Aquatic Remote Sensing

Work Schedule and Flexibilities: 10 hours per week for a full academic term (quarter or semester). Hours per week are flexible based on student's schedule and goals. Preferred hybrid but remote work is also an option. Project Description: Remote Sensing Algorithm Development for Water Quality Monitoring in San Francisco Bay. San Francisco Bay is an economically and environmentally important estuarine system where water quality is dynamic and highly variable. USGS scientists are developing and validating satellite-based algorithms to monitor water quality constituents - including suspended sediment, chlorophyll-a, and dissolved organic matter - using publicly available imagery from Landsat, Sentinel-2, and the new NASA PACE satellite. The student volunteer(s) will support this work by contributing to algorithm testing, validation, and development under the supervision of USGS research scientists. Core tasks may include: Compiling and processing satellite imagery and in-situ water quality datasets using Python Testing published remote sensing algorithms against field measurements from San Francisco Bay Supporting development of novel retrieval approaches, potentially including machine learning methods Contributing to map product generation or validation summaries