1. Introduction
Evapotranspiration (ET
c) is a crucial component of the hydrological cycle and water resource management and plays a key role in the exchange of material and energy within the soil–crop–atmosphere system [
1,
2]. ET
c serves as an important indicator that reflects the water consumption of crops, and an accurate estimation of ET
c can optimize irrigation systems and improve water resource utilization efficiency [
3], which is of great significance to the development of agriculture in China. Historically and spatially, ET
c was estimated using hydrological principles [
4]. In the early 17th century, Dalton proposed a model for calculating water surface ET
c from the saturated state of water vapor pressure, which formed the basis of ET
c theory [
5].
In recent years, satellite remote sensing technology has been increasingly utilized for the estimation of ET
c on a large-scale regional or global level due to its wide monitoring scale. Researchers and scholars demonstrated that remote sensing energy balance models using satellite data, such as the one proposed by Peng et al. [
6], could be effective for estimating ET
c. However, satellite remote sensing has limitations such as low surface resolution, susceptibility to atmospheric factors, and long revisit periods [
7,
8]. In contrast, UAV remote sensing has advantages such as mobility, flexibility, portability, and low cost, and is able to obtain high spatial and temporal resolution data in small- and medium-scale areas, effectively addressing many issues associated with satellite remote sensing [
9,
10]. UAV technology for monitoring soil and vegetation information is characterized by its fast, macroscopic, and convenient capabilities. It has been widely applied in agriculture [
11]. Therefore, based on UAV remote sensing technology, ET
c models can be constructed for farmland. Hoffmann et al. [
12] applied UAVs to obtain high-resolution surface temperatures for the TSEB and DTD algorithms to calculate surface evapotranspiration.
However, most existing remote sensing ET
c models were based on satellite data, and there is a need for more in-depth research on models based on UAV data [
13,
14]. Remote sensing ET
c models can be classified into mechanistic models, empirical regression models, and spatial feature method models. Among these, mechanistic models have been the most widely used, with single-source models, such as the SEBAL model, the S-SEBI model, and the METRIC model [
15,
16,
17], being commonly utilized. The METRIC model, proposed by Allen et al. [
18], has been widely used in many related fields due to its good fit with satellite data and relatively accurate estimation of ET
c. In contrast, multi-source models consider the effects of soil and vegetation on ET
c separately and were shown to provide better results in areas with heterogeneous vegetation cover and varying surface environments [
19]. Typical multi-source models included the SHUTTLEWORTH model, RSEB model, and TSM model [
20,
21,
22]. The RSEB model is an energy balance model designed for remote sensing and was shown to be suitable for estimating ET
c in areas with an inhomogeneous vegetation distribution by Samani et al. [
23]. Ortega-Farías et al. [
24] used the RSEB model to estimate the evapotranspiration in Brazilian olive groves.
Citrus is considered one of the most essential commercial fruits in the global market, where the production of citrus increased substantially from 25.1 Mt to 158.5 Mt worldwide from 1961 to 2020 [
25]. China is the world’s largest citrus producer, accounting for 31% of the global citrus planting area and 25% of the global citrus production [
26]. In the western Hubei region, citrus is the fruit tree with the largest planting area and the most important economic status, and the citrus industry has become one of the pillar industries of rural economic development in this region [
25]. However, there were limited studies on citrus multi-slope ET
c based on UAV multispectral remote sensing data combined with the RSEB model. Therefore, this study focused on citrus in Yichang City, utilizing a UAV remote sensing platform equipped with a multispectral camera to carry out the ET
c inversion of multi-slope Newhall navel oranges in an experimental orchard. This study aimed to use the FAO Penman–Monteith (P-M) model for calculating citrus ET
c as a criterion and construct a modified RSEB model with a high accuracy to achieve the fast, efficient, and accurate ET
c inversion of different slopes in a citrus orchard. The research results of this study will provide technical support and a reference basis for the estimation of ET
c in different terrains at the regional scale using UAV remote sensing images.
