# Estimating the Actual Evapotranspiration Using Remote Sensing and SEBAL Model in an Arid Environment of Northwest China

^{1}

^{2}

^{*}

## Abstract

**:**

^{2}= 0.85). Additionally, SEBAL overestimated ET to some extent compared to the Moderate-Resolution Imaging Spectroradiometer (MODIS) ET (MOD16) product. The daily ET (ET

_{d}) in the Shiyang River Basin showed a single-peak variation during the growing season, with the maximum value occurring around mid-July. Spatially, the ET gradually increased from northeast to southwest with the variation in the land use/land cover (LULC) type. Among the six LULC types, ET

_{d}was higher for woodland, water body, and grassland, all exceeding 5.0 mm/d; farmland and built-up land had ET

_{d}close to 3.9 mm/d; and barren land had the lowest ET

_{d}of below 2.5 mm/d. Furthermore, the standardized regression coefficients indicated that the Normalized Difference Vegetation Index (NDVI) is the main driving factor influencing ET. Overall, the SEBAL model has the potential to estimate spatially actual ET, and the study results provide a scientific basis for water resource accounting and hydrological analysis in arid areas.

## 1. Introduction

_{d}) product for China using SEBAL and found that the ET obtained from SEBAL has better precision than the Moderate Resolution Imaging Spectroradiometer (MODIS) ET (MOD16) data. Gao et al. [26] used SEBAL to estimate the actual ET of the Loess Plateau in China and showed that SEBAL has good applicability. Du et al. [27] used SEBAL and MODIS products to invert the ET of the Sanjiang Plain in China. They showed that the deviation between the seasonal ET of SEBAL and the ground observation was within 8.86%, indicating that the ET estimated by SEBAL could help to solve water resource management problems. Kiptala et al. [28] used the multitemporal MODIS and SEBAL to estimate the ET of different land use types during 2008–2010, and they verified the feasibility of SEBAL in estimating ET from various aspects. The above studies basically concluded that the SEBAL algorithm is sufficiently robust for determining the spatial quantities of actual ET.

_{d}in the Shiyang River Basin during the 2020 growing season (April–October) using SEBAL, (2) analyze the variation characteristics of actual ET

_{d}under different land use/land cover (LULC) types, and (3) quantitatively characterize the driving factors of ET. The study results will provide a reference for crop water demand research and a plant transpiration characteristics analysis as well as rational allocation of water resources in arid regions.

## 2. Materials and Methods

#### 2.1. Study Area Description

^{4}km

^{2}(Figure 1). The basin originates in the northern Qilian Mountains and terminates in the Minqin Oasis, which is a typical oasis-irrigated agricultural area. The area has a temperate continental grassland climate, with perennial drought and little rain, high evaporation, and serious wind and sand hazards. Based on the altitude, rainfall, and evaporation, the Shiyang River Basin can be divided into three climate zones from the south to north: (1) Southern Qilian Mountains alpine semi-arid and semi-humid zone. This region is the water hub of the basin, with an average annual temperature of 2–6 °C, elevation between 2000 and 5000 m, annual precipitation of about 300–600 mm, and potential ET of 700–1200 mm; (2) The central plain is a cool and arid zone. In this region, the average annual temperature is higher than that in the southern Qilian Mountains, elevation is 1500–2000 m, annual precipitation is 150–300 mm, and potential ET is 1300–2000 mm; (3) The northern warm arid zone. In this region, the average annual temperature is greater than 8 °C, elevation is between 1300 and 1500 m, annual precipitation is less than 150 mm, and potential ET is more than 2000 mm.

#### 2.2. Data Collection

#### 2.2.1. MODIS Data

#### 2.2.2. Meteorological Data

#### 2.2.3. Other Data

#### 2.3. SEBAL Model

_{n}is the net radiation (W/m

^{2}), G is the soil heat flux (W/m

^{2}), H is the sensible heat flux (W/m

^{2}), and LE is the latent heat flux associated with ET (W/m

^{2}).

