Inversion of rice canopy chlorophyll content and leaf area index based on coupling of radiative transfer and Bayesian network models

Abstract

The radiative transfer model (RTM) simulates forward spectral reflectance of vegetation and is used to estimate physical parameters using backwards inversion. However, differentiation of spectral reflectances may be hampered due to model parameter combinations, and the cost function within RTM that calculates statistical distance may lead to inconsistent inversions. Bayesian network (BN) is a probabilistic model that is used to solve problems of model ambiguity and incompleteness. Here, we constructed a model to estimate rice growth parameters using data collected by an unmanned aerial vehicle (UAV). We collected rice canopy spectral information using a MiniMCA-6 multispectral camera fitted to an UAV that was used to determine BN structure using parameters derived from the PROSAIL model. We calculated conditional probability distributions of different observed combinations of rice canopy chlorophyll content (CCC) and leaf area index (LAI) and a look up table of maximum conditional probabilities of rice growth parameters based on BN was developed. Results indicated that most accurate inversions of LAI and CCC as BN nodes were achieved at reflectances of 720 nm, 800 nm under the red normalized difference vegetation index and at reflectances of 550, 720, 800 nm under the modified simple ratio index, respectively. Compared with the cost function inversion method, the BN method mitigated the ill-posed problem of inversion and obtained higher inversion accuracy with model test R2, RRMSE, and RE values of 0.81, 0.31, and 0.38, and 0.83, 0.36, and 0.43 for LAI and CCC, respectively. We conclude that application of the BN method to the inversion process of crop RTM could improve inversion accuracy of estimation of crop parameters.