基于地面高光谱遥感的大豆产量估算模型研究

为在田间管理中对作物产量进行估测，通过两年大田试验收集了大豆生殖生长期的高光谱数据及产量数据，基于各生育期一阶微分光谱反射率计算了7个光谱指数：比值指数（Ratio index，RI）、差值指数（Difference index，DI）、归一化光谱指数（Normalized difference vegetation index，NDVI）、土壤调整光谱指数（Soil-adjusted iegetation index，SAVI）、三角光谱指数（Triangular vegetation index，TVI）、改进红边归一光谱指数（Modified normalized difference index，mNDI）和改进红边比值光谱指数（Modified simple ratio，mSR），使用相关矩阵法将光谱指数与大豆产量数据进行相关性分析并提取最佳波长组合，随后将计算结果作为与大豆产量相关的最佳光谱指数，最后将各生育期筛选出的与大豆产量相关系数最高的5个光谱指数作为模型输入变量，利用支持向量机（Support vector machine，SVM）、随机森林（Random forest，RF）和反向神经网络（Back propagation neural network，BPNN）构建大豆产量估算模型并进行验证。结果表明，各生育期（全花期（R2）、全荚期（R4）和鼓粒期（R6））计算的光谱指数与产量的相关系数均高于0.6，相关性较好，其中全荚期的光谱指数FDmSR与大豆产量的相关系数最高，达到0.717；大豆产量最优估算模型的方法是输入变量为全荚期构建的一阶微分光谱指数和RF组合的建模方法，模型验证集R2为0.85，RMSE和MRE分别为272.80kg/hm2和5.12%。本研究成果可为基于高光谱遥感技术的作物产量估测提供理论依据和应用参考。

Soybean Seed Yield Estimation Model Based on Ground Hyperspectral Remote Sensing Technology

To estimate crop yield in field management, hyperspectral data and yield data during the reproductive growth period of soybeans through two years of field experiments were collected. Seven spectral indices were calculated based on first-order spectral reflectance at various growth stages. These indices included the ratio index (RI), difference index (DI), normalized difference vegetation index (NDVI), soil-adjusted vegetation index (SAVI), triangular vegetation index (TVI), modified normalized difference index (mNDI), and modified simple ratio (mSR). A correlation analysis between the spectral indices and soybean yield data were conducted by using the correlation matrix method. The best wavelength combinations to be used as the optimal spectral indices related to soybean yield were extracted. Finally, the five spectral indices with the highest correlation coefficients with soybean yield at different growth stages were selected as input variables for the model. Support vector machine (SVM), random forest (RF), and back propagation neural network (BPNN) were utilized to construct soybean yield estimation models and conducted validation. The results indicated that the spectral indices calculated at different growth stages (full flowering stage (R2), full pod stage (R4), and seed filling stage (R6)) all exhibited a correlation coefficient greater than 0.6 with yield, showing a strong correlation. Among these, the spectral index FDmSR at the full pod stage had the highest correlation with soybean yield, reaching 0.717. The optimal model for soybean yield estimation was built using first-order spectral indices from the full pod stage in combination with RF as input variables, achieving a validation set R2 of 0.85, and RMSE and MRE values of 272.80kg/hm2 and 5.12%, respectively. The research outcome can provide a theoretical basis and practical reference for crop yield estimation based on hyperspectral remote sensing technology.