Water Depth Retrieval in the Everglades Using Cygnss

Quantitative observations of dynamic changes in water extent and depth of the world's wetlands are currently limited by traditional remote sensing methods, which have difficulty observing surface water beneath dense vegetation and clouds. A novel remote sensing technique known as GNSS Reflectometry (GNSS-R) has shown great potential in the detection of terrestrial surface water beneath vegetation. The Cyclone Global Navigation Satellite System (CYGNSS) is a GNSS-R small satellite constellation that exhibits sub-daily revisit rates over tropical wetlands. In this work, we present a retrieval algorithm to predict water depth and surface water extent using CYGNSS observations over the Everglades. We test our algorithm over three regional approaches and varying smoothing filters. Results indicate that CYGNSS signal-to-noise ratio (SNR) is highly correlated with both water depth and extent over shallow, vegetated water.