Digital mapping of soil featured using remote sensing data

Digital soil mapping is achieved using different data and methods in integrated methods. These methods determine the spatial and temporal distribution of soil features. Digital soil mapping can fill the gaps in the knowledge and data of soil in recent years. In early 2000, some effective factors have led to the more successes of the digital soil mapping including: 1) increasing the access to the spatial data such as satellite images, Digital elevation models (DEM), and increasing the number of available maps, 2) increasing the computational abilities in data processing, and 3) development of the data mining tools and Geographic Information Systems (GIS). Moreover, increasing the global demand to reduce uncertainties in the spatial data, repairing existing maps, and the help of global organizations in development of methods are among the other effective factors. Theoretical framework of digital soil mapping was introduced in a large number of researches. In this research, some aspects of digital soil mapping were represented including the structure, history, and explanation of some of its parts. In this regard, two main categories exist including sensing the soil properties close to the soil and soil remote sensing. In the latter one, the most important method is soil spectroscopy. However, soil spectroscopy encountered some problems such as complexity of the spectrum of soil components and overlapping the spectrum of those components. The results showed that the digital soil mapping requires three fundamental parts including: 1) the costs in the form of field campaigns and laboratory observation methods, 2) the process of utilizing spatial and non-spatial inference systems of the soil, and 3) output parts in the form of soil geospatial information systems which include outputs in the form of forecasting rasters along with their uncertainties. The results indicate the success of the proposed methods. Moreover, the developments of digital soil mapping is composed of the access to digital spatial data, abilities in bulk data processing, prevalence of data mining tools, improving the GIS systems, and utilizing geostatistic methods. Furthermore, extracted data from remote sensing data such Landsat, Spot, MODIS, NOAA AVHRR, and IKONOS images can be helpful. However, calibrating satellite data require laboratory measurement such as soil spectrometry.