A Remote Sensing Model to Determine the Critical Groundwater Depth for Soil Salinization

Abstract

【Objective】 Evaporation of groundwater via capillary rise could result in solute accumulation in the proximity of the soil surface, which increases steadily when the groundwater table rises above a critical depth. Knowing this critical depth is crucial for water and salt management but difficult to determine in situ as it depends not only on groundwater depth but also on soil textures and other factors. The purpose of this paper is to propose a remote-sensing method to estimate this critical depth. 【Method】 Based on remote sensing images and ground-truth data, we first calculated the relationship between the normalized difference vegetation index (NDVI), soil salinization index (SI) and groundwater depth. We then proposed a salinization detection index (SDI) to determine the critical groundwater depth and validated it against data measured from a field at Minqin oasis. 【Result】 ①There was an exponential relationship between NDVI and SI; SDI correctly described the relationship between vegetation health and soil salinization across the studied region. ②The spatial distribution of SDI and groundwater depth were inversely correlated, indicating that SDI can be used as a proxy of soil salinization. Critical groundwater depth associated with mild, moderate and severe soil salinization was 4.7 m, 3.2 m and 1.8m respectively. 【Conclusion】 We proposed a remote sensing model to estimate soil salinity and the critical groundwater depth, above which a further rise in groundwater table would cause soil salinization. Application of the model to field data showed that it is accurate and reliable to predict soil salinization and the critical groundwater depths associated with different levels of soil salinization.

Keywords

• soil salinization
• minqin oasis
• remote sensing model
• critical groundwater depth