Abstract
The Leaf Area Index (LAI) was an important structural parameter for quantifying the energy and mass exchange characteristics in ecosystems. There was considerable interest in assessing LAI to evaluate crop growth and production. In this study, hyperspectral vegetation indices (VIs) from in-situ remote sensing measurements were utilized to monitor erect-type corn (Zea mays L.) canopy's LAI. Firstly, LAI gradually increased during the growth process and reached its maximum in the spinning middle stage. Secondly, compared to other VIs, LAI was the most positively related to modified transformed vegetation index (MTVI) (r = 0.867, p 5.48, MTVI and normalized difference vegetation index (NDVI[900, 680]) were easily saturated, while RDVI was far from its saturation state, thus it was suitable to adopt the model based on RDVI to monitor LAI; if LAI ≤ 5.48, it was available to adopt the model based on MTVI to monitor LAI. Finally, the statistical analysis showed that the determination coefficient (R2), relative root mean square error (RRMSE) and monitoring accuracy values of the LAI segmented model based on MTVI and RDVI were 0.909 (p < 0.01), 0.087 and 91.3%, respectively, and the LAI segmented model was significantly better than the models only adopting MTVI, RDVI, or NDVI[900, 680], which the monitoring accuracy increased by 8.0%, 11.6% and 15.1%, respectively. Therefore, the two remote sensing characteristic variable of MTVI and RDVI could be considered as a sensitive indicator as LAI of erect-type corn. In conclusion, this study confirmed the feasibility of utilizing MTVI and RDVI derived from ground-based remotely sensed data to monitor erect-type corn canopy's LAI, and further confirmed that the segmented model based on MTVI and RDVI, used to monitor LAI, can not only improve the monitoring accuracy but also solve the saturation problems that occurred in MTVI and NDVI[900, 680]. Accordingly, the result could supervise erect-type corn growth patterns, predict its production and provide an operational approach to fleetly obtaining accurate and more timely crop growth information in precise agriculture using the remote sensing characteristic variable.
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