This paper represents a review of spaceborne microwave remote sensing utility and recent applications for Arctic-boreal regional (ABR) studies. The targeted audience for the paper is the carbon cycle science community, with a primary focus on monitoring land-atmosphere carbon (CO2 and CH4) fluxes and environmental controls. The authors include a useful summary of microwave remote sensing principles, satellites, and sensors pertinent to ABR applications. They then follow with a detailed review and discussion of major biophysical retrievals and attributes, and data records obtained from microwave sensors, and associated ABR applications from the recent literature. The authors also include an informative review highlighting both the challenges and opportunities of microwave remote sensing. Overall, the paper is well written, comprehensive, and informative; covering most of the major microwave remote sensing derived variables affecting carbon fluxes available from the recent literature. The paper is also timely given the increasing ABR importance in global climate change along with major new microwave satellite missions coming online in the next few years. The paper should be suitable for publication pending moderate revisions to address the following comments.

Despite the focus of this paper on the microwave utility to capture carbon fluxes (both CO2 and CH4) and related environmental attributes, there’s no mention of the application of microwave remote sensing for monitoring fractional surface water (FW) cover dynamics, which contribute strongly to ABR methane emissions (e.g. Watts et al. 2014). The FW parameter is particularly relevant in the ABR owing to the regional abundance of small inland water bodies and wetlands that contribute CH4 emissions, but that can also contaminate microwave land parameter retrievals; here FW is a first-order response variable that has been used to assess and reduce water contamination effects on other ABR land parameter retrievals (e.g. Touati et al. 2019), which might otherwise incur significant bias (e.g. Kim et al. 2019. Remote Sensing 11). SAR is mentioned for wetlands mapping in the paper, which is good, but passive microwave has particular importance by providing full ABR coverage and daily monitoring of FW dynamics. Therefore, FW should be discussed in the paper given its importance for ABR CH4 emissions and significance for other ABR land parameter retrievals.  

Similarly, lake and river ice phenology, including the seasonal timing of ice on/off and ice cover duration, influences the seasonality and magnitude of ABR CH4 emissions, and has been readily documented from the microwave record (e.g. Murfitt and Duguay 2021).  This should also be mentioned in the paper given its importance to CH4 emissions (e.g. Matthews et al. 2020).   

Figure 3a: Include the Feng Yun-3 microwave radiation imager (FY-3 MWRI) in the figure; the global MWRI record extends from 2008-present and provides similar TB observations helping bridge the gap between AMSR-E and AMSR2.

Figure 4: Include FW cover and lake/river ice dynamics, which are particularly important for ABR methane emissions, and where active/passive microwave sensors are well suited for monitoring. Also, it took me a while to figure out that the magnifying glass and green monster J denotes microbial processes; alternatively, it may be better to more clearly represent a below-ground layer in the figure showing soil litter decomposition and Rh; this would also remind the reader about the unique capability of microwave remote sensing to detect below-ground properties.

Figure 4 Cont: Recent work shows some success for active/passive microwave retrievals of surface soil organic carbon, which has strong ABR importance for soil carbon storage and Rh (e.g. Bartsch et al. 2016; Yi et al. 2022). I recommend including this information in the figure and discussion.

Section 3.1. A key challenge in developing effective microwave soil moisture retrievals in the ABR is the predominance of highly organic soils and their unique dielectric properties; these conditions aren’t well represented in traditional dielectric models (although Miranov and a few others have made meaningful advances), which can contribute significant SM retrieval uncertainty. More information is needed on this.   

Table 1: The AMSR-E/2 soil moisture product provided from Du et al., 2017 should be listed as “2002-ongoing”, rather than from 2012.

Ln 268: Include net ecosystem productivity (NEP) in Figure 4 or note the relationship with NEE in the text.

Ln 275: The Jones et al. LST reference should be Jones et al. 2010 (IEEE JSTARS) rather than their 2007 TGARS paper that refers to soil temperature. Here the microwave LST uncertainty is less than the reported soil temperature retrieval RMSE (~3-4K).

Ln 280: Microwave observations have also been used to retrieve soil temperatures in the ABR (e.g. Jones et al. 2007 TGRS); the ability to sense soil temperatures is an important advantage over thermal-IR remote sensing, which can only detect surface skin temperatures and which are subject to significant atmospheric contamination. This should be noted.

Ln 285: The Jones et al. LST reference should be Jones et al. 2010 (IEEE JSTARS). The UM AMSR global land parameter data record (LPDR) includes daily temperature retrievals from both AMSR-E and AMSR2 (rather than just AMSR-E), and extending from 2002-present; although the record isn’t operational as correctly noted.

Table 2: The numbering of SMAP and ASCAT in the column headings should be switched to match the numbering in the table footnote.

Ln 488: “multi-frequency” should be included here, in addition to “multi-polarization” and “multi-angular” measurements; whereby, the variable sensitivity of the different microwave frequencies (e.g. AMSR) has been used to disentangle the integrated microwave signal to obtain multiple complimentary land parameter retrievals (e.g. Du et al. 2017. ESSD).

