Hyperspectral estimation of leaf area index for winter wheat based on Akaike's information criterion

Abstract: Winter wheat leaf area index (LAI) is one of important parameters in describing the canopy structure, which is particularly significant in the analysis of winter wheat growth and the yield prediction. The objective of the study was to demonstrate the feasibility of remote sensing monitoring on winter wheat LAI and its expansibility in spatial and temporal scale. Canopy LAI variables from remote sensing data were investigated using empirical statistics inversion model. This study focused on analyzing the correlation between vegetation index and LAI. After sorting the vegetation index using grey relational analysis (GRA), the number of independent variables of different vegetation indices was chosen to participate in the regression using the partial least squares regression (PLS). Based on these LAI models, the Akaike's information criterion (AIC) values were calculated, and the model with the smallest AIC value was chosen as the optimal winter wheat LAI estimation model, i.e. the optimal winter wheat LAI estimation model was established by integrating the methods of GRA, PLS and AIC. Spectral reflectance of leaves and concurrent LAI parameters of samples were acquired in Tongzhou and Shunyi District, Beijing City, China during 2008-2009, which were for model establishment. Fourteen vegetation indices related to LAI were chosen to evaluate the model of LAI. Firstly, the correlation coefficient was analyzed, and it was found that there was significant negative correlation between transformed chlorophyll absorption in reflectance index / optimized soil adjusted vegetation index (TCARI/OSAVI), structure insensitive pigment index (SIPI), photon radiance index (PRI) and LAI, and significant positive correlation between normalized difference vegetation index (NDVI), simple ratio index (SR), OSAVI, normalized difference vegetation index 705 (NDVI705), modified red edge simple ratio index (MSR705), modified red edge normalized difference vegetation index (mNDVI705), Vogelmann index 1 (VOG1), modified chlorophyll absorption in reflectance index (MCARI2), modified soil adjusted vegetation index (MSAVI), modified simple ratio (MSR) and LAI. Secondly, the related degree order between 14 vegetation indices and winter wheat LAI could be drawn as follows: VOG1 > SIPI > MCARI2 > NDVI > MSR > mNDVI705 > OSAVI > NDVI705 > TCARI / OSAVI > TCARI > PRI > MSAVI > MSR705 > SR. Among them, the biggest GRA correlation degree was VOG1, whose value was 0.9211 and the smallest was SR, whose value was 0.6178. Thirdly, in accordance with the arrangement size of GRA, we used the PLS algorithm to increase the number of independent variables in turn to build 9 winter wheat LAI inversion models. Based on the AIC, we filtered and optimized the 9 winter wheat LAI models. Then, the optimal winter wheat LAI model was constructed by 8 independent variables, which were VOG1, SIPI, MCARI2, NDVI, MSR, mNDVI705, OSAVI and NDVI705. The decision coefficient (R2) and standard error (SE) of the GRA-PLS-AIC method modeling were respectively 0.76 and 0.009, which had a higher ability to predict winter wheat LAI. Considering the temporal characteristics of winter wheat LAI, we incorporated the relevant data from 2009 to 2010 to the model and evaluated its ability of estimating the winter wheat LAI in different years. The R2, relative root mean standard error (RRMSE) and the slope of the fitted line between measured and predicted LAI value in validation set by GRA-PLS-AIC method were respectively 0.63, 0.004 and 0.68. It shows that the model has a higher predictive ability, which lays an important foundation for improving the precision of forecasting winter wheat LAI using remote sensing method.