LAUSR

Abstract Integrating remote sensing and geo-statistical techniques are of expanding field of researches, for soil salinity mapping taking advantage of steadily improving technology for the remote and proximal sensing of land features at the terrain surface. The main objective of the research is to enhance the performance of soil salinity mapping by employing MODIS spectral products and results of laboratory soil analysis into a geo-statistical soil properties analysis. The study area is the Sarvestan region, located in the southeast of Shiraz, Iran. The research followed a stratified random cluster sampling approach for collecting 240 soil samples in 60 geo-referenced soil pits from top of bare soils (5–10 cm). The MODIS data sets used were acquired during soil sampling. Stepwise multiple linear regression (SLMR) was employed for selecting MODIS products, that carry the most information on soil factors. Geo-statistical methods including Ordinary Kriging (OK), Co-kriging (CK), and Regression Kriging (RK) were used in mapping soil properties. Statistical criteria were considered to validate the models developed by SMLR. RK and OK as they are the best in EC and pH prediction. RK presents a higher effectiveness for the soil variables than CK, and confirms the usefulness of coupling SMLR to geostatistical mapping processes. The results indicate that MODIS imageries improve the capability of geostatistical methods for soil salinity mapping. The results showed that the use of MODIS imageries increased the G-Values of RK by 13%, 5%, 3%, 7% and 1% on average for OM, SAR, Na, K, and Mg respectively. RK also showed a good competitiveness for the EC and pH, when compared with OK. The research presentes an integrated helpful and effective mapping tool, especially in areas where there is lack of intensive field data on soil properties.