GOME was a passive nadir-scanning UV/VIS spectrometer onboard ERS-2 . It provided observations of ozone, NO2, clouds, aerosols, and other atmospheric parameters with global coverage every 3 d. GOME had forward and backward scanning sequences with 4 pixels per scan. Forward scans included east, nadir, and west pixels .

SCIAMACHY was a passive UV/VIS/NIR imaging spectrometer on ENVISAT . It featured nadir, limb, and sun/moon occultation viewing. Limb and occultation modes were excluded in this study. SCIAMACHY retrieved trace gases, aerosols, radiation, and cloud properties .

Further details on the characteristics information of each sensor, including the mission life, orbit, and spatial and spectral resolution, are illustrated in Table .

Fully calibrated Level-1 products from GOME (Level-1b version 5.2) and SCIAMACHY (Level-1c version 10) were used to derive TOA reflectance time series. Both GOME Level-1b and SCIAMACHY Level-1c datasets contained solar irradiance and radiance measurements required for reflectance computation. While cloud parameters were included in the GOME Level-1b files, they were not available in the SCIAMACHY Level-1c products. Consequently, cloud parameters for SCIAMACHY were obtained from the corresponding Level-2 products.

The following is the main data from both sensors used:

Reflectance: this parameter refers to the fraction of incoming radiation reflected toward the instruments. This consists of surface reflectance and atmospheric scattering. Reflectance serves as a fundamental parameter for the comparison between sensors and the study of the temporal variability of PICS. In this study, the term “reflectance” refers to TOA reflectance, unless stated otherwise.

The reflectance time series of the PICS were retrieved under conditions of CF less than 0.25. Libya 4, one of the PICS endorsed by CEOS, WGCV, and IVOS, has been extensively used in previous studies for long-term stability assessments and sensor calibration . As a representative example, Libya 4 was selected for detailed visualization of the reflectance time series obtained from the GOME and SCIAMACHY sensors.

Figures  and  present the corresponding heatmaps of reflectance measurements from GOME and SCIAMACHY, respectively. At certain points in time for GOME (Fig. ), higher values of the reflectance were observed across the whole spectrum (horizontal bright lines). In the UV, the value of the reflectance exceeded the average value due to instrument degradation, particularly towards the end of the GOME mission in 2011. The VIS and NIR spectral ranges were more uniform. In the NIR spectral range, O2 A-band was visible with low reflectances around the center of the band. Similar behavior was observed for SCIAMACHY as shown in Fig. . The main difference was the absence of larger variations over long periods in the UV, suggesting better degradation correction compared with GOME.

To gain further insight into the variability of the reflectance time series observed in the heatmaps, three wavelengths were selected. These wavelengths, located approximately at 330 (UV), 450 (VIS), and 770 nm (NIR), were indicated by white dashed lines in the heatmaps. Figures  and  show the corresponding reflectance time series at these wavelengths, derived from GOME and SCIAMACHY data, respectively.

The reflectance time series at the UV wavelength of GOME at 329.5 nm exhibited significant fluctuations, with approximately 47 % of the observations within ±10% of the average value. A noticeable increase in reflectance was observed in 2001 and toward the end of the GOME mission due to degradation effects (see Fig. ). Additionally, the reflectance time series exhibited three clearly separated clusters, likely resulting from the dependence of reflectance on VZA. In contrast, the selected wavelengths in the VIS (436.47 nm) and NIR (736.19 nm) regions demonstrated greater consistency, with 87 % and 96 % of the observations, respectively, remaining within ±10% of their average values. Among them, the VIS wavelength showed a particularly more consistent pattern.

For SCIAMACHY, the average reflectance values at the corresponding wavelengths were comparable to those from GOME (see Fig. ). A similar fluctuation pattern was observed in the UV band (329.98 nm), about 58.3 % of observations were within ±10%. Additionally, sinusoidal-like patterns were more apparent in the SCIAMACHY UV time series than in GOME. Unlike the GOME UV reflectance time series, the scatter distribution did not show distinct clustering. The reflectance time series for the selected VIS (449.94 nm) and NIR (770.04 nm) wavelengths appeared even more consistent than those of GOME, with no evident degradation effects over the mission period.

The derived temporal metrics of the GOME and SCIAMACHY reflectance time series of each spectral channel from the investigated PICS are presented in Figs.  and . These results offered a comprehensive comparison of statistical features across the PICS sites and spectral channels.

