Changes in lake water temperature, observed with the greatest intensity during the last two decades, may significantly affect the functioning of these unique ecosystems. Currently, in situ studies in Poland are conducted only for 38 lakes using the single-point method. The aim of this study was to develop a method for remote sensing monitoring of lake water temperature in a spatio-temporal context based on Landsat 8 imagery. For this purpose, using data obtained for 28 lakes from the period 2013–2020, linear regression (LM) and random forest (RF) models were developed to estimate surface water temperature. In addition, analysis of Landsat Level-2 Surface Temperature Science Product (LST-L2) data provided by United States Geological Survey (USGS) and the National Aeronautics and Space Administration (NASA) was performed. The remaining 10 lakes not previously used in the model development stage were used to validate model performance. The results showed that the most accurate estimation is possible using the RF method for which RMSE = 1.83 °C and R² = 0.89, while RMSE = 3.68 °C and R² = 0.8 for the LST-L2 method. We found that LST-L2 contains a systematic error in the coastal zone, which can be corrected and eventually improve the quality of estimation. The satellite-based method makes it possible to determine water temperature for all lakes in Poland at different times and to understand the influence of climatic factors affecting temperature at the regional scale. On the other hand, spatial presentation of thermics within individual lakes enables understanding the influence of local factors and morphometric conditions.

The division of an arable field into zones of different productivity requires a reliable, discriminatory tool. This hypothesis was validated by analyzing the spatial variability of yield and N indicators in the crop rotation of winter oilseed rape (WOSR)/winter triticale (WTR) during 2016/2017 and 2017/2018 in a field of 30 ha (Przebędowo, Poland). The direct, measurable variables were: yield, N accumulated in—seeds/grain and crop residues, mineral N in spring, and harvest. The basic N indicators were total N uptake (TN), N-partial factor productivity, and N balance (N_b). The attainable yields of WOSR and WTR were 4.93 and 6.51 t ha⁻¹, and a yield gap of −2.04 and −2.10 t ha⁻¹. The management of 50 kg of the non-used N by crops, i.e. nitrogen gap (NG) could cover 36% and 65% of the yield gap (YG), respectively. The N_b, based on N input (N_in = N_min + N_f) and TN, was the key field indicator, defining both yield and NG. Geostatic parameters, i.e., the nugget to sill ratio, spatial dependence range, and mean correlation distance, were very stable (≤0.2–0.17; 94–100 m; 28 m for WOSR and WTR). The spatial stability of N_b, irrespective of the crop and growing conditions, corroborates its suitability for discriminating high and low-productivity field zones.

Measurement of water content in forest habitats is considered essential in ecological research on forests, climate change, or forest management. In the traditional forest habitat classification, two systems of habitat conditions analysis are found: single factor and multifactor methods. Both are laborious and therefore costly. Remote sensing methods provide a low-cost alternative. The aim of the presented study was to find the relationship between the spectral indices obtained from satellite images and the forest habitats moisture indices used traditionally in the Polish forest habitats classification. The scientific hypothesis of the research is as follows: it is possible to assess the variation in the humidity of forest habitats on the basis of spectral indices. Using advanced geographic information system (GIS) technology, 923 research plots were tested, where habitat studies performed with the traditional methods were compared with the analysis of 191 spectral indices calculated for Sentinel-2 image data. The normalized difference vegetation index (NDVI) has proved to be the most useful to the assessing of moisture of forest habitats. The ranking of the most correlated indices was calculated as E_intg—the product of the absolute value of the slope and the mean square error complement, and for the top five indices was as follows: NDVI = 0.248619, EXG = 0.242112, OSAVI = 0.239412, DSWI-4 = 0.238784, and RDVI = 0.236995. The results also highlight the impact of water reservoirs on the humidity and trophicity of forest habitats, showing a decrease in the fertility of habitats with an increase in distance from the water reservoir. The results of the study can be used to preparing maps of the diversity of forest types, especially in hard-to-reach places, as well as to assess changes in the moisture status of habitats, which may be useful, for example, in the assessment of the fire risk of forest habitats. We have proved that NDVI can be used in applications for which it was not originally designed.

Accurate and timely assessment of crop disease severity is crucial for effective management strategies and ensuring sustainable agricultural production. Traditional visual disease scoring methods are subjective and labor-intensive, highlighting the need for automated, objective alternatives. This study evaluates the effectiveness of a model for field-based identification and quantification of stripe rust severity in wheat using red, green, blue RGB imaging. Based on crop reflectance hyperspectra (CRHS) acquired using a FieldSpec ASD spectroradiometer, two complementary approaches were developed. In the first approach, we estimate single leaf disease severity (LDS) under laboratory conditions, while in the second approach, we assess crop disease severity (CDS) from field-based RGB images. The high accuracy of both methods enabled the development of a predictive model for estimating LDS from CDS, offering a scalable solution for precision disease monitoring in wheat cultivation. The experiment was conducted on four winter wheat plots subjected to varying fungicide treatments to induce different levels of stripe rust severity for model calibration, with treatment regimes ranging from no application to three applications during the growing season. RGB images were acquired in both laboratory conditions (individual leaves) and field conditions (nadir and oblique perspectives), complemented by hyperspectral measurements in the 350–2500 nm range. To achieve automated and objective assessment of disease severity, we developed custom image-processing scripts and applied Random Forest classification and regression models. The models demonstrated high predictive performance, with the combined use of nadir and oblique RGB imagery achieving the highest classification accuracy (97.87%), sensitivity (100%), and specificity (95.83%). Oblique images were more sensitive to early-stage infection, while nadir images offered greater specificity. Spectral feature selection revealed that wavelengths in the visible (e.g., 508–563 nm and 621–703 nm) and red-edge/SWIR regions (around 1556–1767 nm) were particularly informative for disease detection. In classification models, shorter wavelengths from the visible range proved to be more useful, while in regression models, longer wavelengths were more effective. The integration of RGB-based image analysis with the Random Forest algorithm provides a robust, scalable, and cost-effective solution for monitoring stripe rust severity under field conditions. This approach holds significant potential for enhancing precision agriculture strategies by enabling early intervention and optimized fungicide application.

Septoria leaf blotch (SLB), caused by Zymoseptoria tritici, is one of the most destructive foliar diseases of wheat and requires accurate methods for early detection and disease severity assessment. This study evaluated the potential of hyperspectral ASD measurements and UAV multispectral imagery combined with machine learning for the detection and quantification of SLB in winter wheat. Six spectral datasets derived from hyperspectral reflectance, UAV multispectral imagery, and vegetation indices were analyzed using CatBoost, Random Forest, and XGBoost algorithms. Random Forest achieved the highest classification performance, reaching an accuracy of 0.9583 and a balanced accuracy of 0.9483. For disease severity prediction, the best performance was obtained using ASD-derived vegetation indices with Random Forest (R² = 0.9199), while CatBoost consistently provided high regression accuracy across hyperspectral datasets. A reduced set of green, red, red-edge, and near-infrared bands produced classification results comparable to those obtained with the full hyperspectral spectrum, indicating that most diagnostic information is concentrated within these spectral regions. Although UAV multispectral data showed lower accuracy than hyperspectral measurements, particularly for disease severity prediction, they enabled effective field-scale disease monitoring. These findings demonstrate that hyperspectral sensing provides a valuable reference for developing accurate disease detection models, whereas UAV multispectral imagery represents a practical and scalable solution for operational precision agriculture.