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1
Moore, J. Keith, Mark R. Abbott, James G. Richman, Walker O. Smith, Timothy J. Cowles, Kenneth H. Coale, Wilford D. Gardner, and Richard T. Barber. "SeaWiFS satellite ocean color data from the Southern Ocean." Geophysical Research Letters 26, no. 10 (May 15, 1999): 1465–68. http://dx.doi.org/10.1029/1999gl900242.
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Jönsson, L. "SeaWiFs satellite data analysis of Black Sea water discharge pattern into the Aegean Sea." Water Science and Technology 46, no. 8 (October 1, 2002): 195–202. http://dx.doi.org/10.2166/wst.2002.0180.
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Satellite data from the SeaWiFS sensor has been used to determine chlorophyll-a contents in the North Aegean Sea using SEADAS 3.3 software. The data is used to extract knowledge on water movements/flow phenomena using chlorophyll as a “tracer” but will also indicate water quality. More than 100 SeaWiFS scenes from 1998 up to 2001 have been analyzed in terms of hydrodynamic phenomena, mainly the transport and spreading pattern of Black Sea Water in the North Aegean Sea but also concerning the water quality and its seasonal and yearly variation at the mouth region of the Dardanelles. Some comparison with earlier studies using NOAA AVHRR thermal data and historical CZCS scenes is also made.
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Djavidnia, S., F. Mélin, and N. Hoepffner. "Comparison of global ocean colour data records." Ocean Science 6, no. 1 (January 27, 2010): 61–76. http://dx.doi.org/10.5194/os-6-61-2010.
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Abstract. The extending record of ocean colour derived information, an important asset for the study of marine ecosystems and biogeochemistry, presently relies on individual satellite missions launched by several space agencies with differences in sensor design, calibration strategies and algorithms. In this study we present an extensive comparative analysis of standard products obtained from operational global ocean colour sensors (SeaWiFS, MERIS, MODIS-Aqua, MODIS-Terra), on both global and regional scales. The analysis is based on monthly mean chlorophyll a (Chl-a) sea surface concentration between 2002 and 2009. Based on global statistics, the Chl-a records appear relatively consistent. The root mean square (RMS) difference Δ between (log-transformed) Chl-a from SeaWiFS and MODIS Aqua amounts to 0.137, with a bias of 0.074 (SeaWiFS Chl-a higher). The difference between these two products and MERIS Chl-a is approximately 0.15. Restricting the analysis to 2007 only, Δ between MODIS Aqua and Terra is 0.142. This global convergence is significantly modulated regionally. Statistics for biogeographic provinces representing a partition of the global ocean, show Δ values varying between 0.08 and 0.3. High latitude regions, as well as coastal and shelf provinces are generally the areas with the largest differences. Moreover, RMS differences and biases are modulated in time, with a coefficient of variation of Δ varying between 10% and 40%, with clear seasonal patterns in some provinces. The comparison of the province-averaged time series obtained from the various satellite products also shows a level of agreement that is geographically variable. Overall, the Chl-a SeaWiFS and MODIS Aqua series appear to have similar levels of variance and display high correlation coefficients, an agreement likely favoured by the common elements shared by the two missions. These results are degraded if the MERIS series is compared to either SeaWiFS or MODIS Aqua. An important outcome of the study is that the results of the inter-comparison analysis are variable with time and location, and therefore globally averaged statistics are not necessarily applicable on a seasonal or regional basis.
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Djavidnia, S., F. Mélin, and N. Hoepffner. "Comparative analysis of the multi-sensor global ocean colour data record." Ocean Science Discussions 6, no. 2 (July 23, 2009): 1611–53. http://dx.doi.org/10.5194/osd-6-1611-2009.
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Abstract. The extending record of ocean colour derived information, an important asset for the study of marine ecosystems and biogeochemistry, presently relies on individual satellite missions launched by several space agencies with differences in sensor design, calibration strategies and algorithms. In this study we present an extensive comparative analysis of standard products obtained from operational global ocean colour sensors (SeaWiFS, MERIS, MODIS-Aqua, MODIS-Terra), on both global and regional scales. The analysis is based on monthly mean chlorophyll-a (Chl-a) surface concentration between 2002 and 2009. Based on global statistics, the Chl-a records appear relatively consistent. The root mean square (RMS) difference Δ between (log-transformed) Chl-a from SeaWiFS and MODIS Aqua amounts to 0.137, with a bias of 0.074 (SeaWiFS Chl-a higher). The difference between these two products and MERIS Chl-a is approximately 0.15. Restricting the analysis to 2007 only, Δ between MODIS Aqua and Terra is 0.142. This global convergence is significantly modulated regionally. Statistics for biogeographic provinces representing a partition of the global ocean, show Δ values varying between 0.08 and 0.3. High latitude regions, as well as coastal and shelf provinces are generally the areas with the largest differences. Moreover, RMS differences and biases are modulated in time, with a coefficient of variation of Δ varying between 10% and 40%, with clear seasonal patterns in some provinces. The comparison of the province-averaged time series obtained from the various satellite products also shows a level of agreement that is geographically variable. Overall, the Chl-a SeaWiFS and MODIS Aqua series appear to have similar levels of variance and display high correlation coefficients, an agreement likely favoured by the common elements shared by the two missions. These results are degraded if the MERIS series is compared to either SeaWiFS or MODIS Aqua. An important outcome of the study is that the results of the inter-comparison analysis are variable with time and location, and therefore globally averaged statistics are not necessarily applicable on a seasonal or regional basis.
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Montes-Hugo, M., H. Bouakba, and R. Arnone. "Performance evaluation of ocean color satellite models for deriving accurate chlorophyll estimates in the Gulf of Saint Lawrence." Biogeosciences Discussions 11, no. 6 (June 17, 2014): 9299–340. http://dx.doi.org/10.5194/bgd-11-9299-2014.
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Abstract. The understanding of phytoplankton dynamics in the Gulf of the Saint Lawrence (GSL) is critical for managing major fisheries off the Canadian East coast. In this study, the accuracy of two atmospheric correction techniques (NASA standard algorithm, SA, and Kuchinke's spectral optimization, KU) and three ocean color inversion models (Carder's empirical for SeaWiFS (Sea-viewing Wide Field-of-View Sensor), EC, Lee's quasi-analytical, QAA, and Garver- Siegel-Maritorena semi-empirical, GSM) for estimating the phytoplankton absorption coefficient at 443 nm (aph(443)) and the chlorophyll concentration (chl) in the GSL is examined. Each model was validated based on SeaWiFS images and shipboard measurements obtained during May of 2000 and April 2001. In general, aph(443) estimates derived from coupling KU and QAA models presented the smallest differences with respect to in situ determinations as measured by High Pressure liquid Chromatography measurements (median absolute bias per cruise up to 0.005, RMSE up to 0.013). A change on the inversion approach used for estimating aph(443) values produced up to 43.4% increase on prediction error as inferred from the median relative bias per cruise. Likewise, the impact of applying different atmospheric correction schemes was secondary and represented an additive error of up to 24.3%. By using SeaDAS (SeaWiFS Data Analysis System) default values for the optical cross section of phytoplankton (i.e., aph(443) = aph(443)/chl = 0.056 m2mg−1), the median relative bias of our chl estimates as derived from the most accurate spaceborne aph(443) retrievals and with respect to in situ determinations increased up to 29%.
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Clay, Stephanie, Angelica Peña, Brendan DeTracey, and Emmanuel Devred. "Evaluation of Satellite-Based Algorithms to Retrieve Chlorophyll-a Concentration in the Canadian Atlantic and Pacific Oceans." Remote Sensing 11, no. 22 (November 7, 2019): 2609. http://dx.doi.org/10.3390/rs11222609.
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Remote-sensing reflectance data collected by ocean colour satellites are processed using bio-optical algorithms to retrieve biogeochemical properties of the ocean. One such important property is the concentration of chlorophyll-a, an indicator of phytoplankton biomass that serves a multitude of purposes in various ocean science studies. Here, the performance of two generic chlorophyll-a algorithms (i.e., a band ratio one, Ocean Colour X (OCx), and a semi-analytical one, Garver–Siegel Maritorena (GSM)) was assessed against two large in situ datasets of chlorophyll-a concentration collected between 1999 and 2016 in the Northeast Pacific (NEP) and Northwest Atlantic (NWA) for three ocean colour sensors: Sea-viewing Wide Field-of-view Sensor (SeaWiFS), Moderate Resolution Imaging Spectroradiometer (MODIS), and Visible Infrared Imaging Radiometer Suite (VIIRS). In addition, new regionally-tuned versions of these two algorithms are presented, which reduced the mean error (mg m−3) of chlorophyll-a concentration modelled by OCx in the NWA from −0.40, −0.58 and −0.45 to 0.037, −0.087 and −0.018 for MODIS, SeaWiFS, and VIIRS respectively, and −0.34 and −0.36 to −0.0055 and −0.17 for SeaWiFS and VIIRS in the NEP. An analysis of the uncertainties in chlorophyll-a concentration retrieval showed a strong seasonal pattern in the NWA, which could be attributed to changes in phytoplankton community composition, but no long-term trends were found for all sensors and regions. It was also found that removing the 443 nm waveband for the OCx algorithms significantly improved the results in the NWA. Overall, GSM performed better than the OCx algorithms in both regions for all three sensors but generated fewer chlorophyll-a retrievals than the OCx algorithms.
