Evaluating the contribution of Sentinel-2 view and illumination geometry to the accuracy of retrieving essential crop parameters

References

• Alexandridis, T. K., G. Ovakoglou, and J. G. P. W. Clevers. 2020. “Relationship Between MODIS EVI and LAI Across Time and Space.” Geocarto international 35 (13): 1385–20. doi:10.1080/10106049.2019.1573928.
   Web of Science ®Google Scholar
• Apolo-Apolo, O. E., M. Pérez-Ruiz, J. Martínez-Guanter, and G. Egea. 2020. “A Mixed Data-Based Deep Neural Network to Estimate Leaf Area Index in Wheat Breeding Trials.” Agronomy 10 (2). doi:10.3390/agronomy10020175.
   Google Scholar
• Azodi, C. B., J. Tang, and S. H. Shiu. 2020. “Opening the Black Box: Interpretable Machine Learning for Geneticists.” Trends in Genetics 36 (6): 442–455. doi:10.1016/j.tig.2020.03.005.
   PubMed Web of Science ®Google Scholar
• Biriukova, K., M. Celesti, A. Evdokimov, J. Pacheco-Labrador, T. Julitta, M. Migliavacca, C. Giardino, et al. 2020. “Effects of Varying Solar-View Geometry and Canopy Structure on Solar-Induced Chlorophyll Fluorescence and PRI.” International Journal of Applied Earth Observation and Geoinformation 89: 102069. doi:10.1016/j.jag.2020.102069.
   Web of Science ®Google Scholar
• Breiman, L. 2001. “Random Forests.” Machine Learning 45 (1): 5–32. doi:10.1023/A:1010933404324.
   Web of Science ®Google Scholar
• Clevers, J. G., and A. A. Gitelson. 2013. “Remote Estimation of Crop and Grass Chlorophyll and Nitrogen Content Using Red-Edge Bands on Sentinel-2 And-3.” International Journal of Applied Earth Observation and Geoinformation 23: 344–351. doi:10.1016/j.jag.2012.10.008.
   Web of Science ®Google Scholar
• Clevers, J., L. Kooistra, and M. Van den Brande. 2017. “Using Sentinel-2 Data for Retrieving LAI and Leaf and Canopy Chlorophyll Content of a Potato Crop.” Remote Sensing 9 (5): 405. doi:10.3390/rs9050405.
   Web of Science ®Google Scholar
• Combal, B., F. Baret, M. Weiss, A. Trubuil, D. Mace, A. Pragnere, R. Myneni, Y. Knyazikhin, and L. Wang. 2003. “Retrieval of Canopy Biophysical Variables from Bidirectional Reflectance: Using Prior Information to Solve the Ill-Posed Inverse Problem.” Remote Sensing of Environment 84 (1): 1–15. doi:10.1016/S0034-4257(02)00035-4.
   Web of Science ®Google Scholar
• Cropestimatesconsortium. 2017. ”Field Crop Boundary Data Layer (Free State Province).” In Field Crop Boundary Data Layer (Free State Province), 2017, ed. CROPESTIMATESCONSORTIUM. Pretoria: Department of Agriculture, Forestry and Fisheries.
   Google Scholar
• Dash, J., and P. J. Curran. 2005. “The MERIS Terrestrial Chlorophyll Index.” International Journal of Remote Sensing 25: 5403–5413.
   Web of Science ®Google Scholar
• Daughtry, C. S. T., C. L. Walthall, M. S. Kim, E. Brown, and J. E. McMurtrey. 2000. “Estimating Corn Leaf Chlorophyll Concentration from Leaf and Canopy Reflectance.” Remote Sensing of Environment 74: 229–239.
   Web of Science ®Google Scholar
• Delegido, J., J. Verrelst, L. Alonso, and J. Moreno. 2011. “Evaluation of Sentinel-2 Red-Edge Bands for Empirical Estimation of Green LAI and Chlorophyll Content.” Sensors 11 (7): 7063–7081. doi:10.3390/s110707063.
   PubMed Web of Science ®Google Scholar
• Delegido, J., J. Verrelst, C. M. Meza, J. P. Rivera, L. Alonso, and J. Moreno. 2013. “A Red-Edge Spectral Index for Remote Sensing Estimation of Green LAI Over Agroecosystems.” European Journal of Agronomy 46: 42–52. doi:10.1016/j.eja.2012.12.001.
