Coupling of machine learning and remote sensing for soil salinity mapping in coastal area of Bangladesh.

Autor: Sarkar SK; Department of Urban and Regional Planning, Khulna University of Engineering & Technology (KUET), Khulna, 9203, Bangladesh. mail4dhrubo@gmail.com., Rudra RR; Department of Urban and Regional Planning, Khulna University of Engineering & Technology (KUET), Khulna, 9203, Bangladesh., Sohan AR; Department of Urban and Regional Planning, Khulna University of Engineering & Technology (KUET), Khulna, 9203, Bangladesh., Das PC; Department of Urban and Regional Planning, Khulna University of Engineering & Technology (KUET), Khulna, 9203, Bangladesh.; Department of Geography, Texas A&M University, College Station, USA., Ekram KMM; Department of Urban and Regional Planning, Khulna University of Engineering & Technology (KUET), Khulna, 9203, Bangladesh.; Population Health Sciences, Harvard University, Cambridge, USA., Talukdar S; Department of Geography, Faculty of Natural Sciences, Jamia Millia Islamia, New Delhi, 110025, India., Rahman A; Department of Geography, Faculty of Natural Sciences, Jamia Millia Islamia, New Delhi, 110025, India., Alam E; Faculty of Resilience, Rabdan Academy, 22401, Abu Dhabi, United Arab Emirates.; Department of Geography and Environmental Studies, University of Chittagong, Chittagong, 4331, Bangladesh., Islam MK; Department of Civil and Environmental Engineering, College of Engineering, King Faisal University, 31982, AlAhsa, Saudi Arabia., Islam ARMT; Department of Disaster Management, Begum Rokeya University, Rangpur, 5400, Bangladesh.; Department of Development Studies, Daffodil International University, Dhaka, 1216, Bangladesh.
Jazyk: angličtina
Zdroj: Scientific reports [Sci Rep] 2023 Oct 10; Vol. 13 (1), pp. 17056. Date of Electronic Publication: 2023 Oct 10.
DOI: 10.1038/s41598-023-44132-4
Abstrakt: Soil salinity is a pressing issue for sustainable food security in coastal regions. However, the coupling of machine learning and remote sensing was seldom employed for soil salinity mapping in the coastal areas of Bangladesh. The research aims to estimate the soil salinity level in a southwestern coastal region of Bangladesh. Using the Landsat OLI images, 13 soil salinity indicators were calculated, and 241 samples of soil salinity data were collected from a secondary source. This study applied three distinct machine learning models (namely, random forest, bagging with random forest, and artificial neural network) to estimate soil salinity. The best model was subsequently used to categorize soil salinity zones into five distinct groups. According to the findings, the artificial neural network model has the highest area under the curve (0.921), indicating that it has the most potential to predict and detect soil salinity zones. The high soil salinity zone covers an area of 977.94 km 2 or roughly 413.51% of the total study area. According to additional data, a moderate soil salinity zone (686.92 km 2 ) covers 30.56% of Satkhira, while a low soil salinity zone (582.73 km 2 ) covers 25.93% of the area. Since increased soil salinity adversely affects human health, agricultural production, etc., the study's findings will be an effective tool for policymakers in integrated coastal zone management in the southwestern coastal area of Bangladesh.
(© 2023. Springer Nature Limited.)
Databáze: MEDLINE
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