Ecohydrological Land Reanalysis: Vegetation Water Content and Soil Moisture Data by Land Data Assimilation

IF 2.4 3区地球科学 Q2 GEOSCIENCES, MULTIDISCIPLINARY

Geoscience Data Journal Pub Date : 2025-08-10 DOI:10.1002/gdj3.70025

Yohei Sawada, Hideyuki Fujii, Hiroyuki Tsutsui, Kentaro Aida, Rigen Shimada, Misako Kachi, Toshio Koike

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引用次数: 0

Abstract

The accurate estimation of terrestrial water and vegetation is a grand challenge in hydrometeorology. Many previous studies developed land data assimilation systems (LDASs) and provided global-scale land surface data sets by integrating numerical simulation and satellite data. However, vegetation dynamics have not been explicitly solved in these land reanalysis data sets. Here we present the newly developed land reanalysis data set, ECoHydrological Land reAnalysis (ECHLA). ECHLA is generated by sequentially assimilating C- and X-band microwave brightness temperature satellite observations into a land surface model which can explicitly simulate the dynamic evolution of vegetation biomass. The ECHLA data set provides semiglobal soil moisture from surface to 1.95 m depth, Leaf Area Index (LAI), and vegetation water content. The ECHLA data set is publicly available in the Japan Aerospace eXploration Agency's repository and is expected to contribute to understanding terrestrial ecohydrological cycles and water-related natural disasters such as drought.

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生态水文土地再分析：基于土地数据同化的植被含水量和土壤水分数据

陆地水体和植被的准确估算是水文气象学的一大挑战。以往的许多研究开发了陆地数据同化系统（LDASs），通过综合数值模拟和卫星数据提供全球尺度的陆地表面数据集。然而，在这些土地再分析数据集中，植被动态还没有得到明确的解决。本文介绍了新开发的土地再分析数据集——生态水文土地再分析（ecachla）。ecla是将C波段和x波段微波亮度温度卫星观测数据依次同化到地表模式中生成的，该模式可以明确地模拟植被生物量的动态演变。ECHLA数据集提供了从地表到1.95 m深度的半全球土壤湿度、叶面积指数（LAI）和植被含水量。ecla数据集在日本宇宙航空研究开发机构的存储库中公开提供，预计将有助于了解陆地生态水文循环和与水有关的自然灾害，如干旱。

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来源期刊

Geoscience Data Journal GEOSCIENCES, MULTIDISCIPLINARYMETEOROLOGY-METEOROLOGY & ATMOSPHERIC SCIENCES

CiteScore

5.90

自引率

9.40%

发文量

审稿时长

4 weeks

期刊介绍： Geoscience Data Journal provides an Open Access platform where scientific data can be formally published, in a way that includes scientific peer-review. Thus the dataset creator attains full credit for their efforts, while also improving the scientific record, providing version control for the community and allowing major datasets to be fully described, cited and discovered. An online-only journal, GDJ publishes short data papers cross-linked to – and citing – datasets that have been deposited in approved data centres and awarded DOIs. The journal will also accept articles on data services, and articles which support and inform data publishing best practices. Data is at the heart of science and scientific endeavour. The curation of data and the science associated with it is as important as ever in our understanding of the changing earth system and thereby enabling us to make future predictions. Geoscience Data Journal is working with recognised Data Centres across the globe to develop the future strategy for data publication, the recognition of the value of data and the communication and exploitation of data to the wider science and stakeholder communities.