# Improving the Operational Simplified Surface Energy Balance Evapotranspiration Model Using the Forcing and Normalizing Operation

^{1}

^{2}

^{3}

^{4}

^{5}

^{6}

^{*}

## Abstract

**:**

## 1. Introduction

^{s}(Ts − Tc)

^{s}is the surface psychrometric constant over a dry-bare surface and is the same as the inverse of the dT (temperature difference, K) parameter in Senay et al. [15]; Ts is the dry-bulb surface temperature (K) derived from the satellite thermal infrared band, and Tc is the wet-bulb reference surface temperature (K) limit; The constant 1 represents the ET fraction value during maximum ETa, i.e., when Ts = Tc.

^{s}) is determined based on energy balance principles. The γ

^{s}parameter was calculated using data from ERA5 (5th generation European Center for Medium-Range Weather Forecasts Reanalysis) for the primary inputs of net radiation parameters [16] and is available for the globe [17].

^{s}may be assumed constant for a given location (1 km × 1 km) and day-of-year, Tc is considered spatiotemporally dynamic and must be determined for each satellite overpass. Earlier versions of SSEBop determined Tc by identifying dense green vegetation using the NDVI (>0.7) and corresponding land surface temperature (Ts) to calibrate the air temperature for establishing Tc for each overpass image (Senay et al. [18]; Senay [6]). Although this approach worked adequately for images with sufficient calibration points (high NDVI), there were at least three major limitations: (1) high NDVI images that meet the NDVI > 0.7 criterion may not be available in arid and semi-arid regions or outside of major growing seasons in different parts of the world, (2) high NDVI calibration landscapes are not uniformly distributed in a given image, thus extrapolating Tc to the entire image from isolated calibration points could introduce errors in hydro-climatically complex regions, and (3) the NDVI threshold could create widely varying Tc ranges because some images may only have a narrow range of NDVI that is closer to the threshold (NDVI = 0.7) while others may have Tc values derived from pixels with NDVI far higher than the 0.7 threshold (e.g., NDVI = 0.9). The difference in Ts between NDVI 0.7 and 0.9 could be as high as 4 to 5 K [16].

## 2. Methods

#### 2.1. Auxiliary Data

#### 2.2. FANO Illustration: Data and Development

^{*}represents the expected land surface temperature difference (K) between the observed Ts (spatial average) and expected wet-bulb (Tc); ΔNDVI

^{*}is the NDVI difference between the observed (spatial average) and theoretical maximum NDVI of 0.9 that would correspond to the wet-bulb pixels; dT

^{*}is the inverse of the surface psychrometric constant that defines the temperature difference between a dry-bare surface and the canopy level air temperature [6]; f is a proportionality “FANO” constant formulated in this study that relates the ratio ΔTs

^{*}/dT

^{*}to ΔNDVI

^{*}. The negative sign indicates the known inverse relation between Ts and NDVI, which translates into a smaller ΔTs at high NDVI and a larger ΔTs at lower NDVI surfaces.

#### 2.2.1. Study Area

^{*}and Ts

^{*}, respectively. The Ts magnitude within the NDVI bin of 0.85–1.0 was considered to represent the wet-bulb with a value of 302.2 K (Table 1). The maximum NDVI associated with the wet-bulb Ts was set to be 0.9 when using surface reflectance-based NDVI. The derivation of the change in NDVI (ΔNDVI

^{*}) and the normalization of change in Ts using dT (i.e., ΔTs*/dT

^{*}) is described below.

^{2}(highest NDVI bin) to 2025 km

^{2}(lowest NDVI bin), respectively (Table 1). This ensures a reliable average value for NDVI and Ts regardless of differences in the number of pixels among the different NDVI bins. The average NDVI ranged from 0.11 in the lowest bin to 0.89 in the highest bin with the corresponding Ts

^{*}varying from 327.5 K to 302.2 K yielding a difference of 25.3 K between the extreme NDVI bins. This observed Ts difference between the low and high NDVI locations within the study site (Figure 2) is coincidentally very close to the theoretically derived dT value of 25.26 K (Table 1). Figure 3 shows the temporal variability of dT for the study site with a peak (~25 K) in the summer and a minimum in the winter (~7 K).