   Web of Science ®Google Scholar
• Dimitrov, P., I. Kamenova, E. Roumenina, L. Filchev, I. Ilieva, G. Jelev, A. Gikov, M. Banov, et al. 2019. “Estimation of Biophysical and Biochemical Variables of Winter Wheat Through Sentinel-2 Vegetation Indices.” Bulgarian Journal of Agricultural Science 25 (5): 819–832.
   Web of Science ®Google Scholar
• Djamai, N., R. Fernandes, M. Weiss, H. Mcnairn, and K. Goïta. 2019. “Validation of the Sentinel Simplified Level 2 Product Prototype Processor (SL2P) for Mapping Cropland Biophysical Variables Using Sentinel-2/MSI and Landsat-8/OLI Data.” Remote Sensing of Environment 225: 416–430. doi:10.1016/j.rse.2019.03.020.
   Web of Science ®Google Scholar
• Durbha, S. S., R. L. King, and N. H. Younan. 2007. “Support Vector Machines Regression for Retrieval of Leaf Area Index from Multiangle Imaging Spectroradiometer.” Remote Sensing of Environment 107 (1–2): 348–361. doi:10.1016/j.rse.2006.09.031.
   Web of Science ®Google Scholar
• Duveiller, G., R. Lopez-Lozano, and A. Cescatti. 2015. “Exploiting the Multi-Angularity of the MODIS Temporal Signal to Identify Spatially Homogeneous Vegetation Cover: A Demonstration for Agricultural Monitoring Applications.” Remote sensing of environment 166: 61–77. doi:10.1016/j.rse.2015.06.001.
   Web of Science ®Google Scholar
• Fawagreh, K., M. M. Gaber, and E. Elyan. 2014. “Random Forests: From Early Developments to Recent Advancements.” Systems Science & Control Engineering: An Open Access Journal 2 (1): 602–609. doi:10.1080/21642583.2014.956265.
   Google Scholar
• Fitzgerald, G., D. Rodriguez, and G. O’Leary. 2010. “Measuring and Predicting Canopy Nitrogen Nutrition in Wheat Using a Spectral Index-The Canopy Chlorophyll Content Index (CCCI).” Field Crops Research 116 (3). doi:10.1016/j.fcr.2010.01.010.
   Web of Science ®Google Scholar
• Frampton, W. J., J. Dash, G. Watmough, and E. J. Milton. 2013. “Evaluating the Capabilities of Sentinel-2 for Quantitative Estimation of Biophysical Variables in Vegetation.” ISPRS Journal of Photogrammetry and Remote Sensing 82: 83–92. doi:10.1016/j.isprsjprs.2013.04.007.
   Web of Science ®Google Scholar
• Galvao, L. S., J. R. dos Santos, D. A. Roberts, F. M. Breunig, M. Toomey, and Y. M. de Moura. 2011. “On Intra-Annual EVI Variability in the Dry Season of Tropical Forest: A Case Study with MODIS and Hyperspectral Data.” Remote Sensing of Environment 115 (9): 2350–2359. doi:10.1016/j.rse.2011.04.035.
   Web of Science ®Google Scholar
• Gara, T. W., R. Darvishzadeh, A. K. Skidmore, T. Wang, and M. Heurich. 2019. “Accurate Modelling of Canopy Traits from Seasonal Sentinel-2 Imagery Based on the Vertical Distribution of Leaf Traits.” Isprs Journal of Photogrammetry and Remote Sensing 157: 108–123. doi:10.1016/j.isprsjprs.2019.09.005.
   Web of Science ®Google Scholar
• Gascon, F., C. Bouzinac, O. Thépaut, M. Jung, B. Francesconi, J. Louis, V. Lonjou, et al. 2017. “Copernicus Sentinel-2A Calibration and Products Validation Status.” Remote Sensing 9 (6): 584. doi:10.3390/rs9060584.
   Web of Science ®Google Scholar
• Gislason, P. O., J. A. Benediktsson, and J. R. Sveinsson. 2006. “Random Forests for Land Cover Classification.” Pattern recognition letters 27 (4): 294–300. doi:10.1016/j.patrec.2005.08.011.
