Aas K, Czado C, Frigessi A, Bakken H. 2009. Pair-copula constructions of multiple dependence. Insurance: Mathematics and Economics, 44, 182–198.

Beget M E, Di Bella C M. 2007. Flooding: The effect of water depth on the spectral response of grass canopies. Journal of Hydrology, 335, 285–294.

Berger K, Atzberger C, Danner M, D’Urso G, Mauser W, Vuolo F, Hank T. 2018. Evaluation of the PROSAIL model capabilities for future hyperspectral model environments: A review study. Remote Sensing, 10, 85.

Brechmann E C, Schepsmeier U. 2013. Modeling dependence with C-and D-vine copulas: The R package CDVine. Journal of Statistical Software, 52, 1–27.

Breiman L. 2001. Random forests. Machine Learning, 45, 5–32.

Caicedo J P R, Verrelst J, Muñoz-Marí J, Moreno J, Camps-Valls G. 2014. Toward a semiautomatic machine learning retrieval of biophysical parameters. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7, 1249–1259.

de Castro A I, Shi Y, Maja J M, Peña J M. 2021. UAVs for vegetation monitoring: Overview and recent scientific contributions. Remote Sensing, 13, 2139.

Cover T, Hart P. 1967. Nearest neighbor pattern classification. IEEE Transactions on Information Theory, 13, 21–27.

Croft H, Chen J M, Luo X, Bartlett P, Chen B, Staebler R M. 2017. Leaf chlorophyll content as a proxy for leaf photosynthetic capacity. Global Change Biology, 23, 3513–3524.

Demmig-Adams B, Adams W W. 1996. The role of xanthophyll cycle carotenoids in the protection of photosynthesis. Trends in Plant Science, 1, 21–26.

Dißmann J, Brechmann E C, Czado C, Kurowicka D. 2013. Selecting and estimating regular vine copulae and application to financial returns. Computational Statistics & Data Analysis, 59, 52–69.

Doktor D, Lausch A, Spengler D, Thurner M. 2014. Extraction of plant physiological status from hyperspectral signatures using machine learning methods. Remote Sensing, 6, 12247–12274.

Duan S B, Li Z L, Wu H, Tang B H, Ma L, Zhao E, Li C. 2014. Inversion of the PROSAIL model to estimate leaf area index of maize, potato, and sunflower fields from unmanned aerial vehicle hyperspectral data. International Journal of Applied Earth Observation and Geoinformation, 26, 12–20.

FAO (Food and Agriculture Organization of the United Nations), IFAD (International Fund for Agricultural Development), UNICEF (the United Nations Children’s Fund), WFP (World Food Programme), WHO (World Health Organization). 2022. The State of Food Security and Nutrition in the World 2022: Repurposing Food and Agricultural Policies to Make Healthy Diets More Affordable. Rome.

Freund Y, Schapire R, Abe N. 1999. A short introduction to boosting. Journal-Japanese Society For Artificial Intelligence, 14, 1612.

Gitelson A A. 2004. Wide dynamic range vegetation index for remote quantification of biophysical characteristics of vegetation. Journal of Plant Physiology, 161, 165–173.

Heiskanen J, Rautiainen M, Stenberg P, Mõttus M, Vesanto V H. 2013. Sensitivity of narrowband vegetation indices to boreal forest LAI, reflectance seasonality and species composition. ISPRS Journal of Photogrammetry and Remote Sensing, 78, 1–14.

Herman J, Usher W. 2017. SALib: An open-source Python library for sensitivity analysis. Journal of Open Source Software, 2, 97.

Impollonia G, Croci M, Blandinières H, Marcone A, Amaducci S. 2022. Comparison of PROSAIL model inversion methods for estimating leaf chlorophyll content and LAI using UAV imagery for hemp phenotyping. Remote Sensing, 14, 5801.

Iwanaga T, Usher W, Herman J, Salib T. 2022. 2.0: Advancing the accessibility and interpretability of global sensitivity analyses. Socio-Environmental Systems Modelling, 4, 18155.

Jacquemoud Stéphane, Baret F. 1990. PROSPECT: A model of leaf optical properties spectra. Remote Sensing of Environment, 34, 75–91.

Kurowicka D, Joe H. 2011. Dependence Modeling: Vine Copula Handbook. World Scientific Publishing Company, NL.

Khorram S, Van der Wiele C F, Koch F H, Nelson S A, Potts M D. 2016. Principles of Applied Remote Sensing. Springer.

Lampayan R M, Rejesus R M, Singleton G R, Bouman B A. 2015. Adoption and economics of alternate wetting and drying water management for irrigated lowland rice. Field Crops Research, 170, 95–108.

Lundberg S M, Erion G, Chen H, DeGrave A, Prutkin J M, Nair B, Katz R, Himmelfarb J, Bansal N, Lee S I. 2020. From local explanations to global understanding with explainable AI for trees. Nature Machine Intelligence, 2, 56–67.

