Comparison of Geostatistical and Random Forest Methods in Mapping Soil Salinity in the Lands of Zahak County of Sistan Plain

Authors

1 Assistant Professor, Soil and Water Research Department, Sistan Agricultural and Natural Resources Research and Education Center, AREEO, Zabol, Iran

2 MSc., Soil and Water Research Department, Sistan Agricultural and Natural Resources Research and Education Center, AREEO, Zabol, Iran

3 Assistant Professor, Soil and Water Research Institute, Agricultural Research, Education and Extension Organization(AREEO), Karaj, Iran

Abstract

Soil maps are the major source of information for land management, natural resources, and environment. Soil salinity is one of the most important factors limiting crop production in Sistan plain. In this research, geostatistical and random forest methods were compared to produce soil salinity maps. Preparation of accurate maps for soil salinity conditions is of great help in proper management of lands in this area. For this purpose, 460 composite soil samples were collected from 0-30 cm depth in 41000 ha of Zahak region of Sistan plain, using 750 m grid network. Then, the electrical conductivity of saturation paste was measured. Afterwards, 361 samples were used for training and 99 for testing. In the geostatistical model, different semi-variogram including circular, spherical, exponential, and Gaussian and different interpolation methods including inverse distance weighting, ordinary kriging, simple kriging, universal kriging and co-kriging were fitted and the best models were selected. In random forest model, digital soil mapping technique was used and environmental covariates were derived from digital elevation model (DEM) map and A Landsat 8 ETM+ image. The results of geostatistical method showed that soil salinity had a medium spatial correlation in the study area and the best semi-variogram and interpolation model were spherical and ordinary kriging, respectively. In random forest model, aspect, NDVI, and NDSI were the most important covariate in predicting soil salinity. The results Root Mean Square Error and Mean Error for the training and testing data showed that random forest method was slightly better than geostatistics. The use of other covariates such as land use and soil series maps can increase accuracy of the maps based on random forest methods, thereby improving decision-making. Using other environmental covariates that were not used in this study such as land unit and soil series map can also improve the accuracy of the map.

Keywords

References

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Iranian Journal of Soil Research
Volume 31, Issue 3
December 2017
Pages 363-373

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