Soil salinity estimation based on machine learning using the GF-3 radar and Landsat-8 data in the Keriya Oasis, Southern Xinjiang, China

Aims Soil salinization has been an important environmental problem globally, particularly in oasis areas in arid zones. The advantages of using multi-source data, combining radar and optical remote sensing data, and applying machine learning-based algorithms to these data could be beneficial for add...

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Veröffentlicht in:	Plant and soil Jg. 498; H. 1-2; S. 451 - 469
Hauptverfasser:	Xiao, Sentian, Nurmemet, Ilyas, Zhao, Jing
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Cham Springer International Publishing 01.05.2024 Springer Springer Nature B.V
Schlagworte:	Agriculture Algorithms Analysis Arid zones Aridity Biomedical and Life Sciences China Digital Elevation Models Earth resources technology satellites Ecology Electrical conductivity Electrical resistivity Landsat Learning algorithms Life Sciences Machine learning Mapping Oases Plant Physiology Plant Sciences Radar Radar data Radar systems Remote sensing Research Article Root-mean-square errors Salinity Salinity effects Salinization Soil chemistry Soil mapping Soil research Soil salinity soil salinization Soil Science & Conservation Soils Soils, Salts in China Random forest model GF-3 data Soil salinization estimation Polarization decomposition Multi-source data prediction
ISSN:	0032-079X, 1573-5036
Online-Zugang:	Volltext
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