Using prosail look-up tables to train random forests regressors for fast live fuel moisture retrieval

Live Fuel Moisture Content (LFMC) is a fundamental variable of fire meteorology, fire behavior models and fire danger indices. The possibility of creating remote sensing LFMC products by directly training machine learning algorithms onto field measurements is severely limited by the sparse geographic distribution of such datasets, which are mostly concentrated in USA, Mediterranean Europe and Australia. Therefore, the physical foundation provided by a Radiative Transfer Model (RTM) such as PROSAIL remains an irreplaceable component of any LFMC product designed for global applicability. However, radiative transfer model inversion usually requires a lot of time and computing power. A Look-Up Table (LUT) approach saves time by running the model forward only during LUT creation, but still requires each row of the LUT to be compared with each observation when searching for the optimal solution. In this paper, we trained a random forest regressor on LUTs generated using PROSAIL and the Jasinski geometric model, aiming to exploit the efficiency of ML regressors to speed up calculation time while still maintaining the foundation of a physically-based approach that enables global applicability. The regressor was trained specifically to invert the LFMC, and was tested using Globe-LFMC v2 field-collected LFMC timeseries as a ground truth. The inversion, while returning results comparable in accuracy with the ones obtained using conventional methods, is now performed virtually instantly.