Characterising dryland salinity in three dimensions

Qingsong Jiang; Jie Peng; Asim Biswas; Jie Hu; Ruiying Zhao; Kang He; Zhou Shi

doi:10.1016/j.scitotenv.2019.05.037

Characterising dryland salinity in three dimensions

Sci Total Environ. 2019 Sep 10:682:190-199. doi: 10.1016/j.scitotenv.2019.05.037. Epub 2019 May 6.

Authors

Qingsong Jiang¹, Jie Peng², Asim Biswas³, Jie Hu⁴, Ruiying Zhao⁴, Kang He⁴, Zhou Shi⁵

Affiliations

¹ Institute of Agricultural Remote Sensing and Information Technology Application, College of Environment and Resource Sciences, Zhejiang University, Hangzhou 310058, China; College of Information Engineering, Tarim University, Alar, Xinjiang 843300, China.
² Institute of Agricultural Remote Sensing and Information Technology Application, College of Environment and Resource Sciences, Zhejiang University, Hangzhou 310058, China; College of Plant Science, Tarim University, Alar, Xinjiang 843300, China.
³ School of Environmental Sciences, University of Guelph, 50 Stone Road East, Guelph, ON N1G 2W1, Canada.
⁴ Institute of Agricultural Remote Sensing and Information Technology Application, College of Environment and Resource Sciences, Zhejiang University, Hangzhou 310058, China.
⁵ Institute of Agricultural Remote Sensing and Information Technology Application, College of Environment and Resource Sciences, Zhejiang University, Hangzhou 310058, China. Electronic address: shizhou@zju.edu.cn.

PMID: 31121345
DOI: 10.1016/j.scitotenv.2019.05.037

Abstract

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.

Keywords: Digital soil mapping; Electrical conductivity; Electromagnetic induction; Soil salinity; Three-dimensional inversion.