Progress and perspectives in data assimilation algorithms for remote sensing and crop growth model
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...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2024-12-01
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| Series: | Science of Remote Sensing |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666017224000300 |
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| Summary: | 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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| ISSN: | 2666-0172 |