On-orbit calibration of SeaWiFS

Eplee, Robert E.; Meister, Gerhard; Patt, Frederick S.; Barnes, Robert A.; Bailey, Sean W.; Franz, Bryan A.; McClain, Charles R.

doi:10.1364/ao.51.008702

Cited by 64 publications

(37 citation statements)

References 34 publications

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“…Thus, S was chosen to be a diagonal matrix, with elements corresponding to I having an uncertainty of 3%, and those corresponding to DoLP and uncertainty of 0.5%. These values correspond to reasonable radiometric uncertainties for characterized orbital instruments (such as Eplee et al (2012)), and to desired polarimetric uncertainties cited for most future polarimetric instrument designs (Kokhanovsky et al (2015)). …”

supporting

confidence: 70%

Remote sensing of aerosols with small satellites in formation flight

Knobelspiesse¹,

Nag²

2018

Preprint

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Abstract. Determination of aerosol optical properties with orbital passive remote sensing is a difficult task, as observations often have limited information. Multi-angle instruments, such as the Multi-angle Imaging SpectroRadiometer (MISR) and the POlarization and Directionality of the Earth's Reflectances (POLDER), seek to address this by making information rich multi-angle observations, which can be used to better retrieve aerosol optical properties. The paradigm for such instruments is that each angle view is made from one platform, with, for example, a gimbaled sensor or multiple fixed view angle sensors. 5This restricts the observing geometry to a plane within the scene Bidirectional Reflectance Distribution Function (BRDF ) observed at the top of the atmosphere (TOA). New technological developments, however, support sensors on small satellites flying in formation, which could be a beneficial alternative. Such sensors may have only one viewing direction each, but the agility of small satellites allows one to control this direction and change it over time. When such agile satellites are flown in formation and their sensors pointed to the same location at approximately the same time, they could sample a distributed set 10 of geometries within the scene BRDF . In other words, observations from multiple satellites can take a variety view zenith and azimuth angles, and are not restricted to one azimuth plane as is the case with a single multi-angle instrument. It is not known, however, if this is as potentially capable as a multi-angle platform for the purposes of aerosol remote sensing. Using a systems engineering tool coupled with an information content analysis technique, we investigate the feasibility of such an approach for the remote sensing of aerosols. These tools test the mean results of all geometries encountered in an orbit. We 15 find that small satellites in formation are equally capable as multi-angle platforms for aerosol remote sensing, as long as their calibration accuracies and measurement uncertainties are equivalent. As long as the viewing geometries are dispersed throughout the BRDF , it appears the quantity of view angles determines the information content of the observations, not the specific observation geometry. Given the smoothly varying nature of BRDF 's observed at the TOA, this is reasonable, and supports the viability of aerosol remote sensing with small satellites flying in formation. The incremental improvement in 20 information content that we found with number of view angles also supports the concept of a resilient mission comprised of multiple satellites that are continuously replaced as they age or fail.

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supporting

confidence: 70%

Remote sensing of aerosols with small satellites in formation flight

Knobelspiesse¹,

Nag²

2018

Preprint

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“…After 2007, the SeaWiFS record actually showed gaps in the series, and after 2012, MODIS data started showing signs of sensor ageing. NASA's work on instruments calibration (Xiong et al 2010;Eplee et al 2012) supports the assumption that no significant artefact results from the instrument calibration history for each mission, an assumption that could be revised as knowledge about the instruments is further improved. It is stressed that the last years of the MODIS record are excluded to avoid artefacts that could come from an insufficiently corrected radiometric degradation of the sensor (Meister and Franz 2014).…”

Section: Datamentioning

confidence: 95%

Impact of inter-mission differences and drifts on chlorophyll-atrend estimates

Mélin

2016

International Journal of Remote Sensing

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The chlorophyll-a (chl-a) concentration is an Essential Climate Variable, and the study of its variability at global scale requires a succession of satellite ocean colour missions to cover a period suitable for climate research. In the context of a multi-mission data record, inter-mission differences can introduce artefacts affecting trend evaluations, and the impact of the bias between the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) and Moderate Resolution Imaging Spectroradiometer (MODIS) chl-a products is shown to be significant in a substantial part of the ocean. The assessment of trends can also be directly impacted by a drift in the chl-a time series resulting from sensor functions. These issues are addressed by a sensitivity analysis that compares slopes of linear regression obtained for varying levels of intermission bias and drift with respect to a 15-year reference series built with SeaWiFS and MODIS data. The relationship, constructed for a representative set of ocean provinces, between bias and the level of significance associated with the comparison of slopes shows that a bias on the order of ±5-6% generally induces a slope that is significantly different from the reference case, while a threshold on bias values not exceeding 2% largely alleviates this effect. Moreover, the study suggests that a drift larger than 2% per decade on the chl-a series can result in misleading conclusions from a trend analysis. All results have a clear regional dependence that needs to be taken into account in bias-correction and merging efforts. Low chl-a regions, such as the oligotrophic subtropical gyres, appear particularly sensitive to perturbations and require still higher levels of consistency and stability.ARTICLE HISTORY

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“…where: A thorough discussion of SeaWiFS instrument calibration will take place in Eplee et al(2012), 11 so a summary of the uncertainties in the calibration coefficients determined from the prelaunch instrument characterization (K c , K rvs ) and from the detailed on-orbit calibration data analysis (K T , K ms ) are presented in Table 1. Uncertainty in counts-to-radiance conversion coefficients K c is the absolute calibration uncertainty and the primary source of the instrument calibration bias.…”

Section: Calibration Of On-orbit Datamentioning

confidence: 99%

Uncertainty assessment of the SeaWiFS on-orbit calibration

et al. 2011

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Ocean color climate data records require water-leaving radiances with 5% absolute and 1% relative accuracies as input. Because of the amplification of any sensor calibration errors by the atmospheric correction, the 1% relative accuracy requirement translates into a 0.1% long-term radiometric stability requirement for top-of-theatmosphere radiances. The rigorous on-orbit calibration program developed and implemented for SeaWiFS by the NASA Ocean Biology Processing Group (OBPG) Calibration and Validation Team (CVT) has allowed the CVT to maintain the stability of the radiometric calibration of SeaWiFS at 0.13% or better over the mission. The uncertainties in the resulting calibrated top-of-the-atmosphere (TOA) radiances can be addressed in terms of accuracy (biases in the measurements), precision (scatter in the measurements), and stability (repeatability of the measurements). The calibration biases of lunar observations relative to the USGS RObotic Lunar Observatory (ROLO) photometric model of the Moon are 2-3%. The biases from the vicarious calibration against the Marine Optical Buoy (MOBY) are 1-2%. The precision of the calibration derived from the solar calibration signal-tonoise ratios are 0.16%, from the lunar residuals are 0.13%, and from the vicarious gains are 0.10%. The long-term stability of the TOA radiances, derived from the lunar time series, is 0.13%. The stability of the vicariouslycalibrated TOA radiances, incorporating the uncertainties in the MOBY measurements and the atmospheric correction, is 0.30%. These results allow the OBPG to produce climate data records from the SeaWiFS ocean color data.

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On-orbit calibration of SeaWiFS

Cited by 64 publications

References 34 publications

Remote sensing of aerosols with small satellites in formation flight

Remote sensing of aerosols with small satellites in formation flight

Impact of inter-mission differences and drifts on chlorophyll-atrend estimates

Uncertainty assessment of the SeaWiFS on-orbit calibration

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