The mean of the GOME reflectance time series in Fig. a demonstrated relatively low reflectance values in UV and VIS and higher reflectance values near the O2 absorption window (O2 A-band). The standard deviation in Fig. b and IQR in Fig. d showed higher UV variability for most sites. However, tighter spreads of the IQR were observed in VIS (see Fig. d). The CV in Fig. c showed notably higher values and a wide range across spectral channels and sites. The CV values were suddenly increased in the O2 A-band. This was likely due to the low mean reflectance value in the absorption window. The slope values in Fig. e showed a notable pattern of degradation across most of the sites, particularly for the short wavelengths in UV. Smaller degradation patterns were observed in the VIS and NIR ranges. GOME reflectance time series demonstrated large magnitudes of skewness in Fig. f and kurtosis in Fig. g, suggesting higher variability and asymmetry across the spectral channels, especially in the O2 A-band in NIR, which could be due to the influence of the clouds and aerosols .

The mean reflectance values of SCIAMACHY were similar to GOME and showed similar patterns across all sites in Fig. a. There were relatively low reflectance values in UV and VIS and higher reflectance values in NIR near the O2 A-band. Low reflectance values were observed within the O2 A-band (759–770 nm), indicating strong atmospheric absorption. The standard deviation in Fig. b and the IQR in Fig. d provided insights into the temporal variability of reflectance. Higher values in the UV range suggested greater fluctuations and noise, while lower values were in the VIS and on both sides of the O2 A-band in NIR. The CV in Fig. c revealed differing levels of stability across spectral bands and among the PICS sites. In the UV, CV tends to increase with wavelength, while in the VIS, it remains relatively consistent. High CV values in the O2 A-band in the NIR. Overall, the results suggested greater temporal stability in the VIS and spectral channels adjacent to the O2 A-band compared to the UV. CV values among the sites range between 0.05 (5 %) and 0.20 (20 %). The slope values in Fig. e, especially in the UV, showed slight changes in the spectral signature of the sites. The oscillations in the slope of the UV channel were possibly caused by residual etalon variations. The dip around 350 nm was likely due to stronger degradation of the Band 2 detector, which was a known characteristic of the instrument. In contrast, many PICS sites exhibited slope values close to zero in the VIS and NIR, suggesting negligible long-term trends and stable reflectance behavior in these regions. The skewness values in Fig. f varied among sites and bands but remained consistent, reflecting differing degrees of asymmetry in the reflectance distributions. The VIS region exhibited more symmetric reflectance distributions compared to the UV. In the NIR, the absorption feature caused noticeable disruptions in symmetry. Similarly, kurtosis values in Fig. g, especially in the UV and NIR, suggested the presence of occasional outliers or extreme values. The VIS region, in contrast, showed fewer such anomalies.

The comparative analysis of temporal metrics for GOME and SCIAMACHY reflectance revealed different sensor performances and varying patterns across both PICS sites and spectral bands. Before assessing the stability of PICS, it is essential to first identify the most stable sensor data to be used in the analysis.

The averages of the metric temporal parameters of each spectral band and site are shown in Fig. . The average values were computed by taking the mean of the parameters across the wavelength ranges defined for each band in Table . Key findings from these temporal metrics for both sensors are presented in Table . It presents the comparison of GOME and SCIAMACHY for each extracted parameter of each band.

GOME and SCIAMACHY shared several characteristics, particularly in their mean reflectance profiles. Both sensors exhibited similar reflectance patterns across the spectral channels (Fig. a). While both sensors displayed high temporal variability in the UV band, they had less variability in VIS and the lowest in NIR (see Fig. b and c).

However, GOME consistently displayed higher temporal variability and drift indicators than SCIAMACHY. Specifically, GOME's standard deviation, IQR, and CV were relatively higher (Fig. b–d); its slope values suggested greater sensor drift (Fig. 7e); skewness was more strongly positive across most sites (Fig. f); kurtosis revealed more frequent extreme reflectance events (Fig. g). Collectively, these findings recommend adopting SCIAMACHY as the primary reference sensor for PICS stability assessments.

To illustrate the effects of SZA and VZA on reflectance, the mean reflectance across all spectral channels was analyzed as an example. Figure a and b present scatter plots of the mean reflectance of the whole spectrum as a function of SZA and VZA, respectively. The black dashed lines represent linear regression fits and illustrate the overall trends in the observations. Consistent positive slopes were observed for all sites with increasing SZA, indicating an increase in reflectance with SZA. In contrast, reflectance exhibited consistent negative slopes with increasing VZA.

Figure c shows the percentage change in reflectance corresponding to a 10° change in the sun-illumination and viewing geometry. The mean reflectance typically varies by 0.5 % per 10° to 2.57 % per 10° increase in SZA, with some sites, such as Arabia1 and Niger1, showing lower sensitivity of approximately 0.5 %. The effect of VZA was more pronounced, exhibiting an inverse relationship with reflectance. The corresponding absolute variations in reflectance ranged from 2 % per 10° to 6 % per 10° change in viewing angle.

