scholarly journals Evolution of Ocean Color Atmospheric Correction: 1970–2005

2021 ◽  
Vol 13 (24) ◽  
pp. 5051
Author(s):  
Howard R. Gordon
Keyword(s):  
Coastal Waters ◽  
Spectral Region ◽  
Ocean Color ◽  
Atmospheric Correction ◽  
Visible Region ◽  
Coastal Areas ◽  
Open Ocean ◽  
Water Properties ◽  
Blue Spectral Region ◽  
Global Data

Retrieval of water properties from satellite-borne imagers viewing oceans and coastal areas in the visible region of the spectrum requires removing the effect of the atmosphere, which contributes approximately 80–90% of the measured radiance over the open ocean in the blue spectral region. The Gordon and Wang algorithm originally developed for SeaWiFS (and used with other NASA sensors, e.g., MODIS) forms the basis for many atmospheric removal (correction) procedures. It was developed for application to imagery obtained over the open ocean (Case 1 waters), where the aerosol is usually non-absorbing, and is used operationally to process global data from SeaWiFS, MODIS and VIIRS. Here, I trace the evolution of this algorithm from early NASA aircraft experiments through the CZCS, OCTS, SeaWiFs, MERIS, and finally the MODIS sensors. Strategies to extend the algorithm to situations where the aerosol is strongly absorbing are examined. Its application to sensors with additional and unique capabilities is sketched. Problems associated with atmospheric correction in coastal waters are described.

scholarly journals Atmospheric Correction of GOCI Using Quasi-Synchronous VIIRS Data in Highly Turbid Coastal Waters

2019 ◽  
Vol 12 (1) ◽  
pp. 89
Author(s):  
Jie Wu ◽  
Chuqun Chen ◽  
Sravanthi Nukapothula
Keyword(s):  
Coastal Waters ◽  
Near Infrared ◽  
Infrared Imaging ◽  
Ocean Color ◽  
Atmospheric Correction ◽  
Turbid Waters ◽  
Aerosol Scattering ◽  
Temporal And Spatial ◽  
Shortwave Infrared ◽  
The Western Pacific

The Geostationary Ocean Color Imager (GOCI) sensor, with high temporal and spatial resolution (eight images per day at an interval of 1 hour, 500 m), is the world’s first geostationary ocean color satellite sensor. GOCI provides good data for ocean color remote sensing in the Western Pacific, among the most turbid waters in the world. However, GOCI has no shortwave infrared (SWIR) bands making atmospheric correction (AC) challenging in highly turbid coastal regions. In this paper, we have developed a new AC algorithm for GOCI in turbid coastal waters by using quasi-synchronous Visible Infrared Imaging Radiometer Suite (VIIRS) data. This new algorithm estimates and removes the aerosol scattering reflectance according to the contributing aerosol models and the aerosol optical thickness estimated by VIIRS’s near-infrared (NIR) and SWIR bands. Comparisons with other AC algorithms showed that the new algorithm provides a simple, effective, AC approach for GOCI to obtain reasonable results in highly turbid coastal waters.

Atmospheric correction of satellite ocean color data in turbid coastal waters

2006 ◽  
Author(s):  
Yu-Hwan Ahn ◽  
Palanisamy Shanmugam ◽  
Joo-Hyung Ryu
Keyword(s):  
Coastal Waters ◽  
Ocean Color ◽  
Atmospheric Correction ◽  
Color Data ◽  
Satellite Ocean Color

scholarly journals Error Budget in the Validation of Radiometric Products Derived from OLCI around the China Sea from Open Ocean to Coastal Waters Compared with MODIS and VIIRS

2019 ◽  
Vol 11 (20) ◽  
pp. 2400 ◽  
Author(s):  
Li ◽  
Jamet ◽  
Zhu ◽  
Han ◽  
Li ◽  
...  
Keyword(s):  
Coastal Waters ◽  
Atmospheric Correction ◽  
Open Ocean ◽  
Error Source ◽  
Error Budget ◽  
Average Percentage ◽  
China Sea ◽  
Primary Error ◽  
Percentage Difference

