Hu, XL; Shi, LS; Lin, L; Magliulo, V (2020). Improving surface roughness lengths estimation using machine learning algorithms. AGRICULTURAL AND FOREST METEOROLOGY, 287, 107956.

Surface roughness lengths, including the aerodynamic roughness length (z(0m)) and the thermodynamic roughness length (z(0h), represented by excess resistance kB(-1)), are crucial parameters in the accurate simulation of surface turbulent fluxes. However, due to insufficient knowledge in the physical mechanisms of surface roughness lengths, there exist considerable uncertainties in physically-based models. In this study, we attempt to overcome this issue by establishing the data-driven surface roughness lengths models, which are based on global observations from the FLUXNET2015 dataset and Moderate Resolution Imaging Spectroradiometer (MODIS). Four machine learning algorithms, including random forest (RF), single hidden layer artificial neural network (ANN), multilayer perceptron (MLP), deep belief network (DBN) are explored. A large number of data from 45 flux tower sites (as many as 44,662 daily z(0m) and 583,484 half-hour kB(-1) observations) are utilized to train and test the data-driven models. Our results show that the data-driven models surprisingly achieve significantly improved estimation of surface roughness lengths and turbulent fluxes than physical models, which indicated the model inadequacy of physical models. RF-driven models achieve the best results. MLP and DBN-driven models of higher complexity are slightly superior to ANN-driven models but exhibit unstable performance. RF and ANN accurately reproduce the unimodal function relationship between leaf area index and z(0m), thus demonstrating that the machine learning methods can extract physical rules from vast numbers of observations. In contrast, MLP and DBN fail to capture this relationship, possibly because of too complicated architecture. It implies that a suitable complexity of machine learning algorithm is critical to excavate true physical mechanism. To the best of our knowledge, this is the first study to demonstrate that machine learning technique can contribute to highly accurate estimations of surface turbulent fluxes by building data-driven surface roughness lengths models.