Satellite rainfall bias assessment for crop growth simulation – A case study of maize growth in Kenya

In this research, the performance of satellite rainfall estimates (SREs) for crop growth simulation was investigated. Rainfall products selected were CHIRPS 2.0, CMORPH 1.0, MSWEP 2.2, and RFE 2.0. In-situ from 20 stations within Lake Victoria basin in Kenya served as reference. evaluated onset days, depths, dry spells, occurrence four stages. Assessment on a daily time step period 2012–2018 point-to-pixel basis. Results showed that SREs exhibit large variation timing arrival. exhibited largest interannual spatial spreads representing spell length during flowering stage with showing best weakest results, respectively. Bias spells smaller early Detecting by weakened growing season progressed. MSWEP, followed RFE2, produced results detecting events, while falsely detected frequent CHIRPS, particularly later generally performed better wet than season. indicated less bias depths stages but deteriorated at least highest spread relative bias, associating biases to severe extreme water stress, based requirement satisfaction index, effects more prevailing ripening Findings study suggest can serve input modelling, validation rain gauge observed counterparts is essential.