Using Deep Learning to Model Elevation Differences between Radar and Laser Altimetry
Abstract
:1. Introduction
2. Data
3. Method
3.1. Data Filtering and Preparation
3.1.1. CryoSat-2 Data
3.1.2. ICESat-2 Data
3.1.3. ArcticDEM Data
3.1.4. Spatio-Temporal Join
3.2. Statistical Assumptions
3.3. CryoSat-2 SARIn vs. ICESat-2 LIDAR Maps
3.4. Models
4. Results
5. Discussion
5.1. Observations
5.1.1. LIDAR-SARIn Differences-Observed Contribution Drivers
5.1.2. LIDAR-SARIn Differences–Spatial and Temporal Trends
5.2. OLS and NN Models-Strengths
5.3. OLS and NN Models-Limitations
6. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Model Input Parameters | Source |
---|---|
Distance to POCA | CryoSat2-SARIn |
Power | CryoSat2-SARIn |
Coherence | CryoSat2-SARIn |
Leading Edge Width | CryoSat2-SARIn |
Relative Elevationi (i = {−3, −2, −1, 1, 2, 3}) | CryoSat2-SARIn |
Relative Elevation Meanj (j = {10, 20}) | CryoSat2-SARIn |
Across Track Slope | ArcticDEM |
Along Track Slope | ArcticDEM |
South/North Aspect Vector Component | ArcticDEM |
West/East Aspect Vector Component | ArcticDEM |
Model | Mean (m) | Standard Error (m) | Median (m) | RMSE (m) | MAD (m) | r |
---|---|---|---|---|---|---|
Ordinary Least Squares (OLS) | −0.175 | 0.020 | −0.105 | 3.183 | 1.102 | 0.372 |
Neural Network (NN) | −0.048 | 0.019 | −0.053 | 2.958 | 0.987 | 0.500 |
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Horton, A.; Ewart, M.; Gourmelen, N.; Fettweis, X.; Storkey, A. Using Deep Learning to Model Elevation Differences between Radar and Laser Altimetry. Remote Sens. 2022, 14, 6210. https://doi.org/10.3390/rs14246210
Horton A, Ewart M, Gourmelen N, Fettweis X, Storkey A. Using Deep Learning to Model Elevation Differences between Radar and Laser Altimetry. Remote Sensing. 2022; 14(24):6210. https://doi.org/10.3390/rs14246210
Chicago/Turabian StyleHorton, Alex, Martin Ewart, Noel Gourmelen, Xavier Fettweis, and Amos Storkey. 2022. "Using Deep Learning to Model Elevation Differences between Radar and Laser Altimetry" Remote Sensing 14, no. 24: 6210. https://doi.org/10.3390/rs14246210