The continuity of ocean color measurements from SeaWiFS to MODIS

Franz, Bryan A.; Werdell, P. Jeremy; Meister, Gerhard; Bailey, Sean W.; Eplee, Robert E.; Feldman, Gene C.; Kwiatkowska, Ewa; McClain, Charles R.; Patt, Frederick S.; Thomas, D. J.

doi:10.1117/12.620069

Cited by 74 publications

(62 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Figure 5 illustrates example results obtained with the application of our POC band ratio algorithms to the satellite imagery from SeaWiFS. Global SeaWiFS data spanning the months of January and July 2005 (900 orbits) were processed from observed top-of-atmosphere radiance to remotesensing reflectance at the native sensor resolution using the standard calibration and atmospheric correction algorithms associated with SeaWiFS Reprocessing 5.1 (Franz et al, 2005). POC was derived from the retrieved satellite R rs using the POC algorithms based on R rs (443)/R rs (555) and R rs (490)/R rs (555) (see the upper part of Table 2, all data).…”

Section: Resultsmentioning

confidence: 99%

Relationships between the surface concentration of particulate organic carbon and optical properties in the eastern South Pacific and eastern Atlantic Oceans

et al. 2008

View full text Add to dashboard Cite

Abstract. We have examined several approaches for estimating the surface concentration of particulate organic carbon, POC, from optical measurements of spectral remotesensing reflectance, R rs (λ), using field data collected in tropical and subtropical waters of the eastern South Pacific and eastern Atlantic Oceans. These approaches include a direct empirical relationship between POC and the blue-to-green band ratio of reflectance, R rs (λ B )/R rs (555), and two-step algorithms that consist of relationships linking reflectance to an inherent optical property IOP (beam attenuation or backscattering coefficient) and POC to the IOP. We considered two-step empirical algorithms that exclusively include pairs of empirical relationships and two-step hybrid algorithms that consist of semianalytical models and empirical relationships. The surface POC in our data set ranges from about 10 mg m −3 within the South Pacific Subtropical Gyre to 270 mg m −3 in the Chilean upwelling area, and ancillary data suggest a considerable variation in the characteristics of particulate assemblages in the investigated waters. The POC algorithm based on the direct relationship between POC and R rs (λ B )/R rs (555) promises reasonably good performance in the vast areas of the open ocean covering different provincesCorrespondence to: D. Stramski (dstramski@ucsd.edu) from hyperoligotrophic and oligotrophic waters within subtropical gyres to eutrophic coastal upwelling regimes characteristic of eastern ocean boundaries. The best error statistics were found for power function fits to the data of POC vs. R rs (443)/R rs (555) and POC vs. R rs (490)/R rs (555). For our data set that includes over 50 data pairs, these relationships are characterized by the mean normalized bias of about 2% and the normalized root mean square error of about 20%. We recommend that these algorithms be implemented for routine processing of ocean color satellite data to produce maps of surface POC with the status of an evaluation data product for continued work on algorithm development and refinements. The two-step algorithms also deserve further attention because they can utilize various models for estimating IOPs from reflectance, offer advantages for developing an understanding of bio-optical variability underlying the algorithms, and provide flexibility for regional or seasonal parameterizations of the algorithms.

show abstract

Section: Resultsmentioning

confidence: 99%

Relationships between the surface concentration of particulate organic carbon and optical properties in the eastern South Pacific and eastern Atlantic Oceans

et al. 2008

View full text Add to dashboard Cite

show abstract

“…However, based on the experience of the ocean color community (e.g., Franz et al, 2005Franz et al, , 2012, consistent algorithms may help. First, however, we have to create consistent input data.…”

Section: R C Levy Et Al: Long-term Global Aerosol Optical Depth Rementioning

confidence: 99%

Towards a long-term global aerosol optical depth record: applying a consistent aerosol retrieval algorithm to MODIS and VIIRS-observed reflectance