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Chen, Chuqun, Lennart Jönsson, and Magnus Larson. "Parameters to Characterize Biological Conditions in Marine and Coastal Waters Retrieved from SeaWiFS Data." Marine Technology Society Journal 36, no. 1 (March 1, 2002): 14–22. http://dx.doi.org/10.4031/002533202787914223.
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SeaWiFS satellite data were employed to demonstrate how the biological conditions in marine and coastal waters may be characterized using the concentration of chlorophyll-α (chl-α) and dissolved organic carbon (DOC) as the leading parameters. In marine waters the standard algorithms from the SeaWiFS Data Analysis System (SeaDAS) package were used to derive the concentrations, whereas in coastal waters special algorithms were developed using field data and a simulation model for the irradiance reflectance. Analysis of the Sea-WiFS data were performed for two study areas, namely the North Aegean Sea between Greece and Turkey and the Pearl River Estuary in southern China. The analysis displayed the temporal and spatial distribution of chl-α and DOC as well as the movement and exchange of water masses. Such results are of great use for monitoring and forecasting the biological conditions in marine and coastal waters.
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Fan, Xia, and Chen. "Intercomparison of Multiple Satellite Aerosol Products against AERONET over the North China Plain." Atmosphere 10, no. 9 (August 21, 2019): 480. http://dx.doi.org/10.3390/atmos10090480.
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In this study, using Aerosol Robotic Network aerosol optical depth (AOD) products at three stations in the North China Plain (NCP)—a heavily polluted region in China—the AOD products from six satellite-borne radiometers: the Moderate Resolution Imagining Spectroradiometer (MODIS), the Multiangle Imaging Spectroradiometer (MISR), Ozone Mapping Imaging (OMI), the Visible Infrared Imaging Radiometer (VIIRS), the Sea-Viewing Wide Field-of-View Sensor (SeaWiFS), and Polarization and Directionality of the Earth’s Reflectances (POLDER), were thoroughly validated, shedding new light on their advantages and disadvantages. The MODIS Deep Blue (DB) products provide more accurate retrievals than the MODIS Dark Target (DT) and other satellite products at the Beijing site (BJ,a megacity), with higher correlations with AERONET (R > 0.93), lower mean absolute bias (MB < 0.012), and higher percentages (>68%) falling within the expected error (EE). All MODIS DT and DB products perform better than the other satellite products at the Xianghe site (XH, a suburb). The MODIS/Aqua DT products at both 3-km and 10-km resolutions performed better than the other space-borne AOD products at the Xinglong site (XL, a rural area at the top of a mountain). MISR, VIIRS, and SeaWiFS tend to underestimate high AOD values and overestimate AOD values under very low AOD conditions in the NCP. Both OMI and POLDER significantly underestimate the AOD. In terms of data volume, MISR with the limited swath width of 380 km has less data volume than the other satellite sensors. MODIS products have the highest sampling rate, especially the MODIS DT and DB merged products, and can be used for various climate study and air-quality monitoring.
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Beaulieu, C., S. A. Henson, J. L. Sarmiento, J. P. Dunne, S. C. Doney, R. R. Rykaczewski, and L. Bopp. "Factors challenging our ability to detect long-term trends in ocean chlorophyll." Biogeosciences Discussions 9, no. 11 (November 20, 2012): 16419–56. http://dx.doi.org/10.5194/bgd-9-16419-2012.
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Abstract. Global climate change is expected to affect the ocean's biological productivity. The most comprehensive information available about the global distribution of contemporary ocean primary productivity is derived from satellite data. Large spatial patchiness and interannual to multidecadal variability in chlorophyll a concentration challenges efforts to distinguish a global, secular trend given satellite records which are limited in duration and continuity. The longest ocean color satellite record comes from the Sea-viewing Wide Field-of-view Sensor (SeaWiFS), which failed in December 2010. The Moderate Resolution Imaging Spectroradiometer (MODIS) ocean color sensors are beyond their originally planned operational lifetime. Successful retrieval of a quality signal from the current Visible Infrared Imager Radiometer Suite (VIIRS) instrument, or successful launch of the Ocean Land Colour Instrument (OLCI) in 2013 will hopefully extend the ocean color time series and increase the potential for detecting trends in ocean productivity in the future. Alternatively, a potential discontinuity in the time series of ocean chlorophyll a, introduced by a change of instrument without overlap and opportunity for cross-calibration, would make trend detection even more challenging. In this paper, we demonstrate that there are a few regions with statistically significant trends over the ten years of SeaWiFS data, but at a global scale the trend is not large enough to be distinguished from noise. We quantify the degree to which red noise (autocorrelation) especially challenges trend detection in these observational time series. We further demonstrate how discontinuities in the time series at various points would affect our ability to detect trends in ocean chlorophyll a. We highlight the importance of maintaining continuous, climate-quality satellite data records for climate-change detection and attribution studies.
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Petrenko, M., and C. Ichoku. "Coherent uncertainty analysis of aerosol measurements from multiple satellite sensors." Atmospheric Chemistry and Physics Discussions 13, no. 2 (February 18, 2013): 4637–85. http://dx.doi.org/10.5194/acpd-13-4637-2013.
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Abstract. Aerosol retrievals from multiple spaceborne sensors, including MODIS (on Terra and Aqua), MISR, OMI, POLDER, CALIOP, and SeaWiFS – altogether, a total of 11 different aerosol products – were comparatively analyzed using data collocated with ground-based aerosol observations from the Aerosol Robotic Network (AERONET) stations within the Multi-sensor Aerosol Products Sampling System (MAPSS, http://giovanni.gsfc.nasa.gov/mapss/ and http://giovanni.gsfc.nasa.gov/aerostat/). The analysis was performed by comparing quality-screened satellite aerosol optical depth or thickness (AOD or AOT) retrievals during 2006–2010 to available collocated AERONET measurements globally, regionally, and seasonally, and deriving a number of statistical measures of accuracy. We used a robust statistical approach to detect and remove possible outliers in the collocated data that can bias the results of the analysis. Overall, the proportion of outliers in each of the quality-screened AOD products was within 12%. Squared correlation coefficient (R2) values of the satellite AOD retrievals relative to AERONET exceeded 0.6, with R2 for most of the products exceeding 0.7 over land and 0.8 over ocean. Root mean square error (RMSE) values for most of the AOD products were within 0.15 over land and 0.09 over ocean. We have been able to generate global maps showing regions where the different products present advantages over the others, as well as the relative performance of each product over different landcover types. It was observed that while MODIS, MISR, and SeaWiFS provide accurate retrievals over most of the landcover types, multi-angle capabilities make MISR the only sensor to retrieve reliable AOD over barren and snow/ice surfaces. Likewise, active sensing enables CALIOP to retrieve aerosol properties over bright-surface shrublands more accurately than the other sensors, while POLDER, which is the only one of the sensors capable of measuring polarized aerosols, outperforms other sensors in certain smoke-dominated regions, including broadleaf evergreens in Brazil and South-East Asia.
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Petrenko, M., and C. Ichoku. "Coherent uncertainty analysis of aerosol measurements from multiple satellite sensors." Atmospheric Chemistry and Physics 13, no. 14 (July 22, 2013): 6777–805. http://dx.doi.org/10.5194/acp-13-6777-2013.
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Abstract. Aerosol retrievals from multiple spaceborne sensors, including MODIS (on Terra and Aqua), MISR, OMI, POLDER, CALIOP, and SeaWiFS – altogether, a total of 11 different aerosol products – were comparatively analyzed using data collocated with ground-based aerosol observations from the Aerosol Robotic Network (AERONET) stations within the Multi-sensor Aerosol Products Sampling System (MAPSS, http://giovanni.gsfc.nasa.gov/mapss/ and http://giovanni.gsfc.nasa.gov/aerostat/. The analysis was performed by comparing quality-screened satellite aerosol optical depth or thickness (AOD or AOT) retrievals during 2006–2010 to available collocated AERONET measurements globally, regionally, and seasonally, and deriving a number of statistical measures of accuracy. We used a robust statistical approach to detect and remove possible outliers in the collocated data that can bias the results of the analysis. Overall, the proportion of outliers in each of the quality-screened AOD products was within 7%. Squared correlation coefficient (R2) values of the satellite AOD retrievals relative to AERONET exceeded 0.8 for many of the analyzed products, while root mean square error (RMSE) values for most of the AOD products were within 0.15 over land and 0.07 over ocean. We have been able to generate global maps showing regions where the different products present advantages over the others, as well as the relative performance of each product over different land cover types. It was observed that while MODIS, MISR, and SeaWiFS provide accurate retrievals over most of the land cover types, multi-angle capabilities make MISR the only sensor to retrieve reliable AOD over barren and snow/ice surfaces. Likewise, active sensing enables CALIOP to retrieve aerosol properties over bright-surface closed shrublands more accurately than the other sensors, while POLDER, which is the only one of the sensors capable of measuring polarized aerosols, outperforms other sensors in certain smoke-dominated regions, including broadleaf evergreens in Brazil and South-East Asia.
