A multi-sensor approach for the on-orbit validation of ocean color satellite data products

Bailey, Sean W.; Werdell, P. Jeremy

doi:10.1016/j.rse.2006.01.015

Cited by 790 publications

(517 citation statements)

References 50 publications

Supporting

Mentioning

498

Contrasting

Unclassified

Order By: Relevance

“…We also acquired coincident Level-2 satellite-to-in situ match-ups for the NASA SeaWiFS and MODISA instruments from the OBPG (http://seabass.gsfc.nasa .gov/seabasscgi/validation_search.cgi). Satellite data processing and quality assurance for the matchups followed Bailey and Werdell [33]. Specifically: (1) temporal coincidence was defined as 3 h; (2) satellite values were the filtered median (via the semiinterquartile range) of all unmasked pixels in a 5 × 5 box centered on the in situ target; and (3) satellite values were excluded when the coefficient of variation for the given product within this box exceeded 0.15.…”

Section: Data Acquisitionmentioning

confidence: 99%

Generalized ocean color inversion model for retrieving marine inherent optical properties

Werdell¹,

Franz²,

Bailey³

et al. 2013

Appl. Opt.

Self Cite

352

317

View full text Add to dashboard Cite

Ocean color measured from satellites provides daily, global estimates of marine inherent optical properties (IOPs). Semi-analytical algorithms (SAAs) provide one mechanism for inverting the color of the water observed by the satellite into IOPs. While numerous SAAs exist, most are similarly constructed and few are appropriately parameterized for all water masses for all seasons. To initiate community-wide discussion of these limitations, NASA organized two workshops that deconstructed SAAs to identify similarities and uniqueness and to progress toward consensus on a unified SAA. This effort resulted in the development of the generalized IOP (GIOP) model software that allows for the construction of different SAAs at runtime by selection from an assortment of model parameterizations. As such, GIOP permits isolation and evaluation of specific modeling assumptions, construction of SAAs, development of regionally tuned SAAs, and execution of ensemble inversion modeling. workshops proposed a preliminary default configuration for GIOP (GIOP-DC), with alternative model parameterizations and features defined for subsequent evaluation. In this paper, we: (1) describe the theoretical basis of GIOP; (2) present GIOP-DC and verify its comparable performance to other popular SAAs using both in situ and synthetic data sets; and, (3) quantify the sensitivities of their output to their parameterization. We use the latter to develop a hierarchical sensitivity of SAAs to various model parameterizations, to identify components of SAAs that merit focus in future research, and to provide material for discussion on algorithm uncertainties and future emsemble applications.

show abstract

Section: Data Acquisitionmentioning

confidence: 99%

Generalized ocean color inversion model for retrieving marine inherent optical properties

Werdell¹,

Franz²,

Bailey³

et al. 2013

Appl. Opt.

Self Cite

352

317

View full text Add to dashboard Cite

show abstract

“…Fulfilling this accuracy requirement is however challenged by uncertainties affecting the absolute and vicarious calibration of the space sensors, the atmospheric correction process and the bio-optical characteristics of the ocean (Gregg and Casey, 2004). Furthermore, global empirical algorithms, such as those used to operationally retrieve CHL, are derived from regression analyses of large in situ databases collected from waters around the world (O'Reilly et al, 1998;O'Reilly et al, 2000;Werdell and Bailey, 2005) and therefore have a tendency to perform well only at global scale (Bailey and Werdell, 2006;Bailey et al, 2000;Gregg and Casey, 2004;Hooker and McClain, 2000;O'Reilly et al, 1998). The accuracy limit for chlorophyll has been shown to be unrealistic for many open ocean regions, such as the Baltic Sea (Darecki and Stramski, 2004), the Southern Ocean (Kahru and Mitchell, 2010) and the Mediterranean Sea (Volpe et al, 2007).…”

Section: G Volpe Et Al: the Mediterranean Ocean Colour Observing Symentioning

confidence: 99%

The Mediterranean Ocean Colour Observing System – system development and product validation

et al. 2012

View full text Add to dashboard Cite

Abstract. This paper presents the Mediterranean Ocean Colour Observing System in the framework of the growing demand of near real-time data emerging within the operational oceanography international context. The main issues related to the satellite operational oceanography are tied to the following: (1) the near real-time ability to track data flow uncertainty sources; (2) in case of failure, to provide backup solutions to end-users; and (3) to scientifically assess the product quality. We describe the major scientific and technological steps made to develop, maintain and improve the operational system and its products. A method for assessing the near real-time product quality is developed and its limitation discussed. Main results are concerned with the degradation, starting from mid-2010, of the MODIS Aqua channel at 443 nm with its successive recovery thanks to the new calibration scheme implemented in the recently released SeaDAS version 6.4. The product validation analysis highlights that SeaWiFS chlorophyll product over the Mediterranean Sea is the best performing in comparison with those of MODIS and MERIS. Despite their general good agreement with in situ observations, MODIS-and MERIS-derived chlorophyll present a slight and systematic underestimation of the in situ counter part. The most relevant implications induced by these results are discussed from an operational point of view.

show abstract

“…Each image contains the amplitude and phase information and some attributions such as corner longitude/latitude location, time, Doppler parameters, and other values processed by the processor. To produce simulated Radarsat-2 and TerraSAR-X images for MCDONNELL DOUGLAS MD-80 aircraft target we use the SAR processing software, Multi-Sensor Processor (MSP) (Bailey and Werdell 2006), which was verified by the general satellite data (Bailey and Werdell 2006). Both the simulated Radarsat-2 and TerraSAR-X images are used to compare and evaluate the results using the LBM method.…”

Section: The Results Of Sar Echo Signal Simulation For Md80mentioning

confidence: 99%

SAR Image Simulations Using the LBM Algorithm on MPI-GPU

Sun¹,

Tsai²,

Chiang³

2016

Terr. Atmos. Ocean. Sci.

View full text Add to dashboard Cite

Synthetic Aperture Radar (SAR) is a powerful tool for studying natural environments under all-weather and day-andnight conditions. SAR system design and data-processing algorithm simulation is noted for its controllable parameters. The satellite SAR echo signal simulation framework has been successfully applied to target recognition based on Radarsat-2 and TerraSAR-X images and in strip map mode. However, such SAR image simulation works only on CPU or GPU (graphics processing units) and requires huge calculations. We developed a "Load-Balancing Model (LBM)" algorithm that uses Message Passing Interface GPU (MPI-GPU) to reduce the inner loop load and improve the computational performance. The LBM algorithm uses MPI-GPU technology to build the simple GPU cluster system. The LBM algorithm is used to separate the intensive computing and controlling tasks for each node, and exploit the contemporary GPU computation capability to accelerate the computing tasks. We conducted a relevant experiment on a target radar cross section (RCS) and improved the performance by a factor of > 40 compared to a 4-core CPU accelerated program.

show abstract

A multi-sensor approach for the on-orbit validation of ocean color satellite data products

Cited by 790 publications

References 50 publications

Generalized ocean color inversion model for retrieving marine inherent optical properties

Generalized ocean color inversion model for retrieving marine inherent optical properties

The Mediterranean Ocean Colour Observing System – system development and product validation

SAR Image Simulations Using the LBM Algorithm on MPI-GPU

Contact Info

Product

Resources

About