A High-Resolution Airborne Color-Infrared Camera Water Mask for the NASA ABoVE Campaign

The airborne AirSWOT instrument suite, consisting of an interferometric Ka-band synthetic aperture radar and color-infrared (CIR) camera, was deployed to northern North America in July and August 2017 as part of the NASA Arctic-Boreal Vulnerability Experiment (ABoVE). We present validated, open (i.e., vegetation-free) surface water masks produced from high-resolution (1 m), co-registered AirSWOT CIR imagery using a semi-automated, object-based water classification. The imagery and resulting high-resolution water masks are available as open-access datasets and support interpretation of AirSWOT radar and other coincident ABoVE image products, including LVIS, UAVSAR, AIRMOSS, AVIRIS-NG, and CFIS. These synergies offer promising potential for multi-sensor analysis of Arctic-Boreal surface water bodies. In total, 3167 km2 of open surface water were mapped from 23,380 km2 of flight lines spanning 23 degrees of latitude and broad environmental gradients. Detected water body sizes range from 0.00004 km2 (40 m2) to 15 km2. Power-law extrapolations are commonly used to estimate the abundance of small lakes from coarser resolution imagery, and our mapped water bodies followed power-law distributions, but only for water bodies greater than 0.34 (±0.13) km2 in area. For water bodies exceeding this size threshold, the coefficients of power-law fits vary for different Arctic-Boreal physiographic terrains (wetland, prairie pothole, lowland river valley, thermokarst, and Canadian Shield). Thus, direct mapping using high-resolution imagery remains the most accurate way to estimate the abundance of small surface water bodies. We conclude that empirical scaling relationships, useful for estimating total trace gas exchange and aquatic habitats on Arctic-Boreal landscapes, are uniquely enabled by high-resolution AirSWOT-like mappings and automated detection methods such as those developed here

Contemporary and historical detection of small lakes using super resolution Landsat imagery: promise and peril

Landsat is the longest-running environmental satellite program and has been used for surface water mapping since its launch in 1972. However, its sustained 30 m resolution since 1982 prohibits the detection of small water bodies, which are globally far more prevalent than large. Remote sensing image resolution is increasingly being enhanced through single image super resolution (SR), a machine learning task typically performed by neural networks. Here, we show that a 10× SR model (Enhanced Super Resolution Generative Adversarial Network, or ESRGAN) trained entirely with Planet SmallSat imagery (3 m resolution) improves the detection of small and sub-pixel lakes in Landsat imagery (30 m) and produces images (3 m resolution) with preserved radiometric properties. We test the utility of these Landsat SR images for small lake detection by applying a simple water classification to SR and original Landsat imagery and comparing their lake counts, sizes, and locations with independent, high-resolution water maps made from coincident airborne camera imagery. SR images appear realistic and have fewer missed detections (type II error) compared to low resolution (LR), but exhibit errors in lake location and shape, and yield increasing false detections (type I error) with decreasing lake size. Even so, lakes between ~500 and ~10,000 m2 in area are better detected with SR than with native-resolution Landsat 8 imagery. SR transformation achieves an F-1 score for water detection of 0.75 compared to 0.73 from native resolution Landsat. We conclude that SR enhancement improves the detection of small lakes sized several Landsat pixels or less, with a minimum mapping unit (MMU) of ~ 2/3 of a Landsat pixel – a significant improvement from previous studies. We also apply the SR model to a historical Landsat 5 image and find similar performance gains, using an independent 1985 air photo map of 242 small Alaskan lakes. This demonstration of retroactively generated 3 m imagery dating to 1985 has exciting applications beyond water detection and paves the way for further SR land cover classification and small object detection from the historical Landsat archive. However, we caution that the approach presented is suitable for landscape-scale inventories of lake counts and lake size distributions, but not for specific geolocational positions of individual lakes. Much work remains to be done surrounding technical and ethical guidelines for the creation, use, and dissemination of SR satellite imagery

Super-Resolution Surface Water Mapping on the Canadian Shield Using Planet CubeSat Images and a Generative Adversarial Network

