Challenges and Evolution of Water Level Monitoring towards a Comprehensive, World-Scale Coverage with Remote Sensing
Surface water availability is a fundamental environmental variable to implement effective climate adaptation and mitigation plans, as expressed by scientific, financial and political stakeholders. Recently published requirements urge the need for homogenised access to long historical records at a gl...
| Published in: | Remote Sensing |
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| Main Authors: | , , , , |
| Format: | Article in Journal/Newspaper |
| Language: | English |
| Published: |
MDPI AG
2022
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| Subjects: | |
| Online Access: | https://doi.org/10.3390/rs14153513 https://doaj.org/article/04e794695e1d4494aeaa0a3bba71f0c0 |
| Summary: | Surface water availability is a fundamental environmental variable to implement effective climate adaptation and mitigation plans, as expressed by scientific, financial and political stakeholders. Recently published requirements urge the need for homogenised access to long historical records at a global scale, together with the standardised characterisation of the accuracy of observations. While satellite altimeters offer world coverage measurements, existing initiatives and online platforms provide derived water level data. However, these are sparse, particularly in complex topographies. This study introduces a new methodology in two steps (1) teroVIR , a virtual station extractor for a more comprehensive global and automatic monitoring of water bodies, and (2) teroWAT , a multi-mission, interoperable water level processor, for handling all terrain types. L2 and L1 altimetry products are used, with state-of-the-art retracker algorithms in the methodology. The work presents a benchmark between teroVIR and current platforms in West Africa, Kazakhastan and the Arctic: teroVIR shows an unprecedented increase from <math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>55</mn><mo>%</mo></mrow></semantics></math> to <math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>99</mn><mo>%</mo></mrow></semantics></math> in spatial coverage. A large-scale validation of teroWAT results in an average of unbiased root mean square error ubRMSE of <math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>0.638</mn></mrow></semantics></math> m on average for 36 locations in West Africa. Traditional metrics (ubRMSE, median, absolute deviation, Pearson coefficient) disclose significantly better values for teroWAT when compared with existing platforms, of the order of 8 cm and ... |
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