Monitoring leaf area index after heading stage using hyperspectral remote sensing data in rice

Leaf area index (LAI), as an important characterization parameter, reflects the canopy structural characteristics of crops. It is commonly used to estimate foliage cover, as well as forecasting crop growth and yield [1,2,3]. Because LAI is functionally linked to the canopy spectral reflectance, its retrieval from remote sensing data has prompted many investigations and studies in recent years. The common and widely used approach has been to develop relationships between ground-measured LAI and vegetation indices [1,4,5]. These vegetation indices performed well at the early stage of crop growth, but the estimation accuracy are greatly decreased in the late growth stages, especially after heading stage. A major problem in the use of these indices arises from the fact that canopy reflectance, it is strongly dependent on both structural and biochemical properties of the canopy [6,7,8]. In the late period of crop growth, panicles changed the canopy structure of crops and affected the crop canopy spectral reflectance [9,10]. This study compared the accuracy of monitoring LAI by using the spectral reflectance that measured the entire canopy and those canopies with panicles removed, and proposed a convenient method to removal of the effect of panicles on canopy reflectance and to enhance the prediction accuracy of LAI after heading stage of rice.