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1
Stocker, Benjamin D., Han Wang, Nicholas G. Smith, Sandy P. Harrison, Trevor F. Keenan, David Sandoval, Tyler Davis, and I. Colin Prentice. "P-model v1.0: an optimality-based light use efficiency model for simulating ecosystem gross primary production." Geoscientific Model Development 13, no. 3 (March 26, 2020): 1545–81. http://dx.doi.org/10.5194/gmd-13-1545-2020.
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Abstract. Terrestrial photosynthesis is the basis for vegetation growth and drives the land carbon cycle. Accurately simulating gross primary production (GPP, ecosystem-level apparent photosynthesis) is key for satellite monitoring and Earth system model predictions under climate change. While robust models exist for describing leaf-level photosynthesis, predictions diverge due to uncertain photosynthetic traits and parameters which vary on multiple spatial and temporal scales. Here, we describe and evaluate a GPP (photosynthesis per unit ground area) model, the P-model, that combines the Farquhar–von Caemmerer–Berry model for C3 photosynthesis with an optimality principle for the carbon assimilation–transpiration trade-off, and predicts a multi-day average light use efficiency (LUE) for any climate and C3 vegetation type. The model builds on the theory developed in Prentice et al. (2014) and Wang et al. (2017a) and is extended to include low temperature effects on the intrinsic quantum yield and an empirical soil moisture stress factor. The model is forced with site-level data of the fraction of absorbed photosynthetically active radiation (fAPAR) and meteorological data and is evaluated against GPP estimates from a globally distributed network of ecosystem flux measurements. Although the P-model requires relatively few inputs, the R2 for predicted versus observed GPP based on the full model setup is 0.75 (8 d mean, 126 sites) – similar to comparable satellite-data-driven GPP models but without predefined vegetation-type-specific parameters. The R2 is reduced to 0.70 when not accounting for the reduction in quantum yield at low temperatures and effects of low soil moisture on LUE. The R2 for the P-model-predicted LUE is 0.32 (means by site) and 0.48 (means by vegetation type). Applying this model for global-scale simulations yields a total global GPP of 106–122 Pg C yr−1 (mean of 2001–2011), depending on the fAPAR forcing data. The P-model provides a simple but powerful method for predicting – rather than prescribing – light use efficiency and simulating terrestrial photosynthesis across a wide range of conditions. The model is available as an R package (rpmodel).
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Sri Rahayu Romadhoni, Linda, Abd Rahman As-syakur, Zainul Hidayah, Dwi Budi Wiyanto, Rahma Safitri, Raden Yusuf Satriyana Utama, I. Made Sara Wijana, Alfandy Putra Anugrah, and I. Made Oka Guna Antara. "Annual characteristics of gross primary productivity (GPP) in mangrove forest during 2016-2020 as revealed by Sentinel-2 remote sensing imagery." IOP Conference Series: Earth and Environmental Science 1016, no. 1 (April 1, 2022): 012051. http://dx.doi.org/10.1088/1755-1315/1016/1/012051.
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Abstract Carbon dioxide (CO2) is the most responsible gas in the phenomenon of global warming on earth because of its greatest concentration and longevity in the long atmosphere. Meanwhile mangrove is one of the blue carbon parameters that can take CO2 for photosynthesis and store it into biomass and sediment, so the existence of Mangrove plays a key role in the balance of the global carboncycle. Gross Primary Productivity (GPP) is one of the key variables in conducting a carboncycle study because the GPP values constitute the total value of carbon fixation by terrestrial ecosystem through vegetation photosynthesis. The aim of this study is to recognize the annual characteristics of GPP values in the mangrove ecosystem using remote sensing satellite Sentinel-2 during the period of 2016 to 2020 in TAHURA Ngurah Rai, Bali, Indonesia. The vegetation photosynthesis model (VPM) model is used to calculate annual GPP by using remote sensing indices of Enhanced Vegetation Index (EVI) and Land Surface Water Index (LSWI). Both remote sensing indices are supported by temperature and solar radiation data to determine photosynthesis fraction. The results of the current study indicated that the annual GPP values in the mangrove forest of TAHURA Ngurah Rai have decreased during the observation year. The total value of GPP in 2016 is reached to 28790 tC m−2 year−1, while the total amount of GPP value in 2020 decrease to 26223 tC m−2 year−1. The decline in GPP values may be due to changes of land cover and mangrove mortality that occurred around Benoa port in 2018.
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Raj, Rahul, Bagher Bayat, Petr Lukeš, Ladislav Šigut, and Lucie Homolová. "Analyzing Daily Estimation of Forest Gross Primary Production Based on Harmonized Landsat-8 and Sentinel-2 Product Using SCOPE Process-Based Model." Remote Sensing 12, no. 22 (November 17, 2020): 3773. http://dx.doi.org/10.3390/rs12223773.
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Vegetation top-of-canopy reflectance contains valuable information for estimating vegetation biochemical and structural properties, and canopy photosynthesis (gross primary production (GPP)). Satellite images allow studying temporal variations in vegetation properties and photosynthesis. The National Aeronautics and Space Administration (NASA) has produced a harmonized Landsat-8 and Sentinel-2 (HLS) data set to improve temporal coverage. In this study, we aimed to explore the potential and investigate the information content of the HLS data set using the Soil Canopy Observation of Photosynthesis and Energy fluxes (SCOPE) model to retrieve the temporal variations in vegetation properties, followed by the GPP simulations during the 2016 growing season of an evergreen Norway spruce dominated forest stand. We optimized the optical radiative transfer routine of the SCOPE model to retrieve vegetation properties such as leaf area index and leaf chlorophyll, water, and dry matter contents. The results indicated percentage differences less than 30% between the retrieved and measured vegetation properties. Additionally, we compared the retrievals from HLS data with those from hyperspectral airborne data for the same site, showing that HLS data preserve a considerable amount of information about the vegetation properties. Time series of vegetation properties, retrieved from HLS data, served as the SCOPE inputs for the time series of GPP simulations. The SCOPE model reproduced the temporal cycle of local flux tower measurements of GPP, as indicated by the high Nash–Sutcliffe efficiency value (>0.5). However, GPP simulations did not significantly change when we ran the SCOPE model with constant vegetation properties during the growing season. This might be attributed to the low variability in the vegetation properties of the evergreen forest stand within a vegetation season. We further observed that the temporal variation in maximum carboxylation capacity had a pronounced effect on GPP simulations. We focused on an evergreen forest stand. Further studies should investigate the potential of HLS data across different forest types, such as deciduous stand.
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Kulmala, L., J. Pumpanen, P. Kolari, P. Muukkonen, P. Hari, and T. Vesala. "Photosynthetic production of ground vegetation in different-aged Scots pine (Pinus sylvestris) forests." Canadian Journal of Forest Research 41, no. 10 (October 2011): 2020–30. http://dx.doi.org/10.1139/x11-121.
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The photosynthesis of ground vegetation is dependent on species composition and environmental factors that are extremely diverse during forest succession. However, present in situ measurements over the gross primary production (GPP) of ground vegetation are unable to cover this variability. The primary objective of the present study was to estimate the GPP of ground vegetation in five differently aged Scots pine (Pinus sylvestris L.) forests in southern Finland during the growing season of 2006 by using temperature, soil moisture, photosynthetically active radiation, and biomass of the ground vegetation to run a known process-based model. The GPP of ground vegetation was ~350 g·m–2 at the 6- and 12-year-old sites and 168, 146, and 41 g·m–2 thereafter at the 20-, 45-, and 120-year-old sites, respectively. The values decreased with stand age, because as the stand ages, light availability decreases, the dominant species below the canopy show lower rates of photosynthesis than species in open areas, and the biomass of the ground vegetation decreases. Grasses and herbs took up nearly half of the value at the youngest site but their role decreased thereafter, whereas low shrubs were responsible for most of the GPP of ground vegetation below closed canopies.
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Yin, Gaofei, Aleixandre Verger, Adrià Descals, Iolanda Filella, and Josep Peñuelas. "A Broadband Green-Red Vegetation Index for Monitoring Gross Primary Production Phenology." Journal of Remote Sensing 2022 (March 19, 2022): 1–10. http://dx.doi.org/10.34133/2022/9764982.
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The chlorophyll/carotenoid index (CCI) is increasingly used for remotely tracking the phenology of photosynthesis. However, CCI is restricted to few satellites incorporating the 531 nm band. This study reveals that the Moderate Resolution Imaging Spectroradiometer (MODIS) broadband green reflectance (band 4) is significantly correlated with this xanthophyll-sensitive narrowband (band 11) (R2=0.98,p<0.001), and consequently, the broadband green-red vegetation index GRVI—computed with MODIS band 1 and band 4—is significantly correlated with CCI—computed with MODIS band 1 and band 11 (R2=0.97,p<0.001). GRVI and CCI performed similarly in extracting phenological metrics of the dates of the start and end of the season (EOS) when evaluated with gross primary production (GPP) measurements from eddy covariance towers. For EOS extraction of evergreen needleleaf forest, GRVI even overperformed solar-induced chlorophyll fluorescence which is seen as a direct proxy of plant photosynthesis. This study opens the door for GPP and photosynthetic phenology monitoring from a wide set of sensors with broadbands in the green and red spectral regions.
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Turner, Alexander J., Philipp Köhler, Troy S. Magney, Christian Frankenberg, Inez Fung, and Ronald C. Cohen. "A double peak in the seasonality of California's photosynthesis as observed from space." Biogeosciences 17, no. 2 (January 29, 2020): 405–22. http://dx.doi.org/10.5194/bg-17-405-2020.
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Abstract. Solar-induced chlorophyll fluorescence (SIF) has been shown to be a powerful proxy for photosynthesis and gross primary productivity (GPP). The recently launched TROPOspheric Monitoring Instrument (TROPOMI) features the required spectral resolution and signal-to-noise ratio to retrieve SIF from space. Here, we present a downscaling method to obtain 500 m spatial resolution SIF over California. We report daily values based on a 14 d window. TROPOMI SIF data show a strong correspondence with daily GPP estimates at AmeriFlux sites across multiple ecosystems in California. We find a linear relationship between SIF and GPP that is largely invariant across ecosystems with an intercept that is not significantly different from zero. Measurements of SIF from TROPOMI agree with MODerate Resolution Imaging Spectroradiometer (MODIS) vegetation indices – the normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and near-infrared reflectance of vegetation index (NIRv) – at annual timescales but indicate different temporal dynamics at monthly and daily timescales. TROPOMI SIF data show a double peak in the seasonality of photosynthesis, a feature that is not present in the MODIS vegetation indices. The different seasonality in the vegetation indices may be due to a clear-sky bias in the vegetation indices, whereas previous work has shown SIF to have a low sensitivity to clouds and to detect the downregulation of photosynthesis even when plants appear green. We further decompose the spatiotemporal patterns in the SIF data based on land cover. The double peak in the seasonality of California's photosynthesis is due to two processes that are out of phase: grasses, chaparral, and oak savanna ecosystems show an April maximum, while evergreen forests peak in June. An empirical orthogonal function (EOF) analysis corroborates the phase offset and spatial patterns driving the double peak. The EOF analysis further indicates that two spatiotemporal patterns explain 84 % of the variability in the SIF data. Results shown here are promising for obtaining global GPP at sub-kilometer spatial scales and identifying the processes driving carbon uptake.
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Walther, Sophia, Luis Guanter, Birgit Heim, Martin Jung, Gregory Duveiller, Aleksandra Wolanin, and Torsten Sachs. "Assessing the dynamics of vegetation productivity in circumpolar regions with different satellite indicators of greenness and photosynthesis." Biogeosciences 15, no. 20 (October 26, 2018): 6221–56. http://dx.doi.org/10.5194/bg-15-6221-2018.
