Quantitative Retrieval of Soil Salinity Using Landsat 8 OLI Imagery
Source abstract
Abstract
Soil salinization is the main reason for declining soil quality and a reduction in agricultural productivity. We derive the spatial distribution of soil moisture from the temperature vegetation dryness index (TVDI) of Landsat TM-8 OLI images to analyze the effect of spatial heterogeneity of soil moisture on the retrieval accuracy of soil salinity. We establish five soil salinity inversion models for different soil moisture levels (drought levels) based on the canopy response salinity index (CRSI), normalized difference vegetation index (NDVI), and automatic water extraction index (AWEI) derived from Landsat TM-8 OLI images. The inversion accuracy of soil salinity is assessed using 42 field samples. The results show that the average accuracies of the five inversion models are higher than that of the traditional soil salinity inversion model of the entire study area. The proposed model underestimates soil salinity in high-moisture areas and overestimates it in drought areas. Therefore, inversion models of soil salinization should consider spatial differences in soil moisture to improve the inversion accuracy.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Soil Geostatistics and Mapping · Soil Moisture and Remote Sensing · Soil and Unsaturated Flow
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Harbin Normal University, Limin Economic Development Zone, No. 1 Shida Road, Harbin 150025, China
Funding
Natural science foundation of Heilongjiang Province
References (31)
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