Evaluation of PenmanMonteith Model Based on Sentinel2 Data for the Estimation of Actual Evapotranspiration in Vineyards
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Field Measurements
2.3. Remote Sensing Data
2.4. Remote SensingBased ET Models
2.4.1. Mapping Evapotranspiration at High Resolution with Internalized Calibration (METRIC)
2.4.2. Priestley–Taylor Two Source Energy Balance (TSEBPT)
2.4.3. Remote Sensing PenmanMonteith (RSPM)
2.4.4. Daily, 7Day and Monthly ET Extrapolation
2.5. Model Assessment
 Bias represents the average difference between the observed and modeled values, so the ideal model gives the lowest value:$$Bias=\frac{1}{n}{\displaystyle \sum}_{i=1}^{n}{P}_{i}{O}_{i}$$$$\%Bias=\frac{1}{n}{\displaystyle \sum}_{i=1}^{n}\frac{{P}_{i}{O}_{i}}{{O}_{i}}\times 100$$
 Root mean squared error (RMSE):$$RMSE=\sqrt{\frac{{{\displaystyle \sum}}_{i=1}^{n}{\left({P}_{i}{O}_{i}\right)}^{2}}{n}}$$
 Relative root mean squared error (%RMSE) which is dimensionless and expresses the error as a fraction of the measured average (O_{avg}), thus the model with the best performance is the one with the lowest %RMSE:$$\%RMSE=\frac{RMSE}{{O}_{avg}}\times 100$$
 Willmott agreement index, which is a skill score that involves the variability of the observed and modeled values. Therefore, the model is perfect if P_{i} = O_{i} and consequently the index = 1. Conversely, if P_{i} = O_{avg} the index = 0 [89]:$${d}_{1}=1\frac{{{\displaystyle \sum}}_{i=1}^{N}\left\left({P}_{i}{O}_{i}\right)\right}{{{\displaystyle \sum}}_{i=1}^{N}\left(\left{P}_{i}{O}_{avg}\right+\left{O}_{i}{O}_{avg}\right\right)}$$
3. Results
3.1. Vineyards Seasonal Observations and Measurements
3.2. Biophysical Variables Based on Remote Sensing Data
3.3. Assessment of Remote SensingBased ET Models
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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BBCH Stage Code  Principal Growth Stage  Description 

11  Leaf Development  First leaf unfolded and spread away from shoot 
53  Inflorescence emergence  Inflorescence clearly visible 
61  Flowering  Beginning of flowering: 10% of flowerhoods fallen 
71  Development of Fruits  Fruit set: fruits begin to swell, remains of flowers lost 
81  Ripening of Berries  Beginning of ripening: berries begin to brighten in color 
Landsat 7  Landsat 8  Sentinel2  

Band  Res. (m)  Band  Res. (m)  Band  Res. (m) 
σ_{RED}  30  σ_{RED}  30  σ_{RED}  10 
σ_{NIR}  30  σ_{NIR}  30  σ_{NIR}  10 
σ_{TIR}  60  σ_{TIR}  100     
Satellite Platform  Parameters  Number of Images 

Landsat 7  NDVI, LST *  9 
Landsat 8  NDVI, LST *  11 
Sentinel2  NDVI, surface albedo (α)  50 
Model Approach  RS Model  Key RS Input  RS Data Source  Modeled Canopy λE  Modeled Soil λE 

TIRET  METRIC  NDVI, LST  LANDSAT 78 
 
TSEBPT  NDVI, LST  LANDSAT 78  λE Rate based on Priestley–Taylor equation.  Energy balance residual from H_{S} as a function of T_{S}, T_{AC}, r_{S}.  
VISET  RSPML  NDVI  SENTINEL2 


RSPMS  NDVI  SENTINEL2 


Modeled Variable  Model  RMSE  %RMSE  d_{1} 

Instantaneous λE (Wm^{−2})  METRIC  55.5  30.8  0.64 
TSEBPT  42.8  23.7  0.62  
RSPML f_{swc}  40.5  21.3  0.65  
RSPMS f_{swc}  28.9  15.2  0.77  
Daily ET mm day^{−1}  METRIC  0.90  31.8  0.50 
TSEBPT  0.85  30.2  0.56  
RSPML f_{swc}  0.63  23.5  0.58  
RSPMS f_{swc}  0.52  19.4  0.70  
7day ET mm 7day^{−1}  METRIC  5.1  30.3  0.55 
TSEBPT  5.0  29.7  0.54  
RSPML f_{swc}  4.4  25.9  0.52  
RSPMS f_{swc}  3.9  23.2  0.56  
Monthly ET mm month^{−1}  METRIC  19.1  25.7  0.47 
TSEBPT  18.8  25.2  0.55  
RSPML f_{swc}  18.0  24.1  0.47  
RSPMS f_{swc}  15.5  20.9  0.51 
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GarcíaGutiérrez, V.; Stöckle, C.; Gil, P.M.; Meza, F.J. Evaluation of PenmanMonteith Model Based on Sentinel2 Data for the Estimation of Actual Evapotranspiration in Vineyards. Remote Sens. 2021, 13, 478. https://doi.org/10.3390/rs13030478
GarcíaGutiérrez V, Stöckle C, Gil PM, Meza FJ. Evaluation of PenmanMonteith Model Based on Sentinel2 Data for the Estimation of Actual Evapotranspiration in Vineyards. Remote Sensing. 2021; 13(3):478. https://doi.org/10.3390/rs13030478
Chicago/Turabian StyleGarcíaGutiérrez, Víctor, Claudio Stöckle, Pilar Macarena Gil, and Francisco Javier Meza. 2021. "Evaluation of PenmanMonteith Model Based on Sentinel2 Data for the Estimation of Actual Evapotranspiration in Vineyards" Remote Sensing 13, no. 3: 478. https://doi.org/10.3390/rs13030478