The influence of landscape on nutrient dynamics in rivers constitutes an important research issue because of its significance with regard to water and land management. In the current study spatial and temporal variability of N-NO3 and P-PO4 concentrations and their landscape dependence was documented in the Świder River catchment in central Poland. From April 2019 to March 2020, water samples were collected from fourteen streams in the monthly timescale and the concentrations of N-NO3 and P-PO4 were correlated with land cover metrics based on the Corine Land Cover 2018 and Sentinel 2 Global Land Cover datasets. It was documented that agricultural lands and forests have a clear seasonal impact on N-NO3 concentrations, whereas the effect of meadows was weak and its direction was dependent on the dataset. The application of buffer zones metrics increased the correlation performance, whereas Euclidean distance scaling improved correlation mainly for forest datasets. The concentration of P-PO4 was not significantly related with land cover metrics, as their dynamics were driven mainly by hydrological conditions. The obtained results provided a new insight into landscape–water quality relationships in lowland agricultural landscape, with a special focus on evaluating the predictive performance of different land cover metrics and datasets.

The search for the best landscape predictors explaining the spatial variability of stream water chemistry is one of the most important and recent research issues. Thus, in the current study, relationships between land cover indices and selected water quality parameters were evaluated regarding the example of 54 lowland temperate streams located in central Poland. From November 2021 to March 2022, water samples were collected in the monthly timescale, and the concentrations of NH4+, NO3−, and NO2−, as well as electrical conductivity, were correlated with the percentage of land cover types calculated for total catchment area, buffer zones, cut buffer zones, and radius. For such computing, Corine Land Cover 2018 and Sentinel 2 Global Land Cover datasets were used. In the case of both datasets, results indicate significant dependence of NO3−, and NO2− concentrations, as well as EC values on cover metrics. Overall, agricultural lands favored higher concentrations of NO3− and NO2−, whereas mainly coniferous forests reduced nitrogen pollution. Significant correlations were not documented in the case of NH4+ ions, the concentrations of which could be linked to point sources from municipal activity. Correlation performance was slightly better in the case of the S2GLC dataset, while the best spatial scales were generally seen for wider buffer zones (250 and 500 m) and total catchment area. The study provided spatially extensive insight into the impact of land cover predictors at different scales on nitrogen compounds in a lowland landscape.