# A Review of Machine Learning Applications in Land Surface Modeling
## 3 Complexity and Limitations of LSMs: Prospect of ML  
The diversity of the interconnected processes in the terrestrial system, and the levels of entanglement present in these processes, pose a hurdle to build tractable land models. The propensity of scientists to focus on their own specific area of interest and the reality that the earth system is indeed complex are both responsible for this complex nature. Often, this reaches a point where no individuals are able to completely understand all aspects of any particular model, and the development teams strive to meet all the requirements placed on modern LSMs [11]. Even though, large uncertainty remains in our understanding and modeling of the interactions between atmospheric and terrestrial branches of the hydrologic cycle due to the non-trivial mechanisms at the land surface. Figure 1 illustrates the convoluted and connected processes in a typical LSM. The major parts, such as, atmosphere, hydrology, urban processes, agriculture; and plant physiology, soil biogeochemistry, soil physics related to each of those, are interlinked in an LSM. These major components are further segregated into smaller yet complicated processes. For example, agriculture includes fertilizer and pesticides usage, biomass burning, harvesting, irrigation, tillage, residual treatment etc. (Figure 1). The interactions are defined by the exchange of information between different parts of the model. However, some of the processes are still oversimplified in modeling. As such, most of the LSMs classify plant species into plant functional types (PFIs), within which the parameters are undifferentiated. Simulations consisting of a limited number of PFIs ignore biodiversity within a simulation grid. This may lead to uncertainty in the strength of climate responses when coupled to a climate model. Furthermore, understanding the combined effects of major greenhouse gases, such as Carbon dioxide, Methane, and Nitrogen dioxide on global warming are still at early stages due to constraints in the measurements of multiple gases. Limited models have the capacity to simulate such effects, which requires realistic carbon and nitrogen cycling processes.  
LSMs are often applied at large spatial scales aimed to simulate the interactions between climate and land surface. Nonetheless, validation data for these models are obtained from flux tower sites. This geographical gap usually limits the accuracy of the models. Microbes may play fundamental roles in altering biogeochemical cycling as 'ecosystem engineers' [41]. However, very few LSMs include the effects of such organisms in an explicit manner. This limits our ability to estimate the climatic impacts of changes in soil biological community composition and diversity. In addition, the unavailability of high-resolution land-surface data affects the LSMs in capturing the effects of spatial heterogeneity. The development of high-accuracy fine resolution data is important for the interpretation of observations and model simulations.  
Some of the limitations of LSMs can be highly benefited from the enormous information currently available from satellite data. However, extracting useful knowledge from terabytes of data provided by observation and LMS simulations is challenging. In contrast, ML models are simple in nature in terms of structure and easy to simulate the output once trained properly. Figure 2 illustrates the general structure of a multilayer perceptron model, which is a commonly used feedforward ANN type ML model and uses supervised learning techniques for training. Compared to LSMs (Figure 1), the structure is much simpler and furthermore, the model parameters are data driven. ML techniques can help