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.