Optimizing UAV broadband albedo survey design and quantifying uncertainties at different measurement scales

The seasonal accumulation and melt of snow represent a dramatic transformation of Earth’s albedo, directly affectingwater and energy cycles. Spatiotemporal estimatesof albedo rely on measurements from NASA satellites (MODIS and Landsat), that require in situ data to calibrate the sensors’raw Earth Observations (EO). However, scaling issues exist between the fixed location in situ and satellite data. Unmanned Aerial Vehicles (UAVs)provide technical and logistical opportunities to bridge the scalingchallenges associated with fixed position in situdata. We testa technological framework and methodology to directly address known albedo scaling issues in snowy landscapes using an integrated albedo sensor framework (ASF) deployed on a UAV. Ourmethods and resulting data will be used to create high resolution (sub-meter) maps of albedo throughout the winter season. These data will be used to optimize survey design, data collection, and processing of albedo data from a UAV. Specifically, we will: 1)Quantify the response of an optimized UAV ASF at different altitudes over a manipulated study area. 2)Quantify scale-dependent factors, optimal flight altitudes, and optimal measurement scales for the UAV ASF during flights across snowy mountain headwaters and boreal forests.3)Disseminate methods and results through a no cost hands-on workshop.4)Develop a schema for archiving UAV data in NASA’s DAAC.These methods and results are of value to NASA, as MODIS and Landsat provide global-scale albedo products. UAVs can measure albedo at multiple scales and locations, bridging the challenges associated with fixed position in situ data and EO.

Contact Info