Abstract
Abstract. Measurement of the areal extent of snow cover with high accuracy plays an important role in hydrological and climate modeling. Remotely-sensed data acquired by earth-observing satellites offer great advantages for timely monitoring of snow cover. However, the main obstacle is the tradeoff between temporal and spatial resolution of satellite imageries. Soft or subpixel classification of low or moderate resolution satellite images is a preferred technique to overcome this problem. The most frequently employed snow cover fraction methods applied on Moderate Resolution Imaging Spectroradiometer (MODIS) data have evolved from spectral unmixing and empirical Normalized Difference Snow Index (NDSI) methods to latest machine learning-based artificial neural networks (ANNs). This study demonstrates the implementation of subpixel snow-covered area estimation based on the state-of-the-art nonparametric spline regression method, namely, Multivariate Adaptive Regression Splines (MARS). MARS models were trained by using MODIS top of atmospheric reflectance values of bands 1-7 as predictor variables. Reference percentage snow cover maps were generated from higher spatial resolution Landsat ETM+ binary snow cover maps. A multilayer feed-forward ANN with one hidden layer trained with backpropagation was also employed to estimate the percentage snow-covered area on the same data set. The results indicated that the developed MARS model performed better than th
Highlights
Snow is an important land cover whose distribution over space and time plays a significant role in various environmental processes
In Multivariate Adaptive Regression Splines (MARS) model building, basis functions (BFs) are fitted in such a way that additive and interactive effects of the predictors are taken into account to determine the response variable (Kuter et al, 2015)
The obtained MARS model had quite satisfying mapping accuracy on the test pixels from the training data set with root mean square error (RMSE) of 0.093 and r of 0.97
Summary
Snow is an important land cover whose distribution over space and time plays a significant role in various environmental processes. Its high reflectance and low thermal reduce energy absorbed by the land surface, while snowpack stores water during the winter and releases it in the spring as snowmelt (Dobreva and Klein, 2011). Continuous monitoring of snow cover and accurate prediction of its areal extent are basically the key factors in order to deepen our understanding for present and future climate, water cycle, and ecological changes (Dobreva and Klein, 2011; Hall et al, 1995).
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