Cross-calibration of MODIS and VIIRS long near infrared bands for ocean color science and applications

doi:10.1016/j.rse.2021.112439

Remote Sensing of Environment

Volume 260, July 2021, 112439

https://doi.org/10.1016/j.rse.2021.112439 Get rights and content

Highlights

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Simultaneous nadir overpass concept expanded for calibration of swath based sensors.
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Calibration of typically un-calibrated band differs by ~3% between MODIS and VIIRS.
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Cross-calibrating this band results leads to some improvement in product agreement.
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Cross-calibrating this band does not resolve most biases between sensors.

Abstract

Generation of consistent multi-sensor datasets is critical to the assessment of long-term global changes using satellite-borne instruments. Recent research suggests, however, that a fundamental assumption in satellite ocean color data processing concerning the calibration of the long near infrared band (i.e., 865 nm for MODIS) may introduce sensor-specific biases in space and/or time, which may also contribute to cross-sensor inconsistency in the derived reflectance data products. As such, it is necessary to assess the calibration of this band across sensors – performed here for MODIS/Aqua and VIIRS/SNPP using ‘simultaneous same view’ matchups (SSV; similar to simultaneous nadir overpass, but allowing for non-nadir measurements). Towards that end, we assess geometric, temporal, and spatial homogeneity metrics to identify SSVs, and develop a band-shifting approach applicable within standard satellite data processing routines to resolve expected spectral differences in the radiometry. We find top-of-atmosphere (TOA) radiance data from VIIRS/SNPP long near infrared band to be approximately 3% higher than the corresponding MODIS/A data. With the expectation that cross-calibrating the NIR_L should improve cross-sensor continuity of downstream geophysical products (e.g., chlorophyll-a), we reprocessed VIIRS data using updated calibration coefficients. While we noticed many minor improvements in cross-sensor continuity in such data products, large-scale geographic and temporal biases between these two datasets still remain. These discontinuities may be the result of disparate errors in polarization correction or atmospheric correction, both of which are modulated by radiant path geometry.

Introduction

Satellite ocean color instruments have provided otherwise unattainable data on spatial and temporal trends in the light field emanating from the world's oceans. However, the physical location of these sensors (on orbit) leads to difficulties in (1) discriminating oceanic from atmospheric signals in the measured total radiance signal; (2) calibrating and validating derived geophysical parameters (e.g., chlorophyll-a concentration; C_a); and (3) cross-calibrating multiple sensors towards longer-term datasets with minimal between-sensor uncertainties. Of these, the latter is particularly important in a climate context, as the usable life of any individual sensor (10 to 20 years at the absolute maximum) is likely too short to capture climate-scale variability (e.g., Lee et al., 2010).

Numerous previous works have investigated such climate-scale variability, using either single-sensor (Gregg et al., 2005; Henson et al., 2010; Siegel et al., 2013; Vantrepotte and Mélin, 2011) or merged-sensor (Gregg and Rousseaux, 2014; Lee et al., 2010; Signorini et al., 2015) datasets. These studies largely show no trend or a declining trend in global C_a, with large regional or basin-scale variability. Nevertheless, most of them note difficulties in statistical assessments of their respective datasets, owing to uncertainties in merging data from multiple sensors, or in drawing conclusions from datasets of too short duration. For example, Signorini et al. (Signorini et al., 2015) considered 16 years of MODIS/A [Moderate Resolution Imaging Spectroradiometer onboard Aqua] and SeaWiFS [Sea-viewing Wide Field-of-View Sensor onboard OrbView2] data. While they note general agreements between trends as derived using these two sensors (and a merged-sensor dataset), data from South Atlantic showed a positive trend using 11 years of SeaWiFS data, but a negative trend using either 11 years of MODIS data or the merged-sensor dataset. Gregg and Rousseaux (Gregg and Rousseaux, 2014) noted that C_a time series that switch from SeaWiFS to MODIS data always showed a negative trend due to a bias between the sensors, and thereby recommended other techniques to reduce cross-sensor differences (Gregg and Casey, 2010). In contrast, for waters offshore China, Zhang et al. (Zhang et al., 2006) noted strong continuity (with no significant bias) in C_a between the same two sensors.

Such cross-sensor comparisons, by definition, require aggregations of large quantities of data, with globally and/or regionally averaged statistics obscuring the complexities of the differences between sensors (Djavidnia et al., 2010; Mélin, 2010). Indeed, Djavidna et al. (Djavidnia et al., 2010) found overall continuity between MODIS- and SeaWiFS- derived C_a, but noted significant modulations of this relationship both regionally and seasonally. Similarly, Melin et al. (Mélin et al., 2016) found spatiotemporal patterns of discontinuities between SeaWiFS, MODIS, and MERIS [Medium Resolution Imaging Spectrometer onboard Envisat] remote sensing reflectance (R_rs, in sr⁻¹) products. For demonstration of these discontinuities between MODIS and VIIRS/SNPP [Visible-Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-orbiting Partnership (SNPP)], Fig. 1 shows the percent difference in R_rs and C_a as calculated using NASA default processing routines (specifically, [(MODIS-VIIRS)/MODIS], see Section 2.4. for processing details). Numerous potential sources of such discontinuities have been offered, including temporal differences in overpass times (Zhang et al., 2006), data quantity (Barnes and Hu, 2015), viewing geometry (Barnes and Hu, 2016), solar geometry (Djavidnia et al., 2010; Mélin et al., 2016; Zibordi et al., 2012), as well as C_a and aerosol optical thickness (Zibordi et al., 2012).