^{*}/dT

^{*}and ∆NDVI

^{*}created using an NDVImax = 0.9 and dT = 25.25 K. The FANO constant f is shown to be 1.23 in this example. However, f is expected to vary among samples (exploratory analysis shows a possible range between 1.10 and 1.40), but an average value of 1.25 is expected to provide a reasonable estimate for general and operational applications. The difference between f = 1.25 and f = 1.10 or between f = 1.25 and f = 1.40 is expected to result in an absolute error of 2 K in the estimation of Tc at low NDVI (0.3) landscapes or only an error of 1 K at high NDVI (0.6) landscapes, which is close to the uncertainty of the Land Surface Temperature and its effect on ETa estimation is relatively small.

^{*}/dT

^{*}) is expected to be 0.0 when the average NDVI is 0.9 because the maximum NDVI is expected to be 0.9. In the SSEBop formulation, ∆Ts

^{*}/dT

^{*}is the same as 1.0−ETf or simply the sensible heat fraction, Hf, in which case a minimum Hf (0.0) and maximum ETf (1.0) is attained at NDVI

^{*}= 0.9. For example, at NDVI

^{*}= 0.1, ∆NDVI

^{*}is 0.8 which translates to ∆Ts

^{*}/dT

^{*}= 0.98 using the y = −1.23x Equation in Figure 4. Thus, Hf is high, close to 1.0, which indicates a negligible ETf around 0.02.

#### 2.2.2. Forcing Operation in FANO: Tc Determination

^{*}= −f∙dT

^{*}· ∆NDVI

^{*}

^{*}= Ts

^{*}− Tc

^{*}

^{*}is the expected (ideal) wet-bulb (cold) reference surface temperature (K) at maximum NDVI (NDVImax) over a grid that includes the “warm” surface temperature (Ts

^{*}); Ts

^{*}is the observed warm surface temperature over a chosen grid size. This is designated as warm surface temperature because it represents the landscape surface temperature with an average NDVI most likely lower than the ideal NDVImax that could correspond to the reference wet-bulb (cold) temperature.

^{*}= NDVI

^{*}− NDVImax

^{*}); NDVI

^{*}is the spatial-average observed NDVI (5 km × 5 km) that corresponds to the observed warm surface temperature (Ts

^{*}).

^{*}can be estimated from an algebraic rearrangement of Equation (3) (Senay Approximation) by combining Equations (4)–(6), leading to the FANO Equation

^{*}= Ts

^{*}− f∙dT

^{*}(NDVImax − NDVI

^{*})

^{*}= Ts

^{*}− 1.25dT

^{*}(0.9 − NDVI

^{*})

^{*}from the observed Ts, NDVI, and predefined dT for any location and date without requiring knowledge of high NDVI calibration points unlike the previous versions of SSEBop. The following sections will also show the procedures used to determine higher resolution (~1 km) Tc from coarse resolution Tc

^{*}(~5 km) using a c factor, like the earlier version of SSEBop [18].

#### 2.2.3. Normalizing Operation in FANO: Parameter and Spatial Scale

^{*}with FANO.

^{*}. However, certain conditions, such as low NDVI associated with low Ts

^{*}from water bodies and wetlands or high NDVI above the NDVI value of 0.9 require Ts values that have not been adjusted via the FANO Equation. The assumption here is that these are already representative of the wet-bulb condition and that Tc

^{*}should be determined using an un-adjusted Ts

^{*}. Referring to Table 2, we note that 5 km pixels that correspond to surface water conditions (NDVI

^{*}< 0) are assigned Ts

^{*}that does not mask out the water pixels. This is a wet-bulb condition; therefore, masking is not necessary. Conditions where NDVI

^{*}> 0.9 (average over 5 km × 5 km) are exceedingly rare. However, if that is encountered, the pixels are assigned a Ts

^{*}that is not modified by the FANO Equation, but it is masked for water pixels. This also amounts to a wet-bulb condition. For these conditions (Table 2), Ts

^{*}values are assigned to the final Tc

^{*}grid. All other pixels return Tc

^{*}derived from Ts

^{*}that is calculated using the FANO Equation.

^{*}> 0.9) or surface water/wet (NDVI

^{*}< 0) are assigned a wet-bulb temperature of the average Ts of all the pixels in a 5 km grid. The layers of Tc

^{*}are mosaicked together based on “priority” (“a” is highest priority and “d” is lowest).

^{*}.

#### 2.2.4. Calculation of c Factor

^{*}is only determined at a coarse resolution (5 km grid or larger) and thus the 1 km air temperature is used to disaggregate and create the final Tc at a 1 km resolution using a similar c factor calculation as in previous publications (e.g., Senay et al. [15,18]. The disaggregation is generally useful in complex topography where the Tc may show a substantial spatial variation within a 5 km grid.

