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Journal articles on the topic 'Sun-induced chlorophyll fluorescence'

Author: Grafiati

Published: 9 March 2023

Last updated: 25 May 2024

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1

Ni, Zhuoya, Qifeng Lu, Hongyuan Huo, and Huili Zhang. "Estimation of Chlorophyll Fluorescence at Different Scales: A Review." Sensors 19, no. 13 (July 8, 2019): 3000. http://dx.doi.org/10.3390/s19133000.

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Measuring chlorophyll fluorescence is a direct and non-destructive way to monitor vegetation. In this paper, the fluorescence retrieval methods from multiple scales, ranging from near the ground to the use of space-borne sensors, are analyzed and summarized in detail. At the leaf-scale, the chlorophyll fluorescence is measured using active and passive technology. Active remote sensing technology uses a fluorimeter to measure the chlorophyll fluorescence, and passive remote sensing technology mainly depends on the sun-induced chlorophyll fluorescence filling in the Fraunhofer lines or oxygen absorptions bands. Based on these retrieval principles, many retrieval methods have been developed, including the radiance-based methods and the reflectance-based methods near the ground, as well as physically and statistically-based methods that make use of satellite data. The advantages and disadvantages of different approaches for sun-induced chlorophyll fluorescence retrieval are compared and the key issues of the current sun-induced chlorophyll fluorescence retrieval algorithms are discussed. Finally, conclusions and key problems are proposed for the future research.

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2

Irteza, S. M., and J. E. Nichol. "MEASUREMENT OF SUN INDUCED CHLOROPHYLL FLUORESCENCE USING HYPERSPECTRAL SATELLITE IMAGERY." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B8 (June 23, 2016): 911–13. http://dx.doi.org/10.5194/isprs-archives-xli-b8-911-2016.

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Solar Induced Chlorophyll Fluorescence (SIF), can be used as an indicator of stress in vegetation. Several scientific approaches have been made and there is considerable evidence that steady state Chlorophyll fluorescence is an accurate indicator of plant stress hence a reliable tool to monitor vegetation health status. Retrieval of Chlorophyll fluorescence provides an insight into photochemical and carbon sequestration processes within vegetation. Detection of Chlorophyll fluorescence has been well understood in the laboratory and field measurement. Fluorescence retrieval methods were applied in and around the atmospheric absorption bands 02B (Red wavelength) approximately 690 nm and 02A (Far red wavelengths) 740 nm. Hyperion satellite images were acquired for the years 2012 to 2015 in different seasons. Atmospheric corrections were applied using the 6S Model. The Fraunhofer Line Discrimanator (FLD) method was applied for retrieval of SIF from the Hyperion images by measuring the signal around the absorption bands in both vegetated and non vegetated land cover types. Absorption values were extracted in all the selected bands and the fluorescence signal was detected. The relationships between NDVI and Fluorescence derived from the satellite images are investigated to understand vegetation response within the absorption bands.

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3

Irteza, S. M., and J. E. Nichol. "MEASUREMENT OF SUN INDUCED CHLOROPHYLL FLUORESCENCE USING HYPERSPECTRAL SATELLITE IMAGERY." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B8 (June 23, 2016): 911–13. http://dx.doi.org/10.5194/isprsarchives-xli-b8-911-2016.

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Solar Induced Chlorophyll Fluorescence (SIF), can be used as an indicator of stress in vegetation. Several scientific approaches have been made and there is considerable evidence that steady state Chlorophyll fluorescence is an accurate indicator of plant stress hence a reliable tool to monitor vegetation health status. Retrieval of Chlorophyll fluorescence provides an insight into photochemical and carbon sequestration processes within vegetation. Detection of Chlorophyll fluorescence has been well understood in the laboratory and field measurement. Fluorescence retrieval methods were applied in and around the atmospheric absorption bands 02B (Red wavelength) approximately 690 nm and 02A (Far red wavelengths) 740 nm. Hyperion satellite images were acquired for the years 2012 to 2015 in different seasons. Atmospheric corrections were applied using the 6S Model. The Fraunhofer Line Discrimanator (FLD) method was applied for retrieval of SIF from the Hyperion images by measuring the signal around the absorption bands in both vegetated and non vegetated land cover types. Absorption values were extracted in all the selected bands and the fluorescence signal was detected. The relationships between NDVI and Fluorescence derived from the satellite images are investigated to understand vegetation response within the absorption bands.

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4

Fournier, A., F. Daumard, S. Champagne, A. Ounis, Y. Goulas, and I. Moya. "Effect of canopy structure on sun-induced chlorophyll fluorescence." ISPRS Journal of Photogrammetry and Remote Sensing 68 (March 2012): 112–20. http://dx.doi.org/10.1016/j.isprsjprs.2012.01.003.

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5

Marler, Thomas E., and Patrick D. Lawton. "Movement Influences Carambola Leaflet Chlorophyll Fluorescence and Temperature under Sunny Conditions." Journal of the American Society for Horticultural Science 120, no. 2 (March 1995): 360–61. http://dx.doi.org/10.21273/jashs.120.2.360.