   Web of Science ®Google Scholar
• Gitelson, A. A., Y. Gritz, and M. N. Merzlyak. 2003. “Relationships Between Leaf Chlorophyll Content and Spectral Reflectance and Algorithms for Non-Destructive Chlorophyll Assessment in Higher Plant Leaves.” Journal of Plant Physiology 160 (3): 271–282. doi:10.1078/0176-1617-00887.
   PubMed Web of Science ®Google Scholar
• Guyot, G., and F. Baret. 1988. Utilisation de la haute re´solution spectrale pour suivre l’e´tat des couverts ve´ge´taux [Utilisation of high spectral resolution for monitoring vegetation condition]. In T.-D.-G. enne and J. J. Hunt (eds.), Proceedings of 4th international colloquim spectral signatures of objects in remote sensing, proceedings of the conference held 18–22 January 1988 in Aussois (Modane), France (pp. 279–286). ESA SP-287. European Space Agency.
   Google Scholar
• Haboudane, D., J. R. Miller, E. Pattey, P. J. Zarco-Tejada, and I. B. Strachan. 2004. “Hyperspectral Vegetation Indices and Novel Algorithms for Predicting Green LAI of Crop Canopies: Modeling and Validation in the Context of Precision Agriculture.” Remote Sensing of Environment 90 (3): 337–352. doi:10.1016/j.rse.2003.12.013.
   Web of Science ®Google Scholar
• Haboudane, D., N. Tremblay, J. R. Miller, and P. Vigneault. 2008. “Remote Estimation of Crop Chlorophyll Content Using Spectral Indices Derived from Hyperspectral Data.” IEEE Transactions on Geoscience and Remote Sensing 46 (2): 423–437. doi:10.1109/TGRS.2007.904836.
   Web of Science ®Google Scholar
• Jacquemoud, S., W. Verhoef, F. Baret, C. Bacour, P. J. Zarco-Tejada, G. P. Asner, C. François, and S. L. Ustin. 2009. “PROSPECT+ SAIL Models: A Review of Use for Vegetation Characterisation.” Remote Sensing of Environment 113: S56–66. doi:10.1016/j.rse.2008.01.026.
   Web of Science ®Google Scholar
• Jay, S., F. Maupas, R. Bendoula, and N. Gorretta. 2017. “Retrieving LAI, Chlorophyll and Nitrogen Contents in Sugar Beet Crops from Multi-Angular Optical Remote Sensing: Comparison of Vegetation Indices and PROSAIL Inversion for Field Phenotyping.” Field Crops Research 210: 33–46. doi:10.1016/j.fcr.2017.05.005.
   Web of Science ®Google Scholar
• Karimi, S., A. A. Sadraddini, A. H. Nazemi, T. Xu, and A. F. Fard. 2018. “Generalizability of Gene Expression Programming and Random Forest Methodologies in Estimating Cropland and Grassland Leaf Area Index.” Computers and Electronics in Agriculture 144: 232–240. doi:10.1016/j.compag.2017.12.007.
   Web of Science ®Google Scholar
• Kganyago, M., C. Adjorlolo, and P.Mhangara. 2022. “Exploring Transferable Techniques to Retrieve Crop Biophysical and Biochemical Variables Using Sentinel-2 Data.” Remote Sensing 14 (16): 3968. doi:10.3390/rs14163968.
   Web of Science ®Google Scholar
• Kganyago, M., P. Mhangara, and C. Adjorlolo. 2021. “Estimating Crop Biophysical Parameters Using Machine Learning Algorithms and Sentinel-2 Imagery.” Remote Sensing 13 (21): 1–21. doi:10.3390/rs13214314.
   PubMed Web of Science ®Google Scholar
• Kganyago, M., P. Mhangara, T. Alexandridis, G. Laneve, G. Ovakoglou, and N. Mashiyi. 2020. “Validation of Sentinel-2 Leaf Area Index (LAI) Product Derived from SNAP Toolbox and Its Comparison with Global LAI Products in an African Semi-Arid Agricultural Landscape.” Remote Sensing Letters 11 (10): 883–892. doi:10.1080/2150704X.2020.1767823.
