Estimating soil salinity from remote sensing and terrain data in southern Xinjiang Province, China - Peng, Jie; Biswas, Asim; Jiang, Qingsong; Zhao, Ruiying; Hu, Jie; Hu, Bifeng; Shi, Zhou (Geoderma, 2019)
Soil salinization is one of the main reasons for soil health and ecosystem deterioration in most degraded arid and semiarid areas. To monitor its spatial variation as precise as possible over a large area, we collected 225 samples using traditional field experiment and laboratory analysis method from the southern part of the Xinjiang Province, China, affected by soil salinity under strong arid climate. Then, we constructed both Cubist and partial least square regression (PLSR) models on electrical conductivity (EC) (150 ground-based measurements as calibration set) using various related covariates (e.g. terrain attributes, remotely sensed spectral indices of vegetation and salinity from landsat8 OLI satellite) that are at the same time period corresponding to soil sampling. Two models were validated using remaining 75 independent ground based measurements and were then used to map the soil salinity over the study area. Finally, the validation results of two models were compared under different intervals of EC, soil moisture content and vegetation coverage. The results indicated that Cubist model could predict EC value with better accuracy and stability under variable environment than PLSR. The R2, RMSE, MAE and RPD of the Cubist model were 0.91, 5.18 dS m−1, 3.76 dS m−1 and 3.15 while corresponding values of the PLSR model were 0.66, 10.46 dS m−1, 8.21 dS m−1 and 1.56 in validation dataset, respectively. Additionally, the map derived from Cubist model revealed more detailed variation information of the spatial distribution of EC than that from PLSR model across the study area. Thus, Cubist model was recommended for mapping soil salinity using indices derived from satellite and terrain in other arid areas. •Soil salinity maps were produced using Cubist and PLSR models.•Cubist was proved a more suitable method for soil salinity mapping than PLSR.•Subregional modeling could improve the prediction results of soil salinity.•The study shows a large variability in EC across various land use.•Vegetation indices, soil salinity spectral indices and terrain attributes are important predictors of EC.