# ET Partitioning Assessment Using the TSEB Model and sUAS Information across California Central Valley Vineyards

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## Abstract

**:**

_{Q}), improved sensible heat flux (H) estimation, regarding the bias, with around 61% and 35% compared with the H from the TSEB-PT and TSEB-2T, respectively. Comparisons among ET partitioning estimates from three different methods (Modified Relaxed Eddy Accumulation—MREA; Flux Variance Similarity—FVS; and Conditional Eddy Covariance—CEC) based on EC flux tower data show that the transpiration estimates obtained from the FVS method are statistically different from the estimates from the MREA and the CEC methods, but the transpiration from the MREA and CEC methods are statistically the same. By using the transpiration from the CEC method to compare with the transpiration modeled by different TSEB models, the TSEB-2T

_{Q}shows better agreement with the transpiration obtained via the CEC method. Additionally, the transpiration estimation from TSEB-2T

_{Q}coupled with different resistance models resulted in insignificant differences. This comparison is one of the first for evaluating ET partitioning estimation from sUAS imagery based on eddy covariance-based partitioning methods.

## 1. Introduction

## 2. Materials and Methods

#### 2.1. Study Area

#### 2.2. Data

#### 2.2.1. sUAS Platform Collection

^{2}resolution, digital surface model (DSM) data at 10 × 10 cm

^{2}resolution, and thermal imagery (Tr) at 60 × 60 cm

^{2}resolution [43]. Images of 6 bands collected via the AggieAir sUAS platform are included as an example, and can be seen in Gao et al. 2022 [30].

#### 2.2.2. Eddy-Covariance Flux Tower Data

^{−2}), latent heat flux (or evapotranspiration rate, LE, Wm

^{−2}), sensible heat flux (H, Wm

^{−2}), and soil surface heat flux (G, Wm

^{−2}) are used in this study to assess the TSEB-PT and TSEB-2T output. More information about the EC flux tower can be found in Kustas et al., 2018 [16] and Bambach et al., 2022 [44], while details about energy closure and ET partitioning for the EC tower data are provided in Section 2.3.3.

#### 2.3. Methodology

^{−1}) and ET partitioning between the EC flux tower monitored data and the TSEB modeling results within the corresponding footprint area. The top 5 boxes, along with surface temperature in the second row, are the inputs for the TSEB models. Canopy height, the ratio of canopy width and height, and fractional cover are obtained with a python program [45]; LAI is obtained from the products of recent studies [30,46], using sUAS information and ground-based LAI measurements via machine learning approach. In this study, the weather data are obtained from the flux tower instrumentation. The TSEB-2T model requires partitioned temperature input (canopy and soil temperature), but other inputs to the two model formulations are the same.

#### 2.3.1. Temperature Separation

#### 2.3.2. TSEB Model

_{nS}). Nieto et al., 2019 [27] show the empirical G/R

_{nS}curve fit as a function of time of the day. Considering that all sUAS images were collected between 10 am to 4 pm, a constant G-ratio value (0.33) is used in this research.

_{C}and T

_{S}. In addition to these component temperatures, the aerodynamic resistance of the canopy (${R}_{x}$) and soil (${R}_{s}$) also affect the H, but a systematic assessment of different methods for defining these resistances within the TSEB context has not been conducted to date. Three different resistance models for canopy and soil were tested in this study for both TSEB-PT and TSEB-2T: Norman and Kustas (called NK resistance model in this paper, expressed by Equations (2) and (3)), McNaughton and Van (MV model, by Equations (4) and (5)), and Choudhury and Monteith (CM model, by Equations (6) and (7)), respectively. Because the separated temperature images illustrated in Section 2.3.1 are used as input for the TSEB-2T model, the TSEB-2T model coupled with QTS in this study is named as TSEB-2T

_{Q}.

^{−1}); ${u}^{*}$ is the friction velocity (ms

^{−1}); ${C}^{\prime}$ is derived from weighting a coefficient in the equation for leaf boundary layer resistance over the height of the canopy [48] and it is assumed to be 90 s

^{1/2}m

^{−1}; LAI is the leaf area index (m

^{2}m

^{−2}); ${l}_{w}$ is the average leaf width (m); ${U}_{{d}_{0}+{z}_{0M}}$ is the wind speed at the heat source-sink (ms

^{−1}); $F$ is the local leaf area index; ${h}_{c}$ is the canopy height; ${\alpha}_{k}$ is the heat diffusion coefficient; $k$ is the von Karman’s constant (0.41); ${z}_{0\_soil}$ is the roughness length of the soil layer; ${d}_{0}$ is the zero-plane displacement height (m); ${z}_{0M}$ is the aerodynamic roughness length for momentum transport (m); $C{M}_{a}$ is the leaf drag coefficient [49]; ${\alpha}^{\prime}$ is the wind extinction coefficient; and ${u}_{c}$ is the wind speed at the canopy interface (ms

^{−1}).

