• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1483-1490
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• 2023

We analyzed the potential for joint utilization of Visible Infrared Imaging Radiometer Suite (VIIRS) satellite imagery Normalized Difference Vegetation Index (NDVI) in crop assessment, considering the aging of MODerate resolution Imaging Spectroradiometer (MODIS) satellites. Over 11 years from 2012 to 2022, we examined the characteristics of NDVI changes in corn and soybean cultivation areas in Illinois, USA. VIIRS and MODIS satellite imagery NDVI exhibited a high correlation coefficient of over 0.98. However, during periods of rapid crop growth or decline, VIIRS NDVI showed values approximately 0.12 to 0.14 higher than MODIS. Estimating crop anomaly classes based on NDVI, we observed similar trends in corn and soybean crop anomaly classes in 2018 and 2019. However, in 2022, there appeared to be a significant divergence in crop anomaly classes, suggesting the need for further investigation. The correlation coefficients between MODIS and VIIRS satellite imagery NDVI and corn and soybean yields were consistently high, exceeding 0.8, indicating the potential for quantity estimation using both MODIS and VIIRS satellite imagery. Specifically, for VIIRS NDVI, excluding the increasing trend in crop quantity estimation for soybeans enhanced the correlation, and compared to MODIS, it showed a consistently high correlation with quantity from approximately 16 days earlier, indicating the potential for early estimation.

• Journal of Korean Society for Geospatial Information Science
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• v.14 no.2 s.36
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• pp.55-62
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• 2006

Droughts have been an important factor in disaster management in Korea because she has been grouped into nations of lack of water. Satellite imagery can be applied to droughts monitoring because it can provide periodic data for large area for long time. This study aims to present a process to analyze droughts in large area using satellite imagery. We estimated evapotranspiration in large area using NDVI data acquired from satellite imagery. For satellite imagery, we dealt with MODIS data operated by NASA. The evapotranspiration estimated from satellite imagery was combined with precipitation data and potential evapotranspiration data to estimate water balances. Using water balances we could analyze droughts effectively in our object area. As the result of this study, we could increase the usability of satellite imagery, especially in droughts analysis.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.398-401
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• 2005

Droughts have been an important factor in disaster management in Korea because she has been grouped into nations of lack of water. Satellite imagery can be applied to droughts monitoring because it can afford periodic data for large area for long time. This study aims to develop a method to analyze droughts in large area using satellite imagery. We estimated evapotranspiration in large area using NDVI data acquired from satellite imagery. For satellite imagery, we dealt with MODIS data operated by NASA. As the result of this study, we improved the usability of satellite imagery, especially in drought analysis.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.29 no.4
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• pp.405-411
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• 2011

In this study we tried to compare and analyze KOMPSAT-2 NOVI and vegetation coverage(VC) which is investigated by fieldwork. To standardize KOMPSAT-2 NOVI, we adjusted NOVI using reference data which is atmospheric corrected MODIS NDVI. Each vegetation coverage point data was surveyed in field using portable GPS and compared with NDVI of satellite imagery. As a results, there was high level of correlation in vegetation coverage and NOVI.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.53 no.4
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• pp.388-393
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• 2008

Earth observation satellite imagery having medium-resolution can provide the useful information very rapidly and cheaply. The objective of this study was to assess the feasibility for monitoring rice growth and yield using medium resolution satellite imagery at Seosan AB reclaimed area, Chung-nam province. Using the LANDSAT imagery at booting stage ($29^{th}$ July 2004), $NDVI_R$ had the most significant linear relationships with rice yield of Seosan AB reclaimed area with the correlation coefficient (r) as 0.68. Therefore, this relationship was established as rice yield equation as function of $NDVI_R$, where excluding the 10 small area having low number of pixel, the determination coefficient ($R^2$) of the linear regression between NDVIred and milled rice yield was improved to 0.66. In addition, raster masking method, which was easier and faster even if a little unaccurate than preexisting method, was established for extracting information paddy field zone. Adaptability of rice yield equation function of $NDVI_R$ on year and region was investigated using rice yield and $NDVI_R$ values, which were extracted with raster masking method, from 7 counties or cities, Kyeong-ki province in 2005. Relationship between observed and calculated rice yield showed 1:1 line indicating that the adaptability was admitted.

• Korean Journal of Soil Science and Fertilizer
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• v.49 no.5
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• pp.495-502
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• 2016