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Li, J., B. E. Carlson, and A. A. Lacis. "Application of spectral analysis techniques to the intercomparison of aerosol data – Part 4: Synthesized analysis of multisensor satellite and ground-based AOD measurements using combined maximum covariance analysis." Atmospheric Measurement Techniques 7, no. 8 (August 14, 2014): 2531–49. http://dx.doi.org/10.5194/amt-7-2531-2014.
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Abstract. In this paper, we introduce the usage of a newly developed spectral decomposition technique – combined maximum covariance analysis (CMCA) – in the spatiotemporal comparison of four satellite data sets and ground-based observations of aerosol optical depth (AOD). This technique is based on commonly used principal component analysis (PCA) and maximum covariance analysis (MCA). By decomposing the cross-covariance matrix between the joint satellite data field and Aerosol Robotic Network (AERONET) station data, both parallel comparison across different satellite data sets and the evaluation of the satellite data against the AERONET measurements are simultaneously realized. We show that this new method not only confirms the seasonal and interannual variability of aerosol optical depth, aerosol-source regions and events represented by different satellite data sets, but also identifies the strengths and weaknesses of each data set in capturing the variability associated with sources, events or aerosol types. Furthermore, by examining the spread of the spatial modes of different satellite fields, regions with the largest uncertainties in aerosol observation are identified. We also present two regional case studies that respectively demonstrate the capability of the CMCA technique in assessing the representation of an extreme event in different data sets, and in evaluating the performance of different data sets on seasonal and interannual timescales. Global results indicate that different data sets agree qualitatively for major aerosol-source regions. Discrepancies are mostly found over the Sahel, India, eastern and southeastern Asia. Results for eastern Europe suggest that the intense wildfire event in Russia during summer 2010 was less well-represented by SeaWiFS (Sea-viewing Wide Field-of-view Sensor) and OMI (Ozone Monitoring Instrument), which might be due to misclassification of smoke plumes as clouds. Analysis for the Indian subcontinent shows that here SeaWiFS agrees best with AERONET in terms of seasonality for both the Gangetic Basin and southern India, while on interannual timescales it has the poorest agreement.
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BARBINI, R., F. COLAO, R. FANTONI, L. FIORANI, A. PALUCCI, E. S. ARTAMONOV, and M. GALLI. "Remotely sensed primary production in the western Ross Sea: results of in situ tuned models." Antarctic Science 15, no. 1 (February 19, 2003): 77–84. http://dx.doi.org/10.1017/s095410200300107x.
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The Southern Ocean plays an important role in the global carbon cycle and, as a consequence, in the planetary climate equilibrium. The Ross Sea is one of the more productive regions in the Southern Ocean, due to strong phytoplankton blooms occurring during summer. Satellite remote sensing is a powerful tool for investigating such phenomena, especially if the bio-optical algorithms are tuned with in situ data. In this paper, after having compared the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) and the ENEA Lidar Fluorosensor (ELF), the SeaWiFS chlorophyll a (Chl a) algorithm is tuned in the Ross Sea by means of the ELF measurements. The Chl a concentrations obtained in this way have been the basis for estimating productivity values and their evolution during summer 1997–98. Three primary production models have been used, providing information on their accuracy and performance in the Antarctic environment. Our investigations suggest that the primary production was lower than usual during the period 3 December 1997–16 January 1998.
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Beaulieu, C., S. A. Henson, Jorge L. Sarmiento, J. P. Dunne, S. C. Doney, R. R. Rykaczewski, and L. Bopp. "Factors challenging our ability to detect long-term trends in ocean chlorophyll." Biogeosciences 10, no. 4 (April 23, 2013): 2711–24. http://dx.doi.org/10.5194/bg-10-2711-2013.
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Abstract. Global climate change is expected to affect the ocean's biological productivity. The most comprehensive information available about the global distribution of contemporary ocean primary productivity is derived from satellite data. Large spatial patchiness and interannual to multidecadal variability in chlorophyll a concentration challenges efforts to distinguish a global, secular trend given satellite records which are limited in duration and continuity. The longest ocean color satellite record comes from the Sea-viewing Wide Field-of-view Sensor (SeaWiFS), which failed in December 2010. The Moderate Resolution Imaging Spectroradiometer (MODIS) ocean color sensors are beyond their originally planned operational lifetime. Successful retrieval of a quality signal from the current Visible Infrared Imager Radiometer Suite (VIIRS) instrument, or successful launch of the Ocean and Land Colour Instrument (OLCI) expected in 2014 will hopefully extend the ocean color time series and increase the potential for detecting trends in ocean productivity in the future. Alternatively, a potential discontinuity in the time series of ocean chlorophyll a, introduced by a change of instrument without overlap and opportunity for cross-calibration, would make trend detection even more challenging. In this paper, we demonstrate that there are a few regions with statistically significant trends over the ten years of SeaWiFS data, but at a global scale the trend is not large enough to be distinguished from noise. We quantify the degree to which red noise (autocorrelation) especially challenges trend detection in these observational time series. We further demonstrate how discontinuities in the time series at various points would affect our ability to detect trends in ocean chlorophyll a. We highlight the importance of maintaining continuous, climate-quality satellite data records for climate-change detection and attribution studies.
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Henson, S. A., J. L. Sarmiento, J. P. Dunne, L. Bopp, I. Lima, S. C. Doney, J. John, and C. Beaulieu. "Is global warming already changing ocean productivity?" Biogeosciences Discussions 6, no. 6 (November 11, 2009): 10311–54. http://dx.doi.org/10.5194/bgd-6-10311-2009.
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Abstract. Global warming is predicted to alter the ocean's biological productivity. But how will we recognise the impacts of climate change on ocean productivity? The most comprehensive information available on the global distribution of ocean productivity comes from satellite ocean colour data. Now that over ten years of SeaWiFS data have accumulated, can we begin to detect and attribute global warming trends in productivity? Here we compare recent trends in SeaWiFS data to longer-term records from three biogeochemical models (GFDL, IPSL and NCAR). We find that detection of real trends in the satellite data is confounded by the relatively short time series and large interannual and decadal variability in productivity. Thus, recent observed changes in chlorophyll, primary production and the size of the oligotrophic gyres cannot be unequivocally attributed to the impact of global warming. Instead, our analyses suggest that a time series of ~40 yr length is needed to distinguish a global warming trend from natural variability. Analysis of modelled chlorophyll and primary production from 2001–2100 suggests that, on average, the global warming trend will not be unambiguously separable from decadal variability until ~2055. Because the magnitude of natural variability in chlorophyll and primary production is larger than, or similar to, the global warming trend, a consistent, decades-long data record must be established if the impact of climate change on ocean productivity is to be definitively detected.
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Jamet, C., S. Thiria, C. Moulin, and M. Crepon. "Use of a Neurovariational Inversion for Retrieving Oceanic and Atmospheric Constituents from Ocean Color Imagery: A Feasibility Study." Journal of Atmospheric and Oceanic Technology 22, no. 4 (April 1, 2005): 460–75. http://dx.doi.org/10.1175/jtech1688.1.
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Abstract This paper presents a neurovariational method for inverting satellite ocean-color signals. The method is based on a combination of neural networks and classical variational inversion. The radiative transfer equations are modeled by neural networks whose inputs are the oceanic and atmospheric parameters, and outputs the top of the atmosphere reflectance at several wavelengths. The procedure consists in minimizing a quadratic cost function that is the distance between the satellite-observed reflectance and the computed neural-network reflectance, the control parameters being the oceanic and atmospheric parameters. First, a feasibility experiment using synthetic data is presented to show that chlorophyll-a can be retrieved with an error of 19.7% when the atmospheric parameters are known exactly. Then both atmospheric and oceanic parameters are relaxed. A first guess for the atmospheric parameters was provided by a direct inverse neural network whose inputs are at near-infrared wavelengths. Sensitivity experiments showed that these parameters can be retrieved with an adequate accuracy. An inversion of a composite SeaWiFS image is presented. Optical thickness and chlorophyll-a both give coherent spatial structures when a background term is added to the cost function. Finally, chlorophyll-a retrievals are compared with SeaWiFS product through in situ data. It shows a better estimation of the chlorophyll-a with the neurovariational inversion for the oligotrophic regions.
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Guðmundsson, Kristinn, Mike R. Heath, and Elizabeth D. Clarke. "Average seasonal changes in chlorophyll a in Icelandic waters." ICES Journal of Marine Science 66, no. 10 (August 13, 2009): 2133–40. http://dx.doi.org/10.1093/icesjms/fsp208.
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Abstract Guðmundsson, K., Heath, M. R., and Clarke, E. D. 2009. Average seasonal changes in chlorophyll a in Icelandic waters. – ICES Journal of Marine Science, 66: 2133–2140. The standard algorithms used to derive sea surface chlorophyll a concentration from remotely sensed ocean colour data are based almost entirely on the measurements of surface water samples collected in open sea (case 1) waters which cover ∼60% of the worlds oceans, where strong correlations between reflectance and chlorophyll concentration have been found. However, satellite chlorophyll data for waters outside the defined case 1 areas, but derived using standard calibrations, are frequently used without reference to local in situ measurements and despite well-known factors likely to lead to inaccuracy. In Icelandic waters, multiannual averages of 8-d composites of SeaWiFS chlorophyll concentration accounted for just 20% of the variance in a multiannual dataset of in situ chlorophyll a measurements. Nevertheless, applying penalized regression spline methodology to model the spatial and temporal patterns of in situ measurements, using satellite chlorophyll as one of the predictor variables, improved the correlation considerably. Day number, representing seasonal variation, accounted for substantial deviation between SeaWiFS and in situ estimates of surface chlorophyll. The final model, using bottom depth and bearing to the sampling location as well as the two variables mentioned above, explained 49% of the variance in the fitting dataset.
