Climate Surpasses Soil Texture in Driving Soil Salinization Alleviation in Arid Xinjiang.
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| Title: | Climate Surpasses Soil Texture in Driving Soil Salinization Alleviation in Arid Xinjiang. |
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| Authors: | Zhao, Jiahao1,2 (AUTHOR), Wu, Hongqi1,2 (AUTHOR), Gu, Haibin1,2 (AUTHOR), Fan, Yanmin1,2 (AUTHOR) fanym@xjau.edu.cn, Zhao, Zhiwen1,2 (AUTHOR), Wang, Pengfei1,2 (AUTHOR), Li, Changlei1,2 (AUTHOR) |
| Source: | Remote Sensing. Dec2025, Vol. 17 Issue 23, p3812. 25p. |
| Subjects: | Soil salinization, Arid regions, Scientific observation, Soil texture, Spatiotemporal processes, Atmosphere, Adaptive natural resource management |
| Geographic Terms: | Xinjiang Uygur Zizhiqu (China) |
| Abstract: | Highlights: What are the main findings? Obtained an inversion model suitable for soil salinization monitoring in arid regions, demonstrating high accuracy and stability. Revealed the spatiotemporal dynamics of soil salinization in southern Xinjiang. Quantitatively elucidated the driving mechanisms of climate and soil texture, highlighting the predominant role of climatic factors across multiple temporal scales. What is the implication of the main finding? Provided a methodological framework for large-scale identification of soil salinization in arid regions. Offered a scientific basis for identifying high-risk areas of soil salinization and implementing targeted monitoring. Strengthened the empirical evidence for the predominant role of climate, thereby supporting the development of adaptive management and differentiated regulation strategies under climate change. Soil salinization in arid regions has drawn considerable attention due to its constraints on agricultural productivity and ecological security. Climate and soil texture, as key drivers at the macroscale, still lack systematic quantitative assessments regarding their mechanisms in shaping the long-term dynamics of salinity, and comparative evaluations of their relative contributions remain insufficient. Therefore, there is an urgent need to explore the spatiotemporal variations in soil salinization in arid regions and its responses to climate and soil texture. This study was based on salinity sampling sites collected in southern Xinjiang in 2023. A Random Forest (RF)-based inversion model was constructed using spectral indices derived from Landsat-9 and Sentinel-2 as environmental predictors. The predictive performance of models using all variables was compared with those using RF-based feature selection. The optimal model was then applied to retrieve soil salinity concentrations for 2008, 2013, 2018, and 2023 at four equidistant time points, enabling the spatiotemporal evolution of soil salinization across the study area to be assessed. Finally, a Boosted Regression Tree (BRT) model was employed to quantify the driving contributions of climate and soil texture. Results showed that the feature-selected Landsat-9 model performed best, with an R2 of 0.747, significantly outperforming the Sentinel-2 model. The mean soil salinity concentration declined rapidly from 2008 to 2013, followed by a relatively slower but sustained decrease thereafter. The proportion of non-salinized land increased from 3.08% to 30.81%. The Sen's slope−Mann−Kendall test indicated that 78.6% of salinity levels exhibited a significant downward trend, while 18.8% showed a slight increase. The relative contribution analysis indicated that climatic factors consistently exerted a stronger influence on the evolution of soil salinization than soil texture. Specifically, the contribution of climatic variables increased from 65.2% in 2008 to 66.8% in 2023, whereas that of soil texture decreased slightly from 34.8% to 33.2%. Among the climatic variables, the effect of potential evapotranspiration gradually weakened, while the impacts of temperature and precipitation continued to intensify. In contrast, soil texture variables played a comparatively minor yet stable role throughout the study period. These findings provide an effective framework for long-term monitoring of soil salinization and offer critical insights for adaptive management in arid regions under climate change. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Highlights: What are the main findings? Obtained an inversion model suitable for soil salinization monitoring in arid regions, demonstrating high accuracy and stability. Revealed the spatiotemporal dynamics of soil salinization in southern Xinjiang. Quantitatively elucidated the driving mechanisms of climate and soil texture, highlighting the predominant role of climatic factors across multiple temporal scales. What is the implication of the main finding? Provided a methodological framework for large-scale identification of soil salinization in arid regions. Offered a scientific basis for identifying high-risk areas of soil salinization and implementing targeted monitoring. Strengthened the empirical evidence for the predominant role of climate, thereby supporting the development of adaptive management and differentiated regulation strategies under climate change. Soil salinization in arid regions has drawn considerable attention due to its constraints on agricultural productivity and ecological security. Climate and soil texture, as key drivers at the macroscale, still lack systematic quantitative assessments regarding their mechanisms in shaping the long-term dynamics of salinity, and comparative evaluations of their relative contributions remain insufficient. Therefore, there is an urgent need to explore the spatiotemporal variations in soil salinization in arid regions and its responses to climate and soil texture. This study was based on salinity sampling sites collected in southern Xinjiang in 2023. A Random Forest (RF)-based inversion model was constructed using spectral indices derived from Landsat-9 and Sentinel-2 as environmental predictors. The predictive performance of models using all variables was compared with those using RF-based feature selection. The optimal model was then applied to retrieve soil salinity concentrations for 2008, 2013, 2018, and 2023 at four equidistant time points, enabling the spatiotemporal evolution of soil salinization across the study area to be assessed. Finally, a Boosted Regression Tree (BRT) model was employed to quantify the driving contributions of climate and soil texture. Results showed that the feature-selected Landsat-9 model performed best, with an R2 of 0.747, significantly outperforming the Sentinel-2 model. The mean soil salinity concentration declined rapidly from 2008 to 2013, followed by a relatively slower but sustained decrease thereafter. The proportion of non-salinized land increased from 3.08% to 30.81%. The Sen's slope−Mann−Kendall test indicated that 78.6% of salinity levels exhibited a significant downward trend, while 18.8% showed a slight increase. The relative contribution analysis indicated that climatic factors consistently exerted a stronger influence on the evolution of soil salinization than soil texture. Specifically, the contribution of climatic variables increased from 65.2% in 2008 to 66.8% in 2023, whereas that of soil texture decreased slightly from 34.8% to 33.2%. Among the climatic variables, the effect of potential evapotranspiration gradually weakened, while the impacts of temperature and precipitation continued to intensify. In contrast, soil texture variables played a comparatively minor yet stable role throughout the study period. These findings provide an effective framework for long-term monitoring of soil salinization and offer critical insights for adaptive management in arid regions under climate change. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs17233812 |
