Final published version
Licence: CC BY-NC-ND: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Article number | 100055 |
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<mark>Journal publication date</mark> | 30/06/2022 |
<mark>Journal</mark> | Science of Remote Sensing |
Volume | 5 |
Publication Status | Published |
Early online date | 20/05/22 |
<mark>Original language</mark> | English |
Continuous monitoring of water bodies is critical for a range of applications including water resource management, natural hazard assessment and climate change analysis. GOES-16 geostationary satellite Advanced Baseline Imager (ABI) imagery has a very fine (per 10 min) temporal resolution, which is essential for frequent monitoring of water body changes. However, GOES-16 ABI imagery has not been applied for water body mapping, as it lacks a green waveband, essential for the construction of water index images used for surface water extraction. Moreover, the spatial resolution of GOES-16 ABI imagery varies from 0.5 km to 2 km, with only the red band having a spatial resolution of 0.5 km. To solve these problems, we constructed a green-like band and applied component substitution (CS), multiresolution analysis (MRA) and geostatistical-based pansharpening to increase the spatial resolution of the original GOES-16 ABI imagery. The results show that the addition of the constructed green-like band increased significantly the accuracy of water body mapping with water indices such as the NDWI and MNDWI. Sharpening the 1 km bands using the 0.5 km red band of GOES-16 ABI imagery further increased the accuracy of water body mapping with greater spatial detail. Notably, experiments performed in this research demonstrated that the order of the two steps (i.e. green-like band construction and multi-spectral image pansharpening), also influences the accuracy of water body mapping. Due to the different ability to preserve spectral information, the strategy of pansharpening-then-construction was more suitable for MRA-based pansharpening, while the opposite was true for CS-based methods, with no significant difference for geostatistical-based methods. An additive wavelet luminance proportion (AWLP) approach in the pansharpening-then-construction strategy was applied to produce 0.5 km time-series water body maps every 10 min within a day for the main part of the Amazon River, as it produced a water body map with the greatest accuracy. The downscaled GOES-16 ABI imagery, with its constructed and sharpened green-like band, has great potential for near real-time fine-scale mapping of surface water bodies.