The accuracy of remote-sensing reflectance (Rrs) estimated from ocean color imagery through the atmospheric correction step is essential in conducting quantitative estimates of the inherent optical properties and biogeochemical parameters of seawater. Therefore, finding the main source of error is the first step toward improving the accuracy of Rrs. However, the classic validation exercises provide only the total error of the retrieved Rrs. They do not reveal the error sources. Moreover, how to effectively improve this satellite algorithm remains unknown. To better understand and improve various aspects of the satellite atmospheric correction algorithm, the error budget in the validation is required. Here, to find the primary error source from the OLCI Rrs, we evaluated the OLCI Rrs product with in-situ data around the China Sea from open ocean to coastal waters and compared them with the MODIS-AQUA and VIIRS products. The results show that the performances of OLCI are comparable to those MODIS-AQUA. The average percentage difference (APD) in Rrs is lowest at 490 nm (18%), and highest at 754 nm (79%). A more detailed analysis reveals that open ocean and coastal waters show opposite results: compared to coastal waters the satellite Rrs in open seas are higher than the in-situ measured values. An error budget for the three satellite-derived Rrs products is presented, showing that the primary error source in the China Sea was the aerosol estimation and the error on the Rayleigh-corrected radiance for OLCI, as well as for MODIS and VIIRS. This work suggests that to improve the accuracy of Sentinel-3A in the coastal waters of China, the accuracy of aerosol estimation in atmospheric correction must be improved.

An Atmospheric Correction Algorithm for Remote Sensing of Bright Coastal Waters Using MODIS Land and Ocean Channels in the Solar Spectral Region

2007 ◽  
Vol 45 (6) ◽  
pp. 1835-1843 ◽  
Author(s):  
Bo-Cai Gao ◽  
Marcos J. Montes ◽  
Rong-Rong Li ◽  
Heidi Melita Dierssen ◽  
Curtiss O. Davis
Keyword(s):  
Remote Sensing ◽  
Coastal Waters ◽  
Spectral Region ◽  
Atmospheric Correction ◽  
Correction Algorithm

scholarly journals Updated MISR Over-Water Research Aerosol Retrieval Algorithm Part 2: A Multi- Angle Aerosol Retrieval Algorithm for Shallow, Turbid, Oligotrophic, and Eutrophic Waters

2018 ◽  
Author(s):  
James A. Limbacher ◽  
Ralph A. Kahn
Keyword(s):  
Remote Sensing ◽  
Coastal Waters ◽  
Ocean Color ◽  
Atmospheric Correction ◽  
Ocean Surface ◽  
Retrieval Algorithm ◽  
Biological Productivity ◽  
Single View ◽  
Aerosol Retrieval ◽  
Ångström Exponent

Abstract. Coastal waters serve as transport pathways to the ocean for all agricultural and other runoff from terrestrial sources; they are also some of the most biologically productive on the planet. Estimating the impact coastal waters have on the global carbon budget requires relating satellite-based remote-sensing retrievals of biological productivity (e.g., Chlorophyll-a concentration) to in-situ measurements taken in near-surface waters. The Multi-angle Imaging SpectroRadiometer (MISR) can uniquely constrain the “atmospheric correction” needed to derive ocean color from remote-sensing imagers. Here, we retrieve aerosol amount and type from MISR over all types of water. The primary limitation is an upper bound on aerosol optical depth (AOD), as the algorithm must be able to distinguish the surface. This updated MISR research aerosol retrieval algorithm (RA) also assumes that light reflection by the underlying ocean surface is Lambertian. The RA computes the ocean surface reflectance (Rrs) analytically for a given AOD, aerosol optical model, and wind speed. We provide retrieval examples over shallow, turbid, and eutrophic waters and introduce a productivity/turbidity index (PTI), calculated from retrieved spectral Rrs, that distinguished water types (similar to NDVI over land). We also validate the new algorithm by comparing spectral AOD and Angstrom exponent (ANG) results with 2419 collocated AERosol RObotic NETwork (AERONET) observations. For AERONET 558 nm interpolated AOD  0.20, the ANG RMSE is 0.25 and r = 0.89. Although MISR RA AOD retrieval quality does not appear to be substantially impacted by the presence of turbid water, MISR RA-retrieved Angstrom exponent seems to suffer from increased uncertainty under such conditions. MISR supplements current ocean color sources in regions where sun glint precludes retrievals from single-view-angle instruments. MISR atmospheric correction should also be more robust than that derived from single-view instruments such as MODIS. This is especially true in regions of shallow, turbid, and eutrophic waters, locations where biological productivity can be high, and single-view angle retrieval algorithms struggle to separate atmospheric from oceanic features.