3. Results and Analysis
3.1. Inversion of Citrus ETc Based on the METRIC Model
Based on the spectral data collected by UAV remote sensing, the METRIC model was constructed. Using the P-M model combined with the meteorological data, the ET
c of the Newhall navel oranges during the experimental period (fruit expansion period and color change and sugar increase period) was calculated separately, and the correlation between the two was compared to evaluate the estimation effect of the METRIC model. The results are shown in
Figure 4,
Figure 5 and
Figure 6. The ET
c values of different slopes inverted by the METRIC model were one-way ANOVA analyzed and correlation analyzed using SPSS 23 software (IBM SPSS Statistics 23) and were multiply compared using Duncan’s test, where different lowercase letters represent significant differences in the ET
c values of different slopes at the
p < 0.05 level, and the standard deviations were calculated, as shown in
Figure 6.
Figure 4 depicts the ET
c change process of the Newhall navel oranges in two growth periods simulated using the METRIC model and P-M formula (average ET
c of the whole experimental area). The lowest value of ET
c estimated based on the METRIC model was 2.30 mm/d during the color change and sugar increase period, while the highest value was 11.98 mm/d during the fruit expansion period.
Figure 5 presents the results of ET
c calculated by the two methods to establish univariate linear regression model. The resulting coefficient of determination (R
2) was 0.396, the root-mean-square error (RMSE) was 4.940, and the systematic error (SE) was 4.570. This indicated that using the METRIC model to calculate the ET
c of the Newhall navel orange had a larger error, and there was a significant difference in the values of the ET
c estimated by the two methods.
From the ET
c space shown in
Figure 6, the average ET
c in the mid-slope treatment was the highest at 8.33 mm/d, followed by the low-slope treatment at 8.28 mm/d, and the smallest in the high-slope treatment at 8.21 mm/d. The differences were relatively small, indicating a stable state in the daily average ET
c of citrus under the three treatments. The overall trend of daily ET
c estimated by the two methods used in this study was basically the same, except for differences in some extreme values between the two growth periods of citrus.
3.2. Influence Factor Analysis of ETc Based on METRIC Model
Based on the METRIC model, the relationship between ET
c and each influencing factor was analyzed, and the results are shown in
Table 1. Evapotranspiration showed a high negative correlation with wind speed (R
2 = 0.584). Higher wind speed attenuates the energy of shortwave radiation and reduces the energy of water molecule transport, thus affecting the evapotranspiration. On the other hand, the correlation between ET
c and temperature was poor (R
2 = 0.234), showing a positive correlation. The appropriate wind speed for dispersing the crop-saturated air had a greater effect on ET
c than the temperature enhancement.
When comparing the relationship between the four types of radiation and the amount of ETc, the net radiation (Rn) and shortwave radiation (Rs) had the greatest impacts on ETc (R2 = 0.437 and 0.422, respectively), whereas upward longwave radiation (RL↑) and downward longwave radiation (RL↓) had relatively smaller impacts (R2 = 0.232). This was attributed to the different roles of each type of radiation. Rn is the combination of two types of radiation (Rns and Rnl). RL↑ represents the energy emitted to the atmosphere by the Earth’s surface after absorbing shortwave radiation, which has a relatively small effect on evaporation. Conversely, RL↓ represents the energy emitted by the atmosphere that is received again by the Earth’s surface; although it also directly provides energy to the ETc process, it has little effect due to its instability and the relatively small energy provided.
Next, the correlations of indirectly influenced factors, such as the vegetation index (NDVI), aerodynamic roughness (
zo), and aerodynamic impedance (
rah), were analyzed. As shown in
Table 1, the correlation between NDVI and ET
c was extremely low (R
2 = 0.036). In contrast, the correlation of
zo and
rah with ET
c was highly significant (R
2 = 0.558 and 0.866, respectively).
zo exhibited a negative correlation with ET
c, while
rah showed a positive correlation with ET
c. A larger
rah makes it more difficult for heat to rise to the atmosphere above the canopy, causing more heat to remain at the surface below the canopy and resulting in increased ET
c.
