High-spatial-resolution surface soil moisture retrieval using the Deep Forest model in the cloud environment over the Tibetan Plateau-Reference-Cited by-同舟云学术

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High-spatial-resolution surface soil moisture retrieval using the Deep Forest model in the cloud environment over the Tibetan Plateau

Published:2024-02-15 Issue: Volume: Page:1-20
ISSN:1009-5020
Container-title:Geo-spatial Information Science
language:en
Short-container-title:Geo-spatial Information Science

Author:

Li Zhenghao¹,Yuan Qiangqiang¹²^ORCID,Su Xin³

Affiliation:

1. School of Geodesy and Geomatics, Wuhan University, Wuhan, China

2. Key Laboratory of Geospace Environment and Geodesy, Ministry of Education, Wuhan University, Wuhan, China

3. School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, China

Funder

National Key R&D Program of China

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/10095020.2024.2307931

Reference64 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

2. Estimating soil moisture using remote sensing data: A machine learning approach

3. Review of Machine Learning Approaches for Biomass and Soil Moisture Retrievals from Remote Sensing Data;Ali I.;Remote Sensing,2015

4. Google Earth Engine Cloud Computing Platform for Remote Sensing Big Data Applications: A Comprehensive Review

5. Dense Temporal Series of C-and L-band SAR Data for Soil Moisture Retrieval Over Agricultural Crops;Balenzano A.;IEEE Journal of Selected Topics in Applied Earth Observations & Remote Sensing,2010

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Spatial Downscaling of Satellite-Based Soil Moisture Products Using Machine Learning Techniques: A Review;Remote Sensing;2024-06-07

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