Spatial estimation of soil organic matter content using remote sensing data in southern Tunisia

Abstract

Learning the spatial distribution of soil organic matter content is essential for the planning of land use and environmental protection. Because laboratory measurement of soil samples is time-consuming and costly, a good alternative is required to estimate spatial content of soil organic matter. This problem can be solved by using remote sensing and GIS techniques. In this study, soil organic matter content was estimated from remote sensing data derived from LandSat8 satellite image by generating a multi linear regression model using the backward regression technique. The multiple regression equation between SOM and remote sensing data was significant with R = 0.678. The resulting multi linear regression equation was then used for the spatial prediction for the entire study area. The predicted SOM derived from remote sensing data was used as auxiliary variable using cokriging spatial interpolation technique. Integrate remote sensing data with cokriging method improves significantly the estimates of surface soil organic matter content.