#### 2.3.1. Net Radiation Flux (R_{n})

_{n}is calculated based on the ground radiation flux balance.

_{s}↓ is the incoming shortwave radiation (W/m

^{2}), R

_{l}↓ is the incoming longwave radiation (W/m

^{2}), R

_{l}↑ is the outgoing longwave radiation (W/m

^{2}), and ${\epsilon}_{g}$ is the land surface emissivity.

_{i}is a weighting coefficient with values of 0.160, 0.291, 0.243, 0.116, 0.112, 0, and 0.081 [33]; and ${\rho}_{i}$ is the reflectance of seven MODIS bands from the MOD09A1 product.

_{s}↓, R

_{l}↓, and R

_{l}↑ are calculated as follows [17,34]:

_{sc}is the solar constant (1367 W/m

^{2}), d

_{r}is the inverse of the square of the relative earth–sun distance, cosθ is the cosine of the zenith angle, $\sigma $ is the Stefan–Boltzmann constant (5.67 × 10

^{−8}W/(m

^{2}·k

^{4})), ${\tau}_{sw}$ is the atmospheric transmissivity, ${\epsilon}_{a}$ is the atmospheric emissivity, and T

_{a}and T

_{s}are the air and land surface temperatures (K), respectively.

#### 2.3.2. Soil Heat Flux (G)

#### 2.3.3. Sensible Heat Flux (H)

^{3}), C

_{p}is the air specific heat (J/(kg·K)), dT is the temperature difference (K), and r

_{ah}is the aerodynamic resistance against heat transfer (s/m).

_{ah}is calculated as follows:

_{f}is the frictional wind speed (m/s) (Equation (10)), and z

_{1}and z

_{2}are 0.01 and 2, respectively.

_{200}is the wind speed at height 200 m and z

_{0m}is the surface roughness (m), which is calculated as follows [36]:

_{s}, as shown in Equation (12):

_{n}− G. Cold pixels were selected in areas with high vegetation cover as well as adequate and sufficient moisture supply, where H was negligible and LE ≈ R

_{n}− G. Herein, the specific hot and cold pixel selection was based on LULC, albedo, T

_{s}, and NDVI [37]. It is important to note that the atmospheric stability conditions significantly influence the aerodynamic resistance, and the atmospheric stability conditions should be considered in the H calculation, especially in dry conditions [23]. Therefore, the Monin-Obukhov similarity theory was adopted for iterative calculation to obtain stable values of H and r

_{ah}. Detailed computational information regarding the iteration process can be found in Allen et al. [23] and Cheng et al. [25].

#### 2.3.4. Daily ET

_{d}). EF refers to the ratio of LE to the available energy. Many researchers have shown that EF can be considered constant throughout the day [38,39], and EF can be obtained as follows:

_{d}can be obtained as follows:

_{n24}is the daily net radiant flux (W/m

^{2}), and G

_{24}is the daily soil heat flux (W/m

^{2}).

#### 2.4. Validation Methods

#### 2.4.1. FAO P-M Equation

_{0}) was calculated using the P–M equation suggested by the FAO in 1998 [40]. Then, the actual ET

_{d}of the five meteorological stations (Minqin, Wuwei, Wushaoling, Gulang, and Yongchang) in the study area on the remote sensing image acquisition dates was determined by combining the crop coefficients to validate the ET estimated by SEBAL. The calculation equation is as follows:

_{2}is the wind speed at 2 m height (m/s), ∆ is the slope of the saturated vapor pressure curve (kPa/°C), and (e

_{a}− e

_{d}) is the water-air pressure difference (kPa). K

_{c}is the crop coefficient.

