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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,So LIM),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So LIM,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-So LIM)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。
Regional spatial information helps decision makers to develop improvement and optimization policies for specific potential and established soil salinization regions in order to avoid irrational allocation of water and land resources in irrigation districts and sustainable degradation of land ecosystems in arid lands. However, existing soil salinity data at the regional scale are retained in a vector manner. There is no spatial variability of the soil properties within the polygons and the lack of real-time updates, which has some limitations on the current practical guidance. As human activity aggravates, soils and their structural degradation are accelerating the damage to soil quality and health. In response, urgent updates or upgrades are needed to characterize soil salinity data in arid area ecosystems to assist in formulating policies to mitigate the salinity of soil salinization. In order to solve this problem, based on the representative level sampling design method (IHSS), a small number of typical samples were obtained, and soil soils inference model (So LIM) Content information. In the case of alluvial plain at the middle and lower altitudes in northern and southern Tianshan Mountains of Xinjiang, the soil salinity content of 0 ~ 10 cm was estimated from only 23 representative samples. The validation data from 94 field samples from three typical oases show that according to Compared with the linear regression model, So LIM has the nonlinear relationship between soil and environmental variables, and the reasoning precision is higher. Therefore, IHSS-So LIM, which is considered as a weighted average of fuzzy membership degree, can obtain better prediction results through smaller modeling points and can be used as an alternative solution to the regional soil salinity reasoning.