A Color‐Index‐Based Empirical Algorithm for Determining Particulate Organic Carbon Concentration in the Ocean From Satellite Observations. Issue 10 (21st October 2018)

• Record Type: Journal Article
• Authors: Le, Chengfeng; Zhou, Xueying; Hu, Chuanmin; Lee, Zhongping; Li, Lin; Stramski, Dariusz
• Is Part Of: Journal of geophysical research. Volume 123:Issue 10(2018)
• Page Start: 7407
• Page End: 7419
• Publication Date: 2018-10-21
• Subjects: particulate organic carbon -- three‐band reflectance difference -- blue‐green reflectance band ratio -- ocean color remote sensing -- global oceans; Oceanography -- Periodicals; 551.4605
• DOI: 10.1029/2018JC014014
• Languages: English
• ISSNs: 2169-9275

Abstract

Abstract: 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 ( Rrs ) 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 (CIPOC ), 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 Rrs . Results show that the statistical parameters characterizing the differences between the satellite‐derived POC and matchup in situ POC are similar when the CIPOC and band ratio algorithms are applied to open ocean waters where the values of CIPOC are relatively low. In coastal waters where the values of CIPOC are generally higher, the statistical parameters of algorithm performance are better for the CIPOC algorithm. In addition, because the CIPOC algorithm is less sensitive to errors and noise in the satellite‐derived Rrs, the image quality obtained with this algorithm can be improved for both open‐ocean and coastal waters. Plain Language Summary: Particulate organic carbon (POC) in the global ocean is linked to many important ocean biogeochemical processes and is responsible for large carbon fluxes. POC variations occur over a broad range of spatial (from regional to global) and temporal (from seasonal to decadal) scales due to various factors. Ocean color data acquired from satellite sensors, such as Sea‐viewing Wide‐Field‐of‐view Sensor (SeaWiFS), Moderate Resolution Imaging Spectroradiometer (MODIS), and Medium‐Resolution Imaging Spectrometer (MERIS), can be used to quantify POC, with the capability for uninterrupted long‐term observations and global coverage. This study demonstrates that the color‐index (band‐difference) approach is applicable to the POC retrieval from remote‐sensing reflectance in both open ocean and coastal waters. Key Points: A color‐index algorithm for estimating POC is proposed The performance of the algorithm is compared with the band‐ratio algorithm Optical proxies for POC estimation are discussed