^{*}is the spatially averaged (5 km) maximum daily air temperature (climatology); and Tc

^{*}is the predicted wet-bulb reference surface temperature as defined earlier at 5 km.

^{*}using the c factor. Note the absence of * in Equation (10), indicating the absence of large area averaging.

#### 2.3. Model Performance Evaluation

#### 2.3.1. Water Balance Evaluation

#### 2.3.2. Evaluation with Flux Tower Data

#### 2.4. Computing Platforms

#### 2.4.1. Google Earth Engine Implementation of SSEBop

#### 2.4.2. USGS On-Demand Overpass SSEBop ETa

## 3. Results

#### 3.1. Water Balance Evaluation

#### 3.2. EC Tower Evaluation

#### 3.3. On-Demand SSEBop Evapotranspiration

## 4. Discussion

#### 4.1. WBET Evaluation

#### 4.2. FANO Constant

#### 4.3. Climatology vs. Annual Gridmet Reference ET

#### 4.4. Challenges and Limitations

^{*}. Future research could look into the sensitivity of the dT parameter and its effect on the final ETa product.

## 5. Conclusions

## Author Contributions

## Funding

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

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**Figure 1.**Location of eddy covariance (EC) towers used in this study; qualified eight-digit Hydrological Unit Code (HUC8 selected, HUC8 FANO) boundaries for the one-to-one evaluation of two versions (v0.1.7 and v0.2.6, respectively) of SSEBop model (red, HUC8 selected) and additional qualified HUC8 with SSEBop v0.2.6 alone (green, HUC8 FANO); unqualified watersheds for water balance-based ET evaluation are shown in blue (HUC8). FANO procedure test site covering the western part of Nevada (Landsat path/row: 043/033) along with the six regions of the conterminous United States are displayed.

**Figure 2.**FANO test site highlighting in gray elevation ranges between 1200 m and 1500 m above mean sea level. Greener colors indicate irrigated lands with green vegetation. Spatially averaged NDVI, Ts, and dT were extracted over the gray region over multiple NDVI bins (Table 1).

**Figure 3.**Timeseries of dT (climatology) for the study region (gray area in Figure 2) showing seasonal evolution that mimics net radiation patterns of the region. Time series represents the period from 1 January through 31 December of a given year.

**Figure 4.**An illustrative Senay Approximation showing the inverse linear relation between a normalized surface temperature difference and NDVI difference (∆Ts

^{*}/dT

^{*}versus ∆NDVI

^{*}). To reduce chart clutter, the * is omitted in the labels; all parameters represent large-area averages determined by the pixel counts in Table 1. Only positive NDVI values over land/vegetated surfaces are valid for the proposed approximation.

**Figure 5.**Flow chart showing FANO filtering and calculation procedures. The * refers to the spatial averaging over 5 km or 100 km area.

**Figure 6.**Annual SSEBop ETa using 5-water year median (2009, 2011, 2013, 2016, 2018) data from Landsat 5/7/8. Data were resampled to 250 m resolution for display. The data are available at https://doi.org/10.5066/P9NKWT3D (accessed on 27 December 2022) [38].

**Figure 7.**Comparison of annual ETa from SSEBop v0.1.7 and SSEBop v0.2.6 (FANO) with water balance (WBET) across HUC8s in the conterminous United States (CONUS). Subfigures (

**a**–

**f**) show the ET comparisons for water years 2009, 2011, 2013, 2016, 2018, and 5-year average, respectively.

**Figure 8.**Comparison of annual ETa from SSEBop v0.1.7 and SSEBop v0.2.6 (FANO) with water balance (WBET) across HUC8s at six different regions of the conterminous United States. Subfigures (

**a**–

**f**) show the ET comparisons for Northeast, Southeast, Midwest, Great Plains, West, and Pacific Northwest regions, respectively.

**Figure 9.**Monthly averages of ETa observations from tower sites for the EC ETa (Flux, blue), SSEBop ETa v0.1.7 (red) and SSEBop ETa v0.2.6 (green) including r, bias and RMSE statistics. (

**a**) monthly averages for all cropland sites (n = 8); (

**b**) monthly averages for all grassland sites (n = 6).

**Figure 10.**Sample of four Landsat SSEBop ETa images downloaded from ESPA for locations in (

**a**) the Central Valley of California for 9 August 2019; Landsat Path/Row (L P/R) 42/35; centroid latitude/longitude (C Lat/Lon) 36.050, −119.447 in decimal degrees, (

