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Remote Sensing 2013, 5(10), 53695396; doi:10.3390/rs5105369
Abstract
: Based on surface energy balance and the assumption of fairly invariant evaporative fraction (EF) during daytime, this study proposes a new parameterization scheme of directly estimating daily EF. Daily EF is parameterized as a function of temporal variations in surface temperature, air temperature, and net radiation. The proposed EF parameterization scheme can well reproduce daily EF estimates from a soilvegetationatmosphere transfer (SVAT) model with a root mean square error (RMSE) of 0.13 and a coefficient of determination (R^{2}) of 0.719. When input variables from in situ measurements at the Yucheng station in North China are used, daily EF estimated by the proposed method is in good agreement with measurements from the eddy covariance system corrected by the residual energy method with an R^{2} of 0.857 and an RMSE of 0.119. MODIS/Aqua remotely sensed data were also applied to estimate daily EF. Though there are some inconsistencies between the remotely sensed daily EF estimates and in situ measurements due to errors in input variables and measurements, the result from the proposed parameterization scheme shows a slight improvement to SEBSestimated EF with remotely sensed instantaneous inputs.1. Introduction
Estimation of evapotranspiration (ET) using remotely sensed data has been a significant topic because of the capability of remote sensing to quickly obtain surface information at large spatial scales with less cost [1–6]. Current models for ET estimation from remotely sensed data, e.g., the surface energy balance system (SEBS), the surface energy balance algorithm for land (SEBAL), and twosource models, depend primarily on observations at the satellite overpass time [7–10]. Because of the influences of atmosphere, observational angular, heterogeneous surfaces, and scale issues, there are some uncertainties in retrieved surface variables from remote sensing [11–13]. Therefore, the accuracy of ET estimates could be largely subjected to retrieval errors in remotely sensed surface variables [14–16].
Evaporative fraction (EF) is an important index for partitioning surface available energy (Q). A number of studies based on in situ measurements as well as analyses from land process modeling showed that EF exhibits a typical concaveup shape and is relatively stable during daytime [17–21]. Therefore, the constant EF method is often used to estimate daily ET from remotely sensed data, which converts the instantaneous ET at the satellite overpass time to daily values under the assumption of selfpreservation of EF in a diurnal cycle [22–25]. EF at the instantaneous scale can be calculated by latent heat flux (LE) and Q from remotely sensed data. Some methods of estimating EF directly from remote sensing have been developed, in which the feature space method is one of the representative parameterizations [26,27]. Directly parameterizing EF can obviate uncertainties caused by the calculation of various resistances [28]. Because the temporal variation of surface variables is less sensitive to the retrieval errors [29,30], the daynight surface temperature difference from MODIS global daily products and the change rate of surface temperature during the morning from MSGSEVIRI data and FY2C data were used to construct the triangle feature space to improve EF estimation [31–33]. However, determination of the dry and wet edges in triangle feature space depends on the domain size and the spatial resolution of remotely sensed images [34,35], which could result in more uncertainties.
The major objective of this study is to develop a new parameterization scheme for directly determining daily EF from temporal variations in surface variables. The proposed method will resolve uncertainties in EF estimation caused by errors in remotely sensed variables. Using simulation from an atmosphereland exchange (ALEX) model [36], a new EF parameterization scheme is proposed in Section 2. The inputs from in situ measurements at the Yucheng station in North China and the MODIS products are also used to analyze performance of the proposed EF method. Data are described in Section 3. Results will be discussed in Section 4. To further demonstrate that the EF estimate from temporal variation is less sensitive to the retrieval error of remotely sensed data, daily EF estimates from remotely sensed data are also compared with the result from the SEBS model with instantaneous inputs. Finally, some conclusions are given in Section 5.
2. Method
2.1. Background of Theory
2.1.1. Radiometric Heat Conductance P_{rad}
According to the boundary similarity theory, it is the aerodynamic temperature (T_{aero}) which determines the loss of sensible heat flux (H) from a surface [37]. T_{aero} is defined as the extrapolation of the air temperature profile down to an effective height within the canopy at which the vegetation components of heat fluxes arise [38,39], but it is not an easily measured variable in reality. For H estimates, T_{aero} is often replaced by surface radiative temperatures (T_{s}) by adding supplementary resistance, defining a radiometric exchange coefficient, or constructing the relationship between T_{aero} and T_{s} [39–41]. In this study, H is expressed by
Figure 2a shows the variation of P_{rad} in a diurnal cycle for the same atmospheric condition but with different wind speed. Because wind speed is the major driving force for heat transfer, the magnitude of P_{rad} is determined primarily by wind speed. For the same surface condition, high wind speed results in a large value in P_{rad}. It is evident that given a certain wind speed, P_{rad} mainly increases with increasing f_{c} (see Figure 2b). This is because the roughness length for heat transfer at the vegetated surface is generally larger than that at the bare soil surface [37]. As a result, P_{rad} is higher at the surface with dense vegetation cover than the bare soil surface. The conclusion of the dependence of P_{rad} on wind speed and f_{c} is similar to the studies from Carlson et al. and Lagouarde et al. [42,43]. They concluded that the heat conductance is highly sensitive to wind speed, roughness, and vegetation amount.
Referencing the study of Carlson et al. [44], it can be inferred from Figure 3 that P_{rad} approximately linearly varies with f_{c} when wind speed is given. The variation in P_{rad} along the vertical axis is caused by soil water content (SWC). For the same atmospheric condition, the RMSE in P_{rad} caused by the approximation of linear function of f_{c} is ∼5 W/(m^{2}·K), i.e., the variation in P_{rad} due to soil water content is ∼5 W/(m^{2}·K). Although soil water content is certainly a critical variable that controls the partitioning of surface available energy into H and LE, the role of SWC on P_{rad} is not significant. This is because radiative T_{s} can reflect surface soil moisture to a certain degree [39].
