Proximal sensor integration for land use classification and soil analysis in a coastal environment

Portable X-ray fluorescence (PXRF) and Nix Pro sensors are efficient tools for rapid in-situ soil analysis. This study combined PXRF and Nix Pro to classify land use and characterize soils from Sandwip Island, Bangladesh. Soil samples from agricultural, abandoned, and seashore areas were analyzed for EC, pH, organic carbon, and texture. Random forest model achieved 84 % classification accuracy, outperforming support vector machines (72 %). Significant soil salinity and management variations were noted, particularly in seashore areas. The findings highlight the potential of these sensors for sustainable soil monitoring, with future work needed to expand applicability to diverse regions and soil types.

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Publication Details

Journal
Case Studies in Chemical and Environmental Engineering
Published
2024-12-19
DOI
https://doi.org/10.1016/j.cscee.2024.101079
Citations
2
Primary Topic
Soil Geostatistics and Mapping
Type
article
Field-Weighted Citation Impact
0.41

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article

Proximal sensor integration for land use classification and soil analysis in a coastal environment

Shovik Deb, David C. Weindorf, Rachna Singh, Sabrina Sharmeen Alam et al.
2 citations
Case Studies in Chemical and Environmental Engineering
Soil Geostatistics and Mapping
0.41
article

Proximal sensor integration for land use classification and soil analysis in a coastal environment

Shovik Deb, David C. Weindorf, Rachna Singh, Sabrina Sharmeen Alam, Somsubhra Chakraborty, Fariha Chowdhury Jain
article en
2 citations

Abstract

Portable X-ray fluorescence (PXRF) and Nix Pro sensors are efficient tools for rapid in-situ soil analysis. This study combined PXRF and Nix Pro to classify land use and characterize soils from Sandwip Island, Bangladesh. Soil samples from agricultural, abandoned, and seashore areas were analyzed for EC, pH, organic carbon, and texture. Random forest model achieved 84 % classification accuracy, outperforming support vector machines (72 %). Significant soil salinity and management variations were noted, particularly in seashore areas. The findings highlight the potential of these sensors for sustainable soil monitoring, with future work needed to expand applicability to diverse regions and soil types.

Case Studies in Chemical and Environmental EngineeringVol. 11
Indian Institute of Technology Kharagpur (IN), Georgia Southern University (US), North Bengal Agricultural University (IN), Schaeffler (Germany) (DE), University of Chittagong (BD)
University of Chittagong
Openalex Percentile: Top 40%
Soil Geostatistics and Mapping
0.41
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Proximal sensor integration for land use classification and soil analysis in a coastal environment — Shovik Deb, David C. Weindorf, et al. · Case Studies in Chemical and Environmental Engineering (2024) | TGRS Research Map | TGRS