Hybrid Methodology Using Sentinel-1/Sentinel-2 for Soil Moisture Estimation
Abstract
:1. Introduction
2. Materials and Methods
2.1. Database
2.1.1. ISMN Soil Moisture Data
2.1.2. Sentinel-1
2.1.3. Sentinel-2
2.1.4. Satellite Data Processing
2.2. Methodology
2.2.1. Change Detection Algorithm
2.2.2. Improved Change Detection Approach
2.2.3. Artificial Neural Network Hybrid Approach
- The VV and VH signals are identified for their high sensitivity to soil moisture.
- The classical change detection SSM index is calculated as a function of radar backscattering coefficients in VV polarization to use it for soil moisture estimation.
- The incidence angle has an effect on the contribution of soil and vegetation components on the radar signal.
- The NDVI index is identified to take into account the effect of vegetation cover on the backscattering signal.
- The VH/VV ratio is identified to take into account the effect of vegetation cover on the backscattering signal [81].
- estimated from the classic change detection approach described in Section 3.1, Equation (2) is also considered as input.
2.3. Statistical Parameters for Accuracy Assessment
3. Results
3.1. Improved Change Detection Approach
3.2. Neural Network Hybrid Approach
4. Discussion
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Network | Country | Number of Selected Stations | SM Sensors | References |
---|---|---|---|---|
AMMA-CATCH | Benin, Niger | 7 | CS616 | Cappelaere et al. [38]; De Rosnay et al. [39]; Lebel et al. [40]; Mougin et al. [41]; Pellarin et al. [42]; Galle et al. [43]. |
BIEBRZA-S-1 | Poland | 8 | GS-3 | Musial et al. 2016 [44] |
COSMOS | USA | 2 | Cosmic-ray-Probe | Zreda et al. [45]; Zreda et al. [46] |
HOBE | Denmark | 3 | Decagon-5TE | Bircher et al. [47]; Jensen et al. [48] |
FLUXNET-AMERIFLUX | USA | 4 | CS655, ThetaProbe-ML3 ThetaProbe-ML2X, | |
FR-Aqui | France | 3 | ThetaProbe ML2X | Al-Yaari et al. [49]; Wigneron et al. [50] |
GROW | UK | 20 | Flower-Power | Zappa et al. [51]; Xaver et al. [52]; Zappa et al. 2020 [53] |
HOAL | Austria | 32 | SPADE-Time-Domain-Transmissivity | Vreugdenhil M. et al. [54]; Blöschl, Günter, et al. [55] |
IPE | Spain | 2 | CS655, ThetaProbe-ML2X | Alday et al. [56] |
MAQU | China | 1 | ECH20-EC-TM | Su et al. [57]; Dente et al. [58] |
MOL-RAO | Germany | 1 | TRIME-EZ | Beyrich F. and Adam W.K. [59] |
NAQU | China | 5 | 5TM | Su et al. 2011 [60] |
REMEDHUS | Spain | 13 | Stevens-Hydra-Probe | Gonzalez-Zamora et al. [61] |
RISMA | Canada | 5 | Hydraprobe-II-Sdi-12 | Canisius F. [62]; L’Heureux J. [63]; Ojo et al. [64] |
RSMN | Romania | 19 | 5TM | |
SCAN | USA | 130 | Hydraprobe-Sdi-12/Ana | Schaefer et al. [65] |
SMOSMANIA | France | 15 | ThetaProbe ML2X | Calvet et al. [66]; Albergel et al. [67]; Calvet et al. [68] |
SNOTEL | USA | 84 | Hydraprobe-Analog-(2.5-Volt) | Leavesley et al. [69] |
TAHMO | Ghana | 3 | TEROS10, TEROS12 | |
TERENO | Poland | 4 | Hydraprobe-II-Sdi-12 | Zacharias et al. [70]; Bogena et al. [71]; Bogena et al. [72] |
USCRN | USA | 77 | Stevens-Hydraprobe-II-Sdi-12 | Bell et al. [73] |
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Nativel, S.; Ayari, E.; Rodriguez-Fernandez, N.; Baghdadi, N.; Madelon, R.; Albergel, C.; Zribi, M. Hybrid Methodology Using Sentinel-1/Sentinel-2 for Soil Moisture Estimation. Remote Sens. 2022, 14, 2434. https://doi.org/10.3390/rs14102434
Nativel S, Ayari E, Rodriguez-Fernandez N, Baghdadi N, Madelon R, Albergel C, Zribi M. Hybrid Methodology Using Sentinel-1/Sentinel-2 for Soil Moisture Estimation. Remote Sensing. 2022; 14(10):2434. https://doi.org/10.3390/rs14102434
Chicago/Turabian StyleNativel, Simon, Emna Ayari, Nemesio Rodriguez-Fernandez, Nicolas Baghdadi, Remi Madelon, Clement Albergel, and Mehrez Zribi. 2022. "Hybrid Methodology Using Sentinel-1/Sentinel-2 for Soil Moisture Estimation" Remote Sensing 14, no. 10: 2434. https://doi.org/10.3390/rs14102434
APA StyleNativel, S., Ayari, E., Rodriguez-Fernandez, N., Baghdadi, N., Madelon, R., Albergel, C., & Zribi, M. (2022). Hybrid Methodology Using Sentinel-1/Sentinel-2 for Soil Moisture Estimation. Remote Sensing, 14(10), 2434. https://doi.org/10.3390/rs14102434