_{d}, the K

_{c}value needs to be first determined. The K

_{c}value was calculated using the dual crop coefficient method [41], which divides the crop evapotranspiration into the plant transpiration coefficient and soil evaporation coefficient. The calculation equation is as follows [42]:

_{cb}is the basic crop coefficient; K

_{e}is the soil evaporation coefficient; NDVI

_{max}and NDVI

_{min}are the monthly maximum and minimum NDVI values, respectively; f

_{c}is the effective area ratio of the vegetation cover to soil surface; and β is an empirical coefficient, which is assumed to be 0.25 herein based on previous studies [43]. The specific values of K

_{c}in this study are shown in Table 2.

#### 2.4.2. Pan Evaporation

_{p}) can be used to estimate the open water evaporation. In this study, the E

_{p}data from Minqin and Yongchang meteorological stations were used to evaluate the inversion accuracy of SEBAL for water evaporation. Because the pan type of the meteorological stations is E-601 (diameter 62 cm), which cannot be used during the freezing period, only the E

_{p}values from the non-freezing period (May–September) were employed for the evaluation. Furthermore, because of the different evaporation conditions of the pan and open water, a conversion coefficient needed to be introduced to correct for E

_{p}[28]:

_{p(w)}is the water evaporation (mm) and K

_{p}is the conversion coefficient. In this study, based on previous related studies [44], the following K

_{p}values were selected: 0.76 (May), 0.75 (June), 0.79 (July), 0.77 (August), and 0.81 (September).

#### 2.4.3. MOD16 ET Product

_{0}between the 1-day and 8-day periods was assumed herein. ET

_{d}can be scaled up to the 8-day scale (ET

_{8d}) as follows [47]:

_{0-d}and ET

_{0-8d}are the daily reference ET and 8-day reference ET, respectively, which were calculated using the FAO P–M equation.

#### 2.5. Principal Component Regression

#### 2.5.1. PCA

^{*}denotes the standardized variables, X denotes the original variables, $\stackrel{-}{X}$ denotes the mean value of X, and SD(X) denotes the standard deviation of X.

- (1)
- Extraction of the principal component (PC). To determine the number of PCs, the cumulative contribution of variance over 85% was used as the selection criterion herein.
- (2)
- Calculation of the PC score. It is expressed as:

#### 2.5.2. MLR

_{i}and b are the regression coefficients, which are usually calculated using the least squares method.

#### 2.6. Technical Process

## 3. Results

#### 3.1. Accuracy Validation of SEBAL ET

^{2}), root mean square error (RMSE), and mean absolute error (MAE) were selected to quantify the accuracy of SEBAL ET. The results show that SEBAL ET and P–M ET exhibited good correlation with R

^{2}of 0.85, MAE of 0.76 mm/d, and RMSE of 0.91 mm/d. Furthermore, the water surface evaporation simulated by SEBAL was evaluated using the E

_{p(w)}values from the meteorological stations (Figure 3b). The results showed good consistency between SEBAL ET and E

_{p(w)}, with R

^{2}of 0.89, MAE of 0.53 mm/d, and RMSE of 0.59 mm/d. The above results denote that the SEBAL results are reliable and valid for the study area.

#### 3.2. Comparison of SEBAL ET and MOD16 ET under Different Land Cover Types

^{2}of 0.52, followed by farmland with R

^{2}of 0.49, and the worst for woodland with R

^{2}of 0.41. Compared to MOD16 ET, SEBAL ET was significantly overestimated for woodland and grassland with MAE of 22.79 and 20.57 mm/8d and RMSE of 17.28 and 21.88 mm/8d, respectively. Additionally, the error was smaller for farmland than that for woodland and grassland, with MAE and RMSE of 11.89 and 13.04 mm/8d, respectively. In general, SEBAL ET was significantly higher than MOD16 ET.