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Abstract. High-latitude treeless ecosystems represent spatially highly heterogeneous landscapes with small net carbon fluxes and a short growing season. Reliable observations and process understanding are critical for projections of the carbon balance of the climate-sensitive tundra. Space-borne remote sensing is the only tool to obtain spatially continuous and temporally resolved information on vegetation greenness and activity in remote circumpolar areas. However, confounding effects from persistent clouds, low sun elevation angles, numerous lakes, widespread surface inundation, and the sparseness of the vegetation render it highly challenging. Here, we conduct an extensive analysis of the timing of peak vegetation productivity as shown by satellite observations of complementary indicators of plant greenness and photosynthesis. We choose to focus on productivity during the peak of the growing season, as it importantly affects the total annual carbon uptake. The suite of indicators are as follows: (1) MODIS-based vegetation indices (VIs) as proxies for the fraction of incident photosynthetically active radiation (PAR) that is absorbed (fPAR), (2) VIs combined with estimates of PAR as a proxy of the total absorbed radiation (APAR), (3) sun-induced chlorophyll fluorescence (SIF) serving as a proxy for photosynthesis, (4) vegetation optical depth (VOD), indicative of total water content and (5) empirically upscaled modelled gross primary productivity (GPP). Averaged over the pan-Arctic we find a clear order of the annual peak as APAR ≦ GPP<SIF<VIs/VOD. SIF as an indicator of photosynthesis is maximised around the time of highest annual temperatures. The modelled GPP peaks at a similar time to APAR. The time lag of the annual peak between APAR and instantaneous SIF fluxes indicates that the SIF data do contain information on light-use efficiency of tundra vegetation, but further detailed studies are necessary to verify this. Delayed peak greenness compared to peak photosynthesis is consistently found across years and land-cover classes. A particularly late peak of the normalised difference vegetation index (NDVI) in regions with very small seasonality in greenness and a high amount of lakes probably originates from artefacts. Given the very short growing season in circumpolar areas, the average time difference in maximum annual photosynthetic activity and greenness or growth of 3 to 25 days (depending on the data sets chosen) is important and needs to be considered when using satellite observations as drivers in vegetation models.
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Agustin, Dewa Ayu Mery, Takahiro Osawa, and I. Putu Gede Ardhana. "Estimation of Carbon Sequestration in Tropical Peat Swamp Forest in Central Kalimantan Using Satellite Based on Primary Productivity." International Journal of Environment and Geosciences 2, no. 2 (December 1, 2018): 55. http://dx.doi.org/10.24843/ijeg.2018.v02.i02.p01.
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One of approach that can be used to estimate the carbon sequestration by vegetation is to calculate the Gross Primary Productivity (GPP) in Central Kalimantan. GPP is total carbon that can be absorbed by vegetation to be used in the process of photosynthesis. The purpose of this study is to estimate the value of GPP using Vegetation Photosynthesis and Respiration Model (VPRM) and analyze the data comparison between GPP value data derived from flux tower and GPP value data from MODIS data. The field data from flux tower was taken by Hirano et al. (2007) from January 1, 2004 to December 31, 2005. The MODIS data is used MODIS Surface Reflectance Level 3 data year 2004 to 2005. According to the result of this study, the maximum GPP value year 2004 and 2005 showed 302.365 gC m-2 per month (February 2004) and 366.841 gC m-2 per month (June 2005). The minimum GPP value year 2004 and 2005 was 166.003 gC m-2 per month (November 2004) and 187.663 gC m-2 per month (March 2005). The total value of GPP in year 2004 was 1,134.231 gC m-2 yr-1 and in year 2005 the value was 1,109.001 gC m-2 yr-1. The correlation coefficient between GPP value from flux tower and GPP value from MODIS – VPRM showed in dry season, r = 0.766 and in rainy season, r = 0,839.
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Zhang, Liangxia, Decheng Zhou, Jiangwen Fan, Qun Guo, Shiping Chen, Ranghui Wang, and Yuzhe Li. "Contrasting the Performance of Eight Satellite-Based GPP Models in Water-Limited and Temperature-Limited Grassland Ecosystems." Remote Sensing 11, no. 11 (June 3, 2019): 1333. http://dx.doi.org/10.3390/rs11111333.
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Models constitute the primary approaches for predicting terrestrial ecosystem gross primary production (GPP) at regional and global scales. Many satellite-based GPP models have been developed due to the simple algorithms and the low requirements of model inputs. The performances of these models are well documented at the biome level. However, their performances among vegetation subtypes limited by different environmental stresses within a biome remains largely unexplored. Taking grasslands in northern China as an example, we compared the performance of eight satellite-based GPP models, including three light-use efficiency (LUE) models (vegetation photosynthesis model (VPM), modified VPM (MVPM), and moderate resolution imaging spectroradiometer GPP algorithm (MODIS-GPP)) and five statistical models (temperature and greenness model (TG), greenness and radiation model (GR), vegetation index model (VI), alpine vegetation model (AVM), and photosynthetic capacity model (PCM)), between the water-limited temperate steppe and the temperature-limited alpine meadow based on 16 site-year GPP estimates at four eddy covariance (EC) flux towers. The results showed that all the GPP models performed better in the alpine meadow, particularly in the alpine shrub meadow (R2 ≥ 0.84), than in the temperate steppe (R2 ≤ 0.68). The performance varied greatly among the models in the temperate steppe, while slight intermodel differences existed in the alpine meadow. Overall, MVPM (of the LUE models) and VI (of the statistical models) were the two best-performing models in the temperate steppe due to their better representation of the effect of water stress on vegetation productivity. Additionally, we found that the relatively worse model performances in the temperate steppe were seriously exaggerated by drought events, which may occur more frequently in the future. This study highlights the varying performances of satellite-based GPP models among vegetation subtypes of a biome in different precipitation years and suggests priorities for improving the water stress variables of these models in future efforts.
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Xin, Q., P. Gong, and W. Li. "Modeling photosynthesis of discontinuous plant canopies by linking the Geometric Optical Radiative Transfer model with biochemical processes." Biogeosciences 12, no. 11 (June 5, 2015): 3447–67. http://dx.doi.org/10.5194/bg-12-3447-2015.
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Abstract. Modeling vegetation photosynthesis is essential for understanding carbon exchanges between terrestrial ecosystems and the atmosphere. The radiative transfer process within plant canopies is one of the key drivers that regulate canopy photosynthesis. Most vegetation cover consists of discrete plant crowns, of which the physical observation departs from the underlying assumption of a homogenous and uniform medium in classic radiative transfer theory. Here we advance the Geometric Optical Radiative Transfer (GORT) model to simulate photosynthesis activities for discontinuous plant canopies. We separate radiation absorption into two components that are absorbed by sunlit and shaded leaves, and derive analytical solutions by integrating over the canopy layer. To model leaf-level and canopy-level photosynthesis, leaf light absorption is then linked to the biochemical process of gas diffusion through leaf stomata. The canopy gap probability derived from GORT differs from classic radiative transfer theory, especially when the leaf area index is high, due to leaf clumping effects. Tree characteristics such as tree density, crown shape, and canopy length affect leaf clumping and regulate radiation interception. Modeled gross primary production (GPP) for two deciduous forest stands could explain more than 80% of the variance of flux tower measurements at both near hourly and daily timescales. We demonstrate that ambient CO2 concentrations influence daytime vegetation photosynthesis, which needs to be considered in biogeochemical models. The proposed model is complementary to classic radiative transfer theory and shows promise in modeling the radiative transfer process and photosynthetic activities over discontinuous forest canopies.
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Cheng, Rui, Troy S. Magney, Debsunder Dutta, David R. Bowling, Barry A. Logan, Sean P. Burns, Peter D. Blanken, et al. "Decomposing reflectance spectra to track gross primary production in a subalpine evergreen forest." Biogeosciences 17, no. 18 (September 15, 2020): 4523–44. http://dx.doi.org/10.5194/bg-17-4523-2020.
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Abstract. Photosynthesis by terrestrial plants represents the majority of CO2 uptake on Earth, yet it is difficult to measure directly from space. Estimation of gross primary production (GPP) from remote sensing indices represents a primary source of uncertainty, in particular for observing seasonal variations in evergreen forests. Recent vegetation remote sensing techniques have highlighted spectral regions sensitive to dynamic changes in leaf/needle carotenoid composition, showing promise for tracking seasonal changes in photosynthesis of evergreen forests. However, these have mostly been investigated with intermittent field campaigns or with narrow-band spectrometers in these ecosystems. To investigate this potential, we continuously measured vegetation reflectance (400–900 nm) using a canopy spectrometer system, PhotoSpec, mounted on top of an eddy-covariance flux tower in a subalpine evergreen forest at Niwot Ridge, Colorado, USA. We analyzed driving spectral components in the measured canopy reflectance using both statistical and process-based approaches. The decomposed spectral components co-varied with carotenoid content and GPP, supporting the interpretation of the photochemical reflectance index (PRI) and the chlorophyll/carotenoid index (CCI). Although the entire 400–900 nm range showed additional spectral changes near the red edge, it did not provide significant improvements in GPP predictions. We found little seasonal variation in both normalized difference vegetation index (NDVI) and the near-infrared vegetation index (NIRv) in this ecosystem. In addition, we quantitatively determined needle-scale chlorophyll-to-carotenoid ratios as well as anthocyanin contents using full-spectrum inversions, both of which were tightly correlated with seasonal GPP changes. Reconstructing GPP from vegetation reflectance using partial least-squares regression (PLSR) explained approximately 87 % of the variability in observed GPP. Our results linked the seasonal variation in reflectance to the pool size of photoprotective pigments, highlighting all spectral locations within 400–900 nm associated with GPP seasonality in evergreen forests.
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Xie, Xinyao, Ainong Li, Huaan Jin, Jinhu Bian, Zhengjian Zhang, and Xi Nan. "Comparing Three Remotely Sensed Approaches for Simulating Gross Primary Productivity over Mountainous Watersheds: A Case Study in the Wanglang National Nature Reserve, China." Remote Sensing 13, no. 18 (September 8, 2021): 3567. http://dx.doi.org/10.3390/rs13183567.
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Light Use Efficiency (LUE), Vegetation Index (VI)-based, and process-based models are the main approaches for spatially continuous gross primary productivity (GPP) estimation. However, most current GPP models overlook the effects of topography on the vegetation photosynthesis process. Based on the structures of a two-leaf LUE model (TL-LUE), a VI-based model (temperature and greenness, TG), and a process-based model (Boreal Ecosystem Productivity Simulator, BEPS), three models, named mountain TL-LUE (MTL-LUE), mountain TG (MTG), and BEPS-TerrainLab, have been proposed to improve GPP estimation over mountainous areas. The GPP estimates from the three mountain models have been proven to align more closely with tower-based GPP than those from the original models at the site scale, but their abilities to characterize the spatial variation of GPP at the watershed scale are not yet known. In this work, the GPP estimates from three LUE models (i.e., MOD17, TL-LUE, and MTL-LUE), two VI-based models (i.e., TG and MTG), and two process-based models (i.e., BEPS and BEPS-TerrainLab) were compared for a mountainous watershed. At the watershed scale, the annual GPP estimates from MTL-LUE, MTG, and BTL were found to have a higher spatial variation than those from the original models (increasing the spatial coefficient of variation by 6%, 8%, and 22%), highlighting that incorporating topographic information into GPP models might improve understanding of the high spatial heterogeneity of the vegetation photosynthesis process over mountainous areas. Obvious discrepancies were also observed in the GPP estimates from MTL-LUE, MTG, and BTL, with determination coefficients ranging from 0.02–0.29 and root mean square errors ranging from 399–821 gC m−2yr−1. These GPP discrepancies mainly stem from the different (1) structures of original LUE, VI, and process models, (2) assumptions associated with the effects of topography on photosynthesis, (3) input data, and (4) values of sensitive parameters. Our study highlights the importance of considering surface topography when modeling GPP over mountainous areas, and suggests that more attention should be given to the discrepancy of GPP estimates from different models.
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Xin, Q., P. Gong, and W. Li. "Modeling photosynthesis of discontinuous plant canopies by linking Geometric Optical Radiative Transfer model with biochemical processes." Biogeosciences Discussions 12, no. 4 (February 27, 2015): 3675–729. http://dx.doi.org/10.5194/bgd-12-3675-2015.