   Web of Science ®Google Scholar
• Kneubühler, M., B. Koetz, S. Huber, N. E. Zimmermann, and M. E. Schaepman. 2008. “Space-Based Spectrodirectional Measurements for the Improved Estimation of Ecosystem Variables.” Canadian Journal of Remote Sensing 34: 192–205.
   Web of Science ®Google Scholar
• Kooistr, L., and J. G. Clevers. 2016. “Estimating Potato Leaf Chlorophyll Content Using Ratio Vegetation Indices.” Remote Sensing Letters 7 (6): 611–620. doi:10.1080/2150704X.2016.1171925.
   Web of Science ®Google Scholar
• Lee, Y. -J., C. -M. Yang, K. -W. Chang, and Y. Shen. 2008. “A Simple Spectral Index Using Reflectance of 735 Nm to Assess Nitrogen Status of Rice Canopy.” Agronomy journal 100 (1): 205–212. doi:10.2134/agronj2007.0018.
   Web of Science ®Google Scholar
• Liang, L., M. Yang, K. Deng, L. Zhang, H. Lin, and Z. Liu. 2011. “A New Hyperspectral Index for the Estimation of Nitrogen Contents of Wheat Canopy.” Shengtai Xuebao/Acta Ecologica Sinica 31: 6594–6605.
   Google Scholar
• Liaw, A., and M. Wiener. 2002. “Classification and Regression by randomForest.” R News 2: 18–22.
   Google Scholar
• Liu, P., L. Hao, C. Pan, D. Zhou, Y. Liu, and G. Sun. 2017. “Combined Effects of Climate and Land Management on Watershed Vegetation Dynamics in an Arid Environment.” The Science of the Total Environment 589: 73–88. doi:10.1016/j.scitotenv.2017.02.210.
   PubMed Web of Science ®Google Scholar
• Liu, J., E. Pattey, and G. Jégo. 2012. “Assessment of Vegetation Indices for Regional Crop Green LAI Estimation from Landsat Images Over Multiple Growing Seasons.” Remote Sensing of Environment 123: 347–358. doi:10.1016/j.rse.2012.04.002.
   Web of Science ®Google Scholar
• Louis, J., V. Debaecker, B. Pflug, M. Main-Knorn, J. Bieniarz, U. Mueller-Wilm, E. Cadau, and F. Gascon. 2016. Sentinel-2 Sen2cor: L2A Processor for Users. Proceedings Living Planet Symposium 2016, Prague, Czech Republic. Spacebooks Online, 1–8.
   Google Scholar
• Lu, S., F. Lu, W. You, Z. Wang, Y. Liu, and K. Omasa. 2018. “A Robust Vegetation Index for Remotely Assessing Chlorophyll Content of Dorsiventral Leaves Across Several Species in Different Seasons.” Plant Methods 14 (1): 1–15. doi:10.1186/s13007-018-0281-z.
   PubMedGoogle Scholar
• Ma, X., A. Huete, N. N. Tran, J. Bi, S. Gao, and Y. Zeng. 2020. “Sun-Angle Effects on Remote-Sensing Phenology Observed and Modelled Using Himawari-8.” Remote Sensing 12 (8): 1339. doi:10.3390/rs12081339.
   Web of Science ®Google Scholar
• Main, R., M. A. Cho, R. Mathieu, M. M. O’Kennedy, A. Ramoelo, and S. Koch. 2011. “An Investigation into Robust Spectral Indices for Leaf Chlorophyll Estimation.” Isprs Journal of Photogrammetry and Remote Sensing 66 (6): 751–761. doi:10.1016/j.isprsjprs.2011.08.001.
   Web of Science ®Google Scholar
• Mao, H., J. Meng, F. Ji, Q. Zhang, and H. Fang. 2019. “Comparison of Machine Learning Regression Algorithms for Cotton Leaf Area Index Retrieval Using Sentinel-2 Spectral Bands.” Applied Sciences 9 (7): 1459. doi:10.3390/app9071459.
   Google Scholar
• Martín-Ortega, P., L. G. García-Montero, and N. Sibelet. 2020. “Temporal Patterns in Illumination Conditions and Its Effect on Vegetation Indices Using Landsat on Google Earth Engine.” Remote Sensing 12 (2): 211. doi:10.3390/rs12020211.
