Global demand for soil data and information for maintaining and improving agricultural productivity and environmental health is soaring.  The accurate and rapid digital maps of soil characteristics are of key importance for evaluation of soil fertility, precision management of crop inputs, estimation of carbon stocks, and modeling ecological responses as well as environmental threats.  

The progress in digital soil mapping (DSM) over the last decade provided an improved choice to monitor and map soil characteristics in space and time.  Previous reviews have discussed the history (McBratney et al. 2003; Hartemink et al. 2013; Minasny and McBratney 2016) and the progress in DSM in general (Grunwald et al. 2011; Zhang et al. 2017).  However, the field of DSM has been moving at an accelerated pace and the progress has been observed in all aspects including data organization and quality, soil sampling, environmental covariates, predictive models, and map validation.  In this special issue, the selected eight papers document some of the scopes, developments and progresses in digital mapping in agriculture and environment.

First four papers documented the progress and developments in predictive models.  Teng et al. (2019) used new methodologies including Collocated CoKriging (ColCOK), geographically weighted regression (GWR) and Random Forest (RF) regression to integrate satellite images, field samples, and ground observations to map the soil loss potential in China.  Cheng et al. (2019) proposed a method of mining soil–environmental relationships from individual soil polygons to update conventional soil maps of the Raffelson watershed in La Crosse County, Wisconsin, United States.  Gao et al. (2019) predicted the spatial variability of soil total nitrogen (TN), total phosphorus (TP) and total potassium (TK) using geostatistical analysis and regression analysis.  Li et al. (2019) evaluated the spatial variability of soil bulk density and its controlling factors at different soil layers in Southwest China’s agricultural intensive area.

The following three papers documented the progress in environmental covariate selection, processing and utilization.  Lu et al. (2019) proposed a framework integrating Pearson correlation analysis, generalized additive models (GAMs), and Random Forest (RF) to select environmental covariates for predictive soil depth mapping in the upper reaches of the Heihe River Basin in Northwest China.  Wu et al. (2019) used the combination of surface albedo computed from moderate resolution imaging spectroradiometer (MODIS) reflectance products and the actual measured soil moisture data to map an albedo/vegetation coverage trapezoid feature space.  Wang et al. (2019) applied natural language processing (NLP) and rule-based techniques to automatically extract and structure information from soil survey reports regarding soil–environment relationships.

The last paper talked about soil sampling.  Guo et al. (2019) employed EM38 data to estimate the spatio-temporal variation of soil salinity in different site-specific management zones.  Fuzzy-k means algorithm was used to divide the site-specific management zones and to help sampling design.  

We believe that the reader both in China and abroad will be interested in these articles and be inspired with the finding of the papers for developing future research on digital mapping in agriculture and environment.  We want to express our deepest appreciation to all the authors for their high-quality contributions and efforts to make this special issue a great success.

Soil moisture and salinity are two crucial coastal saline soil variables, which influence the soil quality and agricultural productivity in the reclaimed coastal region. Accurately characterizing the spatial variability of these soil parameters is critical for the rational development and utilization of tideland resources. In the present study, the spatial variability of soil moisture and salinity in the reclaimed area of Hangzhou gulf, Shangyu City, Zhejiang Province, China, was detected using the data acquired from radar image and the proximal sensor EM38. Soil moisture closely correlates radar scattering coefficient, and a simplified inversion model was built based on a backscattering coefficient extracted from multi-polarization data of ALOS/PALSAR and in situ soil moisture measured by a time domain reflectometer to detect soil moisture variations. The result indicated a higher accuracy of soil moisture inversion by the HH polarization mode than those by the HV mode. Soil salinity is reflected by soil apparent electrical conductivity (ECa). Further, ECa can be rapidly detected by EM38 equipment in situ linked with GPS for characterizing the spatial variability of soil salinity. Based on the strong spatial variability and interactions of soil moisture and salinity, a cokriging interpolation method with auxiliary variable of backscattering coefficient was adopted to map the spatial variability of ECa. When compared with a map of ECa interpolated by the ordinary kriging method, detail was revealed and the accuracy was increased by 15.3%. The results conclude that the integrating active remote sensing and proximal sensors EM38 are effective and acceptable approaches for rapidly and accurately detecting soil moisture and salinity variability in coastal areas, especially in the subtropical coastal zones of China with frequent heavy cloud cover.