TY - JOUR
T1 - Global Estimation of Suspended Particulate Matter From Satellite Ocean Color Imagery
AU - Wei, Jianwei
AU - Wang, Menghua
AU - Jiang, Lide
AU - Yu, Xiaolong
AU - Mikelsons, Karlis
AU - Shen, Fang
N1 - Publisher Copyright:
© 2021. The Authors.
PY - 2021/8
Y1 - 2021/8
N2 - The suspended particulate matter (SPM) concentration (unit: mg l−1) in surface waters is an essential measure of water quality and clarity. Satellite remote sensing provides a powerful tool to derive the SPM with synoptic and repeat coverage. In this study, we developed a new global SPM algorithm utilizing the remote sensing reflectance (Rrs(λ)) at near-infrared (NIR), red, green, and blue bands (NIR-RGB) as input. The evaluations showed that the NIR-RGB algorithm could predict SPM with the median absolute percentage difference of ∼35%–39% over a wide range from ∼0.01 to >2,000 mg l−1. The uncertainty is smaller (29%–37%) for turbid waters where Rrs(671) ≥ 0.0012 sr−1 and slightly higher (41%–44%) for clear waters where Rrs(671) < 0.0012 mg l−1. The algorithm was implemented with the global Rrs(λ) data from the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-orbiting Partnership (SNPP) satellite. We provided a brief characterization of the spatial distribution and temporal trends of the SPM products in global waters based on the monthly SPM composites. Case studies of the SPM time series in coastal and inland waters suggest that the satellite SPM estimations registered spatial and seasonal variation and episodic events in regional scales as well. The VIIRS-generated global SPM maps provide a valuable addition to the existing ocean color products for environmental and climate applications.
AB - The suspended particulate matter (SPM) concentration (unit: mg l−1) in surface waters is an essential measure of water quality and clarity. Satellite remote sensing provides a powerful tool to derive the SPM with synoptic and repeat coverage. In this study, we developed a new global SPM algorithm utilizing the remote sensing reflectance (Rrs(λ)) at near-infrared (NIR), red, green, and blue bands (NIR-RGB) as input. The evaluations showed that the NIR-RGB algorithm could predict SPM with the median absolute percentage difference of ∼35%–39% over a wide range from ∼0.01 to >2,000 mg l−1. The uncertainty is smaller (29%–37%) for turbid waters where Rrs(671) ≥ 0.0012 sr−1 and slightly higher (41%–44%) for clear waters where Rrs(671) < 0.0012 mg l−1. The algorithm was implemented with the global Rrs(λ) data from the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-orbiting Partnership (SNPP) satellite. We provided a brief characterization of the spatial distribution and temporal trends of the SPM products in global waters based on the monthly SPM composites. Case studies of the SPM time series in coastal and inland waters suggest that the satellite SPM estimations registered spatial and seasonal variation and episodic events in regional scales as well. The VIIRS-generated global SPM maps provide a valuable addition to the existing ocean color products for environmental and climate applications.
UR - https://www.scopus.com/pages/publications/85113671026
U2 - 10.1029/2021JC017303
DO - 10.1029/2021JC017303
M3 - 文章
AN - SCOPUS:85113671026
SN - 2169-9275
VL - 126
JO - Journal of Geophysical Research: Oceans
JF - Journal of Geophysical Research: Oceans
IS - 8
M1 - e2021JC017303
ER -