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The Map Data of BRDF-Adjusted Surface Reflectance from GOCI Geostationary Satellite Imagery over Korean Peninsula: Jong-Min Yeom; GEO DATA. 2021;3(4):66-70. Published online December 31, 2021; DOI: https://doi.org/10.22761/DJ2021.3.4.006

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Abstract PDF: In this study, the spatial maps of the BRDF (Bidirectional Reflectance Distribution Function) adjusted surface reflectance (SR) were estimated by using the Geostationary Ocean Color Imager (GOCI) mounted on the Communication, Ocean and Meteorological Satellite (COMS) over the Korean Peninsula. The BRDF-Adjusted surface Reflectance (BAR) is a more effective indicator that not only quantitatively identifies the growth characteristics of vegetation, but also corrects the bidirectional error in the time series observation characteristics, because the surface reflectance is changed according to the solar altitude during the daytime period. Therefore, this BAR products have high data utilization in various fields such as agriculture, environment, land surface information, and atmosphere. In this study, BRDF-adjusted surface reflectance maps were calculated for the Korean peninsula from April 2011 to December 2012 with hourly temporal resolution from 9 a.m. to 4 p.m. For the BAR surface reflectance, the spatial observation range is from latitude 34° N to 39 °N and longitude 125 ° E to 130 ° E, and the spatial resolution is 500 m. The semi-empirical BRDF model was used to calculate the BRDF-adjusted surface reflectance, and the radiometric characteristics of surface reflectance were decomposed into isotropic scattering, geometric scattering, and volumetric scattering. For this model simulation, at least 7 clear pixels are required to fit BRDF model. In this study, unlike the Nadir BRDF-Adjusted surface Reflectance (NBAR) calculation method which was calculated from the existing polar orbiting satellites, semi-empirical BRDF modeling was performed with a value fixed to the satellite viewing angle for each pixel since geostationary satellites of GOCI are difficult to observe in the nadir direction unlike polar satellites. It is more effective to perform BRDF correction by fixing them at the viewing angle in the case of GOCI geostationary satellite.

The Spatial Maps of Paddy Rice Yield over Northeast Asia Using COMS Geostationary Satellite and Reanalysis Meteorological Data: Seungtaek Jeong, Jonghan Ko, Jong-Min Yeom; GEO DATA. 2021;3(2):20-24. Published online July 21, 2021; DOI: https://doi.org/10.22761/DJ2021.3.2.003

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Abstract PDF: This study estimated rice yield maps for Northeast Asia by using the Communication, Ocean and Meteorological satellite (COMS), Terra satellite, and Regional Data Assimilation and Prediction System (RDAPS) of the numerical model. The rice yield is highly useful in the study for crop information monitoring according to climate change as well as agriculture information, industry, and economy. This study produced rice yield maps for Northeast Asia including Korea, North Korea, Japan, and three northeastern provinces of China (Heilongjiang, Jilin, and Liaoning) from 2011 to 2017. The estimated spatial resolution of the rice yield maps in Northeast Asia is 500 m. The spatial observation range is 25 ° N ~ 47 ° N and 115 ° E ~ 145 ° E. In order to estimate rice yield, Remote Sensing-integrated Crop Model was employed in this study. The inputs of the RSCM are vegetation indices from Geostationary Ocean Color Imager (GOCI) of the COMS, solar radiation from Meteorological Imager of the COMS, Land Surface Water Index from the MODerate Resolution Imaging Spectroradiometer, and the temperature from the RDAPS were considered as input data. In particular, this study applied the Bidirectional Reflectance Distribution Function to the GOCI time-series images to calculate more improved vegetation indices by minimizing the directional error generated in the satellite observation location. These indices were very effective in the simulation of the rice yield.

The spatial data of paddy rice classification over Northeast Asia using COMS geostationary satellite: Seungtaek Jeong, Jonghan Ko, Jong-Min Yeom; GEO DATA. 2021;3(1):18-22. Published online March 31, 2021; DOI: https://doi.org/10.22761/DJ2021.3.1.003

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The Korea Aerospace Research Institute (KARI) estimated paddy rice classification maps over Northeast Asia using the Cheonian geostationary orbiting satellite (COMS: Communication, Ocean and Meteorological Satellite) data. In the case of classification map of rice paddy, it is not only used as input data for estimating rice yield, but also for various fields such as agriculture, weather, climate change, bio energy, and ecology. The spatial resolution of the classified rice map is 500 m, and the classification map was estimated yearly temporal resolution from 2011 to 2017. The spatial coverage of the classification map was the Northeast Asia with the latitude 25 ° N ~ 47 ° N and the longitude 115 ° E ~ 145 ° E as shown in Fig. 1 including Heilongjiang Sheng, Jilin Sheng, and Liaoning Sheng. In this classification map of paddy rice, it was calculated by applying geostationary orbiting satellites based on value-added products from Geostationary Ocean Color Imager (GOCI). In this study, we additionally used MODIS Land Surface Water Indices (LSWI) to support rice classification by considering the physical characteristics of rice cultivation area in the transplanting season. Basically, the radiance value of the top of atmosphere (TOP) observed in GOCI satellite was corrected to the surface reflectance at the top of the canopy through radiative transfer model. After that, NDVI, which can reflect the time series growth characteristics of rice, was estimated first. In addition, the MODIS LSWI index was used to determine the rice cultivation area with the NDVI in Northeast Asia by reflecting the water characteristics of the rice cultivation area during the transplanting period. More details of validation results for this algorithm can be found in previous studies.

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Citations to this article as recorded by

The Spatial Maps of Paddy Rice Yield over Northeast Asia Using COMS Geostationary Satellite and Reanalysis Meteorological Data
Seungtaek Jeong, Jonghan Ko, Jong-Min Yeom
GEO DATA.2021; 3(2): 20. CrossRef
The Map Data of BRDF-Adjusted Surface Reflectance from GOCI Geostationary Satellite Imagery over Korean Peninsula
Jong-Min Yeom
GEO DATA.2021; 3(4): 66. CrossRef

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