Day 4

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Paper title Estimation of inter-satellite and inter-track biases of satellite altimetry missions over lakes and reservoirs using surface area from satellite imagery
Authors
  1. Mohammad J. Tourian University of Stuttgart Speaker
  2. Sajedeh Behnia Stuttgart University
  3. Shuhua Yu University of Stuttgart
  4. Omid Elmi Institute of Geodesy, University of Stuttgart
  5. Nico Sneeuw University of Stuttgart, Institute of Geodesy
Form of presentation Poster
Topics
  • A7. Hydrology and Water Cycle
    • A7.01 Inland Water Storage and Runoff: Modeling, In Situ Data and Remote Sensing
Abstract text While multimission satellite altimetry over inland waters has been known and used for more than two decades, monitoring of lakes and reservoirs is far from fully operational. Depending on the spatiotemporal coverage offered by altimetry missions, the concept has its fundamental limitations. However, for medium to large water bodies where altimetry can provide meaningful information, the multimission is still hampered by what is known as inter-satellite bias. Studies have been performed to quantify absolute altimetry biases at calibration sites and relative altimetry biases on a global scale. However, a thorough understanding of the biases between satellites over inland waters has not yet been achieved.

We explore the possibility of resolving the biases between satellites over lakes and reservoirs. Our solution for estimating the biases between overlapping and non-overlapping time series of water levels from different missions and tracks is to rely on the time series of surface area derived from the satellite imagery. The area estimated by the imagery acts as an anchor for the water level variations, making the area-height relationship the basis for estimating the relative biases. We estimate the relative biases by modeling the area-height relationship within a Gauss-Helmert model conditioned on an inequality constraint. For the estimation, we use the expectation maximization algorithm that provides a robust estimate by iteratively adjusting the weights of the observations.

We evaluate our method on a limited number of lakes and reservoirs and validate the results against in situ water level data. Our results show the presence of inter-satellite and also inter-track biases at the decimeter level, which are different from the global bias estimates.