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, R² = 0.64, RMSE = 286.79 g/m², MAE = 236.49 g/m²), models that include such features obtained better estimation accuracy (NDRE*Hcv/GDD, R² = 0.86, RMSE = 178.37 g/m², MAE = 127.34 g/m²). This study suggests that the estimation accuracy of rice biomass can benefit from the utilization of structural and meteorological features.

The accurate quantification of yield in rapeseed is important for evaluating the supply of vegetable oil, especially at regional scales. This study developed an approach to estimate rapeseed yield with remotely sensed canopy spectra and abundance data by spectral mixture analysis. A six-band image of the studied rapeseed plots was obtained by an unmanned aerial vehicle (UAV) system during the rapeseed flowering stage. Several widely used vegetation indices (VIs) were calculated from canopy reflectance derived from the UAV image. And the plot-level abundance of flower, leaf and soil, indicating the fraction of different components within the plot, was retrieved based on spectral mixture analysis on the six-band image and endmember spectra collected in situ for different components. The results showed that for all tested indices VI multiplied by leaf-related abundance closely related to rapeseed yield. The product of Normalized Difference Vegetation Index and short-stalk-leaf abundance was the most accurate for estimating yield in rapeseed under different nitrogen treatments with the estimation errors below 13%. This study gives an important indication that spectral mixture analysis needs to be considered when estimating yield by remotely sensed VI, especially for the image containing obviously spectral different components or for crops which have conspicuous flowers or fruits with significantly different spectra from their leave.

This study developed an approach for remote estimation of Vegetation Fraction (VF) and Flower Fraction (FF) in oilseed rape, which is a crop species with conspicuous flowers during reproduction. Canopy reflectance in green, red, red edge and NIR bands was obtained by a camera system mounted on an unmanned aerial vehicle (UAV) when oilseed rape was in the vegetative growth and flowering stage. The relationship of several widely-used Vegetation Indices (VI) vs. VF was tested and found to be different in different phenology stages. At the same VF when oilseed rape was flowering, canopy reflectance increased in all bands, and the tested VI decreased. Therefore, two algorithms to estimate VF were calibrated respectively, one for samples during vegetative growth and the other for samples during flowering stage. The results showed that the Visible Atmospherically Resistant Index (VARI_green) worked most accurately for estimating VF in flower-free samples with an Root Mean Square Error (RMSE) of 3.56%, while the Enhanced Vegetation Index (EVI2) was the best in flower-containing samples with an RMSE of 5.65%. Based on reflectance in green and NIR bands, a technique was developed to identify whether a sample contained flowers and then to choose automatically the appropriate algorithm for its VF estimation. During the flowering season, we also explored the potential of using canopy reflectance or VIs to estimate FF in oilseed rape. No significant correlation was observed between VI and FF when soil was visible in the sensor’s field of view. Reflectance at 550 nm worked well for FF estimation with coefficient of determination (R²) above 0.6. Our model was validated in oilseed rape planted under different nitrogen fertilization applications and in different phenology stages. The results showed that it was able to predict VF and FF accurately in oilseed rape with RMSE below 6%.

Leaf area index (LAI) estimation is very important, and not only for canopy structure analysis and yield prediction. The unmanned aerial vehicle (UAV) serves as a promising solution for LAI estimation due to its great applicability and flexibility. At present, vegetation index (VI) is still the most widely used method in LAI estimation because of its fast speed and simple calculation. However, VI only reflects the spectral information and ignores the texture information of images, so it is difficult to adapt to the unique and complex morphological changes of rice in different growth stages. In this study we put forward a novel method by combining the texture information derived from the local binary pattern and variance features (LBP and VAR) with the spectral information based on VI to improve the estimation accuracy of rice LAI throughout the entire growing season. The multitemporal images of two study areas located in Hainan and Hubei were acquired by a 12-band camera, and the main typical bands for constituting VIs such as green, red, red edge, and near-infrared were selected to analyze their changes in spectrum and texture during the entire growing season. After the mathematical combination of plot-level spectrum and texture values, new indices were constructed to estimate rice LAI. Comparing the corresponding VI, the new indices were all less sensitive to the appearance of panicles and slightly weakened the saturation issue. The coefficient of determination (R²) can be improved for all tested VIs throughout the entire growing season. The results showed that the combination of spectral and texture features exhibited a better predictive ability than VI for estimating rice LAI. This method only utilized the texture and spectral information of the UAV image itself, which is fast, easy to operate, does not need manual intervention, and can be a low-cost method for monitoring crop growth.

The effective and accurate aboveground biomass (AGB) estimation facilitates evaluating crop growth and site-specific crop management. Considering that rice accumulates AGB mainly through green leaf photosynthesis, we proposed the photosynthetic accumulation model (PAM) and its simplified version and compared them for estimating AGB. These methods estimate the AGB of various rice cultivars throughout the growing season by integrating vegetation index (VI) and canopy height based on images acquired by unmanned aerial vehicles (UAV). The results indicated that the correlation of VI and AGB was weak for the whole growing season of rice and the accuracy of the height model was also limited for the whole growing season. In comparison with the NDVI-based rice AGB estimation model in 2019 data (R² = 0.03, RMSE = 603.33 g/m²) and canopy height (R² = 0.79, RMSE = 283.33 g/m²), the PAM calculated by NDVI and canopy height could provide a better estimate of AGB of rice (R² = 0.95, RMSE = 136.81 g/m²). Then, based on the time-series analysis of the accumulative model, a simplified photosynthetic accumulation model (SPAM) was proposed that only needs limited observations to achieve R² above 0.8. The PAM and SPAM models built by using 2 years of samples successfully predicted the third year of samples and also demonstrated the robustness and generalization ability of the models. In conclusion, these methods can be easily and efficiently applied to the UAV estimation of rice AGB over the entire growing season, which has great potential to serve for large-scale field management and also for breeding.

The dry aboveground biomass (AGB) is an important parameter in assessing crop growth and predicting yield. This study aims to ascertain the optimal methods for the spectroscopic estimation of winter oilseed rape (WOR) biomass. The different fertilizer-N gradients WOR were planted to collect biomass data and canopy hyperspectral data in two years of field experiments. Correlation analyses and partial least squares regression (PLSR) were performed between canopy hyperspectral data and AGB, and the linear and non-linear regression models simulated the quantitative relation between the vegetation indices (VIs) and AGB at four different growth stages (seeding, bolting, flowering, and pod stage). The results indicated that VIs that were derived from canopy hyperspectral data could estimate AGB accurately: (1) At the seeding and bolting stage, the CIred edge showed excellent performance with the higher accuracy (R² ranged from 0.60–0.95) as compared to the other six VIs (Green chlorophyll index (CIgreen), normalized difference vegetation index (NDVI), Green normalized difference vegetation index (GNDVI), ratio vegetation index (RVI), DVI, and soil adjusted vegetation index (SAVI)); (2) Correlation analyses and PLSR can effectively extract the feature wavelengths (800 nm and 1200 nm) for biomass estimation. The modified vegetation indices NDVI (800, 1200) significantly improved AGB estimation accuracy (R² > 0.80, RMSE < 1530 kg/hm², RPD > 2.3) without saturation phenomenon at the total for four stages, and retained good robustness and reduced the influence of flower and pod for estimating AGB; (3) it was vital to pay more attention to the near-infrared (NIR) bands that could represent WOR growth phenology, and selecting suitable VIs and modeling algorithms could also have a relatively large effect on the success of AGB estimation. The overall results indicated that WOR AGB could be reliably estimated by canopy hyperspectral data, although the plant architecture and coverage of WOR were significantly different during its entire growing period.