| 06 Oct 2025
Comparative analysis of GOME and SCIAMACHY reflectance over Pseudo-Invariant Calibration Sites: implications for spectrometers cross-calibration
Abstract. Accurate radiometric cross-calibration is essential for ensuring the consistency and interoperability of multi-sensor satellite observations. Vicarious calibration, a widely adopted approach, relies on the temporal stability of desert-based Pseudo-Invariant Calibration Sites (PICS). However, these sites are limited in spatial extent and have not been systematically examined for cross-calibration with larger pixels, whose dimensions exceed those of PICS. This study establishes a statistical framework to advance cross-calibration of spectrometers over PICS and their surrounding areas. The framework includes performance comparisons of different satellite instruments and the identification of reference and constrained sensors. Furthermore, the temporal stability of PICS across various spectral bands was reassessed using observations from the reference sensor. Stability scores (SS) were derived from a combination of statistical indicators designed to capture temporal variability, distribution symmetry, the occurrence of anomalies, and long-term shifts in the observations.
Decades of surface reflectance data in the ultraviolet, visible, and near-infrared (UV/VIS/NIR) ranges, collected by the Global Ozone Monitoring Experiment (GOME) and Scanning Imaging Absorption Spectrometers for Atmospheric Chartography (SCIAMACHY) spectrometers over 20 PICS sites, were analyzed. The results revealed significant degradation in GOME, particularly in the UV band during 2001 and toward the end of its mission, as evidenced by positively skewed and heavy-tailed distributions. In contrast, SCIAMACHY observations were more uniform and stationary, indicating their greater suitability for assessing PICS stability. The investigated sites were ranked based on the average of SS across the whole investigated spectrum. The analysis revealed a wide range of stability levels among PICS, including intra-site variations across spectral bands. While some sites demonstrated consistently high stability, many were found to be 2–3 times less stable than the most stable sites. Among these robust sites, some have not been recommended by the calibration communities and should be given further consideration. These findings underscore the importance of regularly evaluating PICS and the need to consider spectral-band-specific performance when selecting calibration sites.
General Comments:
This paper presents analysis of GOME and SCIAMACHY surface reflectance data at so-called Pseudo-Invariant Calibration Sites to evaluate cross-calibration between sensors and also temporal stability to evaluate deviations over instrument lifespan. This paper focuses on sites in the North of Africa and Arabian Peninsula, which are valuable due to the need for satellite-validation datasets at high-albedo locations. This work is within scope for AMT. While this study contributes a comprehensive overview of basic metrics for evaluating the suitability of a selection of PICS, my main comment is that the analysis may be improved by analyzing site-to-site differences and evaluating how they reflect in the stability metric.
Specific Comments:
Line 38: what does “spatial uniformity” refer to? Your text states this is based on a series of cloud-free images and seems to imply this uniformity is the peak-to-peak temporal variation in reflectance, but please clarify.
Line 47: re-word “optical sensors”, as that term may include spectrometers.
Line 54: There is only one hypothesis (explicitly) listed.
Table 1: This table summarizes the information in Sections 2.1 and 2.2. I suggest removing Secs. 2.1 and 2.2.
Line 94 and Table A1: It is not clear to me how exactly these sites were chosen. Was the number of data points a factor in this decision? It is mentioned that a subset of the chosen locations are already currently recommended for sensor cross-calibration, so it is a good idea to differentiate those and new locations.
Line 118: CF < 0.25 was used to filter the reflectance time series. Is this heuristically determined? CF 0.25 may be large enough to strongly affect the reflectance at certain wavelengths. It would be good to evaluate how sensitive this threshold is (e.g., at least report how many observations are discarded).
Table 3 and Figure 2 are useful summaries. I wonder if it may be better to shorten the description of such variables by adding another column to Table 3 with each respective calculation, as these are fairly common metrics.
Line 188: Why are these particular wavelengths selected? Is there a wavelength-dependence to this variability?
Figures 7 and 8: Is there a physical interpretation for the negative skewness in the NIR in panel (f)
Figure 12: Consider moving this to the appendix or supplementary, or condense this into a Table. Figure 11 contains the more interesting aspect, which is the wavelength dependence of the SS.
The discussion of Sections 5.2, 5.5, 6.5 are very qualitative in nature as they generally repeat the information in the respective figures.
Line 272: Cloud contamination is mentioned again here — how did your previous CF filtering improve this influence? Are there references in the literature to support differences in cloud formation (brightness, microphysical properties?) between PICS which may explain some site-to-site differences?
Figure 11: UV vs. VIS/NIR SS very different for certain sites. Arabia3 and Algeria1 stand out. What makes Libya 3 distinct from Libya1, 2 and 4 given their geographical proximity? A brief discussion is presented in the paragraph starting Line 339, so this may be expanded. References are lacking in this discussion especially.
Conclusion: I would suggest including a final paragraph for the broader satellite community in how the information gained by analyzing high SS PICS could reflect in other satellite products, such as, as mentioned in the introduction, atmospheric gas measurements.
Technical corrections:
There are many small technical changes, so I would suggest carefully proofreading the manuscript beyond these corrections in your revision:
Line 28: this paragraph only has one sentence - append this to the previous paragraph
Lines 35 and 38: fix in-text citation “… in Cosnefroy et al. (1996)”
Line 64: missing indentation.
Line 70: “over 240-790” -> between 240-790
Equation 1: Dots (assuming multiplication) are not centered with the variables.
Line 126 (and subsequent lines): inconsistent italics for parameters. E.g., CV is italicized in lines 127 and 129 but not Equation 4.
Line 156: observations -> observation
Line 157: 45 (degrees)
Line 170: “see the illustrative diagram in Fig. (2)”
Figure 2: “SS computation” overlaps a vertical line. Also force “SS_NIR” onto one line.
Line 195: “showed a more consistent pattern”
Figures 7: (1) Axis labels are too small, (2) x-axis label should be centered on all three subpanels of each panel, (3) remove some wavelengths from the x axis for the VIS/NIR spectrum to improve readability, (4) the UV subplot for each panel has horizontal lines but the VIS/NIR do not. Is the y-axis to-scale for all three spectral regions? (5) perhaps add some transparency to the lines, as it is impossible to see clearly, especially in the NIR. (6) panel e needs more significant figures.
Same comments for Figure 8, as applicable.
Section 5.3 header: Inconsistent capitalization
Figure 12: The +/- labels for many sites are overlapping, and the two Mauretania labels on the x-axis are mis-aligned
Line 309: Missing period for middle name in Lieuwe G. Telstra?
Line 324, 325: Fix in-text citation for Wang et al. (2022) and Kim et al. (2023)
Line 337: Sudan1 and Arabia2 exhibited a lower score than Libya4
Section 7: rename to Conclusions (because you have more than one conclusion)