We have, for the first time, developed a simple linear regression model based on PALSAR ScanSAR backscattering coefficients (σ0), water levels from Envisat altimetry, and MODIS Vegetation Continuous Field (VCF) product to generate water depth maps over flooded forest in the central Congo Basin. The water depth maps we generated are relative to the lowest water level from Envisat altimetry, which is assumed to be a base level with essentially zero depth. The predicted and observed water depths along the Envisat pass showed excellent agreements with RMS differences of 12.8cm to 17.8cm. The water depth maps were also independently validated with ∂h/∂t obtained from PALSAR interferometry, and storage anomalies estimated by multiplying inundation extents from PALSAR ScanSAR with spatially averaged water level anomalies from Envisat altimetry. The water storage volumes calculated from our water depth maps were calculated to be 11.3±2.0km3, 10.3±2.3km3, and 9.3±1.8km3 for 12/05/2006, 12/08/2007, and 12/10/2008, respectively. It is expected that our method can be applied to other river basins that have flooded forest with satellite radar altimeter over-passes. The water depth maps can be directly used to calibrate and validate the spatial and temporal variation of inundation extent and water depth in the wetland derived from a 2-D hydrodynamic model. Furthermore, our water depth maps can be used as a "true" dataset to perform a pre-launch "virtual mission" study of the Surface Water Ocean Topography (SWOT) mission to be launched in 2020.
Bibliographical notePublisher Copyright:
© 2014 Elsevier Inc.
All Science Journal Classification (ASJC) codes
- Soil Science
- Computers in Earth Sciences