A downscaling-and-fusion framework for generating spatio-temporally complete and fine resolution remotely sensed surface soil moisture-Reference-Cited by-同舟云学术

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A downscaling-and-fusion framework for generating spatio-temporally complete and fine resolution remotely sensed surface soil moisture

Published:2024-06 Issue: Volume:352 Page:110044
ISSN:0168-1923
Container-title:Agricultural and Forest Meteorology
language:en
Short-container-title:Agricultural and Forest Meteorology

Author:

Zhu Zhongzheng^ORCID,Bo Yanchen^ORCID,Sun Tongtong,Zhang Xiaoran,Sun Mei,Shen Aojie^ORCID,Zhang Yusha,Tang Jia,Cao Mengfan^ORCID,Wang Chenyu

Funder

National Natural Science Foundation of China

Publisher

Elsevier BV

Reference77 articles.

1. Generating surface soil moisture at 30 m spatial resolution using both data fusion and machine learning toward better water resources management at the field scale;Abowarda;Remote Sens. Environ.,2021

2. Initial soil moisture retrievals from the METOP-A Advanced Scatterometer (ASCAT);Bartalis;Geophys. Res. Lett.,2007

3. GLDAS Noah Land Surface Model L4 3 hourly 0.25×0.25 degree V2.1;Beaudoing,2020

4. A new global climatology of annual land surface temperature;Bechtel;Remote Sens. (Basel),2015

5. Estimating soil properties from thematic soil maps: the Bayesian maximum entropy approach;Bogaert;Soil Sci. Soc. Am. J.,2002

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