Relevant bibliographies by topics / Canopy chlorophyll content (CCC)

Academic literature on the topic 'Canopy chlorophyll content (CCC)'

Author: Grafiati
Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Canopy chlorophyll content (CCC)"

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Ali, Abebe Mohammed, Roshanak Darvishzadeh, Andrew Skidmore, Marco Heurich, Marc Paganini, Uta Heiden, and Sander Mücher. "Evaluating Prediction Models for Mapping Canopy Chlorophyll Content Across Biomes." Remote Sensing 12, no. 11 (June 1, 2020): 1788. http://dx.doi.org/10.3390/rs12111788.

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Accurate measurement of canopy chlorophyll content (CCC) is essential for the understanding of terrestrial ecosystem dynamics through monitoring and evaluating properties such as carbon and water flux, productivity, light use efficiency as well as nutritional and environmental stresses. Information on the amount and distribution of CCC helps to assess and report biodiversity indicators related to ecosystem processes and functional aspects. Therefore, measuring CCC continuously and globally from earth observation data is critical to monitor the status of the biosphere. However, generic and robust methods for regional and global mapping of CCC are not well defined. This study aimed at examining the spatiotemporal consistency and scalability of selected methods for CCC mapping across biomes. Four methods (i.e., radiative transfer models (RTMs) inversion using a look-up table (LUT), the biophysical processor approach integrated into the Sentinel application platform (SNAP toolbox), simple ratio vegetation index (SRVI), and partial least square regression (PLSR)) were evaluated. Similarities and differences among CCC products generated by applying the four methods on actual Sentinel-2 data in four biomes (temperate forest, tropical forest, wetland, and Arctic tundra) were examined by computing statistical measures and spatiotemporal consistency pairwise comparisons. Pairwise comparison of CCC predictions by the selected methods demonstrated strong agreement. The highest correlation (R2 = 0.93, RMSE = 0.4371 g/m2) was obtained between CCC predictions of PROSAIL inversion by LUT and SNAP toolbox approach in a wetland when a single Sentinel-2 image was used. However, when time-series data were used, it was PROSAIL inversion against SRVI (R2 = 0.88, RMSE = 0.19) that showed greatest similarity to the single date predictions (R2 = 0.83, RMSE = 0.17 g/m2) in this biome. Generally, the CCC products obtained using the SNAP toolbox approach resulted in a systematic over/under-estimation of CCC. RTMs inversion by LUT (INFORM and PROSAIL) resulted in a non-biased, spatiotemporally consistent prediction of CCC with a range closer to expectations. Therefore, the RTM inversion using LUT approaches particularly, INFORM for ‘forest’ and PROSAIL for ‘short vegetation’ ecosystems, are recommended for CCC mapping from Sentinel-2 data for worldwide mapping of CCC. Additional validation of the two RTMs with field data of CCC across biomes is required in the future.
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Sun, Qi, Quanjun Jiao, Xiaojin Qian, Liangyun Liu, Xinjie Liu, and Huayang Dai. "Improving the Retrieval of Crop Canopy Chlorophyll Content Using Vegetation Index Combinations." Remote Sensing 13, no. 3 (January 29, 2021): 470. http://dx.doi.org/10.3390/rs13030470.