Unlike the P-M formula, the METRIC model was constructed by incorporating additional parameters related to the wind speed, such as
zo and
rah, the results are shown in
Table 2. This led to an increased sensitivity to the wind speed factor and reduced sensitivity to the temperature. While the formula does not explicitly describe the water vapor pressure difference, the saturated water vapor pressure is a function of temperature. Therefore, a linear fit of the water vapor pressure difference to the METRIC model revealed a low correlation (R
2 = 0.339).
3.3. Inversion of Citrus ETc Based on the RSEB Model
Based on the UAV spectral remote sensing data, the RSEB model was constructed to estimate the ET
c of the citrus orchard. The effectiveness of the RSEB model was assessed by comparing it with the ET
c data calculated by the P-M model. The optimal model was then derived by comparing it with the METRIC model. The results are shown in
Figure 7,
Figure 8,
Figure 9 and
Figure 10. The ET
c values of different slopes inverted by the RSEB model were one-way ANOVA analyzed and correlation analyzed using SPSS 23 software and were multiply compared using Duncan’s test, where different lowercase letters represent significant differences in the ET
c of different slopes at the
p < 0.05 level, and the standard deviations were calculated, as shown in
Figure 10.
Figure 7 illustrates the changes in ET
c of the Newhall navel oranges during the two growth periods, as estimated by the RSEB model and P-M formula. The lowest value of ET
c based on the RSEB model was 0.63 mm/d at the end of the color change and sugar increase period (close to the fruit-picking period), with the highest value being 10.35 mm/d during the fruit expansion period.
In
Figure 8, the univariate linear regression model for the ET
c results calculated by the two methods is presented, yielding a simulation effect of the two methods (R
2 = 0.486) with a regression equation of y = 0.4357x + 1.1836. The root-mean-square error (RMSE) was 3.010, and the systematic error (SE) was 2.090.
Figure 9 indicates a clear difference in the values of the ET
c estimated by the three methods. The values estimated by the METRIC model were much larger than those estimated by the remaining two methods, while the values estimated by the RSEB model were closer to the results calculated by the P-M formula. Consequently, the RSEB model was a better choice than the METRIC model for estimating the ET
c in citrus zones using UAV multispectral remote sensing.
In
Figure 10, the average ET
c of the different experimental treatments was the highest at the mid-slope position (5.82 mm/d), followed by the high-slope position (5.81 mm/d) and lowest at the low-slope position (5.78 mm/d). Overall, the mean ET
c was relatively stable across the three different treatments, which was consistent with the conclusion of the METRIC model. However, the RSEB model exhibited a more pronounced response to the influence of the surrounding environment, reflecting a more realistic estimation of the mean ET
c among the different treatments of citrus crops. During the two growth periods of the experiment, the values of the daily ET
c estimated by the RSEB model and P-M formula were very close to each other. Most of the time, the ET
c values estimated by the P-M formula were smaller than those estimated by the RSEB model. However, at the end of the color change and sugar increase period and close to the fruit picking period, the values alternated. This could be attributed to the decrease in the water dependence of the mature citrus fruit and the temperature decrease had a great impact on the estimation of the RSEB model. Fitting ET
c using the two models and the P-M formula, respectively, where the RSEB model and the P-M formula fitted ET
c with higher correlation. The results are shown in
Table 3.