Italian Ryegrass (IRG), which is known as high yielding and the highest quality winter annual forage crop, is grown in mid-south area in Korea. The objective of this study was to evaluate the use of unmanned aerial vehicle (UAV) for the monitoring IRG growth. Unmanned aerial vehicle imagery obtained from middle March to late May in Nonsan, Chungcheongnam-do. Unmanned aerial vehicle imagery corrected geometrically and atmospherically to calculate normalized difference vegetation index (NDVI). We analyzed the relationships between $NDVI_{UAV}$ of IRG and biophysical measurements such as plant height, fresh weight, and dry weight over an entire IRG growth period. The similar trend between $NDVI_{UAV}$ and growth parameters was shown. Correlation analysis between $NDVI_{UAV}$ and IRG growth parameters revealed that $NDVI_{UAV}$ was highly correlated with fresh weight (r=0.988), plant height (r=0.925), and dry weight (r=0.853). According to the relationship among growth parameters and $NDVI_{UAV}$, the temporal variation of $NDVI_{UAV}$ was significant to interpret IRG growth. Four different regression models, such as (1) Linear regression function, (2) Linear regression through the origin, (3) Power function, and (4) Logistic function were developed to evaluate the relationship between temporal $NDVI_{UAV}$ and measured IRG growth parameters. The power function provided higher accurate results to predict growth parameters than linear or logistic functions using coefficient of determination. The spatial distribution map of IRG growth was in strong agreement with the field measurements in terms of geographical variation and relative numerical values when $NDVI_{UAV}$ was applied to power function. From these results, $NDVI_{UAV}$ can be used as a new tool for monitoring IRG growth.

• Korean Journal of Soil Science and Fertilizer
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• v.49 no.5
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• pp.420-428
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• 2016

For more than 50 years, satellite images have been used to monitor crop growth. Currently, unmanned aerial vehicle (UAV) imagery is being assessed for analyzing within field spatial variability for agricultural precision management, because UAV imagery may be acquired quickly during critical periods of rapid crop growth. This study refers to the derivation of growth estimating equation for highland Kimchi cabbage using UAV derived normalized difference vegetation index (NDVI) and agro-meteorological factors. Anbandeok area in Gangneung, Gangwon-do, Korea is one of main districts producing highland Kimchi cabbage. UAV imagery was taken in the Anbandeok ten times from early June to early September. Meanwhile, three plant growth parameters, plant height (P.H.), leaf length (L.L.) and outer leaf number (L.N.), were measured for about 40 plants (ten plants per plot) for each ground survey. Six agro-meteorological factors include average temperature; maximum temperature; minimum temperature; accumulated temperature; rainfall and irradiation during growth period. The multiple linear regression models were suggested by using stepwise regression in the extraction of independent variables. As a result, $NDVI_{UAV}$ and rainfall in the model explain 93% of the P.H. and L.L. with a root mean square error (RMSE) of 2.22, 1.90 cm. And $NDVI_{UAV}$ and accumulated temperature in the model explain 86% of the L.N. with a RMSE of 4.29. These lead to the result that the characteristics of variations in highland Kimchi cabbage growth according to $NDVI_{UAV}$ and other agro-meteorological factors were well reflected in the model.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.257-259
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• 2003

Landuse maps are prepared from satellite imagery and field observations were conducted at various locations in the study area. Compared to the field data and NDVI and RVI thematic maps, NDVI is better than RVI, because it compensates for changing illumination conditions, surface slope, aspect and other factors. Clouds, water and snow have negative values for RVI and NDVI. Rock and bare soils have similar reflectance in both NIR and visible band, so RVI and NDVI are near zero. In forest areas with good vegetation cover, NDVI is high and landslide occurrence is less. But if annual and biennial vegetations are present and if cultivation practices are changed frequently, NDVI is medium and landslide occurrence is moderate. In areas where deforestation and settlement is in progress, NDVI is less and landslide occurrence is more. The NDVI FCC thematic map may be used as an important layer in GIS application for landslide studies. Analyzing other layers such as slope, rainfall, soil, geology, drainage, lineament, etc with NDVI FCC layer will give a better idea about the identity of landslide prone areas.

• Korean Journal of Soil Science and Fertilizer
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• v.46 no.6
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• pp.426-433
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• 2013

This study was aimed to classify soil desalination area for cultivation using NDVI (Normalized difference vegetation index) of high-resolution satellite image because the soil salinity affects the change of plant community in reclaimed lands. We measured the soil salinity and NDVI at 28 sites in the Saemangeum reclaimed land in June 2013. In halophyte and non-vegetation sites, no relation was found between NDVI and soil salinity. In glycophyte sites, however, we found that the soil salinity was below 0.1% and NDVI ranged from 0.11 to 0.57 which was greater than the other sites. So, we could distinguish the glycophyte sites from the halophyte sites and non-vegetation, and classify the area that soil salinty was below 0.1%. This technique could save the time and labor to measure the soil salinity in large area for agricultural utilization.

• Korean Journal of Soil Science and Fertilizer
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• v.49 no.6
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• pp.731-742
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• 2016

Recent technological advance in UAV (Unmanned Aerial Vehicle) technology offers new opportunities for assessing crop situation using UAV imagery. The objective of this study was to assess if reflectance and NDVI derived from consumer-grade cameras mounted on UAVs are useful for crop condition monitoring. This study was conducted using a fixed-wing UAV(Ebee) with Cannon S110 camera from March 2015 to March 2016 in the experiment field of National Institute of Agricultural Sciences. Results were compared with ground-based recordings obtained from consumer-grade cameras and ground multi-spectral sensors. The relationship between raw digital numbers (DNs) of UAV images and measured calibration tarp reflectance was quadratic. Surface (lawn grass, stairs, and soybean cultivation area) reflectance obtained from UAV images was not similar to reflectance measured by ground-based sensors. But NDVI based on UAV imagery was similar to NDVI calculated by ground-based sensors.