Characterising dryland salinity in three dimensions - Jiang, Qingsong; Peng, Jie; Biswas, Asim; Hu, Jie; Zhao, Ruiying; He, Kang; Shi, Zhou (The Science of the total environment, 2019)
Due to frequent salt migration and large spatial variability within soil profiles, salinity characterisation by traditional drilling sampling methods is time-consuming and labour-intensive. Thus, it is necessary to develop monitoring technology and three-dimensional (3D) characterisation methods for rapid, non-invasive, and accurate soil salinity measurement. This study presents a new framework combining sensor technology and an inversion algorithm to characterise 3D soil salinity. Four typical land-use types (natural desert, natural vegetation, apple orchard, and winter wheat farmland) in the Aksu region of southern Xinjiang were surveyed and apparent conductivity (ECa) data were recorded at depths of 0.75 m and 1.50 m. ECa data were converted to electrical conductivity and salinity characterisation was conducted following U.S. Salinity Laboratory recommendations. Ordinary Kriging interpolation was used to map the spatial distribution and an iterative inversion model was used to map the vertical distribution of soil salinity. Model parameters were adjusted several times and the accuracy of different inversion algorithms was compared to obtain the best inversion effect. As a result, the Multilevel Orthogonal Inversion model was developed to characterise 3D soil salinity for different land-use types. Due to crop activities including irrigation, managed land use types (apple orchard and winter wheat plots) typically exhibited weaker salinity than natural systems (desert and vegetation plots) but greater spatial variability overall. The proposed framework combining EM (electromagnetic) sensing and the 3D inversion algorithm can effectively characterise and visualise soil salinity for the entire soil profile, which is important for land evaluation and improvement. [Display omitted] •A more rapid and accurate 3D soil salinity measurement method is required•We analyze the effect of agriculture and vegetation on soil salinity distribution•Our method combines electrical conductivity measurement and 3D inversion algorithms•This method provides more effective monitoring and visualisation of soil salinity•Improved land evaluation enables better land management and planning strategies