Description: Northern lakes are considered a major source of atmospheric methane (CH4), a potent GHG. However, large uncertainties in their emissions (7–26 Tg CH4 yr–1) arise from challenges in upscaling field data, including fluxes by ebullition (bubbling), the dominant emission pathway. Remote sensing of ebullition would allow detailed mapping of regional emissions but has hitherto not been developed. Here, we show that lake ebullition can be imaged using synthetic aperture radar remote sensing during ice-cover periods by exploiting the effect of ebullition on the texture of the ice–water interface. Applying this method to five Alaska regions and combining spatial remote sensing information with year-round bubble-trap flux measurements, we create ebullition-flux maps for 5,143 Alaskan lakes. Regional lake CH4 emissions, based on satellite remote sensing analyses, were lower compared to previous estimates based on upscaling from individual lakes and were consistent with independent airborne CH4 observations. Thermokarst lakes formed by thaw of organic-rich permafrost had the highest fluxes, although lake density and lake size distributions also controlled regional emissions. This new remote sensing approach offers an opportunity to improve knowledge about Arctic CH4 fluxes and helps to explain long-standing discrepancies between estimates of CH4 emissions from atmospheric measurements and data upscaled from individual lakes.

Description: Northern lakes are considered a major source of atmospheric methane (CH4), a potent GHG1,2. However, large uncertainties in their emissions (7–26 Tg CH4 yr–1; ref. 2) arise from challenges in upscaling field data, including fluxes by ebullition (bubbling), the dominant emission pathway2. Remote sensing of ebullition would allow detailed mapping of regional emissions but has hitherto not been developed. Here, we show that lake ebullition can be imaged using synthetic aperture radar remote sensing during ice-cover periods by exploiting the effect of ebullition on the texture of the ice–water interface. Applying this method to five Alaska regions and combining spatial remote sensing information with year-round bubble-trap flux measurements, we create ebullition-flux maps for 5,143 Alaskan lakes. Regional lake CH4 emissions, based on satellite remote sensing analyses, were lower compared to previous estimates based on upscaling from individual lakes2,3 and were consistent with independent airborne CH4 observations. Thermokarst lakes formed by thaw of organic-rich permafrost had the highest fluxes, although lake density and lake size distributions also controlled regional emissions. This new remote sensing approach offers an opportunity to improve knowledge about Arctic CH4 fluxes and helps to explain long-standing discrepancies between estimates of CH4 emissions from atmospheric measurements and data upscaled from individual lakes.

Description: Recently, the United States Geological Service (USGS) released a new provisional product which estimates aquatic reflectance from Landsat 8 Operational Land Imager (OLI), called Landsat 8 Provisional Aquatic Reflectance (L8PAR). However, as indicated in the product guide, the use of this product for inland waters needs further verification and improvements. The goal of this study was to determine how the novel L8PAR product performs for different small turbid and eutrophic lakes in Northern Germany compared to in situ measurements of above water remote sensing reflectance (Rrs). For several recent scenes during our monitoring, the L8PAR product failed to produce full data for the lakes of our interest. For the best scene with in situ spectra, L8PAR was not able to retrieve any information for band 1 and not all information for bands 2, 3 and 4. The pixels with valid values for reflectance showed a weak relationship for band 2 (R2 of 0.24) and a medium relationship for bands 3 and 4 (R2 of 0.68 and 0.72, respectively). Compared to other atmospheric correction routines (ACOLITE, C2RCC, C2X, iCOR and L8SR), L8PAR was the only product which was not able to retrieve Rrs for all match up samples. This work provides an evaluation of the L8PAR product for inland waterbodies. Although more analysis and validation need to be conducted, our study suggests that the L8PAR product cannot be used for small inland lakes in its current state and has to be used with care for inland waters in general.

Description: Eutrophication of inland waters is an environmental issue that is becoming more common with climatic variability. Monitoring of this aquatic problem is commonly based on the chlorophyll-a concentration monitored by routine sampling with limited temporal and spatial coverage. Remote sensing data can be used to improve monitoring, especially after the launch of the MultiSpectral Instrument (MSI) on Sentinel-2. In this study, we compared the estimation of chlorophyll-a (chl-a) from different bio-optical algorithms using hyperspectral proximal remote sensing measurements, from simulated MSI responses and from an MSI image. For the satellite image, we also compare different atmospheric corrections routines before the comparison of different bio-optical algorithms. We used in situ data collected in 2019 from 97 sampling points across 19 different lakes. The atmospheric correction assessment showed that the performances of the routines varied for each spectral band. Therefore, we selected C2X, which performed best for bands 4 (root mean square error—RMSE = 0.003), 5 (RMSE = 0.004) and 6 (RMSE = 0.002), which are usually used for the estimation of chl-a. Considering all samples from the 19 lakes, the best performing chl-a algorithm and calibration achieved a RMSE of 16.97 mg/m3. When we consider only one lake chain composed of meso-to-eutrophic lakes, the performance improved (RMSE: 10.97 mg/m3). This shows that for the studied meso-to-eutrophic waters, we can reliably estimate chl-a concentration, whereas for oligotrophic waters, further research is needed. The assessment of chl-a from space allows us to assess spatial dynamics of the environment, which can be important for the management of water resources. However, to have an accurate product, similar optical water types are important for the overall performance of the bio-optical algorithm.