**b**) near the Nile River Delta in Egypt for February 19, 2017; L P/R 176/40; C Lat/Lon 28.825, 31.577, (

**c**) in the state of Bahia, Brazil for 28 April 2017; L P/R 220/69; C Lat/Lon −12.976, −45.766, and (

**d**) near Nanjing, China, in the Yangtze River Delta for 21 July 2017; L P/R 120/38; C Lat/Lon 31.757, 118.841.

**Figure 11.**Sample on-demand SSEBop ETa averages by landcover type showing relative distribution of ETa in different parts of the world for different seasons. Subfigures (

**a**–

**d**) correspond to the Landsat SSEBop ETa in Figure 10a–d.

**Table 1.**Spatial-average values for Landsat NDVI and Ts, and dT parameters corresponding to the 1 July 2020, image. Pixel count refers to the number of 30 m pixels for each parameter. The * indicates spatial averages for each NDVI bin.

NDVI Bin | Pixel Count | NDVI^{*} | dT^{*} | Ts^{*} | ΔTs^{*} | ΔNDVI^{*} | ΔTs^{*}/dT^{*} |
---|---|---|---|---|---|---|---|

0.05–0.15 | 2,249,526 | 0.11 | 25.26 | 327.5 | 25.3 | −0.79 | 1.00 |

0.15–0.25 | 639,361 | 0.18 | 25.26 | 324.8 | 22.6 | −0.72 | 0.90 |

0.25–0.35 | 174,131 | 0.29 | 25.26 | 320.2 | 18.0 | −0.61 | 0.71 |

0.35–0.45 | 140,212 | 0.39 | 25.26 | 317.3 | 15.0 | −0.51 | 0.60 |

0.45–0.55 | 118,247 | 0.50 | 25.26 | 314.7 | 12.5 | −0.40 | 0.49 |

0.55–0.65 | 104,927 | 0.61 | 25.26 | 311.5 | 9.2 | −0.29 | 0.37 |

0.65–0.75 | 78,558 | 0.73 | 25.26 | 308.3 | 6.1 | −0.17 | 0.24 |

0.75–0.85 | 57,827 | 0.82 | 25.26 | 305.2 | 3.0 | −0.08 | 0.12 |

0.85–1.00 | 26,426 | 0.89 | 25.26 | 302.2 | 0.00 | −0.01 | 0.00 |

**Table 2.**NDVI-based filtering procedures for FANO parameterization. Priority in the outcome is ranked from highest (“a”) to lowest (“d”).

Landscape Condition | Filtering Condition | Temperature Assignment | Outcome ^{(priority)} |
---|---|---|---|

FANO land condition | (0 ≤ NDVI^{*} ≤ 0.9) | Tc^{*} = Tc^{*}_{5km} | FANO at 5 km resolution ^{(d)} |

FANO wet condition | (0 ≤ NDVI^{*} ≤ 0.9) & (wet pixels > 10% in 5 km grid) | Tc^{*} = Tc^{*}_{100km} | FANO at 100 km resolution ^{(c)} |

Surface water | Unmasked NDVI^{*} < 0 | Tc^{*} = Ts^{*} | Water pixels retain average surface temperature ^{(b)} |

Dense green vegetation | NDVI^{*} > 0.9 | Tc^{*} = Ts^{*} | High NDVI pixels retain average surface temperature ^{(a)} |

**Table 3.**Summary of HUC8 ETa comparison between SSEBop v0.1.7 and SSEBop v0.2.6 (FANO) with water balance (WBET) for 5-year average (5-y avg.) and individual years. The Pearson correlation coefficient (r) shows the degree of association between the two versions of SSEBop and WBET for the six regions and CONUS.

Region ^{+} | Water Year | WBET mm/yr | n^{1} | r (−) | Bias, mm/yr (%) | MAE, mm/yr (%) | RMSE, mm/yr (%) | ||||
---|---|---|---|---|---|---|---|---|---|---|---|

SSEBop v0.1.7 | SSEBop v0.2.6 (FANO) | SSEBop v0.1.7 | SSEBop v0.2.6 (FANO) | SSEBop v0.1.7 | SSEBop v0.2.6 (FANO) | SSEBop v0.1.7 | SSEBop v0.2.6 (FANO) | ||||

NE | 5-y avg. | 883 | 44 | 0.54 | 0.51 | 104 (12) | 40 (4) | 141 (16) | 97 (11) | 154 (17) | 123 (14) |