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Kulikov, Leonid, Natalia Inkova, Daria Cherniuk, Anton Teslyuk, and Zorigto Namsaraev. "TIEOF: Algorithm for Recovery of Missing Multidimensional Satellite Data on Water Bodies Based on Higher-Order Tensor Decompositions." Water 13, no. 18 (September 18, 2021): 2578. http://dx.doi.org/10.3390/w13182578.
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Satellite research methods are frequently used in observations of water bodies. One of the most important problems in satellite observations is the presence of missing data due to internal malfunction of satellite sensors and poor atmospheric conditions. We proceeded on the assumption that the use of data recovery methods based on spatial relationships in data can increase the recovery accuracy. In this paper, we present a method for missing data reconstruction from remote sensors. We refer our method to as Tensor Interpolating Empirical Orthogonal Functions (TIEOF). The method relies on the two-dimensional nature of sensor images and organizes the data into three-dimensional tensors. We use high-order tensor decomposition to interpolate missing data on chlorophyll a concentration in lake Baikal (Russia, Siberia). Using MODIS and SeaWiFS satellite data of lake Baikal we show that the observed improvement of TIEOF was 69% on average compared to the current state-of-the-art DINEOF algorithm measured in various preprocessing data scenarios including thresholding and different interpolating schemes.
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Li, Hsien-Wen, Chung-Ru Ho, Nan-Jung Kuo, Chun-Te Chen, and Wei-Peng Tsai. "A Caomparison of OCI and SeaWiFS Satellite Imagery in the Waters Adjacent to Taiwan." Terrestrial, Atmospheric and Oceanic Sciences 10, no. 4 (1999): 873. http://dx.doi.org/10.3319/tao.1999.10.4.873(rocsat).
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Heim, Birgit, Hedi Oberhaensli, Susanne Fietz, and Hermann Kaufmann. "Variation in Lake Baikal's phytoplankton distribution and fluvial input assessed by SeaWiFS satellite data." Global and Planetary Change 46, no. 1-4 (April 2005): 9–27. http://dx.doi.org/10.1016/j.gloplacha.2004.11.011.
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Loeb, Norman G., Bruce A. Wielicki, Wenying Su, Konstantin Loukachine, Wenbo Sun, Takmeng Wong, Kory J. Priestley, Grant Matthews, Walter F. Miller, and R. Davies. "Multi-Instrument Comparison of Top-of-Atmosphere Reflected Solar Radiation." Journal of Climate 20, no. 3 (February 1, 2007): 575–91. http://dx.doi.org/10.1175/jcli4018.1.
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Abstract Observations from the Clouds and the Earth’s Radiant Energy System (CERES), Moderate Resolution Imaging Spectroradiometer (MODIS), Multiangle Imaging Spectroradiometer (MISR), and Sea-Viewing Wide-Field-of-View Sensor (SeaWiFS) between 2000 and 2005 are analyzed in order to determine if these data are meeting climate accuracy goals recently established by the climate community. The focus is primarily on top-of-atmosphere (TOA) reflected solar radiances and radiative fluxes. Direct comparisons of nadir radiances from CERES, MODIS, and MISR aboard the Terra satellite reveal that the measurements from these instruments exhibit a year-to-year relative stability of better than 1%, with no systematic change with time. By comparison, the climate requirement for the stability of visible radiometer measurements is 1% decade−1. When tropical ocean monthly anomalies in shortwave (SW) TOA radiative fluxes from CERES on Terra are compared with anomalies in Photosynthetically Active Radiation (PAR) from SeaWiFS—an instrument whose radiance stability is better than 0.07% during its first six years in orbit—the two are strongly anticorrelated. After scaling the SeaWiFS anomalies by a constant factor given by the slope of the regression line fit between CERES and SeaWiFS anomalies, the standard deviation in the difference between monthly anomalies from the two records is only 0.2 W m−2, and the difference in their trend lines is only 0.02 ± 0.3 W m−2 decade−1, approximately within the 0.3 W m−2 decade−1 stability requirement for climate accuracy. For both the Tropics and globe, CERES Terra SW TOA fluxes show no trend between March 2000 and June 2005. Significant differences are found between SW TOA flux trends from CERES Terra and CERES Aqua between August 2002 and March 2005. This discrepancy is due to uncertainties in the adjustment factors used to account for degradation of the CERES Aqua optics during hemispheric scan mode operations. Comparisons of SW TOA flux between CERES Terra and the International Satellite Cloud Climatology Project (ISCCP) radiative flux profile dataset (FD) RadFlux product show good agreement in monthly anomalies between January 2002 and December 2004, and poor agreement prior to this period. Commonly used statistical tools applied to the CERES Terra data reveal that in order to detect a statistically significant trend of magnitude 0.3 W m−2 decade−1 in global SW TOA flux, approximately 10 to 15 yr of data are needed. This assumes that CERES Terra instrument calibration remains highly stable, long-term climate variability remains constant, and the Terra spacecraft has enough fuel to last 15 yr.
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D'Ortenzio, F., and M. Ribera d'Alcalà. "On the trophic regimes of the Mediterranean Sea: a satellite analysis." Biogeosciences Discussions 5, no. 4 (July 25, 2008): 2959–83. http://dx.doi.org/10.5194/bgd-5-2959-2008.
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Abstract. The ten years of the SeaWiFS satellite surface chlorophyll concentration observations, presently available, were used to characterize the biogeography of the Mediterranean Sea and the seasonal cycle of the surface biomass in different areas of the basin. The K-means cluster analysis was applied on the satellite time-series of chlorophyll concentration. The resulting coherent patterns were then explained on the basis of the present knowledge of the basin functioning. Winter biomass enhancements were shown to occur in most of the basin and last for 2–3 months depending on the region. Classical spring bloom regimes were also observed, regularly in the North Western Mediterranean, and intermittently in four others specific areas. The analysis confirmed that the Mediterranean Sea is an ideal area to evaluate the impacts of the external physical forcing on the marine ecosystem functioning.
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D'Ortenzio, F., and M. Ribera d'Alcalà. "On the trophic regimes of the Mediterranean Sea: a satellite analysis." Biogeosciences 6, no. 2 (February 5, 2009): 139–48. http://dx.doi.org/10.5194/bg-6-139-2009.
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Abstract. The ten years of the SeaWiFS satellite surface chlorophyll concentration observations, presently available, were used to characterize the biogeography of the Mediterranean Sea and the seasonal cycle of the surface biomass in different areas of the basin. The K-means cluster analysis was applied on the satellite time-series of chlorophyll concentration. The resulting coherent patterns were then explained on the basis of the present knowledge of the basin's functioning. Winter biomass enhancements were shown to occur in most of the basin and last for 2–3 months depending on the region. Classical spring bloom regimes were also observed, regularly in the North Western Mediterranean, and intermittently in four other specific areas. The geographical correspondence between specific clusters and regions showing high values of mean chlorophyll concentration indicates that, at least in the Mediterranean Sea, accumulations of phytoplankton are observed only where specific temporal trends are present.
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Senghor, Habib, Éric Machu, Frédéric Hourdin, and Amadou Thierno Gaye. "Seasonal cycle of desert aerosols in western Africa: analysis of the coastal transition with passive and active sensors." Atmospheric Chemistry and Physics 17, no. 13 (July 11, 2017): 8395–410. http://dx.doi.org/10.5194/acp-17-8395-2017.
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Abstract. The impact of desert aerosols on climate, atmospheric processes, and the environment is still debated in the scientific community. The extent of their influence remains to be determined and particularly requires a better understanding of the variability of their distribution. In this work, we studied the variability of these aerosols in western Africa using different types of satellite observations. SeaWiFS (Sea-Viewing Wide Field-of-View Sensor) and OMI (Ozone Monitoring Instrument) data have been used to characterize the spatial distribution of mineral aerosols from their optical and physical properties over the period 2005–2010. In particular, we focused on the variability of the transition between continental western African and the eastern Atlantic Ocean. Data provided by the lidar scrolling CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization) onboard the satellite CALIPSO (Cloud Aerosol Lidar and Infrared Pathfinder Satellite Observations) for the period 2007–2013 were then used to assess the seasonal variability of the vertical distribution of desert aerosols. We first obtained a good representation of aerosol optical depth (AOD) and single-scattering albedo (SSA) from the satellites SeaWiFS and OMI, respectively, in comparison with AERONET estimates, both above the continent and the ocean. Dust occurrence frequency is higher in spring and boreal summer. In spring, the highest occurrences are located between the surface and 3 km above sea level, while in summer the highest occurrences are between 2 and 5 km altitude. The vertical distribution given by CALIOP also highlights an abrupt change at the coast from spring to fall with a layer of desert aerosols confined in an atmospheric layer uplifted from the surface of the ocean. This uplift of the aerosol layer above the ocean contrasts with the winter season during which mineral aerosols are confined in the atmospheric boundary layer. Radiosondes at Dakar Weather Station (17.5° W, 14.74° N) provide basic thermodynamic variables which partially give a causal relationship between the layering of the atmospheric circulation over western Africa and their aerosol contents throughout the year. A SSA increase is observed in winter and spring at the transition between the continent and the ocean. The analysis of mean NCEP (National Centers for Environmental Prediction) winds at 925 hPa between 2000 and 2012 suggest a significant contribution of coastal sand sources from Mauritania in winter which would increase SSA over the ocean.
