Open AccessAnalysis of paddy productivity using normalized difference vegetation index value of sentinel-2 and UAV multispectral imagery in the rainy season
Author(s) -
Liyantono,
A. Sianjaya,
IK Sari
Publication year - 2020
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/542/1/012059
Subject(s) - normalized difference vegetation index , environmental science , productivity , vegetation (pathology) , vegetation index , sowing , agriculture , hectare , multispectral image , geography , satellite imagery , regression analysis , forestry , remote sensing , mathematics , statistics , agronomy , leaf area index , biology , medicine , archaeology , pathology , economics , macroeconomics
Rice is one of the primary food sources for people worldwide, especially in Indonesia. In 2018, Indonesia produced approximately 56.5 million tons of unhusked dry rice ready for milling with a total area of around 10.9 million hectares. Until these days, the estimation of rice productivity was done by the Indonesia Statistics Agency (BPS) and the Ministry of Agriculture. Aerial Images and Normalized Difference Vegetation Index (NDVI) were viewed as a tool that helps in monitoring and observing the crops. NDVI was used to analyze the paddy growth from planting until harvesting. Usage of Satellite Sentinel-2 and UAV was meant to compare the accuracy between them. Analysis of paddy productivity was used during the vegetative, reproduction, and maturity phases. The used methods are regression and correlation analysis. The model from the analysis was used to estimate the productivity of paddy. The result shows that the models with the highest correlation from both Sentinel-2 and UAV were obtained from analysis on the vegetative phase. This means the implemented NDVI value was from planting until the peak value in one cultivating season.