Monitoring and evaluating spatial variability of soil salinity in dry and wet seasons in the Werigan-Kuqa Oasis, China, using remote sensing and electromagnetic induction instruments

In arid and semi-arid regions, soil salinization is one of the most critical environmental problems due to its severe effects on agriculture productivity and long-term sustainable development. Monitoring, evaluating and predicting soil salinization are of utmost importance in those regions. The current study proposes an evaluating and predicting approach that is based on remote sensing (e.g., Landsat TM images) and near sensing technologies (e.g., electromagnetic induction device, EM38). We investigated seasonal and spatial changes of soil salinity in a Delta Oasis between the Werigan and Kuqa River in the northern rim of Tarim Basin, Xinjiang, China. Preliminary analysis suggests that apparent soil electrical conductivity obtained from EM38 is highly correlated with soil salinity, which is obtained from post-sampling laboratory tests. The study hence uses the apparent electrical conductivity as a surrogate for soil salinity to understand the spatial pattern of the latter. To understand soil salinity distribution pattern in the study region, we integrated spectral information derived from two Landsat TM images (acquired on April 15, 2011 for the dry season and September 22, 2011 for the wet season), and applied universal kriging, spectral index regression and regression-kriging approaches to obtain the pattern. Results suggest that regression-kriging with nested spherical model produces the closest fit of the observed soil apparent electrical conductivity. Since most previous studies often employ either one or the other approaches in soil salinity monitoring and evaluation, the study suggests that combining remote and near sensing technology provides a rapid and relatively accurate assessment of soil salinity in arid and semi-arid regions, which would be essential to manage and prevent further soil salinization and re-salinization. (C) 2014 Elsevier B.V. All rights reserved.