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Wei, Jing, Yiran Peng, Rashed Mahmood, Lin Sun, and Jianping Guo. "Intercomparison in spatial distributions and temporal trends derived from multi-source satellite aerosol products." Atmospheric Chemistry and Physics 19, no. 10 (May 29, 2019): 7183–207. http://dx.doi.org/10.5194/acp-19-7183-2019.
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Abstract. Satellite-derived aerosol products provide long-term and large-scale observations for analysing aerosol distributions and variations, climate-scale aerosol simulations, and aerosol–climate interactions. Therefore, a better understanding of the consistencies and differences among multiple aerosol products is important. The objective of this study is to compare 11 global monthly aerosol optical depth (AOD) products, which are the European Space Agency Climate Change Initiative (ESA-CCI) Advanced Along-Track Scanning Radiometer (AATSR), Advanced Very High Resolution Radiometer (AVHRR), Multi-angle Imaging SpectroRadiometer (MISR), Moderate Resolution Imaging Spectroradiometer (MODIS), Sea-viewing Wide Field-of-view Sensor (SeaWiFS), Visible Infrared Imaging Radiometer (VIIRS), and POLarization and Directionality of the Earth's Reflectance (POLDER) products. AErosol RObotic NEtwork (AERONET) Version 3 Level 2.0 monthly measurements at 308 sites around the world are selected for comparison. Our results illustrate that the spatial distributions and temporal variations of most aerosol products are highly consistent globally but exhibit certain differences on regional and site scales. In general, the AATSR Dual View (ADV) and SeaWiFS products show the lowest spatial coverage with numerous missing values, while the MODIS products can cover most areas (average of 87 %) of the world. The best performance is observed in September–October–November (SON) and the worst is in June–July–August (JJA). All the products perform unsatisfactorily over northern Africa and Middle East, southern and eastern Asia, and their coastal areas due to the influence from surface brightness and human activities. In general, the MODIS products show the best agreement with the AERONET-based AOD values on different spatial scales among all the products. Furthermore, all aerosol products can capture the correct aerosol trends at most cases, especially in areas where aerosols change significantly. The MODIS products perform best in capturing the global temporal variations in aerosols. These results provide a reference for users to select appropriate aerosol products for their particular studies.
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Banks, Stuart. "SeaWiFS satellite monitoring of oil spill impact on primary production in the Galápagos Marine Reserve." Marine Pollution Bulletin 47, no. 7-8 (July 2003): 325–30. http://dx.doi.org/10.1016/s0025-326x(03)00162-0.
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Loisel, H., E. Bosc, D. Stramski, K. Oubelkheir, and P. Y. Deschamps. "Seasonal variability of the backscattering coefficient in the Mediterranean Sea based on satellite SeaWiFS imagery." Geophysical Research Letters 28, no. 22 (November 15, 2001): 4203–6. http://dx.doi.org/10.1029/2001gl013863.
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Wilson, Cara, and David Adamec. "A global view of bio-physical coupling from SeaWiFS and TOPEX satellite data, 1997-2001." Geophysical Research Letters 29, no. 8 (April 2002): 98–1. http://dx.doi.org/10.1029/2001gl014063.
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Belviso, S., C. Moulin, L. Bopp, and J. Stefels. "Assessment of a global climatology of oceanic dimethylsulfide (DMS) concentrations based on SeaWiFS imagery (1998-2001)." Canadian Journal of Fisheries and Aquatic Sciences 61, no. 5 (May 1, 2004): 804–16. http://dx.doi.org/10.1139/f04-001.
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A method is developed to estimate sea-surface particulate dimethylsulfoniopropionate (DMSPp) and dimethylsulfide (DMS) concentrations from sea-surface concentrations of chlorophyll a (Chl a). When compared with previous studies, the 1° × 1° global climatology of oceanic DMS concentrations computed from 4 years (19982001) of Chl a measurements derived from SeaWiFS (satellite-based, sea-viewing wide field of view sensor) exhibits lower seasonal variability in the southern hemisphere than in the northern hemisphere. A first evaluation of the method shows that it reasonably well represents DMSPp and DMS in the North Atlantic subtropical gyre, in large blooms of mixed populations of diatoms and Phaeocystis spp., and in massive blooms of Phaeocystis spp. but fails for large, almost pure blooms of diatoms. DMSPp and DMS concentrations derived from SeaWiFS were also compared with spatially and temporally coincident in situ measurements acquired independently in the Atlantic between 39°N and 45°N and in subtropical and subantarctic Indian Ocean surface waters. Moderate spring and summer phytoplankton blooms there exhibited similar trends in DMSPp and DMS levels vs. moderate blooms of mixed populations of prymnesiophytes and dinoflagellates investigated by others. Measured DMS largely exceeded simulated DMS concentrations, whereas measured and simulated DMSPp levels were in close agreement. DMS accumulation is tentatively attributed to dinoflagellate DMSP lyase activity.
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Yoon, J., J. P. Burrows, M. Vountas, W. von Hoyningen-Huene, D. Y. Chang, A. Richter, and A. Hilboll. "Changes in atmospheric aerosol loading retrieved from space-based measurements during the past decade." Atmospheric Chemistry and Physics 14, no. 13 (July 4, 2014): 6881–902. http://dx.doi.org/10.5194/acp-14-6881-2014.
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Abstract. The role and potential management of short-lived atmospheric pollutants such as aerosols are currently a topic of scientific and public debates. Our limited knowledge of atmospheric aerosol and its influence on the Earth's radiation balance has a significant impact on the accuracy and error of current predictions of future climate change. In the last few years, there have been several accounts of the changes in atmospheric aerosol derived from satellite observations, but no study considering the uncertainty caused by different/limited temporal sampling of polar-orbiting satellites and cloud disturbance in the trend estimates of cloud-free aerosol optical thickness (AOT). This study presents an approach to minimize the uncertainties by use of weighted least-squares regression and multiple satellite-derived AOTs from the space-born instruments, MODIS (onboard Terra from 2000 to 2009 and Aqua form 2003 to 2008), MISR (Terra from 2000 to 2010), and SeaWiFS (OrbView-2 from 1998 to 2007) and thereby provides more convincing trend estimates for atmospheric aerosols during the past decade. The AOT decreases over western Europe (i.e., by up to about −40% from 2003 to 2008). In contrast, a statistically significant increase (about +34% in the same period) over eastern China is observed and can be attributed to the increase in both industrial output and Asian desert dust.
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von Hoyningen-Huene, W., J. Yoon, M. Vountas, L. G. Istomina, G. Rohen, T. Dinter, A. A. Kokhanovsky, and J. P. Burrows. "Retrieval of spectral aerosol optical thickness over land using ocean color sensors MERIS and SeaWiFS." Atmospheric Measurement Techniques 4, no. 2 (February 3, 2011): 151–71. http://dx.doi.org/10.5194/amt-4-151-2011.
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Abstract. For the determination of aerosol optical thickness (AOT) Bremen AErosol Retrieval (BAER) has been developed. Method and main features on the aerosol retrieval are described together with validation and results. The retrieval separates the spectral aerosol reflectance from surface and Rayleigh path reflectance for the shortwave range of the measured spectrum of top-of-atmosphere reflectance for wavelength less than 0.670 μm. The advantage of MERIS (Medium Resolution Imaging Spectrometer on the Environmental Satellite – ENVISAT – of the European Space Agency – ESA) and SeaWiFS (Sea viewing Wide Field Sensor on OrbView-2 spacecraft) observations is the availability of several spectral channels in the blue and visible range enabling the spectral determination of AOT in 7 (or 6) channels (0.412–0.670 μm) and additionally channels in the NIR, which can be used to characterize the surface properties. A dynamical spectral surface reflectance model for different surface types is used to obtain the spectral surface reflectance for this separation. The normalized differential vegetation index (NDVI), taken from the satellite observations, is the model input. Further surface bi-directional reflectance distribution function (BRDF) is considered by the Raman-Pinty-Verstraete (RPV) model. Spectral AOT is obtained from aerosol reflectance using look-up-tables, obtained from radiative transfer calculations with given aerosol phase functions and single scattering albedos either from aerosol models, given by model package "optical properties of aerosol components" (OPAC) or from experimental campaigns. Validations of the obtained AOT retrieval results with data of Aerosol Robotic Network (AERONET) over Europe gave a preference for experimental phase functions derived from almucantar measurements. Finally long-term observations of SeaWiFS have been investigated for 11 year trends in AOT. Western European regions have negative trends with decreasing AOT with time. For the investigated Asian region increasing AOT have been found.
