# Assessment and Spatial Modelling of Agrochernozem Properties for Reclamation Measurements
## 4 Discussion
Thus, numerous studies have shown good results for spatial prediction of soil properties on arable land using remote sensing data [64; 65]. However, [PERSON] et al. [66] applied a Support Vector Machine method in combination with terrain attributes for modeling soil properties on arable chernozem. The authors achieved RMSE coefficients of 5.33 for Ca, 0.73 for P and 32.34 for K. In another study on the digital mapping of agrochernozems properties, the authors reached RMSE values of 4.4, 27.5 and 14.6 for nitrogen, phosphorus and potassium, respectively, using machine learning methods in combination with remote sensing data and collocated soil parameters maps [67]. Thus, [PERSON] et al. [68] previously showed that the measured collocated soil parameters that were used as covariates (clay, electric conductivity and pH) had more influence for predicting soil organic matter than the environmental parameters due to flat terrain. In another research, it was reported that the key variables for explaining the spatial patterns of SOC concentration were a clay map, remote sensing data and distance from the river [69]. Similarly, [PERSON] et al. [70] showed that a total nitrogen map was the most important variable for the spatial assessment of SOC stocks. Thus, different maps of various soil parameters can be useful information for spatial prediction at different depths in future studies. External influences on soils directly affect qualitative and quantitative indicators, as well as their spatial variability. Since irrigation directly affects soil cover and its properties, we assume that the spatial distribution of properties will also be subject to change. For example, [PERSON] and [PERSON] [71] showed that SOC, total nitrogen and phosphorus concentrations increased significantly as a result of wastewater irrigation in the topsoil layer. The study conducted on three forage farms irrigated for more than 30 years in Saudi Arabia showed that most soil properties were characterized by moderate or strong spatial dependence [72]. At the same time, changes in soil variability can also be accompanied by fertilization, erosion, plot divisions, crop rotations and change or alternation of tillage practices [73; 74]. Thus, permanent irrigation can affect the content and spatial variability of soil parameters, which requires more frequent digital mapping. Further work to improve the predictive accuracy can be achieved by applying additional variables to explain the variation in properties, unmanned aerial vehicles (UAV) [75; 76], predictive techniques that take into account spatial dependence [77] and collecting more soil samples. For example, remote sensing data, such as S2A, has a spatial resolution of 10 m, which may not be sufficient for the digital mapping of soil properties in small areas. Specifically, [PERSON] et al. [27] concluded that microtopography has the most significant impact on soil acidity variability. Moreover, UAV provides more flexibility in the frequency of the survey and minimizes the effects of weather conditions such as cloud cover [78]. Nevertheless, the main limitation of UAV is the spatial coverage, which makes it difficult to map soil properties at the regional level. Nevertheless, the application of UAV seems to be a more rational solution for assessing the spatial distribution of soil properties on irrigated lands.