Amidst these potential sources of uncertainty, a fundamental assumption in system vicarious calibration (SVC) and atmospheric correction of satellite ocean color data concerns the long near infrared (NIR_L) band. In effect, the pre-launch calibration of the NIR_L band is considered sufficient for NASA's current operational SVC and AC procedures. This is supported by findings of Wang and Gordon (Wang and Gordon, 2002) that, based on radiative transfer simulations, moderate (±5%) errors in NIR_L calibration have minimal impacts (2–3%) on subsequently derived remote sensing reflectance (R_rs) in the visible wavelengths. As such, the gain (also termed g-factor; g) for the NIR_L band on any given sensor is not vicariously calibrated after launch, and is assigned a value of 1.0, with the radiance from this band then used “as-is” for calculation of atmospheric aerosol contributions to the measured total radiance prior to vicarious calibration of all other bands. While this overarching assumption may be true when performance is evaluated using discrete data points from either simulations or field measurements, recent research suggests that operational uncertainties resulting from this assumption are not equally distributed in space or time (Barnes et al., 2020).

The focus of this work is the intersection of noted cross-sensor differences in space and time (e.g., Fig. 1; Djavidnia et al., 2010; Mélin et al., 2016) and the sensitivity of R_rs(VIS) to g(NIR_L) within individual sensor datasets (Barnes et al., 2020). As such, the overall objective of this work is to cross-calibrate the NIR_L bands of MODIS/A and VIIRS/SNPP using their on-orbit measurements over global oceans, specifically scaling VIIRS/SNPP to match MODIS/A. Throughout this process, we seek to objectively identify characteristics of MODIS / VIIRS pixel pairs that are appropriate for such cross-calibration. Following this, we assess the impacts of cross-calibrating these NIR_L bands on the continuity of downstream ocean color products including R_rs and C_a (Hu et al., 2012; O'Reilly et al., 2000), with the hypothesis that improving NIR_L cross-calibration will result in greater continuity of downstream products. A fundamental question behind this work is, if MODIS were to replace VIIRS on SNPP (or vice versa, if VIIRS were to replace MODIS on the Aqua satellite), would they measure identical top-of-atmosphere (TOA) radiance over the same ocean pixels?

Section snippets

Cross-calibration approach

Data from five ocean gyres, representing the “clearest ocean waters” (Morel et al., 2010) were used in this study (Fig. 2). Compared to coastal waters, gyre targets are desirable for cross-calibration work as they more likely (1) include negligible water leaving radiance in the NIR (i.e., black-pixel assumption; Gordon and Wang, 1994), (2) are spatially homogeneous, (3) are overlain by primarily marine aerosols, and (4) are less optically complex than coastal waters, reducing uncertainties in

Results

While SSV were identified in each of the 14 overpass matches (Fig. 3), the quantity of SSV data differed greatly by gyre. Only two dates contained SSV in the NPG, with total data quantity of 4316 matchups. As such, NPG data are often excluded in subsequent analyses and discussion. Despite the SAG having only one overpass match, 40 dates of this overpass match included SSV, with total data quantity of 3.6e5 matchups. This is similar to the data quantity in SPG (4.6e5), where SSV were spread out

Discussion

Our analysis of all matchups which met the initial criteria for SSV showed general agreement between the gyres as to the median L_t(M'862)/L_t(V862) (0.95–0.98), with more substantial variability in MAD (Fig. 6). Of all the parameters tested, tightening of the spatial homogeneity metrics (CV and MMR) showed the most prominent impacts on MAD, with all gyres showing steady decreases with more stringent heterogeneity thresholds (Fig. 7). From this, we determined that an MMR threshold of 1.02 or a CV

Conclusions

In this work, we assessed the cross-sensor consistency of the NIR_L bands of two mainstream sensors (MODIS/Aqua and VIIRS/SNPP) while weighing techniques commonly used to identify pixel matchups appropriate for satellite cross-calibration. In doing so, we note metrics for spatial homogeneity and relative azimuth are pivotal towards isolating high-quality simultaneous same view (SSV) matchups. Additionally, we found a ~ 3% difference in the pre-launch calibration of the NIR_L bands of the two

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgments

This work was supported by funding from the United States National Aeronautics and Space Administration (NASA): NNX16AQ71G (BBB and CH), ROSES 17-TASNPP17-0065 (SWB and BAF), and contract 80GSFC20C0044 (NP). The authors wish to thank NASA for providing the data and processing software used in this work, as well as three anonymous reviewers, whose comments led to substantial improvements in this manuscript.

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Cross-calibration of MODIS and VIIRS long near infrared bands for ocean color science and applications

Highlights

Abstract

Introduction

Section snippets

Cross-calibration approach

Results

Discussion

Conclusions

Declaration of Competing Interest

Acknowledgments

Remote Sens. Environ.

Remote Sens. Environ.

Remote Sens. Environ.

Remote Sens. Environ.

Remote Sensing of Environment

Environ.

Remote Sens. Environ.

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Opt. Express

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IEEE Trans. Geosci. Remote Sens.

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