Downscaling Global Gridded Crop Yield Data Products and Crop Water Productivity Mapping Using Remote Sensing Derived Variables in the South Asia

Local scale crop yield and crop water productivity information is critical for informed decision making, crop yield forecasting and crop model calibration applications. In this study, we have attempted to downscale coarse resolution primary season rice yield datasets to a local scale of 500 m using a minimum-median downscaling approach. Sixteen mainland countries in south and southeast Asia region were considered as study region to downscale global rice yield datasets for 2000-2015. Four medium resolution remote sensing derived vegetation indices such as Normalised Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Leaf Area Index (LAI), and Gross Primary Product (GPP) were used to downscale coarse resolution global rice yield datasets. A kharif season district level rice yield data from International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), India was used as a reference dataset to evaluate the downscaled rice yields at the district scale. The proposed downscaling approach performance was satisfactory with a mean absolute error (MAE) range of 0.85-1.2 t/ha which lies in the error range of 10-15% with respect to actual range of reference rice yield datasets. Furthermore, crop water productivity maps at 500 m scale were also developed with the downscaled rice yield and Moderate Resolution Imaging Spectroradiometer (MODIS) Evapotranspiration (ET) data products. Statistical analysis shows that the rice yield and crop water productivity values across different climate zones were statistically significant. Tropical zone-based crop yield and crop water productivity values were showing higher variation when compared to other climate zones with a range of 1-10 t/ha and 1-12.5 kg/m(3), respectively.