## 1 Introduction
Additionally, an Anisotropic Flat Index (AFX) was devised to extract BRDF prototypes from MODIS data, encapsulating the variability of the fundamental dome-bowl anisotropic reflectance patterns of the terrestrial surface. The AFX method augments the understanding of vegetation structure and other anisotropic reflectance properties of the land surface [26]. [PERSON] et al. [23] have devised a comprehensive method for normalizing Landsat reflectance data to BRDF adjusted reflectance, leveraging MODIS BRDF products. Within the scope of their research, a consistent set of BRDF spectral model parameters was established. Subsequently, this fixed parameter set was effectively utilized to produce Nadir BRDF-adjusted Reflectance (NBAR) for Sentinel-2 satellites [17; 18]. Retrieval methods for vegetation parameters such as the LAI, fractional vegetation cover (FVC), chlorophyll, and others, based on remote sensing, are generally categorized into two primary approaches: physical model methods grounded in radiative transfer models, and empirical model methods predicated on vegetation indices [27; 28; 29]. The physical model approach executes a cost function match by simulating the radiative transfer processes within the vegetation canopy and leaf structure to deduce the physical and chemical properties of the vegetation. This method is characterized by its robust universality, as it is not constrained by environmental conditions or crop types. However, it should be noted that the radiative transfer processes in natural settings are influenced by various factors, including the characteristics of the canopy architecture, and are contingent upon several assumed parameters for simulation [30]. Empirical model methods, on the other hand, are predominantly founded upon establishing regression relationships between observedvegetation parameters and corresponding vegetation indices. These indices include, but are not limited to, the NDVI, the ratio vegetation index (RVI), and the enhanced vegetation index 2 (EVI2), among others [31; 32; 33]. The efficacy of empirical models is more reliant on the accuracy of the regression relationships and the reliability of the satellite observation data [34]. Prior research has demonstrated that the Jilin-1 GP01/02 satellites possess an absolute radiometric accuracy that is comparable to that of the Sentinel-2A satellite [35], rendering them suitable for high-precision inversion of the crop LAI [36]. Nevertheless, given the unique flexible side-swing imaging capabilities of the Jilin-1 GP01/02 satellites, further investigation is warranted into the evaluation and correction of the angle effect on crop parameters retrieval by these satellites. This study is primarily concentrated on the correction of the angle effect for high-resolution satellites equipped with side-swing imaging capabilities. We have developed an angle correction methodology that is specifically tailored for the Jilin-1 GP01/02 satellites, enabling accurate reflectance correction under diverse observation geometric conditions. The efficacy of the proposed angle correction method in enhancing the retrieval precision of vegetation indices and crop parameters has been rigorously assessed using an extensive collection of synchronous field measurements. This paper posits that it is imperative to address the angle effect for side-view imaging data obtained from high-resolution satellites. Furthermore, the application of a set of suitably calibrated Anisotropic Flat Index (AFX) parameters can significantly augment the accuracy of crop monitoring endeavors.