## 1 Introduction  
The rapid advancement of remote sensing technology has established it as a crucial tool in the realm of agricultural research. This is attributable to its capability to offer extensive and periodic observations of terrestrial surface data [1; 2; 3]. Nonetheless, the inherent high spatial heterogeneity observed in agricultural lands, coupled with the dynamic nature of crop development, imposes stringent demands on the loneliness, precision, and sequential integrity of satellite-derived remote sensing data [4]. Within the context of contemporary agricultural practices, the accurate derivation of crop parameters, including the vegetation index, leaf area index (LAI), and chlorophyll concentration, is paramount for the monitoring of crop growth, the estimation of yields, and the stewardship of agricultural resources [5; 6]. High-resolution satellites are capable of delivering detailed imagery, which enables the capture of surface features at a finer scale. This enhanced resolution facilitates more precise identification and monitoring of crop growth patterns. However, these high-resolution satellites are inevitably constrained by factors such as optical limitations, bandwidthrestrictions, and orbital configurations, which can impede the simultaneous attainment of high spatial and high temporal resolution data [7]. In recent years, the challenge of acquiring satellite data with both a high spatial resolution (less than 10 m) and a high temporal resolution (less than 5 days) has been addressed through the establishment of satellite constellations utilizing extensive satellite networks [8]. The 'Jilin-1' constellation, developed by Chang Guang Satellite Technology Co., Ltd., is designed to enable rapid updates of global surface information via a network comprising 138 high-resolution satellites. As a provisional measure towards this goal, the flexibility to adjust the satellite's angle of inclination can be exploited to reduce its revisit time. The Jilin-1 GP01/02 satellites constitute pivotal elements within the 'Jilin-1' constellation. Equipped with a 5 m multispectral imager, these satellites possess the capability for side-swing imaging up to \(\pm 45^{\circ}\), enabling daily revisits to local areas through side-view imaging techniques. When juxtaposed with traditional medium-resolution satellites such as the Landsat series and Sentinel-2, the enhanced imaging flexibility of the Jilin-1 satellites presents heightened challenges to satellite data processing and the inversion of vegetation parameters [8; 9; 10]. Among these challenges, angle correction emerges as a critical step for achieving more precise surface albedo measurements. The angle effect in satellite imagery is predominantly influenced by the sensor's observation angle and the solar angle at the time of image acquisition [11]. The Bidirectional Reflectance Distribution Function (BRDF) delineates the relationship between bidirectional reflectance and the spectral, as well as the structural, characteristics of the terrestrial target [12]. It serves as a tool to mitigate discrepancies in observed reflectance that arise from varying incidence and observation angles [13]. MODIS BRDF products have garnered widespread application across a spectrum of satellite sensors, including the Landsat series [14; 15; 16], Sentinel-2 MSI [17; 18], GF-1 WFV [19; 20], GF-4 PMS [21], and SPOT [22], and have even been extended to correct for angle effects in UAV observation data [13]. To streamline the computational process, researchers have formulated a suite of fixed BRDF model parameters predicated on MODIS BRDF, which has been effectively utilized to compute nadir reflectance for both Landsat and Sentinel-2 satellites [17; 18; 23]. The Vermote-Justice-Breon (VJB) method, as developed by [PERSON] et al., is instrumental in correcting the angle effect in time-series MODIS data. This approach adeptly adjusts BRDF parameters via the linear regression relationship with the normalized difference vegetation index (NDVI) [24; 25].