| 引用本文: | 王 坚,杨鹏年,王永鹏,等.基于Sentinel-2影像的渭干河流域春灌前
表层土壤含盐量反演[J].灌溉排水学报,2025,44(10):93-102. |
| WANG Jian,YANG Pengnian,WANG Yongpeng,et al.基于Sentinel-2影像的渭干河流域春灌前
表层土壤含盐量反演[J].灌溉排水学报,2025,44(10):93-102. |
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| 摘要: |
| 【目的】快速、有效地监测新疆干旱区的土壤盐渍化信息。【方法】以新疆渭干河流域作为研究区域，采用Sentinel-2卫星遥感影像和同步采集的0~30 cm实测土壤含盐量数据，基于决策树模型（DT）、梯度提升决策树模型（GBDT）以及随机森林模型（RF），优选最佳土壤含盐量反演模型，探求研究区土壤盐渍化分布特征。【结果】研究区土壤反射率光谱指数与土壤含盐量间存在较高的相关性，部分指数（NDVI、SRSI、SI）相关系数＞0.6，可提升Sentinel-2影像数据反演土壤含盐量的性能；基于随机森林模型（RF）构建的土壤含盐量反演模型具有最高的验证集决定系数（R2=0.613）和最低的平均绝对误差（MAE）和均方根误差（RMSE），分别为2.951 g/kg和4.524 g/kg；研究区非盐土面积增加2 404.17 km2，中度盐土与盐土面积分别减少2 856.21、2 518.06 km2，而轻度盐土与重度盐土面积分别增加1 935.51、1 034.59 km2，整体土壤盐渍化程度降低，流域盐渍化防治工作仍需坚持。【结论】实现了新疆渭干河流域土壤盐渍化程度的有效反演，Sentinel-2卫星多光谱遥感影像的光谱指数数据可作为研究区土壤盐渍化监测的重要数据源，RF模型在处理土壤盐分与遥感数据之间复杂线性关系时具有强适应性，能够有效提升土壤盐渍化监测的精度，可为流域内土壤盐渍化治理与防治提供技术参考。 |
| 关键词: 土壤盐渍化；Sentinel-2遥感影像；光谱指数；随机森林模型；渭干河流域 |
| DOI：10.13522/j.cnki.ggps.2025089 |
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| Mapping soil salinity in the Weigan River Basin using sentinel-2 imagery and machine learning |
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WANG Jian, YANG Pengnian, WANG Yongpeng, SANG Zhida, ZENG Xu
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1. College of Hydraulic and Civil Engineering, Xinjiang Agricultural University, Urumqi 830052, China;
2. Xinjiang Key Laboratory for Hydraulic Engineering Safety and Flood Control, Urumqi 830052, China;
3. Tarim River Basin Administration Bureau Weigan River Water Management Center, Aksu 842000, China
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| Abstract: |
| 【Background and Objective】Soil salinization in Weigan River Basin of Xinjiang poses a significant pressure on agricultural production and ecosystem functions. Timely and accurate monitoring of salt content in the top 0-30 cm soil layer is essential for improving management and mitigation of salt-affected soils. We propose an inversion model in this paper to assess spatiotemporal variation in soil salinity across the basin.【Method】Using Sentinel-2 multispectral imagery and synchronously collected soil salinity data, three machine learning models: decision tree (DT), gradient boosted decision tree (GBDT), and random forest (RF), were constructed and evaluated to calculate soil salinity. We compared the three models first and then selected the most accurate one to calculate the complex relationships between spectral indices and soil salinity. This relationship was used to analyze the spatiotemporal variation in salinization in the basin.【Result】Strong correlations between soil reflectance spectral indices: NDVI, SRSI, and SI, and soil salinity were found, with the correlation coefficients＞0.6. Among all models we compared, the RF-based model was most accurate, with its associated R2, MAE and RMSE being 0.613, 2.951 g/kg and 4.524 g/kg, respectively. Spatial analysis showed that non-saline soil areas in the basin have increased by 2 404.17 km2, while moderately and weakly saline soil areas have decreased by 2 856.21 km2 and 2 518.06 km2, respectively. Despite the decline in soil salinity areas, mildly and heavily saline areas had increased by 1 935.51 km2 and 1 034.59 km2, respectively, highlighting that managing and remediating soil salinization is still a work not yet done.【Conclusion】The RF model combined with Sentinel-2 spectral indices provides an effective and accurate method for monitoring soil salinization at large scales. It can help develop strategies to remediate and manage soil salinity in the studied region. |
| Key words: soil salinization; Sentinel-2 remote sensing imagery; spectral indices; random forest model; Weigan River Basin |