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Abstract. Modeling vegetation photosynthesis is essential for understanding carbon exchanges between terrestrial ecosystems and the atmosphere. The radiative transfer process within plant canopies is one of the key drivers that regulate canopy photosynthesis. Most vegetation cover consists of discrete plant crowns, of which the physical observation departs from the underlying assumption of a homogenous and uniform medium in classic radiative transfer theory. Here we advance the Geometric Optical Radiative Transfer (GORT) model to simulate photosynthesis activities for discontinuous plant canopies. We separate radiation absorption into two components that are absorbed by sunlit and shaded leaves, and derive analytical solutions by integrating over the canopy layer. To model leaf-level and canopy-level photosynthesis, leaf light absorption is then linked to the biochemical process of gas diffusion through leaf stomata. The canopy gap probability derived from GORT differs from classic radiative transfer theory, especially when the leaf area index is high, due to leaf clumping effects. Tree characteristics such as tree density, crown shape, and canopy length affect leaf clumping and regulate radiation interception. Modeled gross primary production (GPP) for two deciduous forest stands could explain more than 80% of the variance of flux tower measurements at both near hourly and daily time scales. We also demonstrate that the ambient CO2 concentration influences daytime vegetation photosynthesis, which needs to be considered in state-of-the-art biogeochemical models. The proposed model is complementary to classic radiative transfer theory and shows promise in modeling the radiative transfer process and photosynthetic activities over discontinuous forest canopies.
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Oliver, Rebecca J., Lina M. Mercado, Doug B. Clark, Chris Huntingford, Christopher M. Taylor, Pier Luigi Vidale, Patrick C. McGuire, et al. "Improved representation of plant physiology in the JULES-vn5.6 land surface model: photosynthesis, stomatal conductance and thermal acclimation." Geoscientific Model Development 15, no. 14 (July 20, 2022): 5567–92. http://dx.doi.org/10.5194/gmd-15-5567-2022.
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Abstract. Carbon and water cycle dynamics of vegetation are controlled primarily by photosynthesis and stomatal conductance (gs). Our goal is to improve the representation of these key physiological processes within the JULES land surface model, with a particular focus on refining the temperature sensitivity of photosynthesis, impacting modelled carbon, energy and water fluxes. We test (1) an implementation of the Farquhar et al. (1980) photosynthesis scheme and associated plant functional type-dependent photosynthetic temperature response functions, (2) the optimality-based gs scheme from Medlyn et al. (2011) and (3) the Kattge and Knorr (2007) photosynthetic capacity thermal acclimation scheme. New parameters for each model configuration are adopted from recent large observational datasets that synthesise global experimental data. These developments to JULES incorporate current physiological understanding of vegetation behaviour into the model and enable users to derive direct links between model parameters and ongoing measurement campaigns that refine such parameter values. Replacement of the original Collatz et al. (1991) C3 photosynthesis model with the Farquhar scheme results in large changes in GPP for the current day, with ∼ 10 % reduction in seasonal (June–August, JJA, and December–February, DJF) mean GPP in tropical forests and ∼ 20 % increase in the northern high-latitude forests in JJA. The optimality-based gs model decreases the latent heat flux for the present day (∼ 10 %, with an associated increase in sensible heat flux) across regions dominated by needleleaf evergreen forest in the Northern Hemisphere summer. Thermal acclimation of photosynthesis coupled with the Medlyn gs scheme reduced tropical forest GPP by up to 5 % and increased GPP in the high-northern-latitude forests by between 2 % and 5 %. Evaluation of simulated carbon and water fluxes by each model configuration against global data products shows this latter configuration generates improvements in these key areas. Thermal acclimation of photosynthesis coupled with the Medlyn gs scheme improved modelled carbon fluxes in tropical and high-northern-latitude forests in JJA and improved the simulation of evapotranspiration across much of the Northern Hemisphere in JJA. Having established good model performance for the contemporary period, we force this new version of JULES offline with a future climate scenario corresponding to rising atmospheric greenhouse gases (Shared Socioeconomic Pathway (SSP5), Representative Concentration Pathway 8.5 (RCP8.5)). In particular, these calculations allow for understanding of the effects of long-term warming. We find that the impact of thermal acclimation coupled with the optimality-based gs model on simulated fluxes increases latent heat flux (+50 %) by the year 2050 compared to the JULES model configuration without acclimation. This new JULES configuration also projects increased GPP across tropical (+10 %) and northern-latitude regions (+30 %) by 2050. We conclude that thermal acclimation of photosynthesis with the Farquhar photosynthesis scheme and the new optimality-based gs scheme together improve the simulation of carbon and water fluxes for the current day and have a large impact on modelled future carbon cycle dynamics in a warming world.
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Chen, Jinghua, Shaoqiang Wang, Bin Chen, Yue Li, Muhammad Amir, Li Ma, Kai Zhu, et al. "Comparative Analysis on the Estimation of Diurnal Solar-Induced Chlorophyll Fluorescence Dynamics for a Subtropical Evergreen Coniferous Forest." Remote Sensing 13, no. 16 (August 9, 2021): 3143. http://dx.doi.org/10.3390/rs13163143.
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Solar-induced chlorophyll fluorescence (SIF) is considered as a prospective indicator of vegetation photosynthetic activity and the ecosystem carbon cycle. The current coarse spatial-temporal resolutions of SIF data from satellite missions and ground measurements still cannot satisfy the corroboration of its correlation with photosynthesis and carbon flux. Practical approaches are needed to be explored for the supplementation of the SIF measurements. In our study, we clarified the diurnal variations of leaf and canopy chlorophyll fluorescence for a subtropical evergreen coniferous forest and evaluated the performance of the canopy chlorophyll concentration (CCC) approach and the backward approach from gross primary production (GPP) for estimating the diurnal variations of canopy SIF by comparing with the Soil Canopy Observation Photosynthesis Energy (SCOPE) model. The results showed that the canopy SIF had similar seasonal and diurnal variations with the incident photosynthetically active radiation (PAR) above the canopy, while the leaf steady-state fluorescence remained stable during the daytime. Neither the CCC nor the raw backward approach from GPP could capture the short temporal dynamics of canopy SIF. However, after improving the backward approach with a correction factor of normalized PAR incident on leaves, the variation of the estimated canopy SIF accounted for more than half of the diurnal variations in the canopy SIF (SIF687: R2 = 0.53, p < 0.001; SIF760: R2 = 0.72, p < 0.001) for the subtropical evergreen coniferous forest without water stress. Drought interfered with the utilization of the improved backward approach because of the decoupling of SIF and GPP due to stomatal closure. This new approach offers new insight into the estimation of diurnal canopy SIF and can help understand the photosynthesis of vegetation for future climate change studies.
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Souza, Maísa Caldas, Marcelo Sacardi Biudes, Victor Hugo de Morais Danelichen, Nadja Gomes Machado, Carlo Ralph de Musis, George Louis Vourlitis, and José de Souza Nogueira. "Estimation of gross primary production of the Amazon-Cerrado transitional forest by remote sensing techniques." Revista Brasileira de Meteorologia 29, no. 1 (March 2014): 01–12. http://dx.doi.org/10.1590/s0102-77862014000100001.
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The gross primary production (GPP) of ecosystems is an important variable in the study of global climate change. Generally, the GPP has been estimated by micrometeorological techniques. However, these techniques have a high cost of implantation and maintenance, making the use of orbital sensor data an option to be evaluated. Thus, the objective of this study was to evaluate the potential of the MODIS (Moderate Resolution Imaging Spectroradiometer) MOD17A2 product and the vegetation photosynthesis model (VPM) to predict the GPP of the Amazon-Cerrado transitional forest. The GPP predicted by MOD17A2 (GPP MODIS) and VPM (GPP VPM) were validated with the GPP estimated by eddy covariance (GPP EC). The GPP MODIS, GPP VPM and GPP EC have similar seasonality, with higher values in the wet season and lower in the dry season. However, the VPM performed was better than the MOD17A2 to estimate the GPP, due to use local climatic data for predict the light use efficiency, while the MOD17A2 use a global circulation model and the lookup table of each vegetation type to estimate the light use efficiency.
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Qiu, Ruonan, Ge Han, Xin Ma, Zongyao Sha, Tianqi Shi, Hao Xu, and Miao Zhang. "CO2 Concentration, A Critical Factor Influencing the Relationship between Solar-induced Chlorophyll Fluorescence and Gross Primary Productivity." Remote Sensing 12, no. 9 (April 27, 2020): 1377. http://dx.doi.org/10.3390/rs12091377.
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The uncertainty of carbon fluxes of the terrestrial ecosystem is the highest among all flux components, calling for more accurate and efficient means to monitor land sinks. Gross primary productivity (GPP) is a key index to estimate the terrestrial ecosystem carbon flux, which describes the total amount of organic carbon fixed by green plants through photosynthesis. In recent years, the solar-induced chlorophyll fluorescence (SIF), which is a probe for vegetation photosynthesis and can quickly reflect the state of vegetation growth, emerges as a novel and promising proxy to estimate GPP. The launch of Orbiting Carbon Observatory 2 (OCO-2) further makes it possible to estimate GPP at a finer spatial resolution compared with Greenhouse Gases Observing Satellite (GOSAT), Global Ozone Monitoring Experiment-2 (GOME-2) and SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY). However, whether the relationship between GPP and SIF is linear or non-linear has always been controversial. In this research, we proposed a new model to estimate GPP using SIF and the atmospheric CO2 concentration from OCO-2 as critical driven factors simultaneously (SIF-CO2-GPP model). Evidences from all sites show that the introduction of the atmospheric CO2 concentration improves accuracies of estimated GPP. Compared with the SIF-CO2-GPP linear model, we found the SIF-GPP model overestimated GPP in summer and autumn but underestimated it in spring and winter. A series of simulation experiments based on SCOPE (Soil-Canopy Observation of Photosynthesis and Energy) was carried out to figure out the possible mechanism of improved estimates of GPP due to the introduction of atmospheric CO2 concentrations. These experiments also demonstrate that there could be a non-linear relationship between SIF and GPP at half an hour timescale. Moreover, such relationships vary with CO2 concentration. As OCO-2 is capable of providing SIF and XCO2 products with identical spatial and temporal scales, the SIF-CO2-GPP linear model would be implemented conveniently to monitor GPP using remotely sensed data. With the help of OCO-3 and its successors, the proposed SIF-CO2-GPP linear model would play a significant role in monitoring GPP accurately in large geographical extents.
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DANELICHEN, VICTOR H. M., MARCELO S. BIUDES, MAÍSA C. S. VELASQUE, NADJA G. MACHADO, RAPHAEL S. R. GOMES, GEORGE L. VOURLITIS, and JOSÉ S. NOGUEIRA. "Estimating of gross primary production in an Amazon-Cerrado transitional forest using MODIS and Landsat imagery." Anais da Academia Brasileira de Ciências 87, no. 3 (July 28, 2015): 1545–64. http://dx.doi.org/10.1590/0001-3765201520140457.
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The acceleration of the anthropogenic activity has increased the atmospheric carbon concentration, which causes changes in regional climate. The Gross Primary Production (GPP) is an important variable in the global carbon cycle studies, since it defines the atmospheric carbon extraction rate from terrestrial ecosystems. The objective of this study was to estimate the GPP of the Amazon-Cerrado Transitional Forest by the Vegetation Photosynthesis Model (VPM) using local meteorological data and remote sensing data from MODIS and Landsat 5 TM reflectance from 2005 to 2008. The GPP was estimated using Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) calculated by MODIS and Landsat 5 TM images. The GPP estimates were compared with measurements in a flux tower by eddy covariance. The GPP measured in the tower was consistent with higher values during the wet season and there was a trend to increase from 2005 to 2008. The GPP estimated by VPM showed the same increasing trend observed in measured GPP and had high correlation and Willmott's coefficient and low error metrics in comparison to measured GPP. These results indicated high potential of the Landsat 5 TM images to estimate the GPP of Amazon-Cerrado Transitional Forest by VPM.
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Zhang, Lifu, Na Qiao, Changping Huang, and Siheng Wang. "Monitoring Drought Effects on Vegetation Productivity Using Satellite Solar-Induced Chlorophyll Fluorescence." Remote Sensing 11, no. 4 (February 13, 2019): 378. http://dx.doi.org/10.3390/rs11040378.
