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A Color‐Index‐Based Empirical Algorithm for Determining Particulate Organic Carbon Concentration in the Ocean From Satellite Observations
Author(s) -
Le Chengfeng,
Zhou Xueying,
Hu Chuanmin,
Lee Zhongping,
Li Lin,
Stramski Dariusz
Publication year - 2018
Publication title -
journal of geophysical research: oceans
Language(s) - English
Resource type - Journals
eISSN - 2169-9291
pISSN - 2169-9275
DOI - 10.1029/2018jc014014
Subject(s) - ocean color , satellite , algorithm , particulate organic carbon , environmental science , remote sensing , particulates , data set , chlorophyll a , computer science , geology , phytoplankton , artificial intelligence , physics , chemistry , organic chemistry , astronomy , nutrient , biochemistry
An empirical algorithm for estimating particulate organic carbon (POC) concentration in the surface ocean from satellite observations is formulated and validated using in situ POC data and remote‐sensing reflectance ( R rs ) data obtained from match‐up satellite ocean color measurements. The algorithm builds upon the band‐difference algorithm concept, which was originally developed for estimating chlorophyll‐a concentration in clear waters. This algorithm utilizes three spectral bands centered approximately at 490, 550, and 670 nm to determine a color index (CI POC ), from which POC can be estimated from satellite measurements. For comparison, the blue‐green band‐ratio algorithm is also formulated using the same data set of in situ POC and satellite‐derived R rs . Results show that the statistical parameters characterizing the differences between the satellite‐derived POC and matchup in situ POC are similar when the CI POC and band ratio algorithms are applied to open ocean waters where the values of CI POC are relatively low. In coastal waters where the values of CI POC are generally higher, the statistical parameters of algorithm performance are better for the CI POC algorithm. In addition, because the CI POC algorithm is less sensitive to errors and noise in the satellite‐derived R rs , the image quality obtained with this algorithm can be improved for both open‐ocean and coastal waters.