| Summary: | Accurate estimation of above ground biomass (AGB) is very important for crop growth monitoring. The objective of this study was to estimate rice biomass by utilizing structural and meteorological features with widely used spectral features. Structural features were derived from the triangulated irregular network (TIN), which was directly built from structure from motion (SfM) point clouds. Growing degree days (GDD) was used as the meteorological feature. Three models were used to estimate rice AGB, including the simple linear regression (SLR) model, simple exponential regression (SER) model, and machine learning model (random forest). Compared to models that do not use structural and meteorological features (NDRE, R<sup>2</sup> = 0.64, RMSE = 286.79 g/m<sup>2</sup>, MAE = 236.49 g/m<sup>2</sup>), models that include such features obtained better estimation accuracy (NDRE*Hcv/GDD, R<sup>2</sup> = 0.86, RMSE = 178.37 g/m<sup>2</sup>, MAE = 127.34 g/m<sup>2</sup>). This study suggests that the estimation accuracy of rice biomass can benefit from the utilization of structural and meteorological features.
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