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Estimates of crop canopy chlorophyll content (CCC) can be used to monitor vegetation productivity, manage crop resources, and control disease and pests. However, making these estimates using conventional ground-based methods is time-consuming and resource-intensive when deployed over large areas. Although vegetation indices (VIs), derived from satellite sensor data, have been used to estimate CCC, they suffer from problems related to spectral saturation, soil background, and canopy structure. A new method was, therefore, proposed for combining the Medium Resolution Imaging Spectrometer (MERIS) terrestrial chlorophyll index (MTCI) and LAI-related vegetation indices (LAI-VIs) to increase the accuracy of CCC estimates for wheat and soybeans. The PROSAIL-D canopy reflectance model was used to simulate canopy spectra that were resampled to match the spectral response functions of the MERIS carried on the ENVISAT satellite. Combinations of the MTCI and LAI-VIs were then used to estimate CCC via univariate linear regression, binary linear regression and random forest regression. The accuracy using the field spectra and MERIS data was determined based on field CCC measurements. All the MTCI and LAI-VI combinations for the selected regression techniques resulted in more accurate estimates of CCC than the use of the MTCI alone (field spectra data for soybeans and wheat: R2 = 0.62 and RMSE = 77.10 μg cm−2; MERIS satellite data for soybeans: R2 = 0.24 and RMSE = 136.54 μg cm−2). The random forest regression resulted in better accuracy than the other two linear regression models. The combination resulting in the best accuracy was the MTCI and MTVI2 and random forest regression, with R2 = 0.65 and RMSE = 37.76 μg cm−2 (field spectra data) and R2 = 0.78 and RMSE = 47.96 μg cm−2 (MERIS satellite data). Combining the MTCI and a LAI-VI represents a further step towards improving the accuracy of estimation CCC based on multispectral satellite sensor data.
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Hoeppner, J. Malin, Andrew K. Skidmore, Roshanak Darvishzadeh, Marco Heurich, Hsing-Chung Chang, and Tawanda W. Gara. "Mapping Canopy Chlorophyll Content in a Temperate Forest Using Airborne Hyperspectral Data." Remote Sensing 12, no. 21 (October 31, 2020): 3573. http://dx.doi.org/10.3390/rs12213573.

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Chlorophyll content, as the primary pigment driving photosynthesis, is directly affected by many natural and anthropogenic disturbances and stressors. Accurate and timely estimation of canopy chlorophyll content (CCC) is essential for effective ecosystem monitoring to allow for successful management interventions to occur. Hyperspectral remote sensing offers the possibility to accurately estimate and map canopy chlorophyll content. In the past, research has predominantly focused on the use of hyperspectral data on canopy chlorophyll content retrieval of crops and grassland ecosystems. Therefore, in this study, a temperate mixed forest, the Bavarian Forest National Park in Germany, was chosen as the study site. We compared different statistical models (narrowband vegetation indices (VIs), partial least squares regression (PLSR) and random forest (RF)) in their accuracy to predict CCC using airborne hyperspectral data. The airborne hyperspectral imagery was acquired by the AisaFenix sensor (623 bands; 3.5 nm spectral resolution in the visible near-infrared (VNIR) region, and 12 nm spectral resolution in the shortwave infrared (SWIR) region; 3 m spatial resolution) on July 6, 2017. In situ leaf chlorophyll content and leaf area index measurements were sampled from the upper canopy of coniferous, mixed, and deciduous forest stands in July and August 2017. The study yielded the highest retrieval accuracies with PLSR (root mean square error (RMSE) = 0.25 g/m2, R2 = 0.66). It further indicated specific spectral regions within the visible (390–400 nm and 470–540 nm), red edge (680–780 nm), near-infrared (1050–1100 nm) and shortwave infrared regions (2000–2270 nm) that were important for CCC retrieval. The results showed that forest CCC can be mapped with relatively high accuracies using image spectroscopy.
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Bai, Xueyuan, Yingqiang Song, Ruiyang Yu, Jingling Xiong, Yufeng Peng, Yuanmao Jiang, Guijun Yang, Zhenhai Li, and Xicun Zhu. "Hyperspectral Estimation of Apple Canopy Chlorophyll Content Using an Ensemble Learning Approach." Applied Engineering in Agriculture 37, no. 3 (2021): 505–11. http://dx.doi.org/10.13031/aea.13935.

