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The archive contains data, analysis scripts, and outputs for evaluating and improving satellite rainfall estimates (SREs) for agro-hydrological applications in data-scarce locations. Data covers (a) assessment of CHIRPS, CMORPH, MSWEP, and RFE satellite rainfall products against rain gauge observations (20 stations, 2012-2018), with bias analysis including onset detection, dry spell identification, and crop water requirement satisfaction metrics; (b) dynamic window-based bias correction methodology with predefined β-values; (c) weighted ensemble of bias-corrected satellite rainfall products to reduce random errors; and (d) error propagation analysis through FAO AquaCrop crop growth model for maize biomass simulation. Contents include raw and processed rainfall data, Jupyter notebooks and Python scripts for analysis, Water Requirement Satisfaction Index calculations, bias correction outputs, AquaCrop simulation inputs and biomass outputs, cross-validation results, and generated figures and statistical summaries supporting the findings."},"dsDescriptionDate":{"typeName":"dsDescriptionDate","multiple":false,"typeClass":"primitive","value":"2026-01-13"}}]},{"typeName":"subject","multiple":true,"typeClass":"controlledVocabulary","value":["Computer and Information Science","Earth and Environmental Sciences","Mathematical Sciences","Physics"]},{"typeName":"keyword","multiple":true,"typeClass":"compound","value":[{"keywordValue":{"typeName":"keywordValue","multiple":false,"typeClass":"primitive","value":"Satellite rainfall estimates"}},{"keywordValue":{"typeName":"keywordValue","multiple":false,"typeClass":"primitive","value":"Bias 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