New empirical backscattering models for estimating bare soil surface parameters-Reference-Cited by-同舟云学术

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New empirical backscattering models for estimating bare soil surface parameters

Published:2020-12-20 Issue:5 Volume:42 Page:1928-1947
ISSN:0143-1161
Container-title:International Journal of Remote Sensing
language:en
Short-container-title:International Journal of Remote Sensing

Author:

Mirmazloumi S. Mohammad¹^ORCID,Sahebi Mahmod Reza²^ORCID,Amani Meisam³^ORCID

Affiliation:

1. Geomatics Division, Centre Tecnològic De Telecomunicacions De Catalunya (CTTC/CERCA), Castelldefels, Spain

2. Faculty of Geodesy and Geomatics Engineering & Remote Sensing Institute, K. N. Toosi University of Technology, Tehran, Iran

3. Wood Environment & Infrastructure Solutions, Ottawa, ON, Canada

Publisher

Informa UK Limited

Subject

General Earth and Planetary Sciences

Link

https://www.tandfonline.com/doi/pdf/10.1080/01431161.2020.1847353

Reference69 articles.

1. Evaluation of empirical and semi-empirical backscattering models for surface soil moisture estimation

2. A Multiple Classifier System to improve mapping complex land covers: a case study of wetland classification using SAR data in Newfoundland, Canada

3. Wetland classification in Newfoundland and Labrador using multi-source SAR and optical data integration

4. Vegetation modeled as a water cloud

5. Assimilation of Sentinel 1 and SMAP – based satellite soil moisture retrievals into SWAT hydrological model: the impact of satellite revisit time and product spatial resolution on flood simulations in small basins

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1. The synergies of SMAP enhanced and MODIS products in a random forest regression for estimating 1 km soil moisture over Africa using Google Earth Engine;Big Earth Data;2023-09-14

2. Machine Learning to Estimate Surface Roughness from Satellite Images;Remote Sensing;2021-09-22

3. Estimation of soil moisture with SAR data in large area based on support vector regression;Thirteenth International Conference on Digital Image Processing (ICDIP 2021);2021-06-30

4. Machine learning inversion approach for soil parameters estimation over vegetated agricultural areas using a combination of water cloud model and calibrated integral equation model;Journal of Applied Remote Sensing;2021-03-08

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