Despite the great possibilities of modern neural network architectures concerning the problems of object detection and recognition, the output of such models is the local (pixel) coordinates of objects bounding boxes in the image and their predicted classes. However, in several practical tasks, it is necessary to obtain more complete information about the object from the image. In particular, for robotic apple picking, it is necessary to clearly understand where and how much to move the grabber. To determine the real position of the apple relative to the source of image registration, it is proposed to use the Intel Real Sense depth camera and aggregate information from its depth and brightness channels. The apples detection is carried out using the YOLOv3 architecture; then, based on the distance to the object and its localization in the image, the relative distances are calculated for all coordinates. In this case, to determine the coordinates of apples, a transition to a symmetric coordinate system takes place by means of simple linear transformations. Estimating the position in a symmetric coordinate system allows estimating not only the magnitude of the shift but also the location of the object relative to the camera. The proposed approach makes it possible to obtain position estimates with high accuracy. The approximate root mean square error is 7–12 mm, depending on the range and axis. As for precision and recall metrics, the first is 100% and the second is 90%.

The integration of water management goals in protected wetland areas agriculturally managed in an intensive manner recalls the comparison of apples (ecological values) and oranges (economic dimension of agriculture). Sustainable wetland management frequently fails if environmental features are not referred to as ecosystem services and quantified in economic terms. In our hydrological-economical study on floodplain wetlands located in the Lower Basin of the Biebrza Valley, we attempt to quantify the monetary value of water storage in the floodplain during flood phenomena as an important ecosystem service. The unit monetary value of water storage in the catchment of Biebrza Valley was assessed on the basis of small artificial water reservoirs, constructed in recent years and located in the area of research, and reached 0.53 EUR·m⁻³·year⁻¹. In a GIS-based study on hydrological floodplain processes in the years 1995–2011, we assessed the average annual volume of active water storage in the floodplain which reached 10.36 M m³ year⁻¹, giving a monetary value of EUR 5.49 million per annum. We propose that the methodology presented in our analysis could be applied as water storage subsidies in valuable floodplains, to prevent their deterioration originating from agriculture intensification.

Future climate change is projected to have significant impact on water resources availability and quality in many parts of the world. The objective of this paper is to assess the effect of projected climate change on water quantity and quality in two lowland catchments (the Upper Narew and the Barycz) in Poland in two future periods (near future: 2021–2050, and far future: 2071– 2100). The hydrological model SWAT was driven by climate forcing data from an ensemble of nine bias-corrected General Circulation Models—Regional Climate Models (GCM-RCM) runs based on the Coordinated Downscaling Experiment—European Domain (EURO-CORDEX). Hydrological response to climate warming and wetter conditions (particularly in winter and spring) in both catchments includes: lower snowmelt, increased percolation and baseflow and higher runoff. Seasonal differences in the response between catchments can be explained by their properties (e.g., different thermal conditions and soil permeability). Projections suggest only moderate increases in sediment loss, occurring mainly in summer and winter. A sharper increase is projected in both catchments for TN losses, especially in the Barycz catchment characterized by a more intensive agriculture. The signal of change in annual TP losses is blurred by climate model uncertainty in the Barycz catchment, whereas a weak and uncertain increase is projected in the Upper Narew catchment.

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.

Long‐term simulated water balance and streamflow data for the territory of Poland with high spatio‐temporal resolution were reconstructed for the period 1951–2020 with a daily time step, using the Soil and Water Assessment Tool (SWAT) hydrological model. The spatial structure includes 4,381 sub‐basins and 47,725 hydrological response units. The SWAT model was extensively calibrated and validated following a two‐stage, multi‐site calibration method for 88 gauging stations across Poland. The simulations show satisfactory performance with a median Kling–Gupta efficiency of 0.73 and 0.7 in the calibration and validation period respectively. The model performance exceeded that of its predecessor, the SWAT model of the Vistula and Odra basins. The simulated hydrological data set covers daily water balance components at the sub‐basin level, including precipitation total, snowfall, snowmelt, evapotranspiration (actual and potential), surface runoff, soil water, percolation, groundwater recharge, groundwater flow, tile flow and water yield (all components in mm), and daily streamflow (m³/s) at the reach level. The data set is stored in an online, publicly available research data archive 4TU.Research Data (Marcinkowski, P., Kardel, I., Płaczkowska, E., Giełczewski, M., Osuch, P., Okruszko, T., Venegas‐Cordero, N., Ignar, S. & Piniewski, M (2021). PL‐SWAT‐51_20: a high‐resolution simulated water balance and streamflow data set for 1951‐2020 for the territory of Poland, 4TU.ResearchData. dataset, https://doi.org/10.4121/16843183).