Prediction of Soil Moisture From Near-Global Cygnss Gnss-Reflectometry Using a Random Forest Machine Learning Model-Reference-Cited by-同舟云学术

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Prediction of Soil Moisture From Near-Global Cygnss Gnss-Reflectometry Using a Random Forest Machine Learning Model

Published:2024-07-07 Issue: Volume: Page:4465-4471
ISSN:
Container-title:IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium
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Author:

Wilson M.D.¹,Datta R.¹,Savarimuthu S.¹,Moller D.²,Ruf C.³

Affiliation:

1. University of Canterbury,Geospatial Research Institute Toi Hangarau,New Zealand

2. University of Auckland,Dept. Electrical, Computer and Software Engineering,New Zealand

3. University of Michigan,Dept. Climate and Space Sciences and Engineering,United States

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10640349/10640352/10642723.pdf?arnumber=10642723

Reference55 articles.

1. Effect of soil moisture and atmospheric humidity on both plant productivity and diversity of native grasslands across the Loess Plateau, China

2. Soil Moisture Remote Sensing: State-of-the-Science

3. Sensitivity Analysis of CYGNSS derived Radar Reflectivity for Soil Moisture Retrieval over India: Initial Results

4. Review and evaluation of remote sensing methods for soil-moisture estimation

5. High Spatio-Temporal Resolution CYGNSS Soil Moisture Estimates Using Artificial Neural Networks

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. An Airborne GNSS-R Driven Low-Latency Flood Assessment Development;IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium;2024-07-07

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