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Sayer, Andrew M., N. Christina Hsu, Jaehwa Lee, Woogyung V. Kim, Sharon Burton, Marta A. Fenn, Richard A. Ferrare, et al. "Two decades observing smoke above clouds in the south-eastern Atlantic Ocean: Deep Blue algorithm updates and validation with ORACLES field campaign data." Atmospheric Measurement Techniques 12, no. 7 (July 4, 2019): 3595–627. http://dx.doi.org/10.5194/amt-12-3595-2019.
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Abstract. This study presents and evaluates an updated algorithm for quantification of absorbing aerosols above clouds (AACs) from passive satellite measurements. The focus is biomass burning in the south-eastern Atlantic Ocean during the 2016 and 2017 ObseRvations of Aerosols above CLouds and their intEractionS (ORACLES) field campaign deployments. The algorithm retrieves the above-cloud aerosol optical depth (AOD) and underlying liquid cloud optical depth and is applied to measurements from the Sea-viewing Wide Field-of-view Sensor (SeaWiFS), Moderate Resolution Imaging Spectroradiometer (MODIS), and Visible Infrared Imaging Radiometer Suite (VIIRS) from 1997 to 2017. Airborne NASA Ames Spectrometers for Sky-Scanning, Sun-Tracking Atmospheric Research (4STAR) and NASA Langley High Spectral Resolution Lidar 2 (HSRL2) data collected during ORACLES provide important validation for spectral AOD for MODIS and VIIRS; as the SeaWiFS mission ended in 2010, it cannot be evaluated directly. The 4STAR and HSRL2 comparisons are complementary and reveal performance generally in line with uncertainty estimates provided by the optimal estimation retrieval framework used. At present the two MODIS-based data records seem the most reliable, although there are differences between the deployments, which may indicate that the available data are not yet sufficient to provide a robust regional validation. Spatiotemporal patterns in the data sets are similar, and the time series are very strongly correlated with each other (correlation coefficients from 0.95 to 0.99). Offsets between the satellite data sets are thought to be chiefly due to differences in absolute calibration between the sensors. The available validation data for this type of algorithm are limited to a small number of field campaigns, and it is strongly recommended that such airborne measurements continue to be made, both over the southern Atlantic Ocean and elsewhere.
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Tamondong, A., T. Nakamura, T. E. A. Quiros, and K. Nadaoka. "TIME SERIES ANALYSIS FOR MONITORING SEAGRASS HABITAT AND ENVIRONMENT IN BUSUANGA, PHILIPPINES USING GOOGLE EARTH ENGINE." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B3-2021 (June 28, 2021): 109–16. http://dx.doi.org/10.5194/isprs-archives-xliii-b3-2021-109-2021.
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Abstract. Seagrasses are marine flowering plants which are part of a highly productive coastal ecosystem and play key roles in the coastal processes. Unfortunately, they are declining in area coverage globally, and seagrass losses can be attributed to climate change such as sea-level rise, increase in sea surface temperature, and decrease in salinity, as well as human-related activities. The objective of this research is to assess the historical changes in the seagrass habitat and environment of Busuanga, Philippines using time series data available in the Google Earth Engine (GEE) platform. These include satellite data such as MODIS, Landsat 5, 7, and 8, and SeaWIFS. Reanalysis data such as HYCOM was also utilized in this research. Results from HYCOM data show that there has been a 0.0098 °C increase in the sea surface temperature per decade in Busuanga while MODIS data indicates an increase of 0.0045 °C per decade. Moreover, HYCOM data also shows an overall average of 0.76 mm in sea surface elevation anomaly and a decreasing trend in salinity values at 0.0026 psu per decade. Chlorophyll-a concentration has a minimal increase based on results from MODIS and SeaWIFS. Aside from changes in water parameters, changes in the land also affect seagrasses. Forest loss may cause increased siltation in the coastal ecosystem which can lead to seagrass loss. Based on the results of Landsat satellite image processing, there has been forest cover loss in Busuanga with the highest loss occurring in 2013 when super typhoon Yolanda ravaged the island. Lastly, results from the linear spectral unmixing of 778 Landsat images from 1987–2000 show that the average percent cover of seagrasses in Busuanga were declining through the years.
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Liu, Yang, and Ronggao Liu. "Evaluation of the Spatial and Temporal Uncertainties Distribution of Daily-Integrated Shortwave Downward Radiation Estimated from Polar-Orbiting Satellite Observation." Journal of Atmospheric and Oceanic Technology 29, no. 10 (October 1, 2012): 1481–91. http://dx.doi.org/10.1175/jtech-d-11-00142.1.
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Abstract The polar-orbiting satellite sensor, which can observe the entire Earth’s surface with good spatial and spectral resolution, is a potential tool for estimation of global downward shortwave radiation (DSR). However, it can only provide a couple of snapshots in one day, which should be extended to daily-integrated DSR in order to drive the ecosystem models. In this paper, the temporal and spatial uncertainties for estimating of daily-integrated DSR from instantaneous measurements of polar-orbiting satellites are evaluated using geostationary Geosynchronous Meteorological Satellite-5 (GMS-5) observations over East Asia. Those overpass times—including 1030, 1200, and 1330 local time (LT), which correspond to Terra/Moderate Resolution Imaging Spectroradiometer (MODIS), Sea-Viewing Wide Field-of-View Sensor (SeaWiFS), and Aqua/MODIS—are evaluated. The combinations of multiobservations are also assessed. The results show that the daily-integrated DSR from polar-orbiting satellite observations of 1030, 1200, and 1330 underestimate solar radiation by 2.16% (0.46 MJ m−2), 5.44% (1.16 MJ m−2), and 5.54% (1.09 MJ m−2), with an RMSE of 2.05 MJ m−2 (12.92%), 2.50 MJ m−2 (13.33%), and 2.34 MJ m−2 (13.95%) in East Asia with large spatial and seasonal variations. In general, the bias is higher in the southern than in the northern part of East Asia. It is low in January, February, and March, then increases from April and reaches the maximum in June and July, and decreases rapidly to lower than 1.0 MJ m−2 in October. The uncertainties of daily-integrated DSR could be reduced by averaging multiday observations or combining of multitime observations. These uncertainties’ distributions are important for evaluation the usability of daily-integrated DSR from polar-orbiting satellite data and probably can be used for its calibration.
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Triantafyllou, G., G. Korres, I. Hoteit, G. Petihakis, and A. C. Banks. "Assimilation of ocean colour data into a Biogeochemical Flux Model of the Eastern Mediterranean Sea." Ocean Science 3, no. 3 (August 21, 2007): 397–410. http://dx.doi.org/10.5194/os-3-397-2007.
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Abstract. An advanced multivariate sequential data assimilation system has been implemented within the framework of the European MFSTEP project to fit a three-dimensional biogeochemical model of the Eastern Mediterranean to satellite chlorophyll data from the Sea-viewing Wide Field-of-view Sensor (SeaWiFS). The physics are described by the Princeton Ocean Model (POM) while the biochemistry of the ecosystem is tackled with the Biogeochemical Flux Model (BFM). The assimilation scheme is based on the Singular Evolutive Extended Kalman (SEEK) filter, in which the error statistics were parameterized by means of a suitable set of Empirical Orthogonal Functions (EOFs). To avoid spurious long-range correlations associated with the limited number of EOFs, the filter covariance matrix was given compact support through a radius of influence around every data point location. Hindcast experiments were performed for one year over 1999 and forced with ECMWF 6 h atmospheric fields. The solution of the assimilation system was evaluated against the assimilated data and the MedAtlas climatology, and by assessing the impact of the assimilation on non-observed biogeochemical processes. It is found that the assimilation of SeaWiFS data improves the overall behavior of the BFM model and efficiently removes long term biases from the model despite some difficulties during the spring bloom period. Results, however, suggest the need of subsurface data to enhance the estimation of the ecosystem variables in the deep layers.
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Li, Z., X. Zhao, R. Kahn, M. Mishchenko, L. Remer, K. H. Lee, M. Wang, I. Laszlo, T. Nakajima, and H. Maring. "Uncertainties in satellite remote sensing of aerosols and impact on monitoring its long-term trend: a review and perspective." Annales Geophysicae 27, no. 7 (July 10, 2009): 2755–70. http://dx.doi.org/10.5194/angeo-27-2755-2009.
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Abstract. As a result of increasing attention paid to aerosols in climate studies, numerous global satellite aerosol products have been generated. Aerosol parameters and underlining physical processes are now incorporated in many general circulation models (GCMs) in order to account for their direct and indirect effects on the earth's climate, through their interactions with the energy and water cycles. There exists, however, an outstanding problem that these satellite products have substantial discrepancies, that must be lowered substantially for narrowing the range of the estimates of aerosol's climate effects. In this paper, numerous key uncertain factors in the retrieval of aerosol optical depth (AOD) are articulated for some widely used and relatively long satellite aerosol products including the AVHRR, TOMS, MODIS, MISR, and SeaWiFS. We systematically review the algorithms developed for these sensors in terms of four key elements that influence the quality of passive satellite aerosol retrieval: calibration, cloud screening, classification of aerosol types, and surface effects. To gain further insights into these uncertain factors, the NOAA AVHRR data are employed to conduct various tests, which help estimate the ranges of uncertainties incurred by each of the factors. At the end, recommendations are made to cope with these issues and to produce a consistent and unified aerosol database of high quality for both environment monitoring and climate studies.