2.1.2. Diurnal Cycle of EF
EF is generally defined as the ratio of LE to Q. Therefore, EF at any time t in a day can be expressed by
From ALEXsimulated data driven by the atmospheric forcing at the Yucheng station in 2010 (details about data can be found in Appendix), the conclusion that daily average EF can be related to daytime average EF and morning average EF for the majority of cases can be justified (see red and green symbols in Figure 5a). Some dispersed data mainly occur under the atmospheric condition on 26 March 2010 (DOY 85). This may be related to relatively low air humidity on this day (see Figure A1a in Appendix). Under the low humidity condition, the assumption of selfpreservation EF may not be valid, e.g., Gentine et al. concluded that the selfpreservation of daytime EF is mainly constrained to high humidity and solar radiation [19]. The daily average EF from in situ measurements at the Yucheng station during the wheat growth period in 2012 (details about data can be found in Section 3.2) is generally greater than the EF during daytime (see Figure 5b). Because of the relatively invariant EF during daytime, daily average EF is further related to an instantaneous EF at 10:30 A.M. (see blue triangles in Figure 5), and the RMSE is within 0.2. Therefore, daily average EF can be estimated by an instantaneous EF during daytime or during the morning to a certain degree, especially under the conditions of clear skies, humid air, and strong solar radiation.
2.2. Parameterization for EF Based on SVAT Modeling
The surface energy balance equation at any time t in a day can be written as
The derivative of Equation (6) with respect to t can be written as,
Combining with Equation (1), if atmosphere and surface properties do not change greatly during a short study period of dt, the following equation can be derived:
From the analysis based on the ALEXsimulated data, there is a strong linear correlation between the values of (T_{s}′(t) − T_{a}′(t))/R_{n}′(t) and (ΔT_{s} − ΔT_{a})/ ΔR_{n} (see Figure 7a, ΔT_{s}, ΔT_{a}, and ΔR_{n} are the differences of T_{s}, T_{a}, and R_{n} between 1:30 P.M./10:30 A.M. and 1:30 A.M./10:30 P.M., respectively.). From in situ measurements at the Yucheng station during the wheat growth period in 2012, the similar results are obtained (see Figure 7b). The values of (T_{s}′(t) − T_{a}′(t))/R_{n}′(t) are almost positively proportioned to the values of (ΔT_{s} − ΔT_{a})/ΔR_{n}, and R^{2} of linear least square fit is greater than 0.8 (see Table 1). The purpose of selecting these moments at 1:30 P.M./10:30 A.M. (MODIS/Aqua or MODIS/Terra daytime overpass time) and 1:30 A.M./10:30 P.M. (MODIS/Aqua or MODIS/Terra nighttime overpass time) was to make this method applicable to MODIS data. The underlying physical mechanism of the linear correlation between (T_{s}′(t) − T_{a}′(t))/R_{n}′(t) and (ΔT_{s} − ΔT_{a})/ΔR_{n} is that the change rate of T_{s} during the morning and its daynight difference are all strongly related to soil moisture or thermal inertia [48]. Therefore, daily EF can be parameterized as Equation (13) as follows,
It is noted that Equations (12,13) have a similar form but with different inputs (i.e., (T_{s}′(t) − T_{a}′(t))/R_{n}′(t) or (ΔT_{s} − ΔT_{a})/ ΔR_{n}). Therefore, coefficients A, B, and C in Equation (13) are different from the values of A, B, and C in Equation (12). For estimating daily EF by Equation (12), geostationary meteorological satellites could provide a good estimate of (T_{s}′(t) − T_{a}′(t))/R_{n}′(t), whereas Equation (13) can accommodate nearpolar orbiting satellite data, e.g., MODIS/Terra, MODIS/Aqua. The same location on the Earth can be observed at least four times around 1:30 A.M, 10:30 A.M., 1:30 P.M., and 10:30 P.M. at local solar time in a day by the MODIS sensor. As a result, there are four different input schemes for Equation (13), i.e., the differences between 1:30 P.M. and 1:30 A.M., the differences between 10:30 A.M. and 10:30 P.M., the differences between 10:30 A.M. and 1:30 A.M., and the differences between 1:30 P.M. and 10:30 P.M.. Different input schemes have different coefficients. For convenience, Equation (13) also represents Equation (12) in the following study. Theoretically, the coefficients in Equation (13) would vary with atmospheric conditions. For each atmosphere, coefficients can be obtained by using data with different f_{c} and SWC conditions simulated from a soilvegetationatmosphere transfer (SVAT) model, and the procedure for obtaining coefficients is shown in Figure 8. In this study, the ALEX model is used. More details about ALEX simulation can be referred to in Appendix. Only the data of daily EF larger than 0 and less than 1 were selected to derive the coefficients in Equation (13) by the least square method. After obtaining the coefficients, daily EF can be calculated by Equation (13). For ALEXsimulated data driven by different atmospheric conditions from four sites (details about atmospheric forcing and these sites are given in Appendix), results from Equation (13) with different input schemes for four sites are listed in Table 2. The R^{2} of daily EF estimates from the developed parameterization scheme with respect to the values from the ALEX model is generally higher than 0.8, and the RMSE is ∼0.1. Among all input schemes, the inputs from the differences between 1:30 P.M. and 1:30 A.M. (around MODIS/Aqua daytime and nighttime overpass times) can generate better results than other inputs schemes.
Coefficients A, B, and C obtained by the least square method for five input schemes and 35 atmospheric forcing from four sites are displayed in Figure 9. It is obvious that coefficients A and B strongly vary with the different atmospheres, whereas coefficient C is almost invariant with atmosphere. In addition, it can be observed that the increase of B corresponds to the decrease of C, and vice versa for many cases. Therefore, an approximate negative relationship between B and C can be inferred. Coefficients A, B, and C in Equation (13) can be assumed to be invariant to a certain degree for all selected atmospheric conditions. When the invariant A, B, and C are obtained by fitting all ALEXsimulated data for 35 atmospheric conditions, daily EF for different input schemes is finally parameterized as those equations listed in Table 3.