The regression analysis revealed a systematic dependence of mean reflectance on both solar illumination and satellite viewing geometry. The magnitude of changes in reflectance was not the same across the PICS. Consequently, the reflectance observations over the PICS were corrected for the effects of both SZA and VZA. To account for spectral variability, the regression was applied separately to each spectral channel, ensuring that angular dependencies were corrected on a channel-by-channel basis.

As an illustrative example, the reflectance differences for the Libya4 site (corrected – original) are provided in the Supplement (see Fig. S1). The effect of the correction was strongest in the UV spectral channels, weaker in the VIS channels, and smallest in the NIR region. The correction applied to the reflectance time series exhibited a clear seasonal dependence, with the largest correction values for the reflectance time series of the summer months (May–September).

The SS for the PICS sites were computed based on corrected SCIAMACHY reflectance observations. The SS was calculated for each spectral channel, excluding the O2 A-band. The scores were normalized and visualized in Fig.  with lower values indicating greater stability.

Some sites demonstrated stable performance across nearly all spectral channels. With the most stable sites being Sudan1, Arabia2, and Libya4. In contrast, other sites showed differences when comparing the stability of the different spectral bands. For instance, Arabia3, Arabia2, Libya3, and Niger2 showed higher stability in the UV band compared to the VIS band. Conversely, sites such as Algeria1, Algeria2, Algeria3, Libya4, and Sudan1 demonstrated greater stability in the VIS band than in the UV. A few sites, including Mali1, Mauritania1, Mauritania2, and Niger1 displayed generally low stability across all spectral regions. Additionally, high SS values were observed across multiple sites, appearing as vertically homogeneous patterns. This effect was particularly pronounced in the UV spectral region, likely due to the strong sensitivity of UV bands to atmospheric influences, such as cloud contamination and aerosols.

Based on the band-wise ranking shown in Fig. S2, the SS values of the PICS span a wide range across the spectral domains. In the UV range, SS varies from 0.11 to 0.75, while in the VIS range, it lies between 0.25 and 0.70. The NIR range exhibits SS values between 0.16 and 0.78. On average, the SS values for the UV and VIS bands are 0.45 and 0.50, respectively, which are higher than the average value for the NIR band (0.40). This indicates that PICS stability exhibits a clear dependence on spectral band. As an illustrative example, the Libyan sites (Libya1, Libya2, Libya3, and Libya4) exhibit SS values of 0.50, 0.38, 0.36, and 0.33, respectively (see Fig. S3). The high SS of Libya1 can be attributed to its being relatively higher SS in the UV and VIS bands compared to the other Libyan sites, as the SS represents a combined measure across all spectral bands.

The influence of geographical proximity is linked to the high potential of similar surface characteristics, which is evident in the band-wise ranking of the PICS, particularly in the NIR band, excluding the O2 A-band. Several geographically close sites showed similar SS values, including Algeria3 and Algeria4, Arabia1 and Arabia2, Mauretania1 and Mauretania2, Libya4 and Egypt1, and Libya3 and Libya2 (see Fig. S2). These geographically driven groupings – most clearly observed in the NIR band – suggest that sites within similar regional and geomorphological settings tend to exhibit comparable temporal characteristics. In contrast, this clustering effect was less pronounced in the VIS and UV bands.

To ensure the robustness and comprehensive assessment of the reflectance time series and the comparison between sensors, additional statistical descriptors were included. Besides the basic statistical parameters and CV, other parameters like IQR, slope, skewness, and kurtosis were included in the assessment. Skewness, kurtosis, and IQR parameters are used to identify the level of symmetry in the observations, reflect the presence of outliers, and quantify the spread of the central portion of the data. The slope serves as an indicator of the degradation in the sensor performance.

With only basic statistical parameters (mean, standard deviation, and CV), there were no significant differences observed for the spectrometer samples. When additional parameters such as slope, skewness, and kurtosis were considered, meaningful differences became apparent (see Fig. ). These high-order statistical features provided greater sensitivity to distributional characteristics, subtle anomalies, and temporal trends, thereby offering a more robust basis for distinguishing the performance and stability of the spectrometers. This multi-parameter approach contributed to a more robust and resilient framework for sensor performance evaluation, radiometer drift monitoring, and identifying the constrained and reference sensors for cross-calibration.

