Remote sensing–based assessment of water quality in small inland waters as a scalable tool for equitable and multisectoral water management

Sustainable water management under a changing hydrological cycle increasingly requires approaches that integrate hydrological processes, water quality dynamics, and decision-making across multiple sectors. Anthropogenic pressures such as urban and industrial wastewater discharge, as well as excessive nutrient inputs from agriculture, exacerbate water quality degradation and ecological stress. This is particularly pronounced in small inland waters, which are often under-monitored yet critical for local water supply, agriculture, and ecosystem services. These challenges are amplified by data scarcity and limited monitoring capacity, constraining equitable water allocation and evidence-based governance.

This contribution presents a remote sensing–based framework for assessing spatio-temporal water quality dynamics in small inland waters, with a focus on supporting multisectoral water management under data-limited conditions. The approach is demonstrated through a case study on the River Aller in Celle, Germany, where the potential impact of a wastewater treatment plant was assessed on the river water quality. Satellite observations are combined with targeted in-situ measurements to evaluate chlorophyll-a (Chl-a) variability as an indicator of eutrophication and ecological pressure.

Two river sections, located upstream and downstream of the wastewater treatment plant, were analysed to assess spatial and temporal differences in Chl-a concentrations. Optical remote sensing data from Sentinel-2 and PlanetScope satellites were integrated with field measurements collected during the summer of 2024. The analysis revealed variable Chl-a concentrations over time, with elevated values downstream of the treatment plant during several sampling periods, indicating a potential influence of treated effluent on eutrophication dynamics.

Statistical analysis showed positive correlations between satellite-derived reflectance and in-situ Chl-a concentrations. For Sentinel-2, the strongest relationships were observed in the red (Band 4) and red-edge (Band 5) bands using Level-2A (bottom-of-atmosphere) data, with the highest Pearson correlation coefficient (r = 0.6) obtained for the red band. These bands (Bands 4 and 5) and data products (Level-2A) were therefore selected for further analysis. Likewise, moderate correlations were also identified using PlanetScope data, particularly in the red and red-edge bands. Although weaker than those obtained from Sentinel-2, these results highlight the potential of high-resolution satellite data, with a spatial resolution of approximately 3 m and near-daily revisit frequency, for monitoring small inland waters. Data at this resolution with improved temporal coverage are particularly valuable where spatial detail is critical and where limited clear-sky conditions constrain data availability.

Empirical models were developed to estimate Chl-a concentrations based on satellite reflectance, demonstrating the value of Earth observation as a complementary tool to conventional monitoring, particularly as an early-warning service in contexts where dense in situ networks are not feasible. By enabling more consistent and spatially extensive monitoring, remote sensing approaches such as those presented here offer a more affordable and scalable alternative to conventional, labor-intensive in-situ sampling. This is particularly important for small inland waters, where consistent long-term monitoring is required to capture spatial heterogeneity and short-term variability relevant for management decisions. In addition, the spatially continuous nature of satellite observations supports reproducible and comparable assessments of water quality dynamics across time and locations, reducing reliance on sparse point-based measurements.