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Leaflets of `Arkin', `B-10', `Kary', and `Sri Kembangan' carambola (Averrhoa carambola L.) trees were restrained in a horizontal position for 3.5 h during midday under full sun conditions to determine the influence of overriding natural leaflet movement on adaxial chlorophyll fluorescence and temperature. Induced chlorophyll fluorescence obtained after 30 minutes of dark adaptation following the period of full sun exposure was affected by leaflet movement. Restrained leaflets exhibited a variable fluorescence (Fv)/peak fluorescence (Fm) of 0.48, while that of unrestrained leaflets was 0.65. Adaxial leaflet temperature of restrained leaflets was 6C higher than that of leaflets that were allowed to move. The influence of leaflet movement on temperature or chlorophyll fluorescence was not different among the four cultivars. However, mean Fv/Fm of `Kary' and `Sri Kembangan' was lower than that of `B-10'. Our results indicate that the ability of carambola to change leaflet angle leads to lower temperature and higher photochemical efficiency than occurs when leaflets are not allowed to move naturally (vertically orient) under full sun conditions.

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6

Pacheco-Labrador, Hueni, Mihai, Sakowska, Julitta, Kuusk, Sporea, et al. "Sun-Induced Chlorophyll Fluorescence I: Instrumental Considerations for Proximal Spectroradiometers." Remote Sensing 11, no. 8 (April 22, 2019): 960. http://dx.doi.org/10.3390/rs11080960.

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Growing interest in the proximal sensing of sun‐induced chlorophyll fluorescence (SIF) has been boosted by space-based retrievals and up-coming missions such as the FLuorescence EXplorer (FLEX). The European COST Action ES1309 “Innovative optical tools for proximal sensing of ecophysiological processes” (OPTIMISE, ES1309; https://optimise.dcs.aber.ac.uk/) has produced three manuscripts addressing the main current challenges in this field. This article provides a framework to model the impact of different instrument noise and bias on the retrieval of SIF; and to assess uncertainty requirements for the calibration and characterization of state-of-the-art SIF-oriented spectroradiometers. We developed a sensor simulator capable of reproducing biases and noises usually found in field spectroradiometers. First the sensor simulator was calibrated and characterized using synthetic datasets of known uncertainties defined from laboratory measurements and literature. Secondly, we used the sensor simulator and the characterized sensor models to simulate the acquisition of atmospheric and vegetation radiances from a synthetic dataset. Each of the sensor models predicted biases with propagated uncertainties that modified the simulated measurements as a function of different factors. Finally, the impact of each sensor model on SIF retrieval was analyzed. Results show that SIF retrieval can be significantly affected in situations where reflectance factors are barely modified. SIF errors were found to correlate with drivers of instrumental-induced biases which are as also drivers of plant physiology. This jeopardizes not only the retrieval of SIF, but also the understanding of its relationship with vegetation function, the study of diel and seasonal cycles and the validation of remote sensing SIF products. Further work is needed to determine the optimal requirements in terms of sensor design, characterization and signal correction for SIF retrieval by proximal sensing. In addition, evaluation/validation methods to characterize and correct instrumental responses should be developed and used to test sensors performance in operational conditions.

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7

Migliavacca, Mirco, Lianhong Gu, Jeffrey D. Woods, and Georg Wohlfahrt. "Editorial special issue: Advancing foundational sun-induced chlorophyll fluorescence science." Agricultural and Forest Meteorology 337 (June 2023): 109499. http://dx.doi.org/10.1016/j.agrformet.2023.109499.

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8

Li, Shilei, Maofang Gao, and Zhao-Liang Li. "Retrieving Sun-Induced Chlorophyll Fluorescence from Hyperspectral Data with TanSat Satellite." Sensors 21, no. 14 (July 18, 2021): 4886. http://dx.doi.org/10.3390/s21144886.

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A series of algorithms for satellite retrievals of sun-induced chlorophyll fluorescence (SIF) have been developed and applied to different sensors. However, research on SIF retrieval using hyperspectral data is performed in narrow spectral windows, assuming that SIF remains constant. In this paper, based on the singular vector decomposition (SVD) technique, we present an approach for retrieving SIF, which can be applied to remotely sensed data with ultra-high spectral resolution and in a broad spectral window without assuming that the SIF remains constant. The idea is to combine the first singular vector, the pivotal information of the non-fluorescence spectrum, with the low-frequency contribution of the atmosphere, plus a linear combination of the remaining singular vectors to express the non-fluorescence spectrum. Subject to instrument settings, the retrieval was performed within a spectral window of approximately 7 nm that contained only Fraunhofer lines. In our retrieval, hyperspectral data of the O2-A band from the first Chinese carbon dioxide observation satellite (TanSat) was used. The Bayesian Information Criterion (BIC) was introduced to self-adaptively determine the number of free parameters and reduce retrieval noise. SIF retrievals were compared with TanSat SIF and OCO-2 SIF. The results showed good consistency and rationality. A sensitivity analysis was also conducted to verify the performance of this approach. To summarize, the approach would provide more possibilities for retrieving SIF from hyperspectral data.

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9

Kohler, Philipp, Luis Guanter, and Christian Frankenberg. "Simplified physically based retrieval of sun-induced chlorophyll fluorescence from GOSAT data." IEEE Geoscience and Remote Sensing Letters 12, no. 7 (July 2015): 1446–50. http://dx.doi.org/10.1109/lgrs.2015.2407051.

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10

Yang, Peiqi, and Christiaan van der Tol. "Linking canopy scattering of far-red sun-induced chlorophyll fluorescence with reflectance." Remote Sensing of Environment 209 (May 2018): 456–67. http://dx.doi.org/10.1016/j.rse.2018.02.029.

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11

ZHAN, Chunhui, Zhaoying ZHANG, and Yongguang ZHANG. "Recent advances in the radiative transfer models of sun-induced chlorophyll fluorescence." National Remote Sensing Bulletin 24, no. 8 (2020): 945–57. http://dx.doi.org/10.11834/jrs.20209379.

