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Downscaling Regional Crop Yields to Local Scale Using Remote Sensing

1
CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS), Borlaug Institute for South Asia (BISA), International Maize and Wheat Improvement Centre (CIMMYT), New Delhi 110012, India
2
Division of Agricultural Physics, Indian Agricultural Research Institute (IARI), ICAR, New Delhi 110012, India
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Author to whom correspondence should be addressed.
Agriculture 2020, 10(3), 58; https://doi.org/10.3390/agriculture10030058
Received: 26 January 2020 / Revised: 7 February 2020 / Accepted: 10 February 2020 / Published: 2 March 2020
Local-scale crop yield datasets are not readily available in most of the developing world. Local-scale crop yield datasets are of great use for risk transfer and risk management in agriculture. In this article, we present a simple method for disaggregation of district-level production statistics over crop pixels by using a remote sensing approach. We also quantified the error in the disaggregated statistics to ascertain its usefulness for crop insurance purposes. The methodology development was attempted in Parbhani district of Maharashtra state with wheat and sorghum crops in the winter season. The methodology uses the ratio of Enhanced Vegetation Index (EVI) of pixel to total EVI of the crop pixels in that district corresponding to the growth phase of the crop. It resulted in the generation of crop yield maps at the 500 m resolution pixel (grid) level. The methodology was repeated to generate time-series maps of crop yield. In general, there was a good correspondence between disaggregated crop yield and sub-district level crop yields with a correlation coefficient of 0.9. View Full-Text
Keywords: crop yield; downscaling; remote sensing; disaggregation crop yield; downscaling; remote sensing; disaggregation
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Shirsath, P.B.; Sehgal, V.K.; Aggarwal, P.K. Downscaling Regional Crop Yields to Local Scale Using Remote Sensing. Agriculture 2020, 10, 58.

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