Selecting the Most Optimum Sentinel-2a Based Vegetation Index to Estimate Leaf Area Index of Three Rice Cultivars

Abstract

The estimation of Leaf area index (LAI) becomes important as LAI is one of parameters in the analysis of crop growth model. The crop growth has different characteristics and its strongly influenced by environmental conditions and factors. The growth tends to occur in a short period of time and covers a large area. Therefore, an approach to analyze the pattern of changes in crop growth based on LAI spatially is needed. Remote sensing offers an effective and efficient approach in monitoring crop growth characteristic, which can be done in a time series with a wide area coverage by detecting and monitoring the physical characteristics of crop. The most famous and commonly used parameters to estimate LAI are vegetation indices which are usually calculated based on the ratio of the red and NIR wavelength, known as spectral signature. The objectives of the research are to examine the spatio-temporal correlation between LAI of three rice cultivars Sentinel-2A based vegetation indices, and to select the most optimum vegetation index in estimating LAI. A synchronization process of the LAI for each plot with the pixel of Sentinel-2A based vegetation index value was carried out. The results of the analysis show that the vegetation index has a strong correlation with LAI. The Comparison of the four calculated vegetation indices in estimating LAI was performed using linear regression model and followed by comparing R-squared, RMSE and Correctness. The EVI2 vegetation index provides the most optimum representation in capturing crop growth patterns based on LAI.