The derived indicators from the reflectance time series of GOME and SCIAMACHY revealed different statistical patterns among the PICS and across spectral channels. These differing patterns indicated that the initial hypothesis – that atmospheric conditions across the sites are relatively similar and that all PICS should exhibit similar behavior – may not hold in all cases. Therefore, PICS should be regularly investigated . This finding underscored the need to quantify and compare the stability of each PICS site individually and across different spectral bands. The metric parameters derived from reflectance observations from both sensors consistently showed that deviation, variation, asymmetry, and degradation were highest in the UV bands, lower in the VIS bands, and lowest in the NIR bands. These results were expected for the following reasons: (i) TOA reflectance was used, which includes contributions from both surface and atmospheric reflectance; (ii) the UV bands are highly sensitive to atmospheric processes and aerosols ; (iii) the VIS bands are influenced by Rayleigh scattering and aerosol variability ; and (iv) the NIR bands (excluding the O2 A-band) are less affected by atmospheric processes and are more sensitive to surface reflectance; therefore, it serves as a baseline for atmospheric correction algorithms .

A comparative analysis of key statistical parameters revealed that SCIAMACHY exhibited more statistically uniform and stable reflectance measurements than GOME. These indicators suggested lower temporal variability and fewer extreme values in SCIAMACHY data, making it a more robust choice. investigated the degradation across the spectral channels of SCIAMACHY and found that even after reaching the nominal lifetime of the mission in 2007, the SCIAMACHY instrument was still in good condition. However, the same study demonstrated an increased degradation in UV channels.

The discrepancy in the derived statistical parameters between GOME and SCIAMACHY might be partly attributed to sensor degradation effects. referred to the severe degradation of the GOME instrument in the UV due to scan mirror degradation. Overall, SCIAMACHY's enhanced design and calibration capabilities resulted in better long-term radiometric stability compared to GOME. and presented a degradation correction approach based on SCIAMACHY's internal white light measurements in combination with direct solar measurements. Consequently, SCIAMACHY was adopted as the reference sensor for evaluating the temporal stability of the PICS. Using SCIAMACHY observations in this paper ensured that the derived SS were based on a more stable and consistent dataset, enhancing the reliability of the site rankings and subsequent cross-calibration of both sensors by .

The reflectance observations collected over PICS exhibited varying degrees of dependence on the SZA and VZA across the sites. An increase in SZA results in a longer atmospheric path length, leading to enhanced scattering and, consequently, TOA reflectance. This dependence on SZA is consistent with previous studies; for example, reported that reflectance tends to increase with both atmospheric aerosol optical depth and SZA. These findings indicated that a significant portion of the observed reflectance variability could be attributed to the sensitivity of TOA reflectance to changes in SZA. Differently, the reflectance time series as a function of VZA exhibited an inverse relationship, which may be attributed to degradation of the scan mirror angle over time, particularly in the UV band.

The correction applied to account for reflectance dependence on variations in SZA and VZA exhibited a clear seasonal pattern, with maximum corrections occurring during summer. Using a reference geometry with SZA=45°, the typically lower SZA values during summer led to increased reflectance after correction to the reference geometry compared with winter. The magnitude of the correction was wavelength dependent, with the largest corrections observed in the UV bands. This behavior was likely associated with enhanced atmospheric transport of aerosols and dust during summer. Previous studies reported a pronounced seasonal dust cycle, characterized by higher dust concentrations in the summer months . Both the central Sahara Desert and the Arabian Peninsula experienced their highest aerosol optical depth during summer . Accounting for these effects was therefore essential for modeling SS and for ensuring comparable values across sites, without the influence of differing illumination and viewing geometries.

SS served as an indicator of temporal variability across the spectral channels. The SS for PICS were estimated based on groups of parameters that directly quantified variability, distribution, and temporal trends. The features describing temporal variability in reflectance were normalized to ensure comparability across sites. The SS was primarily determined by band characteristics. Overall, the values of SS were lower (more stable) in the NIR than in the VIS and UV bands. At longer wavelengths, contributions from surface properties – such as mineral composition, soil texture, surface roughness, and moisture – become increasingly significant , increasing the sensitivity of the metric to surface homogeneity and underlying geophysical conditions.

The band-wise ranking of the PICS showed strong clustering based on geographical proximity, which can be attributed to similar soil mineralogy and morphological conditions. In contrast, less clustering by geographic proximity was observed in the UV and VIS bands. This likely reflects regional differences in meteorological and atmospheric conditions, as these bands are more strongly influenced by atmospheric effects.

Overall, the results demonstrated a wide range of SS across spectral bands and sites. Our findings confirmed that Libya4 – one of the recommended sites for sensor cross-calibration – was among the most stable PICS. Some sites, such as Sudan1 and Arabia2, exhibited lower scores (more stable) than Libya4. These three sites are, therefore, recommended for further cross-calibration activities.

Differing SS across the PICS could refer to the following reasons:

Surface characteristics. Site properties affect radiometric stability; PICS are not fully invariant. Relevant factors include sand topography, grain size , composition, color, material type, and dune displacement . The sun's azimuth relative to dune alignment influences reflectance, with mean values depending on the area size .