Biomass is an important indicator in estimating the growth and yield of crops. Mastering the growth state of crops by evaluating and monitoring biomass is crucial. Correlation analysis was used to identify hyperspectral sensitive bands with the best correlation with biomass, and a hyperspectral vegetation index was constructed. Plant height data was coupled with hyperspectral reflectance and vegetation index data, and a biomass inversion model was constructed using partial least squares (PLS), support vector machine (SVM), and random forest (RF) regression analysis. The R², root mean square error (RMSE), and normalized root mean square error (NRMSE) were calculated to verify the accuracy of the model. Plant height coupled with hyperspectral reflectance and vegetation index data, R², RMSE, and NRMSE of the biomass model establishment and validation, compared with the hyperspectral data alone, improved model accuracy of the three algorithms for maize (Zea mays L.) biomass inversion coupled with plant height data with the relative improvement in accuracy of 13.58, 9.6, and 1.05%, respectively. Additionally, the model becomes more stable, overcoming the spectral saturation of canopy to a certain extent. It provides supporting information for accurate farmland planning, growth monitoring, and yield estimation.

中文翻译：

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结合株高数据的玉米生物量高光谱反演

生物量是估算作物生长和产量的重要指标。通过评估和监测生物量来掌握作物的生长状态至关重要。相关分析用于识别与生物量相关性最好的高光谱敏感波段，并构建高光谱植被指数。植物高度数据与高光谱反射率和植被指数数据相结合，并使用偏最小二乘法 (PLS)、支持向量机 (SVM) 和随机森林 (RF) 回归分析构建生物量反演模型。计算R ²、均方根误差(RMSE) 和归一化均方根误差(NRMSE) 以验证模型的准确性。植物高度加上高光谱反射率和植被指数数据，R²生物量模型建立和验证的RMSE和NRMSE，与单独的高光谱数据相比，提高了玉米（Zea mays L.）生物量反演与株高数据的三种算法的模型精度，精度相对提高分别为 13.58、9.6 和 1.05%。此外，模型变得更加稳定，在一定程度上克服了冠层光谱饱和的问题。它为准确的农田规划、生长监测和产量估算提供支持信息。