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12

ZHANG, Zhaoying, Songhan WANG, Bo QIU, Lian SONG, and Yongguang ZHANG. "Retrieval of sun-induced chlorophyll fluorescence and advancements in carbon cycle application." National Remote Sensing Bulletin 23, no. 1 (2019): 37–52. http://dx.doi.org/10.11834/jrs.20197485.

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13

Jiaochan, HU, LIU Liangyun, and LIU Xinjie. "Assessing uncertainties of sun-induced chlorophyll fluorescence retrieval using Fluor MOD model." National Remote Sensing Bulletin 19, no. 4 (2015): 594–608. http://dx.doi.org/10.11834/jrs.20154053.

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14

Wang, Hongyu, Yiren Ding, Qiushuang Yao, Lulu Ma, Yiru Ma, Mi Yang, Shizhe Qin, Feng Xu, Ze Zhang, and Zhe Gao. "Modeling of Cotton Yield Estimation Based on Canopy Sun-Induced Chlorophyll Fluorescence." Agronomy 14, no. 2 (February 12, 2024): 364. http://dx.doi.org/10.3390/agronomy14020364.

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Cotton yield estimation is of great practical significance to producers, allowing them to make rational management decisions. At present, crop yield estimation methods mainly comprise traditional agricultural yield estimation methods, which have many shortcomings. As an ideal “probe” for detecting crop photosynthesis, sun-induced chlorophyll fluorescence (SIF) can directly reflect the dynamics of actual crop photosynthesis and has the potential to predict crop yield, in order to realize cotton yield estimation based on canopy SIF. In this study, we set up field trials with different nitrogen fertilizer gradients. The changes of canopy SIF and the physiological parameters of cotton in different growth periods were analyzed. To investigate the effects of LAI and AGB on canopy SIF estimation of cotton yield, four algorithms, Ada Boost (Adaptive Boosting), Bagging (Bootstrap Aggregating), RF (Random Forest), and BPNN (Backpropagation Neural Network), were used to construct cotton yield estimation models based on the SIF and SIFy (the normalization of SIF by incident photosynthetically active radiation) for different time and growth periods. The results include the following: (1) The effects of the leaf area index (LAI) and aboveground biomass (AGB) on cotton canopy SIF and cotton yield were similar. The correlation coefficients of LAI and AGB with cotton yield and SIF were significantly positively correlated with each other starting from the budding period, reaching the maximum at the flowering and boll period, and decreasing at the boll period; (2) In different monitoring time periods, the R2 of the cotton yield estimation model established based on SIF and SIFy showed a gradual increase from 10:00 to 14:00 and a gradual decrease from 15:00 to 19:00, while the optimal observation time was from 14:00 to 15:00. The R2 increased with the progression of growth from the budding period to the flowering and boll period and decreased at the boll period, while the optimum growth period was the flowering and boll period; (3) Compared to SIF, SIFy has a superior estimation of yield. The best yield estimation model based on the RF algorithm (R2 = 0.9612, RMSE = 66.27 kg·ha−1, RPD = 4.264) was found in the canopy SIFy of the flowering and boll period at 14:00–15:00, followed by the model utilizing the Bagging algorithm (R2 = 0.8898) and Ada Boost algorithm (R2 = 0.8796). In summary, SIFy eliminates the effect of PAR (photosynthetically active radiation) on SIF and can further improve the estimation of SIF production. This study provides empirical support for SIF estimation of cotton yield and methodological and modeling support for the accurate estimation of cotton yield.

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15

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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16

Kritten, Lena, Rene Preusker, and Jürgen Fischer. "A New Retrieval of Sun-Induced Chlorophyll Fluorescence in Water from Ocean Colour Measurements Applied on OLCI L-1b and L-2." Remote Sensing 12, no. 23 (December 2, 2020): 3949. http://dx.doi.org/10.3390/rs12233949.

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The retrieval of sun-induced chlorophyll fluorescence is greatly beneficial to studies of marine phytoplankton biomass, physiology, and composition, and is required for user applications and services. Customarily phytoplankton chlorophyll fluorescence is determined from satellite measurements through a fluorescence line-height algorithm using three bands around 680 nm. We propose here a modified retrieval, making use of all available bands in the relevant wavelength range, with the goal to improve the effectiveness of the algorithm in optically complex waters. For the Ocean and Land Colour Instrument (OLCI), we quantify a Fluorescence Peak Height by fitting a Gaussian function and related terms to the top-of-atmosphere reflectance bands between 650 and 750 nm. This algorithm retrieves, what we call Fluorescence Peak Height by fitting a Gaussian function upon other terms to top-of-atmosphere reflectance bands between 650 and 750 nm. This approach is applicable to Level-1 and Level-2 data. We find a good correlation of the retrieved fluorescence product to global in-situ chlorophyll measurements, as well as a consistent relation between chlorophyll concentration and fluorescence from radiative transfer modelling and OLCI/in-situ comparison. Evidence suggests, the algorithm is applicable to complex waters without needing an atmospheric correction and vicarious calibration, and features an inherent correction of small spectral shifts, as required for OLCI measurements.

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17

Feng, Huaize, Tongren Xu, Liangyun Liu, Sha Zhou, Jingxue Zhao, Shaomin Liu, Ziwei Xu, et al. "Modeling Transpiration with Sun-Induced Chlorophyll Fluorescence Observations via Carbon-Water Coupling Methods." Remote Sensing 13, no. 4 (February 22, 2021): 804. http://dx.doi.org/10.3390/rs13040804.

