Integration of Leaf Water Content Index (LWCI) and Enhanced Vegetation Index (EVI) for Stress Detection of Rice Plant Using Landsat 8 Satellitte Imagery

Abstract

Rice is the main staple food for Indonesian society. Almost 95% of Indonesians consume rice. Along with the increasing population in Indonesia, the level of rice consumption each year has increased. But on the other hand, the amount of paddy fields has decreased due to the development of settlements and industry. Consequently, the business of fulfilling rice consumption needs should prioritize agricultural intensification method. This agricultural intensification program requires good supporting data. One of the supporting data required is a plant health condition that can be represented in data on rice stress levels. Monitoring the stress level of rice plants can be done using remote sensing methods based on satellite imagery. One of them is Landsat-8 satellite imagery with certain algorithm. In this research, a modification algorithm of Rice Paddy Stress Index (RPSI) was obtained by integrating Leaf Water Canopy Index (LWCI) and Enhanced Vegetation Index (EVI). LWCI is used as a representation of water content in vegetation and EVI is used as a representation of the greenish level of plants associated with chlorophyll content. Plants that experience a decrease in health will decrease the content of chlorophyll and water. The results of this study indicate that in 2015 planting season 2 in Kendal Regency there are 1696.26 ha of rice fields indicated experiencing stress and 3493.85 Ha of rice fields have a potential stress. The result of validation test shows that RPSI algorithm method has 75% accuracy for determining rice stress level.