The aboveground biomass (AGB) is of great practical significance for monitoring crop growth [1] and predicting yield [2]. Therefore, the rapid and accurate prediction of AGB is critical to managing agricultural activities efficiently [3].
The conventional manual field measurement of AGB involves destructive sampling that is time-consuming and laborious [4]. Given these constraints, prompt and accurate monitoring of AGB is critical. Previous studies have demonstrated that multispectral or hyperspectral information from satellites or airborne platforms has been widely used for monitoring leaf area index [5], crop growth [6], nitrogen content [7], and biomass of wheat [8]. However, unfavourable weather conditions such as clouds or fog may lead to a lack of applicable satellite data, which limits the application of this data in crop monitoring. In particular, high temporal resolution is required to explain the spatial specificity in the field during the critical stage of crop monitoring [9]; the accuracy of phenological information needs to be determined from remote sensing observation, which depends largely on the frequency of observations [2]. At the same time, data from remote sensing satellites are usually expensive and require extensive data processing experience.
In recent years, with the development of unmanned aerial vehicles (UAVs) and their application in the field of remote sensing, the use of canopy spectra and UAV images has become a novel method for crop monitoring. For example, researchers have demonstrated the feasibility of using canopy spectra extracted from UAV hyperspectral images combined with partial least squares (PLS) regression to estimate the chlorophyll content of wheat [10]. In addition, colour index and crop surface models have been extracted by using orthogonal correction with ( red-green-blue) RGB UAV images to estimate a leaf area index (LAI) [11], plant height [12], and plant nitrogen content [13]. However, the spectral or image features obtained from a UAV are saturated in the later stage of crop growth, which leads to poor accuracy in the estimation of crop yield. In order to solve this problem, researchers have attempted to combine spectral and image features; they have found that the vegetation indices (VIs) combined with a textural feature index (a Normalized Differential Texture Index, NDTI) extracted from 550 nm and 800 nm band images obtained by UAV multispectral camera provided better results than using traditional textural features and vegetation indices in a rice AGB estimation model [14]. Previous studies have proved the feasibility of UAV-based textural features and VIs along with their combination in wheat AGB estimation and yield detection. However, due to the low resolution of the images obtained by most UAVs, the crop canopy information cannot be fully obtained in the case of large crop coverage in the later stage of growth, which leads to low accuracy in estimating wheat AGB in the later stage of growth [8]. The results show that the accurate estimation of crop AGB using the features obtained by a UAV platform needs to be improved in the later stage of crop growth.
Near-infrared spectroscopy is one of the common methods used to detect crop biomass [15]. Using a hand-held spectrometer to obtain crop canopy reflectance and then extracting a VI or effective wavelengths to estimate wheat biomass has been proven to be an effective method [16]. For example, a power function or exponential function relationship was found between a specific vegetation index (RVI) and AGB of soybean at the seedling stage [17]. However, when the crop biomass reaches a certain range, the crop canopy reflectance tends to be saturated, which leads to the low accuracy of estimated AGB in a crop model based on a VI. In order to reduce the effects of spectral saturation on crop AGB estimation, some researchers have used PLS regression based on band depth and a VI to estimate wheat biomass. However, the results show that the problem of canopy spectral saturation still exists [18]. However, image technology based on consumer grade digital cameras has been commonly used to monitor crop morphology [19], nutrient components [20], and pest status [21]. For example, image information can be obtained through a digital camera, and then the image can be processed into a 3D point cloud data for estimating wheat biomass, crown height, and a harvest index. Research shows that this measurement method has the advantages of high adaptability and robustness [22]. However, when plants grow to a certain level, the model used to estimate AGB based on image features also performs poorly. The results demonstrate that an estimation model based on individual spectral or image features cannot accurately estimate crop AGB in the later stage of growth.
Therefore, the purpose of this study is to evaluate the application of combining ground-scale images with spectral information in estimating the AGB of winter wheat. The image texture, spectral features, and their combinations are used to estimate the AGB of winter wheat in multiple growth stages. In this study, several methods that can be used to predict the AGB of winter wheat are proposed that are based on: (1) spectral features (VIs, effective wavelength), (2) the OTEXS and the ONDTIS calculated from canopy images and side images, and (3) the combination features with the random forest (RF) regression and PLS regression.