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Successfully applied in the carbon research area, sun-induced chlorophyll fluorescence (SIF) has raised the interest of researchers from the water research domain. However, current works focused on the empirical relationship between SIF and plant transpiration (T), while the mechanistic linkage between them has not been fully explored. Two mechanism methods were developed to estimate T via SIF, namely the water-use efficiency (WUE) method and conductance method based on the carbon–water coupling framework. The T estimated by these two methods was compared with T partitioned from eddy covariance instrument measured evapotranspiration at four different sites. Both methods showed good performance at the hourly (R2 = 0.57 for the WUE method and 0.67 for the conductance method) and daily scales (R2 = 0.67 for the WUE method and 0.78 for the conductance method). The developed mechanism methods provide theoretical support and have a great potential basis for deriving ecosystem T by satellite SIF observations.

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18

Zhou, Xijia, Zhigang Liu, Shan Xu, Weiwei Zhang, and Jun Wu. "An Automated Comparative Observation System for Sun-Induced Chlorophyll Fluorescence of Vegetation Canopies." Sensors 16, no. 6 (May 27, 2016): 775. http://dx.doi.org/10.3390/s16060775.

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19

Ma, Yan, Liangyun Liu, Xinjie Liu, and Jidai Chen. "An improved downscaled sun-induced chlorophyll fluorescence (DSIF) product of GOME-2 dataset." European Journal of Remote Sensing 55, no. 1 (January 25, 2022): 168–80. http://dx.doi.org/10.1080/22797254.2022.2028579.

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20

Julitta, Tommaso, Lawrence Corp, Micol Rossini, Andreas Burkart, Sergio Cogliati, Neville Davies, Milton Hom, et al. "Comparison of Sun-Induced Chlorophyll Fluorescence Estimates Obtained from Four Portable Field Spectroradiometers." Remote Sensing 8, no. 2 (February 5, 2016): 122. http://dx.doi.org/10.3390/rs8020122.

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21

Zhang, Lifu, Siheng Wang, Changping Huang, Yi Cen, Yongguang Zhai, and Qingxi Tong. "Retrieval of Sun-Induced Chlorophyll Fluorescence Using Statistical Method Without Synchronous Irradiance Data." IEEE Geoscience and Remote Sensing Letters 14, no. 3 (March 2017): 384–88. http://dx.doi.org/10.1109/lgrs.2016.2644643.

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22

Chou, Shuren, Bin Chen, and Jing M. Chen. "Multi-angular instrument for tower-based observations of canopy sun-induced chlorophyll fluorescence." Instrumentation Science & Technology 48, no. 2 (October 10, 2019): 146–61. http://dx.doi.org/10.1080/10739149.2019.1674326.

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23

Du, Kaiqi, Xia Jing, Yelu Zeng, Qixing Ye, Bingyu Li, and Jianxi Huang. "An Improved Approach to Monitoring Wheat Stripe Rust with Sun-Induced Chlorophyll Fluorescence." Remote Sensing 15, no. 3 (January 24, 2023): 693. http://dx.doi.org/10.3390/rs15030693.

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Sun-induced chlorophyll fluorescence (SIF) has shown potential in quantifying plant responses to environmental changes by which abiotic drivers are dominated. However, SIF is a mixed signal influenced by factors such as leaf physiology, canopy structure, and sun-sensor geometry. Whether the physiological information contained in SIF can better quantify crop disease stresses dominated by biological drivers, and clearly explain the physiological variability of stressed crops, has not yet been sufficiently explored. On this basis, we took winter wheat naturally infected with stripe rust as the research object and conducted a study on the responses of physiological signals and reflectivity spectrum signals to crop disease stress dominated by biological drivers, based on in situ canopy-scale and leaf-scale data. Physiological signals include SIF, SIFyield (normalized by absorbed photosynthetically active radiation), fluorescence yield (ΦF) retrieved by NIRvP (non-physiological components of canopy SIF) and relative fluorescence yield (ΦF-r) retrieved by near-infrared radiance of vegetation (NIRvR). Reflectance spectrum signals include normalized difference vegetation index (NDVI) and near-infrared reflectance of vegetation (NIRv). At the canopy scale, six signals reached extremely significant correlations (P < 0.001) with disease severity levels (SL) under comprehensive experimental conditions (SL without dividing the experimental samples) and light disease conditions (SL<20%). The strongest correlation between NDVI and SL (R=0.69) was observed under the comprehensive experimental conditions, followed by NIRv (R = 0.56), ΦF-r (R=0.53) and SIF (R = 0.51), and the response of ΦF (R = 0.45) and SIFyield (R = 0.34) to SL was weak. Under lightly diseased conditions, ΦF-r (R = 0.62) showed the strongest response to disease, followed by SIFyield (R = 0.60), SIF (R = 0.56) and NIRv (R = 0.54). The weakest correlation was observed between ΦF and SL (R = 0.51), which also showed a result approximating NDVI (R = 0.52). In the case of a high level of crop disease severity, NDVI showed advantages in disease monitoring. In the early stage of crop diseases, which we pay more attention to, compared with SIF and reflectivity spectrum signals, ΦF-r estimated by the newly proposed ‘NIRvR approach’ (which uses SIF together with NIRvR (i.e., SIF/ NIRvR) as a substitute for ΦF) showed superior ability to monitor crop physiological stress, and was more sensitive to plant physiological variation. At the leaf scale, the response of SIF to SL was stronger than that of NDVI. These results validate the potential of ΦF-r estimated by the NIRvR approach to monitoring disease stress dominated by biological drivers, thus providing a new research avenue for quantifying crop responses to disease stress.