et al. 2015

View full text Add to dashboard Cite

Abstract. To answer fundamental questions about aerosols in our changing climate, we must quantify both the current state of aerosols and how they are changing. Although NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) sensors have provided quantitative information about global aerosol optical depth (AOD) for more than a decade, this period is still too short to create an aerosol climate data record (CDR). The Visible Infrared Imaging Radiometer Suite (VIIRS) was launched on the Suomi-NPP satellite in late 2011, with additional copies planned for future satellites. Can the MODIS aerosol data record be continued with VIIRS to create a consistent CDR? When compared to ground-based AERONET data, the VIIRS Environmental Data Record (V_EDR) has similar validation statistics as the MODIS Collection 6 (M_C6) product. However, the V_EDR and M_C6 are offset in regards to global AOD magnitudes, and tend to provide different maps of 0.55 µm AOD and 0.55/0.86 µm-based Ångström Exponent (AE). One reason is that the retrieval algorithms are different. Using the Intermediate File Format (IFF) for both MODIS and VIIRS data, we have tested whether we can apply a single MODISlike (ML) dark-target algorithm on both sensors that leads to product convergence. Except for catering the radiative transfer and aerosol lookup tables to each sensor's specific wavelength bands, the ML algorithm is the same for both. We run the ML algorithm on both sensors between March 2012 and May 2014, and compare monthly mean AOD time series with each other and with M_C6 and V_EDR products.Focusing on the March-April-May (MAM) 2013 period, we compared additional statistics that include global and gridded 1 • × 1 • AOD and AE, histograms, sampling frequencies, and collocations with ground-based AERONET. Over land, use of the ML algorithm clearly reduces the differences between the MODIS and VIIRS-based AOD. However, although global offsets are near zero, some regional biases remain, especially in cloud fields and over brighter surface targets. Over ocean, use of the ML algorithm actually increases the offset between VIIRS and MODIS-based AOD (to ∼ 0.025), while reducing the differences between AE. We characterize algorithm retrievability through statistics of retrieval fraction. In spite of differences between retrieved AOD magnitudes, the ML algorithm will lead to similar decisions about "whether to retrieve" on each sensor. Finally, we discuss how issues of calibration, as well as instrument spatial resolution may be contributing to the statistics and the ability to create a consistent MODIS → VIIRS aerosol CDR.

show abstract

“…SeaWiFS data were used for the period 1998-2007, while MODIS Aqua data were used after July 2007. MODIS and SeaWiFS data sets were already shown to be consistent (Franz et al, 2005). The resulting 16-year satellite database was initially divided on a yearly basis (from July of year T −1 to late June of year T ) and a 3-week (i.e., 24 days) moving average was applied.…”

Section: Datamentioning

confidence: 99%

Interannual variability of the Mediterranean trophic regimes from ocean color satellites

et al. 2016

View full text Add to dashboard Cite

Abstract. D'Ortenzio and Ribera d'Alcalà (2009, DR09hereafter) divided the Mediterranean Sea into "bioregions" based on the climatological seasonality (phenology) of phytoplankton. Here we investigate the interannual variability of this bioregionalization. Using 16 years of available ocean color observations (i.e., SeaWiFS and MODIS), we analyzed the spatial distribution of the DR09 trophic regimes on an annual basis. Additionally, we identified new trophic regimes, exhibiting seasonal cycles of phytoplankton biomass different from the DR09 climatological description and named "Anomalous". Overall, the classification of the Mediterranean phytoplankton phenology proposed by DR09 (i.e., "No Bloom", "Intermittently", "Bloom" and "Coastal"), is confirmed to be representative of most of the Mediterranean phytoplankton phenologies. The mean spatial distribution of these trophic regimes (i.e., bioregions) over the 16 years studied is also similar to the one proposed by DR09, although some annual variations were observed at regional scale. Discrepancies with the DR09 study were related to interannual variability in the sub-basin forcing: winter deep convection events, frontal instabilities, inflow of Atlantic or Black Sea Waters and river run-off. The large assortment of phytoplankton phenologies identified in the Mediterranean Sea is thus verified at the interannual scale, further supporting the "sentinel" role of this basin for detecting the impact of climate changes on the pelagic environment.

show abstract

The continuity of ocean color measurements from SeaWiFS to MODIS

Cited by 74 publications

References 29 publications

Relationships between the surface concentration of particulate organic carbon and optical properties in the eastern South Pacific and eastern Atlantic Oceans

Relationships between the surface concentration of particulate organic carbon and optical properties in the eastern South Pacific and eastern Atlantic Oceans

Towards a long-term global aerosol optical depth record: applying a consistent aerosol retrieval algorithm to MODIS and VIIRS-observed reflectance

Interannual variability of the Mediterranean trophic regimes from ocean color satellites

Contact Info

Product

Resources

About