基于EM38和WorldView-2影像的土壤盐渍化建模研究

Mapping and Modelling of Soil Salinity Using WorldView-2 Data and EM38 in Arid Region of Keriya River,China

在干旱半干旱地区,土壤盐渍化是常见的土地退化问题之一。本研究选取于田县克里雅河上游边缘典型盐渍化区域作为研究靶区,通过EM38大地电导率仪实测土壤表观电导率,提取不同系数下的土壤调节植被指数(SAVI),分析了 SAVI指数与土壤电导率间的相关性,并利用同时期WorldView-2影像的敏感波段建立了基于高分辨率影像数据的土壤盐渍化偏最小二乘回归(PLSR)模型并进行了精度验证。结果表明:①从遥感影像提取SAVI指数时,在系数(Z)调节范围内选取固定系数值,系数值(间隔为0.1)从0.1变化到1.0的过程中,相应提取的SAVI指数与土壤电导率的相关性明显提升,相关性系数(r)从0.30提高到0.50,并通过显著性检验(P<0.01).②选取的SAV1.0、B6、B7、B8四种变量中,以SAVI1.0+B6+B8为变量组合所建立的PLSR模型为最优,该模型较其他变量组合建模的决定系数(R^2p)提高了 0.11,因此,在研究区该模型具有更好的预测能力,模型精度为RMSEc=0.77 dS/m、Rc^2=0.68.RMSEp=0.79dS/m、RP^2=0.66, RPD=2.2。

Soil salinity is one of the factors for land degradation, especially in the arid and semi-arid regions. In this paper, the typical salinity region in the upstream margin of Keriya River in Yutian County of Xinjiang was taken as the study object, EM38 sensor was used to in situ measure soil apparent electrical conductivity (ECa), WorldView-2 images were used to extract adjusted soil vegetation index (SAVI) under different conditions, and PLSR model derived from SAVI and ECa was setup to estimate soil salinization. The results showed that the correlation between SAVI and ECa was increased significantly from 0.30 to 0.5 when the adjusted parameter (L) increased from 0.1to 1.0. The optimal model was established by using the combination of SAVI1.0+B6+B8, its determination coefficient (R^2p) was promoted by 0.11 compared with those of models derived from other variable combination, the validation coefficients were RMSEc=0.77,R^2c=0.68 RMSEp=0.79, R^2p=0.66, RPD=2.2. Therefore, the model derived from different variable combination can provide a fast and accurate method for monitoring soil salinization.