Evaluation of machine learning-dynamical hybrid method incorporating remote sensing data for in-season maize yield prediction under drought-Reference-Cited by-同舟云学术

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Evaluation of machine learning-dynamical hybrid method incorporating remote sensing data for in-season maize yield prediction under drought

Published:2024-05-18 Issue:4 Volume:25 Page:1982-2006
ISSN:1385-2256
Container-title:Precision Agriculture
language:en
Short-container-title:Precision Agric

Author:

Luo Yi,Wang Huijing,Cao Junjun^ORCID,Li Jinxiao,Tian Qun,Leng Guoyong,Niyogi Dev

Funder

National Natural Science Foundation of China

Fundamental Research Funds for the Central Universities

Open Fund of National Engineering Research Center of Geographic Information System, China University of Geosciences

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s11119-024-10149-6.pdf

Reference75 articles.

1. Abatzoglou, J. T., Dobrowski, S. Z., Parks, S. A., & Hegewisch, K. C. (2018). TerraClimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958–2015. Scientific Data, 5(1), 170191. https://doi.org/10.1038/sdata.2017.191

2. Aiken, L. S., West, S. G., Pitts, S. C., Baraldi, A. N., & Wurpts, I. C. (2012). Multiple linear regression. In I. Weiner (Ed.), Handbook of psychology (2nd ed.). Wiley. https://doi.org/10.1002/9781118133880.hop202018

3. Azzari, G., Jain, M., & Lobell, D. B. (2017). Towards fine resolution global maps of crop yields: Testing multiple methods and satellites in three countries. Remote Sensing of Environment, 202, 129–141. https://doi.org/10.1016/j.rse.2017.04.014

4. Benami, E., Jin, Z., Carter, M. R., Ghosh, A., Hijmans, R. J., Hobbs, A., Kenduiywo, B., & Lobell, D. B. (2021). Uniting remote sensing, crop modelling and economics for agricultural risk management. Nature Reviews Earth & Environment, 2(2), 140–159. https://doi.org/10.1038/s43017-020-00122-y

5. Boas, T., Bogena, H., Ryu, D., Vereecken, H., Western, A., & Franssen, H. (2023). Seasonal soil moisture and crop yield prediction with fifth-generation seasonal forecasting system (SEAS5) long-range meteorological forecasts in a land surface modelling approach. Hydrology and Earth System Sciences, 27(16), 3143–3167. https://doi.org/10.5194/hess-27-3143-2023

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