Volume XLII-2/W1
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W1, 31-36, 2016
https://doi.org/10.5194/isprs-archives-XLII-2-W1-31-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W1, 31-36, 2016
https://doi.org/10.5194/isprs-archives-XLII-2-W1-31-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

  26 Oct 2016

26 Oct 2016

ESTIMATION OF SUBPIXEL SNOW-COVERED AREA BY NONPARAMETRIC REGRESSION SPLINES

S. Kuter1,3, Z. Akyürek2,4, and G.-W. Weber3,4 S. Kuter et al.
  • 1Çankırı Karatekin University, Faculty of Forestry, Department of Forest Engineering, 18200, Çankırı, Turkey
  • 2Middle East Technical University, Faculty of Engineering, Department of Civil Engineering, 06800, Ankara, Turkey
  • 3Middle East Technical University, Institute of Applied Mathematics, 06800, Ankara, Turkey
  • 4Middle East Technical University, Graduate School of Natural and Applied Sciences, Department of Geodetic and Geographic Information Technologies, 06800, Ankara, Turkey

Keywords: Remote Sensing, Snow Cover, MARS, Artificial Neural Network, MODIS, Landsat

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