Stochastic bias correction for RADARSAT-2 soil moisture retrieved over vegetated areas
Author:
Affiliation:
1. Future & Fusion Lab of Arch, Civil and Env. Eng, Korea University, Seoul, Republic of Korea;
2. Global Institute for Water Security, School of Environment and Sustainability, University of Saskatchewan, Saskatoon, Saskatchewan, Canada
Funder
National Research Foundation of the Korean government
National Research Foundation of Korean Goverment
Publisher
Informa UK Limited
Subject
Water Science and Technology,Geography, Planning and Development
Link
https://www.tandfonline.com/doi/pdf/10.1080/10106049.2021.2017009
Reference37 articles.
1. Vegetation modeled as a water cloud
2. Semi-empirical calibration of the IEM backscattering model using radar images and moisture and roughness field measurements
3. Toward Global Soil Moisture Monitoring With Sentinel-1: Harnessing Assets and Overcoming Obstacles
4. Multifrequency Soil Moisture Inversion from SAR Measurements with the Use of IEM
5. A multi-objective calibration approach using in-situ soil moisture data for improved hydrological simulation of the Prairies
Cited by 2 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. Bias-Corrected RADARSAT-2 Soil Moisture Dynamics Reveal Discharge Hysteresis at An Agricultural Watershed;Remote Sensing;2023-05-21
2. Time series soil moisture retrieval from SAR data: Multi-temporal constraints and a global validation;Remote Sensing of Environment;2023-03
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