Synchronous Retrieval of LAI and Cab from UAV Remote Sensing: Development of Optimal Estimation Inversion Framework

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.

Synchronous Retrieval of Wheat Cab and LAI from UAV Remote Sensing: Application of the Optimized Estimation Inversion Framework

“…If there are some unknown parameters of a system that need to be estimated, the optimal estimation inversion method is a better solution idea. It finds the optimal parameter by minimizing a cost function, and the function is usually based on the error between the observed data and the model simulated data [24]. We transformed the nonlinear canopy radiative transfer model inversion into a cost function minimization problem.…”

“…To this end, we proposed an inversion method based on the early studies, which use a more rational model and an inversion strategy [24]. The method, based on the theory of optimal estimation [25], builds a cost function by combining the a priori information and the observation error.…”

Crop HTP Technologies: Applications and Prospects

“…In recent decades, high-throughput techniques have been used to measure the LAI of crops such as maize [66], cotton [67], and rapeseed [68]. Zheng et al [69] developed a new unified linearized vector radiative transfer model (UNL-VRTM) based on multispectral data. The forward modeling of the model has a strong coupling between vegetation canopy and the atmospheric environment, and the simulation process is reasonable, which means it can support the synchronous detection of the LAI and Cab.…”

Regional Monitoring of Leaf ChlorophyII Content of Summer Maize by Integrating Multi-Source Remote Sensing Data

“…The physical model considers more factors and complex physical procedures than the other two methods, making it suitable for vegetation parameter inversions in different regions, for different crops, and at different scales; however, this approach is relatively complex and difficult to use [20][21][22]. In contrast, empirical methods establish a statistical relationship between vegetation indices and the physiological parameters of vegetation, offering high computational efficiency and accurate estimates of vegetation-related physiological parameters [23,24]. Representing a relatively recent branch of empirical methods, machine learning algorithms, such as support vector regression (SVR), backpropagation neural networks (BPNNs), Gaussian process regression (GPR) algorithms, random forest algorithms (RF), and deep neural networks (DNN), are widely used in crop growth status monitoring because of their excellent computing efficiency [25][26][27][28][29].…”