数字农科院2.0

Progress and perspectives in data assimilation algorithms for remote sensing and crop growth model

文献类型： 外文期刊

作者： Jianxi Huang;Jianjian Song;Hai Huang;Wen Zhuo;Quandi Niu;Shangrong Wu;Han Ma;Shunlin Liang

作者机构：

关键词： Crop growth models;Crop modeling;Crop yield prediction;Data assimilation;Remote sensing;Remotely sensed parameter

期刊名称： Science of Remote Sensing

ISSN： 2666-0172

年卷期： 2024 年 10 卷

页码：

收录情况： 无来源刊（2024版）

摘要： Combining the advantages of crop growth models and remote sensing observations, data assimilation (DA) has emerged as a vital tool for crop growth monitoring and early-season crop yield forecasting. As an increasing number of related studies have been conducted, data assimilation systems for remote sensing and crop growth models have grown increasingly sophisticated. However, within this context, the research on data assimilation algorithms, as a core component of data assimilation system, highly need investigating the potential. In this review, we discuss the essential differences and inherent connections of various data assimilation algorithms based on Bayes's Theorem. Building upon this foundation, we review the application progress of different DA algorithms data assimilation of remote sensing and crop models. Additionally, we identify the challenges and limitations faced by current data assimilation algorithms in crop practical applications and propose potential directions for future study. As a summary of the entire paper, we provide recommendations for DA algorithm choice strategy in conjunction with specific application scenarios.

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