Identification of Robust Hybrid Inversion Models on the Crop Fraction of Absorbed Photosynthetically Active Radiation Using PROSAIL Model Simulated and Field Multispectral Data  
[PERSON]  
Colege of Hydraulic Science and Engineering, Yangzhou University, Yangzhou 225009, China; [EMAIL_ADDRESS] (J.K.); [EMAIL_ADDRESS] (Z.L. ); [EMAIL_ADDRESS] (M.T. ); mz120221159@stu. numerous Vis, such as the normalized difference VI (NDVI) [8], enhanced VI (EVI) [9] and simple ratio index (SR) [10], have been developed, evaluated and shown to be effective in estimating the FPARs for various vegetation types. However, there are still several issues associated with the VI approach that need to be addressed. First, while VIs can reveal the physiological and ecological characteristics of vegetation, they cannot fully capture the changes in solar effective radiation within the canopy and surface [11; 12]. Furthermore, estimating VIs introduced uncertainty due to factors like variations in solar elevation angle and soil background. Moreover, most VI methods were statistically based and exhibited different relationships between VIs and FPARs for various spatial and temporal vegetation dynamics, resulting in regional restrictions in their application. [PERSON] et al. [13] suggested that VIs could be simply used to determine canopy light absorption without involving the canopy structural level. In contrast, the RTM could elucidate and simulate the process of FPAR change by incorporating explicit geometric models of the canopy structure [14]. [PERSON] et al. [15] quantitatively retrieved the spatial distribution of the absorbed FPAR in salt marsh vegetation from satellite images using the soil-canopy-photochemical and energy flux observation coupling model (SCOPE). [PERSON] et al. [16] employed the modified simultaneous heat and water radiation model (MSHAW) to simulate the vertical profile of FPAR within the canopy. The RTM demonstrated greater adaptability to diverse environments and background conditions for FPAR simulation, but the model uncertainties associated with the input parameters also affected the RTM accuracy. Computational technology advances, more optimization algorithms (e.g., gradient methods, genetic algorithms and simulated annealing) and machine learning algorithms (artificial neural networks (ANNs), support vector regression (SVR) and random forest (RF)) have been combined with RTMs to improve the FPAR inversion accuracy [17; 18]. For example, [PERSON] et al. [19] used the PROSAIL model in conjunction with a neural network algorithm to investigate a series of measured crop FPARs and model the simulated FPARs. In addition to the mechanism-based inversion model, other studies also evaluated robust estimation models through sensitivity analysis, which investigates the impacts of parameter variations on simulation results (e.g., band reflectance and VI) [20; 21]. For example, [PERSON] et al. [22] constructed the relationship between the rescaled VIs (NDVI, OSAVI, etc.) and the observed FPAR, and validated this relationship using independent datasets. [PERSON] et al. [23] analyzed the sensitivity and correlation between the three modified VIs and the FPAR using reflectance data simulated by the PROSAIL model. However, most sensitivity analyses of VIs primarily focus on their influence on LAI changes, and limited studies on FPAR changes have been reported. Although LAI and FPAR are strongly correlated based on the Beer-Lambert law, the relationship is also modulated by the extinction coefficient, introducing a certain degree of uncertainty. Moreover, most previous studies focused on a single empirical or physical method, but few combined multiple methods for FPAR inversion. [PERSON] et al. [24] used the PROSAIL model and a look-up table (LUT) algorithm to investigate the FPAR inversion from satellite data for different vegetation types in various regions. [PERSON] et al. [25] proposed an empirical model calibration method to distinguish vegetation types based on the deviation between the simulated FPAR and MODIS-observed FPAR.