A Comparison between Sentinel-2 and Landsat 8 OLI Satellite Images for Soil Salinity Distribution Mapping Using a Deep Learning Convolutional Neural Network-Reference-Cited by-同舟云学术

A Comparison between Sentinel-2 and Landsat 8 OLI Satellite Images for Soil Salinity Distribution Mapping Using a Deep Learning Convolutional Neural Network

Published:2022-04-18 Issue:3 Volume:48 Page:452-468
ISSN:0703-8992
Container-title:Canadian Journal of Remote Sensing
language:en
Short-container-title:Canadian Journal of Remote Sensing

Author:

Kazemi Garajeh Mohammad¹^ORCID,Blaschke Thomas²^ORCID,Hossein Haghi Vahid³^ORCID,Weng Qihao⁴^ORCID,Valizadeh Kamran Khalil¹^ORCID,Li Zhenlong⁵^ORCID

Affiliation:

1. Department of Remote Sensing and GIS, University of Tabriz, Tabriz, Iran

2. Geoinformatics Z_GIS, University of Salzburg, Salzburg, Austria

3. Department of Geography and Urban Planning, University of Tabriz, Tabriz, Iran

4. Department of Land Surveying and Geo-Informatics, Hong Kong Polytechnic University, Kowloon, China

5. Geoinformation and Big Data Research Laboratory, Department of Geography, University of South Carolina, Columbia, SC, USA

Publisher

Informa UK Limited

Subject

General Earth and Planetary Sciences

Link

https://www.tandfonline.com/doi/pdf/10.1080/07038992.2022.2056435

Reference98 articles.

1. Modelling of soil moisture retention curve using machine learning techniques: Artificial and deep neural networks vs support vector regression models

2. Dynamic Neural Network Modelling of Soil Moisture Content for Predictive Irrigation Scheduling

3. Digital Mapping of Soil Particle-Size Fractions for Nigeria

4. Soil Salinity Mapping and Monitoring in Arid and Semi-Arid Regions Using Remote Sensing Technology: A Review

5. Salinity effects on greenhouse gas emissions from wetland soils are contingent upon hydrologic setting: a microcosm experiment

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