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Around the world, the increasing drought, which is exacerbated by climate change, has significant impacts on vegetation carbon assimilation. Identifying how short-term climate anomalies influence vegetation productivity in a timely and accurate manner at the satellite scale is crucial to monitoring drought. Satellite solar-induced chlorophyll fluorescence (SIF) has recently been reported as a direct proxy of actual vegetation photosynthesis and has more advantages than traditional vegetation indices (e.g., the Normalized Difference Vegetation Index, NDVI and the Enhanced Vegetation Index, EVI) in monitoring vegetation vitality. This study aims to evaluate the feasibility of SIF in interpreting drought effects on vegetation productivity in Victoria, Australia, where heat stress and drought are often reported. Drought-induced variations in SIF and absorbed photosynthetically active radiation (APAR) estimations based on NDVI and EVI were investigated and validated against results indicated by gross primary production (GPP). We first compared drought responses of GPP and vegetation proxies (SIF and APAR) during the 2009 drought event, considering potential biome-dependency. Results showed that SIF exhibited more consistent declines with GPP losses induced by drought than did APAR estimations during the 2009 drought period in space and time, where APAR had obvious lagged responses compared with SIF, especially in evergreen broadleaf forest land. We then estimated the sensitivities of the aforementioned variables to meteorology anomalies using the ARx model, where memory effects were considered, and compared the correlations of GPP anomaly with the anomalies of vegetation proxies during a relatively long period (2007–2013). Compared with APAR, GPP and SIF are more sensitive to temperature anomalies for the general Victoria region. For crop land, GPP and vegetation proxies showed similar sensitivities to temperature and water availability. For evergreen broadleaf forest land, SIF anomaly was explained better by meteorology anomalies than APAR anomalies. GPP anomaly showed a stronger linear relationship with SIF anomaly than with APAR anomalies, especially for evergreen broadleaf forest land. We showed that SIF might be a promising tool for effectively evaluating short-term drought impacts on vegetation productivity, especially in drought-vulnerable areas, such as Victoria.
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De Weirdt, M., H. Verbeeck, F. Maignan, P. Peylin, B. Poulter, D. Bonal, P. Ciais, and K. Steppe. "Seasonal leaf dynamics for tropical evergreen forests in a process based global ecosystem model." Geoscientific Model Development Discussions 5, no. 1 (February 24, 2012): 639–81. http://dx.doi.org/10.5194/gmdd-5-639-2012.
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Abstract. The influence of seasonal phenology in tropical humid forests on canopy photosynthesis remains poorly understood and its representation in global vegetation models highly simplified, typically with no seasonal variability of canopy leaf area properties taken into account. However, recent flux tower and remote sensing studies suggest that seasonal phenology in tropical rainforests exerts a large influence over carbon and water fluxes, with feedbacks that can significantly influence climate dynamics. A more realistic description of the underlying mechanisms that drive seasonal tropical forest photosynthesis and phenology could improve the correspondence of global vegetation model outputs with the wet-dry season biogeochemical patterns measured at flux tower sites. Here, we introduce a leaf Net Primary Production (NPP) based canopy dynamics scheme for evergreen tropical forests in the global terrestrial ecosystem model ORCHIDEE and validated the new scheme against in-situ carbon flux measurements. Modelled Gross Primary Productivity (GPP) patterns are analyzed in details for a flux tower site in French Guiana, in a forest where the dry season is short and where the vegetation is considered to have developed adaptive mechanisms against drought stress. By including leaf litterfall seasonality and a coincident light driven leaf flush and seasonal change in photosynthetic capacity in ORCHIDEE, modelled carbon and water fluxes more accurately represent the observations. The fit to GPP flux data was substantially improved and the results confirmed that by modifying canopy dynamics to benefit from increased light conditions, a better representation of the seasonal carbon flux patterns was made.
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Lin, Shangrong, Jing Li, Qinhuo Liu, Longhui Li, Jing Zhao, and Wentao Yu. "Evaluating the Effectiveness of Using Vegetation Indices Based on Red-Edge Reflectance from Sentinel-2 to Estimate Gross Primary Productivity." Remote Sensing 11, no. 11 (May 31, 2019): 1303. http://dx.doi.org/10.3390/rs11111303.
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Gross primary productivity (GPP) is the most important component of terrestrial carbon flux. Red-edge (680–780 nm) reflectance is sensitive to leaf chlorophyll content, which is directly correlated with photosynthesis as the pigment pool, and it has the potential to improve GPP estimation. The European Space Agency (ESA) Sentinel-2A and B satellites provide red-edge bands at 20-m spatial resolution on a five-day revisit period, which can be used for global estimation of GPP. Previous studies focused mostly on improving cropland GPP estimation using red-edge bands. In this study, we firstly evaluated the relationship between eight vegetation indices (VIs) retrieved from Sentinel-2 imagery in association with incident photosynthetic active radiation (PARin) and carbon flux tower GPP (GPPEC) across three forest and two grassland sites in Australia. We derived a time series of five red-edge VIs and three non-red-edge VIs over the CO2 flux tower footprints at 16-day time intervals and compared both temporal and spatial variations. The results showed that the relationship between the red-edge index (CIr, ρ 783 ρ 705 − 1 ) multiplied by PARin and GPPEC had the highest correlation (R2 = 0.77, root-mean-square error (RMSE) = 0.81 gC∙m−2∙day−1) at the two grassland sites. The CIr also showed consistency (rRMSE defined as RMSE/mean GPP, lower than 0.25) across forest and grassland sites. The high spatial resolution of the Sentinel-2 data provided more detailed information to adequately characterize the GPP variance at spatially heterogeneous areas. The high revisit period of Sentinel-2 exhibited temporal variance in GPP at the grassland sites; however, at forest sites, the flux-tower-based GPP variance could not be fully tracked by the limited satellite images. These results suggest that the high-spatial-resolution red-edge index from Sentinel-2 can improve large-scale spatio-temporal GPP assessments.
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Kimball, J. S., K. C. McDonald, and M. Zhao. "Spring Thaw and Its Effect on Terrestrial Vegetation Productivity in the Western Arctic Observed from Satellite Microwave and Optical Remote Sensing." Earth Interactions 10, no. 21 (December 1, 2006): 1–22. http://dx.doi.org/10.1175/ei187.1.
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Abstract Global satellite remote sensing records show evidence of recent vegetation greening and an advancing growing season at high latitudes. Satellite remote sensing–derived measures of photosynthetic leaf area index (LAI) and vegetation gross and net primary productivity (GPP and NPP) from the NOAA Advanced Very High Resolution Radiometer (AVHRR) Pathfinder record are utilized to assess annual variability in vegetation productivity for Alaska and northwest Canada in association with the Western Arctic Linkage Experiment (WALE). These results are compared with satellite microwave remote sensing measurements of springtime thaw from the Special Sensor Microwave Imager (SSM/I). The SSM/I-derived timing of the primary springtime thaw event was well correlated with annual anomalies in maximum LAI in spring and summer (P ≤ 0.009; n = 13), and GPP and NPP (P ≤ 0.0002) for the region. Mean annual variability in springtime thaw was on the order of ±7 days, with corresponding impacts to annual productivity of approximately 1% day−1. Years with relatively early seasonal thawing showed generally greater LAI and annual productivity, while years with delayed seasonal thawing showed corresponding reductions in canopy cover and productivity. The apparent sensitivity of LAI and vegetation productivity to springtime thaw indicates that a recent advance in the seasonal thaw cycle and associated lengthening of the potential period of photosynthesis in spring is sufficient to account for the sign and magnitude of an estimated positive vegetation productivity trend for the western Arctic from 1982 to 2000.
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Wang, H., I. C. Prentice, and T. W. Davis. "Biophsyical constraints on gross primary production by the terrestrial biosphere." Biogeosciences 11, no. 20 (October 31, 2014): 5987–6001. http://dx.doi.org/10.5194/bg-11-5987-2014.
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Abstract. Persistent divergences among the predictions of complex carbon-cycle models include differences in the sign as well as the magnitude of the response of global terrestrial primary production to climate change. Such problems with current models indicate an urgent need to reassess the principles underlying the environmental controls of primary production. The global patterns of annual and maximum monthly terrestrial gross primary production (GPP) by C3 plants are explored here using a simple first-principles model based on the light-use efficiency formalism and the Farquhar model for C3 photosynthesis. The model is driven by incident photosynthetically active radiation (PAR) and remotely sensed green-vegetation cover, with additional constraints imposed by low-temperature inhibition and CO2 limitation. The ratio of leaf-internal to ambient CO2 concentration in the model responds to growing-season mean temperature, atmospheric dryness (indexed by the cumulative water deficit, Δ E) and elevation, based on an optimality theory. The greatest annual GPP is predicted for tropical moist forests, but the maximum (summer) monthly GPP can be as high, or higher, in boreal or temperate forests. These findings are supported by a new analysis of CO2 flux measurements. The explanation is simply based on the seasonal and latitudinal distribution of PAR combined with the physiology of photosynthesis. By successively imposing biophysical constraints, it is shown that partial vegetation cover – driven primarily by water shortage – represents the largest constraint on global GPP.
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Wang, H., I. C. Prentice, and T. W. Davis. "Biophysical constraints on gross primary production by the terrestrial biosphere." Biogeosciences Discussions 11, no. 2 (February 25, 2014): 3209–40. http://dx.doi.org/10.5194/bgd-11-3209-2014.
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Abstract. Persistent divergences among the predictions of complex carbon cycle models include differences in the sign as well as the magnitude of the response of global terrestrial primary production to climate change. This and other problems with current models indicate an urgent need to re-assess the principles underlying the environmental controls of primary production. The global patterns of annual and maximum monthly terrestrial gross primary production (GPP) by C3 plants are explored here using a simple first-principles model based on the light-use efficiency formalism and the Farquhar model for C3 photosynthesis. The model is driven by incident photosynthetically active radiation (PAR) and remotely sensed green vegetation cover, with additional constraints imposed by low-temperature inhibition and CO2 limitation. The ratio of leaf-internal to ambient CO2 concentration in the model responds to growing-season mean temperature, atmospheric dryness (indexed by the cumulative water deficit, ΔE) and elevation, based on optimality theory. The greatest annual GPP is predicted for tropical moist forests, but the maximum (summer) monthly GPP can be as high or higher in boreal or temperate forests. These findings are supported by a new analysis of CO2 flux measurements. The explanation is simply based on the seasonal and latitudinal distribution of PAR combined with the physiology of photosynthesis. By successively imposing biophysical constraints, it is shown that partial vegetation cover – driven primarily by water shortage – represents the largest constraint on global GPP.
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Rossini, M., S. Cogliati, M. Meroni, M. Migliavacca, M. Galvagno, L. Busetto, E. Cremonese, et al. "Remote sensing-based estimation of gross primary production in a subalpine grassland." Biogeosciences Discussions 9, no. 2 (February 10, 2012): 1711–58. http://dx.doi.org/10.5194/bgd-9-1711-2012.
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Abstract. This study investigates the performances in a terrestrial ecosystem of gross primary production (GPP) estimation of a suite of spectral vegetation indexes (VIs) that can be computed from currently orbiting platforms. Vegetation indexes were computed from near-surface field spectroscopy measurements collected using an automatic system designed for high temporal frequency acquisition of spectral measurements in the visible near-infrared region. Spectral observations were collected for two consecutive years in Italy in a subalpine grassland equipped with an Eddy Covariance (EC) flux tower which provides continuous measurements of net ecosystem carbon dioxide (CO2) exchange (NEE) and the derived GPP. Different VIs were calculated based on ESA-MERIS and NASA-MODIS spectral bands and correlated with biophysical (Leaf Area Index, LAI; fraction of photosynthetically active radiation intercepted by green vegetation, fIPARg), biochemical (chlorophyll concentration) and ecophysiological (green light-use efficiency, LUEg) canopy variables. In this study, the normalized difference vegetation index (NDVI) showed better correlations with LAI and fPARg (r = 0.90 and 0.95, respectively), the MERIS terrestrial chlorophyll index (MTCI) with leaf chlorophyll content (r = 0.91) and the Photochemical Reflectance Index (PRI551), computed as (R531−R551)/(R531+R551) with LUEg (r = 0.64). Subsequently, these VIs were used to estimate GPP using different modelling solutions based on the light-use efficiency model describing the GPP as driven by the photosynthetically active radiation absorbed by green vegetation (APARg) and by the efficiency (ε) with which plants use the absorbed radiation to fix carbon via photosynthesis. Results show that GPP can be successfully modelled with a combination of VIs and meteorological data or VIs only. Vegetation indexes designed to be more sensitive to chlorophyll content explained most of the variability in GPP in the ecosystem investigated, characterized by a strong seasonal dynamic of GPP. Accuracy in GPP estimation slightly improves when taking into account high frequency modulations of GPP driven by incident PAR or modelling LUEg with the PRI in model formulation. Similar results were obtained for both measured daily VIs and VIs obtained as 16-day composite time series and then downscaled from the compositing period to daily scale (resampled data). However, the use of resampled data rather than measured daily input data decreases the accuracy of the total GPP estimation on an annual basis.