#### 3.3. Temporal and Spatial Variation of Actual ET_{d}

_{d}in the Shiyang River Basin simulated by SEBAL. The distribution of ET

_{d}exhibits a unimodal trend during the growing season, with the highest value occurring on DOY 193 in 2020. Specifically, the average ET

_{d}on DOY 113 in 2020 was 2.58 mm/d, and close to 50% of the basin had ET

_{d}of less than 2 mm/d (Figure 5a). The average ET

_{d}on DOY 193 in 2020 increased to 4.77 mm/d, with 62.8% of the area having ET

_{d}of more than 4 mm/d (Figure 5d). The average ET

_{d}on DOY 289 in 2020 decreased to 1.41 mm/d when the number of low-value pixels significantly increased, with nearly three-quarters of the area having an ET

_{d}of less than 2 mm/d (Figure 5g). This trend was observed due to the low temperature, sparse precipitation, and low vegetation coverage in the study area at the initial stage of the growing season, which led to weak transpiration and evaporation. In the middle of the growing season, ET was relatively high due to the gradually increase in temperature, relatively abundant precipitation, increase in snowmelt from the Qilian Mountain, high soil moisture content in farmland supplemented by sufficient irrigation water, and high vegetation cover. However, ET significantly decreased in the late growing season because of the gradual decrease in temperature and precipitation, reduced agricultural irrigation water, slower plant metabolic activity, and crop maturity.

_{d}was calculated for each pixel (Figure 5h). The mean ET

_{d}in the study area varied between 0.23 and 7.83 mm/d during the growing season, with a mean ET

_{d}value of 3.45 mm/d for the entire region. From the overall spatial distribution, the spatial divergence of ET from the northeast to southwest was obvious, showing a gradual increase in ET. The reason for this variation is as follows. The southwestern region of the study area is the Qilian Mountain region, which belongs to the upper reaches of the Shiyang River, and the LULC in this region is mainly woodland and high-cover grassland with lush vegetation growth and relatively sufficient precipitation. This region had high ET values. In the middle region of the study area, the main LULC type is farmland, with high water demand for field crops in the middle of the growing season and sufficient water for irrigation. Thus, the ET values in this area were moderate. Additionally, because the northwestern region of the study area comprises the lower reaches of the Shiyang River, the LULC type is dominated by barren/desert land, precipitation is scarce, vegetation is mostly small shrubs and drought-tolerant herbs, and the surface coverage is low. Therefore, the ET values in this area were low.

#### 3.4. Comparison of ET_{d} in Different LULC Types

_{d}variation pattern and the average ET

_{d}performance under different LULC types during the growing season. The figure shows that the ET

_{d}values in the different LULC types first increased and then decreased during the growing season. The ET

_{d}value was the highest for the woodland with a mean value of 6.33 mm/d, followed by the water body and grassland with average ET

_{d}values of 5.17 and 5.05 mm/d, respectively. The ET

_{d}values for farmland and built-up land were close to each other with values of 3.91 and 3.88 mm/d, respectively, while the ET

_{d}value for barren land was the lowest with an average value of 2.45 mm/d. Therefore, the ET

_{d}in different LULC types revealed the following ET performance during the growing season: woodland > water body > grassland > farmland > built-up land > barren land.

_{d}and area percentage for each LULC type in the study area. As can be seen, barren land had the highest total ET

_{d}value of 5.43 × 10

^{7}m

^{3}, followed by grassland with 4.32 × 10

^{7}m

^{3}. Farmland and woodland had total ET

_{d}values of 2.88 × 10

^{7}m

^{3}and 9.7 × 10

^{6}m

^{3}, respectively, while built-up and water body land had the lowest total ET

_{d}values of 3.60 × 10

^{6}m

^{3}and 3.71 × 10

^{5}m

^{3}, respectively. This is closely related to the area of each LULC type in the study area. Barren land and grassland accounted for 54.24% and 21.08% of the total area of the study area, respectively, while water body and built-up land accounted for only 2.45%.