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HighlightsMonitored the canopy chlorophyll content of apple trees using hyperspectral reflectance information.Constructed support vector machine combination regression model (C-SVR) based on five-fold cross validation and support vector machine regression approach.Compared estimation accuracy of ensemble learning models (C-SVR, RF), machine learning models (SVR, ANN), and PLSR models for apple canopy chlorophyll content.Abstract. Rapidly and effective monitoring of the canopy chlorophyll content (CCC) of apple trees is of great significance for crop stress monitoring in precision agriculture. This study attempted to use hyperspectral vegetation indices (VIs) to estimate the CCC of apple trees based on ensemble learning approach. In this study, vegetation indices combined by any two wavelengths from 400 to 1100 nm were constructed to calculate the correlation coefficient with the CCC in apple. We constructed a partial least squares regression model (PLSR), artificial neural network regression model (ANN), support vector machine regression (SVR), random forest regression (RF) model and support vector machine combination regression model (C-SVR) based on combinations of VIs to improve the estimation accuracy in apple CCC. The results showed that the correlation coefficients between NDVI (949,695), OSAVI (828,705), RDVI (741,725), RVI (716,707), DVI (572,532), and apple CCC were all above 0.76. The CCC estimation model using the RF and C-SVR approach constructed by the NDVI (949,695), OSAVI (828,705), RDVI (741,725), RVI (716,707), and DVI (572,532) achieved the better estimation results, and the R2V, RMSEV, and RPDV values of models were 0.76, 0.131(mg . g-1), 2.04 and 0.78, 0.127(mg . g-1), 2.12, respectively. Compared with the PLSR, ANN, and SVR model, the R2V and RPDV values of C-SVR model were increased by 4%, 1.2%, 3.8%, and 5.0%, 28.4%, 7.1%, respectively. The results show that using C-SVR approach to estimating the apple CCC can realize high accuracy of quantitative estimation. Ensemble learning approach is an effective method for monitoring the nutrient status of fruit trees based on hyperspectral technique. Keywords: Apple tree canopy, Chlorophyll content, Crop stress monitoring, Ensemble learning, Hyperspectral, Vegetation index.
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Zillmann, E., M. Schönert, H. Lilienthal, B. Siegmann, T. Jarmer, P. Rosso, and T. Weichelt. "Crop Ground Cover Fraction and Canopy Chlorophyll Content Mapping using RapidEye imagery." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-7/W3 (April 28, 2015): 149–55. http://dx.doi.org/10.5194/isprsarchives-xl-7-w3-149-2015.

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Remote sensing is a suitable tool for estimating the spatial variability of crop canopy characteristics, such as canopy chlorophyll content (CCC) and green ground cover (GGC%), which are often used for crop productivity analysis and site-specific crop management. Empirical relationships exist between different vegetation indices (VI) and CCC and GGC% that allow spatial estimation of canopy characteristics from remote sensing imagery. However, the use of VIs is not suitable for an operational production of CCC and GGC% maps due to the limited transferability of derived empirical relationships to other regions. Thus, the operational value of crop status maps derived from remotely sensed data would be much higher if there was no need for reparametrization of the approach for different situations. <br><br> This paper reports on the suitability of high-resolution RapidEye data for estimating crop development status of winter wheat over the growing season, and demonstrates two different approaches for mapping CCC and GGC%, which do not rely on empirical relationships. The final CCC map represents relative differences in CCC, which can be quickly calibrated to field specific conditions using SPAD chlorophyll meter readings at a few points. The prediction model is capable of predicting SPAD readings with an average accuracy of 77%. The GGC% map provides absolute values at any point in the field. A high R² value of 80% was obtained for the relationship between estimated and observed GGC%. The mean absolute error for each of the two acquisition dates was 5.3% and 8.7%, respectively.
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Yang, Hongye, Bo Ming, Chenwei Nie, Beibei Xue, Jiangfeng Xin, Xingli Lu, Jun Xue, et al. "Maize Canopy and Leaf Chlorophyll Content Assessment from Leaf Spectral Reflectance: Estimation and Uncertainty Analysis across Growth Stages and Vertical Distribution." Remote Sensing 14, no. 9 (April 28, 2022): 2115. http://dx.doi.org/10.3390/rs14092115.

