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Snow cover and its melt dominate regional climate and water resources in many of the world's mountainous regions. Snowmelt timing and magnitude in mountains are controlled predominantly by absorption of solar radiation and the distribution of snow water equivalent (SWE), and yet both of these are very poorly known even in the best-instrumented mountain regions of the globe. Here we describe and present results from the Airborne Snow Observatory (ASO), a coupled imaging spectrometer and scanning lidar, combined with distributed snow modeling, developed for the measurement of snow spectral albedo/broadband albedo and snow depth/SWE. Snow density is simulated over the domain to convert snow depth to SWE. The result presented in this paper is the first operational application of remotely sensed snow albedo and depth/SWE to quantify the volume of water stored in the seasonal snow cover. The weekly values of SWE volume provided by the ASO program represent a critical increase in the information available to hydrologic scientists and resource managers in mountain regions.

Planetary exploration is full of challenges. Data bandwidth is very limited between planetary rovers and ground-based data system. What’s worse, even though NASA has accumulated over 34 million images from various missions, it requires significant effort and is hardly possible for any scientist to go through all of them. In order to improve the degree of automation and the efficiency of these processes, we propose a system leveraging machine learning for planetary rovers to actively look for scientifically interesting and valuable features according to text instructions from scientists and prioritize the images captured onboard with those features for downlink. Such an image prioritization mechanism can also be naturally applied to content-based image search through text description in any local planetary image data server, allowing scientists to search for images with desired features without going through them one by one. Besides theoretical and engineering details of our proposed approach, we also present both quantitative and qualitative evaluation of the system along with some concrete examples.