Using Artificial Neural Network Algorithm and Remote Sensing Vegetation Index Improves the Accuracy of the Penman-Monteith Equation to Estimate Cropland Evaptranspiration  
[PERSON]  
1 Centre for Remote Sensing and Digital Earth, College of Computer Science and Technology, Qingdao University, Qingdao 266071, China; [EMAIL_ADDRESS] (Y.L. ); [EMAIL_ADDRESS] (S.Z. ); [EMAIL_ADDRESS] (L.T)  
[PERSON]  
1 Centre for Remote Sensing and Digital Earth, College of Computer Science and Technology, Qingdao University, Qingdao 266071, China; [EMAIL_ADDRESS] (Y.L. ); [EMAIL_ADDRESS] (S.Z. ); [EMAIL_ADDRESS] (L.T)  
[PERSON]  
2 Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China; [EMAIL_ADDRESS] [EMAIL_ADDRESS] [EMAIL_ADDRESS] [EMAIL_ADDRESS] [EMAIL_ADDRESS] [EMAIL_ADDRESS] [EMAIL_ADDRESS] [EMAIL_ADDRESS] [EMAIL_ADDRESS] [EMAIL_ADDRESS] 22222221@gdu  
surface energy balance model and PM-based approach using remote sensing (RS)-derived leaf area index (LAI), to estimate _Gs_ at two Australian flux stations, and the PM-based method proved better. [PERSON] et al. [6] found that the surface conductivity model of [PERSON] et al. [5] was unreliable when used to estimate the global ET of 19 AmeriFlux sites due to the oversimplified estimates of surface conductance. Therefore, the canopy conductance and ET algorithms based on the PM method of [PERSON] et al. [5] were improved by using the RS and global meteorological data. The algorithm of [PERSON] et al. [6] considered the surface energy partitioning process and the environmental constraints of ET, but the performance of [PERSON] et al. [6] still remains uncertain. [PERSON] et al. [7] further improved the global terrestrial ET algorithm and showed the improved algorithm performed better compared to the original. Based on [PERSON] et al. [5] and [PERSON] et al. [6], [PERSON] et al. [8] developed a biophysical model to estimate _Gs_ and introduced a simpler soil evaporation algorithm than the MOD16 algorithm [6] to calculate daily average evaporation. The results showed that the PM equation, incorporated with the RS leaf area index, could more reliably estimate the evaporation rate. However, the performances of the model degraded if a fixed value of maximum stomatal conductance (g\({}_{\text{scx}}\)) was used to estimate the surface conductance across a wide range of vegetation categories [8]. [PERSON] et al. [9] further developed the _Gs_ formula and calculated the land surface ET at a spatial resolution of 0.05 \({}^{\circ}\) using the PM equation. [PERSON] et al. [10] reversed the PM equation to obtain the _Gs_ of the plant canopy, and then the estimated _Gs_ was used to retrieve actual ET using the parameterized PM equation. [PERSON] et al. [11] also applied a semi-empirical model dependent on photosynthesis [12] to estimate canopy _Gs_. Because the method of [PERSON] et al. [12] restricted the applicability of the model, [PERSON] et al. [13] used a simple biophysical model to calculate _Gs_, and then the computed _Gs_ was used to calculated global ET based on the PM equation. [PERSON] et al. [14] estimated _Gs_ by integrating the radiometric surface temperature into a combined structure of the PM model and the Shuttleworth-Wallace model and used the simplified surface energy balance model to estimate ET. The method of [PERSON] et al. [13] used the leaf area index and surface meteorological data, while [PERSON] et al. [14] did not use any leaf-scale empirical parameter model to determine _Gs_ and ET. However, the method of [PERSON] et al. [14] had a tendency to overestimate _Gs_. For areas with limited data, the method of [PERSON] et al. [14] was considered to be further improved. Therefore, [PERSON] et al. [15] used RS and reanalysis data to develop an automatic multi-model to estimate regional ET in important areas. In order to reduce the uncertainties in ET estimation due to the difficulty in estimating _Gs_, semi-empirical models that use machine learning (ML) to more accurately calculate the _Gs_ in the PM equation were proposed [16; 17; 18].