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Chen, Shuobo, Li Zhai, Yu'an Zhou, Jiayang Xie, Yiwen Shao, Wen Wang, Hongye Li, Yong He, and Haiyan Cen. "Early diagnosis and mechanistic understanding of citrus Huanglongbing via sun-induced chlorophyll fluorescence." Computers and Electronics in Agriculture 215 (December 2023): 108357. http://dx.doi.org/10.1016/j.compag.2023.108357.

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25

De Cannière, S., M. J. Baur, D. Chaparro, T. Jagdhuber, and F. Jonard. "Water availability and atmospheric dryness controls on spaceborne sun-induced chlorophyll fluorescence yield." Remote Sensing of Environment 301 (February 2024): 113922. http://dx.doi.org/10.1016/j.rse.2023.113922.

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26

Chen, Zhizhong, Mei Zan, Jingjing Kong, Shunfa Yang, and Cong Xue. "Phenology of Vegetation in Arid Northwest China Based on Sun-Induced Chlorophyll Fluorescence." Forests 14, no. 12 (November 24, 2023): 2310. http://dx.doi.org/10.3390/f14122310.

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The accurate monitoring of vegetation phenology is critical for carbon sequestration and sink enhancement. Vegetation phenology in arid zones is more sensitive to climate responses; therefore, it is important to conduct research on phenology in arid zones in response to global climate change. This study compared the applicability of the enhanced vegetation index (EVI), which is superior in arid zones, and global solar-induced chlorophyll fluorescence (GOSIF), which has a high spatial resolution, in extracting vegetation phenology in arid zones, and explored the mechanism of the differences in the effects of environmental factors on the phenology of different vegetation types. Therefore, this study employed a global solar-induced chlorophyll fluorescence (GOSIF) dataset to determine the start and end of the vegetation growth season (SOSSIF and EOSSIF, respectively) in the arid zone of Northwest China from 2001 to 2019. The results were compared with those from the EVI-based MODIS climate product MCD12Q2 (SOSEVI and EOSEVI). Variations in the sensitivity of these climatic datasets concerning temperature, precipitation, and standardised precipitation evapotranspiration index (SPEI) were assessed through partial correlation analysis. Results: Compared to the MCD12Q2 climatic products, SOSSIF and EOSSIF closely matched the observed climate data in the study area. Spring onset was delayed at higher altitudes and latitudes, and the end of the growing season occurred earlier in these areas. Both SOSSIF and EOSSIF significantly advanced from 2001 to 2019 (trend degrees −0.22 and −0.48, respectively). Spring vegetation phenology was chiefly influenced by precipitation while autumn vegetation phenology was driven by both precipitation and SPEI. GOSIF-based climate data provides a more accurate representation of vegetation phenology compared to traditional vegetation indices. The findings of this study contribute to a deeper understanding of the potential ability of EVI and SIF to reveal the influence of vegetation phenology on the carbon cycle.

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Gao, Sicong, William Woodgate, Xuanlong Ma, and Tanya M. Doody. "Prediction of Open Woodland Transpiration Incorporating Sun-Induced Chlorophyll Fluorescence and Vegetation Structure." Remote Sensing 16, no. 1 (December 28, 2023): 143. http://dx.doi.org/10.3390/rs16010143.

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Transpiration (T) represents plant water use, while sun-induced chlorophyll fluorescence (SIF) emitted during photosynthesis, relates well to gross primary production. SIF can be influenced by vegetation structure, while uncertainties remain on how this might impact the relationship between SIF and T, especially for open and sparse woodlands. In this study, a method was developed to map T in riverine floodplain open woodland environments using satellite data coupled with a radiative transfer model (RTM). Specifically, we used FluorFLiES, a three-dimensional SIF RTM, to simulate the full spectrum of SIF for three open woodland sites with varying fractional vegetation cover. Five specific SIF bands were selected to quantify their correlation with field measured T derived from sap flow sensors. The coefficient of determination of the simulated far-red SIF and field measured T at a monthly scale was 0.93. However, when comparing red SIF from leaf scale to canopy scale to predict T, performance declined by 24%. In addition, varying soil reflectance and understory leaf area index had little effect on the correlation between SIF and T. The method developed can be applied regionally to predict tree water use using remotely sensed SIF datasets in areas of low data availability or accessibility.

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28

De Cannière, Simon, Harry Vereecken, Pierre Defourny, and François Jonard. "Remote Sensing of Instantaneous Drought Stress at Canopy Level Using Sun-Induced Chlorophyll Fluorescence and Canopy Reflectance." Remote Sensing 14, no. 11 (May 31, 2022): 2642. http://dx.doi.org/10.3390/rs14112642.

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Climate change amplifies the intensity and occurrence of dry periods leading to drought stress in vegetation. For monitoring vegetation stresses, sun-induced chlorophyll fluorescence (SIF) observations are a potential game-changer, as the SIF emission is mechanistically coupled to photosynthetic activity. Yet, the benefit of SIF for drought stress monitoring is not yet understood. This paper analyses the impact of drought stress on canopy-scale SIF emission and surface reflectance over a lettuce and mustard stand with continuous field spectrometer measurements. Here, the SIF measurements are linked to the plant’s photosynthetic efficiency, whereas the surface reflectance can be used to monitor the canopy structure. The mustard canopy showed a reduction in the biochemical component of its SIF emission (the fluorescence emission efficiency at 760 nm—ϵ760) as a reaction to drought stress, whereas its structural component (the Fluorescence Correction Vegetation Index—FCVI) barely showed a reaction. The lettuce canopy showed both an increase in the variability of its surface reflectance at a sub-daily scale and a decrease in ϵ760 during a drought stress event. These reactions occurred simultaneously, suggesting that sun-induced chlorophyll fluorescence and reflectance-based indices sensitive to the canopy structure provide complementary information. The intensity of these reactions depend on both the soil water availability and the atmospheric water demand. This paper highlights the potential for SIF from the upcoming FLuorescence EXplorer (FLEX) satellite to provide a unique insight on the plant’s water status. At the same time, data on the canopy reflectance with a sub-daily temporal resolution are a promising additional stress indicator for certain species.

