Soil Moisture Estimates in a Grass Field Using Sentinel-1 Radar Data and an Assimilation Approach
Abstract
:1. Introduction
- -
- To test the potential of Sentinel 1 for soil moisture estimation in a grass field characterized by typical Mediterranean water-limited conditions;
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- To compare and revise some common retrieval models for soil moisture estimation from Sentinel 1 data, proposing a simplified and robust solution to account for vegetation attenuation effects on radar backscattering using simultaneous Sentinel 2 optical data;
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- To develop and test an operational approach to assimilate Sentinel 1 observations in a land surface model, to demonstrate the potential of the use of the new satellite sensors in soil moisture predictions in a grass field.
2. Materials and Methods
2.1. Sardinian Case Study
2.2. Satellite Data
2.3. Methods for Soil Moisture Retrieval from Sentinel 1 Data
2.3.1. The Revised Change Detection Method
2.3.2. The Semi-Empirical Model of Dubois et al. 1995
2.3.3. The Physical Model of Fung et al., 1992
2.3.4. Removal of Grass Cover Contribution from Radar Backscattering
2.4. Data Assimilation Approach
2.4.1. The Land Surface Model
2.4.2. The Assimilation Approach Using the EnKF
3. Results
3.1. Soil Moisture Estimation from Radar
3.2. Soil Moisture Assimilation in a Land Surface Model
4. Discussion
4.1. Soil Moisture Estimation from Radar Satellite Observations
4.2. Soil Moisture Assimilation in a Land Surface Model
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Statistical Index | Year | CD | CD, WCM | DU WCM | DU sD(NDVI) | FU, s = 1, CL = 0.5 | FU, sF (NDVI),CL = 0.5 | FU, sF (NDVI), CLF(NDVI) |
---|---|---|---|---|---|---|---|---|
rmse | 2016 | 0.10 | 0.09 | 0.22 | 0.07 | 0.15 | 0.16 | 0.16 |
2017 | 0.10 | 0.09 | 0.24 | 0.04 | 0.12 | 0.18 | 0.08 | |
2018 | 0.12 | 0.12 | 0.23 | 0.13 | 0.16 | 0.06 | 0.19 | |
2016–2018 | 0.10 | 0.10 | 0.23 | 0.08 | 0.14 | 0.16 | 0.14 | |
Rm | 2016 | 0.94 | 1.11 | 0.53 | 0.96 | 2.06 | 1.43 | 2.14 |
2017 | 0.80 | 0.95 | 0.47 | 1.01 | 1.66 | 1.47 | 1.31 | |
2018 | 0.90 | 1.06 | 0.54 | 1.24 | 1.94 | 0.92 | 1.87 | |
2016–2018 | 0.88 | 1.04 | 0.51 | 1.03 | 1.88 | 1.34 | 1.70 | |
Rs | 2016 | 1.71 | 1.33 | 2.94 | 1.05 | 2.67 | 1.69 | 1.83 |
2017 | 1.34 | 1.15 | 2.25 | 0.97 | 2.05 | 1.86 | 1.30 | |
2018 | 1.87 | 1.60 | 3.50 | 1.84 | 3.10 | 0.93 | 0.86 | |
2016–2018 | 1.54 | 1.29 | 2.68 | 1.09 | 2.43 | 1.61 | 1.31 | |
R2 | 2016 | 0.28 | 0.38 | 0.36 | 0.67 | 0.31 | 0.07 | 0.16 |
2017 | 0.57 | 0.53 | 0.45 | 0.93 | 0.55 | 0.01 | 0.80 | |
2018 | 0.12 | 0.11 | 0.09 | 0.03 | 0.12 | 0.57 | 0.06 | |
2016–2018 | 0.35 | 0.37 | 0.33 | 0.58 | 0.34 | 0.01 | 0.22 | |
Slope | 2016 | 0.31 | 0.46 | 0.20 | 0.78 | 0.21 | −0.16 | 0.22 |
2017 | 0.56 | 0.63 | 0.30 | 0.99 | 0.36 | −0.06 | 0.69 | |
2018 | 0.19 | 0.20 | 0.08 | 0.10 | 0.11 | 0.82 | −0.29 | |
2016–2018 | 0.38 | 0.47 | 0.21 | 0.70 | 0.24 | −0.06 | 0.36 | |
Intercept | 2016 | 0.17 | 0.10 | 0.38 | 0.06 | 0.06 | 0.19 | 0.05 |
2017 | 0.14 | 0.09 | 0.36 | 0.00 | 0.05 | 0.16 | 0.01 | |
2018 | 0.21 | 0.17 | 0.41 | 0.16 | 0.09 | 0.05 | 0.19 | |
2016–2018 | 0.17 | 0.11 | 0.38 | 0.06 | 0.06 | 0.17 | 0.05 |
Parameter | Description | Value | |
---|---|---|---|
Grass | WV | ||
rs,min [s m−1] | Minimum stomatal resistance | 100 | 280 |
Tmin [°K] | Minimum temperature | 272.15 | 272.15 |
Topt [°K] | Optimal temperature | 295.15 | 292.15 |
Tmax [°K] | Maximum temperature | 313.15 | 318.15 |
θwp [–] | Wilting point | 0.08 | 0.05 |
θlim [–] | Limiting soil moisture for vegetation | 0.20 | 0.15 |
ω [HPa−1] | Slope of the f3 relation | 0.01 | 0.01 |
zom,v [m] | Vegetation momentum roughness length | 0.05 | 0.5 |
zov,v [m] | Vegetation water vapor roughness length | zom/7.4 | zom/2.5 |
zom,bs [m] | Bare soil momentum roughness length | 0.015 | |
zov,bs [m] | Bare soil water vapor roughness length | zom/10 | |
θs [–] | Saturated soil moisture | 0.53 | |
b [–] | Slope of the retention curve | 8 | |
ks [m/s] | Saturated hydraulic conductivity | 5 × 10−6 | |
∣ψs∣ [m] | Air entry suction head | 0.79 | |
drz [m] | Root zone depth | 0.19 |
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Montaldo, N.; Fois, L.; Corona, R. Soil Moisture Estimates in a Grass Field Using Sentinel-1 Radar Data and an Assimilation Approach. Remote Sens. 2021, 13, 3293. https://doi.org/10.3390/rs13163293
Montaldo N, Fois L, Corona R. Soil Moisture Estimates in a Grass Field Using Sentinel-1 Radar Data and an Assimilation Approach. Remote Sensing. 2021; 13(16):3293. https://doi.org/10.3390/rs13163293
Chicago/Turabian StyleMontaldo, Nicola, Laura Fois, and Roberto Corona. 2021. "Soil Moisture Estimates in a Grass Field Using Sentinel-1 Radar Data and an Assimilation Approach" Remote Sensing 13, no. 16: 3293. https://doi.org/10.3390/rs13163293