Hyperspectral Estimation Model for Predicting LAI of Rice in Ningxia Irrigation Zone

Leaf area index (LAI) is one of the important parameters for evaluating rice growth status. Hyperspectral remote sensing is a new technical approach that can be used to acquire LAI information quickly and nondestructively. This study aims to explore the best vegetation index and monitoring model for rice LAI inversion. This study was carried out in Ningxia irrigation zone, where the rice was planted in different fertilizer level. Then the correlation between vegetation index and LAI was analyzed and four inversion models were constructed for estimating LAI by using correlation analysis and regression analysis. The result revealed that the LAI value increased with the increase of nitrogen level, and it reached a maximum value at booting stage and then drops down. The reflectance of rice canopy at the wavebands 400~722 nm and 1 990~2 090 nm was very significantly negatively correlated with LAI and that of which at near infrared region (760~1 315 nm) was very significantly positively correlated with LAI. The tests with independent dataset suggested that the rice LAI monitoring models with radio vegetation index RVI _{(850, 750)} as the variable could give an accurate LAI estimation. These results provided an insight for monitoring the rice LAI in different regions.