Spatial Interpolation of Gravimetric Soil Moisture Using EM38-mk Induction and Ensemble Machine Learning (Case Study from Dry Steppe Zone in Volgograd Region)

Abstract

The implementation of the sustainable management of the interaction between agriculture and the environment requires an increasingly deep understanding and numerical description of the soil genesis and properties of soils. One of the areas of application of relevant knowledge is digital irrigated agriculture. During the development of such technologies, the traditional methods of soil research can be quite expensive and time consuming. Proximal soil sensing in combination with predictive soil mapping can significantly reduce the complexity of the work. In this study, we used topographic variables and data from the Electromagnetic Induction Meter (EM38-mk) in combination with soil surface hydrological variables to produce cartographic models of the gravimetric soil moisture for a number of depth intervals. For this purpose, in dry steppe zone conditions, a test site was organized. It was located at the border of the parcel containing the irrigated soybean crop, where 50 soil samples were taken at different points alongside electrical conductivity data (ECa) measured in situ in the field. The modeling of the gravimetric soil moisture was carried out with the stepwise inclusion of independent variables, using methods of ensemble machine learning and spatial cross-validation. The obtained cartographic models showed satisfactory results with the best performance R2cv 0.59–0.64. The best combination of predictors that provided the best results of the model characteristics for predicting gravimetric soil moisture were geographical variables (buffer zone distances) in combination with the initial variables converted into the principal components. The cartographic models of the gravimetric soil moisture variability obtained this way can be used to solve the problems of managed irrigated agriculture, applying fertilizers at variable rates, thereby optimizing the use of resources by crop producers, which can ultimately contribute to the sustainable management of natural resources.

Determination of Soil Electrical Conductivity and Moisture on Different Soil Layers Using Electromagnetic Techniques in Irrigated Arid Environments in South Africa

“…We found that soils within the study area had no sign of salinity, with ECa values < 100 mSm −1 . The findings of this study relate to the findings of other studies [43,65], which also used the EM38-mk to estimate soil moisture based on ECa measurements, and found similar trends in terms of correlations and behavior of ECa under given SWC conditions.…”

“…The findings of this study demonstrate that the soil water content is the main driver that allows the flow of electrical current in the soil medium, making it easier for the electromagnetic induction device to determine the soil's apparent electrical conductivity. Such findings are also reported by others [36,43,65]. The linear relationship between soil water content and the soil's electrical conductivity made it possible to easily convert ECa (mS/m) into SWC%, influenced largely by irrigation in the area.…”

Modelling soil moisture using climate data and normalized difference vegetation index based on nine algorithms in alpine grasslands

“…For example, both random forest and support-vector machine provide some usersadjusted model parameters, but multiple linear regression does not provide (Han et al, 2022b;Tian and Fu, 2022). The predicted accuracies of SM estimation based on the constructed random forest models in this study were no lower than those found in earlier studies performed on (Zeng et al, 2015;Bai et al, 2017;Deng et al, 2018;Tong et al, 2021;Wu and Wen, 2022) and beyond (Zormand et al, 2017;Ma et al, 2020;Yuan et al, 2020;Zhang et al, 2022a;Wang et al, 2022c;Jarray et al, 2022;Manninen et al, 2022;Zeyliger et al, 2022) Tibetan Plateau. For example, an earlier study demonstrated that SM at the depth of 0-10 cm, according to the global land data assimilation system (GLDAS) Noah, can only explain about 71% of the variation in observed SM at the depth of 0-10 cm in Naqu, and the RMSE values between the GLDAS Noah and observed SM were 4.47-5.03 (Chen et al, 2021a).…”