Preliminary Validation Of Leaf Area Index Sensor In Huailai

Leaf area index (LAI) is a key variable in many land surface models that involve energy and mass exchange between vegetation and the environment. In recent years, extracting vegetation structure parameters from digital photography becomes a widely used indirect method to estimate LAI for its simplicity and ease of use. A Leaf Area Index Sensor (LAIS) system was developed to continuously monitor the growth of crops in several sampling points in Huailai, China. The system applies 3G/WIFI communication technology to remotely collect crop photos in real-time. Then the crop photos are automatically processed and LAI is estimated based on the improved leaf area index of Lang and Xiang (LAILX) algorithm in LAIS. The objective of this study is to primarily verify the LAI estimated from LAIS (Lphoto) through comparing them with the destructive green LAI (Ldest). Ldest was measured across the growing season ntil maximum canopy development while plants are still green. The preliminary verification shows that Lphoto corresponds well with the Ldest (R2=0.975). In general, LAI could be accurately estimated with LAIS and its LAI shows high consistency compared with the destructive green LAI. The continuous LAI measurement obtained from LAIS could be used for the validation of remote sensing LAI products.