Contribution of ECOSTRESS thermal imagery to wetland mapping: Application to heathland ecosystems

This is the authors' abstract. We don't add key points for this paper.

• Synergy of day and night thermal ECOSTRESS time-series for wetland mapping. • ECOSTRESS imagery outperformed thermal Landsat and ASTER data for wetland mapping. • Fusion of thermal, optical and topographic satellite data improves wetland mapping. While wetlands have been extensively studied using optical and radar satellite imagery, thermal imagery has been used less often due its low spatial – temporal resolutions and challenges for emissivity estimation. Since 2018, spaceborne thermal imagery has gained interest due to the availability of ECOSTRESS data, which are acquired at 70 m spatial resolution and a 3–5 revisit time. This study aimed at comparing the contribution of ECOSTRESS time-series to wetland mapping to that of other thermal time-series (i.e., Landsat-TIRS, ASTER-TIR), Sentinel-1 SAR and Sentinel-2 optical satellite time-series, and topographical variables derived from satellite data. The study was applied to a 209 km 2 heathland site in north-western France that includes riverine, slope, and flat wetlands. The method used consisted of four steps: (i) four-year time-series (2019–2022) were aggregated into dense annual time-series; (ii) the temporal dimension was reduced using functional principal component analysis (FPCA); (iii) the most discriminating components of the FPCA were selected based on recursive feature elimination; and (iv) the contribution of each sensor time-series to wetland mapping was assessed based on the accuracy of a random forest model trained and tested using reference field data. The results indicated that an ECOSTRESS time-series that combined day and night acquisitions was more accurate (overall F1-score: 0.71) than Landsat-TIRS and ASTER-TIR time-series (overall F1-score: 0.40–0.62). A combination of ECOSTRESS thermal images, Sentinel-2 optical images, Sentinel-1 SAR images, and topographical variables outperformed the sensor-specific accuracies (overall F1-score: 0.87), highlighting the synergy of thermal, optical, and topographical data for wetland mapping.