3.4. Influence Factor Analysis of ETc Based on the RSEB Model
The effectiveness of the impact factors was first analyzed in terms of the basic meteorological factors, which were used to explore the reason why the RSEB model fit the P-M formula better than the METRIC model. The basic meteorological factors included temperature, wind speed, and radiation. Unlike the P-M formula and the METRIC model, the RSEB model had a very detailed classification of temperature, which was divided into the atmospheric temperature Ta (the same as that used in the P-M formula), surface temperature Ts (the same as that used in the METRIC model), and canopy temperature Tc, which was unique to the RSEB model. The results of fitting the basic meteorological factors were as follows: The R2 for wind speed was 0.282. The linear relationship was negatively correlated, which was close to the results of the previous subsection, but the wind speed factor had a much smaller effect on the RSEB model than on the METRIC model.
Comparing the three temperature and ET
c fits,
Ta,
Ts, and
Tc had different fits (the R
2 values were 0.770, 0.258, and 0.348, respectively), with
Ta having the highest effect on the ET
c value, which was close to the P-M formula. This indicated that the temperature factor had the same effect on the RSEB model as that of the P-M formula. Comparing the three kinds of radiation and ET
c values, the fit of emitted longwave radiation was much better than that of soil-emitted longwave radiation and canopy-emitted longwave radiation (the R
2 values were 0.756, 0.260, and 0.355, respectively), the results are shown in
Table 4. The fit of the ET
c values of the P-M formula to the emitted longwave radiation values yielded a better fit (R
2 = 0.562), which indicated that radiation was also a major influence factor of the P-M formula and the RSEB model. The effect of the temperature factor on the RSEB model was consistent with that of the P-M formula and both were positively correlated in the same way.
The fits of the two most important factors affecting ET
c, H sensible heat flux and G soil heat flux, were analyzed. Through
Table 5, we found that the RSEB model fitted well to the H sensible heat flux (R
2 = 0.999) and less well to the G soil heat flux (R
2 = 0.394). This finding was completely opposite to the P-M formula, which showed a fit to H of R
2 = 0.493 and fit to G of R
2 = 0.851. Both fits were not optimal, and there was some error in the estimates.
3.5. Inversion of ETc from Citrus Orchard
The RSEB model, which yielded higher results in the three precision validations, was chosen as the primary model for ET
c in citrus zones. However, the precision was not satisfactory based on the evaluation results of the three precision validation indicators. Therefore, the RSEB model was revised to ensure that the inversion results were more reliable and accurate compared with the original. The specific revisions are shown in
Table 6.
The results in
Table 6 represent the ET
c results calculated by RSEB multiplied by several conversion factors of 0.70, 0.65, 0.64, 0.63, and 0.60 before comparing them with the standard values. Where R
2 represents no change in the degree of fit, the RMSE and SE values were significantly decreased, indicating that the corrected fit was improved. Since there was not much difference in the RMSE after the correction of several conversion factors of 0.65, 0.64, 0.63, 0.60, the final correction factor of 0.64 was chosen since it had the smallest systematic error (SE).
Using the modified RSEB model (0.64 RSEB) in a time series, the four trees were divided into a group of nine sets of three different treatments, and the results of the inversion of ET
c from citrus fields in the top slope treatment (No. 13, No. 2, No. 20), the medium slope treatment (No. 1, No. 23, No. 10), and the bottom slope treatment (No. 24, No. 12, and No. 3) are shown in
Figure 11,
Figure 12 and
Figure 13.
The inversion of ETc of citrus on different slopes was realized by using UAV multispectral remote sensing with the RSEB model and simple corrections. It was evident that the ETc of crops under the same treatment was generally consistent and decreased with time, with some variations in specific values. Notably, the ETc value of the medium slope treatment was 3.745 mm/d greater than that of the high-slope treatment and 3.747 mm/d greater than that of the low-slope treatment due to the vigorous growth of weeds. The average ETc of the top slope treatment was 0.063 mm/d more than that of the bottom slope treatment, indicating that the ETc varied greatly when there was a difference in the amount of weed cover. Conversely, the height of the slope did not significantly impact the ETc, suggesting that the effect of weeds should be carefully considered when planting mandarin oranges in the field. Timely weeding is crucial, as excessive weeds could consume more soil water content, leading to insufficient irrigation for the mandarin oranges, and subsequently causing a decrease in fruit quality. When setting up the citrus field trial treatments, strict control over the weed coverage, height, and species is essential. The next step could involve quantifying the weed coverage, allowing for more accurate and controlled trial results.