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Accurate estimation of the canopy chlorophyll content (CCC) plays a key role in quantitative remote sensing. Maize (Zea mays L.) is a high-stalk crop with a large leaf area and deep canopy. It has a non-uniform vertical distribution of the leaf chlorophyll content (LCC), which limits remote sensing of CCC. Therefore, it is crucial to understand the vertical heterogeneity of LCC and leaf reflectance spectra to improve the accuracy of CCC monitoring. In this study, CCC, LCC, and leaf spectral reflectance were measured during two consecutive field growing seasons under five nitrogen treatments. The vertical LCC profile showed an asymmetric ‘bell-shaped’ curve structure and was affected by nitrogen application. The leaf reflectance also varied greatly between spatio–temporal conditions, which could indicate the influence of vertical heterogeneity. In the early growth stage, the spectral differences between leaf positions were mainly concentrated in the red-edge (RE) and near-infrared (NIR) regions, whereas differences were concentrated in the visible region during the mid-late filling stage. LCC had a strong linear correlation with vegetation indices (VIs), such as the modified red-edge ratio (mRER, R2 = 0.87), but the VI–chlorophyll models showed significant inversion errors throughout the growth season, especially at the early vegetative growth stage and the late filling stage (rRMSE values ranged from 36% to 87.4%). The vertical distribution of LCC had a strong correlation with the total chlorophyll in canopy, and sensitive leaf positions were identified with a multiple stepwise regression (MSR) model. The LCC of leaf positions L6 in the vegetative stage (R2-adj = 0.9) and L11 + L14 in the reproductive stage (R2-adj = 0.93) could be used to evaluate the canopy chlorophyll status (L12 represents the ear leaf). With a strong relationship between leaf spectral reflectance and LCC, CCC can be estimated directly by leaf spectral reflectance (mRER, rRMSE = 8.97%). Therefore, the spatio–temporal variations of LCC and leaf spectral reflectance were analyzed, and a higher accuracy CCC estimation approach that can avoid the effects of the leaf area was proposed.
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Jiao, Quanjun, Qi Sun, Bing Zhang, Wenjiang Huang, Huichun Ye, Zhaoming Zhang, Xiao Zhang, and Binxiang Qian. "A Random Forest Algorithm for Retrieving Canopy Chlorophyll Content of Wheat and Soybean Trained with PROSAIL Simulations Using Adjusted Average Leaf Angle." Remote Sensing 14, no. 1 (December 25, 2021): 98. http://dx.doi.org/10.3390/rs14010098.

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Canopy chlorophyll content (CCC) is an important indicator for crop-growth monitoring and crop productivity estimation. The hybrid method, involving the PROSAIL radiative transfer model and machine learning algorithms, has been widely applied for crop CCC retrieval. However, PROSAIL’s homogeneous canopy hypothesis limits the ability to use the PROSAIL-based CCC estimation across different crops with a row structure. In addition to leaf area index (LAI), average leaf angle (ALA) is the most important canopy structure factor in the PROSAIL model. Under the same LAI, adjustment of the ALA can make a PROSAIL simulation obtain the same canopy gap as the heterogeneous canopy at a specific observation angle. Therefore, parameterization of an adjusted ALA (ALAadj) is an optimal choice to make the PROSAIL model suitable for specific row-planted crops. This paper attempted to improve PROSAIL-based CCC retrieval for different crops, using a random forest algorithm, by introducing the prior knowledge of crop-specific ALAadj. Based on the field reflectance spectrum at nadir, leaf area index, and leaf chlorophyll content, parameterization of the ALAadj in the PROSAIL model for wheat and soybean was carried out. An algorithm integrating the random forest and PROSAIL simulations with prior ALAadj information was developed for wheat and soybean CCC retrieval. Ground-measured CCC measurements were used to validate the CCC retrieved from canopy spectra. The results showed that the ALAadj values (62 degrees for wheat; 45 degrees for soybean) that were parameterized for the PROSAIL model demonstrated good discrimination between the two crops. The proposed algorithm improved the CCC retrieval accuracy for wheat and soybean, regardless of whether continuous visible to near-infrared spectra with 50 bands (RMSE from 39.9 to 32.9 μg cm−2; R2 from 0.67 to 0.76) or discrete spectra with 13 bands (RMSE from 43.9 to 33.7 μg cm−2; R2 from 0.63 to 0.74) and nine bands (RMSE from 45.1 to 37.0 μg cm−2; R2 from 0.61 to 0.71) were used. The proposed hybrid algorithm, based on PROSAIL simulations with ALAadj, has the potential for satellite-based CCC estimation across different crop types, and it also has a good reference value for the retrieval of other crop parameters.
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Červená, L., G. Pinlová, Z. Lhotáková, E. Neuwirthová, L. Kupková, M. Potůčková, J. Lysák, P. Campbell, and J. Albrechtová. "DETERMINATION OF CHLOROPHYLL CONTENT IN SELECTED GRASS COMMUNITIES OF KRKONOŠE MTS. TUNDRA BASED ON LABORATORY SPECTROSCOPY AND AERIAL HYPERSPECTRAL DATA." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B3-2022 (May 30, 2022): 381–88. http://dx.doi.org/10.5194/isprs-archives-xliii-b3-2022-381-2022.