For all simulated data from the ALEX model based on 35 atmospheric conditions at four sites, the daily EF estimates from Equations (14–18) are plotted in Figure 10a–e, respectively. It can be found that Equation (15) with the inputs of the differences between Aqua daytime and nighttime can better estimate daily EF than other input schemes, with an R^{2} of 0.719, an RMSE of 0.130, and a mean bias of 0.017. When Equation (17) is used with the inputs of the differences between Terra daytime and Aqua nighttime, daily EF estimates are slightly worse than the results from Equation (15), showing an RMSE of 0.137 and a mean bias of 0.033. The worst result is from Equation (16) with differences between Terra daytime and nighttime (see also the results shown in Table 2). This indicates that the differences of T_{s}, T_{a}, and R_{n} between Aqua/Terra daytime and Aqua nighttime can better reflect the variation of surface heat fluxes in a day than the differences from Terra overpass times. When the change rates of T_{s}, T_{a}, and R_{n} during the morning are used as inputs, the result from Equation (14) is not satisfied. On the one hand, the error may be from the weak ability of change rate of T_{s}, T_{a}, and R_{n} during the morning reflecting surface heat fluxes, and on the other hand, the approximation of linear relationship of T_{s}, T_{a}, and R_{n} with time during the morning also brings certain errors in daily EF estimates. Therefore, for the determination of daily EF, Equation (15) with the inputs of the differences between Aqua daytime and nighttime is recommended.
3. Data
To further validate Equation (15) for daily EF estimation, input variables of ΔT_{s}, ΔT_{a}, ΔR_{n}, and f_{c} from in situ measurements and MODIS/Aqua products would be applied.
3.1. In situ Measurements
In situ measurements, including meteorological variables, radiation data, and fluxes data, are from the Yucheng station (36.8291°N, 116.5703°E) in North China. Considering that the assumption of selfpreservation EF during daytime may not be valid under the conditions of low solar radiation and air humidity, days with daily average incoming solar radiation <200 W/m^{2} and average relative humidity of air <20% were excluded. Daily average incoming solar radiation <200 W/m^{2} generally occurred in the winter or the cloud skies. Based on the available remotely sensed data and in situ measurements, 16 clear days during the wheat growth period in 2012 were finally selected. Surface characteristics of the Yucheng station during the wheat growth period in 2012, i.e., f_{c} and crop height, are shown in Figure 11. f_{c} is calculated by weekly measured leaf area index (LAI) from a portable leaf area meter (LI3000) with the assumption of a random and spherical leaf angle distribution, i.e., f_{c} = 1 − exp(−0.5LAI). Meteorological variables were routinely measured at the heights of 2.89 m. Radiation data, including downwelling and upwelling shortwave and longwave radiations, were from a CNR1 radiometer installed at the height of 3.98 m. T_{s} is not measured at the surface, which is calculated by the measured downwelling and upwelling longwave radiation with a surface emissivity of 0.98 in this study. H and LE were observed from an eddy covariance (EC) system installed at the height of 2.68 m. G was measured by a single HFP01 soil heat flux plate at 2 cm below the surface. All data are at a 30min interval.
3.2. Remotely Sensed Data
Remotely sensed data used in this study include MYD021KM, MYD03, MYD05_L2, MYD11_L2, and MOD13A2/MYD13A2 products. MYD021KM, MYD03, MYD11_L2 and MYD05_L2 are used to estimate R_{n} at Aqua daytime and nighttime overpass times by the methods proposed by Tang et al. [49,50]. Because there is no incoming solar radiation at nighttime, R_{n} at Aqua nighttime overpass time equal to the net longwave radiation from Tang and Li’s method [50]. Both MOD13A2 and MYD13A2 NDVI products are jointly used to calculate f_{c} every eight days using the formula proposed by Carlson and Ripley [51], i.e.,
4. Results and Discussions
4.1. Daily EF Estimates with In situ Measurements as Inputs
When ΔT_{s}, ΔT_{a}, ΔR_{n}, and f_{c} required by Equation (15) are from measurements at the Yucheng station during the wheat growth period in 2012, daily EF estimates are shown in Figure 12a. Compared with the measured daily EF that is calculated by LE from EC and R_{n} from CNR1 radiometer (see open squares in Figure 12a), the serious discrepancies appear. Two main reasons can result in the discrepancies: one is from the lack of energybalance closure in ECbased measurements, and the other is from errors in f_{c} measurements. As shown in Figure 13, at the daily scale, the RMSE of energy balance closure from EC measurements is 17.0 W/m^{2}. When daily average R_{n} − G is under 100 W/m^{2}, daily average H + LE from EC measurements is generally higher than daily average R_{n} − G, whereas for those cases of daily average R_{n} − G greater than 100 W/m^{2}, ECmeasured H + LE is less than the values of R_{n} − G. The residual energy (RE) method and the Bowen ratio (BR) method are often used to correct the lack of energy balance closure from EC measurements. RE method is to assume that the imbalance energy is due to the underestimation of LE measurements, whereas BR method is to partition the imbalance energy into H and LE according to Bowen ratio [53]. After ECmeasured LE was corrected by RE and BR methods, the daily EF estimates can be improved to a certain extent (see black squares and cross symbols in Figure 12a) with an R^{2} of ∼0.6 and an RMSE of ∼0.24. From Figure 11, it can be observed that f_{c} from MODIS is different from measured values, and is underestimated for most of days. However, f_{c} from LAI measured by LI3000 at a point scale was not able to reflect the vegetation cover at large scales. Therefore, f_{c} from MODIS NDVI products instead of groundbased measurements was also applied to Equation (15). Results in Figure 12b show that daily EF estimates from in situ measurements but f_{c} from MODIS data as inputs are closer to the EF measurements corrected by RE method with an R^{2} of 0.857, an RMSE of 0.119, and a mean bias of 0.049. The results are comparable with the accuracy of Equation (15) driven by the ALEXsimulated data shown in Figure 10b. Therefore, if input variables are accurate, Equation (15) with the differences of T_{a}, T_{s} and R_{n} between 1:30 P.M. and 1:30 A.M. as inputs should give reasonable daily EF estimates.
