# A Review of Machine Learning Applications in Land Surface Modeling
## 4 Major Applications of Machine Learning in Land Surface Modeling
### Crop Yield Prediction  
Crop yield is often predicted leveraging the physics based LSMs. Early and reliable crop yield forecasts are critical for farmers and decision-makers in food security policymaking, planning, and trade. Agriculture is greatly affected by weather and climate, and hence, large-scale crop growth simulations under a changing climate in a regional and global scale remains a priority. At the same time, the climate is affected by the extension of agricultural practices due to land-atmosphere interactions. The majority of the studies implemented a 'hybrid' approach, combining process-based modeling and ML, to provide a better yield estimate than those could provide individually.  
[PERSON] et al. [56] provided a framework to combine RF (their ML technique) and APSIM (a process-based crop model) to predict the annual variation of sugarcane yield in Australia. Biomass indices, an output from the crop model, were identified to be critical as a predictor for the ML model for the next month's yield prediction. They also considered observed rainfall, temperature, radiation, and seasonal climate indices as input features for ML algorithms. This is an example of ML aiding the investigation of the relative importance of several predictors. [PERSON] et al. [57] implemented a similar approach, with addition of climate extreme indices, to refine the prediction of wheat yield in Australia. They compared the performance of a process-based model ASPIM with several combinations of the process-based model and ML models, such as ASPIM+ multiple linear regression (MLR) and ASPIM+ MLR+ RF. They reported an additional improvement of 19% in the yield prediction accuracy as compared to ASPIM+MLR and 33% as compared to APSIM alone when the RF was included in the modeling chain.  
[PERSON] et al. [58] developed a two-step approach to constrain the projected GPP at the end of the 21 st century in the Representative Concentration Pathway 8.5 scenario with ML. They fed observational data into the ML algorithms that have been trained on Coupled Model Intercomparison Project (CMIP5) data to learn the relationships between present-day carbon estimates and the future scenario GPP (target variables). Their ML model showed superior performance to the CMIP5 ensemble mean. It also predicted an increasing GPP in the high latitudes. [PERSON] et al. [59] built novel crop meta-models from coarser GCMs to derive estimates of crop yield at a high spatial resolution without requiring the crop model set up at high-resolution. ML methods used in this study were RF and gradient boosting regression tree (GBRT). They were able to go to 0.25\({}^{\circ}\) from 0.5\({}^{\circ}\) and the results showed high accuracy (R\({}^{2}\) > 0.96) in predicting maize yields, ET and crop available water.  
In a recent study, [PERSON] et al. [60] showed that adding physics-based crop model variables as input features to ML models can improve the performance of ML models by 29% on average in the US Corn Belt. They compared the performances of several ML models such as RF, linear regression (LR), least absolute shrinkage and selection operator (LASSO) regression, Light Gradient Boost (LightGBM), Extreme Gradient Boost (XGBoost), and also an ensemble of them to investigate their added value individually and in combination. This study also assessed the relative importance of various variables from the process-based models as input features to different ML models. As such, they included phenology related variables, crop-related variables, and soil-related variables separately to investigate their individual added value in model predictions. The results indicated that the soil-water related variables were most important for crop yield prediction over the US corn belt.