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Abstract. The study focuses on the determination of chlorophyll content in four prevailing grasses in the relict arctic-alpine tundra located in the Krkonoše Mountains National Park, Czech Republic. We compared two methods for determination of leaf chlorophyll content (LCC) – spectrophotometric determination in the laboratory, and the LCC assessed by fluorescence portable chlorophyll meter CCM-300. Relationships were established between the LCCs and vegetation indices calculated from leaf spectra acquired with contact probe coupled with an ASD FieldSpec4 Wide-Res spectroradiometer. Canopy chlorophyll contents (CCC) were computed from the LCCs and green leaf area index (LAI), and modelled based on the field spectra measured by the spectroradiometer and the hyperspectral images acquired by Headwall Nano-Hyperspec® mounted on the DJI Matrice 600 Pro drone. The calculations are performed on datasets acquired in June, July and August 2020 together and separately for species and months. In general, the correlations based on June datasets work the best at both levels: median R2 for all indices was 0.52 for all species together at leaf level and median R2 = 0.47 at the canopy level (vegetation indices computed from field spectra). Canopy chlorophyll content map was created based on the results of stepwise multiple linear regression. The R2 was 0.42 when using four wavelengths from the red and red edge spectral region. We attribute the weak model performance to a combination of several factors: leaf structure may bias LCC from laboratory measurements, effects of LAI variability on CCC, and the sampling design, probably not covering the whole phenology equally for all studied species.
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Kamenova, Ilina, Petar Dimitrov, and Rusina Yordanova. "Evaluation of RapidEye vegetation indices for prediction of biophysical/biochemical variables of winter wheat." Aerospace Research in Bulgaria 30 (2018): 63–74. http://dx.doi.org/10.3897/arb.v30.e06.

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The aim of the study is to evaluate the possibility for using RapidEye data for prediction of Leaf Area Index (LAI), fraction of Absorbed Photosynthetically Active Radiation (fAPAR), fraction of vegetation Cover (fCover), leaf Chlorophyll Concentration (CC) and Canopy Chlorophyll Content (CCC) of winter wheat. The relation of a number of vegetation indices (VIs) with these crop variables are accessed based on a regression analysis. Indices, which make use of the red edge band, such as Chlorophyll Index red edge (CIre) and red edge Normalized Difference Vegetation Index (reNDVI), were found most useful, resulting in linear models with R2 of 0.67, 0.71, 0.72, and 0.76 for fCover, LAI, CCC, and fAPAR respectively. CC was not related with any of the VIs.
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Brown, Luke A., Booker O. Ogutu, and Jadunandan Dash. "Estimating Forest Leaf Area Index and Canopy Chlorophyll Content with Sentinel-2: An Evaluation of Two Hybrid Retrieval Algorithms." Remote Sensing 11, no. 15 (July 25, 2019): 1752. http://dx.doi.org/10.3390/rs11151752.