To understand the impact of error in input variables of Equation (15) on daily EF estimates, a sensitivity and error analysis based on in situ measurements but f_{c} from MODIS data were performed. Each input variable varies under a given set of reference values, a 1 K step and the upper and lower limits of ±5 K for ΔT_{s} and ΔT_{a}, a 50 W/m^{2} step and the ±250 W/m^{2} range for ΔR_{n}, and a 0.1 step and the ±0.5 range for f_{c}. The range of variations and the averaged variations in daily EF estimates at each step of ΔT_{s}, ΔT_{a}, ΔR_{n}, and f_{c} are displayed in Figure 14a–d, respectively. In general, EF in Equation (15) is negatively correlated to ΔT_{s} and f_{c}, and positively correlated to ΔT_{a} and ΔR_{n}. The same error in ΔT_{s} and ΔT_{a} leads to the same error for daily EF estimates. 2 K variations in ΔT_{s} and ΔT_{a} lead to an averaged variation <0.1 in daily EF estimates. Error in EF caused by the underestimation of ΔR_{n} is generally higher than the error caused by the overestimation of ΔR_{n}, but the underestimation of 100 W/m^{2} in ΔR_{n} leads to the averaged error in EF <0.1. In addition, the variation of 0.2 in f_{c} also leads to the variation of ∼0.1 in EF. The range of variations in daily EF estimation caused by the error in inputs variables indicates that daily EF estimates from Equation (15) may be more sensitive to f_{c} and the underestimation of ΔR_{n} than other inputs.
4.2. Application to Satellite Data
A flowchart showing procedures for daily EF estimation from MODIS/Aqua data is presented in Figure 15. Input variables ΔT_{s}, ΔR_{n}, and f_{c} required by Equation (15) are all obtained from MODIS/Aqua products, whereas ΔT_{a} is from in situ measurements. Although MYD07_L2 products can provide atmosphere profile data, the retrieved atmosphere temperature at the bottom of the atmosphere was not used in this study because of different spatial resolutions between the MYD07_L2 product (5 km) and other MODIS/Aqua products (1 km) used in this study and less available data at the Yucheng station.
For the selected 16 clear days at the Yucheng station during wheat growth period in 2012, daily EF calculated by Equation (15) from MODIS/Aqua data is shown in Figure 16. Compared with the daily EF from EC measurements and the values corrected by the RE or BR method, RMSE is about 0.24. For the majority of cases, the results estimated by Equation (15) are generally consistent with the measurements, but several large discrepancies deteriorate the overall results. One of the reasons of the discrepancies between daily EF estimates from remotely sensed data and the measurements may be ascribed to the difference in spatial scale between the MODIS observation and EC measurements. The nominal spatial resolution of MODIS senor at the thermal infrared bands is 1 km, whereas the footprint of EC at the height of 2.68 m would be far less than MODIS observation scale [3,54]. Because of the heterogeneous nature of land surface, surface heat fluxes vary at spatial scales [20]. Therefore, the ECmeasured surface fluxes may not represent the values at MODIS pixel scale. Although it is not appropriate to compare EF estimates from MODIS data with EC measurements because of different scales of observation, only the result shown in Figure 16 is given because of the lack of surface flux observation at MODIS pixel scale.
In addition, retrieval errors in T_{s} and R_{n} also lead to the discrepancies between daily EF estimates from Equation (15) and EC measurements. T_{s} is directly from the MYD11_L2 product, which is produced daily at 5minute increments using the generalized splitwindow algorithm [55]. Compared with the measured T_{s} that is calculated by CNR1 measurements, MYD11_L2 products underestimated T_{s} by 2.6 K at Aqua daytime overpass time and by 0.4 K at nighttime overpass time. As a result, the underestimation in ΔT_{s} is reduced to 2.2 K with an R^{2} of 0.840 and an RMSE of 4.2 K (see Figure 17a). Though both R_{n} at Aqua daytime and nighttime overpass times estimated by Tang’s method are all higher than the measured R_{n} (see Figure 17b), the quantity of overestimation of 123.7 W/m^{2} in ΔR_{n} is still large because of the obvious overestimation of R_{n} at Aqua daytime overpass time. The quantity of R_{n} at nighttime is negative, so the value of ΔR_{n} is greater than R_{n} at daytime.
4.3. Comparison with SEBSEstimated EF
SEBS is a representative onesource energy balance model of estimating surface heat fluxes from remotely sensed data. In the SEBS model, EF is formulated on the basis of the energy balance at limiting cases [9]. Because the limiting cases are calculated by an equation similar to the PenmanMonteith combination equation rather than using the spatial information from remotely sensed data like triangletype methods, the SEBS model cannot be restricted by the research domain in theory. The SEBS model estimates EF by the onetime observed remotely sensed data. Because of the relatively invariant EF during diurnal cycle, the instantaneous EF during daytime is often considered as the daily EF to estimate daily ET. By using MODIS products at Aqua daytime overpass time, daily EF at the Yucheng station can also be estimated by the SEBS model. From Figure 18a, it can be observed that the daily EF estimated by Equation (15) with remotely sensed data as inputs is well correlated with the SEBSestimated value with an R^{2} of 0.838 and an RMSE of 0.124. However, when the difference between T_{s} and T_{a} at Aqua daytime overpasses, the time required by the SEBS model is less than 0, SEBSestimated EF is greater than 1 and is also greater than EF from Equation (15). When SEBSestimated EF is compared with the values from in situ measurements, the results shown in Figure 18b are inferior to the results from Equation (15) as shown in Figure 16 (see Table 4), and more dispersed data appear. This indicates that the EF estimates from instantaneous inputs are more sensitive to the retrieval error of remotely sensed data at onetime observation, whereas the application of the temporal variation in surface variables can reduce the uncertainties caused by retrieval errors in remotely sensed data. In addition, although daily EF can be approximated by instantaneous EF during daytime, daily EF is essentially different from instantaneous values. Instantaneous EF instead of daily EF would result in some errors, whereas Equation (15) can directly determine daily EF. Equation (15) needs fewer input variables than the SEBS model: only ΔT_{s}, ΔT_{a}, ΔR_{n}, and f_{c} are required. These input variables can be easily obtained from remote sensing. Therefore, Equation (15) has the potential to estimate surface ET at the regional scale.