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Li, Longlei, and Irina Sokolik. "Analysis of Dust Aerosol Retrievals Using Satellite Data in Central Asia." Atmosphere 9, no. 8 (July 24, 2018): 288. http://dx.doi.org/10.3390/atmos9080288.
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Several long-term monitoring of aerosol datasets from the Moderate Resolution Imaging Spectroradiometer (MODIS) on board Terra/Aqua, Multi-angle Imaging SpectroRadiometer (MISR), Sea-Viewing Wide Field-of-View Sensor (SeaWiFS) were used to derive the dust aerosol optical depth (DOD) in Central Asia based on the Angstrom exponent parameter and/or the particle shape. All sensors agree very well on the interannual variability of DOD. The seasonal analysis of DOD and dust occurrences identified the largest dust loading and the most frequent dust occurrence in the spring and summer, respectively. No significant trend was found during the research period in terms of both DOD and the dust occurrence. Further analysis of Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) aerosol products on a case-by-case basis in most dust months of 2007 suggested that the vertical structure is varying in terms of the extension and the dust loading from one event to another, although dust particles of most episodes have similar physical characteristics (particle shape and size). Our analysis on the vertical structure of dust plumes, the layer-integrated color ratio and depolarization ratio indicates a varied climate effect (e.g., the direct radiative impact) by mineral dust, dependent on the event being observed in Central Asia.
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Barnes, Brian B., and Chuanmin Hu. "Dependence of satellite ocean color data products on viewing angles: A comparison between SeaWiFS, MODIS, and VIIRS." Remote Sensing of Environment 175 (March 2016): 120–29. http://dx.doi.org/10.1016/j.rse.2015.12.048.
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Mélin, Frédéric, Giuseppe Zibordi, and Samuel Djavidnia. "Development and validation of a technique for merging satellite derived aerosol optical depth from SeaWiFS and MODIS." Remote Sensing of Environment 108, no. 4 (June 2007): 436–50. http://dx.doi.org/10.1016/j.rse.2006.11.026.
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Falke, Stefan R., Rudolf B. Husar, and Bret A. Schichtel. "Fusion of SeaWiFS and TOMS Satellite Data with Surface Observations and Topographic Data during Extreme Aerosol Events." Journal of the Air & Waste Management Association 51, no. 11 (November 2001): 1579–85. http://dx.doi.org/10.1080/10473289.2001.10464386.
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Schaeffer, Blake A., James D. Hagy, Robyn N. Conmy, John C. Lehrter, and Richard P. Stumpf. "An Approach to Developing Numeric Water Quality Criteria for Coastal Waters Using the SeaWiFS Satellite Data Record." Environmental Science & Technology 46, no. 2 (January 5, 2012): 916–22. http://dx.doi.org/10.1021/es2014105.
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von Hoyningen-Huene, W., J. Yoon, M. Vountas, L. G. Istomina, G. Rohen, T. Dinter, A. A. Kokhanovsky, and J. P. Burrows. "Retrieval of spectral aerosol optical thickness over land using ocean color sensors MERIS and SeaWiFS." Atmospheric Measurement Techniques Discussions 3, no. 3 (May 12, 2010): 2107–64. http://dx.doi.org/10.5194/amtd-3-2107-2010.
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Abstract. For the determination of aerosol optical thickness (AOT) Bremen AErosol Retrieval (BAER) has been developed. Method and main influences on the aerosol retrieval are described together with validation and results. The retrieval separates the spectral aerosol reflectance from surface and Rayleigh path reflectance for the shortwave range of the measured spectrum of top-of-atmosphere reflectance less than 0.670 μm. The advantage of MERIS (Medium Resolution Imaging Spectrometer on ENVISAT) and SeaWiFS (Sea viewing Wide Fiels Sensor on OrbView-2) observations are the existence of several spectral channels in the blue and visible range enabling the spectral determination of AOT in 7 (or 6) channels (0.412–0.670 μm) and additionally channels in the NIR, which can be used to characterize the surface properties. A dynamical spectral surface reflectance model for different surface types is used to obtain the spectral surface reflectance for this separation. Normalized differential vegetation index (NDVI), taken from the satellite observations, is the model input. Further surface BRDF is considered by the Raman-Pinty-Verstraete (RPV) model. Spectral AOT is obtained from aerosol reflectance using look-up-tables, obtained from radiative transfer calculations with given aerosol phase functions and single scattering albedos either from aerosol models, given by OPAC or from experimental campaigns. Validations of the obtained AOT retrieval results with AERONET data over Europe gave a preference for experimental phase functions derived from almucantar measurements. Finally long-term observations of SeaWiFS have been investigated for trends in AOT.
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Sachoemar, Suhendar. "VARIABILITY OF SEA SURFACE CHLOROPHYLL-A, TEMPERATURE AND FISH CATCH WITHIN INDONESIAN REGION REVEALED BY SATELLITE DATA." Marine Research in Indonesia 37, no. 2 (March 4, 2015): 75–87. http://dx.doi.org/10.14203/mri.v37i2.25.
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The investigation of sea surface chlorophyll-a (SSC) and sea surface temperature (SST) in relation to fish catch variability within the Indonesian region were conducted by using satellite data of NOAA-AVHRR, SeaWiFs and Aqua MODIS. The investigation focused in the region of the coastal area of Java, Lampung Bay and South Kalimantan as representation of the environment diversities of the Indonesian seas. The result shows that seasonal variation in fish productivity has a strong correlation with SSC variability. High fish productivity corresponded well with high concentration of SSC, and the productivity tended to decrease when the SSC concentration was declined. High SSC variability in the coastal area of Java and Lampung Bay was governed by the upwelling that induced high nutrient load into the sea surface during the southeast monsoon, while in the northern coastal area of Java and South Kalimantan, it was governed by high precipitation ocurring during the northwest monsoon that enhanced the nutrient load through the rivers and coastal discharge.
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Ahmadi, Bonyad, Mehdi Gholamalifard, Tiit Kutser, Stefano Vignudelli, and Andrey Kostianoy. "Spatio-Temporal Variability in Bio-Optical Properties of the Southern Caspian Sea: A Historic Analysis of Ocean Color Data." Remote Sensing 12, no. 23 (December 4, 2020): 3975. http://dx.doi.org/10.3390/rs12233975.
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Currently, satellite ocean color imageries play an important role in monitoring of water properties in various oceanic, coastal, and inland ecosystems. Although there is a long-time and global archive of such valuable data, no study has comprehensively used these data to assess the changes in the Caspian Sea. Hence, this study assessed the variability of bio-optical properties of the upper-water column in the Southern Caspian Sea (SCS) using the archive of the Sea-Viewing Wide Field-of-View Sensor (SeaWiFS) and the Moderate Resolution Imaging Spectroradiometer (MODIS). The images acquired from SeaWiFS (January 1998 to December 2002) and MODIS Aqua (January 2003 to December 2015) satellites were used to investigate the spatial–temporal variability of bio-optical properties including Chlorophyll-a (Chl-a), attenuation coefficient, and remote sensing reflectance, and environmental parameters such as sea surface temperature (SST), wind stress and the El Nino-southern oscillation (ENSO) phenomena at different time lags in the study area. The trend analysis demonstrated an overall increase of 0.3358 mg m−3 in the Chl-a concentration during 1998–2015 (annual increase rate of 0.018 mg m−3 year−1) and four algal blooms and in turn an abnormal increase in Chl-a concentration were occurred in August 2001, September 2005, 2009, and August 2010. The linear model revealed that Chl-a in the northern and middle part of the study area had been influenced by the attenuation coefficient after a one-month lag time. The analysis revealed a sharp decline in Chl-a concentration during 2011–2015 and showed a high correlation with the turbidity and attenuation coefficient in the southern region, while Kd_490nm and remote sensing reflectance did a low one. Generally, Chl-a concentration exhibited a positive correlation with the attenuation coefficient (r = 0.63) and with remote sensing reflectance at the 555 nm (r = 0.111). This study can be used as the basis for predictive modeling to evaluate the changes of water quality and bio-optical indices in the Southern Caspian Sea (SCS).
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Radiarta, I. Nyoman. "SATELLITE-MEASURED SPATIAL AND TEMPORAL CHLOROPHYLL-A VARIABILITY IN THE GULF OF TOMINI, SULAWESI." Indonesian Aquaculture Journal 4, no. 2 (December 31, 2009): 147. http://dx.doi.org/10.15578/iaj.4.2.2009.147-152.
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Chlorophyll-a concentration, an index of phytoplankton biomass, is an important parameter for fisheries resources and marine aquaculture development. Spatial and temporal variability of surface cholophyll-a (chl-a) concentration and water condition in the Gulf of Tomini were investigated using monthly climatologies the Sea-viewing Wide Field-of-view sensor (SeaWiFS), sea surface temperature (SST), and wind data from January 2000 to December 2007. The results showed seasonal variation of chla and SST in the Gulf of Tomini. High chl-a concentrations located in the eastern part of the gulf were observed during the southeast monsoon in August. During the northwest monsoon, chl-a concentrations were relatively low (<0.2 mg m-3) and distributed uniformly throughout most of the region. Chl-a concentrations peaked in August at every year, and chl-a concentrations were observed low in November at every year from 2000 to 2007. SSTs were relatively high (> 28oC) during the northwest monsoon, but low during the southeast monsoon. High wind speed was coincided with high chl-a concentrations. Local forcing such as sea surface heating and wind condition are the mechanisms that controlled the spatial and temporal variations of chlorophyll concentrations.