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Pinto, Francisco, Marco Celesti, Kelvin Acebron, Giorgio Alberti, Sergio Cogliati, Roberto Colombo, Radosław Juszczak, et al. "Dynamics of sun‐induced chlorophyll fluorescence and reflectance to detect stress‐induced variations in canopy photosynthesis." Plant, Cell & Environment 43, no. 7 (May 3, 2020): 1637–54. http://dx.doi.org/10.1111/pce.13754.

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Cao, Junjun, Qi An, Xiang Zhang, Shan Xu, Tong Si, and Dev Niyogi. "Is satellite Sun-Induced Chlorophyll Fluorescence more indicative than vegetation indices under drought condition?" Science of The Total Environment 792 (October 2021): 148396. http://dx.doi.org/10.1016/j.scitotenv.2021.148396.

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Dechant, Benjamin, Youngryel Ryu, Grayson Badgley, Philipp Köhler, Uwe Rascher, Mirco Migliavacca, Yongguang Zhang, et al. "NIRVP: A robust structural proxy for sun-induced chlorophyll fluorescence and photosynthesis across scales." Remote Sensing of Environment 268 (January 2022): 112763. http://dx.doi.org/10.1016/j.rse.2021.112763.

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Pinto, Francisco, Mark Müller-Linow, Anke Schickling, M. Cendrero-Mateo, Agim Ballvora, and Uwe Rascher. "Multiangular Observation of Canopy Sun-Induced Chlorophyll Fluorescence by Combining Imaging Spectroscopy and Stereoscopy." Remote Sensing 9, no. 5 (April 28, 2017): 415. http://dx.doi.org/10.3390/rs9050415.

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Cendrero-Mateo, Wieneke, Damm, Alonso, Pinto, Moreno, Guanter, et al. "Sun-Induced Chlorophyll Fluorescence III: Benchmarking Retrieval Methods and Sensor Characteristics for Proximal Sensing." Remote Sensing 11, no. 8 (April 22, 2019): 962. http://dx.doi.org/10.3390/rs11080962.

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The interest of the scientific community on the remote observation of sun‐induced chlorophyll fluorescence (SIF) has increased in the recent years. In this context, hyperspectral ground measurements play a crucial role in the calibration and validation of future satellite missions. For this reason, the European cooperation in science and technology (COST) Action ES1309 OPTIMISE has compiled three papers on instrument characterization, measurement setups and protocols, and retrieval methods (current paper). This study is divided in two sections; first, we evaluated the uncertainties in SIF retrieval methods (e.g., Fraunhofer line depth (FLD) approaches and spectral fitting method (SFM)) for a combination of off-the-shelf commercial spectrometers. Secondly, we evaluated how an erroneous implementation of the retrieval methods increases the uncertainty in the estimated SIF values. Results show that the SFM approach applied to high-resolution spectra provided the most reliable SIF retrieval with a relative error (RE) ≤6% and <5% for F687 and F760, respectively. Furthermore, although the SFM was the least affected by an inaccurate definition of the absorption spectral window (RE = 5%) and/or interpolation strategy (RE = 15%–30%), we observed a sensitivity of the SIF retrieval for the simulated training data underlying the SFM model implementation.

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Celesti, Marco, Christiaan van der Tol, Sergio Cogliati, Cinzia Panigada, Peiqi Yang, Francisco Pinto, Uwe Rascher, Franco Miglietta, Roberto Colombo, and Micol Rossini. "Exploring the physiological information of Sun-induced chlorophyll fluorescence through radiative transfer model inversion." Remote Sensing of Environment 215 (September 2018): 97–108. http://dx.doi.org/10.1016/j.rse.2018.05.013.

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Yang, Peiqi, Christiaan van der Tol, Wout Verhoef, Alexander Damm, Anke Schickling, Thorsten Kraska, Onno Muller, and Uwe Rascher. "Using reflectance to explain vegetation biochemical and structural effects on sun-induced chlorophyll fluorescence." Remote Sensing of Environment 231 (September 2019): 110996. http://dx.doi.org/10.1016/j.rse.2018.11.039.

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Dechant, Benjamin, Youngryel Ryu, Grayson Badgley, Yelu Zeng, Joseph A. Berry, Yongguang Zhang, Yves Goulas, et al. "Canopy structure explains the relationship between photosynthesis and sun-induced chlorophyll fluorescence in crops." Remote Sensing of Environment 241 (May 2020): 111733. http://dx.doi.org/10.1016/j.rse.2020.111733.

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37

Rossini, M., L. Nedbal, L. Guanter, A. Ač, L. Alonso, A. Burkart, S. Cogliati, et al. "Red and far red Sun‐induced chlorophyll fluorescence as a measure of plant photosynthesis." Geophysical Research Letters 42, no. 6 (March 18, 2015): 1632–39. http://dx.doi.org/10.1002/2014gl062943.

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Jurásek, A., J. Leugner, and J. Martincová. "Growth and physiological state of beech seedlings grown in a nursery in different light conditions." Journal of Forest Science 56, No. 10 (September 30, 2010): 442–50. http://dx.doi.org/10.17221/8/2010-jfs.

