Downscaling Global Gridded Crop Yield Data Products and Crop Water Productivity Mapping Using Remote Sensing Derived Variables in the South Asia
Author:
Funder
SERB, India
Publisher
Springer Science and Business Media LLC
Subject
Plant Science,Agronomy and Crop Science
Link
https://link.springer.com/content/pdf/10.1007/s42106-022-00223-2.pdf
Reference48 articles.
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2. Beck, H. E., Zimmermann, N. E., McVicar, T. R., Vergopolan, N., Berg, A., & Wood, E. F. (2018). Present and future Köppen-Geiger climate classification maps at 1-km resolution. Sci Data, 5, 180214. https://doi.org/10.1038/sdata.2018.214
3. Budde, M. E., Tappan, G., Rowland, J., Lewis, J., & Tieszen, L. L. (2004). Assessing land cover performance in Senegal, West Africa using 1-km integrated NDVI and local variance analysis. Journal of Arid Environments, 59, 481–498. https://doi.org/10.1016/J.JARIDENV.2004.03.020
4. Cazcarro, I., Martín-Retortillo, M., & Serrano, A. (2019). Reallocating regional water apparent productivity in the long term: Methodological contributions and application for Spain. Regional Environmental Change, 19, 1455–1468. https://doi.org/10.1007/s10113-019-01485-9
5. Chen, Y., Zhang, Z., Tao, F., Wang, P., & Wei, X. (2017). Spatio-temporal patterns of winter wheat yield potential and yield gap during the past three decades in North China. F. Crop. Res., 206, 11–20. https://doi.org/10.1016/J.FCR.2017.02.012
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