Abstract:It is of great significance to estimate the aboveground biomass of rice accurately and timely for precision management of rice field. However, the existing researches focus on using single UAV remote sensing data, which is difficult to achieve accurate estimation of aboveground biomass in the late growth stage of rice due to the spectral saturation effect. To this end, the drone multispectral remote sensing images, meteorological data, and aboveground dry biomass data of rice during the 2023 and 2024 growing seasons were collected. A multi?source feature fusion model for aboveground biomass estimation was constructed to achieve accurate and effective estimation throughout the entire growth period and across multiple growing seasons.The results showed that the vegetation index，vegetation index and texture characteristics，vegetation index and texture characteristics and effective product temperature as the input variable，using multiple linear regression（MLR）, random forest（RF）, partial least squares（PLS）and support vector machine（SVM） to establish the rice ground biomass estimation model. The accuracy was gradually improved and the model accuracy established by the RF algorithm was the highest. With the vegetation index as the model input variable，the adjusted coefficient of determination (adjusted R2) during flowering，late flowering and all reproductive stages were 0.71,0.67 and 0.7，respectively，root mean square error (RMSE) were 268.62 g/m2，300.29 g/m2 and 249.43 g/m2，respectively. With the vegetation index and texture features as the model input variables，the corresponding adjustment R2 were respectively 0.75, 0.72 and 0.74，RMSE were 213.79 g/m2，239.81 g/m2 and 289.46 g/m2，respectively. With vegetation index and texture characteristics and effective product temperature as input variables，the corresponding adjustment R2 were respectively 0.84, 0.87 and 0.87，and RMSE were 176.9 g/m2，162.81 g/m2 and 163.08 g/m2. Using 2024 data as validation, RF achieved an adjusted R2 of 0.60 and RMSE of 288.19 g/m2 across the entire growth cycle, enabling precise estimation of aboveground dry biomass in rice across growing seasons. The proposed integrated approach combining UAV remote sensing and meteorological data provided a robust method for accurate aboveground biomass estimation throughout the growth cycle and across seasons, offering technical support for precision rice management in smart agriculture.
