Sentinel-1&2 Multitemporal Water Surface Detection Accuracies, Evaluated at Regional and Reservoirs Level
| Autor: | Sylvain Ferrant, Thomas Ledauphin, Mauricio C. R. Cordeiro, Santiago Peña-Luque, Jerome Maxant, Jean-Michel Martinez |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Science
Detector water cycle water surfaces reservoirs multi-temporal water detection radar imaging optical imaging area monitoring Context (language use) law.invention Water scarcity law Radar imaging General Earth and Planetary Sciences Environmental science Satellite Radar Water cycle Scale (map) Remote sensing |
| Zdroj: | Remote Sensing, Vol 13, Iss 3279, p 3279 (2021) Remote Sensing; Volume 13; Issue 16; Pages: 3279 |
| ISSN: | 2072-4292 |
| Popis: | Water stock monitoring is a major issue for society on a local and global scale. Sentinel-1&2 satellites provide frequent acquisitions to track water surface dynamics, proxy variables to enable water surface volume monitoring. How do we combine such observations along time for each sensor? What advantages and disadvantages of single-date, monthly or time-windowed estimations? In this context, we analysed the impact of merging information through different types and lengths of time-windows. Satellite observations were processed separately on optical (Sentinel-2) and radar (Sentinel-1) water detectors at 10 m resolution. The analysis has been applied at two scales. First, validating with 26 large scenes (110 × 110 km) in different climatic zones in France, time-windows yielded an improvement on radar detection (F1-score improved from 0.72 to 0.8 for 30 days on average logic) while optical performances remained stable (F1-score 0.89). Second, validating reservoir area estimations with 29 instrumented reservoirs (20–1250 ha), time-windows presented in all cases an improvement on both optical and radar error for any window length (5–30 days). The mean relative absolute error in optical area detection improved from 16.9% on single measurements to 12.9% using 15 days time-windows, and from 22.15% to 15.1% in radar detection). Regarding reservoir filling rates, we identified an increased negative bias for both sensors when the reservoir is nearly full. This work helped to compare accuracies of separate optical and radar capabilities, where optical statistically outperforms radar at both local and large scale to the detriment of less frequent measurements. Furthermore, we propose a geomorphological indicator of reservoirs to predict the quality of radar area monitoring (R2 = 0.58). In conclusion, we suggest the use of time-windows on operational water mapping or reservoir monitoring systems, using 10–20 days time-windows with average logic, providing more frequent and faster information to water managers in periods of crisis (e.g., water shortage) compared to monthly estimations. |
| Databáze: | OpenAIRE |
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