Aerosol optical thickness (AOT) and water vapor (WV) was retrieved using Geo-KOMSAT2A satellite (GK-2A, Geostationary Satellite) launched in 2018, and operated by the Meteorological Satellite Center in Korea. GK-2A is equipped with 16 bands, including 4 visible bands, and is taking pictures of the East Asia region every 2 minutes. We utilized visible, near infrared, and shortwave infrared bands on AOT and WV retrieval. These bands are simultaneously affected by atmospheric scattering and surface reflection, so the contribution of each element can be simulated using the atmospheric radiation transfer model (RTM model). The first is to generate Look-up Table using RTM model of SBDART (Santa Barbara DISORT Atmospheric Radiative Transfer), the second is the atmospheric correction reflectivity calculation process. The third, WV and AOT was retrieved, and the results were compared and verified with ground measurement data. Finally, atmospheric correction was experimentally performed for polar orbit satellite (KOMSAT 3, 3A) images using WV and AOT as input variables.
In this study, we performed the vicarious radiometric calibration of KOMPSAT-3A multispectral bands by using 6S radiative transfer model, radiometric tarps, MFRSR measurements. Furthermore, to prepare the accurate input parameter, we also did experiment work to measure the BRDF of radiometric tarps based on hyperspectral gonioradiometer to compensate the observation geometry difference between satellite and ASD Fieldspec 3. Also, we measured point spread function (PSF) by using the bright star and corrected multispectral bands based on the Wiener filter. For accurate atmospheric constituent effects such as aerosol optical depth, column water, and total ozone, we used MFRSR instrument and estimated related optical depth of each gases. Based on input parameters for 6S radiative transfer model, we simulated top of atmosphere (TOA) radiance by observed by KOMPSAT-3A and matched-up the digital number. Consequently, DN to radiance coefficients was determined based on aforementioned methods and showed reasonable statistics results.
The GRAMI crop growth model uses remote sensing data and thus has the potential to produce maps of crop growth and yield. A pixel-based crop information delivery system (CIDS) to simulate and map rice (Oryza sativa) growth and yield was developed using GRAMI. The GRAMI-rice model was parameterized using field data obtained at Chonnam National University, Gwangju, Republic of Korea, in 2011 and 2012. The model was separately validated using field data obtained at the same research site in 2009 and 2010. The model was then integrated into the CIDS to produce two-dimensional (2-D) maps of crop growth and yield. Simulated values of rice growth and yield agreed well with the corresponding measurements in both parameterization and evaluation. The simulated yields were in statistical agreement with the corresponding measured yields according to paired t tests (p=0.415 for parameterization and p=0.939 for validation). The CIDS accurately produced 2-D maps of rice growth and yield. The GRAMI-rice CIDS has simple input requirements and will be useful for regional rice growth monitoring and yield mapping projects.
Global climate changes as well as abnormal climate phenomena have affected the agricultural environment on a great
scale. Thus, there is a strong need for countermeasures by making full use of agriculture related information. As
agricultural lands in South Korea are mostly operated by private farmers on a small parcel level, it is difficult to gather
information for an overview on changing crop condition and to construct database necessary for disease management,
production estimation and compensation measures on a regional or governmental level. The objective of this study is to
evaluate the multispectral reflectance characteristics of RapidEye image data to classify agricultural land cover as well as
crop condition in South Korea. As the RapidEye sensor offers the spectral information in red edge range as a first
multispectral satellite system, we focus on the usefulness of red edge reflectance for identifying crop species and for
interpreting crop growth or stress condition.
Change detection using satellite imagery has been increasing the need for effective land management, land
environmental changes. Utilizing remote sensing data analysis is high application possibility about management
in the field of environmental changes, because relatively wide area in a short-term is to get the visual
information. The principal objective of this study was to provide that statistic approaches to determine dynamic
thresholds for detection of significant change using image differencing of NDVI (Normalized Difference
Vegetation Index). Dynamic threshold look-up-table obtained from statistics (per-pixel standard deviations over
10 years) of 10-year wide-swath satellite data (SPOT/VEGETATION) was used to apply Landsat-based change
detection. Two areas is utilized in research using Landsat 7 ETM+ images that have resolution 30×30 m. When
achieve changed detection taking advantage of image differencing technique which is one of the changed
detection technique, it choose more dynamic critical value taking advantage of middle and low resolution
satellite data. As a result, it is effective that takes advantage of NDVI value more than reflection value and
method to decide change standard is effective that take advantage of statistics.
The fluctuation of vegetation water condition around desert area is one of most important parameters to interpret the
desertification expansion. United Nations reported that about 35 million square kilometers of land are subject to
desertification. Historically, many parts of China have been suffered from severe desertification. This paper attempts an
analysis for spatio-temporal variation characteristics of vegetation drought status around China and Mongolia desert with
remotely sensed data. Time series images (1 January, 1999 - 31 December 2006) obtained from SPOT/VEGETATION
were used to monitor inter-annual variability of water condition. SPOT/VEGETATION satellite, which has a fine
temporal resolution and sensitive to vegetation growth, could be very useful to detect large scale dynamics of
environmental changes and desertification progress. The main objective of the study is analyzing water status around
China and Mongolia desert and predicting a risk area of desertification. In this study, NDWI (Normalized Difference
Water Index) is used to monitor vegetation water condition (drought status) over the study area. To interpret the
relationship between vegetation drought status and vigor, NDVI (Normalized Difference Vegetation Index) was
employed in ensemble with NDWI. Annual total precipitation from NCEP/NCAR reanalysis data is used as subsidiary
data. The study area from 73°36´E to 120°41´E longitude and from 30°81´N to 52°13´N longitude in northern China and
whole Mongolia. NDWI value around desert has a range from -0.05 to -0.35 and NDWI values are decreased during the
study period. Each year precipitation patterns are similar to yearly mean NDWI value. The study detected several areas
where NDWI is dramatically decreased for 8 years, especially northeast part of Mongolian Gobi desert and southeast part
of China Taklamakan desert.
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