# A Review of Machine Learning Applications in Land Surface Modeling
## 4 Major Applications of Machine Learning in Land Surface Modeling  
### Estimation of Evapotranspiration  
Evapotranspiration (ET) is perhaps the most discussed variable in the land surface modeling since it is a major part of both water and energy (in the form of LH) balance. It is also a critical factor in the carbon cycle, acting as a trade-off between photosynthesis and transpiration. Since direct measurement of global terrestrial ET is implausible, one of the central uses of LSMs is providing ET estimates using other comprehensively measured hydrometeorological variables. Some noteworthy applications regarding ET estimation are listed below.  
[PERSON] et al. [42] developed an ANN approach to estimate monthly LH, SH, and gross primary productivity (GPP) at a global scale for 8 years at 1\({}^{\circ}\)\(\times\) 1\({}^{\circ}\) resolution using remotely sensed solar-induced fluorescence (SIF) measurements in addition to conventional precipitation, temperature, soil moisture, snow cover and net radiation (Rn) as inputs. When compared to eddy covariance measurements from FLUXNET, their method 'WECANN' outperformed traditional surface fluxes products provided by Global Land Evaporation Amsterdam Model (GLEAM), Moderate Resolution Imaging Spectroradiometer (MODIS) and European Centre for Medium-Range Weather Forecasts (ECMWF). The ML retrievals also demonstrated capability to represent the extents of several extreme drought and heat events. They were also able to analyze the effects of extreme climatic events on surface turbulent fluxes and GPP. Prior to this study, neural networks were primarily applied to satellite observations to estimate LH [43]. Some studies also applied generalized neural networks and artificial intelligence models at a local-regional scale [44; 45; 46; 47]. However, these techniques, when applied to the global scale, often failed to predict extremes and perform outside their calibration range [48].  
A major limitation of traditional ML methods in Earth Science, as pointed out by [PERSON] et al. [49], was the issue of not conserving the surface energy budget, leading to unrealistic predictions of various surface fluxes causing problems in implementation with a coupled atmospheric model. Physics-based models, although complicated, tend to have superior interpretability [50]. To overcome this, [49] developed the first 'physics-constrained' ML model for ET estimation, which applied ML techniques while keeping the energy conservation equations valid, to provide a global ET estimate. Their hybrid model was able to learn the nonlinear relations from the data while obeying the physical laws. The study was significantly robust as they used 82 eddy covariance sites from FLUXNET with nine different PFTs and implemented a feedforward ANN escorted by that. This kind of modeling can enhance the capability to simulate ET during extreme climatic conditions like heatwaves and droughts.  
Another study, [PERSON] et al. [51], recently attempted to estimate global terrestrial ET (GTET) using two machine learning algorithms, RF, and Model Tree Ensemble and predicted its annual mean, interannual variability, and trends. They concluded that the utilization of satellite retrievals and deep-learning methods, and model-data fusion advances the predictive understanding of GTET. Critically, the ML methodology provided similar results to remote sensing-based products indicating a significant increasing trend in GTET.