Ma Weitong, Cui Xin, Han Wenting, Zhang Huihui, Zhang Liyuan
College of Water Resources and Architectural Engineering, Northwest A&F University, Yangling, Shaanxi 712100, China.
Institute of Water-Saving Agriculture in Arid Areas of China, Northwest A&F University, Yangling, Shaanxi 712100, China.
iScience. 2025 Jun 27;28(8):113020. doi: 10.1016/j.isci.2025.113020. eCollection 2025 Aug 15.
In the context of global land degradation and increasing salinization of cultivated land, accurately and sustainably estimating soil salinization is essential for effective land management. This study explores a novel approach to improve soil salt content (SSC) monitoring by minimizing the influence of long-term environmental variability and incorporating relevant environmental factors. Focusing on exposed cultivated land in arid regions, we analyzed the sensitivity of multispectral indices and environmental factors to SSC during pre-seeding and post-harvest bare soil periods. Our findings indicate that soil mechanical composition and salinity indices exhibited strong correlations with SSC, which further enhanced when the periods were analyzed separately. Dividing the bare soil periods improved the model performance, increasing R by 10.2%-55.7%, and the support vector regression model performed better, with an R of 0.77, RMSE of 0.11%. This approach offers a robust framework for precision agriculture and sustainable land management in salinity-affected regions.
在全球土地退化和耕地盐碱化加剧的背景下，准确且可持续地估算土壤盐碱化对于有效的土地管理至关重要。本研究探索了一种新方法，通过最小化长期环境变异性的影响并纳入相关环境因素来改进土壤盐分含量（SSC）监测。聚焦于干旱地区的裸露耕地，我们分析了播种前和收获后裸土期多光谱指数和环境因素对SSC的敏感性。我们的研究结果表明，土壤机械组成和盐分指数与SSC表现出强相关性，当分别分析各时期时相关性进一步增强。划分裸土期提高了模型性能，决定系数R提高了10.2% - 55.7%，支持向量回归模型表现更好，决定系数R为0.77，均方根误差RMSE为0.11%。该方法为受盐碱化影响地区的精准农业和可持续土地管理提供了一个强大的框架。
