# Tracking Dynamic Northern Surface Water Changes with High-Frequency Planet CubeSat Imagery
###### Abstract  
Recent deployments of CubeSat imagers by companies such as Planet may advance hydrological remote sensing by providing an unprecedented combination of high temporal and high spatial resolution imagery at the global scale. With approximately 170 CubeSats orbiting at full operational capacity, the Planet CubeSat constellation currently offers an average revisit time of <1 day for the Arctic and near-daily revisit time globally at 3 m spatial resolution. Such data have numerous potential applications for water resource monitoring, hydrologic modeling and hydrologic research. Here we evaluate Planet CubeSat imaging capabilities and potential scientific utility for surface water studies in the Yukon Flats, a large sub-Arctic wetland in north central Alaska. We find that surface water areas delineated from Planet imagery have a normalized root mean square error (NRMSE) of <11% and geolocation accuracy of <10 m as compared with manual delineations from high resolution (0.3-0.5 m) WorldView-2/3 panchromatic satellite imagery. For a 625 km\({}^{2}\) subarea of the Yukon Flats, our time series analysis reveals that roughly one quarter of 268 lakes analyzed responded to changes in Yukon River discharge over the period 23 June-1 October 2016, one half steadily contracted, and one quarter remained unchanged. The spatial pattern of observed lake changes is heterogeneous. While connections to Yukon River control the hydrologically connected lakes, the behavior of other lakes is complex, likely driven by a combination of intricate flow paths, underlying geology and permafrost. Limitations of Planet CubeSat imagery include a lack of an automated cloud mask, geolocation inaccuracies, and inconsistent radiometric calibration across multiple platforms. Although these challenges must be addressed before Planet CubeSat imagery can achieve its full potential for large-scale hydrologic research, we conclude that CubeSat imagery offers a powerful new tool for the study and monitoring of dynamic surface water bodies. Arctic hydrology; CubeSats; remote sensing of lakes and rivers; Yukon Flats 20179130603390/rs9121306 Issue 201791306 Issue 201791306 Issue 201791306 Issue 201791306 Issue 201791306 Issue 201791306 Issue  
Although mapping surface water extent using optical imagery is well established [1; 5], a tradeoff between high spatial versus high temporal resolution sensors has traditionally prevented study of dynamic, fine-scale changes in surface water extent over short temporal scales. Surface water mapping has a long legacy with Landsat sensors (TM, ETM, OLI) due to their relatively high spatial resolution (30 m), high radiometric quality, and long term continuous record [6], yet their historical 16-day revisit time, typically lower due to cloud cover, limits the applicability of Landsat for tracking sub-seasonal patterns in surface water extent. The twin Sentinel-2 satellites now offer more frequent returns (5-10 days) at 10 m resolution, but cloud obscuration still lowers the actual revisit frequency. MODIS imagery is often used to study large, dynamic hydrologic systems such as rivers (e.g., [7]), wetlands (e.g., [8]) and lakes (e.g., [9]) due to its daily periodicity, but the coarse resolution of MODIS visible/NIR bands (250-500 m) means only coarse scale variability can be distinguished. Combining MODIS and Landsat observations can enable near-daily, 30 m land surface monitoring, though the results are sensitive to the blending algorithm used [10; 11; 12]. This compromise between high spatial and temporal resolution has thus limited the ability to track both fine-scale, sub-seasonal patterns in surface water extent. High frequency observations of surface water extent are particularly critical for study of dynamic high latitude hydrologic processes. The high latitudes contain the highest proportion of small water bodies on Earth [13; 14], nearly all of which are unvisited and unstudied. Due to strong seasonality, northern lakes and rivers are highly dynamic.