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Rossini, M., S. Cogliati, M. Meroni, M. Migliavacca, M. Galvagno, L. Busetto, E. Cremonese, et al. "Remote sensing-based estimation of gross primary production in a subalpine grassland." Biogeosciences 9, no. 7 (July 12, 2012): 2565–84. http://dx.doi.org/10.5194/bg-9-2565-2012.
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Abstract. This study investigates the performances in a terrestrial ecosystem of gross primary production (GPP) estimation of a suite of spectral vegetation indexes (VIs) that can be computed from currently orbiting platforms. Vegetation indexes were computed from near-surface field spectroscopy measurements collected using an automatic system designed for high temporal frequency acquisition of spectral measurements in the visible near-infrared region. Spectral observations were collected for two consecutive years in Italy in a subalpine grassland equipped with an eddy covariance (EC) flux tower that provides continuous measurements of net ecosystem carbon dioxide (CO2) exchange (NEE) and the derived GPP. Different VIs were calculated based on ESA-MERIS and NASA-MODIS spectral bands and correlated with biophysical (Leaf area index, LAI; fraction of photosynthetically active radiation intercepted by green vegetation, fIPARg), biochemical (chlorophyll concentration) and ecophysiological (green light-use efficiency, LUEg) canopy variables. In this study, the normalized difference vegetation index (NDVI) was the index best correlated with LAI and fIPARg (r = 0.90 and 0.95, respectively), the MERIS terrestrial chlorophyll index (MTCI) with leaf chlorophyll content (r = 0.91) and the photochemical reflectance index (PRI551), computed as (R531-R551)/(R531+R551) with LUEg (r = 0.64). Subsequently, these VIs were used to estimate GPP using different modelling solutions based on Monteith's light-use efficiency model describing the GPP as driven by the photosynthetically active radiation absorbed by green vegetation (APARg) and by the efficiency (ε) with which plants use the absorbed radiation to fix carbon via photosynthesis. Results show that GPP can be successfully modelled with a combination of VIs and meteorological data or VIs only. Vegetation indexes designed to be more sensitive to chlorophyll content explained most of the variability in GPP in the ecosystem investigated, characterised by a strong seasonal dynamic of GPP. Accuracy in GPP estimation slightly improves when taking into account high frequency modulations of GPP driven by incident PAR or modelling LUEg with the PRI in model formulation. Similar results were obtained for both measured daily VIs and VIs obtained as 16-day composite time series and then downscaled from the compositing period to daily scale (resampled data). However, the use of resampled data rather than measured daily input data decreases the accuracy of the total GPP estimation on an annual basis.
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Jiang, Chongya, Kaiyu Guan, Genghong Wu, Bin Peng, and Sheng Wang. "A daily, 250 m and real-time gross primary productivity product (2000–present) covering the contiguous United States." Earth System Science Data 13, no. 2 (February 9, 2021): 281–98. http://dx.doi.org/10.5194/essd-13-281-2021.
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Abstract. Gross primary productivity (GPP) quantifies the amount of carbon dioxide (CO2) fixed by plants through photosynthesis. Although as a key quantity of terrestrial ecosystems, there is a lack of high-spatial-and-temporal-resolution, real-time and observation-based GPP products. To address this critical gap, here we leverage a state-of-the-art vegetation index, near-infrared reflectance of vegetation (NIRV), along with accurate photosynthetically active radiation (PAR), to produce a SatelLite Only Photosynthesis Estimation (SLOPE) GPP product for the contiguous United States (CONUS). Compared to existing GPP products, the proposed SLOPE product is advanced in its spatial resolution (250 m versus >500 m), temporal resolution (daily versus 8 d), instantaneity (latency of 1 d versus >2 weeks) and quantitative uncertainty (on a per-pixel and daily basis versus no uncertainty information available). These characteristics are achieved because of several technical innovations employed in this study: (1) SLOPE couples machine learning models with MODIS atmosphere and land products to accurately estimate PAR. (2) SLOPE couples highly efficient and pragmatic gap-filling and filtering algorithms with surface reflectance acquired by both Terra and Aqua MODIS satellites to derive a soil-adjusted NIRV (SANIRV) dataset. (3) SLOPE couples a temporal pattern recognition approach with a long-term Cropland Data Layer (CDL) product to predict dynamic C4 crop fraction. Through developing a parsimonious model with only two slope parameters, the proposed SLOPE product explains 85 % of the spatial and temporal variations in GPP acquired from 49 AmeriFlux eddy-covariance sites (324 site years), with a root-mean-square error (RMSE) of 1.63 gC m−2 d−1. The median R2 over C3 and C4 crop sites reaches 0.87 and 0.94, respectively, indicating great potentials for monitoring crops, in particular bioenergy crops, at the field level. With such a satisfactory performance and its distinct characteristics in spatiotemporal resolution and instantaneity, the proposed SLOPE GPP product is promising for biological and environmental research, carbon cycle research, and a broad range of real-time applications at the regional scale. The archived dataset is available at https://doi.org/10.3334/ORNLDAAC/1786 (download page: https://daac.ornl.gov/daacdata/cms/SLOPE_GPP_CONUS/data/, last access: 20 January 2021) (Jiang and Guan, 2020), and the real-time dataset is available upon request.
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Thum, Tea, Sönke Zaehle, Philipp Köhler, Tuula Aalto, Mika Aurela, Luis Guanter, Pasi Kolari, et al. "Modelling sun-induced fluorescence and photosynthesis with a land surface model at local and regional scales in northern Europe." Biogeosciences 14, no. 7 (April 11, 2017): 1969–87. http://dx.doi.org/10.5194/bg-14-1969-2017.
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Abstract. Recent satellite observations of sun-induced chlorophyll fluorescence (SIF) are thought to provide a large-scale proxy for gross primary production (GPP), thus providing a new way to assess the performance of land surface models (LSMs). In this study, we assessed how well SIF is able to predict GPP in the Fenno-Scandinavian region and what potential limitations for its application exist. We implemented a SIF model into the JSBACH LSM and used active leaf-level chlorophyll fluorescence measurements (Chl F) to evaluate the performance of the SIF module at a coniferous forest at Hyytiälä, Finland. We also compared simulated GPP and SIF at four Finnish micrometeorological flux measurement sites to observed GPP as well as to satellite-observed SIF. Finally, we conducted a regional model simulation for the Fenno-Scandinavian region with JSBACH and compared the results to SIF retrievals from the GOME-2 (Global Ozone Monitoring Experiment-2) space-borne spectrometer and to observation-based regional GPP estimates. Both observations and simulations revealed that SIF can be used to estimate GPP at both site and regional scales. At regional scale the model was able to simulate observed SIF averaged over 5 years with r2 of 0.86. The GOME-2-based SIF was a better proxy for GPP than the remotely sensed fAPAR (fraction of absorbed photosynthetic active radiation by vegetation). The observed SIF captured the seasonality of the photosynthesis at site scale and showed feasibility for use in improving of model seasonality at site and regional scale.
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Xin, Fengfei, Xiangming Xiao, Osvaldo M. R. Cabral, Paul M. White, Haiqiang Guo, Jun Ma, Bo Li, and Bin Zhao. "Understanding the Land Surface Phenology and Gross Primary Production of Sugarcane Plantations by Eddy Flux Measurements, MODIS Images, and Data-Driven Models." Remote Sensing 12, no. 14 (July 8, 2020): 2186. http://dx.doi.org/10.3390/rs12142186.
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Sugarcane (complex hybrids of Saccharum spp., C4 plant) croplands provide cane stalk feedstock for sugar and biofuel (ethanol) production. It is critical for us to analyze the phenology and gross primary production (GPP) of sugarcane croplands, which would help us to better understand and monitor the sugarcane growing condition and the carbon cycle. In this study, we combined the data from two sugarcane EC flux tower sites in Brazil and the USA, images from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor, and data-driven models to study the phenology and GPP of sugarcane croplands. The seasonal dynamics of climate, vegetation indices from MODIS images, and GPP from two sugarcane flux tower sites (GPPEC) reveal the temporal consistency in sugarcane phenology (crop calendar: green-up dates and harvesting dates) as estimated by the vegetation indices and GPPEC data. The Land Surface Water Index (LSWI) is shown to be useful to delineate the phenology of sugarcane croplands. The relationship between the sugarcane GPPEC and the Enhanced Vegetation Index (EVI) is stronger than the relationship between the GPPEC and the Normalized Difference Vegetation Index (NDVI). We ran the Vegetation Photosynthesis Model (VPM), which uses the light use efficiency (LUE) concept and is driven by climate data and MODIS images, to estimate the daily GPP at the two sugarcane sites (GPPVPM). The seasonal dynamics of the GPPVPM and GPPEC at the two sites agreed reasonably well with each other, which indicates that VPM is a powerful tool for estimating the GPP of sugarcane croplands in Brazil and the USA. This study clearly highlights the potential of combining eddy covariance technology, satellite-based remote sensing technology, and data-driven models for better understanding and monitoring the phenology and GPP of sugarcane croplands under different climate and management practices.
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Li, Jianying, Jin-Soo Kim, and Jong-Seong Kug. "The Impact of the 20–50-Day Atmospheric Intraseasonal Oscillation on the Gross Primary Productivity between the Yangtze and Yellow Rivers." Journal of Climate 33, no. 8 (April 15, 2020): 2967–84. http://dx.doi.org/10.1175/jcli-d-19-0575.1.
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AbstractGiven their high carbon uptake, the terrestrial ecosystems in the East Asia summer monsoon (EASM) region play an irreplaceable role in the global carbon cycle. Because the rich vegetation growth over East Asia benefits mainly from the sufficient water supply brought by the EASM, which is characterized by a strong intraseasonal oscillation (ISO), the intraseasonal spatiotemporal variations and underlying drivers of photosynthesis activity over East Asia have been comprehensively investigated using the daily gross primary productivity (GPP) and meteorological data. Strong intraseasonal fluctuations of GPP have been identified over the area between the Yangtze and Yellow Rivers (YYR) with a magnitude of 0.4 gC m−2 day−1. The mean power spectrum suggests that 20–50-day variation is the major component of the intraseasonal GPP anomalies over the YYR during the summers of 1980–2013. The 20–50-day ISO of YYR GPP anomalies is modulated by the local 20–50-day precipitation variation via soil moisture, with precipitation (soil moisture) leading GPP by 10 (7) days. The 20–50-day YYR precipitation anomalies are in turn controlled by tropical ISO signals, particularly the convective activity over the western North Pacific. This leading relationship between the 20–50-day atmospheric ISO and GPP suggests a potential for extended-range predictability of vegetation growth.
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Alsafadi, Karam, Shuoben Bi, Bashar Bashir, Safwan Mohammed, Saad Sh Sammen, Abdullah Alsalman, Amit Kumar Srivastava, and Ahmed El Kenawy. "Assessment of Carbon Productivity Trends and Their Resilience to Drought Disturbances in the Middle East Based on Multi-Decadal Space-Based Datasets." Remote Sensing 14, no. 24 (December 9, 2022): 6237. http://dx.doi.org/10.3390/rs14246237.
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Gross primary production (GPP) is a key component in assessing the global change in carbon uptake and in evaluating the impacts of climate change on terrestrial ecosystems. A decrease in the photosynthetic rate due to stomata closing by vegetation could have an impact on GPP. Nonetheless, the uncertainty in long-term GPP patterns and their resilience to drought disturbances has not yet been examined thoroughly. In this study, four state-of-the-art GPP datasets, including the revised EC-LUE algorithm-driven GPP (GLASS-GPP), the EC flux tower upscaling-based GPP (FluxCom-GPP), the MODIS algorithm-driven GPP model (GIMMS-GPP), and the vegetation photosynthesis model-GPP (VPM-GPP), were used to assess GPP characteristics in the Middle East region for 36 years spanning the period of 1982 to 2016. All investigated datasets revealed an increasing trend over the study period, albeit with a more pronounced upward trend for the VPM-GPP dataset in the most recent decades (2000–2016). On the other hand, FluxCom-GPP exhibited less variability than the other datasets. In addition, while GLASS-GPP presented a significant increasing trend in some parts of the region, significant negative trends dominated the other parts. This study defined six significant drought episodes that occurred in the Middle East region between 1982 and 2017. The most severe drought events were recorded in 1985, 1989–1990, 1994, 1999–2001, 2008, and 2015, spreading over more than 15% of the total area of the region. The extreme droughts accounted for a high decline in GPP in the north of Iraq, the northeast of Syria, and the southwest of Iran, where 20.2 and 40.8% of the ecosystem’s GPP were severely non-resilient to drought according to the GLASS and VPM-based GPP responses, respectively. The spatial distribution patterns of the correlations between the SEDI and GPP products were somewhat similar and coherent. The highest positive correlations were detected in the central and western parts of Turkey, the western and northeastern parts of Iran, and north Iraq, which showed anomalous r values (r = 0.7), especially for the SEDI-VPM and SEDI-FluxCom GPP associations. The findings of this study can provide a solid base for identifying at-risk regions in the Middle East in terms of climate change impacts, which will allow for better management of ecosystems and proper implementation of climate policies.