   Web of Science ®Google Scholar
• Matsushita, B., W. Yang, J. Chen, Y. Onda, and G. Qiu. 2007. “Sensitivity of the Enhanced Vegetation Index (EVI) and Normalised Difference Vegetation Index (NDVI) to Topographic Effects: A Case Study in High-Density Cypress Forest.” Sensors 7 (11): 2636–2651. doi:10.3390/s7112636.
   PubMed Web of Science ®Google Scholar
• Middleton, E. M. 1991. “Solar Zenith Angle Effects on Vegetation Indices in Tallgrass Prairie.” Remote sensing of environment 38 (1): 45–62. doi:10.1016/0034-4257(91)90071-D.
   Web of Science ®Google Scholar
• Mohanty, S., and R. Codell. 2002. “Sensitivity Analysis Methods for Identifying Influential Parameters in a Problem with a Large Number of Random Variables.” WIT Transactions on Modelling and Simulation 31.
   Google Scholar
• Mutanga, O., E. Adam, and M. A. Cho. 2012. “High Density Biomass Estimation for Wetland Vegetation Using WorldView-2 Imagery and Random Forest Regression Algorithm.” International Journal of Applied Earth Observation and Geoinformation 18: 399–406. doi:10.1016/j.jag.2012.03.012.
   Web of Science ®Google Scholar
• Nagol, J. R., J. O. Sexton, D. -H. Kim, A. Anand, D. Morton, E. Vermote, and J. R. Townshend. 2015. “Bidirectional Effects in Landsat Reflectance Estimates: Is There a Problem to Solve?” Isprs Journal of Photogrammetry and Remote Sensing 103: 129–135. doi:10.1016/j.isprsjprs.2014.09.006.
   Web of Science ®Google Scholar
• Nguy-Robertson, A., A. Gitelson, Y. Peng, A. Viña, T. Arkebauer, and D. Rundquist. 2012. “Green Leaf Area Index Estimation in Maize and Soybean: Combining Vegetation Indices to Achieve Maximal Sensitivity.” Agronomy journal 104 (5): 1336–1347. doi:10.2134/agronj2012.0065.
   Web of Science ®Google Scholar
• Nicodemus, F. E., J. C. Richmond, J. J. Hsia, I. Ginsberg, and T. Limperis. 1977. Geometrical Considerations and Nomenclature for Reflectance. Washington D.C., USA: U.S. Department of Commerce, National Bureau of Standards.
   Google Scholar
• Ollinger, S. V. 2011. “Sources of Variability in Canopy Reflectance and the Convergent Properties of Plants.” The New Phytologist 189 (2): 375–394. doi:10.1111/j.1469-8137.2010.03536.x.
   PubMed Web of Science ®Google Scholar
• Pasqualotto, N., J. Delegido, S. van Wittenberghe, M. Rinaldi, and J. Moreno. 2019. “Multi-Crop Green LAI Estimation with a New Simple Sentinel-2 LAI Index (SeLi).” Sensors (Switzerland) 19 (4). doi:10.3390/s19040904.
   Google Scholar
• Pasqualotto, N., G. D’Urso, S. F. Bolognesi, O. R. Belfiore, S. van Wittenberghe, J. Delegido, A. Pezzola, C. Winschel, et al. 2019. “Retrieval of Evapotranspiration from Sentinel-2: Comparison of Vegetation Indices, Semiempirical Models and SNAP Biophysical Processor Approach.” Agronomy 9 (10). doi:10.3390/agronomy9100663.
   Google Scholar
• Pocewicz, A., L. A. VIerling, L. B. Lentile, and R. Smith. 2007. “View Angle Effects on Relationships Between MISR Vegetation Indices and Leaf Area Index in a Recently Burned Ponderosa Pine Forest.” Remote Sensing of Environment 107 (1–2): 322–333. doi:10.1016/j.rse.2006.06.019.
   Web of Science ®Google Scholar
• Rahman, H., D. Quadir, A. Zahedul Islam, and S. Dutta. 1999. “Viewing Angle Effect on the Remote Sensing Monitoring of Wheat and Rice Crops.” Geocarto international 14 (1): 74–79. doi:10.1080/10106049908542097.
