当作物生物量较大时, 现有植被指数由于受饱和问题限制, 不能较好的估算作物生物量。 针对此问题, 尝试将波段深度分析与偏最小二乘回归(partial least square regression, PLSR)结合, 提高对大田冬小麦生物量的估算精度, 并将两者结合建立的模型与应用代表性植被指数建立的模型进行生物量估算精度比较。 波段深度分析主要对冬小麦冠层光谱550～750 nm范围进行, 采用波段深度、波段深度比(band depth ratio, BDR)、 归一化波段深度指数和归一化面积波段深度对波段深度信息进行表征。 在建立的模型中, 波段深度分析和PLSR结合的估算精度比应用植被指数模型的精度高, 其中BDR与PLSR结合的估算精度最高(R2=0.792, RMSE=0.164 kg·m-2)。 研究结果表明波段深度分析与PLSR结合能较好的克服生物量较大时存在的饱和问题, 提高冬小麦生物量的估算精度。

The major limitation of using existing vegetation indices for crop biomass estimation is that it approaches a saturation level asymptotically for a certain range of biomass. In order to resolve this problem, band depth analysis and partial least square regression (PLSR) were combined to establish winter wheat biomass estimation model in the present study. The models based on the combination of band depth analysis and PLSR were compared with the models based on common vegetation indexes from the point of view of estimation accuracy, subsequently. Band depth analysis was conducted in the visible spectral domain (550~750 nm). Band depth, band depth ratio (BDR), normalized band depth index, and band depth normalized to area were utilized to represent band depth information. Among the calibrated estimation models, the models based on the combination of band depth analysis and PLSR reached higher accuracy than those based on the vegetation indices. Among them, the combination of BDR and PLSR got the highest accuracy (R2=0.792, RMSE=0.164 kg·m-2). The results indicated that the combination of band depth analysis and PLSR could well overcome the saturation problem and improve the biomass estimation accuracy when winter wheat biomass is large.