Interpretation of ASCAT Radar Scatterometer Observations Over Land: A Case Study Over Southwestern France
Abstract
:1. Introduction
- The ability of the WCM to simulate ASCAT σ° observations using SSM values simulated by the ISBA LSM and satellite-derived LAI, in contrasting land-cover conditions over southwestern France,
- The statistical distribution of the parameters of the WCM,
- The response of observed and simulated σ° to LAI and SSM across seasons,
- The response of observed and simulated σ° to a rapid change in vegetation cover, and
- The feasibility of building an observation operator for the assimilation of ASCAT σ° observations into the ISBA LSM.
2. Material and Methods
2.1. Study Area
2.2. SSM Simulations
2.3. ASCAT σ° Observations
2.4. LAI Observations
2.5. The Water Cloud Model (WCM)
2.6. Model Calibration
2.7. Implementation of σ° Simulations
2.8. Statistical Analysis
3. Results
3.1. Parameter Values
3.2. Performance of the WCM
3.3. Landes Forest: Impact of the Klaus Storm
4. Discussion
4.1. Is the WCM Able to Represent Vegetation Effects?
4.2. Can the WCM be Improved?
4.3. Can the WCM Be Used as an Observation Operator?
4.4. Are Satellite-Derived LAI Values Reliable?
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
Abbreviations
ASCAT | Advanced Scatterometer |
CNRM | Centre National de Recherches Météorologiques |
CGLS | Copernicus Global Land Service |
ECMWF | European Centre for Medium-Range Weather Forecasts |
ERA-5 | ECMWF Reanalysis 5th generation |
ISBA | Interactions between Soil, Biosphere, and Atmosphere |
JJA | June–July–August |
LAI | Leaf Area Index |
LDAS | Land Data Assimilation System |
LSM | Land Surface Model |
MAM | March–April–May |
MODIS | Moderate Resolution Imaging Spectroradiometer |
NDVI | Normalized Difference Vegetation Index |
PROBA-V | Project for On-Board Autonomy—Vegetation |
RMSD | Root Mean Square Deviation |
SCE-UA | Shuffled Complex Evolution Algorithm |
SLA | Specific Leaf Area |
SMOS | Soil Moisture and Ocean Salinity |
SPOT-VGT | Vegetation sensor on SPOT (‘Système probatoire d’observation de la Terre’ or ‘Satellite pour l’observation de la Terre’) satellite |
SSM | Surface Soil Moisture |
SURFEX | Surface Externalisée (externalized surface models) |
VOD | Vegetation Optical Depth |
VWC | Vegetation Water Content |
WCM | Water Cloud Model |
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Time period | Parameter | Median [Minimum, Maximum] | Standard deviation | Skewness |
---|---|---|---|---|
2010–2013 (calibration period) | A | 0.14 [0.07, 0.20] | 0.02 | –0.51 |
B | 0.36 [0.20, 1.71] | 0.21 | 2.30 | |
C (dB) | −17.9 [−20.0, −15.1] | 0.8 | 0.10 | |
D (dB) | 27.9 [24.9, 29.7] | 0.7 | –0.66 | |
2010–2016 | A | 0.14 [0.07, 0.20] | 0.02 | –0.49 |
B | 0.41 [0.22, 1.50] | 0.19 | 1.93 | |
C (dB) | −17.6 [−20.0, −14.9] | 0.8 | –0.04 | |
D (dB) | 28.0 [24.9, 29.7] | 0.6 | –0.62 |
Scores | R Median [Minimum, Maximum] (n) | RMSD in dB Median [Minimum, Maximum] | ||||
---|---|---|---|---|---|---|
Seasons | All | MAM | JJA | All | MAM | JJA |
Calibration (2010–2013) | 0.67 [0.16,0.80] (209327) | 0.60 [0.02,0.77] (45348) | 0.44 [–0.18,0.71] (90082) | 0.35 [0.22,0.68] | 0.39 [0.23,0.86] | 0.32 [0.17,0.54] |
Validation (2014–2016) | 0.69 [0.17,0.82] (204520) | 0.60 [–0.04,0.80] (45805) | 0.47 [–0.38,0.68] (83139) | 0.36 [0.20,0.68] | 0.36 [0.19,0.66] | 0.32 [0.18,0.75] |
Time Period | WCM Parameters | Scores | ||||
---|---|---|---|---|---|---|
A (–) | B (–) | C (in dB) | D (in dB) | R (n) | RMSD (in dB) | |
Pre-storm (January 2007 to January 2009) | 0.13 | 0.19 | −16.5 | 27.3 | 0.74 (503) | 0.33 |
Forest degradation (February 2009 to December 2012) | 0.13 | 0.20 | −16.5 | 27.3 | 0.79 (1027) | 0.42 |
Forest regeneration (January 2013 to December 2016) | 0.12 | 0.29 | −16.5 | 27.9 | 0.80 (1203) | 0.40 |
Agricultural Areas | WCM Parameters | Scores | ||||
---|---|---|---|---|---|---|
A (–) | B (–) | C (in dB) | D (in dB) | R (n) | RMSD (in dB) | |
Lomagne (all seasons) | 0.13 | 0.51 | −16.9 | 27.7 | 0.74 (1954) | 0.65 |
Lomagne (MAM only) | 0.11 | 0.28 | −18.3 | 29.0 | 0.77 (499) | 0.54 |
Bas-Armagnac | 0.14 | 0.34 | −17.9 | 27.5 | 0.77 (2669) | 0.38 |
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Shamambo, D.C.; Bonan, B.; Calvet, J.-C.; Albergel, C.; Hahn, S. Interpretation of ASCAT Radar Scatterometer Observations Over Land: A Case Study Over Southwestern France. Remote Sens. 2019, 11, 2842. https://doi.org/10.3390/rs11232842
Shamambo DC, Bonan B, Calvet J-C, Albergel C, Hahn S. Interpretation of ASCAT Radar Scatterometer Observations Over Land: A Case Study Over Southwestern France. Remote Sensing. 2019; 11(23):2842. https://doi.org/10.3390/rs11232842
Chicago/Turabian StyleShamambo, Daniel Chiyeka, Bertrand Bonan, Jean-Christophe Calvet, Clément Albergel, and Sebastian Hahn. 2019. "Interpretation of ASCAT Radar Scatterometer Observations Over Land: A Case Study Over Southwestern France" Remote Sensing 11, no. 23: 2842. https://doi.org/10.3390/rs11232842
APA StyleShamambo, D. C., Bonan, B., Calvet, J.-C., Albergel, C., & Hahn, S. (2019). Interpretation of ASCAT Radar Scatterometer Observations Over Land: A Case Study Over Southwestern France. Remote Sensing, 11(23), 2842. https://doi.org/10.3390/rs11232842