Applicability of Different Assimilation Algorithms in Crop Growth Model Simulation of Evapotranspiration
2024
Jingshu Wang | Ping Li | Rutian Bi | Lishuai Xu | Peng He | Yingjie Zhao | Xuran Li
Remote sensing spatiotemporal fusion technology can provide abundant data source information for assimilating crop growth model data, enhancing crop growth monitoring, and providing theoretical support for crop irrigation management. This study focused on the winter wheat planting area in the southeastern part of the Loess Plateau, a typical semi-arid region, specifically the Linfen Basin. The SEBAL and ESTARFM were used to obtain 8 d, 30 m evapotranspiration (ET) for the growth period of winter wheat. Then, based on the &lsquo:localization&rsquo: of the CERES-Wheat model, the fused results were incorporated into the data assimilation process to further determine the optimal assimilation method. The results indicate that (1) ESTARFM ET can accurately capture the spatial details of SEBAL ET (R >: 0.9, p <: 0.01). (2) ESTARFM ET can accurately capture the spatial details of SEBAL ET (R >: 0.9, p <: 0.01). The calibrated CERES-Wheat ET characteristic curve effectively reflects the ET variation throughout the winter wheat growth period while being consistent with the trend and magnitude of ESTARFM ET variation. (3) The correlation between Ensemble Kalman filter (EnKF) ET and ESTARFM ET (R2 = 0.7119, p <: 0.01) was significantly higher than that of Four-Dimensional Variational data assimilation (4DVar) ET (R2 = 0.5142, p <: 0.01) and particle filter (PF) ET (R2 = 0.5596, p <: 0.01). The results of the study provide theoretical guidance to improve the yield and water use efficiency of winter wheat in the region, which will help promote sustainable agricultural development.
Afficher plus [+] Moins [-]Mots clés AGROVOC
Informations bibliographiques
Cette notice bibliographique a été fournie par Multidisciplinary Digital Publishing Institute
Découvrez la collection de ce fournisseur de données dans AGRIS