## 1 Introduction  
Winter oilseed rape (WOR) is one of the major commercial crops, being grown mainly in temperate regions [1; 2; 3]. It is cultivated mostly for its oil-rich seeds that are widely used for food, biofuel, and medicine [4; 5]. The dry aboveground biomass (AGB) is an important parameter in indicating crop growth status, farmers need crop biomass information at different growth stages for guiding their applying fertilizer, and the early estimation of AGB can also be utilized for yield prediction [6]. Traditionally, AGB estimation was based on ground destructive sampling, which was both time-consuming and unsuitable for large areas [7; 8; 9]. In contrast, with the development of remote sensing technology, the high resolution, strong continuity, and massive remote sensing information provided a cost-effective method for avoiding sampling bias and opened a new perspective for quantifying and estimating AGB at both the local and regional scale [10]. Remote sensing had been proven to be a reliable method in monitoring plant growth and nutritional status over large areas [11], and is gradually used to assess plant and leaf nitrogen concentration, chlorophyll density for fertilization, and irrigation of WOR [12; 13; 14]. Most previous studies adopted large-scale satellite image for WOR AGB estimation. [PERSON] et al. [15] extracted 27 parameters from the C band synthetic aperture radar (SAR) image to estimate AGB with a determination coefficient (R\({}^{2}\)) of 0.765 and root mean squared error (RMSE) of 73.20 g/m\({}^{2}\). The fully polarimetric SAR data was also used to estimate dry and fresh biomass with an R\({}^{2}\) of 0.85 and 0.76 and RMSE of 41.6 g/m\({}^{2}\) and 533.5 g/m\({}^{2}\), respectively [16]. However, the use of the SAR image remained challenging due to its high costs and algorithm complexity for large scale biomass estimation; it was also hypersensitive to the underlying surface, which might greatly influence the accuracy of AGB estimation [17]. When compared with SAR satellite sensors, high spatial resolution optical satellite sensors had a relatively shorter revisiting cycle and they were efficient in capturing finer plant growth information [18]. [PERSON] et al. [19] collected the optimum vegetation indices (VIs) from the time-series high spatial resolution satellite image (the Pleiades-1, Worldview-2, and Spot-6) for the whole growth period of WOR AGB estimation, with the accuracy of inversion model (R\({}^{2}\) = 0.77 and RMSE = 104.64 g/m\({}^{2}\)). Although these studies had demonstrated the successful estimation of biomass by satellite image, the satellite image was still difficult and expensive to acquire crop growth information of small plots due to a long visiting cycle and cloud coverage, especially for the detailed canopy structural information [20; 21]. The plant architecture and coverage of WOR were significantly different during its entire growing period since leaves, buds, flowers, and pods appeared gradually [22]. When considering these restrictions, the remote sensing techniques that are based on canopy hyperspectral data are needed for accurately monitoring WOR AGB. In general, canopy hyperspectral data was commonly used in precision agriculture to non-destructively estimate agronomic parameters, including AGB [23; 24]. The results of several studies showed that hyperspectral VIs were considered to be more sensitive in quantifying vegetation biomass [25; 26]. For example, the normalized difference vegetation index (NDVI) that was associated with the visible and near-infrared wavebands was mostly recommended in monitoring the biophysical characteristics (biomass, leaf area, leaf gap fraction, and plant cover) [27]. [PERSON] et al. [28] found that a linear model based on NDVI (740 nm, 771 nm) produced higher accuracy than that on a traditional NDVI (665 nm, 801 nm) for grass biomass estimation. [PERSON] et al.