#### 2.3.3. Validation Data from the Eddy Covariance Tower

#### Energy Components

#### Transpiration

_{2}measurements from eddy covariance measurements to estimate soil evaporation from plant transpiration, and compared results with the modified Relaxed Eddy Accumulation (MREA) method and the Flux Variance Similarity (FVS) method. They found that the CEC and MREA framework can be used as a qualitative measure to identify stomatal and non-stomatal components. Methods to evaluate the transpiration modeled by the TSEB models using these measurements are explained in Section 3.2.1.

## 3. Results and Discussion

#### 3.1. TSEB Modeling Results

#### 3.1.1. TSEB Component Comparison Considering Different Resistance Models

_{Q}) coupled with different resistance models (NK, CM, and MV). In Figure 5, observed H and LE have been adjusted for closure using the technique discussed in Section 2.3.3.

_{Q}shows better agreement with measurement fluxes. This shows that the QTS method considering shadow and extreme pixel-value effects, characteristics of the high-resolution pixel within the smallest TSEB modeling domain, in general improved the flux estimation.

_{Q}is small, the TSEB-2T

_{Q}coupled with the NK model was adopted in this research for energy component estimation.

#### 3.1.2. Time-Based Performance of the TSEB-2T_{Q} NK Model

_{Q}coupled with the NK model in estimating energy components at each time period. Table A3 contains the corresponding metrics associated with the comparisons displayed in Figure 6.

_{Q}model is more appropriate at the AF time period than for the LS and SN time periods. For example, the labeled points in Figure 6a,b, “RIP760 20180806 10:41” and “RIP760 20180805 12:33”, indicate that G was overestimated, indicating that the G ratio should be smaller than 0.33. This behavior was also noted by Nieto et al., 2019 [27], who found that a double asymmetric sigmoid function gave better results than using a constant value, and better fits the observations than the sinusoidal function proposed by Santanello and Friedl., 2003 [53].

_{Q}is minimized in the AF period. Examining scenes where outliers in H and LE are observed in Figure 6c showed no significant issues from the QTS model based on the separated average soil and canopy temperatures within the corresponding footprint area, in comparison with the remaining image dates (Table A4), so the cause of poor performance is unknown.

#### 3.2. Transpiration

#### 3.2.1. Transpiration Estimation via CEC, MREA, and FVS

#### 3.2.2. Transpiration Comparison

_{Q}. The first factor is the corresponding “p-adj” values, which are 0.900 (higher than most other “p-adj” values, and higher than α = 0.05). The second is that the corresponding “Mean difference” is smaller than 10 Wm

^{−2}, which is generally smaller than other experiments.

_{Q}, in general, is closer to the transpiration estimated via the CEC method.

## 4. Conclusions

_{Q}(TSEB-2T model coupled with the QTS method for temperature separation) coupled with the NK (Norman and Kustas) resistance model can appropriately provide energy-component estimations. The ET partitioning comparison regarding transpiration illustrated that all TSEB models are statistically acceptable for ET partitioning, but the TSEB-2T

_{Q}showed better agreement with the CEC method. Further work, focused on augmenting the EC flux tower system with measurements of ET for the interrow, upgrading the sUAS image processing system for creating near-real time products, and implementing a 3SEB formulation to explicitly account for the interrow cover crop, is necessary to accurately estimate vine transpiration [55]. These advancements will improve management practices that promote great water use efficiency in vineyards and will improve growers’ and researchers’ understanding of the role of cover crop and vine water use at the canopy and sub-block scale.

## Author Contributions

## Funding

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## Appendix A

Study Sites | Latitude | Longitude | Elevation above the Sea Level (m) |
---|---|---|---|

SLM | 38°16′49.76″ | −121°7′3.35″ | 40 |

BAR | 38°45′4.91″ | −122°58′28.77″ | 120 |

RIP760 | 36°50′20.52″ | −120°12′36.60″ | 62 |

RIP720 | 36°50′57.27″ | −120°10′26.50″ | 62 |

**Table A2.**The flight date and time of the sUAS platform over vineyards. Azimuth and elevation of the sun corresponding to the time are also shown.

