ESTIMATION OF SUBPIXEL SNOW-COVERED AREA BY NONPARAMETRIC REGRESSION SPLINES
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 te...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2016-10-01
|
| Series: | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Online Access: | http://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLII-2-W1/31/2016/isprs-archives-XLII-2-W1-31-2016.pdf |
| Summary: | 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 |
|---|---|
| ISSN: | 1682-1750 2194-9034 |