Modeling crop yield using NDVI-derived VGM metrics across different climatic regions in the USA
Author:
Publisher
Springer Science and Business Media LLC
Subject
Health, Toxicology and Mutagenesis,Atmospheric Science,Ecology
Link
https://link.springer.com/content/pdf/10.1007/s00484-023-02478-4.pdf
Reference36 articles.
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3. Bolton DK, Friedl MA (2013) Forecasting crop yield using remotely sensed vegetation indices and crop phenology metrics. Agric For Meteorol 173:74–84. https://doi.org/10.1016/j.agrformet.2013.01.007
4. Clemente JP, Fontanelli G, Ovando GG, Roa YLB, Lapini A, Santi E (2020) Google Earth Engine: Application of Algorithms for Remote Sensing of Crops In Tuscany (Italy). IEEE Latin Am GRSS ISPRS Remote Sens Conf (LAGIRS) 2020:195–200. https://doi.org/10.1109/LAGIRS48042.2020.9165561
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