A Color-index Based Empirical Algorithm For Estimating POC In Global Ocean

Particulate organic carbon(POC)is an organic particulate matter in seawater which is consisted of biological and non-biological components.POC plays an important role in the marine carbon cycle,and more and more attention has been paid to related researches.Ocean color remote sensing provides an effective way to obtain large-scale data in a short time,and plays an important role in monitoring the distribution and dynamic change of POC in the sea area.Although there are many algorithms for deriving POC concentration in the ocean from satellite observations,the algorithm suitable for global ocean water can be further improved.Based on the in situ POC and the match-up satellite remote sensing reflectance data(Rrs),an empirical algorithm for estimating POC in sea surface based on color index(CIPOC)is established and validated.The three band-difference algorithm was originally developed to estimate the concentration of chlorophyll-a in open ocean.This approach is based on optical classification and utilizes three spectral bands centered approximately at 490 nm,550 nm,and 670 nm to determine a color index(CIPOC)from which POC can be derived from different ocean color sensors,such as SeaWiFS,MODIS/Aquaand MERIS.The current remote sensing inversion of global POC is the standard blue-green band ratio(BG)algorithm.For objective comparison,the new BG algorithm is also formulated using the same dataset of in situ POC and satellite-derived Rrs and the same classification method.After using SeaWiFS with a total of 297 data to establish the two different inversion model,SeaWiFS(455),MODIS/Aqua(899),and MERIS(72)data are used for validated.Results show that the statistical parameters characterizing the differences between the in situ POC and the matchup satellite-derived POC are similar when the CIPOC and BG algorithms are applied to open ocean where the values of CIPOC are relatively low.The statistical parameters of algorithm performance are better for the CIPOC algorithm in coastal waters where the values of CIPOC are generally higher.In addition,we also use long-term observations,global monthly average image data and regional image data for further applicability evaluation.It is also found that CIPOC algorithm performs well in coastal waters.Moreover,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 in both the open ocean areas and the coastal waters.