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Accurate information about growing crops allows for regulating the internal stocks of agricultural products and drawing strategies for negotiating agricultural commodities on financial markets. Machine learning methods are widely implemented for crop type recognition and classification based on satellite images. However, field classification is complicated by class imbalance and aggregation of pixel-wise into field-wise forecasting. We propose here a Bayesian methodology for the aggregation of classification results. We report the comparison of class balancing techniques. We also report the comparison of classical machine learning methods and the U-Net convolutional neural network for classifying crops using a single satellite image. The best result for single-satellite-image crop classification was achieved with an overall accuracy of 77.4% and a Macro F1-score of 0.66. Bayesian aggregation for field-wise classification improved the result obtained using majority voting aggregation by 1.5%. We demonstrate here that the Bayesian aggregation approach outperforms the majority voting and averaging strategy in overall accuracy for the single-image crop classification task. [ABSTRACT FROM AUTHOR]

Nowadays trace metal contamination of soils represents an important environmental hazard. Nevertheless, the use of some secondary waste products as amendments may restore the common soil functions. This paper focuses on the chemical and biological influence of wood biochar (BC), wood ash (WA) and humic substances (HS), alone and in the mixtures, on a heavily multi-contaminated sandy loam soil. The soil was amended by above-mentioned materials to follow a pH-increasing design (pH Ca from 6.0 to 6.5, 7.0 and 7.5); soil samples were analyzed after 3, 30, and 60 days using a set of variables, namely the plant-available trace element concentrations (Cu, Cd, and Zn), microbial biomass carbon (Cmic), and microbial quotient (qCO 2 ), as well as toxicity to Sinapis alba and Daphnia magna . Wood ash and WA + HS were the most efficient treatments to decrease mobile Cd and Zn concentrations in the soil, while HS, BC, and BC + HS combinations were the most effective in reducing the Cu mobility. The effect of BC and WA on the Cmic and qCO 2 was mostly negative, whereas adding HS markedly increased Cmic and reduced qCO 2 in soil. After amendment applications, the root elongation of mustard was significantly increased in HS and combined treatments (BC + HS, WA + HS). Additionally, BC + HS, WA + HS and WA 8.4% significantly decreased the toxicity of leachates to D. magna to the low-, or non-toxic levels. Our results suggest that the combination of amendments with HS can be a suitable remediation strategy for heavily contaminated soils. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]