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Padalia, H., S. Kumari, S. K. Sinha, S. Nandy, and P. Chauhan. "INTRA- AND INTER-ANNUAL TRENDS OF SUN-INDUCED FLUORESCENCE (SIF) FOR CONTRASTING VEGETATION TYPES OF INDIA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B3-2020 (August 21, 2020): 1047–53. http://dx.doi.org/10.5194/isprs-archives-xliii-b3-2020-1047-2020.
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Abstract. The photosynthesis governs productivity and health of the forests. Traditionally, remote sensing derived reflectance measures have been used to assess forest phenology, productivity and stress. The chlorophyll pigments absorb solar radiation, and emit fluorescence in far red region of electromagnetic spectrum. Chlorophyll fluorescence directly relates to the photosynthetic activity of the plants. Measurement of chlorophyll fluorescence from space has recently been achieved in the form of Sun-Induced Fluorescence (SIF). But SIF response have been found variable with respect to variation in vegetation type, hence, there is a need to study SIF response of tropical forests of India considering their wide extent, contribution to national carbon cycle and climate resilience. In this study, intra- and inter-annual GOME-2 and OCO-2 SIF responses of contrasting Indian tropical forest types viz., dry deciduous (Betul, Madhya Pradesh), moist deciduous (Kalahandi, Orissa) and wet evergreen forests (Uttara Kannada, Karnataka) has been investigated with respect to rainfall, NDVI and GPP trends. The results show that dry, moist and wet forests of India have differences in photosynthetic activity at intra- and inter-annual scale. GOME-2 SIF observations were more variables than OCO-2 SIF, particularly during green-up and senescence phase. SIF explained higher seasonality for dry deciduous followed by moist deciduous and wet evergreen. Annually integrated SIF (proxy of GPP) was in order: wet evergreen > moist deciduous > dry deciduous.
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Du, Shanshan, Liangyun Liu, Xinjie Liu, Jian Guo, Jiaochan Hu, Shaoqiang Wang, and Yongguang Zhang. "SIFSpec: Measuring Solar-Induced Chlorophyll Fluorescence Observations for Remote Sensing of Photosynthesis." Sensors 19, no. 13 (July 8, 2019): 3009. http://dx.doi.org/10.3390/s19133009.
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Solar-induced chlorophyll fluorescence (SIF) is regarded as a proxy for photosynthesis in terrestrial vegetation. Tower-based long-term observations of SIF are very important for gaining further insight into the ecosystem-specific seasonal dynamics of photosynthetic activity, including gross primary production (GPP). Here, we present the design and operation of the tower-based automated SIF measurement (SIFSpec) system. This system was developed with the aim of obtaining synchronous SIF observations and flux measurements across different terrestrial ecosystems, as well as to validate the increasing number of satellite SIF products using in situ measurements. Details of the system components, instrument installation, calibration, data collection, and processing are introduced. Atmospheric correction is also included in the data processing chain, which is important, but usually ignored for tower-based SIF measurements. Continuous measurements made across two growing cycles over maize at a Daman (DM) flux site (in Gansu province, China) demonstrate the reliable performance of SIF as an indicator for tracking the diurnal variations in photosynthetically active radiation (PAR) and seasonal variations in GPP. For the O2–A band in particular, a high correlation coefficient value of 0.81 is found between the SIF and seasonal variations of GPP. It is thus concluded that, in coordination with continuous eddy covariance (EC) flux measurements, automated and continuous SIF observations can provide a reliable approach for understanding the photosynthetic activity of the terrestrial ecosystem, and are also able to bridge the link between ground-based optical measurements and airborne or satellite remote sensing data.
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Xue, Wei, Seungtaek Jeong, Jonghan Ko, and John Tenhunen. "Linking canopy reflectance to crop structure and photosynthesis to capture and interpret spatiotemporal dimensions of per-field photosynthetic productivity." Biogeosciences 14, no. 5 (March 15, 2017): 1315–32. http://dx.doi.org/10.5194/bg-14-1315-2017.
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Abstract. Nitrogen and water availability alter canopy structure and physiology, and thus crop growth, yielding large impacts on ecosystem-regulating/production provisions. However, to date, explicitly quantifying such impacts remains challenging partially due to lack of adequate methodology to capture spatial dimensions of ecosystem changes associated with nitrogen and water effects. A data fitting, where close-range remote-sensing measurements of vegetation indices derived from a handheld instrument and an unmanned aerial vehicle (UAV) system are linked to in situ leaf and canopy photosynthetic traits, was applied to capture and interpret inter- and intra-field variations in gross primary productivity (GPP) in lowland rice grown under flooded conditions (paddy rice, PD) subject to three nitrogen application rates and under rainfed conditions (RF) in an East Asian monsoon region of South Korea. Spatial variations (SVs) in both GPP and light use efficiency (LUEcabs) early in the growing season were enlarged by nitrogen addition. The nutritional effects narrowed over time. A shift in planting culture from flooded to rainfed conditions strengthened SVs in GPP and LUEcabs. Intervention of prolonged drought late in the growing season dramatically intensified SVs that were supposed to seasonally decrease. Nevertheless, nitrogen addition effects on SV of LUEcabs at the early growth stage made PD fields exert greater SVs than RF fields. SVs of GPP across PD and RF rice fields were likely related to leaf area index (LAI) development less than to LUEcabs, while numerical analysis suggested that considering strength in LUEcabs and its spatial variation for the same crop type tends to be vital for better evaluation in landscape/regional patterns of ecosystem photosynthetic productivity at critical phenology stages.
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Wang, S., and X. Mo. "Comparison of multiple models for estimating gross primary production using remote sensing data and fluxnet observations." Proceedings of the International Association of Hydrological Sciences 368 (May 6, 2015): 75–80. http://dx.doi.org/10.5194/piahs-368-75-2015.
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Abstract. In this study, gross primary production (GPP) estimated from a temperature and greenness (TG) model, a greenness and radiation (GR) model, a vegetation photosynthesis model (VPM), and a MODIS product have been compared with eddy covariance measurements in cropland during 2003–2005. Results showed that the determination coefficients (R2) between fluxnet GPP and estimated GPP were all greater than 0.74, indicating that all these models offered reliable estimates of GPP. We also found that the VPM-based GPP estimates performed a bit better (R2 is 0.82, and RMSE is 16.75 gC m−2 (8 day)−1) than other models, mainly due to its comprehensive consideration of the stresses from light, temperature and water. The actual GPP was overestimated in the non-growing season and underestimated in the growing season by MOD_GPP. The validation confirms that the above three models may be used to estimate crop production in the North China Plain, but there are still significant uncertainties.
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Gensheimer, Johannes, Alexander J. Turner, Philipp Köhler, Christian Frankenberg, and Jia Chen. "A convolutional neural network for spatial downscaling of satellite-based solar-induced chlorophyll fluorescence (SIFnet)." Biogeosciences 19, no. 6 (March 31, 2022): 1777–93. http://dx.doi.org/10.5194/bg-19-1777-2022.
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Abstract. Gross primary productivity (GPP) is the sum of leaf photosynthesis and represents a crucial component of the global carbon cycle. Space-borne estimates of GPP typically rely on observable quantities that co-vary with GPP such as vegetation indices using reflectance measurements (e.g., normalized difference vegetation index, NDVI, near-infrared reflectance of terrestrial vegetation, NIRv, and kernel normalized difference vegetation index, kNDVI). Recent work has also utilized measurements of solar-induced chlorophyll fluorescence (SIF) as a proxy for GPP. However, these SIF measurements are typically coarse resolution, while many processes influencing GPP occur at fine spatial scales. Here, we develop a convolutional neural network (CNN), named SIFnet, that increases the resolution of SIF from the TROPOspheric Monitoring Instrument (TROPOMI) on board of the satellite Sentinel-5P by a factor of 10 to a spatial resolution of 500 m. SIFnet utilizes coarse SIF observations together with high-resolution auxiliary data. The auxiliary data used here may carry information related to GPP and SIF. We use training data from non-US regions between April 2018 until March 2021 and evaluate our CNN over the conterminous United States (CONUS). We show that SIFnet is able to increase the resolution of TROPOMI SIF by a factor of 10 with a r2 and RMSE metrics of 0.92 and 0.17 mW m−2 sr−1 nm−1, respectively. We further compare SIFnet against a recently developed downscaling approach and evaluate both methods against independent SIF measurements from Orbiting Carbon Observatory 2 and 3 (together OCO-2/3). SIFnet performs systematically better than the downscaling approach (r=0.78 for SIFnet, r=0.72 for downscaling), indicating that it is picking up on key features related to SIF and GPP. Examination of the feature importance in the neural network indicates a few key parameters and the spatial regions in which these parameters matter. Namely, the CNN finds low-resolution SIF data to be the most significant parameter with the NIRv vegetation index as the second most important parameter. NIRv consistently outperforms the recently proposed kNDVI vegetation index. Advantages and limitations of SIFnet are investigated and presented through a series of case studies across the United States. SIFnet represents a robust method to infer continuous, high-spatial-resolution SIF data.
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Tagesson, Torbern, Jonas Ardö, Bernard Cappelaere, Laurent Kergoat, Abdulhakim Abdi, Stéphanie Horion, and Rasmus Fensholt. "Modelling spatial and temporal dynamics of gross primary production in the Sahel from earth-observation-based photosynthetic capacity and quantum efficiency." Biogeosciences 14, no. 5 (March 17, 2017): 1333–48. http://dx.doi.org/10.5194/bg-14-1333-2017.
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Abstract. It has been shown that vegetation growth in semi-arid regions is important to the global terrestrial CO2 sink, which indicates the strong need for improved understanding and spatially explicit estimates of CO2 uptake (gross primary production; GPP) in semi-arid ecosystems. This study has three aims: (1) to evaluate the MOD17A2H GPP (collection 6) product against GPP based on eddy covariance (EC) for six sites across the Sahel; (2) to characterize relationships between spatial and temporal variability in EC-based photosynthetic capacity (Fopt) and quantum efficiency (α) and vegetation indices based on earth observation (EO) (normalized difference vegetation index (NDVI), renormalized difference vegetation index (RDVI), enhanced vegetation index (EVI) and shortwave infrared water stress index (SIWSI)); and (3) to study the applicability of EO upscaled Fopt and α for GPP modelling purposes. MOD17A2H GPP (collection 6) drastically underestimated GPP, most likely because maximum light use efficiency is set too low for semi-arid ecosystems in the MODIS algorithm. Intra-annual dynamics in Fopt were closely related to SIWSI being sensitive to equivalent water thickness, whereas α was closely related to RDVI being affected by chlorophyll abundance. Spatial and inter-annual dynamics in Fopt and α were closely coupled to NDVI and RDVI, respectively. Modelled GPP based on Fopt and α upscaled using EO-based indices reproduced in situ GPP well for all except a cropped site that was strongly impacted by anthropogenic land use. Upscaled GPP for the Sahel 2001–2014 was 736 ± 39 g C m−2 yr−1. This study indicates the strong applicability of EO as a tool for spatially explicit estimates of GPP, Fopt and α; incorporating EO-based Fopt and α in dynamic global vegetation models could improve estimates of vegetation production and simulations of ecosystem processes and hydro-biochemical cycles.
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Whitley, Rhys, Jason Beringer, Lindsay B. Hutley, Gab Abramowitz, Martin G. De Kauwe, Remko Duursma, Bradley Evans, et al. "A model inter-comparison study to examine limiting factors in modelling Australian tropical savannas." Biogeosciences 13, no. 11 (June 3, 2016): 3245–65. http://dx.doi.org/10.5194/bg-13-3245-2016.
