Soil Salinity, Its Inversion and Spatial Distribution Characteristics in Agricultural Fields Using Remote Sensing Data

Abstract

Soil salinization is an urgent problem in the arid and semi-arid regions that damages soil ecology and affects agricultural growths. Timely supervision and monitoring of soil salinity are essential to reach the most sustainable improvement goals in arid and semi-arid regions. In the present study, the soil of Aktau region in Xinjiang, China was collected to build a remote sensing based inversion model for identifying soil salinity hazards. Results showed that Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and Difference Vegetation Index (DVI) were correlated (P < 0.01) with the model inversion, having correlation coefficients of -0.735, -0.858, and -0.774, respectively. All these were suitable for the construction of the soil salinity inversion model where the optimal parameters of model accuracy were above 85% and prediction results were accurate credible and consistent with the measured data. The NDWI extracted from multispectral images was used as the key parameter of the soil salinity inversion model, which could obtain a better spatial distribution of soil salinity. The remote sensing inversion model of soil salinity provides a theoretical basis for the management of soil salinization and sustainable utilization of agricultural resources in the Aktau region of Xinjiang. Bangladesh J. Bot. 52(2): 437-443, 2023 (June) Special