Geo-Intelligent Retrieval Framework Based on Machine Learning in the Cloud Environment: A Case Study of Soil Moisture Retrieval-Reference-Cited by-同舟云学术

Geo-Intelligent Retrieval Framework Based on Machine Learning in the Cloud Environment: A Case Study of Soil Moisture Retrieval

Published:2023 Issue: Volume:61 Page:1-15
ISSN:0196-2892
Container-title:IEEE Transactions on Geoscience and Remote Sensing
language:
Short-container-title:IEEE Trans. Geosci. Remote Sensing

Author:

Li Zhenghao¹,Yuan Qiangqiang²^ORCID,Zhang Liangpei³^ORCID

Affiliation:

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

2. School of Geodesy and Geomatics and the Collaborative Innovation Center of Geospatial Technology, Wuhan University, Wuhan, China

3. State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing and the Collaborative Innovation Center of Geospatial Technology, Wuhan University, Wuhan, China

Funder

National Key Research and Development Program of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Earth and Planetary Sciences,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/36/10006360/10143395.pdf?arnumber=10143395

Reference59 articles.

1. Multi-source hydrological soil moisture state estimation using data fusion optimisation

2. Validation practices for satellite soil moisture retrievals: What are (the) errors?

3. Downscaling of Surface Soil Moisture Retrieval by Combining MODIS/Landsat and In Situ Measurements

4. On the measurement of bias in geographically weighted regression models

5. Deep learning in environmental remote sensing: Achievements and challenges

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