Satellite-based snow-cover monitoring is performed using optical, synthetic aperture radar (SAR), and passivemicrowave sensors. Effects of forest canopy on the observed signal need to be considered with all of these sensor types. Various models describing the interaction of electromagnetic radiation with forest canopy have been developed, but many of these are overly complex with high computational and ancillary data requirements. However, for retrieval purposes, simple models are preferred. This work aims at increasing the understanding of the effect of forest canopy on remote sensing observations of snow-covered terrain for both microwave and optical regimes and at quantifying the capability of simple zeroth-order models in simulating these effects. To achieve these goals, a spatial analysis of optical, SAR, and passive-microwave remote sensing data in the northern boreal forest region was performed. Model parameters for vegetation transmissivity as well as the properties of the underlying surface were optimized by utilizing lidar-ranging- and Landsat-based simplified proxy parameters describing forest canopy closure and stem volume. The results demonstrated that despite using these relatively simple proxies, a zeroth-order model can accurately estimate the extinction of electromagnetic signals in a forest, particularly for passive microwave and optical data. The SAR model successfully estimated the median of the observations, but larger scatter of the observations was reflected by a higher root mean square error and lower correlation between models and observations. Due to both good estimation accuracy and simplicity, the presented models can be considered to be applicable in existing snow retrieval algorithms.
|Journal||IEEE Transactions on Geoscience and Remote Sensing|
|Publication status||Published - 2015|
|MoE publication type||A1 Journal article-refereed|