Sites | Year | Month | Day | Time Flight | Azimuth | Elevation |
---|---|---|---|---|---|---|

RIP 720-1 RIP 720-2 RIP 720-3 RIP 720-4 | 2018 | 6 | 19 | 11:20 | 144.1 | 74.0 |

2018 | 6 | 19 | 13:17 | 236.1 | 68.8 | |

2018 | 6 | 19 | 15:38 | 269.8 | 41.8 | |

2018 | 7 | 12 | 12:29 | 201.0 | 74.2 | |

2018 | 7 | 12 | 15:32 | 266.5 | 43.1 | |

2018 | 7 | 13 | 10:40 | 123.3 | 66.3 | |

2018 | 7 | 13 | 15:22 | 264.6 | 45.1 | |

2018 | 8 | 5 | 10:44 | 132.4 | 63.3 | |

2018 | 8 | 5 | 12:33 | 198.9 | 69.2 | |

2018 | 8 | 6 | 10:41 | 131.2 | 62.8 | |

2019 | 5 | 4 | 10:25 | 130.1 | 60.9 | |

RIP 760 | 2018 | 6 | 19 | 11:20 | 144.1 | 74.0 |

2018 | 6 | 19 | 13:17 | 236.1 | 68.8 | |

2018 | 6 | 19 | 15:38 | 269.8 | 41.8 | |

2018 | 7 | 12 | 12:29 | 201.0 | 74.2 | |

2018 | 7 | 12 | 15:32 | 266.5 | 43.1 | |

2018 | 7 | 13 | 10:40 | 123.3 | 66.3 | |

2018 | 8 | 5 | 10:44 | 132.4 | 63.3 | |

2018 | 8 | 5 | 12:33 | 198.9 | 69.2 | |

2018 | 8 | 6 | 10:41 | 131.2 | 62.8 | |

BAR012 | 2017 | 8 | 8 | 10:52 | 144.9 | 63.6 |

2017 | 8 | 9 | 10:43 | 141.1 | 62.3 | |

2019 | 6 | 27 | 10:41 | 131.9 | 68.9 | |

2019 | 6 | 27 | 12:07 | 193.6 | 74.2 | |

2019 | 6 | 27 | 14:21 | 255.2 | 54.7 | |

2019 | 7 | 29 | 10:51 | 140.8 | 65.8 | |

2019 | 7 | 29 | 13:09 | 224.2 | 64.4 | |

2019 | 7 | 30 | 10:28 | 130.9 | 62.5 | |

2019 | 7 | 30 | 13:09 | 223.9 | 64.2 | |

2019 | 7 | 30 | 15:40 | 264.2 | 37.5 | |

SLM001 | 2014 | 8 | 9 | 10:41 | 136.3 | 61.5 |

2015 | 6 | 2 | 10:43 | 131.9 | 67.9 | |

2015 | 6 | 2 | 14:07 | 250.2 | 57.2 | |

2015 | 7 | 11 | 10:35 | 125.1 | 65.5 | |

2015 | 7 | 11 | 14:14 | 250.1 | 57.3 | |

2019 | 5 | 3 | 10:38 | 139.1 | 62.0 | |

SLM002 | 2014 | 8 | 9 | 10:41 | 136.3 | 61.5 |

2015 | 6 | 2 | 10:43 | 131.9 | 67.9 | |

2015 | 6 | 2 | 14:07 | 250.2 | 57.2 | |

2015 | 7 | 11 | 10:35 | 125.1 | 65.5 | |

2015 | 7 | 11 | 14:14 | 250.1 | 57.3 |

**Table A3.**The performance of the TSEB-2T model coupled with the Norman and Kustas (NK) resistance model at different research sites with different times shown by different evaluation metrics. “LS” stands for the results that occurred in Landsat time; “SN” near solar noon; and “AF” afternoon. The unit of RMSE and Bias is Wm

^{−2}.

Time Periods | Net Radiation | Ground Heat Flux | Sensible Heat Flux | Latent Heat Flux | ||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|

N | RMSE | Bias | r | N | RMSE | Bias | r | N | RMSE | Bias | r | N | RMSE | Bias | r | |

LS | 29 | 21 | −9 | 0.91 | 29 | 45 | −28 | −0.43 | 29 | 66 | 2 | 0.33 | 29 | 68 | 16 | 0.62 |

SN | 17 | 21 | −3 | 0.79 | 17 | 40 | −28 | −0.38 | 17 | 69 | −23 | 0.63 | 17 | 81 | 49 | 0.64 |

AF | 14 | 29 | −23 | 0.96 | 14 | 20 | −12 | 0.64 | 14 | 58 | 8 | 0.63 | 14 | 56 | −22 | 0.46 |

**Table A4.**Separated average soil and canopy temperatures within the corresponding footprint area via the QTS model (the temperature unit is °C).

