Leaf Area Index Inversion of Summer Maize at Multiple Growth Stages based on BP Neural Network

Leaf Area Index (LAI) is an important vegetation structure parameter in biogeochemical cycling. In view of the lack of LAI inversion in the multiple growth period of summer maize based on GF-1 WFV satellite images in China, this study constructs a BP neural network model (BP1 model and BP2 model) based on different hidden layers, and compares and analyzes the accuracy of the inversion between the BP1 model, BP2 model and 6 statistical models (NDVI、RVI、DVI、EVI、SAVI、ARVI). Based on the measured data, BP1 model and BP2 model are used to map the LAI dynamic changes of summer maize. The results show that LAI has good correlation with 6 common statistical models, and the fitting degree of the NDVI exponential equation regression model is the best. The overallR ² of BP neural network model is slightly smaller than the statistical model, while RMSE is less than the statistical model, and the errors with the measured value is smaller than the statistical model. So both the statistical model and the BP neural network model have advantages and disadvantages. The BP2 model is superior to the BP1 model inR ² and RMSE, and can obtain more accurate inversion values, and the overall prediction accuracy of BP2 is higher. Based on the BP neural network simulation of summer maize growth period inversion, the LAI value presents a slow increase to the gradual decrease of S type change process, which is basically in line with the crop growth rules. The study combines with the BP neural network model established by different hidden layers to provide a method for the application of GF-1 satellite in the application of crop leaf area index multiple growth period inversion.