title: Mapping spatial variability of soil salinity in a coastal paddy field based on electromagnetic sensors.
abstract: In coastal China, there is an urgent need to increase land area for agricultural production and urban development, where there is a rapid growing population. One solution is land reclamation from coastal tidelands, but soil salinization is problematic. As such, it is very important to characterize and map the within-field variability of soil salinity in space and time. Conventional methods are often time-consuming, expensive, labor-intensive, and unpractical. Fortunately, proximal sensing has become an important technology in characterizing within-field spatial variability. In this study, we employed the EM38 to study spatial variability of soil salinity in a coastal paddy field. Significant correlation relationship between ECa and EC1:5 (i.e. r >0.9) allowed us to use EM38 data to characterize the spatial variability of soil salinity. Geostatistical methods were used to determine the horizontal spatio-temporal variability of soil salinity over three consecutive years. The study found that the distribution of salinity was heterogeneous and the leaching of salts was more significant in the edges of the study field. By inverting the EM38 data using a Quasi-3D inversion algorithm, the vertical spatio-temporal variability of soil salinity was determined and the leaching of salts over time was easily identified. The methodology of this study can be used as guidance for researchers interested in understanding soil salinity development as well as land managers aiming for effective soil salinity monitoring and management practices. In order to better characterize the variations in soil salinity to a deeper soil profile, the deeper mode of EM38 (i.e., EM38v) as well as other EMI instruments (e.g. DUALEM-421) can be incorporated to conduct Quasi-3D inversions for deeper soil profiles.
authors.affiliation: Institute of Agricultural Remote Sensing and Information Technology Application, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, China; Institute of Agricultural Economics and Information, Henan Academy of Agricultural Sciences, Zhengzhou, China.
authors.forename: Yan
authors.initials: Y
authors.lastname: Guo
authors.affiliation: School of Biological, Earth and Environmental Science, The University of New South Wales, Kensington, NSW 2052, Australia.
authors.forename: Jingyi
authors.initials: J
authors.lastname: Huang
authors.affiliation: Institute of Agricultural Remote Sensing and Information Technology Application, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, China.
authors.forename: Zhou
authors.initials: Z
authors.lastname: Shi
authors.affiliation: School of Tourism and Urban Management, Jiangxi University of Finance and Economics, Nanchang, China.
authors.forename: Hongyi
authors.initials: H
authors.lastname: Li
mesh_terms: D000465:Algorithms; D000069599:Crop Production; D004784:Environmental Monitoring / Q000295:instrumentation* / Q000379:methods*; D054712:Salinity; D012987:Soil / Q000737:chemistry*
chemical_list: D012987:Soil