ABSTRACT
Soil, an important component of land resource inventory is necessary to deal with the issues of food security, environmental problem, sustainability of natural resources and climate change impact on soil resource degradation 274and their sustainability. Satellite remote sensing data and integrated use of Geographic Information Systems (GIS) and Global Positioning System (GPS) technologies offers unique potential in soil resource inventory. Optical remote sensing data are commonly being used for retrieving thematic information. Several Global freely available Digital Elevation Models (DEMs) are very useful sources to derive topographic parameters facilitating in digital soil mapping. The growth of open source softwares has a particular impact on the potential to analyze geospatial data. They are providing large opportunities for predictive soil mapping to generate accurate spatially explicit soil maps. Soil surveyors follows soil–landscape model to capture the relationship between soil and their formative environment. The spatial extents of these soil formative environments are interpreted using satellite data to delineate soil–landscape units known as physiographic units for soil mapping. In digital soil mapping, laboratory analyzed soil data and remote sensing data are integrated with statistical methods to infer spatial pattern and distribution of soil properties. It follows SCORPAN equation in spatial prediction of soil types. Various approaches such as multivariate statistical analysis, geostatistics, artificial neural network (ANN), expert systems, etc., have emerged as advanced tools in digital soil mapping. Statistical regression models, generalized linear models (GLMs) and general additive models (GAMs) are used in predictive soil mapping. Geostatistics in association with GIS serves as an advanced method in quantifying the spatial pattern of soil properties and environmental variables. Hyperspectral remote sensing data are being used to derive spectral information of soil properties and to map them quantitatively. However, to retrieve soil hydrological properties, microwave remote sensing data are used.
