Temporal NDVI analysis to detect the effects of seawater intrusion on rice growth in coastal areas. Issue 1 (February 2021)

• Record Type: Journal Article
• Authors: Tivianton, T A; Barus, B; Purwanto, M Y J; Anwar, S; Widiatmaka, ; Laudiansyah, R
• Is Part Of: IOP conference series. Volume 662:Issue 1(2021)
• Publication Date: 2021-02
• Subjects: salinization -- NDVI -- crop growth -- Sentinel-2 -- coastal; Earth sciences -- Periodicals; Environmental sciences -- Congresses; Environmental sciences -- Periodicals; 550.5
• DOI: 10.1088/1755-1315/662/1/012021
• Languages: English
• ISSNs: 1755-1307

Abstract

Abstract: As population size grows over time, staple food production also needs to keep up with increased annual demands. In Indonesia, the agricultural sector applies intensification and extensification to maximize rice productivity. However, farm extensification can instead decline productivity, should it sprawl into marginal lands like the study area that has been affected by sea-level rise impact, i.e., surface saltwater intrusion. Therefore, this study set out to differentiate paddies into segments affected and unaffected by salinity based on discernible variation in rice growth stages. These stages were determined using a vegetation index, NDVI (Normalized Difference Vegetation Index), calculated from time-series Sentinel-2 L2A+B image data from 2015 until 2020. The resulting temporal NDVI showed two cropping patterns year-round but with different planting times. In salinity-unaffected paddy segments, farmers began the inundation-transplanting stage in late March and ended the cropping season with fallow in August. Meanwhile, in salinity-affected segments, the cropping stages were the opposite: inundation in early April and fallow in early September. The measurable impact of salinity was apparent at the vegetative-generative stage, where salinity-affected paddies had the highest NDVI of 0.64–0.65, whereas those unaffected had the highest NDVI of 0.7–0.75. These index values indicate an impaired rice growth rate due to salinity effects. Compared with the field-measured data, the NDVI showed 85% accuracy, with a Kappa coefficient of 0.87. Meanwhile, the NDVI-EC correlation test produced R-values of 63–85%. Overall, this research has confirmed that remote sensing image and technology can acquire variable data that explain salinity effects on coastal paddies.