Estimating leaf chlorophyll concentration in soybean using random forests and field imaging spectroscopy

摘要

An accurate quantitative estimation of crop chlorophyll content is of great importance for a wide range of monitoring crop grow health condition and estimating biomass, since radiative transfer model are complex caused by the nonlinear relationship between crop spectral and chlorophyll content and the uncertainties in the land surface systems, traditional inversion techniques can not satisfied with the demand of accurate estimation of chlorophyll content. Alternatively, random forests are able to cope with the strong nonlinearity of the functional dependence between the biophysical parameter and the observed reflected radiance, it may therefore be more suitable candidates for estimating crop biochemistry parameters from inversion of radiative transfer model. It is crucial to apply random forests for inversion of radiative transfer model, so as to construct hyperspectral estimation model for crop chlorophyll content. The aim of this paper is to explore the feasibility of using random forests and field imaging spectroscopy for the estimating leaf chlorophyll concentration in soybean. Field spectroscopy was carried out with an ASD FieldSpec3 in summer 2009, at the farmlands of city of Chang'chun, Jinlin province. The measured spectral range was between 350–2500 nm with a sampling interval of 1.4 nm in the 350–1000 nm range and 2 nm in the 1000–2500 nm range, and the spectral range between 350–1250 nm was used for the retrieval of leaf chlorophyll concentration. Leaf chlorophyll concentration in soybean was measured by SPAD-502. Each sample sites was recorded with a Global Position System (GPS). Firstly, a training data set through PROSPECT was established to link soybean spectrum and the corresponding chlorophyll content. Secondly, random forests were adopted to train the training data set, in order to establish leaf chlorophyll content estimation model. Thirdly, a validation data set was established based on proximal hyperspectral dat- , and the leaf estimation model of chlorophyll concentration was applied to the validation data set to estimate leaf chlorophyll content of soybean in the research area. The estimation model yielded results with a coefficient of determination of 0.9317 and a mean square error (MSE) of 74.2569. The results indicate that model based on random forests and field imaging spectroscopy can estimate leaf chlorophyll content of soybean accurately, and it can solve soybean chlorophyll content inversion problem even with inadequate samples. Random forests and field imaging spectroscopy would be used as a new quickly and nondestructive method to estimate the chlorophyll content of the soybean. Future study will concentrated on scaled up the field estimation model to satellite remote sensing level, which will monitor the soybean's health condition in a large scale.