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Abstract. The savanna ecosystem is one of the most dominant and complex terrestrial biomes, deriving from a distinct vegetative surface comprised of co-dominant tree and grass populations. While these two vegetation types co-exist functionally, demographically they are not static but are dynamically changing in response to environmental forces such as annual fire events and rainfall variability. Modelling savanna environments with the current generation of terrestrial biosphere models (TBMs) has presented many problems, particularly describing fire frequency and intensity, phenology, leaf biochemistry of C3 and C4 photosynthesis vegetation, and root-water uptake. In order to better understand why TBMs perform so poorly in savannas, we conducted a model inter-comparison of six TBMs and assessed their performance at simulating latent energy (LE) and gross primary productivity (GPP) for five savanna sites along a rainfall gradient in northern Australia. Performance in predicting LE and GPP was measured using an empirical benchmarking system, which ranks models by their ability to utilise meteorological driving information to predict the fluxes. On average, the TBMs performed as well as a multi-linear regression of the fluxes against solar radiation, temperature and vapour pressure deficit but were outperformed by a more complicated nonlinear response model that also included the leaf area index (LAI). This identified that the TBMs are not fully utilising their input information effectively in determining savanna LE and GPP and highlights that savanna dynamics cannot be calibrated into models and that there are problems in underlying model processes. We identified key weaknesses in a model's ability to simulate savanna fluxes and their seasonal variation, related to the representation of vegetation by the models and root-water uptake. We underline these weaknesses in terms of three critical areas for development. First, prescribed tree-rooting depths must be deep enough, enabling the extraction of deep soil-water stores to maintain photosynthesis and transpiration during the dry season. Second, models must treat grasses as a co-dominant interface for water and carbon exchange rather than a secondary one to trees. Third, models need a dynamic representation of LAI that encompasses the dynamic phenology of savanna vegetation and its response to rainfall interannual variability. We believe that this study is the first to assess how well TBMs simulate savanna ecosystems and that these results will be used to improve the representation of savannas ecosystems in future global climate model studies.
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Whitley, R., J. Beringer, L. Hutley, G. Abramowitz, M. G. De Kauwe, R. Duursma, B. Evans, et al. "A model inter-comparison study to examine limiting factors in modelling Australian tropical savannas." Biogeosciences Discussions 12, no. 23 (December 2, 2015): 18999–9041. http://dx.doi.org/10.5194/bgd-12-18999-2015.
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Abstract. Savanna ecosystems are one of the most dominant and complex terrestrial biomes that derives from a distinct vegetative surface comprised of co-dominant tree and grass populations. While these two vegetation types co-exist functionally, demographically they are not static, but are dynamically changing in response to environmental forces such as annual fire events and rainfall variability. Modelling savanna environments with the current generation of terrestrial biosphere models (TBMs) has presented many problems, particularly describing fire frequency and intensity, phenology, leaf biochemistry of C3 and C4 photosynthesis vegetation, and root water uptake. In order to better understand why TBMs perform so poorly in savannas, we conducted a model inter-comparison of 6 TBMs and assessed their performance at simulating latent energy (LE) and gross primary productivity (GPP) for five savanna sites along a rainfall gradient in northern Australia. Performance in predicting LE and GPP was measured using an empirical benchmarking system, which ranks models by their ability to utilise meteorological driving information to predict the fluxes. On average, the TBMs performed as well as a multi-linear regression of the fluxes against solar radiation, temperature and vapour pressure deficit, but were outperformed by a more complicated nonlinear response model that also included the leaf area index (LAI). This identified that the TBMs are not fully utilising their input information effectively in determining savanna LE and GPP, and highlights that savanna dynamics cannot be calibrated into models and that there are problems in underlying model processes. We identified key weaknesses in a model's ability to simulate savanna fluxes and their seasonal variation, related to the representation of vegetation by the models and root water uptake. We underline these weaknesses in terms of three critical areas for development. First, prescribed tree-rooting depths must be deep enough, enabling the extraction of deep soil water stores to maintain photosynthesis and transpiration during the dry season. Second, models must treat grasses as a co-dominant interface for water and carbon exchange, rather than a secondary one to trees. Third, models need a dynamic representation of LAI that encompasses the dynamic phenology of savanna vegetation and its response to rainfall interannual variability. We believe this study is the first to assess how well TBMs simulate savanna ecosystems, and that these results will be used to improve the representation of savannas ecosystems in future global climate model studies.
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Gea-Izquierdo, G., F. Guibal, R. Joffre, J. M. Ourcival, G. Simioni, and J. Guiot. "Modelling the climatic drivers determining photosynthesis and carbon allocation in evergreen Mediterranean forests using multiproxy long time series." Biogeosciences Discussions 12, no. 3 (February 6, 2015): 2745–86. http://dx.doi.org/10.5194/bgd-12-2745-2015.
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Abstract. Climatic drivers limit several important physiological processes involved in ecosystem carbon dynamics including gross primary productivity (GPP) and carbon allocation in vegetation. Climatic variability limits these two processes differently. We developed an existing mechanistic model to analyse photosynthesis and variability in carbon allocation in two evergreen species at two Mediterranean forests. The model was calibrated using a combination of eddy covariance CO2 flux data, dendrochronological time series of secondary growth and forest inventory data. The model was modified to be climate explicit in the key processes addressing acclimation of photosynthesis and allocation. It succeeded to fit both the high- and the low-frequency response of stand GPP and carbon allocation to the stem. This would support its capability to address both carbon source and sink limitations. Simulations suggest a decrease in mean stomatal conductance in response to environmental changes and an increase in mean annual intrinsic water use efficiency (iWUE) in both species during the last 50 years. However, this was not translated on a parallel increase in ecosystem water use efficiency (WUE). A long-term decrease in annual GPP matched the local trend in precipitation since the 1970s observed in one site. In contrast, GPP did not show a negative trend and the trees buffered the climatic variability observed at the site where long-term precipitation remained stable. In our simulations these temporal changes would be partly related to increasing [CO2] because the model includes biochemical equations where photosynthesis is directly linked to [CO2]. Long-term trends in GPP did not match those in growth, in agreement with the C-sink hypothesis. There is a great potential to use the model with abundant dendrochronological data and analyse forest performance under climate change. This would help to understand how different interfering environmental factors produce instability in the climatic signal expressed in tree-rings.
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Zhou, Xuqiang, Xufeng Wang, Songlin Zhang, Yang Zhang, and Xuejie Bai. "Combining Phenological Camera Photos and MODIS Reflectance Data to Predict GPP Daily Dynamics for Alpine Meadows on the Tibetan Plateau." Remote Sensing 12, no. 22 (November 13, 2020): 3735. http://dx.doi.org/10.3390/rs12223735.
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Gross primary production (GPP) is the overall photosynthetic fixation of carbon per unit space and time. Due to uncertainties resulting from clouds, snow, aerosol, and topography, it is a challenging task to accurately estimate daily GPP. Daily digital photos from a phenological camera record vegetation daily greenness dynamics with little cloud or aerosol disturbance. It can be fused with satellite remote sensing data to improve daily GPP prediction accuracy. In this study, we combine the two types of datasets to improve the estimation accuracy of GPP for alpine meadow on the Tibetan Plateau. To examine the performance of different methods and vegetation indices (VIs), three experiments were designed. First, GPP was estimated with the light use efficiency (LUE) model with the green chromatic coordinate (GCC) from the phenological camera and vegetation index from MODIS, respectively. Second, GPP was estimated with the Backpropagation neural network machine learning algorithm (BNNA) method with GCC from the phenological camera and vegetation index from MODIS, respectively. Finally, GPP was estimated with the BNNA method using GCC and vegetation index as inputs at the same time. Compared with eddy covariance GPP, GPP predicted by the BNNA method with GCC and vegetation indices as inputs at the same time showed the highest accuracy of all the experiments. The results indicated that GCC had a higher accuracy than NDVI and EVI when only one vegetation index data was used in the LUE model or the BNNA method. The R2 of GPP estimated by BNNA and GPP from eddy covariance increased by 0.12 on average, RMSE decreased by 1.13 g C·m−2·day−1 on average, and MAD decreased by 0.87 g C·m−2·day−1 on average compared with GPP estimated by the traditional LUE model and GPP from eddy covariance. This study puts forth a new way to improve the estimation accuracy of GPP on the Tibetan Plateau. With the emergence of a large number of phenological cameras, this method has great potential for use on the Tibetan Plateau, which is heavily affected by clouds and snow.
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Kayiranga, Alphonse, Baozhang Chen, Fei Wang, Winny Nthangeni, Adil Dilawar, Yves Hategekimana, Huifang Zhang, and Lifeng Guo. "Spatiotemporal Variation in Gross Primary Productivity and Their Responses to Climate in the Great Lakes Region of Sub-Saharan Africa during 2001–2020." Sustainability 14, no. 5 (February 24, 2022): 2610. http://dx.doi.org/10.3390/su14052610.
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The impacts of climate on spatiotemporal variations of eco-physiological and bio-physical factors have been widely explored in previous research, especially in dry areas. However, the understanding of gross primary productivity (GPP) variations and its interactions with climate in humid and semi-humid areas remains unclear. Based on hyperspectral satellite remotely sensed vegetation phenology processes and related indices and the re-analysed climate datasets, we investigated the seasonal and inter-annual variability of GPP by using different light-use efficiency (LUE) models including the Carnegie-Ames-Stanford Approaches (CASA) model, vegetation photosynthesis models (VPMChl and VPMCanopy) and Moderate Resolution Imaging Spectroradiometer (MODIS) GPP products (MOD17A2H) during 2001–2020 over the Great Lakes region of Sub-Saharan Africa (GLR-SSA). The models’ validation against the in situ GPP-based upscaled observations (GPP-EC) indicated that these three models can explain 82%, 79% and 80% of GPP variations with root mean square error (RMSE) values of 5.7, 8.82 and 10.12 g C·m−2·yr−1, respectively. The spatiotemporal variations of GPP showed that the GLR-SSA experienced: (i) high GPP values during December-May; (ii) high annual GPP increase during 2002–2003, 2011–2013 and 2015–2016 and annual decreasing with a marked alternation in other years; (iii) evergreen broadleaf forests having the highest GPP values while grasslands and croplands showing lower GPP values. The spatial correlation between GPP and climate factors indicated 60% relative correlation between precipitation and GPP and 65% correction between surface air temperature and GPP. The results also showed high GPP values under wet conditions (in rainy seasons and humid areas) that significantly fell by the rise of dry conditions (in long dry season and arid areas). Therefore, these results showed that climate factors have potential impact on GPP variability in this region. However, these findings may provide a better understanding of climate implications on GPP variability in the GLR-SSA and other tropical climate zones.
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Xu, Xiaojun, Yan Tang, Yiling Qu, Zhongsheng Zhou, and Junguo Hu. "Global Vegetation Photosynthetic Phenology Products Based on MODIS Vegetation Greenness and Temperature: Modeling and Evaluation." Remote Sensing 13, no. 24 (December 14, 2021): 5080. http://dx.doi.org/10.3390/rs13245080.
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Land surface phenology (LSP) products that are derived from different data sources have different definitions and biophysical meanings. Discrepancies among these products and their linkages with carbon fluxes across plant functional types and climatic regions remain somewhat unclear. In this study, to differentiate LSP related to gross primary production (GPP) from LSP related to remote sensing data, we defined the former as vegetation photosynthetic phenology (VPP), including the starting and ending days of GPP (SOG and EOG, respectively). Specifically, we estimated VPP based on a combination of observed VPP from 145 flux-measured GPP sites together with the vegetation index and temperature data from MODIS products using multiple linear regression models. We then compared VPP estimates with MODIS LSP on a global scale. Our results show that the VPP provided better estimates of SOG and EOG than MODIS LSP, with a root mean square error (RMSE) for SOG of 12.7 days and a RMSE for EOG of 10.5 days. The RMSE was approximately three weeks for both SOG and EOG estimates of the non-forest type. Discrepancies between VPP and LSP estimates varied across plant functional types (PFTs) and climatic regions. A high correlation was observed between VPP and LSP estimates for deciduous forest. For most PFTs, using VPP estimates rather than LSP improved the estimation of GPP. This study presents a useful method for modeling global VPP, investigates in detail the discrepancies between VPP and LSP, and provides a more effective global vegetation phenology product for carbon cycle modeling than the existing ones.
