The contamination of soil with petroleum products is a major environmental problem. Petroleum products are common soil contaminants as a result of human activities, and they are causing substantial changes in the biological (particularly microbiological) processes, chemical composition, structure and physical properties of soil. The main objective of this study was to assess the impact of soil moisture on CO₂ efflux from diesel-contaminated albic podzol soils. Two contamination treatments (3000 and 9000 mg of diesel oil per kg of soil) were prepared for four horizons from two forest study sites with different initial levels of soil water repellency. CO₂ emissions were measured using a portable infrared gas analyser (LCpro+, ADC BioScientific, UK) while the soil samples were drying under laboratory conditions (from saturation to air-dry). The assessment of soil water repellency was performed using the water drop penetration time test. An analysis of variance (ANVOA) was conducted for the CO₂ efflux data. The obtained results show that CO₂ efflux from diesel-contaminated soils is higher than efflux from uncontaminated soils. The initially water-repellent soils were found to have a bigger CO₂ efflux. The non-linear relationship between soil moisture content and CO₂ efflux only existed for the upper soil horizons, while for deeper soil horizons, the efflux is practically independent of soil moisture content. The contamination of soil by diesel leads to increased soil water repellency.

Despite covering only 2–6% of land, wetland ecosystems play an important role at the local and global scale. They provide various ecosystem services (carbon dioxide sequestration, pollution removal, water retention, climate regulation, etc.) as long as they are in good condition. By definition, wetlands are rich in water ecosystems. However, ongoing climate change with an ambiguous balance of rain in a temperate climate zone leads to drought conditions. Such periods interfere with the natural processes occurring on wetlands and restrain the normal functioning of wetland ecosystems. Persisting unfavorable water conditions lead to irreversible changes in wetland habitats. Hence, the monitoring of habitat changes caused by an insufficient amount of water (plant water stress) is necessary. Unfortunately, due to the specific conditions of wetlands, monitoring them by both traditional and remote sensing techniques is challenging, and research on wetland water stress has been insufficient. This paper describes the adaptation of the thermal water stress index, also known as the crop water stress index (CWSI), for wetlands. This index is calculated based on land surface temperature and meteorological parameters (temperature and vapor pressure deficit—VPD). In this study, an unmanned aerial system (UAS) was used to measure land surface temperature. Performance of the CWSI was confirmed by the high correlation with field measurements of a fraction of absorbed photosynthetically active radiation (R = −0.70) and soil moisture (R = −0.62). Comparison of the crop water stress index with meteorological drought indices showed that the first phase of drought (meteorological drought) cannot be detected with this index. This study confirms the potential of using the CWSI as a water stress indicator in wetland ecosystems.

Surface soil moisture (SSM) is one of the factors affecting plant growth. Methods involving direct soil moisture measurement in the field or requiring laboratory tests are commonly used. These methods, however, are laborious and time-consuming and often give only point-by-point results. In contrast, SSM can vary across a field due to uneven precipitation, soil variability, etc. An alternative is using satellite data, for example, optical data from Sentinel-2 (S2). The main objective of this study was to assess the accuracy of SSM determination based on S2 data versus standard measurement techniques in three different agricultural areas (with irrigation and drainage systems). In the field, we measured SSM manually using non-destructive techniques. Based on S2 data, we estimated SSM using the optical trapezoid model (OPTRAM) and calculated eighteen vegetation indices. Using the OPTRAM model gave a high SSM estimating accuracy (R² = 0.67, RMSE = 0.06). The use of soil porosity in the OPTRAM model significantly improved the results. Among the vegetation indices, at the NDVI ≤ 0.2, the highest value of R² was obtained for the STR to OPTRAM index, while at the NDVI > 0.2, the shadow index had the highest R² comparable with OPTRAM.