Academic Journal of Agriculture & Life Sciences, 2025, 6(1); doi: 10.25236/AJALS.2025.060103.
Zhi Wu1,2, Jinzhi Gu1,2, Junwei Xuan1,2, Xinshuo Liu1,2, Yu Wei1,2
1College of Resources and Environment, Xinjiang Agricultural University, Xinjiang Uygur Autonomous Region, Urumqi, 830052, China
2Xinjiang Key Laboratory of Soil and Plant Ecological Processes, Urumqi, 830052, China
Soil salinization in Northwest China, caused by its arid climate, high evaporation, and inappropriate irrigation methods, is a severe environmental issue affecting resources and the ecosystem. The study area is the Anjihai Irrigation District in Northwest China, where regression analysis was applied to soil salinity data and Landsat TM/ETM+ imagery, with the multi-year average vegetation index as the independent variable, to model the spatial distribution of soil salinity. The results indicate that the best correlation between soil salinity and vegetation index occurs in the 30–60 cm soil layer. Using the multi-year average vegetation index as a covariate significantly improves the accuracy of soil salinity remote sensing inversion in the study area. The soil in the region is primarily non-salinized and lightly to moderately salinized, with salinized soils mainly distributed in the northwestern, central, and eastern parts of the study area. This study provides valuable insights into the spatial distribution of soil salinity in the Anjihai Irrigation District. It demonstrates the potential of using vegetation indices to improve remote sensing inversion accuracy for soil salinity assessment.
Soil salinity, Remote sensing, Vegetation index, Inversion model
Zhi Wu, Jinzhi Gu, Junwei Xuan, Xinshuo Liu, Yu Wei. Soil Salinity Inversion in the Anjihai Irrigation Zone of Northwest China Based on Landsat Data. Academic Journal of Agriculture & Life Sciences (2025), Vol. 6, Issue 1: 17-24. https://doi.org/10.25236/AJALS.2025.060103.
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