Data-driven Exploratory Analysis for Raster Data Using Self-Organizing Maps Regressor

Abstract

Sugarcane is one of the plantation commodities in Indonesia which has a big potential. Sugarcane growth consists of 4 phases that happen in a year. In the Grand Growth phase, sugarcane needs an appropriate condition to grow well and enter the next phase. The factors that affect sugarcane’s Grand Growth phase are water, temperature, and sunlight. Rainfall is one of the sugarcane water sources needed, but the rainfall intensity is different, and the rainfall distribution is uneven every year. The uneven rainfall caused water stress in a sugarcane plantation. That is why it is necessary to identify the water content in sugarcane plantations to maintain the quality of sugarcane. This study predicted the water content of sugarcane plantations so the areas indicated with water stress can be anticipated. Raster data are collected from Landsat-8 satellite imagery and analyzed using one of the data-driven exploration analysis methods, PCA (Principal Component Analysis), to analyze the overlay of the Landsat 8 imageries of the sugarcane plantation area. After that, the raster data were processed to calculate the water index of the sugarcane plantation, known as NDWI (Normalized Different Water Index). NDWI values of the sugarcane plantation area are converted into an array and then become data input for the Self-Organizing Map Regressor algorithm to predict the water content of the sugarcane plantation. The results are predicted water index values for the sugarcane plantation with 72% accuracy.