Site | Date | Time | Sonic Air Temperature | Soil Temperature | Canopy Temperature | Soil–Canopy Temperature Difference |
---|---|---|---|---|---|---|

SLM001 | 20150711 | 14:14 | 28.1 | 32.9 | 28.7 | 4.2 |

SLM002 | 20150711 | 14:14 | 30.7 | 32.9 | 28.7 | 4.2 |

BAR012 | 20190627 | 14:21 | 25.7 | 31.0 | 26.6 | 4.4 |

BAR012 | 20190730 | 15:40 | 30.9 | 34.2 | 29.4 | 4.8 |

RIP760 | 20180619 | 15:38 | 32.1 | 36.2 | 31.6 | 4.6 |

RIP720-1 | 20180619 | 15:38 | 34.0 | 35.5 | 32.1 | 3.4 |

RIP720-1 | 20180712 | 15:32 | 38.3 | 36.8 | 33.1 | 3.7 |

RIP720-1 | 20180713 | 15:22 | 38.1 | 36.7 | 33.3 | 3.4 |

RIP720-2 | 20180619 | 15:38 | 34.5 | 37.3 | 32.5 | 4.8 |

RIP720-2 | 20180712 | 15:32 | 38.8 | 37.8 | 33.0 | 4.8 |

RIP720-2 | 20180713 | 15:22 | 38.5 | 38.6 | 34.4 | 4.2 |

RIP720-3 | 20180713 | 15:22 | 38.5 | 35.1 | 31.1 | 4.0 |

RIP720-4 | 20180619 | 15:38 | 35.9 | 35.6 | 31.8 | 3.8 |

RIP720-4 | 20180713 | 15:22 | 40.5 | 37.1 | 32.9 | 4.2 |

**Table A5.**ANOVA and Tukey test results showing the difference between the transpiration calculated via the CEC method and the transpiration modeled via the TSEB models. The null hypothesis is that the mean transpiration between different groups is the same. “Mean difference” is the mean difference between “Group 1” and “Group 2.” “Lower boundary” and “Upper boundary” are the lower and upper 95% confidence interval boundaries, respectively. “CEC” represents the transpiration calculated via the CEC method. The unit for “Mean difference,” “Lower boundary,” and “Upper boundary” is Wm

^{−2}.

Group 1 | Group 2 | Mean Difference | p-Adj | Lower Boundary | Upper Boundary | The Mean Transpiration Is the Same |
---|---|---|---|---|---|---|

CEC | TSEB-PT (NK) | −25 | 0.674 | −69 | 18 | YES |

CEC | TSEB-PT (CM) | −16 | 0.900 | −60 | 27 | YES |

CEC | TSEB-PT (MV) | −32 | 0.372 | −75 | 12 | YES |

CEC | TSEB-2T (NK) | −36 | 0.194 | −80 | 7 | YES |

CEC | TSEB-2T (CM) | −30 | 0.456 | −74 | 13 | YES |

CEC | TSEB-2T (MV) | −39 | 0.132 | −82 | 5 | YES |

CEC | TSEB-2T_{Q} (NK) | −10 | 0.900 | −53 | 34 | YES |

CEC | TSEB-2T_{Q} (CM) | −7 | 0.900 | −51 | 36 | YES |

CEC | TSEB-2T_{Q} (MV) | −9 | 0.900 | −53 | 34 | YES |

**Table A6.**Metrics for model evaluation shown in Figure 8. N is the number of scatters in Figure 8; RMSE is the root mean square error; Bias is the mean bias computed as the observed minus the predicted; r is the Pearson correlation coefficient between the observed and the predicted; and d is Willmott’s index of agreement.

