Applicability of Different Assimilation Algorithms in Crop Growth Model Simulation of Evapotranspiration

Applicability of Different Assimilation Algorithms in Crop Growth Model Simulation of Evapotranspiration

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 southe...

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Bibliographic Details
Main Authors: Jingshu Wang, Ping Li, Rutian Bi, Lishuai Xu, Peng He, Yingjie Zhao, Xuran Li
Format: Article
Language:English
Published: MDPI AG 2024-11-01
Series:Agronomy
Subjects:
winter wheat
Linfen Basin
ESTARFM
CERES-Wheat
assimilation
Online Access:https://www.mdpi.com/2073-4395/14/11/2674
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Summary: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 ‘localization’ 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, <i>p</i> < 0.01). (2) ESTARFM ET can accurately capture the spatial details of SEBAL ET (R > 0.9, <i>p</i> < 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 (R<sup>2</sup> = 0.7119, <i>p</i> < 0.01) was significantly higher than that of Four-Dimensional Variational data assimilation (4DVar) ET (R<sup>2</sup> = 0.5142, <i>p</i> < 0.01) and particle filter (PF) ET (R<sup>2</sup> = 0.5596, <i>p</i> < 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.
ISSN:2073-4395

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