Amanjit Premsagar

Effective monitoring of inland waters requires approaches capable of capturing high spatial and temporal variability. Traditional in situ sampling provides accurate point measurements but lacks spatial coverage, while satellite remote sensing is often limited by coarse spatial resolution and cloud cover. To address these limitations, this study developed and validated an integrated monitoring platform combining an Autonomous Surface Vehicle (ASV) and a drone-based multispectral imaging system for flexible, high-resolution water quality monitoring. The study was conducted in two contrasting aquatic environments in Alabama: the North River–Lake Tuscaloosa system and the Sardine Pass and Duck Skiff Pass tidal inlets in Mobile Bay. A HyCAT ASV equipped with a YSI EXO2 multiparameter sonde collected continuous in situ measurements of turbidity, chlorophyll-a (Chl-a), and fluorescent dissolved organic matter (fDOM), which served as water-truth for a MicaSense Dual multispectral camera onboard a DJI Inspire-2 drone platform acquiring imagery in 10 spectral bands at ~8 cm spatial resolution. Machine learning models, including ensemble and Random Forest approaches, were developed and compared with traditional empirical algorithms. Ensemble models consistently outperformed empirical approaches, while Random Forest models achieved the highest accuracy and best generalization across variable environmental conditions. Compared with Sentinel-2 and Landsat-8 imagery, the drone-derived maps resolved fine-scale spatial variability, including sediment plumes and near-shore gradients, that could not be detected by satellite sensors. To facilitate operational implementation, the RS-WaterQuality Mapper software tool was expanded to support ensemble and Random Forest analyses for MicaSense imagery. Overall, the integrated ASV–drone system demonstrated substantial advantages over traditional sampling and satellite remote sensing, including rapid deployment, user-controlled acquisition timing, high spatial resolution, and improved monitoring of small and optically complex water bodies, highlighting its potential for adaptive water resource management and early warning applications.

Effective water management requires frequent monitoring, but traditional methods are limited in spatiotemporal scope. While satellite remote sensing provides extensive data, a gap persists in accessible, integrated software for non-specialists. To address this, we developed the RS-WaterQuality Mapper, an open-source Python plugin for QGIS. This toolbox provides a complete, scientifically robust workflow for aquatic remote sensing, from aqua-focused atmospheric correction to the application of advanced machine learning models. A key innovation is the implementation of a multi-predictor ensemble model based on spectral-space partitioning, which enhances predictive accuracy in optically complex inland waters. Built on optimized Python libraries and a multi-processing architecture, the tool ensures computational efficiency for processing large satellite scenes. The toolbox’s utility was validated in diverse case studies—a U.S. reservoir, a Kenyan saline lake, and a U.S. river system—demonstrating strong performance (R ² >0.80). By embedding state-of-the-art science into a familiar GIS environment, the RS-WaterQuality Mapper empowers a global community of researchers and water resource managers to leverage satellite data for more effective ecosystem management.