A Machine Learning Based Downscaling Approach to Produce High Spatio-Temporal Resolution Land Surface Temperature of the Antarctic Dry Valleys from MODIS Data
Abstract
:1. Introduction
2. Research Area
3. Material and Methods
3.1. Data and Pre-Processing
3.1.1. Landsat LST
3.1.2. MODIS LST
3.1.3. High Resolution Predictors for LST
3.2. Compilation of the Training and Validation Data Sets
3.3. Training and Validation
4. Results
4.1. Model Selection and Evaluation
4.2. Selected Features
4.3. Area of Applicability
4.4. Performance over Time and Land Cover Types
5. Discussion
5.1. Variable Selection
5.2. Model Evaluation
5.3. Scope of Applicability
5.4. Comparison to Other Studies
5.5. Current Limitations and Future Perspectives of the Downscaled Data Set
6. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
LST | Land Surface Temperature |
RF | Random Forest |
GBM | Gradient Boosting Machine |
NN | Neural Net |
CV | Cross Validation |
MDV | McMurdo Dry Valleys |
TWI | Topographic Wetness Index |
REMA | Reference Elevation Model of Antarctica |
RAMP | Radarsat Antarctic Mapping Project Digital Elevation Model |
RMSE | Root Mean Square Error |
AOA | Area of Applicability |
FFS | Forward Feature Selection |
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Predictor Variable | Connection to High Resolution LST | Original Spatial Resolution (m) | Temporal Resolution | Source |
---|---|---|---|---|
MODIS LST | variable to be downscaled | 1000 | subdaily | [50,51] |
DEM | meteorological lapse | 8 (200 for filling NA) | static | REMA [52] & RAMP [53] |
incidence angle | solar insolation | 8 | subdaily | DEM + MODIS capturing time |
hillshading | direct or diffuse solar insolation | 8 | subdaily | DEM + MODIS capturing time |
slope | possibility for water accumulation | 8 | static | DEM |
aspect | direct or diffuse insolation for different periods of time per day | 8 | static | DEM |
land surface type | albedo | 30 | static | Landsat classification [45] |
soil map | water retention capacity of soil types and albedo | from vector | static | Landcare Research [54] |
TWI | water content affects LST, TWI proxi for hydrologic routing | 8 | static | DEM via SAGA TWI algorithm |
Terra/Aqua | Acquisitions from Terra are temporally further apart from the response variable | whole scene | spatial constant per scene | MODIS filename |
Algorithm (Caret Method) | Hyperparameter | Tested Value Final Model | Optimal Value Final Model |
---|---|---|---|
Random Forest (rf) | mtry | 2 to 4 with increment 1 | 2 |
Gradient Boosting (gbm) | number of trees max depth of interactions shrinkage min observations in terminal nodes | 100 to 500 with increment 100 3 to 14 with increment 2 0.01, 0.05, 0.1 10 | 500 3 0.01 10 |
Artificial Neural Net (nnet) | size decay | 1, 2, 3, 5, 10, 20 0.5, 0.1, 1 × 10 bis 1 × 10 | 20 0.0001 |
Spatial | Temporal | Spatio-Temporal | ||||
---|---|---|---|---|---|---|
R | RMSE | R | RMSE | R | RMSE | |
RF | 0.83 | 2.99 | 0.80 | 3.24 | 0.78 | 3.32 |
NN | 0.8 | 3.26 | 0.75 | 3.69 | 0.73 | 3.74 |
GBM | 0.73 | 3.68 | 0.72 | 3.70 | 0.7 | 3.58 |
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Lezama Valdes, L.-M.; Katurji, M.; Meyer, H. A Machine Learning Based Downscaling Approach to Produce High Spatio-Temporal Resolution Land Surface Temperature of the Antarctic Dry Valleys from MODIS Data. Remote Sens. 2021, 13, 4673. https://doi.org/10.3390/rs13224673
Lezama Valdes L-M, Katurji M, Meyer H. A Machine Learning Based Downscaling Approach to Produce High Spatio-Temporal Resolution Land Surface Temperature of the Antarctic Dry Valleys from MODIS Data. Remote Sensing. 2021; 13(22):4673. https://doi.org/10.3390/rs13224673
Chicago/Turabian StyleLezama Valdes, Lilian-Maite, Marwan Katurji, and Hanna Meyer. 2021. "A Machine Learning Based Downscaling Approach to Produce High Spatio-Temporal Resolution Land Surface Temperature of the Antarctic Dry Valleys from MODIS Data" Remote Sensing 13, no. 22: 4673. https://doi.org/10.3390/rs13224673