Impacts of cropland masks on the explanation of crop yield anomaly in Africa

We provide main scripts for the manuscript: Decoding cropland mask effects on the explanatory power of remote sensing and reanalyzed climate data on yield anomalies in Africa.No new data are generated in this study. Yield and harvest area of maize, millet, and sorghum can be accessed through the FAO (2024); MIRCA2000 data is available through Portmann et al. (2010); SPAM data is available through Yu et al. (2020); GLC_FCS30D data is available through Zhang et al. (2024); African administration boundary is from the U.S. Department of State (2017); MODIS LST data is obtained from the NASA (2024a); MODIS ET data is obtained from the NASA (2024b); GPM v6 data is obtained from the NASA (2024c); ERA5-Land data is obtained from the ECMWF (2024); MODIS, GPM v6, and ERA5-Land are pre-processed on the Google Earth Engine Platform (Gorelick et al., 2017). Python 3.10 and R 4.3.3 used for data processing, regression modelling, and figure visualization.