# A Review of Machine Learning Applications in Land Surface Modeling
## 7 Conclusions  
The implementation of ML techniques in earth system modeling has been widely accepted by many researchers in recent years. Direct application of such techniques, particularly on LSM, is quite recent. LSM plays an important role in simulating the feedback and interactions between the land surface and the atmosphere. As we escalate toward rapid changes in the climate and land use, the future of the terrestrial biosphere and hydrology remains a global concern. LSMs are the fundamental tools to simulate and predict the state of the terrestrial surface and play a critical role in optimizing the amount of carbon dioxide to limit global warming. This provides motivation for improving the performance of LSMs.  
The complex nature of linked land-surface processes makes it difficult to create a tractable LSM. The interactive components include atmosphere, urban environment, canopy processes, agriculture, hydrology, soil dynamics, snow physics, soil processes and so on. Recently with the need for hydrodigists, ecologists, biogeochemists, physiologists, the structure of LSMs is extremely complex. We argue that ML methods can help us achieve better performance of LSMs, make it efficient and also create a flexible structure of the models. With the help of ML and adequate data in recent ages, some critical applications of LSM have already been upgraded.  
The main improvements were in the areas of ET estimation, parameter calibration, crop yield, model benchmarking, and uncertainty quantification. ET estimation is important in earth system modeling as ET is an important part of both water and energy cycles. ML methods were able to use satellite data and improve global ET estimates. The major complexity of the LSMs come from a significant number of parameterized processes within LSMs. ML surrogates were able to replace some of these processes to improve the model performance and reduce the uncertainty. Crop yield prediction majorly benefitted from the 'hybrid' model approach where process-based models and ML models were combined to achieve better yield predictions. Model benchmarking is a relatively newer concept but an extremely important part of LSM improvement, and ML has been helpful in this context too.  
The widely used ML techniques along with land surface modeling are ANN, RF, and modified versions of Gradient trees. Supervised learning is often used with the help of these techniques to improve the LSM performance. Other ML techniques are also often used along with these, such as genetic algorithms, SVM, GBRT, 'Bagging,' MCMC, etc. We foresee that there is scope to apply the recent DL methods like CNN and LSTM regression to improve several parts of the land surface modeling, following other applications in water science and earth system modeling. For example, high-resolution data preparation and improved data assimilation techniques can leverage these advanced methods.  
Additional improvements are possible in the areas of complexity reduction of LSMs, multiple parameter calibration, structure revision, uncertainty reduction, and sustainable crop yield estimate. Interpretability of ML models should be carefully investigated to further explore the underlying physics and processes behind the yet less-known land surface-related processes.  
Conceptualization, S.P.; P.S.; methodology, S.P.; PS.; writing--original draft preparation, S.P.; writing--review and editing, P.S. and S.P.; visualization, S.P.; All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
**Acknowledgments:** We would like to thank the Department of Atmospheric Science of University of Illinois at Urbana-Champaign for providing us with resources for the study. We gratefully thank the three anonymous reviewers for their valuable comments and suggestions, which helped to improve the paper. We also thank the editor for editorial guidance.  
**Conflicts of Interest:** The authors declare no conflict of interest.