*2.2. General Issues*

The general conditions of SNOs and radiometric intercomparison have been well described [9–11,24] and the details are not repeated here. The focus here is on issues pertaining to the capability of the radiometric intercomparison methodology.

#### 2.2.1. SNO Geolocation and Scenes

The occurrences of SNOs are determined by the flight trajectories of the satellites. Figure 2 displays the SNO locations for three pairs of satellites—Sentinel-3A versus SNPP for the year 2017 (red triangles), Terra (green stars), and Aqua (blue squares) versus SNPP (green stars) up to end of 2017—showing the northern polar region in Figure 2a and the southern polar region in Figure 2b. The 2017 SNO subsets are highlighted for Terra versus SNPP (magenta triangles) and Aqua versus SNPP (cyan squares); this is to illustrate that SNO locations do not repeat yearly. The SNOs of Sentinel-3A versus SNPP, with descending node for the former and ascending node for the latter, are concentrated within a tight circular band at around N71◦ latitude just inside the Artic Zone, with no occurrences in the southern region. On the other hand, the SNOs of Terra versus SNPP, also in opposing descending and ascending node, occur over both northern and southern regions, tracing out near the N68 and S68 circulars. The SNOs of Aqua versus SNPP, both ascending node, occur over both northern and southern polar regions in an interesting three-arm spiral pattern. This illustrates that different orbits and flight parameters map out different SNO locations, and therefore the reflectance property and the common atmospheric conditions of these SNO scenes are important factors. For example, Aqua versus SNPP commonly crosses over icy scenes of Antarctica, which have scene radiance commonly above 50 watt/m2/sr/μm, thus easily saturate SNPP VIIRS M6 and many MODIS bands of low dynamic range.

(a)

**Figure 2.** The precise SNOs of Sentinel-3A versus SNPP satellite (green stars) in 2017, Aqua versus SNPP satellite for the entire SNPP mission (blue squares) and in 2017 (cyan squares), and Terra versus SNPP satellite for the entire SNPP mission (red diamonds) and in 2017 (magenta diamonds), over (**a**) northern polar region and (**b**) southern polar region.

Figure 3 shows the daily frequency of precise SNO events for the year 2017. An interesting finding is the extended four-month periods of missing SNOs events for Sentinel-3A versus SNPP satellites (green bars) that run from October to February. Although not shown, the late 2016 and early 2018 periods are also without SNO occurrences for Sentinel-3A versus SNPP. A quick check confirms that Sentinel-3A OLCI observational coverage changes throughout the year and does not extend beyond 71◦ latitude during those four-month gaps, and therefore, despite any actual SNO events of the two satellites, there is no OLCI data available. Another interesting result of Sentinel-3A versus SNPP is that the SNOs cluster in distinctive days, 45 days of multiple SNO occurrences that further group into 13 clusters, thus showing that mismatching flight parameters, such as 16-day versus 27-day repeat cycle for this case, can generate interesting occurrences.

**Figure 3.** SNO occurrences in 2017 for Sentinel-3A versus SNPP (green bars), Terra versus SNPP (magenta triangles), and Aqua versus SNPP (cyan squares).

#### 2.2.2. Spectral Match

The matching of two bands for radiometric comparison is customarily made according to their spectral proximity to ensure comparable radiometric responses over SNO scenes. In reality, most band pairs have RSR differences that induce yearly variability into the comparison time series. On the other hand, some band pairs not showing good spectral match, such as Aqua MODIS B3 versus SNPP VIIRS M3 with limited RSR overlap as shown in Figure 1, can still generate usable comparison time series [6,7]. A more extreme example is Aqua MODIS B7 (2130 nm) versus SNPP VIIRS M11 (2257 nm) [25] for which the two RSRs do not overlap but a marginally usable time series can still be generated. The impact of RSR mismatch and the full range of possibilities beyond the standard spectral-matching practice are not fully understood and should be pursued in future studies.

## 2.2.3. Dynamic Range

The limitation due the dynamic range is briefly presented here only for clarifications. The narrow dynamic range of a band can set a limitation impossible to overcome. For example, Aqua MODIS B15 (748 nm) versus SNPP VIIRS M6 (746 nm), with *LMAX* of 3.5 and 41 watt/m2/sr/μm, respectively, hardly generates any successful outcomes as both bands saturate over the higher-latitude icy polar scenes where their SNOs commonly occur. However, future sensors with progressively wider dynamic range are less likely to encounter saturation. For instance, Sentinel-3A OLCI bands already have sufficient dynamic range and show no saturation issue for this study. But for band M6 of all VIIRS builds at only 41 watt/m2/sr/μm *LMAX*, the success of SNOs involving VIIRS M6 is limited.

