基于无人机多光谱遥感的冬油菜地上部生物量估算

地上部生物量(Above-ground biomass, AGB)是判断作物生长发育的重要指标，对作物不同生长阶段地上部生物量进行快速、准确、无损遥感监测对精准农业生产具有重要意义。本文在西北关中地区开展田间试验，以不同水氮处理下冬油菜为研究对象，通过对其生理生长指标以及产量进行分析，确定I2N3(越冬期和蕾薹期补灌，施氮量为280 kg/hm²)处理为该地适宜的水氮管理策略。使用无人机获取冬油菜营养生长期和生殖生长期多光谱图像，采用阈值法对多光谱图像中的阴影和土壤背景进行掩膜处理，提取各波段反射率，构建植被指数。将冬油菜地上部生物量实测数据与21个光谱变量进行相关性分析，筛选出各生长阶段相关系数绝对值排名前8个光谱变量作为输入量，通过随机森林(RF)、支持向量机(SVM)、遗传算法优化支持向量机(GA-SVM)和粒子群优化支持向量机(PSO-SVM)构建不同生长阶段冬油菜地上部生物量估算模型，确定最佳估算模型。结果表明，全生长阶段和生殖生长阶段红光波段反射率显著性最强且稳定，相关系数分别达到0.835和0.754;PSO-SVM模型更适合用于反演关中地区冬油菜不...

Estimation of Winter Rapeseed Above-ground Biomass Based on UAV Multi-spectral Remote Sensing

Above-ground biomass (AGB) is an important index to judge the growth and development of crops. Rapid, accurate and non-destructive remote sensing monitoring of AGB at different growth stages of crops is of great significance to precision agricultural production. A field experiment was carried out in Guanzhong area of Northwest China. Winter rapeseed under different water and nitrogen treatments was used as the research object. The multi-spectral images of winter rapeseed in vegetative and reproductive growth periods were obtained by UAV, and the AGB measured data of winter rapeseed were obtained by field experiment. The shadow and soil background in multi-spectral image were masked by threshold method, and the reflectance of each band was extracted to construct vegetation index. The correlation analysis between the measured data of winter rapeseed AGB and spectral variables was carried out, and the top eight spectral variables with the absolute value of correlation coefficient in each growth stage were selected as input variables. The AGB estimation model of winter rapeseed at different growth stages was constructed by random forest (RF), support vector machine (SVM), genetic algorithm optimized support vector machine (GA-SVM) and particle swarm optimization support vector machine (PSO-SVM) to determine the best estimation model. The results showed that the red band reflectance in the whole growth stage and reproductive growth stage was the most significant and stable, and the correlation coefficients were 0.835 and 0.754, respectively. The NBI in the vegetative growth stage was the most significant and stable, and the correlation coefficient was 0.846. The PSO-SVM was more suitable for the inversion of AGB at different growth stages of winter oilseed. The validation set R2 of the whole growth period, vegetative growth period and reproductive growth period were 0.866, 0.962 and 0.789, respectively.