HOW DO FORESTS AFFECT BOTH SNOW PROPERTIES AND THE DETECTION OF SNOW PROPERTIES BY REMOTE SENSING? WHAT FOREST METRICS ARE MOST RELEVANT TO SNOW REMOTE SENSING AND AT WHAT SCALES? WHAT ARE OPTIMAL SYNERGISTIC COMBINATIONS OF REMOTE SENSING TECHNOLOGIES FOR MAPPING AND CHARACTERIZING SNOW IN FORESTED REGIONS? IN WHAT WAYS CAN SNOW HYDROLOGY MODELS BE USED TO AUGMENT REMOTE SENSING CAPABILITIES WHERE DETECTION OF SNOW PROPERTIES IS LIMITED? THESE ARE THE MOTIVATING SCIENCE QUESTIONS FOR SNOW REMOTE SENSING IN FORESTED REGIONS WITH APPLICATIONS TO TERRESTRIAL HYDROLOGY AND LAND SURFACE MODELING. AT PRESENT WE LACK THE REMOTE SENSING CAPACITY TO CONSISTENTLY PRODUCE ACCURATE ESTIMATES OF KEY SNOW PROPERTIES SUCH AS SNOW-COVERED AREA (SCA) SNOW ALBEDO SNOW WATER EQUIVALENT (SWE) SNOWMELT STATUS AND SNOW GRAIN SIZE IN FORESTED ECOSYSTEMS. FORESTS OBSCURE SNOW FROM SATELLITE OBSERVATIONS AND CONCURRENTLY MODIFY SNOWPACK PROPERTIES. FORESTS MODIFY SNOW ACCUMULATION THROUGH CANOPY INTERCEPTION AND ALSO INFLUENCE SNOWPACK EVOLUTION BY REDUCING WIND SPEED AND INCOMING SHORTWAVE RADIATION RADIATING LONGWAVE ENERGY AND DECREASING SNOW ALBEDO. IF WE ARE TO EFFECTIVELY DETECT AND CHARACTERIZE SNOW IN FORESTED REGIONS WE MUST FIRST UNDERSTAND THE ABILITIES AND LIMITATIONS OF OPTICAL AND MICROWAVE REMOTE SENSING INSTRUMENTS IN FOREST ENVIRONMENTS. FURTHERMORE WE MUST EXAMINE AND EVALUATE THE SYNERGISTIC CAPABILITIES OF SNOWPACK MODELS TO SUPPLY KEY INFORMATION NEEDED BY ALGORITHMS TO DETERMINE SCA SWE ETC. FROM OPTICAL AND MICROWAVE INSTRUMENTS. AS A MEMBER OF THE SCIENCE DEFINITION TEAM FOR THE NEXT GENERATION CLPX NOLIN WILL FOCUS ON THESE SNOW-FOREST QUESTIONS WORKING CLOSELY WITH OTHER TEAM MEMBERS TO IDENTIFY NEEDS CAPABILITIES AND UNCERTAINTIES IN SNOW REMOTE SENSING IN FORESTED REGIONS. IN THIS CAPACITY NOLIN AND HER STUDENTS WILL ACHIEVE THE FOLLOWING SPECIFIC OBJECTIVES: 1. EVALUATE FOREST STRUCTURE METRICS THAT AFFECT SNOW REMOTE SENSING FROM STAND TO WATERSHED TO LARGER SCALES; 2. IMPROVE AND VALIDATE MODEL PARAMETERIZATIONS OF FOREST EFFECTS ON SNOW CHARACTERISTICS: INCLUDING FOREST STRUCTURE AND FOREST LITTER (THE DIRECT EFFECT OF FOREST LITTER ON SNOW ALBEDO AND THE INDIRECT EFFECTS OF FOREST LITTER ON SNOWPACK PHYSICAL PROPERTIES SUCH AS GRAIN SIZE AND DENSITY); 3. EVALUATE THE USE OF A SNOW MODEL TO INFORM REMOTE SENSING ALGORITHMS WHERE DETECTION OF SNOW PROPERTIES IS LIMITED BY FOREST COVER; 4. RECOMMEND STRATEGIES FOR IMPROVING REMOTE SENSING OF SNOW IN FORESTED REGIONS FOR CURRENT AND FUTURE NASA MISSIONS. THESE OBJECTIVES WILL BE ACHIEVED THROUGH (A) DERIVING FOREST STRUCTURE METRICS FROM LIDAR AND EVALUATE THEIR RELATIONSHIPS WITH KEY SNOW PROPERTIES; (B) CORRELATING THESE STAND-SCALE METRICS WITH LANDSAT MULTISPECTRAL IMAGE DATA AT THE WATERSHED SCALE; (C) IMPROVING A REPRESENTATIVE SNOW MODEL THROUGH COLLABORATIVELY REFINING A FOREST-SNOW SUB-MODEL THAT INTEGRATES THE WATERSHED-SCALE LANDSATDERIVED FOREST METRICS; (D) USING THE SNOW MODEL TO SIMULATE SUB-CANOPY SNOW CHARACTERISTICS AND ASSESSING THE RESULTS ACROSS SITES; AND (D) COLLABORATING WITH CLPX TEAM MEMBERS TO IDENTIFY FOREST AREAS WHERE THE NEW FOREST METRICS AND IMPROVED MODELING OF FOREST SNOW CAN INFORM SNOW REMOTE SENSING ALGORITHMS. PI NOLIN HAS THREE DECADES OF EXPERIENCE IN REMOTE SENSING OF SNOW FIELD MEASUREMENTS IN SNOW HYDROLOGY SNOW MODELING AND MORE RECENTLY MEASUREMENTS OF FOREST-SNOW INTERACTIONS. SHE IS A RECOGNIZED LEADER IN SNOW REMOTE IN 2011 SHE ESTABLISHED AND HAS SINCE MAINTAINED THE FOREST ELEVATIONAL SNOW TRANSECT (FOREST). SINCE 1997 SHE HAS BEEN A MEMBER OF THE NASA MISR SCIENCE TEAM AND SERVED AS VICE CHAIR OF THE WATER PANEL FOR THE 2007 DECADAL SURVEY. SHE CURRENTLY SERVES ON THE NASA ADVISORY COUNCIL-EARTH SCIENCE SUBCOMMITTEE.