There is a growing awareness among farmers about the importance of soil for sustaining crop production and soil health. Marked interests in “environmentally-friendly” soil fertilization in agriculture has attracted significant attention in the last decades. A wide range of commercial humic products (HPs) are used as soil supplements to improve the nutrient utilization efficiency of plants as well as to increase crop production. However, the implications of increased HPs use for soil biology are being questioned, but a comprehensive review on this topic is lacking. The aims of this review are to understand behavior of HPs applied to soil, with a special focus on environmental protection issues, and to summarize the available data how HPs may influence the soil microbial communities, including fungi and bacteria strains. The interaction between HPs and soil inhabitants is highly complex and is controversial to a certain extent for several reasons. First, applying HPs may promote specific bacteria communities (Acidobacteria, Actinobacteria, Bacteriodeites, etc.) rather than other microbiome species. Soil enzymes such as laccase, peroxidase and dehydrogenase were seemingly affected as well. Applying HPs may result in the microbial respiration of CH₄, preventing the production of CO₂ from microbial respiration. These results would potentially lead to an overall decrease in the microbial respiration of CH₄, which is associated with a deficit in CO₂. Furthermore, HPs can control the nematode population in different soils. Finally, applying HPs in terms of physiological functioning can also have various consequences for different groups of soil fungi. However, the conditions that promote specific effects (chemical composition of HPs, environmental conditions, etc.) have yet to be investigated. Given the surveyed data, we conclude that there are gaps in the current knowledge of this topic. We propose an integrated approach including a targeted research involving not only species of plants but also the integrated chemical, toxicological and biological analyses as a useful approach of soil health protection.

Usage of multispectral satellite imaging data opens vast possibilities for monitoring and quantitatively assessing properties or objects of interest on a global scale. Machine learning and computer vision (CV) approaches show themselves as promising tools for automatizing satellite image analysis. However, there are limitations in using CV for satellite data. Mainly, the crucial one is the amount of data available for model training. This paper presents a novel image augmentation approach called MixChannel that helps to address this limitation and improve the accuracy of solving segmentation and classification tasks with multispectral satellite images. The core idea is to utilize the fact that there is usually more than one image for each location in remote sensing tasks, and this extra data can be mixed to achieve the more robust performance of the trained models. The proposed approach substitutes some channels of the original training image with channels from other images of the exact location to mix auxiliary data. This augmentation technique preserves the spatial features of the original image and adds natural color variability with some probability. We also show an efficient algorithm to tune channel substitution probabilities. We report that the MixChannel image augmentation method provides a noticeable increase in performance of all the considered models in the studied forest types classification problem.

Sustainable management of the environment is based on the preservation of natural resources, first of all, freshwater—both surface and groundwater—from exhaustion and contamination. Thus, development of adequate monitoring solutions, including fast and adaptive modelling approaches, are of high importance. Recent progress in machine learning techniques provide an opportunity to improve the prediction accuracy of the spatial distribution of properties of natural objects and to automate all stages of this process to exclude uncertainties caused by handcrafting. We propose a technique to construct the weighted Water Quality Index (WQI) and the spatial prediction map of the WQI in tested area. In particular, WQI is calculated using dimensionality reduction technique (Principal Component Analysis), and spatial map of WQI is constructed using Gaussian Process Regression with automatic kernel structure selection using Bayesian Information Criterion (BIC). We validate our approach on a new dataset for groundwater quality in the New Moscow region, where groundwater is mostly used for drinking purposes. According to estimated WQI values, groundwater quality across the study region is relatively high, with few points, less than 0.5% of all observations, severely contaminated. Estimated WQIs then were used to construct spatial distribution models, GPR-BIC approach was compared with ordinary Kriging (OK), Universal Kriging (UK) with exponential, Gaussian, polynomial and periodic kernels. Quality of models was assessed using cross-validation scheme, according to which BIC-GPR approach showed better performance on average with 15% higher $R^2$ score comparing to other Kriging models. We show that the proposed geospatial interpolation is a potentially powerful and adaptable tool for predicting the spatial distribution of properties of natural resources.