资源简介

摘要： 叶面积指数（Leaf Area Index,LAI）是生物地球化学循环中重要的植被结构参数。针对目前基于我国GF-1 WFV卫星影像的夏玉米多生育期LAI反演研究较少的问题，基于不同隐含层构建BP神经网络模型（BP1模型和BP2模型），对比分析BP1模型、BP2模型和6种统计模型（NDVI、RVI、DVI、EVI、SAVI、ARVI）反演之间的精度差异，并根据实测数据绘制BP1模型和BP2模型的夏玉米多生育期LAI动态变化图。结果表明:LAI与6种常用的统计模型均有良好相关性，其中NDVI指数方程式回归模型拟合度最优；BP神经网络模型整体R 2略小于统计模型，而RMSE则小于统计模型，取得了与实测值差异更小的结果，统计模型与BP神经网络模型各有优劣之处；BP2模型在R 2和RMSE均优于BP1模型，能获得更为精确的反演值，BP2整体预测精度更高；基于BP神经网络模拟夏玉米生育期反演，LAI值呈现缓慢升高—快速增长—逐渐减小的S型变化过程，基本符合作物生长规律。该研究结合不同隐含层建立的BP神经网络模型，为GF-1卫星在作物叶面积指数多生育期反演的应用推广提供了方法支撑。 关键词： 夏玉米;  叶面积指数;  BP神经网络模型;  统计模型;  多生育期     Abstract: 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.

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