Improved use of satellite rainfall estimates for crop growth simulation

Crop growth simulation models (CGSMs) are essential tools for assessing crop production under varying environmental conditions, particularly in rainfed agriculture where rainfall serves as the primary water source. These models require accurate rainfall data to simulate soil moisture dynamics and crop performance. However, obtaining reliable rainfall data from ground-based gauge networks remains challenging due to sparse networks, equipment malfunctions, and restricted data access. Satellite-based rainfall estimates (SREs) offer promising alternatives by providing spatially and temporally consistent rainfall data, but they contain systematic (bias) and random errors that can propagate through CGSMs, leading to unreliable crop growth simulations. While bias correction methods exist, they are primarily developed for hydrological applications using correction windows of short duration and fixed length that may not be directly suitable for agro-hydrological applications where crop responses to soil moisture vary across growth stages.
This research evaluated the applicability of SREs for CGSMs, focusing on rainfed maize growing in the Lake Victoria Basin, Kenya. Four objectives were addressed: (1) assessing SRE accuracy in representing key rainfall characteristics across maize growth stages, (2) developing an adaptive SRE bias correction method using meaningful correction window sizes defined by crop-relevant thresholds, (3) developing ensemble approaches for random error correction, and (4) evaluating the impact of bias and random error corrected SREs on crop growth simulations.
The research demonstrated that no single SRE product consistently outperformed others across all crop growth stages, highlighting limitations of individual products. A novel bias correction approach was developed using Crop Water Requirement Satisfaction Index (WRSI) error propagation to determine meaningful correction windows, identifying a 23.5% WRSI error threshold for triggering corrections. To address persistent random errors, a two-step weighted ensemble method was developed that reduced rainfall estimation errors by 25–30% compared to simple averaging.
Application of corrected SREs in the AquaCrop-OSPy CGSM demonstrated substantial improvements: bias decreased from -26.9% to -0.4%, normalized root mean square error reduced from 44.5% to 6.2%, and crop failure detection accuracy improved from 30–83% to 89%. This research provides operational frameworks for reliable SRE integration, enabling improved crop production assessments for food security applications in data-scarce regions.