The Canadian Shield, the world’s largest exposure of glaciated crystalline bedrock, is the most lake-rich region on Earth. Recent studies using high-resolution CubeSat satellite imagery have revealed its surface water hydrology to be surprisingly dynamic at fine spatial scales. Here we test whether super-resolution (SR), the resampling of coarse imagery to a finer-than-native resolution, can detect such changes. We degrade high-resolution Planet CubeSat images of the Shield, then resample the coarsened imagery back to its native resolution using both traditional cubic resampling and a generative adversarial network, a type of neural network often used for SR. To test classification accuracy from the generated SR imagery, we apply the same water classification to both resampling methods and find similar performance based on confusion matrices with the control case of high-resolution imagery. Next, we compare fine-scale shoreline mapping in SR imagery, cubic resampling, and in-situ field surveys. SR shorelines outperform those from cubic resampling, with an increase in the modified kappa coefficient from −0.070 to 0.073. Potential applications include improved mapping of Shield lakes and retroactive application of SR to coarser-resolution satellite datasets to infer historical changes in fine-scale surface water dynamics

Tracking transient boreal wetland inundation with Sentinel-1 SAR: Peace-Athabasca Delta, Alberta and Yukon Flats, Alaska

Accurate and frequent mapping of transient wetland inundation in the boreal region is critical for monitoring the ecological and societal functions of wetlands. Satellite Synthetic Aperture Radar (SAR) has long been used to map wetlands due to its sensitivity to surface inundation and ability to penetrate clouds, darkness, and certain vegetation canopies. Here, we track boreal wetland inundation by developing a two-step modified decision-tree algorithm implemented in Google Earth Engine using Sentinel-1 C-band SAR and Sentinel-2 Multispectral Instrument (MSI) time-series data as inputs. This approach incorporates temporal as well as spatial characteristics of SAR backscatter and is evaluated for the Peace-Athabasca Delta, Alberta (PAD), and Yukon Flats, Alaska (YF) from May 2017 to October 2019. Within these two boreal study areas, we map spatiotemporal patterns in wetland inundation classes of Open Water (OW), Floating Plants (FP), Emergent Plants (EP), and Flooded Vegetation (FV). Temporal variability, frequency, and maximum extents of transient wetland inundation are quantified. Retrieved inundation estimates are compared with in-situ field mapping obtained during the NASA Arctic-Boreal Vulnerability Experiment (ABoVE), and a multi-temporal Landsat-derived surface water map. Over the 2017–2019 study period, we find that fractional inundation area ranged from 18.0% to 19.0% in the PAD, and from 10.7% to 12.1% in the YF. Transient wetland inundation covered ~595 km2 of the PAD, comprising ~9.1% of its landscape, and ~102 km2 of the YF, comprising ~3.6%. The implications of these findings for wetland function monitoring, and estimating landscape-scale methane emissions are discussed, together with limitations and uncertainties of our approach. We conclude that time series of Sentinel-1 C-band SAR backscatter, screened with Sentinel-2 MSI optical imagery and validated by field measurements, offer a valuable tool for tracking transient boreal wetland inundation

Geospatial Analysis of Alaskan Lakes Indicates Wetland Fraction and Surface Water Area Are Useful Predictors of Methane Ebullition