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Leroux, Delphine, Jean-Christophe Calvet, Simon Munier, and Clément Albergel. "Using Satellite-Derived Vegetation Products to Evaluate LDAS-Monde over the Euro-Mediterranean Area." Remote Sensing 10, no. 8 (July 31, 2018): 1199. http://dx.doi.org/10.3390/rs10081199.
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Within a global Land Data Assimilation System (LDAS-Monde), satellite-derived Surface Soil Moisture (SSM) and Leaf Area Index (LAI) products are jointly assimilated with a focus on the Euro-Mediterranean region at 0.5∘ resolution between 2007 and 2015 to improve the monitoring quality of land surface variables. These products are assimilated in the CO2 responsive version of ISBA (Interactions between Soil, Biosphere and Atmosphere) land surface model, which is able to represent the vegetation processes including the functional relationship between stomatal aperture and photosynthesis, plant growth and mortality (ISBA-A-gs). This study shows the positive impact on SSM and LAI simulations through assimilating their satellite-derived counterparts into the model. Using independent flux estimates related to vegetation dynamics (evapotranspiration, Sun-Induced Fluorescence (SIF) and Gross Primary Productivity (GPP)), it is also shown that simulated water and CO2 fluxes are improved with the assimilation. These vegetation products tend to have higher root-mean-square deviations in summer when their values are also at their highest, representing 20–35% of their absolute values. Moreover, the connection between SIF and GPP is investigated, showing a linear relationship depending on the vegetation type with correlation coefficient values larger than 0.8, which is further improved by the assimilation.
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Cheng, Xiangfen, Yu Zhou, Meijun Hu, Feng Wang, Hui Huang, and Jinsong Zhang. "The Links between Canopy Solar-Induced Chlorophyll Fluorescence and Gross Primary Production Responses to Meteorological Factors in the Growing Season in Deciduous Broadleaf Forest." Remote Sensing 13, no. 12 (June 17, 2021): 2363. http://dx.doi.org/10.3390/rs13122363.
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Solar-induced chlorophyll fluorescence (SIF) is a hopeful indicator, which along with remote sensing, is used to measure the photosynthetic efficiency and gross primary production (GPP) of vegetation in regional terrestrial ecosystems. Studies have found a significant linear correlation between SIF and GPP in a variety of ecosystems. However, this relationship has mainly been established using SIF and GPP data derived from satellite remote sensing and continuous ground-based observations, respectively, which are difficult to accurately match. To overcome this, some studies have begun to use tower-based automatic observation instruments to study the changes of near-surface SIF and GPP. This study conducts continuous simultaneous observation of SIF, carbon flux, and meteorological factors on the forest canopy of a cork oak plantation during the growing season to explore how meteorological factors impact on canopy SIF and its relationship with GPP. This research found that the canopy SIF has obvious diurnal and day-to-day variations during the growing season but overall is relatively stable. Furthermore, SIF is greatly affected by incident radiation in different weather conditions and can change daily. Meteorological factors have a major role in the relationship between SIF and GPP; overall, the relationship shows a significant linear regression on the 30 min scale, but weakens when aggregating to the diurnal scale. Photosynthetically active radiation (PAR) drives SIF on a daily basis and changes the relationship between SIF and GPP on a seasonal timescale. As PAR increases, the daily slopes of the linear regressions between SIF and GPP decrease. On the 30 min timescale, both SIF and GPP increase with PAR until it reaches 1250 μmol·m−2·s−1; subsequently, SIF continues to increase while GPP decreases and they show opposite trends. Soil moisture and vapor pressure deficit influence SIF and GPP, respectively. Our findings demonstrate that meteorological factors affect the relationship between SIF and GPP, thereby enhancing the understanding of the mechanistic link between chlorophyll fluorescence and photosynthesis.
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Vesala, T., S. Launiainen, P. Kolari, J. Pumpanen, S. Sevanto, P. Hari, E. Nikinmaa, et al. "Autumn temperature and carbon balance of a boreal Scots pine forest in Southern Finland." Biogeosciences 7, no. 1 (January 13, 2010): 163–76. http://dx.doi.org/10.5194/bg-7-163-2010.
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Abstract. We analyzed the dynamics of carbon balance components: gross primary production (GPP) and total ecosystem respiration (TER), of a boreal Scots pine forest in Southern Finland. The main focus is on investigations of environmental drivers of GPP and TER and how they affect the inter-annual variation in the carbon balance in autumn (September–December). We used standard climate data and CO2 exchange measurements collected by the eddy covariance (EC) technique over 11 years. EC data revealed that increasing autumn temperature significantly enhances TER: the temperature sensitivity was 9.5 gC m−2 °C−1 for the period September–October (early autumn when high radiation levels still occur) and 3.8 gC m−2 °C−1 for November–December (late autumn with suppressed radiation level). The cumulative GPP was practically independent of the temperature in early autumn. In late autumn, air temperature could explain part of the variation in GPP but the temperature sensitivity was very weak, less than 1 gC m−2 °C−1. Two models, a stand photosynthesis model (COCA) and a global vegetation model (ORCHIDEE), were used for estimating stand GPP and its sensitivity to the temperature. The ORCHIDEE model was tested against the observations of GPP derived from EC data. The stand photosynthesis model COCA predicted that under a predescribed 3–6 °C temperature increase, the temperature sensitivity of 4–5 gC m−2 °C−1 in GPP may appear in early autumn. The analysis by the ORCHIDEE model revealed the model sensitivity to the temporal treatment of meteorological forcing. The model predictions were similar to observed ones when the site level 1/2-hourly time step was applied, but the results calculated by using daily meteorological forcing, interpolated to 1/2-hourly time step, were biased. This is due to the nonlinear relationship between the processes and the environmental factors.
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Lin, S., J. Li, and Q. Liu. "ESTIMATING GROSS PRIMARY PRODUCTION IN CROPLAND WITH HIGH SPATIAL AND TEMPORAL SCALE REMOTE SENSING DATA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3 (April 30, 2018): 1009–14. http://dx.doi.org/10.5194/isprs-archives-xlii-3-1009-2018.
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Satellite remote sensing data provide spatially continuous and temporally repetitive observations of land surfaces, and they have become increasingly important for monitoring large region of vegetation photosynthetic dynamic. But remote sensing data have their limitation on spatial and temporal scale, for example, higher spatial resolution data as Landsat data have 30-m spatial resolution but 16&thinsp;days revisit period, while high temporal scale data such as geostationary data have 30-minute imaging period, which has lower spatial resolution (&gt;&thinsp;1&thinsp;km). The objective of this study is to investigate whether combining high spatial and temporal resolution remote sensing data can improve the gross primary production (GPP) estimation accuracy in cropland. For this analysis we used three years (from 2010 to 2012) Landsat based NDVI data, MOD13 vegetation index product and Geostationary Operational Environmental Satellite (GOES) geostationary data as input parameters to estimate GPP in a small region cropland of Nebraska, US. Then we validated the remote sensing based GPP with the in-situ measurement carbon flux data. Results showed that: 1) the overall correlation between GOES visible band and in-situ measurement photosynthesis active radiation (PAR) is about 50&thinsp;% (R<sup>2</sup>&thinsp;=&thinsp;0.52) and the European Center for Medium-Range Weather Forecasts ERA-Interim reanalysis data can explain 64&thinsp;% of PAR variance (R<sup>2</sup>&thinsp;=&thinsp;0.64); 2) estimating GPP with Landsat 30-m spatial resolution data and ERA daily meteorology data has the highest accuracy(R<sup>2</sup>&thinsp;=&thinsp;0.85, RMSE &lt;&thinsp;3&thinsp;gC/m<sup>2</sup>/day), which has better performance than using MODIS 1-km NDVI/EVI product import; 3) using daily meteorology data as input for GPP estimation in high spatial resolution data would have higher relevance than 8-day and 16-day input. Generally speaking, using the high spatial resolution and high frequency satellite based remote sensing data can improve GPP estimation accuracy in cropland.
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Cañizares, M., A. Moreno, S. Sánchez-Ruiz, and M. A. Gilabert. "Variabilidad de la eficiencia en el uso del carbono a partir de datos MODIS." Revista de Teledetección, no. 48 (June 20, 2017): 1. http://dx.doi.org/10.4995/raet.2017.7044.
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<p>Carbon use efficiency (CUE) describes how efficiently plants incorporate the carbon fixed during photosynthesis into biomass gain and can be calculated as the ratio between net primary production (NPP) and gross primary production (GPP). In this work, annual CUE has been obtained from annual GPP and NPP MODIS products for the peninsular Spain study area throughout eight years. CUE is spatially and temporally analyzed in terms of the vegetation type and annual precipitation and annual average air temperature. Results show that dense vegetation areas with moderate to high levels of precipitation present lower CUE values, whereas more arid areas present the highest CUE values. However, the temperature effect on the spatial variation of CUE is not well characterized. On the other hand, inter-annual variations of CUE of different ecosystems are discussed in terms of inter-annual variations of temperature and precipitation. It is shown that CUE exhibited a positive correlation with precipitation and a negative correlation with temperature in most ecosystems. Thus, CUE decreases when the ecosystem conditions change towards aridity.</p>
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Gourlez de la Motte, Louis, Quentin Beauclaire, Bernard Heinesch, Mathias Cuntz, Lenka Foltýnová, Ladislav Šigut, Natalia Kowalska, et al. "Non-stomatal processes reduce gross primary productivity in temperate forest ecosystems during severe edaphic drought." Philosophical Transactions of the Royal Society B: Biological Sciences 375, no. 1810 (September 7, 2020): 20190527. http://dx.doi.org/10.1098/rstb.2019.0527.
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Severe drought events are known to cause important reductions of gross primary productivity ( GPP ) in forest ecosystems. However, it is still unclear whether this reduction originates from stomatal closure (Stomatal Origin Limitation) and/or non-stomatal limitations (Non-SOL). In this study, we investigated the impact of edaphic drought in 2018 on GPP and its origin (SOL, NSOL) using a dataset of 10 European forest ecosystem flux towers. In all stations where GPP reductions were observed during the drought, these were largely explained by declines in the maximum apparent canopy scale carboxylation rate V CMAX,APP (NSOL) when the soil relative extractable water content dropped below around 0.4. Concurrently, we found that the stomatal slope parameter ( G 1 , related to SOL) of the Medlyn et al . unified optimization model linking vegetation conductance and GPP remained relatively constant. These results strengthen the increasing evidence that NSOL should be included in stomatal conductance/photosynthesis models to faithfully simulate both GPP and water fluxes in forest ecosystems during severe drought. This article is part of the theme issue ‘Impacts of the 2018 severe drought and heatwave in Europe: from site to continental scale’.
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Xin, Qinchuan, Yongjiu Dai, and Xiaoping Liu. "A simple time-stepping scheme to simulate leaf area index, phenology, and gross primary production across deciduous broadleaf forests in the eastern United States." Biogeosciences 16, no. 2 (January 25, 2019): 467–84. http://dx.doi.org/10.5194/bg-16-467-2019.
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Abstract. Terrestrial plants play a key role in regulating the exchange of energy and materials between the land surface and the atmosphere. Robust models that simulate both leaf dynamics and canopy photosynthesis are required to understand vegetation–climate interactions. This study proposes a simple time-stepping scheme to simulate leaf area index (LAI), phenology, and gross primary production (GPP) when forced with climate variables. The method establishes a linear function between steady-state LAI and the corresponding GPP. The method applies the established function and the MOD17 algorithm to form simultaneous equations, which can be solved together numerically. To account for the time-lagged responses of plant growth to environmental conditions, a time-stepping scheme is developed to simulate the LAI time series based on the solved steady-state LAI. The simulated LAI time series is then used to derive the timing of key phenophases and simulate canopy GPP with the MOD17 algorithm. The developed method is applied to deciduous broadleaf forests in the eastern United States and is found to perform well for simulating canopy LAI and GPP at the site scale as evaluated using both flux tower and satellite data. The method also captures the spatiotemporal variation of vegetation LAI and phenology across the eastern United States compared with satellite observations. The developed time-stepping scheme provides a simplified and improved version of our previous modeling approach to simulate leaf phenology and can potentially be applied at regional to global scales in future studies.