Paper Info
Title
Improving Mountainous Snow Cover Fraction Mapping via Artificial Neural Networks Combined With MODIS and Ancillary Topographic Data
Abstract
A multilayer feedforward artificial neural network (ANN) is developed for mountainous fractional snow cover (FSC) mapping. This is trained with back propagation to learn the relationship between FSC and Moderate Resolution Imaging Spectroradiometer (MODIS) products (reflectance at seven bands, normalized difference snow index, land surface temperature (LST), and FSC) and elevation. In this paper, images from Landsat Enhanced Thematic Mapper Plus (ETM+) and MODIS products from three periods are chosen to test and validate the proposed method at the Heihe River Basin. Three binary snow cover maps derived from Landsat ETM+ images are used to calculate FSC. Two of these maps are first used to train, calibrate, and test the ANN. The other independent image is used to test the generalization ability of network. Results show that the ANN can easily incorporate auxiliary information to improve the accuracy of snow cover mapping effectively. It is also capable of mapping snow cover fraction in a complicated mountainous area with considerable generalization. For the nonindependent test set, the performance evaluation results show that the improvements of ANN-based methods are apparent compared with MODIS FSC products (higher correlation coefficient, lower root-mean-square error, and more accurate total snow cover area). For the temporal/temporal-spatial independent test set, ANN-based methods perform slightly worse than the nonindependent test set, but the accuracy of the ANN methods still shows some improvement. Elevation, LST, and FSC play more important roles in the training process of the ANN. Overall, experiment 8, which integrated all input information, is approved the best in all test sets.
Year
DOI
Venue
2014
10.1109/TGRS.2013.2290996
IEEE T. Geoscience and Remote Sensing
Keywords
Field
DocType
snow,terrain mapping,correlation coefficient,remote sensing,snow cover area,mountainous fractional snow cover mapping,ann training,landsat etm+ image,lst,china,moderate resolution imaging spectroradiometer (modis),mountainous area,mountainous fsc mapping,backpropagation,hydrological techniques,network generalization ability,ancillary topographic data,back propagation,binary snow cover map,topography (earth),artificial neural network (ann),feedforward neural nets,elevation,modis data,auxiliary information,fractional snow cover (fsc),geophysical image processing,landsat enhanced thematic mapper plus,mountainous snow cover fraction mapping,heihe river basin,root mean square error,land surface temperature,temporal-spatial independent test set,modis product,generalisation (artificial intelligence),landsat etm+ product,multilayer feedforward ann,multilayer feedforward artificial neural network,moderate resolution imaging spectroradiometer,normalized difference snow index,neural nets,learning
Meteorology,Correlation coefficient,Thematic Mapper,Moderate-resolution imaging spectroradiometer,Topographic map,Remote sensing,Elevation,Backpropagation,Snow,Mathematics,Test set
Journal
Volume
Issue
ISSN
52
9
0196-2892
Citations 
PageRank 
References 
2
0.39
3
Authors
2
Name
Order
Citations
PageRank
Jinliang Hou121.07
Chunlin Huang2367.22