5. Conclusion
On the basis of surface energy balance and the assumption of selfpreservation EF (evaporative fraction) during daytime, this study developed a new parameterization scheme for deriving daily EF from temporal variations of T_{s} (surface temperature), T_{a} (air temperature), and R_{n} (net radiation). Among various input schemes as to temporal variations, the differences of T_{s}, T_{a}, and R_{n} between 1:30 P.M. and 1:30 A.M. (around at Aqua overpass times), i.e., ΔT_{s}, ΔT_{a}, and ΔR_{n}, can produce better estimates for daily EF with an R^{2} (coefficient of determination) of 0.719 and an RMSE (root mean square error) of 0.130 with respect to ALEX (atmosphereland exchange model)based estimates. When the input scheme in combination with input variables from groundbased measurements is used to estimate daily EF at the Yucheng station during the wheat growth period in 2012, the results agreed well with the daily EF corrected by RE (residual energy) method with an R^{2} of 0.857 and an RMSE of 0.119. Sensitivity and error analysis show that variations in input variables of 2 K in ΔT_{s} and ΔT_{a}, 100 W/m^{2} in ΔR_{n}, and 0.2 in f_{c} (fractional vegetation cover) could lead to errors <0.1 for daily EF estimates. Although daily EF estimates in combination with remotely sensed inputs are not in good agreement with the measured measurements, they are correlated with results from the SEBS (surface energy balance system) model, and are slightly superior to SEBSestimated EF with remotely sensed instantaneous inputs.
The developed EF parameterization scheme in this study required ΔT_{s}, ΔT_{a}, ΔR_{n}, and f_{c} as inputs. The input requirements can be satisfied by remotely sensed data from the MODIS sensor or geostationary meteorological satellites. ΔT_{s}, ΔT_{a} and ΔR_{n} rather than absolute T_{s}, T_{a} and R_{n} can diminish uncertainties in surface flux estimates caused by errors in remotely sensed data to a certain degree. Another advantage of the proposed method is that it directly determines daily EF without the need for the calculation of various resistances which requires many surface parameters. Fewer input requirements will enable the developed approach to estimate surface evapotranspiration over datasparse region or at regional scale because some surface parameters are not easily measured at a large spatial scale. In addition, the accuracy of daily EF estimates is also independent of the errors in daily net radiation estimates.
Because the parameterization scheme for daily EF estimation is developed based on the assumption of selfpreservation EF during daytime, when the assumption cannot be met, it may not be applicable to estimating daily EF. Therefore, the method may be more appropriate to estimate surface fluxes under the conditions of clear skies, humid air, and strong solar radiation because of relative invariant EF at such conditions. The results from ALEXsimulated data and the measurements at the Yucheng station can demonstrate that the coefficients in the proposed parameterization scheme are not sitespecific and do not strongly depend on the atmospheric conditions. However, this is only a preliminary conclusion. More validation across various land surface types needs to be performed in the future to further evaluate the robustness of the proposed EF parameterization scheme.
Acknowledgments
This work was partly supported by the National Natural Science Foundation of China under Grant 41201366 and 41101332 and by the China Postdoctoral Science Foundation funded project under Grant 07Z7602MZ1. Jing Lu is financially supported by the China Scholarship Council for her stay in ICube, Strasbourg, France.
Conflict of Interest
The authors declare no conflict of interest.
References
 Vinukollu, R.K.V.R.K.; Wood, E.F.; Ferguson, C.R.; Fisher, J.B. Global estimates of evapotranspiration for climate studies using multisensor remote sensing data: Evaluation of three processbased approaches. Remote Sens. Environ 2011, 115, 801–823. [Google Scholar]
 Anderson, M.C.; Allen, R.G.; Morse, A.; Kustas, W.P. Use of Landsat thermal imagery in monitoring evapotranspiration and managing water resources. Remote Sens. Environ 2012, 122, 50–65. [Google Scholar]
 Tang, R.L.; Li, Z.L.; Jia, Y.; Li, C.; Sun, X.; Kustas, W.P.; Anderson, M.C. An intercomparison of three remote sensingbased energy balance models using Large Aperture Scintillometer measurements over a wheatcorn production region. Remote Sens. Environ 2011, 115, 3187–3202. [Google Scholar]
 Teixeira, A.H. de C.; Bastiaanssen, W.G.M.; Ahmad, M.D.; Bos, M.G. Determining regional actual evapotranspiration of irrigated crops and natural vegetation in the São Francisco River Basin (Brazil) using remote sensing and penmanmonteith equation. Remote Sens 2010, 2, 1287–1319. [Google Scholar]
 Ruhoff, A.L.; Paz, A.R.; Collischonn, W.; Aragao, L.E.O.C.; Rocha, H.R.; Malhi, Y.S. A MODISbased energy balance to estimate evapotranspiration for clearsky days in Brazilian Tropical Savannas. Remote Sens 2012, 4, 703–725. [Google Scholar]
 Johnson, L.F.; Trout, T.J. Satellite NDVI assisted monitoring of vegetable crop evapotranspiration in California’s San Joaquin Valley. Remote Sens 2012, 4, 439–455. [Google Scholar]
 Long, D.; Singh, V.P. A twosource trapezoid model for evapotranspiration (TTME) from satellite imagery. Remote Sens. Environ 2012, 121, 370–388. [Google Scholar]
 Bastiaanssen, W.; Menenti, M.; Feddes, R.; Holtslag, A. A remote sensing surface energy balance algorithm for land (SEBAL). 1. Formulation. J. Hydrol 1998, 212, 198–212. [Google Scholar]
 Su, Z. The surface energy balance system (SEBS) for estimation of turbulent heat fluxes. Hydrol. Earth Syst. Sci 2002, 6, 85–99. [Google Scholar]
 Norman, J.M.; Kustas, W.P.; Humes, K.S. Source approach for estimating soil and vegetation energy fluxes in observations of directional radiometric surfacetemperature. Agric. For. Meteorol 1995, 77, 263–293. [Google Scholar]
