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The recent paper in Geosciences, “Understanding the Permafrost−Hydrate System and Associated Methane Releases in the East Siberian Arctic Shelf” by Shakhova, Semiletov, and Chuvilin, (henceforth “S2019”), contains a number of false statements about our 2016 paper, “Methane fluxes from the sea to the atmosphere across the Siberian shelf seas”, (henceforth “T2016”). S2019 use three paragraphs of section 5 of their paper to claim methodological errors and issues in T2016. Notably they claim that in T2016, we systematically removed data outliers including data with high methane concentrations; this claim is false. While we appreciate that flawed methodologies can be a problem in any area of science, in this case, the claims made in S2019 are simply false. In this comment, we detail the incorrect claims made in S2019 regarding T2016, and then discuss some additional problematic aspects of S2019.

Monitoring high latitude wetlands is required to understand feedbacks between terrestrial carbon pools and climate change. Hydrological variability is a key factor driving biogeochemical processes in these ecosystems and effective assessment tools are critical for accurate characterization of surface hydrology, soil moisture, and water table fluctuations. Operational satellite platforms provide opportunities to systematically monitor hydrological variability in high latitude wetlands. The objective of this research application was to integrate high temporal frequency Synthetic Aperture Radar (SAR) and high spatial resolution Light Detection and Ranging (LiDAR) observations to assess hydroperiod at a mire in northern Sweden. Geostatistical and polarimetric (PLR) techniques were applied to determine spatial structure of the wetland and imagery at respective scales (0.5 m to 25 m). Variogram, spatial regression, and decomposition approaches characterized the sensitivity of the two platforms (SAR and LiDAR) to wetland hydrogeomorphology, scattering mechanisms, and data interrelationships. A Classification and Regression Tree (CART), based on random forest, fused multi-mode (fine-beam single, dual, quad pol) Phased Array L-band Synthetic Aperture Radar (PALSAR) and LiDAR-derived elevation to effectively map hydroperiod attributes at the Swedish mire across an aggregated warm season (May–September, 2006–2010). Image derived estimates of water and peat moisture were sensitive (R2 = 0.86) to field measurements of water table depth (cm). Peat areas that are underlain by permafrost were observed as areas with fluctuating soil moisture and water table changes.