Qi Yaqin, Chen Xi, Chen Zhengchao, Zhang Xin, Shen Congju, Chen Yan, Peng Yuanying, Chen Bing, Wang Qiong, Liu Taijie, Zhang Hao
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China.
Research Institute, Xinjiang Academy Agricultural and Reclamation Science, Shihezi 832003, China.
Life (Basel). 2025 Jan 7;15(1):62. doi: 10.3390/life15010062.
The spectral vegetation indices derived from remote sensing data provide a detailed spectral analysis for assessing vegetation characteristics. This study investigated the relationship between cotton yield and canopy spectral indices to develop yield estimation models. Spectral reflectance data were collected at various growth stages using an ASD FieldSpec Pro VNIR 2500 spectrometer. Six prediction models were developed using spectral vegetation indices, including the Normalized Difference Vegetation Index () and Ratio Vegetation Index (), to estimate the Leaf Area Index () and above-ground biomass. For estimation using the , the power function model () demonstrated higher precision, with a multiple correlation coefficient of = 0.8184 and the smallest root mean square error ( = 0.3613). These results confirm the strong predictive capacity of for , with the power function model offering the best estimation accuracy. In estimating above-ground biomass using , the power function model of achieved the higher correlation ( = 0.8851) for fresh biomass with an of 0.1033, making it the most accurate. For dry biomass, the exponential function model () was the most precise, achieving an value of 0.8456 and the lowest value of 0.0076. These findings highlight the potential of spectral remote sensing for accurately predicting cotton canopy structural parameters and biomass weights. By integrating spectral analysis techniques with remote sensing, this research offers valuable insights for precision cotton planting and field management, enabling optimized agricultural practices and enhanced vegetation health monitoring.
从遥感数据中得出的光谱植被指数为评估植被特征提供了详细的光谱分析。本研究调查了棉花产量与冠层光谱指数之间的关系，以建立产量估算模型。使用ASD FieldSpec Pro VNIR 2500光谱仪在不同生长阶段收集光谱反射率数据。利用光谱植被指数，包括归一化植被指数（）和比值植被指数（），建立了六个预测模型，以估算叶面积指数（）和地上生物量。对于使用进行估算，幂函数模型（）显示出更高的精度，复相关系数=0.8184，均方根误差最小（=0.3613）。这些结果证实了对具有很强的预测能力，幂函数模型提供了最佳估算精度。在使用估算地上生物量时，的幂函数模型对鲜生物量的相关性更高（=0.8851），为0.1033，是最准确的。对于干生物量，指数函数模型（）最精确，值为0.8456，值最低为0.0076。这些发现突出了光谱遥感在准确预测棉花冠层结构参数和生物量权重方面的潜力。通过将光谱分析技术与遥感相结合，本研究为精准棉花种植和田间管理提供了有价值的见解，有助于实现优化农业实践和加强植被健康监测。