SE | 5-y avg. | 1033 | 246 | 0.36 | 0.27 | 110 (11) | 6 (1) | 134 (13) | 95 (9) | 160 (16) | 129 (12) |

MW | 5-y avg. | 672 | 279 | 0.87 | 0.73 | 50 (7) | −25 (−4) | 59 (9) | 73 (11) | 76 (11) | 87 (13) |

GP | 5-y avg. | 626 | 242 | 0.96 | 0.95 | 44 (7) | −6 (−1) | 103 (16) | 78 (13) | 128 (20) | 99 (16) |

W | 5-y avg. | 383 | 136 | 0.93 | 0.95 | −51 (−13) | −19 (−5) | 79 (21) | 63 (17) | 104 (27) | 80 (21) |

P NW | 5-y avg. | 398 | 53 | 0.88 | 0.91 | −25 (−6) | −2 (−1) | 68 (17) | 52 (13) | 86 (22) | 68 (17) |

CONUS | 2009 | 702 | 1000 | 0.92 | 0.92 | 14 (2) | −28 (−4) | 101 (14) | 94 (13) | 128 (18) | 122 (17) |

2011 | 640 | 751 | 0.89 | 0.91 | −5 (−1) | −34 (−5) | 108 (17) | 100 (16) | 138 (22) | 126 (20) | |

2013 | 684 | 946 | 0.95 | 0.93 | 27 (4) | −27 (−4) | 98 (14) | 87 (13) | 128 (19) | 113 (16) | |

2016 | 780 | 1024 | 0.92 | 0.91 | 35 (4) | −37 (−5) | 120 (15) | 106 (14) | 150 (19) | 134 (17) | |

2018 | 805 | 773 | 0.93 | 0.90 | 28 (3) | −39 (−5) | 105 (13) | 109 (13) | 133 (17) | 139 (17) | |

5-y avg. | 705 | 1000 | 0.95 | 0.94 | 48 (7) | −8 (−1) | 95 (13) | 78 (11) | 122 (17) | 104 (14) |

^{1}: n = number of HUC8s for ETa comparison between SSEBop (identical HUCs on two versions) and WBET.

^{+}: NE = Northeast; SE: Southeast; MW: Midwest; GP = Great Plains; W: West; P NW = Pacific Northwest.

**Table 4.**Flux Tower Alfalfa Reference Evapotranspiration (ETr) comparison with Gridmet Alfalfa Reference Evapotranspiration for 23 Ameriflux eddy covariance (EC) Towers. ETr values in the first two columns refer to the two Gridmet daily datasets used in the comparison: Climatology 1981–2010 average ETr and annual ETr (for the same years used in the ETa validation). Values are the average of all overpass days from all towers (n = 925) with the standard deviation (STD) given in brackets.

Gridmet Version | Tower ETr (mm) [STD] | GMET ETr (mm) [STD] | Bias (mm) [%] | RMSE (mm) [%] | r (−) |
---|---|---|---|---|---|

Climatology * | 5.84 [2.98] | 5.83 [2.24] | −0.01 [−0.2%] | 1.86 [32%] | 0.78 |

Annual | 5.84 [2.98] | 6.76 [3.06] | 0.91 [15.6%] | 1.98 [34%] | 0.83 |

**Table 5.**Comparison between SSEBop and flux tower ETa using two model versions (v0.1.7 and v0.2.6) and two reference ET sources (annual and climatology Gridmet) over 23 Ameriflux eddy covariance (EC) towers. Top section compares the Gridmet climatology 1981–2010 ETr and the bottom section compares the Gridmet annual ETr (for the same years used in the ETa validation) on satellite overpass days. ETa values for the towers and SSEBop ETa are the average of all overpass days from all towers (n = 1115) with the standard deviation (STD) given in brackets.

SSEBop Version | Gridmet Version | Tower ETa (mm) [STD] | SSEBop ETa (mm) [STD] | Bias (mm) | RMSE (mm) | r (−) | Percent Bias (%) |
---|---|---|---|---|---|---|---|

v0.1.7 | Climatology * | 2.32 [2] | 3.23 [1.78] | 0.91 | 1.76 | 0.69 | 39.2% |

v0.2.6 | Climatology * | 2.32 [2] | 2.39 [1.94] | 0.08 | 1.36 | 0.76 | 3.0% |

v0.1.7 | Annual ** | 2.32 [2] | 3.2 [1.96] | 0.88 | 1.88 | 0.65 | 37.9% |

v0.2.6 | Annual ** | 2.32 [2] | 2.4 [2.06] | 0.08 | 1.47 | 0.74 | 3.4% |

**Table 6.**Overpass actual ET (ETa) comparison between SSEBop and flux tower categorized by landcover (as reported by Ameriflux). SSEBop was forced with the climatology Gridmet 1981–2010 ETr without any scaling factor.

