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Prediction of Active Microwave Backscatter Over Snow-Covered Terrain Across Western Colorado Using a Land Surface Model and Support Vector Machine Regression
- Source :
- IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 14, Pp 2403-2417 (2021)
- Publication Year :
- 2022
-
Abstract
- The main objective of this article is to develop a physically constrained support vector machine (SVM) to predict C-band backscatter over snow-covered terrain as a function of geophysical inputs that reasonably represent the relevant characteristics of the snowpack. Sentinel-1 observations, in conjunction with geophysical variables from the Noah-MP land surface model, were used as training targets and input datasets, respectively. Robustness of the SVM prediction was analyzed in terms of training targets, training windows, and physical constraints related to snow liquid water content. The results showed that a combination of ascending and descending overpasses yielded the highest coverage of prediction (15.2%) while root mean square error (RMSE) ranged from 2.06 to 2.54 dB and unbiased RMSE ranged from 1.54 to 2.08 dB, but that the combined overpasses were degraded compared with ascending-only and descending-only training target sets due to the mixture of distinctive microwave signals during different times of the day (i.e., 6 A.M. versus 6 P.M. local time). Elongation of the training window length also increased the spatial coverage of prediction (given the sparsity of the training sets), but resulted in introducing more random errors. Finally, delineation of dry versus wet snow pixels for SVM training resulted in improving the accuracy of predicted backscatter relative to training on a mixture of dry and wet snow conditions. The overall results suggest that the prediction accuracy of the SVM was strongly linked with the first-order physics of the electromagnetic response of different snow conditions. ispartof: IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING vol:14 pages:2403-2417 ispartof: location:United States status: published
- Subjects :
- Technology
Atmospheric Science
010504 meteorology & atmospheric sciences
Geophysics. Cosmic physics
0211 other engineering and technologies
C-BAND SAR
02 engineering and technology
LEARNING PREDICTIONS
01 natural sciences
Remote Sensing
Engineering
NASA land information system (LIS)
Snow
CLIMATE-CHANGE
BRIGHTNESS TEMPERATURE
SEASONAL SNOW
synthetic aperture radar (SAR)
Ocean engineering
Geography, Physical
Physical Sciences
SIR-C/X-SAR
support vector machine (SVM)
Land surface model
Synthetic aperture radar
Backscatter
Mean squared error
ERS-1 SAR DATA
TIME-SERIES
Terrain
WET-SNOW
snow-covered terrain
Land surface
Training
Computers in Earth Sciences
Imaging Science & Photographic Technology
TC1501-1800
021101 geological & geomatics engineering
0105 earth and related environmental sciences
Remote sensing
Science & Technology
Support vector machines
QC801-809
Training (meteorology)
Engineering, Electrical & Electronic
Snowpack
Support vector machine
Physical Geography
WATER EQUIVALENT
Subjects
Details
- ISSN :
- 19391404 and 24032417
- Volume :
- 14
- Database :
- OpenAIRE
- Journal :
- IEEE journal of selected topics in applied earth observations and remote sensing
- Accession number :
- edsair.doi.dedup.....7062f6bf16bdfd68a619d468646269d5