Arctic-boreal lakes emit methane (CH4), a powerful greenhouse gas. Recent studies suggest ebullition might be a dominant methane emission pathway in lakes but its drivers are poorly understood. Various predictors of lake methane ebullition have been proposed but are challenging to evaluate owing to different geographical characteristics, field locations, and sample densities. Here we compare large geospatial data sets of lake area, lake perimeter, permafrost, land cover, temperature, soil organic carbon content, depth, and greenness with remotely sensed methane ebullition estimates for 5,143 Alaskan lakes. We find that lake wetland fraction (LWF), a measure of lake wetland and littoral zone area, is a leading predictor of methane ebullition (adj. R2 = 0.211), followed by lake surface area (adj. R2 = 0.201). LWF is inversely correlated with lake area, thus higher wetland fraction in smaller lakes might explain a commonly cited inverse relationship between lake area and methane ebullition. Lake perimeter (adj. R2 = 0.176) and temperature (adj. R2 = 0.157) are moderate predictors of lake ebullition, and soil organic carbon content, permafrost, lake depth, and greenness are weak predictors. The low adjusted R2 values are typical and informative for methane attribution studies. Our leading model, which uses lake area, temperature, and LWF (adj. R2 = 0.325, n = 5,130) performs slightly better than leading multivariate models from similar studies. Our results suggest landscape-scale geospatial analyses can complement smaller field studies, for attributing Arctic-boreal lake methane emissions to readily available environmental variables.</p

Peace-Athabasca Delta water surface elevations and slopes mapped from AirSWOT Ka-band InSAR

In late 2023 the Surface Water and Ocean Topography (SWOT) satellite mission will release unprecedented high-resolution measurements of water surface elevation (WSE) and water surface slope (WSS) globally. SWOT’s exciting Ka-band near-nadir wide-swath interferometric radar (InSAR) technology could transform studies of surface water hydrology, but remains highly experimental. We examine Airborne SWOT (AirSWOT) data acquired twice over Canada’s Peace-Athabasca Delta (PAD), a large, low-gradient, ecologically important riverine wetland complex. While noisy and susceptible to “dark water” (low-return) data losses, spatially averaged AirSWOT WSE observations reveal a broad-scale water-level decline of ~44 cmn (σ =271 cm) between 9 July and 13 August 2017, similar to a ~56 cm decline (σ=33 cm) recorded by four in situ gauging stations. River flow directions and WSS are correctly inferred following filtering and reach-averaging of AirSWOT data, but ~10 km reaches are essential to retrieve them. July AirSWOT observations suggest steeper WSS down an alternate flow course (Embarras River–Mamawi Creek distributary) of the Athabasca River, consistent with field surveys conducted the following year. This signifies potential for the Athabasca River to avulse northward into Mamawi Lake, with transformative impacts on flooding, sedimentation, ecology, and human activities in the PAD. Although AirSWOT differs from SWOT, we conclude SWOT Ka-band InSAR observations may detect water level changes and avulsion potentials in other low-gradient deltas globally.</p

Hydrologic and Landscape Controls on Dissolved Organic Matter Composition Across Western North American Arctic Lakes

Northern high-latitude lakes are hotspots for cycling dissolved organic carbon (DOC) inputs from allochthonous sources to the atmosphere. However, the spatial distribution of lake dissolved organic matter (DOM) is largely unknown across Arctic-boreal regions with respect to the surrounding landscape. We expand on regional studies of northern high-latitude DOM composition by integrating DOC concentrations, optical properties, and molecular-level characterization from lakes spanning the Canadian Taiga to the Alaskan Tundra. Lakes were sampled during the summer from July to early September to capture the growing season. DOM became more optically processed and molecular-level aromaticity increased northward across the Canadian Shield to the southern Arctic and from interior Alaska to the Tundra, suggesting relatively greater DOM incorporation from allochthonous sources. Using water isotopes (δ18O-H2O), we report a weak overall trend of increasing DOC and decreasing aromaticity in lakes that were hydrologically isolated from the landscape and enriched in δ18O-H2O, while within-region trends were stronger and varied depending on the landscape. Finally, DOC correlated weakly with chromophoric dissolved organic matter (CDOM) across the study sites, suggesting that autochthonous and photobleached DOM were a major component of the DOC in these regions; however, some of the northernmost and wetland-dominated lakes followed pan-Arctic riverine DOC-CDOM relationships, indicating strong contributions from allochthonous inputs. As many lakes across the North American Arctic are experiencing changes in temperature and precipitation, we expect the proportions of allochthonous and autochthonous DOM to respond with aquatic optical browning with greater landscape connectivity and more internally produced DOM in hydrologically isolated lakes