| 17 Jul 2026
First comparison of XCO2 products from DQ-1 ACDL and passive optical satellites
Abstract. Spaceborne active CO2 IPDA lidar provides an active remote-sensing approach for XCO2 observations that differs from passive NIR/SWIR remote sensing. However, comparisons between DQ-1 ACDL and passive optical satellite XCO2 products remain limited. Here we compare DQ-1 ACDL with OCO-2, OCO-3, GOSAT, and GOSAT-2 XCO2 products from June 2022 to December 2024 using a unified framework that combines CAMS-based spatiotemporal coherence assessment, satellite sampling availability, daily 2° aggregation, spatiotemporal collocation, and XCO2 column-definition correction. The results show that DQ-1 is broadly consistent with OCO-2 and OCO-3 over coherent and well-sampled regions, with mean differences mostly below 0.5 ppm and similar spatial distributions, meridional structures, and regional monthly variations. Nighttime DQ-1 XCO2 is also consistent with CAMS-derived XCO2, indicating stable performance relative to an external model reference. Independent validation against TCCON shows that daytime DQ-1 XCO2 has a correlation coefficient of 0.92, a mean bias of 0.21 ppm, and an RMSE of 1.49 ppm, with statistics comparable to GOSAT. Regional results further indicate that DQ-1 does not simply duplicate existing passive satellite observations, but provides complementary XCO2 observational coverage under high aerosol loading, at high latitudes, and in regions where passive observations are limited. Overall, DQ-1 ACDL is comparable to passive satellite XCO2 products under the unified framework, and can serve as an important complement to existing passive satellite XCO2 observations, supporting future active–passive joint XCO2 constraints, regional carbon flux inversions, and emission monitoring.
The reviewed manuscript discusses the XCO2 product retrieved from the DQ-1 ACDL space-borne lidar and compares it both with other space-borne XCO2 measurements and with the reference ground-based stations. The need for accurate CO2 measurements is obvious, its mean value characterizes global changes, whereas its sources and sinks tell us about carbon cycle, emissions, and transport effects. Compared to existing and past space-borne passive measurements (SCIAMACHY, GOSATs, OCOs, TanSat), the space-borne lidar has certain advantages. It uses a controlled source of probing light whereas the passive sounders depend on the incident solar radiance that is not pointed the same way throughout the globe. In particular, this changes the observability of the nighttime part of the globe that is completely inaccessible for the passive XCO2 sounders relying on reflected sunlight (OCO-2, GOSAT, TanSat). The thermal-infrared emission sounders like AIRS, IASI, or CrIS do observe CO2 emissions, but their channels are characterized by altitude-dependent contribution functions related to CO2 concentrations, whereas the differential absorption method probes the whole atmospheric column down to the ground. For this method, the absence of solar photons contaminating the signals makes the nighttime observations preferable to daytime ones. From this point of view, ACDL might serve either as a reference for other sounders or as a strong supplement to existing XCO2 data.
The manuscript introduces the problem, discusses an extensive list of comparison datasets that includes not only spaceborne passive remote sensors like OCOs or GOSATs, but also a network of ground-based Fourier-spectrometers TCCON that retrieve XCO2 from solar near-infrared absorption spectra and serve as a reference for space-borne observations.
Throughout the manuscript, the authors show different cross-comparisons between the space-borne passive, space-borne active, and ground-based observations, and conclude that the overall agreement between the compared instruments is astoundingly good, with bias less than 0.3 ppmv and RMSE less than 1.5ppmv for all instruments except GOSAT-2. The correlation coefficients between the datasets are on the order of 0.91-0.95. The manuscript is topical, it fits well the scope of the journal, is well organized and well-written.
I believe it can be published in the journal with minor revisions, which I propose for the discussion below.
Minor comments
The abstract relies heavily on acronyms (XCO2, IPDA, TCCON, and the various instrument names) without expanding any of them at first use. This is fine for a specialist audience already familiar with the history of spaceborne XCO2 observations, but makes the abstract nearly unreadable for a general reader outside this subfield. Since the abstract is often the only part of the paper read by non-specialists, could the authors, please, either expand all acronyms at first use or rewrite the abstract in plain language?
Lacking elements of the method
Lines 55-72 and/or lines 102-113: it makes sense to add some information about the optical path of ACDL to explain why the vertically resolved lidar signal does not carry the information about the CO2 and only the column value is retrievable. The references given in this section explain this, but adding a couple of sentences regarding the method will simplify the flow.
Neither of these paragraphs discusses the differences of daytime and nighttime observations. Since the online and offline channels share nearly the same additive solar background but different signal levels, an imperfectly subtracted background biases the online/offline ratio toward unity and, therefore, biases XCO2 low – an effect that should vanish at night. Could the authors show a daytime-vs-nighttime comparison over similar scenes to bound this residual bias, if not already addressed in the cited references? Maybe it makes sense to provide a theoretical estimate for exactly the same atmospheric column to exclude any effects caused by diurnal change.
A related point: the differential absorption method requires a valid hard target (surface or opaque cloud top) to close the two-way path. Could the authors please note the fraction of shots lost to a missing hard target and whether this loss is geographically or seasonally structured?
The structure and the order of presentation
The validation with TCCON observation appears only after the diagnosis of residuals in passive spaceborne instrument comparison, whereas I’d say that TCCON observations should have more weight and therefore precede them. See the other comment regarding the order of presentation and TCCON measurements below.
Section 3.1 is dedicated to sampling and takes a lot of space – I am not sure that it belongs to the Results. In principle, it could have been reduced or moved to Appendix without losing the main message of the section.
Throughout the Results section, some of the differences between the datasets are explained whereas the other ones are just reported. Maybe it makes sense to give the priorities or weights to these differences: for example, one chooses TCCON as a reference as in Section 3.7 and discusses the differences only with respect to these observations. Again, this will require a slight reorganization of the order of sections. If the authors believe that TCCON comparisons are of lesser importance, I’d like to understand, why – for me, they are a good candidate for the reference ones because they depend on smaller number of unknowns.
For the figures showing geographical distribution of XCO2 differences, did the authors try to subset the datasets over the oceans using the surface winds values? Does the smoother sea produce better results?
Technical corrections
Fig. 1: the panels share the same color bar and OX/OY axes. In this case it would be logical to merge them and keep only 2xOX axis labels, 3xOY axis labels, and 1 color bar.