Comparison and Validation of MODIS Bio-Optical Algorithm in the Arctic Waters

Evaluation of MODIS bio-optical algorithms in the Arctic waters

P. Shanmugam*, S.P. Tiwari*, Y.H.Ahn**, J.E. Moon** and J.H. Ryu**

*Department of Ocean Engineering, Indian Institute of Technology Madras, Chennai – 600036, India **Ocean Satellite Remote Sensing & Observation Technology Research Department, Korea Ocean Research & Development Institute, Seoul – 425 600, Korea,

Summary A data set containing chlorophyll-a (Chl-a), absorption coefficients of coloured dissolved organic matter (aCDOM) and phytoplankton (aph), and remote sensing reflectances collected from field measurements in coincidence with MODIS observations during summer 2007 and 2008 was used to evaluate the performance of several standard bio-optical algorithms -3 in the Artic Sea, where the Chl-a concentration varied from 0.01 to 5.0 mg m , aCDOM at -1 -1 400 nm from 0.01 to 1 m , and aph at 400 nm from 0.005 to 5 m . Comparison of MODIS-observed remote sensing reflectances with in situ measurements showed good correlation at regional level, but with significant overestimation at 412nm and 443nm and underestimation at 551nm and 667nm wavebands. It was traced that higher MODIS remote sensing reflectances were likely caused by sub-pixel/adjacent effects of the ice cover in the region and improbable negative remote sensing reflectances in the blue bands by sub-pixel cloud contamination and known atmospheric correction failure in high latitude waters. All the MODIS pigment algorithms examined showed a systematic and significant overestimation particularly in low chlorophyll regimes, whereas MODIS_CZCS_Chl and MODIS_DAAC-v4_Chl algorithms yielded lower mean bias (MNB) and RMS errors than other algorithms. The performance of MODIS_OC3_Chl, MODIS_DC_Chl (Default case), and MODIS_DC_case-2_Chl (Default case) were however found relatively satisfactory than that of MODIS_case2_Chl and MODIS_case- 1_Chl algorithms in these waters (Table 1). The algorithms for estimating the absorption coefficients of CDOM and phytoplankton showed the worst performance among all the algorithms examined, with MNB and RMS error of 10.5% and 55.66% for aCDOM and 130% and 638% for aph (Table 2) This suggests the apparent problems of the standard bio-optical algorithms and that new approaches for ocean colour algorithms are required in the high latitude Arctic Sea. The analysis also reveals that the atmospheric correction currently in use for MODIS usually fails to retrieve upwelling radiances emerging from the Arctic Sea and the cloud detection algorithm neglects to mask the contaminated pixels by clouds (Fig. 1). Algorithm Field data N MNB RMS (%) (%) MODIS_OC3_Chl Chl-a 6.40 39 24

MODIS_DC_Chl Chl-a 6.44 38 24 (Default case)

MODIS_DAAC-v4_ Chl-a 6.25 38.45 24 Chl MODIS_case-1_Chl Chl-a For low_X 11 60.60 24 For high_X 12 65 24 MODIS_case2_Chl Chl-a 9.4 55.57 24

MODIS_DC_case- Chl-a 6.61 38.8 24 2_Chl (Default case) MODIS_CZCS-Chl CZCS-pigm for low_X 4.5 31.25 24 For high_X 9 52.8

Table 1. Summary of error analysis for the standard MODIS_Chl algorithms in the Arctic waters.

Parameter MNB RMS N (%) (%)

aCDOM (400) 10.47 55.66 23

a (400) 130.25 638.083 23 ph Table 2. Summary of error analysis for the standard MODIS CDOM and phytoplankton absorption algorithms in the Arctic waters. Fig. 1. Comparison of MODIS remote sensing reflectance with in-situ data. (a) Chl = 1.9 -3 -3 -3 -3 -1 mg m , SS = 1.2 g m , aCDOM = 0.1; (b) Chl = 3.7 mg m , SS = 3 g m , aCDOM = 0.2 m . The difference between MODIS remote sensing reflectance with in-situ data is likely due to adjacency effect and possibly sub-pixel ice contamination (a) and sub-pixel contamination of clouds (b)