TY - JOUR

T1 - Automatic mapping of 500 m daily open water body fraction in the American continent using GOES-16 ABI imagery

AU - Wang, Xia

AU - Atkinson, Peter

AU - Zhang, Yihang

AU - Li, Xiaodong

AU - Zhang, Kerong

PY - 2024/4/1

Y1 - 2024/4/1

N2 - Timely and large-area monitoring of terrestrial water bodies using remote sensing data is essential to protect water resource security for humans and ecosystems. Although efforts have been made to monitor inter-monthly water bodies at the regional, continental and global scales, surface open water bodies may experience rapid changes within a short duration. At the same time, the data gaps caused by the long revisit frequencies of satellite sensor imaging systems and the extremely cloudy and rainy weather in certain areas mean that a suitable operational method for recording such rapid changes over large areas does not yet exist. The recently launched operational geostationary satellite sensor GOES-16 ABI can provide multi-scale optical images every 10 min in the Americas, and it has great potential to provide a new solution for timely open surface water monitoring. In this research, a framework based on a multi-scale non-linear mixture model (NLMM) was proposed for the automatic estimation of 500 m daily open water body fraction maps across the full extent of the American continent from GOES-16 ABI imagery. Specifically, an exterior spectral library was created firstly from many pairs of training GOES-16 ABI images and 500 m water body fraction maps derived from Landsat images, and an inherited interior pure spectral library was then derived from a pair of the testing GOES-16 ABI image and the corresponding pure water and non-water (i.e., vegetation and soil/rock substrate) fractions extracted from the monthly water recurrence dataset. Furthermore, a synthetic interior mixed spectral library was built finally based on the linear and non-linear mixtures of randomly selected pure endmembers of water and non-water (i.e., vegetation and soil/rock substrate) in the testing image. All the exterior and interior pure and synthetic mixed spectral libraries were employed jointly in the NLMM machine learning method for estimating daily water body fractions with GOES-16 ABI images. Experiments show that the integration of all three different spectral libraries produced 500 m open water body fraction maps with the greatest accuracies. Compared to traditional linear spectral unmixing and a water index thresholding method, the proposed method produced more accurate results both quantitatively and visually. Moreover, the proposed method can produce time-series daily 500 m open water body fraction maps with minimal cloud coverage for the full extent of the Americas by combining dense GOES-16 ABI images at half-hour intervals, opening up great possibilities for near real-time rapid open water body changes monitoring, such as flooding and drought, over large areas, even with a high frequency of cloud cover.

AB - Timely and large-area monitoring of terrestrial water bodies using remote sensing data is essential to protect water resource security for humans and ecosystems. Although efforts have been made to monitor inter-monthly water bodies at the regional, continental and global scales, surface open water bodies may experience rapid changes within a short duration. At the same time, the data gaps caused by the long revisit frequencies of satellite sensor imaging systems and the extremely cloudy and rainy weather in certain areas mean that a suitable operational method for recording such rapid changes over large areas does not yet exist. The recently launched operational geostationary satellite sensor GOES-16 ABI can provide multi-scale optical images every 10 min in the Americas, and it has great potential to provide a new solution for timely open surface water monitoring. In this research, a framework based on a multi-scale non-linear mixture model (NLMM) was proposed for the automatic estimation of 500 m daily open water body fraction maps across the full extent of the American continent from GOES-16 ABI imagery. Specifically, an exterior spectral library was created firstly from many pairs of training GOES-16 ABI images and 500 m water body fraction maps derived from Landsat images, and an inherited interior pure spectral library was then derived from a pair of the testing GOES-16 ABI image and the corresponding pure water and non-water (i.e., vegetation and soil/rock substrate) fractions extracted from the monthly water recurrence dataset. Furthermore, a synthetic interior mixed spectral library was built finally based on the linear and non-linear mixtures of randomly selected pure endmembers of water and non-water (i.e., vegetation and soil/rock substrate) in the testing image. All the exterior and interior pure and synthetic mixed spectral libraries were employed jointly in the NLMM machine learning method for estimating daily water body fractions with GOES-16 ABI images. Experiments show that the integration of all three different spectral libraries produced 500 m open water body fraction maps with the greatest accuracies. Compared to traditional linear spectral unmixing and a water index thresholding method, the proposed method produced more accurate results both quantitatively and visually. Moreover, the proposed method can produce time-series daily 500 m open water body fraction maps with minimal cloud coverage for the full extent of the Americas by combining dense GOES-16 ABI images at half-hour intervals, opening up great possibilities for near real-time rapid open water body changes monitoring, such as flooding and drought, over large areas, even with a high frequency of cloud cover.

KW - Open water body fraction

KW - GOES-16 ABI

KW - Non-linear spectral unmixing

KW - Machine learning

KW - Flooding

KW - Drought

U2 - 10.1016/j.rse.2024.114040

DO - 10.1016/j.rse.2024.114040

M3 - Journal article

VL - 304

JO - Remote Sensing of Environment

JF - Remote Sensing of Environment

SN - 0034-4257

M1 - 114040

ER -