scholarly journals Updated MISR over-water research aerosol retrieval algorithm – Part 2: A multi-angle aerosol retrieval algorithm for shallow, turbid, oligotrophic, and eutrophic waters

2019 ◽  
Vol 12 (1) ◽  
pp. 675-689 ◽  
Author(s):  
James A. Limbacher ◽  
Ralph A. Kahn
Keyword(s):  
Remote Sensing ◽  
Coastal Waters ◽  
Ocean Color ◽  
Atmospheric Correction ◽  
Ocean Surface ◽  
Retrieval Algorithm ◽  
Biological Productivity ◽  
Single View ◽  
Aerosol Retrieval ◽  
Ångström Exponent

Abstract. Coastal waters serve as transport pathways to the ocean for all agricultural and other runoff from terrestrial sources, and many are the sites for upwelling of nutrient-rich, deep water; they are also some of the most biologically productive on Earth. Estimating the impact coastal waters have on the global carbon budget requires relating satellite-based remote-sensing retrievals of biological productivity (e.g., chlorophyll a concentration) to in situ measurements taken in near-surface waters. The Multi-angle Imaging SpectroRadiometer (MISR) can uniquely constrain the “atmospheric correction” needed to derive ocean color from remote-sensing imagers. Here, we retrieve aerosol amount and type from MISR over all types of water. The primary limitation is an upper bound on aerosol optical depth (AOD), as the algorithm must be able to distinguish the surface. This updated MISR research aerosol retrieval algorithm (RA) also assumes that light reflection by the underlying ocean surface is Lambertian. The RA computes the ocean surface reflectance (Rrs) analytically for a given AOD, aerosol optical model, and wind speed. We provide retrieval examples over shallow, turbid, and eutrophic waters and introduce a productivity and turbidity index (PTI), calculated from retrieved spectral Rrs, that distinguished water types (similar to the the normalized difference vegetation index, NDVI, over land). We also validate the new algorithm by comparing spectral AOD and Ångström exponent (ANG) results with 2419 collocated AErosol RObotic NETwork (AERONET) observations. For AERONET 558 nm interpolated AOD < 1.0, the root-mean-square error (RMSE) is 0.04 and linear correlation coefficient is 0.95. For the 502 cloud-free MISR and AERONET collocations with an AERONET AOD > 0.20, the ANG RMSE is 0.25 and r is 0.89. Although MISR RA AOD retrieval quality does not appear to be substantially impacted by the presence of turbid water, the MISR-RA-retrieved Ångström exponent seems to suffer from increased uncertainty under such conditions. MISR supplements current ocean color sources in regions where sunglint precludes retrievals from single-view-angle instruments. MISR atmospheric correction should also be more robust than that derived from single-view instruments such as the Moderate Resolution Imaging Spectroradiometer (MODIS). This is especially true in regions of shallow, turbid, and eutrophic waters, locations where biological productivity can be high, and single-view-angle retrieval algorithms struggle to separate atmospheric from oceanic features.

scholarly journals Assessment of Polymer Atmospheric Correction Algorithm for Hyperspectral Remote Sensing Imagery over Coastal Waters

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4125
Author(s):  
Mariana A. Soppa ◽  
Brenner Silva ◽  
François Steinmetz ◽  
Darryl Keith ◽  
Daniel Scheffler ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Satellite Data ◽  
Coastal Waters ◽  
Spectral Region ◽  
Atmospheric Correction ◽  
Hyperspectral Remote Sensing ◽  
Water Colour ◽  
Percentage Difference ◽  
Green Spectral Region

