Uncertainties of SeaWiFS and MODIS remote sensing reflectance: Implications from clear water measurements

Hu, Chuanmin M.; Feng, Lian; Lee, Zhongping

doi:10.1016/j.rse.2013.02.012

Cited by 110 publications

(130 citation statements)

References 55 publications

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“…We have shown that over the full range of in situ Chla, all satellite sensors estimate Chla reasonably well with median absolute percent errors below 30% and R 2 of about 0.8 or higher, which is consistent with other studies (e.g., [20]) and meets the original goal of 35% accuracy [21] set for SeaWiFS for retrieving ocean Chla in Case 1 waters. However, at medium and high Chla, the accuracy drops dramatically and in different ways for the different sensors.…”

Section: Discussionsupporting

confidence: 88%

Evaluation of Satellite Retrievals of Ocean Chlorophyll-a in the California Current

et al. 2014

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Retrievals of ocean surface chlorophyll-a concentration (Chla) by multiple ocean color satellite sensors (SeaWiFS, MODIS-Terra, MODIS-Aqua, MERIS, VIIRS) using standard algorithms were evaluated in the California Current using a large archive of in situ measurements. Over the full range of in situ Chla, all sensors produced a coefficient of determination (R 2 ) between 0.79 and 0.88 and a median absolute percent error (MdAPE) between 21% and 27%. However, at in situ Chla > 1 mg m −3 , only products from MERIS (both the ESA produced algal_1 and NASA produced chlor_a) maintained reasonable accuracy (R 2 from 0.74 to 0.52 and MdAPE from 23% to 31%, respectively), while the other sensors had R 2 below 0.5 and MdAPE higher than 36%. We show that the low accuracy at medium and high Chla is caused by the poor retrieval of remote sensing reflectance.

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Section: Discussionsupporting

confidence: 88%

Evaluation of Satellite Retrievals of Ocean Chlorophyll-a in the California Current

et al. 2014

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“…This confirms the validity of previous results based on the assumption that the atmospheric correction process does not significantly affect the noise-related error on chl-a. In opposition to previous studies which focus on ocean colour sensors (Jolivet et al 2007;Hu, Feng, and Lee 2013), we demonstrated that noise-related uncertainty derived from atmospheric correction is less important than the direct effect of noise associated to TOA measurements in the visible range. This FCI specificity can be explained by the application of a dedicated atmospheric correction algorithm and by the radiometric performance of the FCI sensor which is far from that of typical ocean colour sensors.…”

Section: Impact Of Atmospheric Correction On Chl-a Final Errorcontrasting

confidence: 99%

The potential use of geostationary MTG/FCI to retrieve chlorophyll-a concentration at high temporal resolution for the open oceans

Lavigne

Ruddick

2018

International Journal of Remote Sensing

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In a few years, the Flexible Combined Imager (FCI) on-board Meteosat Third Generation will provide images of European Seas, the Atlantic Ocean, and the Mediterranean Sea every 2.5 min (regions above 30°N) or 10 min (full disk). Although dedicated to meteorological applications, this sensor has blue, green, and red spectral bands allowing to consider the adaptation of a bandratio algorithm to retrieve chlorophyll-a concentration (chl-a). However, the radiometric specification of the FCI sensor is far from the minimum requirement recommended for ocean colour sensors and the validity of FCI data for oceanic applications is not clear. This present article aims to determine if, and under which conditions, chl-a could be estimated from FCI data. From the National Aeronautics and Space Administration bio-Optical Marine Algorithm data set in situ data set, a blue green bandratio algorithm adapted to FCI spectral characteristics is proposed. Then, the impact of FCI radiometric noise on chl-a estimations is investigated in detail. Results show that noise-induced chl-a error increases with chl-a and solar zenith angle. For a chl-a estimation based on a unique pixel, this error ranges between 20% and 100% which prevents any direct utilisation and suggests that it is necessary to degrade the spatio-temporal resolution to obtain an acceptable noise-related uncertainty on chl-a. With a spatial (9 pixels) and temporal (1 h) averaging process, chl-a can be estimated with a noise-induced error less than 10% for chl-a up to 5 mg m −3 and solar zenith angle lower than 60°. Our analysis also showed that the noise-related error associated to the atmospheric correction process can be neglected compared to the radiometric noise of the visible bands themselves if it is assumed that aerosol type is uniform over large areas (9 km × 9 km boxes). ARTICLE HISTORY