Lundberg S M, Lee S I. 2017. A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30, 4768–4777

Nguy-Robertson A, Gitelson A, Peng Y, Viña A, Arkebauer T, Rundquist D. 2012. Green leaf area index estimation in maize and soybean: Combining vegetation indices to achieve maximal sensitivity. Agronomy journal, 104, 1336–1347.

Sagi O, Rokach L. 2018. Ensemble learning: A survey. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 8, e1249.

Sit M, Demiray B, Xiang Z, Ewing G, Sermet Y, Demir I. 2020. A comprehensive review of deep learning applications in hydrology and water resources. Water Science and Technology, 82, 2635-2670.

Stoner E R, Baumgardner M. 1981. Characteristic variations in reflectance of surface soils. Soil Science Society of America Journal, 45, 1161–1165.

Sun J, Wang L, Shi S, Li Z, Yang J, Gong W, Wang S, Tagesson T. 2022. Leaf pigment retrieval using the PROSAIL model: Influence of uncertainty in prior canopy-structure information. The Crop Journal, 10, 1251–1263.

Sun T, Fang H, Liu W, Ye Y. 2017. Impact of water background on canopy reflectance anisotropy of a paddy rice field from multi-angle measurements. Agricultural and Forest Meteorology, 233, 143–152.

Svozil D, Kvasnicka V, Pospichal J. 1997. Introduction to multi-layer feed-forward neural networks. Chemometrics and Intelligent Laboratory Systems, 39, 43–62.

Swinehart D F. 1962. The beer-lambert law. Journal of Chemical Education, 39, 333.

Verrelst J, Malenovský Z, Van der Tol C, Camps-Valls G, Gastellu-Etchegorry J P, Lewis P, North P, Moreno J. 2019. Quantifying vegetation biophysical variables from imaging spectroscopy data: A review on retrieval methods. Surveys in Geophysics, 40, 589–629.

Wan L, Cen H, Zhu J, Zhang J, Zhu Y, Sun D, Du X, Zhai L, Weng H, Li Y, Li X, Bao Y, Shou J, He Y. 2020. Grain yield prediction of rice using multi-temporal UAV-based RGB and multispectral images and model transfer – a case study of small farmlands in the South of China. Agricultural and Forest Meteorology, 291, 108096.

Wan L, Zhang J, Dong X, Du X, Zhu J, Sun D, Liu Y, He Y, Cen H. 2021a. Unmanned aerial vehicle-based field phenotyping of crop biomass using growth traits retrieved from PROSAIL model. Computers and Electronics in Agriculture, 187, 106304.

Wan L, Zhu J, Du X, Zhang J, Han X, Zhou W, Li X, Liu J, Liang F, He Y, Cen H. 2021b. A model for phenotyping crop fractional vegetation cover using imagery from unmanned aerial vehicles. Journal of Experimental Botany, 72, 4691–4707.

Wu T, Liu K, Cheng M, Gu Z, Guo W, Jiao X. 2025. Paddy field scale evapotranspiration estimation based on two-source energy balance model with energy flux constraints and UAV multimodal data. Remote Sensing, 17, 1662.

Wu T, Zhang W, Wu S, Cheng M, Qi L, Shao G, Jiao X. 2023. Retrieving rice (Oryza sativa L.) net photosynthetic rate from UAV multispectral images based on machine learning methods. Frontiers in Plant Science, 13, 1088499.

Xiao X, Boles S, Frolking S, Li C, Babu J Y, Salas W, Moore B. 2006. Mapping paddy rice agriculture in South and Southeast Asia using multi-temporal MODIS images. Remote Sensing of Environment, 100, 95–113.

Yang Z, Tian J, Wang Z, Feng K. 2022. Monitoring the photosynthetic performance of grape leaves using a hyperspectral-based machine learning model. European Journal of Agronomy, 140, 126589.

Yuan D, Zhang S, Li H, Zhang J, Yang S, Bai Y. 2022. Improving the gross primary productivity estimate by simulating the maximum carboxylation rate of the crop using machine learning algorithms. IEEE Transactions on Geoscience and Remote Sensing, 60, 1–15.

Zeng Y, Hao D, Huete A, Dechant B, Berry J, Chen J M, Joiner J, Frankenberg C, Bond-Lamberty B, Ryu Y. 2022. Optical vegetation indices for monitoring terrestrial ecosystems globally. Nature Reviews Earth & Environment, 3, 477–493.

Zhang Y, Jin X, Shi L, Wang Y, Qiao H, Zha Y. 2025. A hybrid method for water stress evaluation of rice with the radiative transfer model and multidimensional imaging. Plant Phenomics, 7, 100016.

Zhou G, Tian C, Han Y, Niu C, Miao H, Jing G, Lopez F P A, Yan G, Najjar H S M, Zhao F, Sathyendranath S. 2024. Canopy reflectance modeling of row aquatic vegetation: AVRM and AVMC. Remote Sensing of Environment, 311, 114296.