Satellite rainfall bias correction incorporating effects on simulated crop water requirements

Calisto kennedy Omondi*, Tom H.M. Rientjes, Martijn j. Booij, Andrew D. Nelson

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

23 Downloads (Pure)

Abstract

Satellite rainfall estimates (SRE) offer spatial-temporal rainfall representations in regions with limited ground-based gauge rainfall measurements. However, differences exist between SRE and gauge measured rainfall, which needs assessment and reduction. This study presents a method to correct errors in SRE to make their use in agro-hydrological applications and models meaningful. The main scientific objective is the determination of effective window sizes for SRE bias correction. To conclude on effective window sizes, the crop water requirement satisfaction index (WRSI) for gauged rainfall, uncorrected SRE and bias corrected SRE were estimated and propagation effects of SRE errors on respective WRSI estimates were assessed. WRSI indicates how much of the crop water needs are satisfied by rainfall. The Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) SRE was bias corrected using gauged rainfall data from 20 stations in the Lake Victoria basin of Kenya from 2012 to 2018. The results show that the error in WRSI can serve to determine effective window sizes for SRE bias correction rather than using SRE bias error itself. This proposed correction method resulted in improved estimates of WRSI.
Original languageEnglish
Pages (from-to)2269-2288
Number of pages20
JournalInternational journal of remote sensing
Volume45
Issue number7
Early online date19 Mar 2024
DOIs
Publication statusPublished - 2 Apr 2024

Keywords

  • UT-Hybrid-D

Fingerprint

Dive into the research topics of 'Satellite rainfall bias correction incorporating effects on simulated crop water requirements'. Together they form a unique fingerprint.

Cite this