Development of Spatio-Temporal Gap-Filling Technique for NDVI Images
Sun-Hwa Kim, Jeong Eun, Taeho Kim · 대한원격탐사학회지 · 2024
Time-series Normalized Difference Vegetation Index (NDVI) data extracted from optical satellite images is the most widely used data for predicting crop growth and yield.In the time series pattern of NDVI, variation due to the growth cycle of crops and reduction due to clouds are often seen.In particular, the rainy season in Korea is an important growing period for crops, but it is very difficult to use optical satellite images due to many clouds.In this study, a spatio-temporal gap-filling technique was developed for Sentinel-2A/B NDVI images obtained in rice paddy in Dangjin.The spatial gap-filling technique is used to correct the boundary of a missing area or a small-area missing area by obtaining the NDVI information of normal pixels at the periphery.Afterward, pixels in large areas of missing areas are temporally gap-filled by applying a Gaussian Process Regression (GPR) model to data acquired before and after the target period.The spatio-temporal gap-filling technique developed in this study showed a Root Mean Squared Error (RMSE) of less than 0.15 for the periodically composited NDVI image, and the clouds were removed and the corrected image could be confirmed with visual interpretation.In addition, for daily NDVI images with a large number of clouds, it was found that RMSE was 0.11 to 0.17 depending on the amount of clouds.The algorithm was developed as a program using Python and takes less than 5 minutes to process.In the future, it will be provided to experts who use agricultural and forestry satellite images.This algorithm will be tested on a variety of land cover areas, including mountainous areas as well as agricultural areas, and will be applied to longer time series data.Through this, we plan to analyze the applicability not only to the vegetation index but also to other biophysical variables required for crop monitoring.