Application of digital soil mapping methods for identifying salinity management classes based on a study on coastal central China

Abstract

In coastal China, there is an urgent need to increase land for agriculture. One solution is land reclamation from coastal tidelands, but soil salinization poses a problem. Thus, there is need to map saline areas and identify appropriate management strategies. One approach is the use of digital soil mapping. At the first stage, auxiliary data such as remotely sensed multispectral imagery can be used to identify areas of low agricultural productivity due to salinity. Similarly, proximal sensing instruments can provide data on the distribution of soil salinity. In this study, we first used multispectral QuickBird imagery (Bands 1–4) to provide information about crop growth and then EM38 data to indicate relative salt content using measurements of apparent soil electrical conductivity (ECa) in the horizontal (ECh) and vertical (ECv) modes of operation. Second, we used a fuzzy k‐means (FKM) algorithm to identify three salinity management zones using the normalized difference vegetation index (NDVI), ECh and ECv/ECh. The three identified classes were statistically different in terms of auxiliary and topsoil properties (e.g. soil organic matter) and more importantly in terms of the distribution of soil salinity (ECe) with depth. The resultant three classes were mapped to demonstrate that remote and proximally sensed auxiliary data can be used as surrogates for identifying soil salinity management zones.

Towards decadal soil salinity mapping using Landsat time series data

“…It is accepted that passive remote sensing can only sense the superficial soil, while subsoil salinity may be a major cause of yield reduction particularly over dryland regions (Rengasamy et al, 2003). However, there has reported a relation between topsoil and subsoil salinity (Ben-Dor et al, 2008;Guo et al, 2013). Thus, satellite derived soil salinity may have an implication for subsoil salinity, which can be a concern for agriculture.…”

Multitemporal soil pattern analysis with multispectral remote sensing data at the field-scale

“…Spatial and geostatistical techniques, such as inverse distance calculations, various kriging-procedures, fuzzy clustering algorithms (Birrell et al, 1996;Gotway et al, 1996;Guo et al, 2013;Liu et al, 2008;López-Granados et al, 2005;Sumfleth and Duttmann, 2008;Triantafilis et al, 2013;Yan et al, 2007;Zhu et al, 2013), can transfer punctual quantitative soil property data to fine-scale soil maps, depending on the sampling strategy and the accuracy of the extensive, time-consuming and costly field survey. Non-invasive proximal and remote sensors and their corresponding physical-based or empirical-based data analysis methods have been approved as potentially effective, rapid and cost efficient (Mulder et al, 2011) and provide continuous, direct or indirect data on physiochemical soil properties depending on spatial, temporal or spectral sensor resolution.…”

Mapping soil salinity in 3‐dimensions using an EM38 and EM4Soil inversion modelling at the reconnaissance scale in central Morocco

“…There is a growing body of literature showing increasing research in developing countries aimed at using such an approach to measure and map soil salinity. This includes Indonesia (McLeod et al ., ), Turkey (Cetin et al ., ), Uzbekistan (Akramkhanov et al ., ) and China (Yao & Yang, ; Guo et al ., ) where in coastal tidelands susceptibility to shallow water tables and saline soil conditions after reclamation requires knowledge for monitoring and management. In Africa, similar work has also been undertaken including South Africa (Johnston et al ., ), Senegal (Ceuppens & Wopereis, ; Barbiéro et al ., ) and Tunisia (Aragüés et al ., ).…”