 Li, Z.L.; Tang, B.H.; Wu, H.; Ren, H.; Yan, G.; Wan, Z. Satellitederived land surface temperature: Current status and perspectives. Remote Sens. Environ 2013, 131, 14–37. [Google Scholar]
 Wu, H.; Li, Z.L. Scale issues in remote sensing: A review on analysis, processing and modeling. Sensors 2009, 9, 1768–1793. [Google Scholar]
 Li, Z.L.; Wu, H.; Wang, N.; Qiu, S.; Sobrino, J.A.; Wan, Z.; Tang, B.H.; Yan, G. Land surface emissivity retrieval from satellite data. Int. J. Remote Sens 2013, 34, 3084–3127. [Google Scholar]
 Li, Z.L.; Tang, R.L.; Wan, Z.; Bi, Y.; Zhou, C.; Tang, B.H.; Yan, G.; Zhang, X. A review of current methodologies for regional evapotranspiration estimation from remotely sensed data. Sensors 2009, 9, 3801–3853. [Google Scholar]
 Kalma, J.D.; McVicar, T.R.; McCabe, M.F. Estimating land surface evaporation: A review of methods using remotely sensed surface temperature data. Surv. Geophys 2008, 29, 421–469. [Google Scholar]
 Wang, K.C.; Dickinson, R.E. A review of global terrestrial evapotranspiration: Observation, modeling, climatology, and climatic variability. Rev. Geophys. 2012. [Google Scholar] [CrossRef]
 Crago, R.D. Conservation and variability of the evaporative fraction during the daytime. J. Hydrol 1996, 180, 173–194. [Google Scholar]
 Lhomme, J.P.; Elguero, E. Examination of evaporative fraction diurnal behaviour using a soilvegetation model coupled with a mixedlayer model. Hydrol. Earth Syst. Sci 1999, 3, 259–270. [Google Scholar]
 Gentine, P.; Entekhabi, D.; Polcher, J. The diurnal behavior of evaporative fraction in the soilvegetationatmospheric boundary layer continuum. J. Hydrometeorol 2011, 12, 1530–1546. [Google Scholar]
 Lu, J.; Li, Z.L.; Tang, R.L.; Tang, B.H.; Wu, H.; Yang, F.; Labed, J.; Zhou, G. Evaluating the SEBSestimated evaporative fraction from MODIS data for a complex underlying surface. Hydrol. Process. 2012. [Google Scholar] [CrossRef]
 Nichols, W.E.; Cuenca, R.H. Evaluation of the evaporative fraction for parameterization of the surface energy balance. Water Resour. Res 1993, 29, 3681–3690. [Google Scholar]
 Colaizzi, P.; Evett, S.; Howell, T.; Tolk, J. Comparison of five models to scale daily evapotranspiration from onetimeofday measurements. Trans. ASAE 2006, 49, 1409–1417. [Google Scholar]
 Sugita, M.; Brutsaert, W. Daily evaporation over a region from lower boundary layer profiles measured with radiosondes. Water Resour. Res 1991, 27, 747–752. [Google Scholar]
 Tang, R.L.; Li, Z.L.; Sun, X. Temporal upscaling of instantaneous evapotranspiration: An intercomparison of fourmethods using eddy covariance measurements and MODIS data. Remote Sens. Environ. 2013. [Google Scholar] [CrossRef]
 Cammalleri, C.; Anderson, M.; Kustas, W. Upscaling of evapotranspiration fluxes from instantaneous to daytime scales for thermal remote sensing applications. Hydrol. Earth Syst. Sci 2013, 10, 7325–7350. [Google Scholar]
 Jiang, L.; Islam, S. A methodology for estimation of surface evapotranspiration over large areas using remote sensing observations. Geophys. Res. Lett 1999, 26, 2773–2776. [Google Scholar]
 Roerink, G.; Su, Z.; Menenti, M. SSEBI: A simple remote sensing algorithm to estimate the surface energy balance. Phys. Chem. Earth Part. B Hydrol. Oceans Atmos 2000, 25, 147–157. [Google Scholar]
 Tang, R.L; Li, Z.L.; Tang, B.H. An application of the TsVI triangle method with enhanced edges determination for evapotranspiration estimation from MODIS data in arid and semiarid regions: Implementation and validation. Remote Sens. Environ 2010, 114, 540–551. [Google Scholar]
 Anderson, M.C.; Norman, J.M.; Diak, G.R.; Kustas, W.P.; Mecikalski, J.R. A twosource timeintegrated model for estimating surface fluxes using thermal infrared remote sensing. Remote Sens. Environ 1997, 60, 195–216. [Google Scholar]
 Norman, J.M.; Kustas, W.P.; Prueger, J.H.; Diak, G.R. Surface flux estimation using radiometric temperature: A dual temperaturedifference method to minimize measurement errors. Water Resour. Res 2000, 36, 2263–2274. [Google Scholar]
 Wang, K.C.; Li, Z.Q.; Cribb, M. Estimation of evaporative fraction from a combination of day and night land surface temperatures and NDVI: A new method to determine the PriestleyTaylor parameter. Remote Sens. Environ 2006, 102, 293–305. [Google Scholar]
 Stisen, S.; Sandholt, I.; Norgaard, A.; Fensholt, R.; Jensen, K.H. Combining the triangle method with thermal inertia to estimate regional evapotranspiration—Applied to MSGSEVIRI data in the Senegal River basin. Remote Sens. Environ 2008, 112, 1242–1255. [Google Scholar]
 Shu, Y.Q.; Stisen, S.; Jensen, K.H.; Sandholt, I. Estimation of regional evapotranspiration over the North China Plain using geostationary satellite data. Int. J. Appl. Earth Obs 2011, 13, 192–206. [Google Scholar]
 Long, D.; Singh, V.P.; Scanlon, B.R. Deriving theoretical boundaries to address scale dependencies of triangle models for evapotranspiration estimation. J. Geophys. Res 2012, 117, D05113. [Google Scholar]
 Long, D.; Singh, V.P. Assessing the impact of endmember selection on the accuracy of satellitebased spatial variability models for actual evapotranspiration estimation. Water Resour. Res 2013, 49, 2601–2618. [Google Scholar]
 Anderson, M.C.; Norman, J.M.; Meyers, T.P.; Diak, G.R. An analytical model for estimating canopy transpiration and carbon assimilation fluxes based on canopy lightuse efficiency. Agric. For. Meteorol 2000, 101, 265–289. [Google Scholar]
 Brutsaert, W. Evaporation into the Atmosphere: Theory, History, and Applications; D. Reidel: Dordrecht, The Netherlands, 1982. [Google Scholar]
 Kalma, J.; Jupp, D. Estimating evaporation from pasture using infrared thermometry: Evaluation of a onelayer resistance model. Agric. For. Meteorol 1990, 51, 223–246. [Google Scholar]
 Chehbouni, A.; Lo Seen, D.; Njoku, E.; Monteny, B. Examination of the difference between radiative and aerodynamic surface temperatures over sparsely vegetated surfaces. Remote Sens. Environ 1996, 58, 177–186. [Google Scholar]
