Evaluation of CryoSat-2 water level derived from different retracking scenarios over selected inland water bodies

As the first satellite altimetry mission operating in sar (delay-Doppler) mode, CryoSat-2 is an interesting mission to analyze its performance for water level monitoring over inland water bodies. It offers the opportunity to make comparison of sar and conventional altimeters by a multi-mode altimeter mounted on the same platform with a long repeat orbit. This comparison gives us more knowledge about the performance of the sar altimeter. Even tough it is not possible to perform it over same objects. In this paper we analyze the CryoSat-2 performance for water level monitoring via full- and sub-waveform retracking against in-situ gauge and L2 products of other satellite altimetry missions, e.g. Envisat and Jason-2. To this end, we retrack the full-waveforms and sub-waveforms with different empirical and physical retracking algorithms such as ocog , threshold, β -parameters and samosa 3. We evaluate its capability in all measurement modes, i.e. lrm , sar and sari n, over inland water bodies located in different climatic zones. We selected study areas with different shapes and sizes. Relative to in situ measurements we find a precision of the CryoSat-2 lrm mode of 15 cm rms over Qinghai lake (China) and 13 cm over Erie lake ( usa ). This is an improvement over Envisat, yielding precision of 17 cm in both cases. For the sar mode over Neagh lake (Northern Ireland) and Derg lake (Ireland) we obtain 15 cm and 13 cm where Envisat yields 28 cm and 100 cm , respectively. The sari n mode’s precision is assessed over Nasser lake (Egypt) with 25 cm rms and Athabasca lake (Canada) with 16 cm . Over these lakes Jason-2 achieved 54 cm and Envisat 19 cm , respectively. The most precise results of CryoSat-2 are obtained with our retracking and sub-waveform selection scenarios. Comparing CryoSat-2 results from our retracking scenarios using L1b data with those results obtained from L2 products (data) of this mission shows an improvement of 4–17 cm. The minimum and maximum improvements belong to Erie and Derg lakes respectively, the largest and smallest lakes. From the waveform analysis over lakes with different shapes and sizes, we found that the first and the mean-all sub-waveforms (mean correction from all sub-waveforms) retracked with the threshold and samosa 3 (only for sar mode) retrackers are appropriate to retrieve water level variation of small lakes and complex shaped lakes in this study. Over large lakes the full-waveform retracking leads to better results. In the case of icy-lake objects, sub-waveform retracking scenarios (the first and mean-all sub-waveforms) are more precise than the other scenarios. These are our findings over few samples, though more samples need to be analyzed to support them strongly.