Addressing spatial gaps in ESA CCI soil moisture product: A hierarchical reconstruction approach using deep learning model-Reference-Cited by-同舟云学术

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Addressing spatial gaps in ESA CCI soil moisture product: A hierarchical reconstruction approach using deep learning model

Published:2024-08 Issue: Volume:132 Page:104003
ISSN:1569-8432
Container-title:International Journal of Applied Earth Observation and Geoinformation
language:en
Short-container-title:International Journal of Applied Earth Observation and Geoinformation

Author:

Ding Tao^ORCID,Zhao Wei^ORCID,Yang Yanqing

Publisher

Elsevier BV

Reference60 articles.

1. The k-means algorithm: A comprehensive survey and performance evaluation;Ahmed;Electronics,2020

2. Temporal and spatial changes of soil moisture and its response to temperature and precipitation over the Tibetan Plateau;Bai;Hydrolog. Sci. J.,2019

3. Evaluation of 18 satellite- and model-based soil moisture products using in situ measurements from 826 sensors;Beck;Hydrol. Earth Syst. Sci.,2021

4. High-resolution observations from space to address new applications in hydrology;Brocca;The Innovation,2023

5. A review on semi-supervised clustering;Cai;Inf. Sci. (N Y).,2023

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