# A Review of Machine Learning Applications in Land Surface Modeling
## 6 Possible future directions  
Combining the applications of ML in past literature and keeping in mind the still existing problems in land surface modeling, we provide some future directions to improve the current status of land modeling leveraging machine learning techniques. 1. ML techniques can be a suitable way to reduce the process complexity of the LSMs. Currently, the complex interactive processes of the terrestrial system and way they are represented in conventional LSMs makes them intractable. It is often difficult to assess the added value of a complex process given its cost of burdening the model. ML can help in this regard to take a modular approach, where modelers can represent a complex process in multiple ways and test the model performance easily. This will help in reducing the structural uncertainty of the models as well. The model intercomparison projects (e.g. ; MstMIP) are often constrained by the under-sampling of the potential range of model configurations. Such technique will also help us in collaborative development of the model, rather than adding processes on a specific need basis. 2. Processes in the model which have little physics knowledge or complex calculation, but more data availability, could be replaced by a ML-based surrogate. As mentioned in Section 4.4, there have been some attempts to predict important land surface properties by a hybrid modeling approach, but we are still lacking in exploring some of the more fundamental variables which can be easily provided by ML applications as the amount of observations increase in recent days. For example, hydrology, phenology and snow cover fraction. Hydrologic observations are increasing all over the globe with advanced velocimetry techniques and phenology is easily obtained now from satellite remote sensing data. Improving these components leveraging ML will help upgrade the overall LSM performance. 3. Parametric uncertainty can be investigated, and parameters can be better optimized following [PERSON] et al. [55]. However, we need to consider longer data records (now possible with the availability of computing resources) and more output variables. Good quality globally gridded observations are now available. For example, Atmospheric CO\({}_{2}\) from National Oceanic and Atmospheric Administration (NOAA), Commonwealth Scientific and Industrial Research Organisation (CSIRO), gridded FLUXNET from Max Planck Institute for Biogeochemistry (MPI-BCG), river flow (GRDC), albedo (MODIS), and so on (ILAMB project, etc.). 4. Data assimilation is proved to be an important tool to improve model simulations at different earth system modeling applications [80, 81]; and advancement in ML can further enhance that. Conventional data assimilation methods include Kalman filter and variational approaches. These methods have underlying assumptions of normality, Markovian processes, zero error covariance and similar ML algorithms, being completely data-driven, may improve the assimilation in terms of speed, accuracy, and efficiency. 5. There are several avenues for better crop yield prediction. Soil hydrology related variables such as soil moisture or drought indices (obtained from either remote sensing, in-situ measurements, or process-based models) can further improve the ML predictions on crop yield. Precision agriculture is another advanced recent agricultural technology which includes sensors, robotics, and AI to assemble 'big data' which can further be processed with ML models to develop a sustainable agricultural practice with enhanced yield. We can use such data and the extracted information to assess the yield variability among regions to make informed decisions [82]. As such, the yield stability maps, included in the landscape, can provide information about environment friendly areas and constant low yield areas. We should focus on intensifying the high yield areas sustainably. On the other hand, low yield zones can be improved by perennial bioenergy crops and recycling of plant available nutrients. 6.