Comparison of SPAD Value and LAI Spectral Estimation of Soybean Leaves Based on Different Analysis Models

In order to explore the effective analytical model and methods for estimating leaf SPAD value and LAI by spectroscopic technique in soybean drum-grain stage, this study used soybean at seed-filling stage in the field as the test material, conducted three different time periods at 9:45-10:15（10 AM）, 11:45-12:15（12 PM） and 13:45-14:15（2 PM） to measure the canopy full-band spectral reflectance, used extreme learning machine（ELM）, partial least squares regression（PLSR）, support vector machine（SVM） and random forest（RF） to build soybean leaf SPAD value and LAI estimation models, and compared the estimation accuracy of the analysis results of different models. The results showed that in each model, the fitting accuracy of the spectral reflectance measured by 12 PM and 2 PM with the SPAD value and LAI of soybean leaves was higher than that of 10 AM. The R~2 of the RF-based soybean leaf SPAD value estimation model validation set was 0.910, the RMSE was 2.006, and the MRE was 3.684. The RF-based soybean LAI estimation model validation set was with R~2 of 0.916, RMSE was 0.209, MRE was 4.383, compared with ELM, PLSR and SVM, it had higher estimation accuracy. The results also showed that the full-band spectral reflectance took on at 11:45-12:15 and 13:45-14:15 with the RF model could be used to estimate the SPAD value and LAI of soybean leaves, which could obtain more accurate results.