# A Review of Machine Learning Applications in Land Surface Modeling
## 4 Major Applications of Machine Learning in Land Surface Modeling
### Parameter Estimation and Uncertainty Assessment  
LSMs are not properly equipped to accurately simulate the real world and lead to significant uncertainty when trying to capture the land-atmosphere interactions. One major reason behind this is the use of parameterizations for complex processes such as ET estimation, which includes an abundance of soil and vegetation parameters. For example, current global LSMs have over 20 parameters that are linked to just ET estimation [52]. Other sources of uncertainty in the LSMs include internal variability and forcing uncertainty.  
[PERSON] et al. [52] provided global parameter estimates at 5 km spatial resolution for the Noah LSM using 85 eddy covariance sites in the global FLUXNET network and linked the most sensitive parameters to local environmental characteristics using an MLalgorithm--extremely randomized trees (Extra-trees). They concluded that FLUXNET could be potentially used to tune the parameters of LSM. The calibration led to a significant enhancement in model performance in terms of Kling-Gupta Efficiency increasing from 0.54 to 0.71. Furthermore, their leave-one-out cross validation method revealed the potential to relate calibrated model parameters to local environmental conditions.  
Uncertainty quantification of LSMs is closely related to the parameter optimization problem. As such, it is far-fetched to obtain the accurate set of sensitive parameters for all observable variables, and different combinations of parameters may show similar skill to reproduce observations, also termed as 'equilibrium' in hydrology [53]. An alternative method is to construct the probability density function of parameters, explaining the uncertainty in parameter estimation. [PERSON] [54] applied ML techniques for parameter optimization and uncertainty assessment with global satellite observations. The technique was to create an ML model, which emulates the relationship between model parameters and LSM outputs and is computationally cheaper than LSM. They applied this technique, involving ML and Markov Chain Monte Carlo (MCMC), to EcoHydro-SiB (a modified version of [20]) model over a part of the Sahel region in Africa, and was successful in obtaining the nonparametric posterior distribution of four unknown parameters and enhanced the soil moisture and vegetation dynamics simulation skill of the LSM. The addition of ML techniques made optimization 50,000 times faster. They highlighted the importance of selecting suitable prior, consideration of error in meteorological forcing, and inclusion of satellite observations in the modeling chain.  
In a recent study, [PERSON] et al. [55] investigated the biogeophysical parameter space of CLM5 and determined the sensitivity of parameters to get insight into the role of parameter choices on the overall model uncertainty. They implemented an ML approach to globally calibrate six parameters of CLM5, selected by a sensitivity analysis, to the observations of water and carbon fluxes. Specifically, they trained their feedforward ANNs to emulate CLM5 outputs given parameters as predictors. The trained ML models were able to estimate global optimal parameter values and quantify the contribution of parameter uncertainty to the overall uncertainty in LSM simulated GPP and LH. Moreover, they were able to inspect several interpretation methods to better understand the inner workings of the ANN as emulators of CLM.