Evaluations of Machine Learning-Based CYGNSS Soil Moisture Estimates against SMAP Observations
Abstract
:1. Introduction
2. Datasets
2.1. Cyclone Global Navigation Satellite System
2.2. International Soil Moisture Network
2.3. Ancillary Data
2.4. SMAP Radiometer Soil Moisture Data
3. Methodology
3.1. Training of Random Forest Model
3.2. Quasi-Global Application and Evaluation of the Model
4. Results
4.1. Quasi-Global Performance Results of the Proposed ML-Based SM Retrieval
4.2. Performance Evaluation of SMAP and ML Model at ISMN Sites
4.3. Spatial and Temporal Analysis of CYGNSS Soil Moisture Retrievals
5. Discussion
6. Conclusions
Supplementary Materials
Author Contributions
Funding
Conflicts of Interest
Appendix A. Retrieval Comparisons
References
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Spatial Resolution | Time Resolution | # of Samples | RMSD cm cm | Mean ubRMSD (std.) cm cm | R-Value | |
---|---|---|---|---|---|---|
for all grids | 9 km × 9 km | concurrent | 1.64 × 108 | 0.11 | 0.054 (±0.027) | 0.63 |
3-day | 1.07 × 108 | 0.11 | 0.049 (±0.025) | 0.63 | ||
week | 7.13 × 107 | 0.11 | 0.044 (±0.024) | 0.63 | ||
month | 2.24 × 107 | 0.11 | 0.034 (±0.023) | 0.64 | ||
36 km × 36 km | concurrent | 2.95 × 107 | 0.11 | 0.050 (±0.024) | 0.65 | |
3-day | 1.66 × 107 | 0.12 | 0.045 (±0.024) | 0.65 | ||
week | 7.82 × 106 | 0.12 | 0.040 (±0.023) | 0.66 | ||
month | 1.95 × 106 | 0.12 | 0.032 (±0.022) | 0.66 | ||
for SMAP recommended grids | 9 km × 9 km | concurrent | 1.16 × 108 | 0.066 | 0.044 (±0.021) | 0.66 |
3-day | 9.14 × 107 | 0.066 | 0.041 (±0.022) | 0.66 | ||
week | 5.99 × 107 | 0.065 | 0.038 (±0.021) | 0.68 | ||
month | 1.87 × 107 | 0.065 | 0.031 (±0.021) | 0.71 | ||
36 km × 36 km | concurrent | 2.12 × 107 | 0.070 | 0.045 (±0.021) | 0.69 | |
3-day | 1.16 × 107 | 0.071 | 0.042 (±0.022) | 0.70 | ||
week | 5.51 × 106 | 0.071 | 0.037 (±0.022) | 0.72 | ||
month | 1.39 × 106 | 0.070 | 0.030 (±0.022) | 0.74 |
In-Situ vs. | RMSE | Mean ubRMSE cm cm | Median ubRMSE cm cm | R |
---|---|---|---|---|
CYGNSS (training) | 0.067 | 0.052 | 0.049 | 0.83 |
CYGNSS (5-fold) | 0.072 | 0.055 | 0.051 | 0.80 |
SMAP | 0.112 | 0.054 | 0.052 | 0.59 |
Region | Location | Dominant Land Cover | Climate Zone |
---|---|---|---|
Midwest US | 85W:105W–32N:37N | Grass | Cfa |
India | 70E:90E–10N:20N | Croplands | Aw |
Sahara | −15W:15 E–7N:20N | Grass | BWh, Aw |
Australia | 138E:153 E–27S:37S | Open shrublad | BWh |
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Senyurek, V.; Lei, F.; Boyd, D.; Gurbuz, A.C.; Kurum, M.; Moorhead, R. Evaluations of Machine Learning-Based CYGNSS Soil Moisture Estimates against SMAP Observations. Remote Sens. 2020, 12, 3503. https://doi.org/10.3390/rs12213503
Senyurek V, Lei F, Boyd D, Gurbuz AC, Kurum M, Moorhead R. Evaluations of Machine Learning-Based CYGNSS Soil Moisture Estimates against SMAP Observations. Remote Sensing. 2020; 12(21):3503. https://doi.org/10.3390/rs12213503
Chicago/Turabian StyleSenyurek, Volkan, Fangni Lei, Dylan Boyd, Ali Cafer Gurbuz, Mehmet Kurum, and Robert Moorhead. 2020. "Evaluations of Machine Learning-Based CYGNSS Soil Moisture Estimates against SMAP Observations" Remote Sensing 12, no. 21: 3503. https://doi.org/10.3390/rs12213503