Machine Learning-Based CYGNSS Soil Moisture Estimates over ISMN sites in CONUS
Abstract
:1. Introduction
2. Datasets
2.1. Cyclone Global Navigation Satellite System
2.2. International Soil Moisture Network
2.3. Ancillary Data
2.4. Quality Control Mechanisms
3. Methodology
3.1. Machine Learning Algorithm and Feature Selection
3.2. Performance Metrics and Evaluation
3.3. Machine Learning Framework Summary
4. Results
4.1. Examination of Different Machine Learning Algorithms and Input Features
4.2. Overall Performance of the Machine Learning Retrieval Model
4.3. Effect of Underlying Land Surface Conditions
4.4. Temporal Variations of Predicted Soil Moisture Retrievals
5. Discussion
6. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Initial | Quality Control Mechanisms and the Ratio on the Raw Dataset | % | Final | |||
---|---|---|---|---|---|---|
# of Sites | # of Data | # of Sites | # of Data | |||
COSMOS | 14 | 7381 | CYGNSS quality flags | 27 | 5 | 1923 |
SCAN | 104 | Incidence angle > 65∘ | 3 | 68 | ||
USCRN | 53 | Rx_gain < 0 | 27 | 33 | 8679 | |
2017 | 225 | Peak power delay row bin | 20 | 89 | 7580 | |
2018 | 219 | Water land percent > 2% | 16.6 | 99 | 9485 | |
2019 | 222 | Elevation > 600 m (for 2017) | 11 | 100 | ||
Overall | 234 | Urban areas | 0.9 | 106 | ||
VWC > 5 kg/m2 |
Input Group | Feature Name | Description |
---|---|---|
CYGNSS | Reflectivity | Reflectivity calculated via [13] |
TES | Slope of the trailing edge of the reflectivity | |
LES | Leading edge slope of the reflectivity | |
SP incidence angle | Incidence angle of specular point | |
Topography | Elevation | Mean elevation for each specular point 3-km grid |
Slope | Mean Slope for each specular point 3-km grid | |
MODIS | NDVI | Mean normalized difference vegetation index |
VWC | Mean vegetation water content | |
H-value | Dominant land cover type based roughness parameter | |
Soil texture | Soil clay ratio | Mean clay proportion for each specular point 3-km grid |
Soil silt ratio | Mean silt proportion for each specular point 3-km grid | |
Soil sand ratio | Mean sand proportion for each specular point 3-km grid |
Validation Method | Overall Performance | Average of Sites | |||||
---|---|---|---|---|---|---|---|
RMSE | ubRMSE | R | RMSE (std.) | bias (std.) | ubRMSE (std.) | R value (std.) | |
5fold | 0.0523 | 0.0523 | 0.89 | 0.050 () | 0.011 () | 0.047 () | 0.56 () |
Site independent | 0.0883 | 0.0883 | 0.64 | 0.084 () | 0.056 () | 0.054 () | 0.42 () |
year based (2019) | 0.0639 | 0.0639 | 0.84 | 0.06 () | 0.027 () | 0.05 () | 0.49 () |
year based (2018) | 0.0586 | 0.0584 | 0.86 | 0.055 () | 0.024 () | 0.047 () | 0.43 () |
year based (2017) | 0.0602 | 0.0599 | 0.84 | 0.058 () | 0.027 () | 0.048 () | 0.40 () |
RMSE | Bias | ubRMSE | R | |
---|---|---|---|---|
Overall | 0.049 | −1 × 10−4 | 0.049 | 0.9 |
Average of sites | 0.048() | 0.0085() | 0.046() | 0.58() |
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Senyurek, V.; Lei, F.; Boyd, D.; Kurum, M.; Gurbuz, A.C.; Moorhead, R. Machine Learning-Based CYGNSS Soil Moisture Estimates over ISMN sites in CONUS. Remote Sens. 2020, 12, 1168. https://doi.org/10.3390/rs12071168
Senyurek V, Lei F, Boyd D, Kurum M, Gurbuz AC, Moorhead R. Machine Learning-Based CYGNSS Soil Moisture Estimates over ISMN sites in CONUS. Remote Sensing. 2020; 12(7):1168. https://doi.org/10.3390/rs12071168
Chicago/Turabian StyleSenyurek, Volkan, Fangni Lei, Dylan Boyd, Mehmet Kurum, Ali Cafer Gurbuz, and Robert Moorhead. 2020. "Machine Learning-Based CYGNSS Soil Moisture Estimates over ISMN sites in CONUS" Remote Sensing 12, no. 7: 1168. https://doi.org/10.3390/rs12071168
APA StyleSenyurek, V., Lei, F., Boyd, D., Kurum, M., Gurbuz, A. C., & Moorhead, R. (2020). Machine Learning-Based CYGNSS Soil Moisture Estimates over ISMN sites in CONUS. Remote Sensing, 12(7), 1168. https://doi.org/10.3390/rs12071168