   Google Scholar
• Ranson, K. J., C. S. T. Daughtry, and L. L. Biehl. 1986. “Sun Angle, View Angle, and Background Effects on Spectral Response of Simulated Balsam Fir Canopies.” Photogrammetric Engineering and Remote Sensing 52: 649–658.
   Web of Science ®Google Scholar
• Richter, K., T. B. Hank, W. Mauser, and C. Atzberger. 2012. “Derivation of Biophysical Variables from Earth Observation Data: Validation and Statistical Measures.” Journal of Applied Remote Sensing 6 (1): 063557. doi:10.1117/1.JRS.6.063557.
   Web of Science ®Google Scholar
• Roosjen, P. P., B. Brede, J. M. Suomalainen, H. M. Bartholomeus, L. Kooistra, and J. G. Clevers. 2018. “Improved Estimation of Leaf Area Index and Leaf Chlorophyll Content of a Potato Crop Using Multi-Angle Spectral Data–Potential of Unmanned Aerial Vehicle Imagery.” International Journal of Applied Earth Observation and Geoinformation 66: 14–26. doi:10.1016/j.jag.2017.10.012.
   Web of Science ®Google Scholar
• Rouse, J. J., 1973. Monitoring the Vernal Advancement and Retrogradation (Green Wave Effect) of Natural Vegetation.
   Google Scholar
• Roy, D., Z. Li, and H. Zhang. 2017a. “Adjustment of Sentinel-2 Multispectral Instrument (MSI) Red-Edge Band Reflectance to Nadir BRDF Adjusted Reflectance (NBAR) and Quantification of Red-Edge Band BRDF Effects.” Remote Sensing 9 (12): 1325. doi:10.3390/rs9121325.
   Web of Science ®Google Scholar
• Roy, D. P., J. Li, H. K. Zhang, L. Yan, H. Huang, and Z. Li. 2017b. “Examination of Sentinel-2A Multispectral Instrument (MSI) Reflectance Anisotropy and the Suitability of a General Method to Normalise MSI Reflectance to Nadir BRDF Adjusted Reflectance.” Remote Sensing of Environment 199: 25–38. doi:10.1016/j.rse.2017.06.019.
   Web of Science ®Google Scholar
• Scahaaf, C. B., F. Gao, A. H. Strahler, W. Lucht, X. Li, T. TSANG, N. C. Strugnell, et al. 2002. “First Operational BRDF, Albedo Nadir Reflectance Products from MODIS.” Remote Sensing of Environment 83 (1–2): 135–148. doi:10.1016/S0034-4257(02)00091-3.
   Web of Science ®Google Scholar
• Schlemmer, M., A. Gitelson, J. Schepers, R. Ferguson, Y. Peng, J. Shanahan, and D. Rundquist. 2013. “Remote Estimation of Nitrogen and Chlorophyll Contents in Maize at Leaf and Canopy Levels.” International Journal of Applied Earth Observation and Geoinformation 25: 47–54. doi:10.1016/j.jag.2013.04.003.
   Web of Science ®Google Scholar
• Shah, S. H., Y. Angel, R. Houborg, S. Ali, and M. F. Mccabe. 2019. “A Random Forest Machine Learning Approach for the Retrieval of Leaf Chlorophyll Content in Wheat.” Remote Sensing 11 (8): 920. doi:10.3390/rs11080920.
   Web of Science ®Google Scholar
• Sharma, R. C., K. Kajiwara, and Y. Honda. 2013. “Estimation of Forest Canopy Structural Parameters Using Kernel-Driven Bi-Directional Reflectance Model Based Multi-Angular Vegetation Indices.” Isprs Journal of Photogrammetry and Remote Sensing 78: 50–57. doi:10.1016/j.isprsjprs.2012.12.006.
   Web of Science ®Google Scholar
• Sibanda, M., O. Mutanga, T. S. Dube, T. Vundla, and P. L. Mafongoya. 2019. “Estimating LAI and Mapping Canopy Storage Capacity for Hydrological Applications in Wattle Infested Ecosystems Using Sentinel-2 MSI Derived Red Edge Bands.” GIScience & Remote Sensing 56 (1): 68–86. doi:10.1080/15481603.2018.1492213.