#### 3.5. Analysis of Driving Factors for ET

#### 3.5.1. Correlation Analysis

#### 3.5.2. Principal Component Regression

_{1}, PC

_{2}, and PC

_{3}) were extracted based on the principle that the cumulative contribution of variance was greater than 85%, which is shown in Table 3. The eigenvalues of PC

_{1}, PC

_{2}, and PC

_{3}were 5.309, 0.967, and 0.746, respectively, and the cumulative variance contribution of the three PCs was 87.78%, signifying that the selected PCs covered almost all of the information about the indicators.

_{1}, PC

_{2}, and PC

_{3}using Equation (23):

^{2}of 0.935:

## 4. Discussion

#### 4.1. Accuracy Assessment of ET Estimation Using SEBAL

^{2}of 0.85 and MAE and RMSE of 0.76 and 0.91 mm/d, respectively. Additionally, the E

_{p(w)}values observed at the meteorological stations were used to verify the water surface evaporation simulated by SEBAL, and the results showed a good correlation with the R

^{2}of 0.89 and MAE and RMSE of 0.53 and 0.59 mm/d, respectively. Compared to the results of previous studies (Table 4), the errors observed herein are acceptable, indicating that the estimation of the actual ET in the study area using SEBAL is feasible.

_{d}[45]. (2) Several assumptions in the estimation of H using SEBAL could cause an overestimation of ET if not applied correctly, especially in arid regions and/or sparse canopies [59]. To estimate H, the SEBAL algorithm introduced a temperature gradient dependent on two extreme pixels (cold and hot pixels), and the user’s subjective decision in selecting these hot and cold pixel points (although there were many suggestions) could introduce uncertainty into the modeling results. In addition to H, the calculation of R

_{n}and G through some empirical formulae could result in uncertainties in ET estimates [60,61].

#### 4.2. Analysis of the ET_{d} with Different LULC Types

_{d}distribution in the Shiyang River Basin showed a unimodal variation, with the maximum value occurring in mid-July. The same conclusion was reported by Liu et al. [29] using the MOD16 ET product, who suggested that the distribution trend of ET

_{d}was related to changes in irrigation water, temperature, precipitation, and vegetation within the Shiyang River Basin. Spatially, ET in the study area decreased from the southwest to northeast. Although the southwest region of the basin has a higher altitude and lower temperature, there are more woodlands and high cover grasslands with high precipitation and a sufficient water supply. The northeastern region of the basin is at a lower altitude, but it is mostly sparse grassland and barren land with insufficient water supply. Therefore, in the study area, the ET in the northeast is much lower than that in the southwest.

_{d}values of different LULC types had the order of woodland > water body > grassland > farmland > built-up land > barren land. This is similar to the findings of Kiptala et al. [28], who suggested that the ET values were the highest in the water body and woodland, followed by irrigated farmland, while grassland and barren land had the lowest ET values. Woodland has the dual functions of water conservation and transpiration, which can provide a good water supply for ET; therefore, its ET is relatively high. However, the percentage of woodland in the study area was less than 4%, while barren and grassland accounted for more than 75% of the study area. Thus, the total ET

_{d}of the barren land and grassland was much higher than that of the woodland. Moreover, farmland accounted for about 18% of the study area and had a high ET; therefore, farmland is the third highest LULC type in terms of total ET

_{d}. The built-up land and water body only accounted for approximately 2.5% of the study area, and hence, both had a considerably low total ET

_{d}.

#### 4.3. Impact of Environmental Factors on ET

^{2}of 0.935. The standardized regression coefficients showed that the influence degree of environmental factors on ET in descending order was NDVI, albedo, LST, DEM, D, W, P, and H. Lin et al. [63] used a ridge regression model to investigate the driving forces of ET in the Sanjiang Plain of China and showed that precipitation was the primary factor impacting ET in this region, followed by NDVI, which is somewhat different from the findings of this study. This is because the Sanjiang Plain belongs to the humid zone, where precipitation is the main source of ET. However, precipitation is scarce in the Shiyang River Basin and the main source of ET is irrigation or groundwater, which leads to a reduced effect of precipitation on ET. Additionally, Yang et al. [64] explored the influencing factors of ET in the Haihe River Basin using the structural equation model. They showed that the direct effect of meteorological factors on ET was not significant and that it tended to indirectly affect ET by influencing vegetation changes, providing a reasonable explanation for the fact that the degree of influence of the meteorological factors (P, W, and H) on ET was less than that of NDVI on ET in this study.

