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. 2016 Dec 7;16(12):2075.
doi: 10.3390/s16122075.

Chlorophyll-a Estimation Around the Antarctica Peninsula Using Satellite Algorithms: Hints from Field Water Leaving Reflectance

Chen Zeng  1   2 , Huiping Xu  3   4 , Andrew M Fischer  5
Affiliations

Chlorophyll-a Estimation Around the Antarctica Peninsula Using Satellite Algorithms: Hints from Field Water Leaving Reflectance

Chen Zeng et al. Sensors (Basel). .

Abstract

Ocean color remote sensing significantly contributes to our understanding of phytoplankton distribution and abundance and primary productivity in the Southern Ocean (SO). However, the current SO in situ optical database is still insufficient and unevenly distributed. This limits the ability to produce robust and accurate measurements of satellite-based chlorophyll. Based on data collected on cruises around the Antarctica Peninsula (AP) on January 2014 and 2016, this research intends to enhance our knowledge of SO water and atmospheric optical characteristics and address satellite algorithm deficiency of ocean color products. We collected high resolution in situ water leaving reflectance (±1 nm band resolution), simultaneous in situ chlorophyll-a concentrations and satellite (MODIS and VIIRS) water leaving reflectance. Field samples show that clouds have a great impact on the visible green bands and are difficult to detect because NASA protocols apply the NIR band as a cloud contamination threshold. When compared to global case I water, water around the AP has lower water leaving reflectance and a narrower blue-green band ratio, which explains chlorophyll-a underestimation in high chlorophyll-a regions and overestimation in low chlorophyll-a regions. VIIRS shows higher spatial coverage and detection accuracy than MODIS. After coefficient improvement, VIIRS is able to predict chlorophyll a with 53% accuracy.

Keywords: MODIS; VIIRS; chlorophyll-a estimation; skylight downwelling radiance; water leaving reflectance.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
(a) Map of sample distribution in SO around Antarctic Peninsula with topography from ETOPO1 [16]; (b) Map of detail sample distribution during 10 February to 19 February 2014; (c) Map of detail sample distribution during 6 January to 27 January 2016.
Figure 2
Figure 2
Skylight downwelling radiance distribution from AP and MODTRAN simulation. The histogram is the skylight1 subtracting from the MODTRAN Mid-Latitude summer simulation.
Figure 3
Figure 3
Water leaving reflectance spectrums around AP. The dot line is from our measurements, the lower three dash dot lines are in situ water spectrums in AP from Dierssen and Smith [8], and the other three dash dot lines with asterisk in legend are global water spectrum models from Morel and Maritorena [15]. The value ahead of each line is blue-green band ratio, max(Rrs(blue,443), Rrs(blue,488))/Rrs(green,555).
Figure 4
Figure 4
Blue-green band ratio model sensitivity on chlorophyll-a estimation, calculated from estimated residual error.
Figure 5
Figure 5
(a) Correlations between Rrs band ratio and in situ chlorophyll-a for MODIS matching pairs and its corresponding in situ Rrs; (b) Correlations between MODIS and in situ blue and green band Rrs; (c) Correlations between Rrs band ratio and in situ chlorophyll-a for VIIRS matching pairs and its corresponding in situ Rrs; (d) Correlations between VIIRS and in situ blue and green band Rrs; (e) Correlations between satellite estimated chlorophyll-a (VIIRS and MODIS, respectively) and in situ chlorophyll-a; (f) Correlations between various algorithms estimated chlorophyll-a and in situ chlorophyll-a. Algorithms include OC3M, OC3V, in situ Rrs band ratio algorithm (this manuscript), VIIRS Rrs band ratio algorithm (this manuscript).
Figure 5
Figure 5
(a) Correlations between Rrs band ratio and in situ chlorophyll-a for MODIS matching pairs and its corresponding in situ Rrs; (b) Correlations between MODIS and in situ blue and green band Rrs; (c) Correlations between Rrs band ratio and in situ chlorophyll-a for VIIRS matching pairs and its corresponding in situ Rrs; (d) Correlations between VIIRS and in situ blue and green band Rrs; (e) Correlations between satellite estimated chlorophyll-a (VIIRS and MODIS, respectively) and in situ chlorophyll-a; (f) Correlations between various algorithms estimated chlorophyll-a and in situ chlorophyll-a. Algorithms include OC3M, OC3V, in situ Rrs band ratio algorithm (this manuscript), VIIRS Rrs band ratio algorithm (this manuscript).
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