TSEB-PT | TSEB-2T | TSEB-2T_{Q} | |||||||
---|---|---|---|---|---|---|---|---|---|

NK | CM | MV | NK | CM | MV | NK | CM | MV | |

N | 50 | 50 | 50 | 50 | 50 | 50 | 50 | 50 | 50 |

RMSE | 71 | 68 | 77 | 84 | 77 | 83 | 72 | 70 | 71 |

Bias | 25 | 16 | 32 | 36 | 30 | 39 | 10 | 7 | 9 |

r | 0.58 | 0.58 | 0.56 | 0.54 | 0.55 | 0.56 | 0.54 | 0.54 | 0.54 |

d | 0.73 | 0.73 | 0.72 | 0.71 | 0.72 | 0.72 | 0.73 | 0.72 | 0.73 |

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**Figure 1.**Study areas in California and the position of EC flux towers at each research site. The position of each EC flux tower within the respective research sites is marked by a red cross and the corresponding tower name in white font.

**Figure 2.**Flowchart showing the process of comparing ET rate and ET partitioning from TSEB models within the footprint area. The top 5 boxes, along with surface temperature in the second row, are the inputs for the TSEB models. The ET rate and T/ET were extracted within the corresponding footprint area and then compared with the EC flux tower monitored data.

**Figure 3.**Flowchart showing the ideal temperature separation process for a single TSEB model pixel (3.6 m resolution).

**Figure 4.**One example showing the performance of the method in one TSEB modeling pixel (3.6 m resolution grid) to separate the temperature as canopy and soil temperature. (

**a**) Spectral image at 0.15 m resolution, along with (

**b**) co-collected temperature image and (

**c**) generated NDVI image at 0.6 m resolution. Pixels highlighted with the dashed line in (

**b**,

**c**) represent the locations of shadow at 0.6 m pixel scale, and the 0.15 m red pixels in (

**b**) represent shadow locations at 0.15 m pixel scale; (

**d**) linear relationship between temperature and NDVI considering 36 pairs of pixels within the 3.6 m grid. The red points highlighted by dashed lines represent the temperatures from the shadow pixels. The pure vegetation zone whose x-axis value is higher than 0.70 and the pure soil zone whose x-axis value is lower than 0.40 are displayed at each side of the x-axis; (

**e**) Within the pure vegetation zone, pixels with temperatures higher than its 75th percentile temperature are highlighted by dash-lined boxes; (

**f**) pixel locations where the temperature is higher than its 75th percentile temperature are highlighted on the temperature image; (

**g**) box plots for soil region, NDVI $\in $ [0, 0.40], vegetation region, NDVI $\in $ [0.70, 1], and the middle part region, NDVI $\in $ (0.4, 0.7). The 50th and 75th percentile temperatures within the pure vegetation zone are shown on the right side; (

**h**) linear relationship between temperature and NDVI obtained by eliminating vegetation-temperature pixels above the 75th percentile temperature, highlighted by the red dashed-line box.

**Figure 5.**Scatter plots showing the comparison between energy balance components measured from the EC flux tower (y-axis) and the modeled energy balance components from TSEB-PT, TSEB-2T, and TSEB-2T

_{Q}(rows 1–3) using the NK, CM and MV (columns 1–3) resistance formulations (x-axis).

**Figure 6.**Scatter plots illustrating the performance of the TSEB-2T

_{Q}model coupled with the Norman and Kustas (NK) resistance model at different time periods.

**Figure 7.**Scatter plots showing the difference between the transpiration estimated based on different methods (CEC, MREA, and FVS). The red dashline is a reference 1:1 line.

**Figure 8.**The comparison between the transpiration based on the CEC method and the transpiration modeled via the TSEB models (different TSEB models with different resistance models).

**Table 1.**Statistics of the goodness of fit showing the performance of each TSEB modeling result within the footprint area. N is the number of cases used for validation, RMSE is the root mean square error (Wm

^{−2}), Bias is the mean bias computed as the measured minus the modeled (Wm

^{−2}), r is the Pearson correlation coefficient between the measured and modeled, and d is the Willmott’s index of agreement [51]. When N is different in different groups, d is still calculated but not a representative metric to compare the model performance.

