Estimation and mapping of surface soil properties in the Caucasus Mountains, Azerbaijan using high-resolution remote sensing data

Soil surveys and mapping with traditional methods are time-consuming and expensive especially in mountainous areas while demand for detailed soil information is steadily increasing. This study tested two spatial hybrid approaches to predict and map basic soil properties using high resolution digital elevation model (DEM) and multispectral satellite imagery in a study area located in the Caucasus Mountains, Azerbaijan. Terrain attributes and spectral indices extracted from DEM with 12.5 m spatial resolution and Pléiades-1 data were used as auxiliary variables. A total of 115 soil samples were collected from the surface layer of 423 ha area and tested for soil organic carbon, soil reaction (pH in H2O and KCl solutions), calcium carbonate (CaCO3), sand, silt, clay and hygroscopic water content. The predictive capability of Universal Kriging (UK) and Random Forest Kriging (RFK) was evaluated using spatial cross-validation technique. To model and quantify the associated uncertainty of these models a probabilistic framework, kriging variance approach was applied. The uncertainty models were validated using independent and randomly selected control points (20% of the reference samples). For this, the actual fraction of true values falling within symmetric prediction intervals was calculated and visualized known as accuracy plot. Although the performances of the tested models were similar, RFK was superior in view of both accuracy and computed biases. The models were capable of delineating spatial pattern, mostly elevation dependent as well as the local patterns attributed by e.g., variations in vegetation, land use and soil erosion. UK model produced a few local erratic spatial patterns (e.g., in the case of pH) corresponding to the artifacts such as roads and houses in the image that should be considered in future applications. When comparing the uncertainties, both the models produced considerable underestimations and overestimations depending on soil property. RFK provided better uncertainty estimation for the most of soil properties than UK, the latter technique was more appropriate for the clay and pHKCl prediction. This case study confirmed the importance of assumptions made in uncertainty modelling and quantification. Those soil properties were therefore reliably predicted that their residuals were compatible with the normality assumption and showed apparent spatial correlation, e.g., both the models severely overestimated uncertainty of CaCO3 due to lack of normality assumption and low spatial correlation. This study showed that high resolution remote sensing data are promising, and the procedure presented in this study can be reliably used to map the studied soil properties and extended to partially larger adjacent areas characterized by similar environmental conditions in the Caucasus Mountains. However, with respect to future digital soil mapping, we assume that it is important to consider sampling design, testing other modelling approaches their uncertainties and multi-scale digital terrain analysis as well.