A Comparison of Different Remote Sensors for Ramie Leaf Area Index Estimation

Abstract

Leaf area index (LAI) is an important parameter indicating crop growth. At present, spectral technology has developed into a popular means for LAI monitoring, which can provide accurate estimation results by constructing a model with crop spectral information and a ground LAI value. Spectral range and data type may affect the performance of the model, but few studies have compared the estimation accuracy of crop LAI using different spectral sensors, especially in ramie. In this study, we compared the potential to estimate the ramie LAI of a hyperspectral sensor with a multispectral sensor. A handheld hyperspectral sensor and an airborne multispectral sensor were used to collect spectral data from multiple growth stages, and then four machine learning algorithms were used to construct the LAI estimation model. The results showed that the R2 of the hyperspectral model was 0.702, while the R2 of the multispectral model was 0.816. The cropped hyperspectral data was less sensitive to LAI than the multispectral data with the same spectral band, due to the result of radiation area and data type. The accuracy of the ramie LAI estimation model constructed with all stage data was improved (R2 = 0.828). In conclusion, the airborne multi-spectrometer is more suitable for monitoring ramie LAI in the field.

Keywords

• ramie
• leaf area index
• multi-source remote sensing
• spectral technology