TY - JOUR

T1 - Near real-time surface water extraction from GOES-16 geostationary satellite ABI images by constructing and sharpening the green-like band

AU - Wang, X.

AU - Gong, J.

AU - Zhang, Y.

AU - Atkinson, P.M.

PY - 2022/6/30

Y1 - 2022/6/30

N2 - 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.

AB - 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.

KW - Water body extraction

KW - Near real-time

KW - GOES-16 ABI imagery

KW - Green-like band construction

KW - Geostationary satellite

U2 - 10.1016/j.srs.2022.100055

DO - 10.1016/j.srs.2022.100055

M3 - Journal article

VL - 5

JO - Science of Remote Sensing

JF - Science of Remote Sensing

SN - 2666-0172

M1 - 100055

ER -