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Estimates of biophysical and biochemical variables such as leaf area index (LAI) and canopy chlorophyll content (CCC) are a fundamental requirement for effectively monitoring and managing forest environments. With its red-edge bands and high spatial resolution, the Multispectral Instrument (MSI) on board the Sentinel-2 missions is particularly well-suited to LAI and CCC retrieval. Using field data collected throughout the growing season at a deciduous broadleaf forest site in Southern England, we evaluated the performance of two hybrid retrieval algorithms for estimating LAI and CCC from MSI data: the Scattering by Arbitrarily Inclined Leaves (SAIL)-based L2B retrieval algorithm made available to users in the Sentinel Application Platform (SNAP), and an alternative retrieval algorithm optimised for forest environments, trained using the Invertible Forest Reflectance Model (INFORM). Moderate performance was associated with the SNAP L2B retrieval algorithm for both LAI (r2 = 0.54, RMSE = 1.55, NRMSE = 43%) and CCC (r2 = 0.52, RMSE = 0.79 g m−2, NRMSE = 45%), while improvements were obtained using the INFORM-based retrieval algorithm, particularly in the case of LAI (r2 = 0.79, RMSE = 0.47, NRMSE = 13%), but also in the case of CCC (r2 = 0.69, RMSE = 0.52 g m−2, NRMSE = 29%). Forward modelling experiments confirmed INFORM was better able to reproduce observed MSI spectra than SAIL. Based on our results, for forest-related applications using MSI data, we recommend users seek retrieval algorithms optimised for forest environments.
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Dissertations / Theses on the topic "Canopy chlorophyll content (CCC)"

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Gao, Jincheng. "Canopy chlorophyll estimation with hyperspectral remote sensing." Diss., Manhattan, Kan. : Kansas State University, 2006. http://hdl.handle.net/2097/252.

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Jiang, Jingyi. "Retrieving leaf and canopy characteristics from their radiative properties using physically based models : from laboratory to satellite observations Estimation of leaf traits from reflectance measurements: comparison between methods based on vegetation indices and several versions of the PROSPECT model a model of leaf optical properties accounting for the differences between upper and lower faces Speeding up 3D radiative transfer simulations: a physically based approximation of canopy reflectance dependency on wavelength, leaf biochemical composition and soil reflectance Effective GAI for crops is best estimated from reflectance observations as compared to GAI and LAI Optimal learning for GAI and chlorophyll estimation from 1D and 3D radiative transfer model inversion: the case of wheat and maize crops observed by Sentinel2." Thesis, Avignon, 2019. http://www.theses.fr/2019AVIG0708.