4. Discussion
In this study, by comparing the inversion accuracy of two models, METRIC and RSEB, it was found that the RSEB model performed better. Overall, the inversion accuracy of the two models needed to be improved, which might have been due to the existence of artificial errors in the actual measurement process, and a sudden change in the weather could also make the meteorological data have errors [
38], which, in turn, affected the verification accuracy of the P-M formula. In this study, only two of the more commonly used energy balance models were selected, and the accuracy of more models still needs to be subsequently verified. In addition, Liu et al. pointed out that eliminating the soil background is the key to obtaining accurate canopy spectral information [
39]. Therefore, subsequent experiments should enhance access to citrus spectral data to further validate this idea. In this study, only multispectral remote sensing data were used to invert citrus evapotranspiration, and thermal infrared remote sensing fused with multispectral remote sensing will be used to further verify the inversion accuracy in the later stage.
This experiment was conducted during two critical growth periods, the fruit expansion stage and the color change and sugar increase stage, while it was crucial for guaranteeing the quality of citrus fruit and water-saving irrigation to meet the water consumption of citrus in all growth periods using precise irrigation [
40], and the subsequent research will be carried out on the ET of citrus throughout the whole growth period.
In this specific experiment, the evapotranspiration was not significantly affected by the height of the slope, where the evapotranspiration was the largest in the middle slope, which may have been caused by the larger weed cover in this slope. In further citrus field experiment treatment settings, there should be strict control over the weed cover, height, and species, and thus, the next step can include a quantitative assessment of the weed cover so that the results of the experiment will be more accurate and controlled. Alternatively, research could be conducted specifically on the effects of different weed covers on citrus evapotranspiration.
5. Conclusions
(1) The ETc of the Newhall navel oranges in the citrus orchard was estimated using the METRIC model, where the lowest values occurred during the color transition and sugar enhancement period, and the highest values occurred during the fruit expansion period. Among the different treatments, the mean ETc was the highest in the mid-slope treatment, followed by the low-slope treatment, and lowest in the high-slope treatment. The accuracy of the ETc estimation using the METRIC model was verified against the P-M model, with R2 = 0.396, RMSE = 4.940, and systematic error (SE) = 4.570.
(2) The estimation of the ETc of Newhall navel oranges in the citrus orchard using the RSEB model revealed the lowest value during the color transition and sugar enhancement period, and the highest value during the fruit expansion period. Among the different treatments, the mean ETc was highest in the mid-slope treatment, followed by the high-slope treatment, and lowest in the low-slope treatment. The accuracy of the ETc estimation using the RSEB model was verified against the P-M model, with R2 = 0.486, RMSE = 3.010, and systematic error (SE) = 2.090. The RSEB model inversion was found to be better than that of the METRIC model.
(3) The optimal modification coefficient of the RSEB model was determined to be 0.64. After the modification, RMSE = 1.470, SE = 0.003, and R2 remained unchanged, resulting in more accurate and reliable calculation results. Based on the corrected RSEB model for the ETc inversion in citrus zones, the mean ETc in the medium slope treatment was 3.745 mm/d higher than that in the high-slope treatment, and 3.747 mm/d higher than that in the low-slope treatment, with the high-slope treatment being 0.063 mm/d higher than the low-slope treatment.
In this study, by using multispectral remote sensing by UAV, constructing the RSEB model, and correcting key parameters, we could realize rapid and accurate estimation of the citrus ETc for different slopes. This can provide theoretical and technical support for the study of the citrus water–heat cycle in farmland and the formulation of irrigation systems.