Study of soil salinity in agricultural land using Remote Sensing data in Amudarya district, Uzbekistan

\nOne of the primary concerns in agricultural regions is the presence of soil salinity, which is especially prominent in the Amudarya district of Uzbekistan. This investigation aims to evaluate the salinity of the local soil utilizing data acquired through remote sensing. By integrating remote sensing data with modeling methodologies, this study offers a comprehensive understanding of the patterns of soil salinity. The outcomes of the study demonstrate the effectiveness of this approach, with a 72% accuracy in interpolation and a Kappa value of 81%. The study underscores the critical importance of amalgamating modeling techniques and remote sensing data to monitor local changes in soil salinity and assess the potential impact of climate change on soil salinity levels. These findings are of utmost significance in facilitating the planning of water resources and agricultural management, ultimately supporting the sustainable utilization of farmland in the region.\n

Publication Details

Journal
Springer Link (Chiba Institute of Technology)
Published
2024-11-13
DOI
https://doi.org/10.1051/e3sconf/202459001011/pdf
Primary Topic
Engineering and Agricultural Innovations
Type
article
Field-Weighted Citation Impact
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article

Study of soil salinity in agricultural land using Remote Sensing data in Amudarya district, Uzbekistan

Springer Link (Chiba Institute of Technology)
Engineering and Agricultural Innovations
article

Study of soil salinity in agricultural land using Remote Sensing data in Amudarya district, Uzbekistan

article en

Abstract

\nOne of the primary concerns in agricultural regions is the presence of soil salinity, which is especially prominent in the Amudarya district of Uzbekistan. This investigation aims to evaluate the salinity of the local soil utilizing data acquired through remote sensing. By integrating remote sensing data with modeling methodologies, this study offers a comprehensive understanding of the patterns of soil salinity. The outcomes of the study demonstrate the effectiveness of this approach, with a 72% accuracy in interpolation and a Kappa value of 81%. The study underscores the critical importance of amalgamating modeling techniques and remote sensing data to monitor local changes in soil salinity and assess the potential impact of climate change on soil salinity levels. These findings are of utmost significance in facilitating the planning of water resources and agricultural management, ultimately supporting the sustainable utilization of farmland in the region.\n

Springer Link (Chiba Institute of Technology)
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Engineering and Agricultural Innovations
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