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Seedlings of European beech of two populations (from the 4<sup>th</sup> and 7<sup>th</sup> forest altitudinal zone) were grown in a shaded and unshaded plastic greenhouse. The objective was to compare seedling growth and the function of assimilatory organs and to determine their reactions after transfer to different light conditions.Seedlings grown in the unshaded plastic greenhouse (the sun variant) were taller and stronger at the end of the first growing season and had the higher weight and volume of shoots and root systems than seedlings grown in the shade. A higher number of leaves, larger total leaf area and higher dry matter of leaves per 1 plant were determined in seedlings grown in the sun. The average area of one leaf was larger in seedlings grown in the shade. The higher photosynthetic electron transport rate (ETR) determined from the light curves of chlorophyll fluorescence in seedlings grown in the sun was apparently connected with the higher photosynthetic rate and more intensive growth of these seedlings. The transfer of seedlings from full sun to shade resulted only in small changes in chlorophyll fluorescence (Fv/Fm, ETR). On the contrary, the transfer of seedlings from the shaded plastic greenhouse to the sun induced photoinhibition leading to a significant reduction in the maximum quantum yield of photochemistry Fv/Fm and in the photosynthetic electron transport rate (ETR).

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Jia, Min, Jie Zhu, Chunchen Ma, Luis Alonso, Dong Li, Tao Cheng, Yongchao Tian, Yan Zhu, Xia Yao, and Weixing Cao. "Difference and Potential of the Upward and Downward Sun-Induced Chlorophyll Fluorescence on Detecting Leaf Nitrogen Concentration in Wheat." Remote Sensing 10, no. 8 (August 20, 2018): 1315. http://dx.doi.org/10.3390/rs10081315.

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Precise detection of leaf nitrogen concentration (LNC) is helpful for nutrient diagnosis and fertilization guidance in farm crops. Numerous researchers have estimated LNC with techniques based on reflectance spectra or active chlorophyll fluorescence, which have limitations of low accuracy or small scale in the field. Given the correlation between chlorophyll and nitrogen contents, the response of sun-induced chlorophyll fluorescence (SIF) to chlorophyll (Chl) content reported in a few papers suggests the feasibility of quantifying LNC using SIF. Few studies have investigated the difference and power of the upward and downward SIF components on monitoring LNC in winter wheat. We conducted two field experiments to evaluate the capacity of SIF to monitor the LNC of winter wheat during the entire growth season and compare the differences of the upward and downward SIF for LNC detection. A FluoWat leaf clip coupled with a ASD spectrometer was used to measure the upward and downward SIF under sunlight. It was found that three (↓FY687, ↑FY687/↑FY739, and ↓FY687/↓FY739) out of the six SIF yield (FY) indices examined were significantly correlated to the LNC (R2 = 0.6, 0.51, 0.75, respectively). The downward SIF yield indices exhibited better performance than the upward FY indices in monitoring the LNC with the ↓FY687/↓FY739 being the best FY index. Moreover, the LNC models based on the three SIF yield indices are insensitive to the chlorophyll content and the leaf mass per area (LMA). These findings suggest the downward SIF should not be neglected for monitoring crop LNC at the leaf scale, although it is more difficult to measure with current instruments. The downward SIF could play an increasingly important role in understanding of the SIF emission for LNC detection at different scales. These results could provide a solid foundation for elucidating the mechanism of SIF for LNC estimation at the canopy scale.

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Zhou, Yu-an, Zichen Huang, Weijun Zhou, and Haiyan Cen. "Optimized Transfer Learning for Chlorophyll Content Estimations across Datasets of Different Species Using Sun-Induced Chlorophyll Fluorescence and Reflectance." Remote Sensing 16, no. 11 (May 23, 2024): 1869. http://dx.doi.org/10.3390/rs16111869.

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Remote sensing-based techniques have been widely used for chlorophyll content (Cab) estimations, while they are challenging when transferred across different species. Sun-induced chlorophyll fluorescence (SIF) provides a new approach to address these issues. This research explores whether SIF has transferability for Cab estimation and to enhance between-species transferability. Here, three rice datasets and a rapeseed dataset were collected. Initially, direct transfer models were constructed using partial least squares regression (PLSR) based on SIF yield (SIFY) and reflectance, respectively. Subsequently, methods were employed within the rice datasets to improve the models’ transferability. Finally, the between-species transferability of two data sources was validated in the rapeseed dataset. Direct transfer models indicated that the reflectance-based model exhibited a higher accuracy in predicting Cab when the training dataset acquired sufficient features, whereas the SIFY-based model showed better performance with fewer features. Spectral preprocessing methods can enhance the transferability, especially for SIFY-based models. In addition, supplementing 10% of out-of-sample data significantly improved the transferability. The proposed methods only require a small amount of new data to extend the original model for predicting Cab in other species. Specifically, the new method reduced the average RMSE based on SIFY and reflectance models by 23.59% and 35.51%, respectively.

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Liu, Ying, Chaoya Dang, Hui Yue, Chunguang Lyu, and Xuehui Dang. "Enhanced drought detection and monitoring using sun-induced chlorophyll fluorescence over Hulun Buir Grassland, China." Science of The Total Environment 770 (May 2021): 145271. http://dx.doi.org/10.1016/j.scitotenv.2021.145271.

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42

Guo, Meng, Jing Li, Shubo Huang, and Lixiang Wen. "Feasibility of Using MODIS Products to Simulate Sun-Induced Chlorophyll Fluorescence (SIF) in Boreal Forests." Remote Sensing 12, no. 4 (February 19, 2020): 680. http://dx.doi.org/10.3390/rs12040680.

