# A Review of Machine Learning Applications in Land Surface Modeling
###### Abstract  
Machine learning (ML), as an artificial intelligence tool, has acquired significant progress in data-driven research in Earth sciences. Land Surface Models (LSMs) are important components of the climate models, which help to capture the water, energy, and momentum exchange between the land surface and the atmosphere, providing lower boundary conditions to the atmospheric models. The objectives of this review paper are to highlight the areas of improvement in land modeling using ML and discuss the crucial ML techniques in detail. Literature searches were conducted using the relevant key words to obtain an extensive list of articles. The bibliographic lists of these articles were also considered. To date, ML-based techniques have been able to upgrade the performance of LSMs and reduce uncertainties by improving evapotranspiration and heat fluxes estimation, parameter optimization, better crop yield prediction, and model benchmarking. Widely used ML techniques used for these purposes include Artificial Neural Networks and Random Forests. We conclude that further improvements in land modeling are possible in terms of high-resolution data preparation, parameter calibration, uncertainty reduction, efficient model performance, and data assimilation using ML. In addition to the traditional techniques, convolutional neural networks, long short-term memory, and other deep learning methods can be implemented. machine learning; land surface; land-atmosphere interactions; parameterizations; model uncertainty [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35][36][37  
improved estimates of land surface radiation, ecology, hydrology, biology are now possible. However, representing the land-surface interactions properly in the model still remains a challenge. Specifically, in the areas of complex terrain and strong surface-atmosphere coupling. Major components of an LSM include (1) atmospheric forcing, (2) physical, chemical, and biological processes, (3) parameters, and (4) outputs. The adoption of ML in land surface modeling has so far been gradual, but the related fields are now highly emerging. ML-based methods can create viable, complementary avenues toward knowledge discovery in land modeling. ML is often categorized into traditional methods and deep learning (DL) based methods. While traditional methods include random forests (RF), support vector machines (SVM), classification and regression trees (CART); DL techniques include Deep Learning Artificial Neural Network (DL-ANN), Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN) etc. Overall, ML models have repeatedly outperformed simpler statistical models and provided improved generalized solutions to unforeseen circumstances in many applications of Earth Sciences. The recent growth of big hydrologic data through remote sensing and data compilation has fostered the development. The difference between the process-based LSMs and ML models is that the model structure for LSMs is based on the underlying physical principles that we believe are valid, whereas ML model structure is completely data-driven. Despite this fundamental difference, they can complement each other. Two strategies popular in literature in this regard are: (1) integrating LSMs and ML, (2) introducing physical constraints on ML models. Strategy (1) Is quite straightforward. One common way to implement it is to ensure the application of pure ML models only when the climatology is similar to the training data; otherwise, in case of rare events, the combined model relies on the LSM. In this way, modelers can avoid the uncertainty coming from ML models in the extreme cases while making the model less computationally expensive in general application. On the other hand, (2) acknowledges that data-driven algorithms often fail to satisfy the well-known physics law constraints as they are not generally enforced with those.