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“…Some manual elimination of false positives was also needed for SeaWiFS data, particularly along cloud edges. SeaWiFS data have lower signal-to-noise ratio than MODIS data and therefore higher levels of noise variance (Hu et al, 2013). Some areas, e.g., the Bay of Gdansk and the Gulf of Riga, are often turbid (e.g., Liblik and Lips, 2011) and the detection of cyanobacteria accumulations there is sometimes ambiguous.…”

Section: Ocean Color Sensorsmentioning

confidence: 99%

Multidecadal time series of satellite-detected accumulations of cyanobacteria in the Baltic Sea

2014

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Abstract. Cyanobacteria, primarily of the species Nodularia spumigena, form extensive surface accumulations in the Baltic Sea in July and August, ranging from diffuse flakes to dense surface scums. The area of these accumulations can reach ∼ 200 000 km 2 . We describe the compilation of a 35-year-long time series of cyanobacteria surface accumulations in the Baltic Sea using multiple satellite sensors. This appears to be one of the longest satellite-based time series in biological oceanography. The satellite algorithm is based on remote sensing reflectance of the water in the red band, a measure of turbidity. Validation of the satellite algorithm using horizontal transects from a ship of opportunity showed the strongest relationship with phycocyanin fluorescence (an indicator of cyanobacteria), followed by turbidity and then by chlorophyll a fluorescence. The areal fraction with cyanobacteria accumulations (FCA) and the total accumulated area affected (TA) were used to characterize the intensity and extent of the accumulations. The fraction with cyanobacteria accumulations was calculated as the ratio of the number of detected accumulations to the number of cloud-free sea-surface views per pixel during the season (July-August). The total accumulated area affected was calculated by adding the area of pixels where accumulations were detected at least once during the season. The fraction with cyanobacteria accumulations and TA were correlated (R 2 = 0.55) and both showed large interannual and decadalscale variations. The average FCA was significantly higher for the second half of the time series (13.8 %, 1997-2013) than for the first half (8.6 %, 1979-1996). However, that does not seem to represent a long-term trend but decadal-scale oscillations. Cyanobacteria accumulations were common in the 1970s and early 1980s (FCA between 11-17 %), but rare (FCA below 4 %) during 1985-1990; they increased again starting in 1991 and particularly in 1999, reaching maxima in FCA (∼ 25 %) and TA (∼ 210 000 km 2 ) in 2005 and 2008. After 2008, FCA declined to more moderate levels (6-17 %). The timing of the accumulations has become earlier in the season, at a mean rate of 0.6 days per year, resulting in approximately 20 days advancement during the study period. The interannual variations in FCA are positively correlated with the concentration of chlorophyll a during July-August sampled at the depth of ∼ 5 m by a ship of opportunity, but interannual variations in FCA are more pronounced as the coefficient of variation is over 5 times higher.

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Uncertainties of SeaWiFS and MODIS remote sensing reflectance: Implications from clear water measurements

Cited by 110 publications

References 55 publications

Evaluation of Satellite Retrievals of Ocean Chlorophyll-a in the California Current

Evaluation of Satellite Retrievals of Ocean Chlorophyll-a in the California Current

The potential use of geostationary MTG/FCI to retrieve chlorophyll-a concentration at high temporal resolution for the open oceans

Multidecadal time series of satellite-detected accumulations of cyanobacteria in the Baltic Sea

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