###### Abstract  
UAV (unmanned aerial vehicle) remote sensing provides the feasibility of high-throughput phenotype nondestructive acquisition at the field scale. However, accurate remote sensing of crop physicochemical parameters from UAV optical measurements still needs to be further studied. For this purpose, we put forward a crop phenotype inversion framework based on the optimal estimation (OE) theory in this paper, originating from UAV low-altitude hyperspectral/multispectral data. The newly developed unified linearized vector radiative transfer model (UNL-VRTM), combined with the classical PROSAIL model, is used as the forward model, and the forward model was verified by the wheat canopy reflectance data, collected using the FieldSpec Handheld in Qi County, Henan Province. To test the self-consistency of the OE-based framework, we conducted forward simulations for the UAV multispectral sensors (DJI P4 Multispectral) with different observation geometries and aerosol loadings, and a total of 801 sets of validation data were obtained. In addition, parameter sensitivity analysis and information content analysis were performed to determine the contribution of crop parameters to the UAV measurements. Results showed that: (1) the forward model has a strong coupling between vegetation canopy and atmosphere environment, and the modeling process is reasonable. (2) The OE-based inversion framework can make full use of the available radiometric spectral information and had good convergence and self-consistency. (3) The UAV multispectral observations can support the synchronous retrieval of LAI (leaf area index) and Cab (chlorophyll a and b content) based on the proposed algorithm. The proposed inversion framework is expected to be a new way for phenotypic parameter extraction of crops in field environments and had some potential and feasibility for UAV remote sensing. c 2023 13 119 11  
accuracy of the empirical method mainly depends on the spectral indices and the number of samples [15; 16; 17]. Since the spectral indices are usually constructed from measurements at several wavelengths, these methods can hardly make use of all the observation information. Moreover, the application of the empirical model approach is limited [18; 19; 20]. The physical model method builds a forward model from the solar radiation to the crop canopy, and then to the sensor [21; 22]. Then, crop parameters can be obtained by finding the minimum of the cost function. The cost function is usually a multivariable nonlinear function and mathematical tool, which is constructed by model simulation results and actual measurements, as the Least Squares method can be used to solve the cost function [23; 24]. However, the iterative process requires repeated forward model calculation, which is time-consuming, and prior constraints are also required to avoid falling into local optimality [25; 26]. Look-up Tables (LUTs) are widely used to replace the real-time calculation of the forward model, which can greatly reduce the computation load [27; 28]. However, LUTs are not suitable for multiparameter inversion because the size of the LUT grows exponentially with the number of parameters. At present, the physical models are rare for the UAV remote sensing of crop parameters. The PROSAIL model, widely used to simulate canopy reflectance from crop physicochemical and structural parameters, does not consider solar radiation and atmospheric environment yet [29; 30]. The typical atmospheric radiative transfer models, such as 6 SV (Second Simulation of a Satellite Signal in the Solar Spectrum--vector) [31], MODTRAN (moderate-resolution atmospheric transmission) [32], RT3 (the polarized radiative transfer) [33], and UNL-VRTM [34], can be used for UAV remote sensing. However, the inputs of these models do not include the crop phenotype parameters. The 6 SV provides a Python interface for PROSAIL but does not have open-source code [35].