 Lhomme, J.; Chehbouni, A.; Monteny, B. Sensible heat fluxradiometric surface temperature relationship over sparse vegetation: Parameterizing B1. Bound.Lay. Meteorol 2000, 97, 431–457. [Google Scholar]
 Sun, J.; Mahrt, L. Determination of surface fluxes from the surface radiative temperature. J. Atmos. Sci 1995, 52, 1096–1106. [Google Scholar]
 Carlson, T.N.; Buffum, M.J. On estimating total daily evapotranspiration from remote surface temperature measurements. Remote Sens. Environ 1989, 29, 197–207. [Google Scholar]
 Lagouarde, J.P.; McAneney, K. Daily sensible heat flux estimation from a single measurement of surface temperature and maximum air temperature. Bound.Lay. Meteorol 1992, 59, 341–362. [Google Scholar]
 Carlson, T.N.; Capehart, W.J.; Gillies, R.R. A new look at the simplified method for remote sensing of daily evapotranspiration. Remote Sens. Environ 1995, 54, 161–167. [Google Scholar]
 Brutsaert, W.; Sugita, M. Application of selfpreservation in the diurnal evolution of the surface energy budget to determine daily evaporation. J. Geophys. Res 1992, 97, 18377–18382. [Google Scholar]
 Daughtry, C.; Kustas, W.; Moran, M.; Pinter, P.; Jackson, R.; Brown, P.; Nichols, W.; Gay, L. Spectral estimates of net radiation and soil heat flux. Remote Sens. Environ 1990, 32, 111–124. [Google Scholar]
 Choudhury, B.J.; Idso, S.B.; Reginato, R.J. Analysis of an empirical model for soil heat flux under a growing wheat crop for estimating evaporation by an infraredtemperature based energy balance equation. Agric. For. Meteorol 1987, 39, 283–297. [Google Scholar]
 Van de Griend, A.A.; Camillo, P.J.; Gurney, R.J. Discrimination of soil physical parameters, thermal inertia, and soil moisture from diurnal surface temperature fluctuations. Water Resour. Res 1985, 21, 997–1009. [Google Scholar]
 Tang, B.H.; Li, Z.L.; Zhang, R. A direct method for estimating net surface shortwave radiation from MODIS data. Remote Sens. Environ 2006, 103, 115–126. [Google Scholar]
 Tang, B.H.; Li, Z.L. Estimation of instantaneous net surface longwave radiation from MODIS cloudfree data. Remote Sens. Environ 2008, 112, 3482–3492. [Google Scholar]
 Carlson, T.N.; Ripley, D.A. On the relation between NDVI, fractional vegetation cover, and leaf area index. Remote Sens. Environ 1997, 62, 241–252. [Google Scholar]
 Prihodko, L.; Goward, S.N. Estimation of air temperature from remotely sensed surface observations. Remote Sens. Environ 1997, 60, 335–346. [Google Scholar]
 Twine, T.; Kustas, W.; Norman, J.; Cook, D.; Houser, P.; Meyers, T.; Prueger, J.; Starks, P.; Wesely, M. Correcting eddycovariance flux underestimates over a grassland. Agric. For. Meteorol 2000, 103, 279–300. [Google Scholar]
 McCabe, M.F.; Wood, E.F. Scale influences on the remote estimation of evapotranspiration using multiple satellite sensors. Remote Sens. Environ 2006, 105, 271–285. [Google Scholar]
 Wan, Z.; Dozier, J. A generalized splitwindow algorithm for retrieving landsurface temperature from space. IEEE T. Geosci. Remot 1996, 34, 892–905. [Google Scholar]
 Campbell, G.S.; Norman, J.M. Introduction to Environmental Biophysics; Springer Verlag: New York, NY, USA, 1998. [Google Scholar]
Appendix
Simulated data used in this study are from an atmosphereland exchange (ALEX) model. ALEX is a twosource dynamic model of heat, water and carbon exchange between a vegetated surface and the atmosphere. Details of the model can be found in Anderson et al. [36]. Atmosphere forcing, soil properties and vegetation characteristics are required as inputs of the ALEX model. Forcing data from four different sites are displayed in Figure A1. Criteria for selecting forcing data include that: (1) cloudfree day, (2) daily average incoming solar radiation greater than 200 W/m^{2}, and (3) relative humidity of air not less than 20%. These criteria are to ensure that the implicit assumption of selfpreservation evaporative fraction (EF) during daytime in the daily EF parameterization scheme can be satisfied. The main input quantities required by the ALEX model and the values used in model simulation are listed in Table A1.
(a)  

Quantities  Units  Values  
Site  Yucheng  
Longitude  116.5703  
Latitude  36.8291  
Year  2010  
Date  26 Mar, 24 Apr, 29 May, 28 June, 21 July  17 Aug, 11 Sep, 4 Oct.  
Measure height  m  2.93  4.2  
Vegetation type  C_{3} grass  corn  
Vegetation height  m  0–0.6  0–2.4  
Leaf area index  m^{2}/m^{2}  0–10  
Rooting depth  m  0.5  
Soil texture  loam  
sand  0.42  
silt  0.4  
clay  0.18  
Bulk density  g/cm^{3}  1.5  
Moisture release parameter  4.5  
Air entry potential  J/kg  −1.1  
Saturated hydraulic conductivity  K·g·s/m^{3}  3.7 × 10^{−4}  
soil water content (0–2 m)  m^{3}/m^{3}  0.09–0.21, 0.43 
(b)  

Quantities  Units  Values  
Site  Goodwin  Cottonwood  Audubon  
Longitude  −89.7735  −101.8466  −110.5092  
Latitude  34.2547  43.95  31.5907  
Year  2006  2008  2006  
Date  9 Feb, 14 Mar, 12 Apr, 12 May, 15 June, 17 July, 17 Aug, 13 Sep, 7 Oct  10 Mar, 14 Apr, 17 May, 14 June, 13 July, 17 Aug, 14 Sep, 7 Oct  19 Feb, 21 Mar, 15 Apr, 4 May, 10 June, 20 July, 27 Aug, 16 Sep, 20 Oct, 2 Nov  
Measure height  m  4  5  4 
Vegetation type  C_{3} grass  soybean  desert C_{3}type shrubs  
Vegetation height  m  0–1  0–1  0–0.6 
Leaf area index  m^{2}/m^{2}  0–10  0–10  0–10 
Rooting depth  m  1  2  0.5 
Soil texture  clay loam  clay loam  silt loam  
sand  0.32  0.32  0.2  
silt  0.34  0.34  0.65  
clay  0.34  0.34  0.15  
Bulk density  g/cm^{3}  1.5  1.5  1.5 
Moisture release parameter  5.2  5.2  4.7  
Air entry potential  J/kg  −2.6  −2.6  −2.1 
Saturated hydraulic conductivity  K·g·s/m^{3}  6.4 × 10^{−4}  6.4 × 10^{−4}  1.9 × 10^{−4} 
soil water content (0–2 m)  m^{3}/m^{3}  0.13–0.25, 0.43  0.13–0.25, 0.43  0.11–0.23, 0.43 
Note: The values of inputs related to the hydraulic properties required by the ALEX model are from Table 9.1 in the book edited by Campbell and Norman [56].
