将卫星衍生植被变量整合到ISBA模型:一种序列数据同化方法

2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS Pub Date : 2021-07-11 DOI:10.1109/IGARSS47720.2021.9553560

J. Calvet, B. Bonan, Anthony Mucia, D. Shamambo, Yongjun Zheng, C. Albergel

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引用次数: 0

摘要

CNRM开发了全球土地数据同化系统(LDAS-Monde)。它使用土壤、生物圈和大气相互作用(ISBA)陆地表面模型的一个版本，能够模拟光合作用和植物生长。植被变量如叶面积指数(LAI)和地表土壤水分可以在模型中共同同化。由于完全由光合作用驱动的物候，LAI的顺序同化成为可能。模拟的LAI是灵活的，可以在给定的日期进行分析。此外，单独利用LAI同化可以分析根区土壤水分。目前正在研究1级微波观测的同化。介绍了LDAS-Monde的最新研究成果和应用前景。

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Integrating Satellite-Derived Vegetation Variables into the ISBA Model: A Sequential Data Assimilation Approach

A global land data assimilation system (LDAS-Monde) was developed by CNRM. It uses a version of the interactions between soil, biosphere, and atmosphere (ISBA) land surface model able to simulate photosynthesis and plant growth. Vegetation variables such as leaf area index (LAI) and surface soil moisture can be jointly assimilated in the model. Sequential assimilation of LAI is possible thanks to the fully photosynthesis-driven phenology. The simulated LAI is flexible and can be analyzed at a given date. Also, the assimilation of LAI alone can be used to analyze the root-zone soil moisture. The assimilation of level 1 microwave observations is being investigated. Recent results and potential applications of LDAS-Monde are presented.

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来源期刊

2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS

CiteScore

1.20

自引率

0.00%

发文量