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Gao, Bo-Cai, and Rong-Rong Li. "Improving Water Leaving Reflectance Retrievals from ABI and AHI Data Acquired Over Case 2 Waters from Present Geostationary Weather Satellite Platforms." Remote Sensing 12, no. 19 (October 7, 2020): 3257. http://dx.doi.org/10.3390/rs12193257.
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The current generation of geostationary weather satellite instruments, such as the Advanced Baseline Imagers (ABIs) on board the US NOAA GOES 16 and 17 satellites and the Advanced Himawari Imagers (AHIs) on board the Japanese Himawari-8/9 satellites, have six channels located in the visible to shortwave IR (SWIR) spectral range. These instruments can acquire images over both land and water surfaces at spatial resolutions between 0.5 and 2 km and with a repeating cycle between 5 and 30 min depending on the mode of operation. The imaging data from these instruments have clearly demonstrated the capability in detecting sediment movements over coastal waters and major chlorophyll blooms over deeper oceans. At present, no operational ocean color data products have been produced from ABI data. Ocean color data products have been operationally generated from AHI data at the Japan Space Agency, but the spatial coverage of the products over very turbid coastal waters are sometimes lacking. In this article, we describe atmospheric correction algorithms for retrieving water leaving reflectances from ABI and AHI data using spectrum-matching techniques. In order to estimate aerosol models and optical depths, we match simultaneously the satellite-measured top of atmosphere (TOA) reflectances on the pixel by pixel basis for three channels centered near 0.86, 1.61, and 2.25 μm (or any combinations of two channels among the three channels) with theoretically simulated TOA reflectances. We demonstrate that water leaving reflectance retrievals can be made from ABI and AHI data with our algorithms over turbid case two waters. Our spectrum-matching algorithms, if implemented onto operational computing facilities, can be complimentary to present operational ocean versions of atmospheric correction algorithms that are mostly developed based on the SeaWiFS type of two-band ratio algorithm.
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Kiyofuji, H., T. Hokimoto, and S. I. Saitoh. "Predicting the Spatiotemporal Chlorophyll-$a$Distribution in the Sea of Japan Based on SeaWiFS Ocean Color Satellite Data." IEEE Geoscience and Remote Sensing Letters 3, no. 2 (April 2006): 212–16. http://dx.doi.org/10.1109/lgrs.2005.861931.
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Sathyendranath, Shubha, Robert Brewin, Carsten Brockmann, Vanda Brotas, Ben Calton, Andrei Chuprin, Paolo Cipollini, et al. "An Ocean-Colour Time Series for Use in Climate Studies: The Experience of the Ocean-Colour Climate Change Initiative (OC-CCI)." Sensors 19, no. 19 (October 3, 2019): 4285. http://dx.doi.org/10.3390/s19194285.
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Ocean colour is recognised as an Essential Climate Variable (ECV) by the Global Climate Observing System (GCOS); and spectrally-resolved water-leaving radiances (or remote-sensing reflectances) in the visible domain, and chlorophyll-a concentration are identified as required ECV products. Time series of the products at the global scale and at high spatial resolution, derived from ocean-colour data, are key to studying the dynamics of phytoplankton at seasonal and inter-annual scales; their role in marine biogeochemistry; the global carbon cycle; the modulation of how phytoplankton distribute solar-induced heat in the upper layers of the ocean; and the response of the marine ecosystem to climate variability and change. However, generating a long time series of these products from ocean-colour data is not a trivial task: algorithms that are best suited for climate studies have to be selected from a number that are available for atmospheric correction of the satellite signal and for retrieval of chlorophyll-a concentration; since satellites have a finite life span, data from multiple sensors have to be merged to create a single time series, and any uncorrected inter-sensor biases could introduce artefacts in the series, e.g., different sensors monitor radiances at different wavebands such that producing a consistent time series of reflectances is not straightforward. Another requirement is that the products have to be validated against in situ observations. Furthermore, the uncertainties in the products have to be quantified, ideally on a pixel-by-pixel basis, to facilitate applications and interpretations that are consistent with the quality of the data. This paper outlines an approach that was adopted for generating an ocean-colour time series for climate studies, using data from the MERIS (MEdium spectral Resolution Imaging Spectrometer) sensor of the European Space Agency; the SeaWiFS (Sea-viewing Wide-Field-of-view Sensor) and MODIS-Aqua (Moderate-resolution Imaging Spectroradiometer-Aqua) sensors from the National Aeronautics and Space Administration (USA); and VIIRS (Visible and Infrared Imaging Radiometer Suite) from the National Oceanic and Atmospheric Administration (USA). The time series now covers the period from late 1997 to end of 2018. To ensure that the products meet, as well as possible, the requirements of the user community, marine-ecosystem modellers, and remote-sensing scientists were consulted at the outset on their immediate and longer-term requirements as well as on their expectations of ocean-colour data for use in climate research. Taking the user requirements into account, a series of objective criteria were established, against which available algorithms for processing ocean-colour data were evaluated and ranked. The algorithms that performed best with respect to the climate user requirements were selected to process data from the satellite sensors. Remote-sensing reflectance data from MODIS-Aqua, MERIS, and VIIRS were band-shifted to match the wavebands of SeaWiFS. Overlapping data were used to correct for mean biases between sensors at every pixel. The remote-sensing reflectance data derived from the sensors were merged, and the selected in-water algorithm was applied to the merged data to generate maps of chlorophyll concentration, inherent optical properties at SeaWiFS wavelengths, and the diffuse attenuation coefficient at 490 nm. The merged products were validated against in situ observations. The uncertainties established on the basis of comparisons with in situ data were combined with an optical classification of the remote-sensing reflectance data using a fuzzy-logic approach, and were used to generate uncertainties (root mean square difference and bias) for each product at each pixel.
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Radiarta, I. Nyoman, Sei-Ichi Saitoh, and Hajime Yasui. "Aquaculture site selection for Japanese kelp (Laminaria japonica) in southern Hokkaido, Japan, using satellite remote sensing and GIS-based models." ICES Journal of Marine Science 68, no. 4 (November 17, 2010): 773–80. http://dx.doi.org/10.1093/icesjms/fsq163.
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Abstract Radiarta, I N., Saitoh, S-I., and Yasui, H. 2011. Aquaculture site selection for Japanese kelp (Laminaria japonica) in southern Hokkaido, Japan, using satellite remote sensing and GIS-based models. – ICES Journal of Marine Science, 68: 773–780. Japanese kelp (Laminaria japonica) is an important species cultured and harvested in Japan. The most suitable areas for hanging culture in southern Hokkaido were determined using geographic information system (GIS) models and a multicriteria evaluation approach. Analyses of physical parameters (sea surface temperature and suspended solid from SeaWiFS and MODIS) and available bathymetric data indicated that some 74% (1139 km2) of the total potential area with bottom depths <60 m had the two highest suitability scores. A local sensitivity analysis indicated that suspended solids were more important than temperature in affecting model output. This study demonstrates that GIS databases of different formats and sources can be used effectively to construct spatial models for kelp aquaculture.
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Hsu, N. C., R. Gautam, A. M. Sayer, C. Bettenhausen, C. Li, M. J. Jeong, S. C. Tsay, and B. N. Holben. "Global and regional trends of aerosol optical depth over land and ocean using SeaWiFS measurements from 1997 to 2010." Atmospheric Chemistry and Physics Discussions 12, no. 3 (March 29, 2012): 8465–501. http://dx.doi.org/10.5194/acpd-12-8465-2012.
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Abstract. Both sensor calibration and satellite retrieval algorithm play an important role in the ability to determine accurately long-term trends from satellite data. Owing to the unprecedented accuracy and long-term stability of its radiometric calibration, the SeaWiFS measurements exhibit minimal uncertainty with respect to sensor calibration. In this study, we take advantage of this well-calibrated set of measurements by applying a newly-developed aerosol optical depth (AOD) retrieval algorithm over land and ocean to investigate the distribution of AOD, and to identify emerging patterns and trends in global and regional aerosol loading during its 13-yr mission. Our results indicate that the averaged AOD trend over global ocean is weakly positive from 1998 to 2010 and comparable to that observed by MODIS but opposite in sign to that observed by AVHRR during overlapping years. On a smaller scale, different trends are detected for different regions. For example, large upward trends are found over the Arabian Peninsula that indicate a strengthening of the seasonal cycle of dust emission and transport processes over the whole region as well as over downwind oceanic regions. In contrast, a negative-neutral tendency is observed over the desert/arid Saharan region as well as in the associated dust outflow over the North Atlantic. Additionally, we found decreasing trends over the Eastern US and Europe, and increasing trends over countries such as China and India that are experiencing rapid economic development. In general, these results are consistent with those derived from ground-based AERONET measurements.