| 摘要: |
| 为及时、准确地应用多源遥感数据提取干旱和半干旱区域土壤盐渍化反演特征及获取土壤盐渍化程度的空间分布数据，以吉林省西部大安市为研究区，利用Sentinel-1 SAR、Sentinel-2 MSI多源遥感数据和DEM数据，构建土壤盐分含量（Soil Salt Content，SSC）反演特征集，结合BorutaShap算法优选特征，通过耦合地理加权回归（Geographically weighted regression，GWR）与极限梯度提升树（XGBoost）回归构建土壤盐渍化反演模型，并与XGBoost回归、GWR反演结果对比分析。结果表明：SSC反演特征集中，盐分指数、植被指数在BorutaShap算法中取得了较高的重要性排名，是大安市SSC反演的重要特征。GWR模型的R2和RMSE分别为0.48和4.83 g/kg，XGBoost回归模型的R2和RMSE分别为0.54和4.35 g/kg，耦合GWR与XGBoost回归构建的土壤盐渍化反演模型预测精度得到显著提高，R2与RMSE分别达到0.63和3.71 g/kg。依据该模型反演结果，大安市各类盐渍土分布存在较强的空间异质性，土壤盐分含量呈现出由东南向西北逐渐递减的趋势，与实地调查基本一致。综上，耦合GWR与XGBoost回归模型充分考虑了反演特征的空间异质性和非线性关系，可有效提高SSC反演精度，可获得更符合实际的SSC空间分布，可用于干旱和半干旱地区土壤盐分含量的反演。 |
| 关键词: 土壤盐渍化 遥感反演 地理加权回归 XGBoost 特征优选 吉林省西部 |
| DOI：10.11841/j.issn.1007-4333.2024.02.01 |
| 投稿时间：2023-06-19 |
| 基金项目:国家重点研发项目（2021YFD1500202） |
|
| Inversion of soil salinization in western Jilin Province based on XGBoost and Geographically Weighted Regression |
|
LI Chunze1, ZHANG Chao1*, ZHANG Haoyuan1, YANG Cuicui1, LI Shaner1, YUN Wenju2
|
| (1.College of Land Science and Technology, China Agricultural University, Beijing 100193, China;2.Land Consolidation Center of the Ministry of Natural Resources, Beijing 100035, China) |
| Abstract: |
| In order to timely and accurately applying multi-source remote sensing data to extract soil salinization inversion characteristics in arid and semi-arid areas and obtain spatial distribution data of soil salinization degree. Taking Da'an City in western Jilin Province as the research area， Sentinel-1 SAR， Sentinel-2 MSI multi-source remote sensing data and DEM data were used to construct a soil salt content （SSC） inversion feature set. Combined with the BorutaShap algorithm to optimize features， the coupling Geographically Weighted Regression （GWR） with XGBoost regression was used to construct a soil salinization inversion model. The results were compared with XGBoost regression and GWR inversion results. The results show that： The SSC inversion features of this study are concentrated. Salinity index and vegetation index achieve high importance ranking in the BorutaShap algorithm and are important features of SSC inversion in Da’an City. In the soil salinization inversion model constructed， the R2 and RMSE of the GWR model are 0.48 and 4.83 g/kg， respectively， and the R2 and RMSE of the XGBoost regression model are 0.54 and 4.35 g/kg， respectively. The prediction accuracy of the soil salinization inversion model constructed by coupling GWR and XGBoost regression is significantly improved， with R2 and RMSE reaching 0.63 and 3.71 g/kg， respectively. According to the inversion results of this model， there is strong spatial heterogeneity in the distribution of various types of saline soil in Da’an City. The SSC shows a gradually decreasing trend from southeast to northwest， which is basically consistent with the field survey. In summary， the coupled GWR and XGBoost regression model constructed in this study fully considers the spatial heterogeneity and nonlinear relationship of the inversion characteristics， which effectively improves the SSC inversion accuracy. A more realistic SSC spatial distribution can be obtained. This model can be used for the inversion of soil salinity content in arid and semi-arid areas. |
| Key words: soil salinization remote sensing inversion geographically weighted regression XGBoost feature optimization western part of Jilin Province |