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Solar-induced chlorophyll fluorescence (SIF) is a novel approach to gain information about plant activity from remote sensing observations. However, there are currently no continuous SIF data produced at high spatial resolutions. Many previous studies have discussed the relationship between SIF and gross primary production (GPP) and showed a significant correlation between them, but few researchers have focused on forests, which are one the most important terrestrial ecosystems. This study takes Greater Khingan Mountains, a typical boreal forest in China, as an example to explore the feasibility of using MODerate resolution Imaging Spectroradiometer (MODIS) products and Orbiting Carbon Observatory-2 (OCO-2) SIF data to simulate continuous SIF at higher spatial resolutions. The results show that there is no significant correlation between SIF and MODIS GPP at a spatial resolution of 1 km; however, significant correlations between SIF and the enhanced vegetation index (EVI) were found during growing seasons. Furthermore, the broadleaf forest has a higher SIF than coniferous forest because of the difference in leaf and canopy bio-chemical and structural characteristic. When using MODIS EVI to model SIF, linear regression models show average performance (R2 = 0.58, Root Mean Squared Error (RMSE) = 0.14 from Julian day 145 to 257) at a 16-day time scale. However, when using MODIS EVI and temperature, multiple regressions perform better (R2 = 0.71, RMSE = 0.13 from Julian day 145 to 241). An important contribution of this paper is the analysis of the relationships between SIF and vegetation indices at different spatial resolutions and the finding that the relationships became closer with a decrease in spatial resolution. From this research, we conclude that the SIF of the boreal forest investigated can mainly be explained by EVI and air temperature.

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Wang, Na, Jan G. P. W. Clevers, Sebastian Wieneke, Harm Bartholomeus, and Lammert Kooistra. "Potential of UAV-based sun-induced chlorophyll fluorescence to detect water stress in sugar beet." Agricultural and Forest Meteorology 323 (August 2022): 109033. http://dx.doi.org/10.1016/j.agrformet.2022.109033.

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De Cannière, S., M. Herbst, H. Vereecken, P. Defourny, and F. Jonard. "Constraining water limitation of photosynthesis in a crop growth model with sun-induced chlorophyll fluorescence." Remote Sensing of Environment 267 (December 2021): 112722. http://dx.doi.org/10.1016/j.rse.2021.112722.

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45

Huot, Yannick, Catherine A. Brown, and John J. Cullen. "New algorithms for MODIS sun-induced chlorophyll fluorescence and a comparison with present data products." Limnology and Oceanography: Methods 3, no. 2 (February 2005): 108–30. http://dx.doi.org/10.4319/lom.2005.3.108.

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Damm, A., L. Guanter, V. C. E. Laurent, M. E. Schaepman, A. Schickling, and U. Rascher. "FLD-based retrieval of sun-induced chlorophyll fluorescence from medium spectral resolution airborne spectroscopy data." Remote Sensing of Environment 147 (May 2014): 256–66. http://dx.doi.org/10.1016/j.rse.2014.03.009.

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Zhang, Yao, Xiangming Xiao, Cui Jin, Jinwei Dong, Sha Zhou, Pradeep Wagle, Joanna Joiner, et al. "Consistency between sun-induced chlorophyll fluorescence and gross primary production of vegetation in North America." Remote Sensing of Environment 183 (September 2016): 154–69. http://dx.doi.org/10.1016/j.rse.2016.05.015.

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van der Tol, Christiaan, Micol Rossini, Sergio Cogliati, Wouter Verhoef, Roberto Colombo, Uwe Rascher, and Gina Mohammed. "A model and measurement comparison of diurnal cycles of sun-induced chlorophyll fluorescence of crops." Remote Sensing of Environment 186 (December 2016): 663–77. http://dx.doi.org/10.1016/j.rse.2016.09.021.

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Jia, Liping, Yi He, Wanqing Liu, Yaru Zhang, and Yanlin Li. "Assessment of Drought Events in Southwest China in 2009/2010 Using Sun-Induced Chlorophyll Fluorescence." Forests 14, no. 1 (December 27, 2022): 49. http://dx.doi.org/10.3390/f14010049.

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With the increasing trend of global warming, drought events frequently occur, which have an impact on human life and the environment. In this study, an extreme drought event in Southwest China in 2009/2010 was used as an example to explore the potential of using satellite observations of sun-induced chlorophyll fluorescence (SIF) for drought monitoring. The results indicated that the SIF observations show more proper responses to drought than EVI, which underestimated the losses by approximately 50%. The SIF reduction in this drought event (19% in March 2010 and 11% in May 2010) was more obvious than that of the enhanced vegetation index (EVI) (4% and 5%). The drought severity index (DSI) overestimates the drought during most dry months. SIF can be a reliable tool for monitoring drought in a timely and accurate manner. In addition, the significant correlation coefficient with SIF and ET (reaching 0.8 at the beginning and end of the drought stage), indicates the ability of SIF to reveal the interaction of carbon and water during drought, which provides us with ideas for future research on the terrestrial carbon–water cycle.

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Dong-zhi, ZHAO, ZHANG Feng-shou, DU Fei, ZHAO Ling, and Guo Hao. "Interpretation of Sun-induced Fluorescence Peak of Chlorophyll a on Reflectance Spectrum of Algal Waters." National Remote Sensing Bulletin, no. 3 (2005): 265–70. http://dx.doi.org/10.11834/jrs.20050339.

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