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
in the Artic Sea, where the Chl-a concentration varied from 0.01 to 5.0 mg m-3, aCDOM at
400 nm from 0.01 to 1 m-1, and aph at 400 nm from 0.005 to 5 m-1. 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_case1_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
MODIS_OC3_Chl
Chl-a
MNB
(%)
6.40
MODIS_DC_Chl
(Default case)
Chl-a
6.44
38
24
MODIS_DAAC-v4_
Chl
MODIS_case-1_Chl
Chl-a
6.25
38.45
24
Chl-a
For low_X
For high_X
Chl-a
11
12
9.4
60.60
65
55.57
24
24
24
Chl-a
6.61
38.8
24
CZCS-pigm for low_X
For high_X
4.5
9
31.25
52.8
24
MODIS_case2_Chl
MODIS_DC_case2_Chl
(Default case)
MODIS_CZCS-Chl
RMS
(%)
39
24
Table 1. Summary of error analysis for the standard MODIS_Chl algorithms in the Arctic
waters.
Parameter
MNB
(%)
aCDOM (400) 10.47
a
ph
(400)
130.25
RMS
(%)
55.66
N
638.083
23
23
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
mg m-3, SS = 1.2 g m-3, aCDOM = 0.1; (b) Chl = 3.7 mg m-3, SS = 3 g m-3, aCDOM = 0.2 m-1.
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)