## 1 Introduction
[PERSON] and [PERSON] used vegetation index to monitor dry matter weight [31; 32], but the accuracy of the model mainly depended on the relationship between LAI or SPAD and dry matter [33]. For this reason, some scholars insist on using vegetation index to monitor the dry matter weight of individual organs to improve accuracy [34]. On the other hand, partial least-squares regression (PLSR), support vector machine (SVM), random forest (RF), artificial neural networks (ANNs), and other machine learning regression models are used to improve the accuracy and efficiency of monitoring [35]. [PERSON] [36] used hyperspectral imagery to establish an accurate regression model of LAI and chlorophyll based on PLSR. The PROSAIL model, as a widely used physical model, has become one of the most important tools for estimating various vegetation parameters. [PERSON] [37] used this model to retrieve the LAI and chlorophyll content of rice from unmanned, aerial, vehicle-based hyperspectral images. Furthermore, a hybrid method combining physical models and statistical methods was proposed by [PERSON] [38] to estimate the LAI values of crops. In practice, the hyperspectral reflectance characteristics of rice should be extended to a larger geographic space. Therefore, the extraction of spectral information on ground objects (such as the spectral reflectance characteristics of rice-growing regions) and land classification have become hot topics, especially based on hyperspectral images. For example, by comparing N-FINDR, VCA, and other algorithms, it was concluded that the ant colony optimization (ACO) could accurately extract endmembers, which are some pixels including only one ground object in the image [39]. Further, the high-accuracy model for estimating rice yield was established by integrating vegetation index that recalculated from endmember spectra with abundance data [40]. On the other hand, with the development of machine learning and deep learning in recent years, it has become possible to process and analyze hyperspectral images to classify land cover. Convolutional neural networks (CNNs) and recurrent neural networks (RNNs) have been proven to efficiently classify hyperspectral images with high accuracy [41; 42]. Furthermore, considering the topological relationship between pixels, the graph neural network (GCN) has also begun to be used for hyperspectral image classification [43]. However, GCNs also have some drawbacks, such as high storage and computational cost, and the need to retrain the model when new data are fed. Therefore, a new supervised version of GCNs, called miniGCNs, was developed to solve these problems [44]. As one of the main food crops, rice accounts for 40% of the total grain production in China. The lower Yangtze River region is an important rice production area in China. Changes in SSI, especially the diffuse radiation fraction, will have remarkable effects on rice growth and yield in this region. In recent years, because of the increasing concentration of aerosols in the atmosphere, the diffuse radiation fraction is changing. However, there are few studies on analyzing rice growth and the hyperspectral reflectance characteristics of canopy under this changing situation. Our research was based on rice field experiments in two years and comprised two main parts. Firstly, with the experimental data, the change in LAI, SPAD, and hyperspectral reflectance characteristics, including the band reflectivity, red-edge parameters, and absorption features parameters, was analyzed in two scenarios. In the next part, the paper attempted to explore the underlying reasons for these different hyperspectral reflectance characteristics and to provide useful information for relevant research.