#### 4.4. Limitations and Outlook

_{d}estimates, and developing automatic identification procedures for cold and hot pixels to eliminate user subjectivity. Additionally, the present study area lacks in situ flux data to validate the estimates of ET and other fluxes. Therefore, the estimation results should be validated by multiple in situ flux data as far as possible in the future.

## 5. Conclusions

^{2}= 0.85) and E

_{p(w)}(R

^{2}= 0.89), indicating that SEBAL has the potential to estimate the actual ET in the Shiyang River Basin. Moreover, SEBAL significantly overestimated ET compared to MOD16, which could be caused by the underestimation of ET from the MOD16 algorithm and the uncertainty of SEBAL itself. The ET in the study area exhibited a single-peak variation during the growing season, with the peak occurring in mid-July. Spatially, the ET values were higher in the woodland and grassland in the southwestern part of the study area and lower in the sparse grasslands and desert areas in the northeastern part. The ET values in different LULC types were in the order of woodland > water body > grassland > cropland > building land > barren land. Additionally, the correlation analysis showed that ET was significantly correlated with LST, DEM, albedo, and NDVI, with r values exceeding 0.8. The PCR concluded that NDVI was the major driving factor impacting ET, and the direct effect of meteorological factors (precipitation, wind speed, and sunshine hours) on ET was not significant. Furthermore, it is important to note that there was still uncertainty in the estimation of the surface energy components using SEBAL as well as difficulty in validating the accuracy of the estimation results. Therefore, future studies will concentrate on the improvement of the SEBAL algorithm and the multi-scale and multi-method accuracy validation of the simulation results.

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

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**Figure 1.**Map of the study area. (

**a**) Location of Shiyang River Basin in northwest China and locations of meteorological stations; (

**b**) land use/land cover map of the study area in 2020.

**Figure 4.**Comparison of SEBAL ET and MOD16 ET for different LULC types in DOY 193–DOY 200 in 2020. (

**a**) Farmland; (

**b**) woodland; and (

**c**) grassland.

**Figure 5.**Spatial and temporal distribution of ET

_{d}in the study area in 2020. (

**a**) DOY 113 (22 April); (

**b**) DOY 145 (24 May); (

**c**) DOY 177 (25 June); (

**d**) DOY 193 (11 July); (

**e**) DOY 225 (12 August); (

**f**) DOY 257 (13 September); (

**g**) DOY 289 (15 October); (

**h**) 7-day mean ET.

**Figure 6.**(

**a**) Variation of ET

_{d}in the growing season under different LULC types; (

**b**) average ET

_{d}of different LULC types; (

**c**) total ET

_{d}and area percentage for each LULC type in the study area. Bars indicate standard deviation.

**Figure 7.**Correlation analysis diagram between ET and (

**a**) NDVI, (

**b**) LST, (

**c**) albedo, (

**d**) DEM, (

**e**) D, (

**f**) W, (

**g**) H, and (

**h**) P.

**Figure 8.**Correlation coefficients between the driving factors. ** represents sig values less than 0.01 (p < 0.01).

Data Product | Satellite Imagery | Temporal Resolution | Spatial Resolution |
---|---|---|---|

MOD11A1/A2 | LST/Emissivity | Daily/8 d | 1 km |

MOD13A1 | NDVI | 16 d | 0.5 km |

MOD09A1 | Albedo | 8 d | 0.5 km |

MOD16A2 | ET_{8d} | 8 d | 0.5 km |

Station | April | May | June | July | August | September | October |
---|---|---|---|---|---|---|---|

