A new method for rapid construction of multi-band vegetation index

• A new method of constructing vegetation index was proposed. • The new method can quickly realize the construction of vegetation index. • Five-band vegetation index had good effect. • New five-band vegetation indices were constructed. The aboveground dry biomass (AGDB), leaf area index (LAI), chlorophyll density (CHD), and plant water content (PWC) are important growth physiological parameters that can reflect the growth status of winter wheat. Monitoring the growth physiological parameters of winter wheat by constructing vegetation index is a common method that can quickly obtain the growth physiological parameters of winter wheat. In order to further improve the accuracy of using vegetation index method to monitor the growth physiological parameters of winter wheat, this study proposed a new method based on the genetic algorithm that can quickly construct multi-band vegetation index. The main results were as follows: Compared with the traditional random bands combination to construct the vegetation index, the results of using the rapid method proposed in this study to construct the vegetation index were reliable, and can greatly reduce the time required to construct the vegetation index, which only took about 17.7044 s on average. In the four arithmetic operations used, the priority should be given to Subtraction(Sub.) and Division(Div.), combined with Addition(Add.) and Multiplication(Mul.) operations to construct multi-band vegetation index can achieve good monitoring results. In this study, AGDB, LAI, CHD, and PWC reached the highest accuracy when using ’Sub.-Add.-Sub.-Sub., ’Div.-Div.-Mul.-Mul.’, ’Sub.-Add.-Sub.-Add.’, and ’Div.-Sub.-Div.-Add.’ to construct the five-band vegetation index, respectively, with validation R 2 (R 2 v ) of 0.6104, 0.6849, 0.7019, and 0.8960, and validation RMSE (RMSE v ) of 0.4508, 2.1025, 1.1473, and 3.4683. Using ’Sub.-Add.-Sub.-Sub.’ to construct five-band vegetation index could simultaneously monitor four growth physiological parameters. The R 2 v of AGDB, LAI, CHD, and PWC monitored by it was 0.6104, 0.6326, 0.6791, and 0.8425 respectively, and the RMSE v was 0.4508, 2.2702, 1.1903, and 4.2692, respectively. In a word, using the rapid method proposed in this study to construct vegetation index can not only greatly reduce the time required to construct vegetation index, but also has high reliability and reliable operation results.