Exploring Bangladesh's Soil Moisture Dynamics via Multispectral Remote Sensing Satellite Image



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Submitted Jun 5, 2023
Published Oct 5, 2023
10.24018/ejgeo.2023.4.5.415

Abstract viewed = 428 times

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Accurate monitoring and mapping of soil moisture are essential for sustainable agricultural practices, water resource management, and climate studies. This study aims to explore the mapping of soil moisture in Bangladesh using multispectral remote-sensing satellite images. The purpose of this study is to prepare a map of soil moisture aiming to help government authorities in developing agricultural activities to accelerate the sustainable development of the rural economy in Bangladesh. A total of 14 Landsat scenes of paths 135-139 and rows 42-46 covers the entire Bangladesh. Thus, a set of Landsat imagery (a total of 14 scenes) for the year 2022 was used in this study to map the soil moisture of Bangladesh through the application of Geographical Information System (GIS) and Remote Sensing. Satellite Image preprocessing, correction, and analysis were done with ENVI software (version 5.1, developed by Research Systems, Inc., USA) and the ArcGIS software (version 10.6, developed by Environmental Systems Research Institute, USA). For the study of the long-term variation of soil moisture over Bangladesh and its seasonal characteristics, a soil moisture map can be used. In addition, to improve the climate model over Bangladesh, an up-to-date soil moisture map will be very helpful. The objective of this study is to provide accurate and detailed up-to-date spatial soil moisture information at reduced cost and time which is essential for environment modeling, risk assessment, decision-making for different government agencies and development partners, and help toward socio-economic development. In this study, the map shows soil moisture as very wet, wet, dry, and very dry soils of Bangladesh. The overall land cover classification accuracy was 92.56%, with a Kappa value 0.90 for Random Forest and the overall soil classification accuracy was 87.27%, with a Kappa value 0.858 for maximum likelihood classification indicating good consistency.
Keywords: agriculture GIS Remote Sensing Satellite Image Soil Moisture Dynamics

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