Evaluation of SSM/I filtering algorithm for snow cover identification in Northern New York State

Snow-cover parameters are being increasingly used as input to hydrological models. An accurate knowledge of the onset of snow melts and snow water equivalent values are important variables in different hydrological applications such as Hooding prediction, reservoir management and agricultural activities. However, the traditional field sampling methods and the ground-based data collection are often very sparse, time consuming, and expensive compared to the coverage provided by remote sensing techniques. Microwave remote sensing techniques have been investigated by numerous researchers using various sensors and have been demonstrated to be effective for monitoring snow pack parameters such as spatial and temporal distribution, snow water equivalent, depth, and snow condition (wet/dry state). Those researches have resulted that the microwave brightness temperature and the microwave backscattering are related to the snow cover structure with different correlation degrees. The primary objective of this research is to produce a spatial estimation of snow water equivalent in a timely fashion with sufficient spatial and temporal resolution using multi-source microwave and optical data. The final product of this project will be an additional tool for flood warning and water resource forecasts, which can be an additional input to the actual hydrological models. The contribution of remote sensing snow related information into the advanced hydrologic prediction system (AHPS) operated by NWS/NOAA (with 4 km grid resolution) will be also evaluated. This paper presents the first step of this project: data collection and evaluation