For each atmosphere, five different soil water contents from wilting point to field capacity and the approximate saturated soil water of 0.43 m^{3}/m^{3} are assigned in ALEX simulation. The leaf area index (LAI) varies from 0 to 10 m^{2}/m^{2}, which corresponds to fractional vegetation cover (f_{c}) from 0 to 1 with an interval of 0.1. Vegetation height in simulation linearly varies with f_{c}. Because the case of high f_{c} with wilting soil water rarely occurs in reality, the cases of f_{c} greater than 0.5 at wilting soil water content were removed. As a result, 61 cases for each atmosphere are finally formed.
Data  Inputs  Linear Relationships  R^{2}  RMSE 

ALEXsimulated data  Aqua daytimeAqua nighttime  Y = 0.6619X − 0.0003  0.808  0.0047 
Terra daytimeTerra nighttime  Y = 1.3435X − 0.0046  0.874  0.0038  
Terra daytimeAqua nighttime  Y = 1.0790X − 0.0017  0.882  0.0037  
Aqua daytimeTerra nighttime  Y = 0.7858X − 0.0023  0.821  0.0045  
In situ measurements  Aqua daytimeAqua nighttime  Y = 0.6066X − 0.0021  0.891  0.0029 
Terra daytimeTerra nighttime  Y = 0.7854X − 0.0001  0.827  0.0036  
Terra daytimeAqua nighttime  Y = 0.8130X − 0.0006  0.853  0.0033  
Aqua daytimeTerra nighttime  Y = 0.5996X − 0.0017  0.894  0.0028 
Sites  Inputs  R^{2}  RMSE  BIAS 

Yucheng  Change rate during the morning  0.817  0.107  0.020 
Aqua daytimeAqua nighttime  0.878  0.083  −0.002  
Terra daytimeTerra nighttime  0.810  0.103  −0.001  
Terra daytimeAqua nighttime  0.869  0.086  0.007  
Aqua daytimeTerra nighttime  0.835  0.098  −0.009  
Goodwind  Change rate during the morning  0.806  0.106  0.023 
Aqua daytimeAqua nighttime  0.847  0.093  0.019  
Terra daytimeTerra nighttime  0.787  0.129  0.045  
Terra daytimeAqua nighttime  0.826  0.106  0.029  
Aqua daytimeTerra nighttime  0.840  0.098  0.023  
Cottonwood  Change rate during the morning  0.787  0.124  0.029 
Aqua daytimeAqua nighttime  0.877  0.087  0.008  
Terra daytimeTerra nighttime  0.839  0.100  0.009  
Terra daytimeAqua nighttime  0.850  0.111  0.033  
Aqua daytimeTerra nighttime  0.860  0.093  −0.007  
Audubon  Change rate during the morning  0.827  0.101  0.001 
Aqua daytimeAqua nighttime  0.858  0.092  −0.005  
Terra daytimeTerra nighttime  0.737  0.137  0.034  
Terra daytimeAqua nighttime  0.837  0.104  0.019  
Aqua daytimeTerra nighttime  0.823  0.103  −0.005 
Inputs  Equations 

Change rate during the morning 
$${\mathit{EF}}_{\text{daily}}=1(2.06\times {{f}_{\text{c}}}^{2}+38.42\times {f}_{\text{c}}+15.74)\frac{{{T}_{\text{s}}}^{\prime}(t){{T}_{\text{a}}}^{\prime}(t)}{{{R}_{\text{n}}}^{\prime}(t)}$$

Aqua daytimeAqua nighttime 
$${\mathit{EF}}_{\text{daily}}=1(14.74\times {{f}_{\text{c}}}^{2}+40.11\times {f}_{\text{c}}+14.57)\frac{\mathrm{\Delta}{T}_{\text{s}}\mathrm{\Delta}{T}_{\text{a}}}{\mathrm{\Delta}{R}_{\text{n}}}$$

Terra daytimeTerra nighttime 
$${\mathit{EF}}_{\text{daily}}=1(87.38\times {{f}_{\text{c}}}^{2}+83.11\times {f}_{\text{c}}+27.19)\frac{\mathrm{\Delta}{T}_{\text{s}}\mathrm{\Delta}{T}_{\text{a}}}{\mathrm{\Delta}{R}_{\text{n}}}$$

Terra daytimeAqua nighttime 
$${\mathit{EF}}_{\text{daily}}=1(57.02\times {{f}_{c}}^{2}+71.17\times {f}_{c}+21.58)\frac{\mathrm{\Delta}{T}_{\text{s}}\mathrm{\Delta}{T}_{\text{a}}}{\mathrm{\Delta}{R}_{\text{n}}}$$

Aqua daytimeTerra nighttime 
$${\mathit{EF}}_{\text{daily}}=1(37.35\times {{f}_{c}}^{2}+49.30\times {f}_{\text{c}}+17.45)\frac{\mathrm{\Delta}{T}_{\text{s}}\mathrm{\Delta}{T}_{\text{a}}}{\mathrm{\Delta}{R}_{\text{n}}}$$

EC  RE  BR  

R^{2}  RMSE  BIAS  R^{2}  RMSE  BIAS  R^{2}  RMSE  BIAS  
New method  0.139  0.215  0.051  0.394  0.242  0.093  0.382  0.210  0.066 
SEBS  0.125  0.268  0.053  0.292  0.275  0.096  0.296  0.249  0.069 
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