An Evaluation of EO-1 Hyperion Data for Estimating Leaf Area Index of Rubber Plantations

Abstract

The leaf area index (LAI) is one of the most important parameters for quantifying the physical conditions of rubber plantations. Modern remote sensing tools such as hyperspectral sensors can be effectively used for estimating the LAI of crops. Unfortunately, only few examples are found in the literature on the application of hyperspectral data for estimating rubber LAI and the understanding of the underlying mechanisms remain unclear. Thus, the aim of this study is to explore one step beyond the existing research. The current study is the first time that the capability of hyperspectral data for estimating the LAI of rubber plantations has been investigated. Four popular vegetation indices (i.e., Simple Ratio index (SR705), Modified Simple Ratio Index (MSR705), Normalized Difference Vegetation Index (NDVI705), and Modified Soil Adjusted Vegetation Index (MSAVI705)) and the EO-1 Hyperion image of the rubber plantations in Pak Chom District, Loei Province, Thailand were the chosen for the investigation. Despite additional fine-tuning need to be done on the statistical model parameters, the proposed models reveal significant high statistical correlations. The best-fitted model was determined to be the MSAVI705 model (R2 = 0.775), which possesses the lowest RMSE values (=0.160). It is anticipated that the methodology presented in this study can be used as a guideline for estimating the LAI of rubber plantations in other areas.