干旱区大尺度土壤盐度信息环境建模——以新疆天山南北中低海拔冲积平原为例

区域空间信息有助于决策者针对特定潜在和既定的土壤盐渍化区域制定改良和优化政策,以避免灌区水土资源的不合理配置和干旱区土地生态系统持续性退化。然而现存区域尺度土壤盐度数据以矢量方式留存,多边形内部土壤属性无空间变异性,缺乏实时更新,对当下实际指导作用具有一定的局限性。随着人类活动的加剧,土壤及其结构性退化正加速危害土壤质量和健康。对此,急需更新或升级,用于刻画干旱区生态系统中土壤盐度数据,以辅助制定相关政策,减缓土壤盐渍化的危害。针对此问题,本文基于代表性等级的采样设计方法（Integrative Hierarchical Sampling Strategy, IHSS）,获取少量典型样点,结合土壤—环境推理模型（soil land inference model, SoLIM）,尝试推理区域尺度土壤盐分含量信息。研究以新疆天山南北中低海拔冲积平原为案例,仅以23个代表性样本,推理陆表（0~10 cm）土壤盐分含量,源自3个典型绿洲94个野外样本的验证数据显示,依据评判标准,预测结果与实际情况较为相符,与线性回归模型相比,具备处理土壤与环境变量之间非线性关系的SoLIM,推理精度更高。所以,研究认为模糊隶属度加权平均的方法（IHSS-SoLIM）可以通过较小的建模点得到更好的预测效果,可作为区域尺度土壤盐度推理的备选方案。

Environmental modeling of large-scale soil salinity information in an arid region: A case study of the low and middle altitude alluvial plain north and south of the Tianshan Mountains,Xinjiang

Regional information on the spatial distribution of soil salinity can be used as guidance in avoiding the continued degradation of land and water resources. However, most regional soil salinity maps are produced through a conventional direct-linking method derived from historic observations. Such maps lack spatial details and are limited in describing the evolution of soil salinization in particular instances. In anthropogenic regions, soil change, and soil formation and degradation, have accelerated, jeopardizing soil quality and health. The need for up-to-date soil and environmental data that characterize the physicochemical, biological, and hydrological conditions of arid ecosystems across continents has intensified (e.g. soil salinization in arid land). Digital soil mapping (DSM) and modeling techniques have been widely used in the past few decades. To overcome these limitations, we employed a method that included an integrative hierarchical-sampling strategy (IHSS) and the Soil Land Inference Model (SoLIM) to map soil salinity over a regional area. This case study, the Xinjiang Uygur Autonomous Region of China, demonstrates that the employed method can produce soil salinity maps at a higher level of spatial detail and accuracy. Twenty-three representative points are determined. The results show that: (1) the prediction is accurate in Kuqa Oasis (R²=0.70, RPD=1.55, RMSE=12.86) and Keriya Oasis (R²=0.75, RPD=1.66, RMSE=10.92), and performed a little better than in Fukang Oasis (R²=0.77, RPD=2.01, RMSE=6.32), according to the evaluation criteria. (2) Based on all validation samples from three oases, accuracy estimation shows that the employed method (R²= 0.74, RPD=1.67, RMSE=11.18) performed better than the multiple linear regression model (R²=0.60, RPD= 1.47, RMSE=14.45). Finally, this study concludes that the employed method can serve as an alternative model for soil salinity mapping on a large scale.