#### 2.2.4. Spatial Resolution

The central goal of this study is to assess the capability of radiometric intercomparison and the achievable statistics in different regimes of spatial resolution. That is, how well can intersensor comparison assess radiometric performance of a sensor at different pixel sizes? As the regime reaches the 1-km resolution or so, the number of pixels in a small but realistic sized area selected for comparison becomes sufficient to allow standard statistical sampling. For example, at 1-km regime, a small area of 32 × 32 km-square contains 1024 pixels, which is sufficient for robust statistics under favorable scene conditions. The current result, such as shown in Chu et al. [7], suggests that the precision result of the time series at 1-km spatial regime is ~1%. Below the 1-km regime, the greater pixel density then give more samples per unit area as well as greater flexibility to enable more powerful sampling analysis—it may be possible to reach precision result much tighter than 1%. At coarser spatial resolution, for example at 5-km pixel size, to have 1000 pixels require an area size of 160 × 160 km-square, and that extent is too large to realize "nadir-only" condition. Therefore, the result using large coarser pixel size is likely to have large-area effects to render the result unreliable.

Most SNPP VIIRS and Aqua MODIS bands are moderate bands, at 750-m and 1-km spatial resolution respectively, and their intercomparison at the 1-km regime have demonstrated precision result at ~1% [7]. But Aqua MODIS and SNPP VIIRS also contain imagery bands with resolutions as fine as 250 m, and furthermore, the OLCI spatial resolution is ~300 m. The examination at regimes finer than 1-km can therefore assess the capability at higher imaging capability. The coming era will have more such higher spatial resolution imaging sensors, such as OLCI already in operation.

#### **3. The Examination of Radiometric Intersensor Comparison**

This study generalizes the methodology used in the Aqua MODIS versus SNPP VIIRS inter-RSB comparison by Chu et al. [7] to focus on three key criteria—area size, pixel homogeneity, and pixel sample size. The small area is a way to approximate the "nadir-only" condition, while homogeneity and sample size constraints are containment strategies to minimize a generally persistent scene-based variability of ~2% significantly impacting the comparison result. This persistent broad-scale variability—the "scaling phenomenon"—renders the use of larger area and sample size to improve statistics useless and is a key motivator of this study. The earlier assessment [7] suggests that the scaling phenomenon arises out of some mid- to large-scale scene conditions in the southern polar region, including Antarctica, where SNOs commonly occur. The application of the constraints to each SNO event successfully circumvented the variability to achieve a better precision at about 1%. This study clarifies how scene-based variability can impact intercomparison and why improvements can be made. The northern polar region is also shown to have the same 2% scene-based variability.

#### *3.1. Procedure and Setup*

Given an SNO event precisely determined within a single pixel of nadir crossing, a small area centered on the nadir crossing is used for pixel-based radiometric intercomparison. The radiance pixels of the two sensors within the small area are matched pair-by-pair via geolocation information. Each pair of collocated pixels is used to compute a pixel-based radiometric ratio of radiance. For this analysis, SNPP VIIRS radiance is taken to be the common radiometric reference against that of MODIS or OLCI. A fixed number of pixels of the best homogeneity quality, to be explained below, is selected for the computation of population statistics. The population average and the relative standard deviation (STD) of all qualified pixel-based ratios represent the ratio and the precision, or error bar, of the SNO event. The low radiance bias of MODIS and the impact of the solar zenith angle (SZA) are two issues briefly discussed here for clarification but are not used for analysis.

First, specific only to the inter-RSB comparison of MODIS versus SNPP VIIRS, a radiance cut of the 20% of the lowest radiance is imposed to remove biased cases occurring at low radiance, as was first done by Chu et al. [7] for Aqua MODIS-based result. The low radiance values from the two sensors are actually in good agreement on absolute terms, but nevertheless can result in large relative bias primarily as numerical artifact due to the low radiance value in the denominator. This low-radiance bias is also quickly confirmed to be true for Terra MODIS versus SNPP VIIRS. High radiance cases also possesses a few outliers, possibly associated with band response near saturation, thus the highest 10% of the radiance are removed as a safety measure. On the other hand, the OLCI-based comparison result does not exhibit bias at either low or high radiance. This points to MODIS possibly having some calibration issues, such as incorrect characterization of nonlinearity at low radiance, but is in any case a calibration issue not examined here.

The second issue concerns the impact of the SZA dependence, which imparts to radiance a distinctive seasonal pattern. However, the "nadir-only" restriction effectively cancels out the SZA effect in the radiometric comparison because the SZAs of the two sensors are effectively identical across the small area. Figure 4 shows the SZA correction ratio of Terra versus SNPP (magenta triangle), Aqua versus SNPP (cyan squares), and Sentinel-3A versus SNPP (green stars) for the year 2017. The error

bars are mostly ~0.1% and smaller. Given the yearly cycle of the SZA, the demonstrated stability in the year 2017 is sufficient to show that the SZA correction factor will not impart to comparison time series any seasonal modulation or multiyear drift. The small random variability can be attributed to the time difference that is also random from one SNO event to next; furthermore, the accuracy of geolocation data can also be questioned at the level of 0.1%. Thus it is not necessary to include the SZA correction factor in the "nadir-only" framework.

**Figure 4.** Solar zenith angle (SZA) correction factors in the year 2017 for Terra versus SNPP satellite (magenta triangles), Aqua versus SNPP satellites (cyan squares), and Sentinel-3A versus SNPP satellite (green stars) demonstrating stable trends.
