干旱区生态环境知识资源中心

This paper presents the potential of the four-image spectral endmember (EM) space comprising sand (SL), green vegetation (GV), saline land (SA), and dark materials (DA), unmixed from Landsat TM/ETM+ to map dryland agricultural soil alkalinity and salinity (i.e., soil alkalinity (pH) and soil electrical conductivity (EC)) in the shallow root zone (0-20 cm) using partial least squares regression (PLSR) and an artificial neural network (ANN). The results reveal that SA, SL, and GV fractions at the subpixel level, and land surface temperature (LST) are necessary independent variables for soil EC modeling in Minqin Oasis, a temperate-arid system in China. The R-2 (coefficient of determination) of the optimized parameters with the ANN model was 0.79, the root mean squared error (RMSE) was 0.13, and the ratio of prediction to deviation (RPD) was 1.95 when evaluated against all sampled data. In addition to the aforementioned four variables, the DA fraction and the recent historical SA fraction (SAH) in the spring dry season in 2008 were also helpful for soil pH modeling. The model performance is R-2 = 0.76, RMSE = 0.24, and RPD = 1.96 for all sampled data. In summary, the stable EMs and LST space of TM imagery with an ANN approach can generate near-real-time regional soil alkalinity and salinity estimations in the cropping period. This is the case even in the critical agronomic range (EC of 0-20 dSm(-1) and pH of 7-9) at which researchers and policy-makers require near-real-time crop management information.