GLOBAL OPTIMIZATION OF SOIL TEXTURE FROM A LONG-TERM SATELLITE SOIL MOISTURE DATASET

被引：0

作者：

He, Qing ^{[1
,2
]}

Lu, Hui ^{[2
,3
]}

He, Kaixun ^{[2
]}

Zhou, Jianhong ^{[2
]}

Xu, Yawei ^{[2
]}

Yang, Kun ^{[2
]}

Shi, Jiancheng ^{[4
]}

机构：

[1] Univ Tokyo, Dept Civil Engn, Tokyo 1138656, Japan

[2] Tsinghua Univ, Dept Earth Syst Sci, Minist Educ, Key Lab Earth Syst Modeling, Beijing 100084, Peoples R China

[3] Minist Educ, Ecol Field Stn East Asian Migratory Birds, Beijing 100084, Peoples R China

[4] Chinese Acad Sci, Natl Space Sci Ctr, Beijing 100190, Peoples R China

来源：

IGARSS 2023 - 2023 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM | 2023年

关键词：

RETRIEVAL;

D O I：

10.1109/IGARSS52108.2023.10282295

中图分类号：

P [天文学、地球科学];

学科分类号：

07 ;

摘要：

Soil texture is a fundamental soil property and serve as a crucial input to many Land Surface Models (LSMs). However, current soil texture datasets used in LSMs are usually extrapolated from in-situ scale geological surveys, which may contain high uncertainties due to the mismatch in spatial scales. Here, we propose a method to optimize several currently existing soil texture datasets by using a long-term satellite soil moisture dataset. The optimized soil texture datasets may provide an opportunity to improve land surface simulations in LSMs.

引用

页码：3206 / 3208

页数：3

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