Landcover | SSEBop Version | Count | Average Tower ETa (mm) [STD] | Average SSEBop ETa (mm) [STD] | Bias (mm) | RMSE (mm) | r (−) | Percent Bias (%) |
---|---|---|---|---|---|---|---|---|

Cropland | v0.1.7 | 295 | 3.13 [2.26] | 3.47 [1.92] | 0.34 | 1.48 | 0.77 | 11% |

Cropland | v0.2.6 | 295 | 3.13 [2.26] | 2.91 [2.22] | −0.22 | 1.21 | 0.86 | −7% |

Grassland | v0.1.7 | 400 | 2.1 [1.97] | 3.08 [1.63] | 0.98 | 1.88 | 0.61 | 47% |

Grassland | v0.2.6 | 400 | 2.1 [1.97] | 2.06 [1.64] | −0.04 | 1.35 | 0.73 | −2% |

**Table 7.**Summary of non-matching (different n values) HUC8 ETa comparison between SSEBop v0.1.7 and SSEBop v0.2.6 (FANO) with water balance (WBET) for CONUS and the six regions. Bias, MAE, and RMSE are yearly magnitudes (mm/year) with percent of the 5-year average shown in brackets (%).

Statistics | CONUS | Northeast | Southeast | Midwest | Great Plains | West | Pacific Northwest | |||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|

v0.2.6 | v0.1.7 | v0.2.6 | v0.1.7 | v0.2.6) | v0.1.7 | v0.2.6 | v0.1.7 | v0.2.6 | v0.1.7 | v0.2.6 | v0.1.7 | v0.2.6 | v0.1.7 | |

n | 1222 | 1079 | 44 | 44 | 261 | 247 | 285 | 281 | 415 | 264 | 161 | 184 | 56 | 59 |

r | 0.94 | 0.96 | 0.51 | 0.54 | 0.24 | 0.34 | 0.74 | 0.87 | 0.95 | 0.96 | 0.94 | 0.92 | 0.90 | 0.84 |

Bias, mm (%) | −7 (−1) | 43 (6) | 40 (4) | 104 (12) | 14 (1) | 112 (11) | −24 (−4) | 50 (8) | −12 (−2) | 39 (6) | −15 (−4) | −52 (−14) | 4 (1) | −10 (−3) |

MAE, mm (%) | 74 (11) | 94 (14) | 97 (11) | 141 (16) | 98 (9) | 135 (13) | 72 (11) | 60 (9) | 66 (11) | 99 (16) | 60 (16) | 77 (20) | 55 (14) | 74 (19) |

RMSE, mm (%) | 97 (14) | 121 (18) | 123 (14) | 154 (17) | 130 (13) | 163 (16) | 86 (13) | 77 (11) | 87 (14) | 124 (20) | 78 (21) | 100 (27) | 72 (18) | 91 (23) |

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## Share and Cite

**MDPI and ACS Style**

Senay, G.B.; Parrish, G.E.L.; Schauer, M.; Friedrichs, M.; Khand, K.; Boiko, O.; Kagone, S.; Dittmeier, R.; Arab, S.; Ji, L.
Improving the Operational Simplified Surface Energy Balance Evapotranspiration Model Using the Forcing and Normalizing Operation. *Remote Sens.* **2023**, *15*, 260.
https://doi.org/10.3390/rs15010260

**AMA Style**

Senay GB, Parrish GEL, Schauer M, Friedrichs M, Khand K, Boiko O, Kagone S, Dittmeier R, Arab S, Ji L.
Improving the Operational Simplified Surface Energy Balance Evapotranspiration Model Using the Forcing and Normalizing Operation. *Remote Sensing*. 2023; 15(1):260.
https://doi.org/10.3390/rs15010260

**Chicago/Turabian Style**

Senay, Gabriel B., Gabriel E. L. Parrish, Matthew Schauer, MacKenzie Friedrichs, Kul Khand, Olena Boiko, Stefanie Kagone, Ray Dittmeier, Saeed Arab, and Lei Ji.
2023. "Improving the Operational Simplified Surface Energy Balance Evapotranspiration Model Using the Forcing and Normalizing Operation" *Remote Sensing* 15, no. 1: 260.
https://doi.org/10.3390/rs15010260