Resumen
Accurately estimating aboveground dry biomass (ADB) is crucial. The ADB of rice has primarily been estimated using vegetation indices with several discrete bands; nevertheless, these indices cannot take advantage of continuous bands available with hyperspectral remote sensing. This study analyzed the quantitative relationship between canopy hyperspectral characteristic parameters (HCPs) and the ADB of rice. Twenty HCPs were used, including red edge area (SDr), blue edge area (SDb), and others. The variable-screening methods involved stepwise regression (SR), a regression coefficient (RC), variable importance in projection (vip), and random forest (RF). Stepwise and partial least squares regression methods were employed with traditional linear regression as well as machine learning methods including random forest (RF), a support vector machine (SVM), a BP artificial neural network (BPNN), and an extreme learning machine. Whole- and screening-variable models were constructed to estimate rice ADB at jointing, booting, heading, and maturing stages and across growth stages. Screening-variable models include SVM models based on SR (SVM-sr), RF models based on vip (RF-vip), and others. The results show that the HCPs had a significant correlation with ADB containing elements in the red edge region, namely SDr, SDr/SDb, and (SDr - SDb)/(SDr + SDb) at each growth stage. In addition, the screening performance of vip and SR was better than that of RC and RF, and fewer variables were screened. Moreover, the HCPs of the red edge region were screened using different screening methods at each growth stage. Among them, SDr/SDb and (SDr - SDb)/(SDr + SDb) appeared frequently, indicating they are important. Furthermore, at each growth stage, ADB could be well-estimated using diverse models with the RF modeling method based on vip screening variables found to be the best modeling method for ADB estimation; the independent variables of the RF-vip model involved the (SDr - SDb)/(SDr + SDb) at each growth stage.