Unmanned Aerial System-Based Wheat Biomass Estimation Using Multispectral, Structural and Meteorological Data

Rapid estimation of above-ground biomass (AGB) with high accuracy is essential for monitoring crop growth status and predicting crop yield. Recently, remote sensing techniques using unmanned aerial systems (UASs) have exhibited great potential in obtaining structural information about crops and identifying spatial heterogeneity. However, methods of data fusion of different factors still need to be explored in order to enhance the accuracy of their estimates. Therefore, the objective of this study was to investigate the combined metrics of different variables (spectral, structural and meteorological factors) for AGB estimation of wheat using UAS multispectral data. UAS images were captured on two selected growing dates at a typical reclaimed cropland in the North China Plain. The spectral response was determined using the highly correlated vegetation index (VI). A structural metric, the canopy height model (CHM), was produced using UAS-based multispectral images. The measure of growing degree days (GDD) was selected as a meteorological proxy. Subsequently, a structurally–meteorologically weighted canopy spectral response metric (SM-CSRM) was derived by the pixel-level fusion of CHM, GDD and VI. Both correlation coefficient analysis and simple function fitting were implemented to explore the highest correlation between the measured AGB and each proposed metric. The optimal regression model was built for AGB prediction using leave-one-out cross-validation. The results showed that the proposed SM-CSRM generally improved the correlation between wheat AGB and various VIs and can be used for estimating the wheat AGB. Specifically, the combination of MERIS terrestrial chlorophyll index (MTCI), vegetation-masked CHM (mCHM) and normalized GDD (nGDD) achieved an optimal accuracy (R 2 = 0.8069, RMSE = 0.1667 kg/m 2 , nRMSE = 19.62%) through the polynomial regression method. This improved the nRMSE by 3.44% compared to the predictor using MTCI × mCHM. Moreover, the pixel-level fusion method slightly enhanced the nRMSE by ~0.3% for predicted accuracy compared to the feature-level fusion method. In conclusion, this paper demonstrated that an SM-CSRM using pixel-level fusion with canopy spectral, structural and meteorological factors can obtain a good level of accuracy for wheat biomass prediction. This finding could benefit the assessment of reclaimed cropland or the monitoring of crop growth and field management in precision agriculture.