AirSWOT, an experimental airborne Ka-band interferometric synthetic aperture radar, was developed for hydrologic research and validation of the forthcoming Surface Water and Ocean Topography (SWOT) satellite mission (to be launched in 2021). AirSWOT and SWOT aim to improve understanding of surface water processes by mapping water surface elevation (WSE) and water surface slope (WSS) in rivers, lakes, and wetlands. However, the utility of AirSWOT for these purposes remains largely unexamined. We present the first investigation of AirSWOT WSE and WSS surveys over complex, low-relief, wetland-river hydrologic environments, including (1) a field-validated assessment of AirSWOT WSE and WSS precisions for lakes and rivers in the Yukon Flats Basin, an Arctic-Boreal wetland complex in eastern interior Alaska; (2) improved scientific understanding of surface water flow gradients and the influence of subsurface permafrost; and (3) recommendations for improving AirSWOT precisions in future scientific and SWOT validation campaigns. AirSWOT quantifies WSE with an RMSE of 8 and 15 cm in 1 and 0.0625 km 2 river reaches, respectively, and 21 cm in lakes. This indicates good utility for studying hydrologic flux, WSS, geomorphic processes, and coupled surface/subsurface hydrology in permafrost environments. This also suggests that AirSWOT supplies sufficient precision for validating SWOT WSE and WSS over rivers, but not lakes. However, improvements in sensor calibration and flight experiment design may improve precisions in future deployments as may modifications to data processing. We conclude that AirSWOT is a useful tool for bridging the gap between field observations and forthcoming global SWOT satellite products.This study deployed AirSWOT and a field team to eastern interior Alaska in June 2015 for testing over the Yukon Flats Basin (YFB), a protected wetland area within the Yukon Flats National Wildlife Refuge, which straddles the Arctic circle (Figure 1a). The YFB has complex, low-relief topography and is underlain by PITCHER ET AL.

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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 km 2 of open surface water were mapped from 23,380 km 2 of flight lines spanning 23 degrees of latitude and broad environmental gradients. Detected water body sizes range from 0.00004 km 2 (40 m 2 ) to 15 km 2 . 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) km 2 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.

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Landslide event inventories are a vital resource for landslide susceptibility and forecasting applications. However, landslide inventories can vary in accuracy, availability, and timeliness as a result of varying detection methods, reporting, and data availability. This study presents an approach to use publicly available satellite data and open-source software to automate a landslide detection process called the Sudden Landslide Identification Product (SLIP). SLIP utilizes optical data from the Landsat-8 Operational Land Imager sensor, elevation data from the Shuttle Radar Topography Mission, and precipitation data from the Global Precipitation Measurement mission to create a reproducible and spatially customizable landslide identification product. The SLIP software applies change-detection algorithms to identify areas of new bare-earth exposures that may be landslide events. The study also presents a precipitation monitoring tool that runs alongside SLIP called the Detecting Real-Time Increased Precipitation (DRIP) model that helps to identify the timing of potential landslide events detected by SLIP. Using SLIP and DRIP together, landslide detection is improved by reducing problems related to accuracy, availability, and timeliness that are prevalent in the state of the art for landslide detection. A case study and validation exercise in Nepal were performed for images acquired between 2014 and 2015. Preliminary validation results suggest 56% model accuracy, with errors of commission often resulting from newly cleared agricultural areas. These results suggest that SLIP is an important first attempt in an automated framework that can be used for medium-resolution regional landslide detection, although it requires refinement before being fully realized as an operational tool.