Minqin | 0.53 | 0.69 | 1.29 | 1.29 | 1.26 | 0.76 | 0.37 |

Wuwei | 0.51 | 0.51 | 1.28 | 1.28 | 1.25 | 0.66 | 0.51 |

Wushaoling | 0.38 | 0.86 | 1.12 | 1.13 | 1.08 | 0.97 | 0.48 |

Gulang | 0.34 | 0.75 | 0.94 | 1.27 | 1.02 | 0.64 | 0.41 |

Yongchang | 0.34 | 0.52 | 1.00 | 1.02 | 1.19 | 0.71 | 0.55 |

Principal Components | Initial Eigenvalues and Variance Contribution Rates | Extracted Eigenvalues and Variance Contribution Rates | ||||
---|---|---|---|---|---|---|

Eigenvalues | Variance Contribution Rates/% | Cumulative Contribution Rates/% | Eigenvalues | Variance Contribution Rates/% | Cumulative Contribution Rates/% | |

PC_{1} | 5.309 | 66.362 | 66.362 | 5.309 | 66.362 | 66.362 |

PC_{2} | 0.967 | 12.085 | 78.448 | 0.967 | 12.085 | 78.448 |

PC_{3} | 0.746 | 9.331 | 87.779 | 0.746 | 9.331 | 87.779 |

PC_{4} | 0.379 | 4.741 | 92.52 | |||

PC_{5} | 0.306 | 3.827 | 96.346 | |||

PC_{6} | 0.191 | 2.390 | 98.736 | |||

PC_{7} | 0.052 | 0.645 | 99.381 | |||

PC_{8} | 0.05 | 0.619 | 100 |

References | Study Area | Validation Methods | Temporal/ Spatial Resolution | Time | Accuracy Evaluation Results | ||
---|---|---|---|---|---|---|---|

R^{2} | MAE (mm/d) | RMSE (mm/d) | |||||

Li et al. [50] | Agro-pastoral ecotone in northwest China | FAO P-M equation | Daily/1 km | 2015 | 0.76 | 0.79 | 0.94 |

Kong et al. [51] | Ordos Basin in China | FAO P-M equation | Daily/30 m | 2015–2016 | 0.99 | 0.88 | 0.97 |

Ghaderi et al. [52] | Ein Khosh Plain in Iran | FAO P-M equation | Daily/1 km | 2015 | 0.97 | 0.22 | 0.47 |

Rahimzadegan and Janani [53] | A pistachio farm in Semnan Province, Iran | FAO P-M equation | Daily/30 m | 2013–2017 | 0.80 | 2.09 | 2.48 |

Liu et al. [49] | Nukus irrigation area of Amu River Basin | Pan evaporation | Daily/30 m | 2019 | 0.81 | / | 1.76 |

Yang et al. [54] | Agro-pastoral ecotone in northwest China | Pan evaporation | Daily/30 m | 2016–2017 | 0.81 | / | 0.90 |

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**MDPI and ACS Style**

Chen, X.; Yu, S.; Zhang, H.; Li, F.; Liang, C.; Wang, Z.
Estimating the Actual Evapotranspiration Using Remote Sensing and SEBAL Model in an Arid Environment of Northwest China. *Water* **2023**, *15*, 1555.
https://doi.org/10.3390/w15081555

**AMA Style**

Chen X, Yu S, Zhang H, Li F, Liang C, Wang Z.
Estimating the Actual Evapotranspiration Using Remote Sensing and SEBAL Model in an Arid Environment of Northwest China. *Water*. 2023; 15(8):1555.
https://doi.org/10.3390/w15081555

**Chicago/Turabian Style**

Chen, Xietian, Shouchao Yu, Hengjia Zhang, Fuqiang Li, Chao Liang, and Zeyi Wang.
2023. "Estimating the Actual Evapotranspiration Using Remote Sensing and SEBAL Model in an Arid Environment of Northwest China" *Water* 15, no. 8: 1555.
https://doi.org/10.3390/w15081555