Ln 490: Here, introduce FW abundance as a key challenge for remote sensing of ABR land parameters, which is then expanded upon below (Ln 501).

Ln 510: Here, clarify that the tower network is particularly sparse in the ABR, while referring back to Figure 1. The sparse regional tower network adds to the difficulty for effective regional calibration and validation given the strong ABR heterogeneity.

References:

Bartsch, A., et al. 2016. Can C-band synthetic aperture radar be used to estimate soil organic carbon storage in tundra? Biogeosciences 13, 5453-5470.

Du, J., et al. 2017. Satellite microwave assessment of Northern Hemisphere lake ice phenology from 2002 to 2015. The Cryosphere 11, 47063.

Jones, L.A., et al. 2010. Satellite microwave remote sensing of daily land surface air temperature minima and maxima from AMSR-E. IEEE JSTARS 3, 1, 111-123.

Matthews, E., et al. 2020. Methane emission from high latitude lakes: methane-centric lake classification and satellite-driven annual cycle of emissions. Scientific Reports 10:12465.

Murfitt, J., and C.R. Duguay, 2021. 50 years of lake ice research from active microwave remote sensing: Progress and prospects. Remote Sensing of Environment 264, 112616.

Watts, J.D., et al. 2014. Surface water inundation in the boreal-Arctic: potential impacts on regional methane emissions. Environmental Research Letters 9, 075001.

Yi, Y., et al. 2022. Potential satellite monitoring of surface organic soil properties in arctic tundra from SMAP. Water Resources Research 58(4).

The high latitudes are experiencing significant environmental changes that necessitate precise, large-scale, and long-term measurements of the interconnected hydrological and ecological systems. Microwave remote sensing is a critical tool for tracking gradual and sudden changes in the region and revealing the underlying relationships and mechanisms. This paper offers a timely and perceptive overview of microwave remote sensing in high-latitude environments, which is likely to benefit the scientific community and aid in the advancement of remote sensing research in carbon studies. Overall, the paper was clearly structured and nicely written. However, there are a few corrections and improvements to be made before I recommend it for publication:

(1) Fig. 3(a): please change “Forest” to “Vegetation”;

(2) Fig. 3(b): The figure illustrates three soil-vegetation interaction components, which are not shown in the associated equation and labels. A more complete equation consistent with the figure and accounting for the first-order scattering process is preferred.

(3) Fig. 4(a): Radar observations similar to passive microwave remote sensing are also affected by vegetation water content. Please consider to have “Vegetation water storage” under “Passive and Active” category.

(4) Table 1: For the AMSR-E/2 column, the temporal coverage should start from 2002 instead of 2012.

(5) Line 310: The statement “The rapid decrease of εsoil in freezing soils translates into a much higher microwave emission and backscattering from the surface” is not accurate. The decrease of soil dielectric constant typically corresponds to weaker radar backscattering from soil. However, for a complex high-latitude scenario with mixed soil, snow and vegetation, landscape freeze/thaw transitions can cause both enhanced or weakened microwave scattering depending on its frequencies. A nice reference explaining the rationale can be found at:

             Zwieback, S., Bartsch, A., Melzer, T. and Wagner, W., 2011. Probabilistic Fusion of Ku- and C-band Scatterometer Data for Determining the Freeze/Thaw State. IEEE transactions on geoscience and remote sensing, 50(7), pp.2583-2594.

(6) A recent work focusing on the possibility of detangling AGB and the vegetation water content from VOD (e.g. Line 392) can be found at:

              Dou, Y., Tian, F., Wigneron, J.P., Tagesson, T., Du, J., Brandt, M., Liu, Y., Zou, L., Kimball, J.S. and Fensholt, R., 2023. Reliability of using vegetation optical depth for estimating decadal and interannual carbon dynamics. Remote Sensing of Environment, 285, p.113390.

(7) For section 5.2, I recommend additional review of recent machine-learning based downscaling studies, which help to resolve the spatial heterogeneity of land parameters. For example, below is a recent paper on soil moisture downscaling:

              Du, J., Kimball, J.S., Bindlish, R., Walker, J.P. and Watts, J.D., 2022. Local Scale (3-m) Soil Moisture Mapping Using SMAP and Planet SuperDove. Remote Sensing, 14(15), p.3812.

(8) I also recommend the authors add a short summary of the satellite GNSS-R technique for high-latitude studies. The novel approach shows promise in soil moisture, vegetation, water body and freeze/thaw detections. Here is a nice reference:

             Rautiainen, K., Comite, D., Cohen, J., Cardellach, E., Unwin, M. and Pierdicca, N., 2021. Freeze–Thaw Detection Over High-Latitude Regions by Means of GNSS-R Data. IEEE Transactions on Geoscience and Remote Sensing, 60, pp.1-13.