Disentangling Vegetation Water Content and Surface Soil Moisture from Sentinel-1 Sar Time Series Through Asynchronous Convolutional Neural Network-Reference-Cited by-同舟云学术

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Disentangling Vegetation Water Content and Surface Soil Moisture from Sentinel-1 Sar Time Series Through Asynchronous Convolutional Neural Network

Published:2024-07-07 Issue: Volume: Page:1474-1477
ISSN:
Container-title:IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium
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Author:

Shi Changjiang¹,Zhang Zhijie²,Wang Baohui³,Zhang Wanchang¹,Yi Yaning⁴

Affiliation:

1. Aerospace Information Research Institute, Chinese Academy of Sciences,Key Laboratory of Digital Earth Science,Beijing,China,100094

2. Utah State University,Quinney College of Natural Resources,Department of Environment and Society,Logan,UT,USA,84322

3. Beihang University,School of Software,Beijing,China,100191

4. Ministry of Emergency Management of China,National Institute of Natural Hazards,Beijing,China,100085

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10640349/10640352/10642855.pdf?arnumber=10642855

Reference10 articles.

1. Advances in soil moisture retrieval from synthetic aperture radar and hydrological applications

2. Soil moisture estimation over vegetated terrains using multitemporal remote sensing data

3. Stochastic ensemble methods for multi-SAR-mission soil moisture retrieval

4. A Method for Estimating Soil Moisture from ERS Scatterometer and Soil Data

5. Dense Temporal Series of C- and L-band SAR Data for Soil Moisture Retrieval Over Agricultural Crops

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