Review of the manuscript titled "Constraining 2010-2020 Amazonian carbon flux estimates with satellite solar-induced fluorescence (SIF)" by Dayalu et al.

I find the manuscript quite long, but given the data and methods used, the length is justified. The manuscript is well-written, with extensive and intensive data usage (Flux sites, Aircraft profiles, SIF data, XCO2 data from satellites and different NEE simulations). The analysis performed is robust with clear results. The discussion is quite thorough as well, but more detailed discussion on the limitations of the calibration process, especially given the spatial and temporal variability in the Amazon would be desirable.

I recommend minor revisions and have following questions/clarifications?

1. Is there a reason why the authors did not extend the analysis from 2001-2020 (instead of 2010-2020)? I see that the CSIF is available since 2001, and the EC flux datasets are also available during the 2001-2010 period.

2. Equation 8: Although the authors clarify why they stick with the bold terms of the eq. 8 for the analysis, it would be great if there is some information about how the other terms of the equation affects the final NEE estimations.

3. Figures 1 & 6. Please get rid of the rainbow color palette (the rainbow colour map is not perceptually uniform) and consider replacing it with the color palette of Figure 9.

4. When comparing the fluxes with SiB4 (section 3.3.2), it would be great if authors clarify whether these differences are due to the different model structures or input data, or whether they reflect genuine differences in carbon dynamics that should be explored further.

5. It would be good to also discuss the results shown by VPRM-SIFg in different season with respect to with previous (conflicting) results in the literature by Gatti et al., (2014), Saleska et al. 2005, Huete et al (2006) (cited in the manuscript) and Brando et al., 2010 (https://www.pnas.org/doi/abs/10.1073/pnas.0908741107). 

This study focuses on improving the estimation of Amazonian carbon fluxes, particularly the Net Biome Exchange (NBE), which encompasses biogenic and wildfire fluxes. The authors highlight the challenges in quantifying Amazonia's carbon balance due to anthropogenic disturbances and the need for more reliable long-term data. To address this, they utilize solar-induced fluorescence (SIF) data from NASA's OCO-2 satellite and other observations to enhance the Vegetation Photosynthesis and Respiration Model (VPRM). They compare different VPRM versions and the Simple Biosphere 4 (SiB4) model and further optimize these models using OCO-2 CO2 column observations. The study reveals that SIF-based VPRM versions, especially VPRM_SIFg, outperform traditional ones in capturing CO2 fluxes across various timescales and moisture conditions. The researchers underscore the importance of SIF in improving carbon flux estimations and understanding Amazon's response to environmental changes.

There are some issues that, if the authors address, can make the study more robust:

(1)    The VPRM model calibration relies on a limited dataset from eight eddy flux sites, potentially hindering the model's ability to represent the diverse and heterogeneous Amazonian ecosystems accurately. (if possible) expanding the calibration dataset to include more sites and diverse vegetation types would improve the model's applicability and robustness.

(2)   The available eddy flux data used for calibration spans a period from 2001 to 2015, which is offset from the study period of 2014-2020. This temporal mismatch might affect the model's accuracy in capturing recent carbon dynamics, as ecosystem states and environmental conditions could have changed over time. Therefore, it becomes essential to incorporate uncertainty in VPRM parameters in inversion runs. These uncertainties can be obtained from the hessian of non-linear optimization procedure.

(3)   Assessment of transport model errors is required to understand their influence in determining the coherence between observations and fluxes. This can be done through ensemble runs. The authors can also convolve the posterior uncertainty to see the envelope of uncertainty surrounding true observations and convolved observation that is forward operator*posterior fluxes.

(4)   While the study briefly touches upon the impact of fires on carbon fluxes, a more comprehensive evaluation of fire influences, including the effects of fire severity and frequency on ecosystem recovery and carbon balance, would provide a deeper understanding of the Amazon's response to fire disturbances. This can be done by OSSE kind of run to see its impact on the flux.

(5)   Finally (this is the biggest issue), the study primarily focuses on a wet season case study in 2016 and lacks a long-term validation of the improved VPRM model against independent observations. Conducting a multi-year validation using additional data sources, such as atmospheric CO2 measurements or biomass inventories, would strengthen the confidence in the model's performance and its ability to capture interannual and seasonal carbon trends.

Other comments:

*How are the authors dealing with negative SIF values in their models? If they keep them, GPP will become positive in SIF-based equations. The authors technically are not completely replacing EVI and other scalars by SIF as CSIF is itself derived from MODIS reflectance. It would be good to know how does SIF directly obtained from OCO-2 perform in the VPRM models. Why rely on CSIF when SIF is directly available from OCO-2? Note that OCO-2 SIF also comes with uncertainty, whereas CSIF does not include uncertainty estimates. If there are cloud cover issues, then the distribution of CSIF can be compared with OCO-2. Also, if there are cloud cover issues, then CSIF is mainly influenced by MODIS. I suggest a few things authors can do:

• Replace CSIF (derived from OCO-2) with OCO-2 SIF (native OCO-2 SIF)
• Compare the ECDF of CSIF with OCO-2. Check if they are similar or not. Use two samples, Anderson-Darling or other statistical tests, to ensure they carry the same information. If they are statistically different, then making any conclusions about SIF improving VPRM estimates would be difficult.
• Run this analysis with CSIF, GOSIF, and other SIF products. Please also check the annual variability in CSIF.

All this is required to make sure that acceptance of the new model is not an artifact of CSIF.

*Evaluation of VPRM models against observation is OK, but it depends on uncertainty. Clarify this. Show the posterior flux match of each of them against observations and whether they are within each other uncertainty bounds or they are outside uncertainty bounds, in which case they can be rejected outright.

* The study could benefit from adding a flowchart to provide a clear visual representation of its methodology. This would be particularly helpful in understanding the complex workflow and interconnections between the different components of the study, such as data processing, model calibration, regional inversion, and model evaluation.

*Many of the steps the authors took for their assessment need to be formalized to understand better what is being done. For example, "We bootstrap CT2019 background concentrations and vertical profile measurements at each vertical level to dimensions that enable merging with the month of hourly ….." I was utterly lost here. I do not know what is being done. Line 360 to the end of the methodology section requires a significant rewrite for clarity. Have a clear flowchart + equations + Jupiter notebook. How is this all connected to equation 10. What kind of bootstrap is it?

*Line 330 "The footprint domain is outlined in Figure Error! Reference source not found.b". Correct this.

* In Figure 3. VPRM Model-Observation (Night-time) Respiration residuals (I think this is a boxplot). It would also be good to see this as a frequency or histogram plot, as clearly, the bars are not uncertainty estimates. Therefore, we need to know the proportion of residual per/quantile. The authors should explain the relevance of these results in the caption.

* In Figure 4. Panel(a) bars are not uncertainty estimates. They incorrectly imply uncertainty when it is something else. Clarify and, if possible, plot them in a way so that people, by just looking at them, do not think that these are estimates of uncertainty