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http://dx.doi.org/10.7780/kjrs.2022.38.6.1.19

Evaluation of NDVI Retrieved from Sentinel-2 and Landsat-8 Satellites Using Drone Imagery Under Rice Disease  

Ryu, Jae-Hyun (Climate Change Assessment Division, National Institute of Agricultural Sciences)
Ahn, Ho-yong (Climate Change Assessment Division, National Institute of Agricultural Sciences)
Na, Sang-Il (Climate Change Assessment Division, National Institute of Agricultural Sciences)
Lee, Byungmo (Climate Change Assessment Division, National Institute of Agricultural Sciences)
Lee, Kyung-do (Climate Change Assessment Division, National Institute of Agricultural Sciences)
Publication Information
Korean Journal of Remote Sensing / v.38, no.6_1, 2022 , pp. 1231-1244 More about this Journal
Abstract
The frequency of exposure of field crops to stress situations is increasing due to abnormal weather conditions. In South Korea, large-scale diseases in representative paddy rice cultivation area were happened. There are limits to field investigation on the crop damage due to large-scale. Satellite-based remote sensing techniques are useful for monitoring crops in cities and counties, but the sensitivity of vegetation index measured from satellite under abnormal growth of crop should be evaluated. The goal is to evaluate satellite-based normalized difference vegetation index (NDVI) retrieved from different spatial scales using drone imagery. In this study, Sentinel-2 and Landsat-8 satellites were used and they have spatial resolution of 10 and 30 m. Drone-based NDVI, which was resampled to the scale of satellite data, had correlation of 0.867-0.940 with Sentinel-2 NDVI and of 0.813-0.934 with Landsat-8 NDVI. When the effects of bias were minimized, Sentinel-2 NDVI had a normalized root mean square error of 0.2 to 2.8% less than that of the drone NDVI compared to Landsat-8 NDVI. In addition, Sentinel-2 NDVI had the constant error values regardless of diseases damage. On the other hand, Landsat-8 NDVI had different error values depending on degree of diseases. Considering the large error at the boundary of agricultural field, high spatial resolution data is more effective in monitoring crops.
Keywords
UAV; Rice disease; Crop; Sensitivity test;
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Times Cited By KSCI : 7  (Citation Analysis)
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