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La mesure des caractéristiques des feuilles et du couvert végétal par télédétection est un moyen efficace et non destructif d’effectuer un suivi des cultures, que ce soit pour la prise de décision dans la gestion d’itinéraires techniques an agriculture de précision ou pour le phénotypage au champ pour améliorer l'efficacité de la sélection variétale. Grâce à l’augmentation de la puissance de calcul des machines et à la disponibilité croissante d'images à haute résolution spatiale, les méthodes d’estimation peuvent maintenant bénéficier de simulations plus précises des modèles de transfert radiatif (RT) dans la végétation. L'objectif de ce travail est de proposer et d'évaluer des moyens efficaces pour estimer les caractéristiques des feuilles et du couvert végétal à partir d'observations rapprochées ou de télédétection en utilisant des modèles RT basés sur une description réaliste de la structure des feuilles et du couvert. Au niveau des feuilles, nous avons d'abord évalué la capacité des différentes versions du modèle PROSPECT à estimer des variables biochimiques comme la chlorophylle (Cab), la teneur en eau et en matière sèche. Nous avons ensuite proposé le modèle FASPECT pour décrire les différences de propriétés optiques entre les faces supérieure et inférieure des feuilles en considérant un système à quatre couches. Après avoir étalonné les coefficients d'absorption spécifiques des principaux constituants de la feuille, nous avons validé FASPECT sur 8 jeux de données. Nous avons montré que les spectres de réflectance et de transmittance des deux faces sont simulés avec une très bonne précision, et même meilleure que PROSPECT pour la face supérieure. De même, en mode inverse, les performances d'estimation de la teneur en matière sèche sont considérablement améliorées avec FASPECT par rapport à PROSPECT, et restent du même ordre de grandeur pour la chlorophylle et l’eau. Au niveau du couvert végétal, nous avons utilisé le simulateur de rendu physique réaliste LuxCoreRender pour calculer le transfert radiatif à partir d'une description 3D de l’architecture de la culture. Nous avons d’abord vérifié ses bonnes performances par comparaison aux modèles 3D les plus récents en utilisant ROMC (RAMI On Line Model Checker). Afin d’accélérer les simulations, nous avons développé une méthode qui repose sur l’utilisation d’un nombre limité de propriétés optiques du sol et des feuilles. Pour estimer les variables d'état du couvert végétal (indice de surface verte, GAI, contenu en chlorophylle du couvert (CCC) ou des feuilles (Cab), nous avons ensuite entrainé des algorithmes d’apprentissage automatique à partir de bases de données « culture spécifique » simulées avec LuxCoreRender pour le blé et le maïs et d’une base de données générique simulée avec le modèle 1D PROSAIL de transfert radiatif. Les résultats sur des simulations et sur des données in situ combinés aux images SENTINEL2 ont montré que les algorithmes spécifiques aux cultures surpassent les algorithmes génériques pour les trois variables, en particulier lorsque la structure du couvert s’éloigne de l'hypothèse 1D du milieu turbide, comme dans le cas du maïs où la structure en rang domine pendant toute une partie de la saison de croissance
Measuring leaf and canopy characteristics from remote sensing acquisitions is an effective and non destructive way to monitor crops both for decision making within the smart agriculture practices or for phenotyping under field conditions to improve the selection efficiency. With the advancement of computer computing power and the increasing availability of high spatial resolution images, retrieval methods can now benefit from more accurate simulations of the Radiative Transfer (RT) models within the vegetation. The objective of this work is to propose and evaluate efficient ways to retrieve leaf and canopy characteristics from close and remote sensing observations by using RT models based on a realistic description of the leaf and canopy structures. At the leaf level, we first evaluated the ability of the different versions of the PROSPECT model to estimate biochemical variables like chlorophyll (Cab), water and dry matter content. We then proposed the FASPECT model to describe the optical properties differences between the upper and lower leaf faces by considering a four-layer system. After calibrating the specific absorption coefficients of the main absorbing material, we validated FASPECT against eight measured ground datasets. We showed that FASPECT simulates accurately the reflectance and transmittance spectra of the two faces and overperforms PROSPECT for the upper face measurements. Moreover, in the inverse mode, the dry matter content estimation is significantly improved with FASPECT as compared to PROSPECT. At the canopy level, we used the physically based and unbiased rendering engine, LuxCoreRender to compute the radiative transfer from a realistic 3D description of the crop structure. We checked its good performances by comparison with the state of the art 3D RT models using the RAMI online model checker. Then, we designed a speed-up method to simulate canopy reflectance from a limited number of soil and leaf optical properties. Based on crop specific databases simulated from LuxCoreRender for wheat and maize and crop generic databases simulated from a 1D RT model, we trained some machine learning inversion algorithms to retrieve canopy state variables like Green Area Index GAI, Cab and Canopy Chlorophyll Content (CCC). Results on both simulations and in situ data combined with SENTINEL2 images showed that crop specific algorithms outperform the generic one for the three variables, especially when the canopy structure breaks the 1D turbid medium assumption such as in maize where rows are dominant during a significant part of the growing season
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Schlemmer, Michael R. "Examining leaf and canopy optical properties for the assessment of chlorophyll content to determine nitrogen management strategies." 2008. http://proquest.umi.com/pqdweb?did=1625771201&sid=27&Fmt=2&clientId=14215&RQT=309&VName=PQD.

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Thesis (Ph.D.)--University of Nebraska-Lincoln, 2008.
Title from title screen (site viewed Mar. 10, 2009). PDF text: vi, 121 p. : ill. (some col.) ; 1 Mb. UMI publication number: AAT 3336809. Includes bibliographical references. Also available in microfilm and microfiche formats.
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Book chapters on the topic "Canopy chlorophyll content (CCC)"

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Shanahan, John F., Kyle H. Holland, James S. Schepers, Dennis D. Francis, Michael R. Schlemmer, and Robert Caldwell. "Use of a Crop Canopy Reflectance Sensor to Assess Corn Leaf Chlorophyll Content." In ASA Special Publications, 135–50. Madison, WI, USA: American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America, 2015. http://dx.doi.org/10.2134/asaspecpub66.c11.

