## 1 Introduction  
Based on observational data, the surface solar irradiation (SSI) decreased from 1950 to 1990, which is called global dimming, with a mean decreasing amplitude of 5 W/m\({}^{2}\) per decade [1, 2]. After 1990, SSI increased in Europe and North America (0.66 W/m\({}^{2}\)); however, in the southern and eastern regions of China [3, 4], SSI is still decreasing, while in these regions, the diffuse radiation and diffuse radiation fraction are increasing [5, 6, 7]. Based on [PERSON]'s study, the diffuse radiation fraction in China has steadily increased after 1994 [6]. By analyzing the data from 1981 to 2010, [PERSON] found that China's diffuse radiation has increased by 7.03 MJm\({}^{-2}\) yr\({}^{-1}\) per decade [5]. The main cause of these phenomena is the increase in aerosol in the atmosphere, which has been confirmed by data analysis and physical models [6; 8; 9; 10; 11]. Solar radiation, which is the energy source of photosynthesis for food crops, has a significant effect on crop growth, dry matter accumulation, and crop productivity. Through observation, it is found that a reduction in solar radiation will lead to a significant drop in ecosystem primary productivity (GPP) [12]. However, some studies show that with the decrease in solar radiation, the fraction of diffuse radiation will increase, which can enhance the radiation use efficiency of the canopy [13]. Further, the increased fraction of diffuse radiation has a strong fertilization effect on crop yield and GPP of ecosystems [14; 15; 16; 17]. For rice, the increased fraction of diffuse radiation also has a fertilization effect. The LAI, SPAD, and leaf nitrogen concentration of rice increase with the increase in diffuse radiation fraction. This shows that the increase in diffuse radiation fraction can also raise the grain filling rate and increase the yield [18]. [PERSON] used a 3D model to simulate the growth of rice. He found that the LAI at the bottom of the rice would increase with the increase in diffuse radiation fraction, which increased the photosynthetic rate of the rice population [19]. Hyperspectral remote sensing, which possesses high spectral resolution, can acquire a large amount of information to accurately express the effects of various external environmental stresses on rice [20; 21]. For example, [PERSON] indicates that under flood stress, the hyperspectral reflectance of rice canopy in the near-infrared region changes significantly; in addition, its first derivative hyperspectral reflectance reveals a more obvious phenomenon, i.e., a double reflection peak at 680-760 nm [22]. The temperature stress also has a significant impact on hyperspectral reflectance at visible and near-infrared regions [23]. More specifically, the increasing temperature leads to an increase in reflectance in the visible region and a decrease in the near-infrared region [24]. In addition, with the increasing atmospheric concentration of carbon dioxide, the reflectance rates at the green peak and red bandalso show remarkable changes [25]. Therefore, hyperspectral reflectance makes it possible to monitor rice growth. Generally, there are three types of methods for monitoring rice growth: empirical statistical, physical, and a hybrid of both. For the empirical model, on the one hand, it is very important to select reasonable hyperspectral parameters. The vegetation indices constructed by reflectance are one of the common hyperspectral parameters. [PERSON], [PERSON], and [PERSON] et al. used some specific vegetation indices to monitor the LAI and SPAD values of rice [23; 24; 26; 27]. Compared with vegetation indices, [PERSON] and [PERSON] found the model incorporated with absorption band width performs better in simulating LAI [28; 29]. [PERSON] also highlighted that the rice LAI monitoring model, which uses first and second derivative hyperspectral data, has better accuracy and stability [30].