Improving the practicability of remote sensing data-assimilation-based crop yield estimations over a large area using a spatial assimilation algorithm and ensemble assimilation strategies

• Published in: Agricultural and forest meteorology Vol. 291; p. 108082
• Main Authors: Chen, Yi, Tao, Fulu
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 15.09.2020
• Subjects: algorithms; China; Crop model; crop models; crop yield; Data assimilation; forests; leaf area index; Leaf area index (LAI); meteorology; Remote sensing; Yield estimation; China; Leaf area index (LAI); Crop model; Yield estimation; Remote sensing; Data assimilation
• ISSN: 0168-1923; 1873-2240
• DOI: 10.1016/j.agrformet.2020.108082

•Spatial assimilation algorithm is robust in mitigating impact of biased observation.•Historical experiences of assimilation weights are not suitable for coming season.•Ensemble strategy can well drive assimilation without knowing optimal weights. Assimilating remote sensing data with crop growth model is a promising method to estimate crop yields over a large area. However, the method is always subject to the problems with biases in remote sensing products and assimilation weights in practical applications. In this study, we demonstrated the robustness of a ‘spatial assimilation’ method in dealing with the biases in three different remote sensing leaf area index (LAI) products. We further explored assimilation strategies for determining the assimilation weights when using ‘spatial assimilation’ method. Three different remote sensing LAI products were assimilated with MCWLA-Wheat model in the North China Plain during 2008–2015. The results demonstrated that the ‘spatial assimilation’ method was robust in mitigating the influences of biased LAI values and easily coupled with various LAI products based on different sources and retrieving algorithms. Furthermore, we found that the historical experiences of the optimal assimilation weights were not suitable to directly drive data assimilation in the coming seasons. Thus data-assimilation strategies to estimate crop yields without prior knowledge on the optimal assimilation weights were investigated. Two ensemble-mean-based assimilation strategies were recommended, which could reach 84 ~ 98% of yield estimation accuracy using the optimal assimilation weights. This study provides reliable and promising solutions for yield estimation over a large area using data assimilation without being limited by the biased state variables in remote sensing products and the lack of prior knowledge on the optimal assimilation weights. The ‘spatial assimilation’ method and the proposed ensemble-mean-based assimilation strategies have great potentials for wide applications, laying solid foundations for developing crop growth monitoring and yield forecasting system.