   Web of Science ®Google Scholar
• Sola, I., A. García-Martín, L. Sandonís-Pozo, J. Álvarez-Mozos, F. Pérez-Cabello, M. González-Audícana, and R. M. Llovería. 2018. “Assessment of Atmospheric Correction Methods for Sentinel-2 Images in Mediterranean Landscapes.” International Journal of Applied Earth Observation and Geoinformation 73: 63–76. doi:10.1016/j.jag.2018.05.020.
   Web of Science ®Google Scholar
• Tierney, L. 2012. “The R Statistical Computing Environment.” In Statistical Challenges in Modern Astronomy V. Lecture Notes in Statistics, edited by E. Feigelson and G. Babu. Vol. 902. New York, NY: Springer. doi: 10.1007/978-1-4614-3520-4_41.
   Google Scholar
• Verrelst, J., J. P. Rivera, F. Veroustraete, J. MUÑOZ-MARÍ, J. G. Clevers, G. Camps-Valls, and J. Moreno. 2015. “Experimental Sentinel-2 LAI Estimation Using Parametric, Non-Parametric and Physical Retrieval Methods–A Comparison.” ISPRS Journal of Photogrammetry and Remote Sensing 108: 260–272. doi:10.1016/j.isprsjprs.2015.04.013.
   Web of Science ®Google Scholar
• Verrelst, J., M. E. Schaepman, B. Koetz, and M. Kneubühler. 2008. “Angular Sensitivity Analysis of Vegetation Indices Derived from CHRIS/PROBA Data.” Remote Sensing of Environment 112 (5): 2341–2353. doi:10.1016/j.rse.2007.11.001.
   Web of Science ®Google Scholar
• Vierling, L. A., D. W. Deering, and T. F. Eck. 1997. “Differences in Arctic Tundra Vegetation Type and Phenology as Seen Using Bidirectional Radiometry in the Early Growing Season.” Remote sensing of environment 60 (1): 71–82. doi:10.1016/S0034-4257(96)00139-3.
   Web of Science ®Google Scholar
• Viña, A., A. A. Gitelson, A. L. Nguy-Robertson, and Y. Peng. 2011. “Comparison of Different Vegetation Indices for the Remote Assessment of Green Leaf Area Index of Crops.” Remote sensing of environment 115 (12): 3468–3478. doi:10.1016/j.rse.2011.08.010.
   Web of Science ®Google Scholar
• Weiss, M., and F. Baret 2020. S2ToolBox Level 2 products: LAI, FAPAR, FCOVER. V2.0 ed.
   Google Scholar
• Weiss, M., F. Jacob, and G. Duveiller. 2020. “Remote Sensing for Agricultural Applications: A Meta-Review.” Remote sensing of environment 236: 111402. doi:10.1016/j.rse.2019.111402.
   Web of Science ®Google Scholar
• Xie, Q., J. Dash, A. Huete, A. Jiang, G. Yin, Y. Ding, D. Peng, et al. 2019. “Retrieval of Crop Biophysical Parameters from Sentinel-2 Remote Sensing Imagery.” International Journal of Applied Earth Observation and Geoinformation 80: 187–195. doi:10.1016/j.jag.2019.04.019.
   Web of Science ®Google Scholar
• Xu, X., J. Lu, N. Zhang, T. Yang, J. He, X. Yao, T. Cheng, Y. Zhu, W. Cao, and Y. Tian. 2019. “Inversion of Rice Canopy Chlorophyll Content and Leaf Area Index Based on Coupling of Radiative Transfer and Bayesian Network Models.” Isprs Journal of Photogrammetry and Remote Sensing 150: 185–196. doi:10.1016/j.isprsjprs.2019.02.013.
   Web of Science ®Google Scholar
• Yin, G., J. Li, B. Xu, Y. Zeng, S. W, K. Yan, A. Verger, and G. Liu. 2020. “PLC-C: An Integrated Method for Sentinel-2 Topographic and Angular Normalization.” IEEE Geoscience and Remote Sensing Letters 18 (8): 1446–1450. doi:10.1109/LGRS.2020.3001905.
   Web of Science ®Google Scholar