TSEB-PT (NK) | TSEB-PT (CM) | TSEB-PT (MV) | TSEB-2T (NK) | TSEB-2T (CM) | TSEB-2T (MV) | TSEB-2T_{Q} (NK) | TSEB-2T_{Q} (CM) | TSEB-2T_{Q} (MV) | ||
---|---|---|---|---|---|---|---|---|---|---|

Net radiation | N | 60 | 60 | 60 | 60 | 60 | 60 | 60 | 60 | 60 |

RMSE | 22 | 22 | 22 | 21 | 21 | 21 | 23 | 23 | 23 | |

Bias | −4 | −5 | −4 | −5 | −5 | −5 | −10 | −10 | −10 | |

r | 0.96 | 0.97 | 0.97 | 0.97 | 0.97 | 0.97 | 0.97 | 0.97 | 0.97 | |

d | 0.98 | 0.98 | 0.98 | 0.98 | 0.98 | 0.98 | 0.98 | 0.98 | 0.98 | |

Ground heat flux | N | 60 | 60 | 60 | 60 | 60 | 60 | 60 | 60 | 60 |

RMSE | 41 | 40 | 41 | 41 | 41 | 41 | 39 | 39 | 39 | |

Bias | −27 | −26 | −27 | −26 | −26 | −26 | −24 | −24 | −24 | |

r | 0.25 | 0.24 | 0.25 | 0.26 | 0.26 | 0.26 | 0.27 | 0.27 | 0.27 | |

d | 0.52 | 0.52 | 0.52 | 0.54 | 0.54 | 0.54 | 0.55 | 0.55 | 0.55 | |

Sensible heat flux | N | 60 | 60 | 60 | 60 | 60 | 60 | 60 | 60 | 60 |

RMSE | 78 | 85 | 84 | 71 | 71 | 69 | 65 | 71 | 65 | |

Bias | 21 | 45 | 17 | −16 | 14 | −19 | −3 | 26 | −3 | |

r | 0.63 | 0.62 | 0.61 | 0.62 | 0.60 | 0.64 | 0.63 | 0.61 | 0.63 | |

d | 0.78 | 0.74 | 0.76 | 0.78 | 0.77 | 0.79 | 0.77 | 0.75 | 0.77 | |

Latent heat flux | N | 60 | 60 | 60 | 60 | 60 | 60 | 60 | 60 | 60 |

RMSE | 82 | 84 | 90 | 80 | 73 | 81 | 69 | 71 | 70 | |

Bias | −7 | −32 | −3 | 34 | 3 | 36 | 16 | −13 | 16 | |

r | 0.53 | 0.55 | 0.51 | 0.55 | 0.57 | 0.58 | 0.58 | 0.59 | 0.58 | |

d | 0.73 | 0.73 | 0.71 | 0.71 | 0.76 | 0.72 | 0.75 | 0.78 | 0.75 |

**Table 2.**ANOVA and Tukey test results showing the difference between the transpiration estimated based on different methods (CEC, MREA, and FVS). The null hypothesis is that the mean transpiration between different groups is the same (shown in the last column). “Mean difference” is the mean difference between “Group 1” and “Group 2.” “Lower boundary” and “Upper boundary” are the lower and upper 95% confidence interval boundaries, respectively. The unit for “Mean difference,” “Lower boundary,” and “Upper boundary” is Wm

^{−2}.

Group 1 | Group 2 | Mean Difference | p-Adj | Lower Boundary | Upper Boundary | The Mean Transpiration Is the Same |
---|---|---|---|---|---|---|

CEC | FVS | −84 | 0.004 | −152 | −15 | NO |

CEC | MREA | 0 | 0.900 | −69 | 68 | YES |

MREA | FVS | −84 | 0.004 | −152 | −15 | NO |

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**MDPI and ACS Style**

Gao, R.; Torres-Rua, A.F.; Nieto, H.; Zahn, E.; Hipps, L.; Kustas, W.P.; Alsina, M.M.; Bambach, N.; Castro, S.J.; Prueger, J.H.;
et al. ET Partitioning Assessment Using the TSEB Model and sUAS Information across California Central Valley Vineyards. *Remote Sens.* **2023**, *15*, 756.
https://doi.org/10.3390/rs15030756

**AMA Style**

Gao R, Torres-Rua AF, Nieto H, Zahn E, Hipps L, Kustas WP, Alsina MM, Bambach N, Castro SJ, Prueger JH,
et al. ET Partitioning Assessment Using the TSEB Model and sUAS Information across California Central Valley Vineyards. *Remote Sensing*. 2023; 15(3):756.
https://doi.org/10.3390/rs15030756

**Chicago/Turabian Style**

Gao, Rui, Alfonso F. Torres-Rua, Hector Nieto, Einara Zahn, Lawrence Hipps, William P. Kustas, Maria Mar Alsina, Nicolas Bambach, Sebastian J. Castro, John H. Prueger,
and et al. 2023. "ET Partitioning Assessment Using the TSEB Model and sUAS Information across California Central Valley Vineyards" *Remote Sensing* 15, no. 3: 756.
https://doi.org/10.3390/rs15030756