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Conference papers on the topic "Canopy chlorophyll content (CCC)"

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Jin, Xu, and Meng Jihua. "Retrieval Of canopy chlorophyll content for spring corn using multispectral remote sensing data." In 2014 Third International Conference on Agro-Geoinformatics. IEEE, 2014. http://dx.doi.org/10.1109/agro-geoinformatics.2014.6910668.

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Clevers, J. G. P. W., and L. Kooistra. "Using hyperspectral remote sensing data for retrieving total canopy chlorophyll and nitrogen content." In 2011 3rd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS). IEEE, 2011. http://dx.doi.org/10.1109/whispers.2011.6080916.

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Xuqing Li, Xiangnan Liu, Zhihong Du, and Cuicui Wang. "A random forest model for estimating Canopy Chlorophyll Content in rice using hyperspectral measurements." In 2013 10th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD). IEEE, 2013. http://dx.doi.org/10.1109/fskd.2013.6816256.

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Ai, Jinquan, Wei Gao, Runhe Shi, Chao Zhang, Zhibin Sun, Wenhui Chen, Chaoshun Liu, and Yuyan Zeng. "In situ hyperspectral data analysis for canopy chlorophyll content estimation of an invasive speciesspartina alterniflorabased on PROSAIL canopy radiative transfer model." In SPIE Optical Engineering + Applications, edited by Wei Gao, Ni-Bin Chang, and Jinnian Wang. SPIE, 2015. http://dx.doi.org/10.1117/12.2186973.

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Cui, Zhaoyu, and John Kerekes. "Potential of Red Edge Spectral Bands in Future Landsat Satellites on Agroecosystem Canopy Chlorophyll Content Retrieval." In IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2019. http://dx.doi.org/10.1109/igarss.2019.8898783.

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Zhang, Qingyuan, and Elizabeth M. Middleton. "Introduction to fraction of absorbed par by canopy chlorophyll (fAPARchl) and canopy leaf water content derived from hyperion, simulated HyspIRI and MODIS images." In IGARSS 2010 - 2010 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2010. http://dx.doi.org/10.1109/igarss.2010.5649467.

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Li, Dong, Hengbiao Zheng, Xiaoqing Xu, Ning Lu, Xia Yao, Jiale Jiang, Xue Wang, et al. "BRDF Effect on the Estimation of Canopy Chlorophyll Content in Paddy Rice from UAV-Based Hyperspectral Imagery." In IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2018. http://dx.doi.org/10.1109/igarss.2018.8517684.

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Pasqualotto, Nieves, Salvatore Falanga Bolognesi, Oscar Rosario Belfiore, Jesus Delegido, Guido D'Urso, and Jose Moreno. "Canopy chlorophyll content and LAI estimation from Sentine1-2: vegetation indices and Sentine1-2 Leve1-2A automatic products comparison." In 2019 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor). IEEE, 2019. http://dx.doi.org/10.1109/metroagrifor.2019.8909218.

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Laurent, V. C. E., W. Verhoef, M. E. Schaepman, A. Damm, and J. G. P. W. Clevers. "Mapping LAI and chlorophyll content from at-sensor APEX data using a Bayesian optimisation of a coupled canopy-atmosphere model." In IGARSS 2012 - 2012 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2012. http://dx.doi.org/10.1109/igarss.2012.6352321.

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Jiang, J., M. Weiss, S. Liu, and F. Baret. "The impact of canopy structure assumption on the retrieval of GAI and Leaf Chlorophyll Content for wheat and maize crops." In IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2019. http://dx.doi.org/10.1109/igarss.2019.8899064.

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You might also be interested in the extended bibliographies on the topic 'Canopy chlorophyll content (CCC)' for particular source types:
Journal articles
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