Spaceborne imaging spectroscopy, also called hyperspectral remote sensing, has shown huge potential to improve current water colour retrievals and, thereby, the monitoring of inland and coastal water ecosystems. However, the quality of water colour retrievals strongly depends on successful removal of the atmospheric/surface contributions to the radiance measured by satellite sensors. Atmospheric correction (AC) algorithms are specially designed to handle these effects, but are challenged by the hundreds of narrow spectral bands obtained by hyperspectral sensors. In this paper, we investigate the performance of Polymer AC for hyperspectral remote sensing over coastal waters. Polymer is, in nature, a hyperspectral algorithm that has been mostly applied to multispectral satellite data to date. Polymer was applied to data from the Hyperspectral Imager for the Coastal Ocean (HICO), validated against in situ multispectral (AERONET-OC) and hyperspectral radiometric measurements, and its performance was compared against that of the hyperspectral version of NASA’s standard AC algorithm, L2gen. The match-up analysis demonstrated very good performance of Polymer in the green spectral region. The mean absolute percentage difference across all the visible bands varied between 16% (green spectral region) and 66% (red spectral region). Compared with L2gen, Polymer remote sensing reflectances presented lower uncertainties, greater data coverage, and higher spectral similarity to in situ measurements. These results demonstrate the potential of Polymer to perform AC on hyperspectral satellite data over coastal waters, thus supporting its application in current and future hyperspectral satellite missions.

scholarly journals Inversion of multiangular polarimetric measurements over open and coastal ocean waters: a joint retrieval algorithm for aerosol and water-leaving radiance properties

2019 ◽  
Vol 12 (7) ◽  
pp. 3921-3941 ◽  
Author(s):  
Meng Gao ◽  
Peng-Wang Zhai ◽  
Bryan A. Franz ◽  
Yongxiang Hu ◽  
Kirk Knobelspiesse ◽  
...  
Keyword(s):  
Optical Properties ◽  
Coastal Waters ◽  
Ocean Color ◽  
Atmospheric Correction ◽  
Coastal Ocean ◽  
High Spectral Resolution ◽  
Radiative Transfer Model ◽  
Retrieval Algorithm ◽  
Transfer Model ◽  
Ocean Water

Abstract. Ocean color remote sensing is a challenging task over coastal waters due to the complex optical properties of aerosols and hydrosols. In order to conduct accurate atmospheric correction, we previously implemented a joint retrieval algorithm, hereafter referred to as the Multi-Angular Polarimetric Ocean coLor (MAPOL) algorithm, to obtain the aerosol and water-leaving signal simultaneously. The MAPOL algorithm has been validated with synthetic data generated by a vector radiative transfer model, and good retrieval performance has been demonstrated in terms of both aerosol and ocean water optical properties (Gao et al., 2018). In this work we applied the algorithm to airborne polarimetric measurements from the Research Scanning Polarimeter (RSP) over both open and coastal ocean waters acquired in two field campaigns: the Ship-Aircraft Bio-Optical Research (SABOR) in 2014 and the North Atlantic Aerosols and Marine Ecosystems Study (NAAMES) in 2015 and 2016. Two different yet related bio-optical models are designed for ocean water properties. One model aligns with traditional open ocean water bio-optical models that parameterize the ocean optical properties in terms of the concentration of chlorophyll a. The other is a generalized bio-optical model for coastal waters that includes seven free parameters to describe the absorption and scattering by phytoplankton, colored dissolved organic matter, and nonalgal particles. The retrieval errors of both aerosol optical depth and the water-leaving radiance are evaluated. Through the comparisons with ocean color data products from both in situ measurements and the Moderate Resolution Imaging Spectroradiometer (MODIS), and the aerosol product from both the High Spectral Resolution Lidar (HSRL) and the Aerosol Robotic Network (AERONET), the MAPOL algorithm demonstrates both flexibility and accuracy in retrieving aerosol and water-leaving radiance properties under various aerosol and ocean water conditions.

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