Snow cover is biggest single component of cryosphere. The Snow is covering the ground in the Northern Hemisphere approximately 50% in winter season and is one of climate factors that affects Earth’s energy budget because it has higher reflectance than other land types. Also, snow cover has an important role about hydrological modeling and water resource management. For this reason, accurate detection of snow cover acts as an essential element for regional water resource management. Snow cover detection using satellite-based data have some advantages such as obtaining wide spatial range data and time-series observations periodically. In the case of snow cover detection using satellite data, the discrimination of snow and cloud is very important. Typically, Misclassified cloud and snow pixel can lead directly to error factor for retrieval of satellite-based surface products. However, classification of snow and cloud is difficult because cloud and snow have similar optical characteristics and are composed of water or ice. But cloud and snow has different reflectance in 1.5 ~ 1.7 μm wavelength because cloud has lower grain size and moisture content than snow. So, cloud and snow shows difference reflectance patterns change according to wavelength. Therefore, in this study, we perform algorithm for classifying snow cover and cloud with satellite-based data using Dynamic Time Warping (DTW) method which is one of commonly used pattern analysis such as speech and fingerprint recognitions and reflectance spectral library of snow and cloud. Reflectance spectral library is constructed in advance using MOD21km (MODIS Level1 swath 1km) data that their reflectance is six channels including 3 (0.466μm), 4 (0.554μm), 1 (0.647μm), 2 (0.857μm), 26 (1.382μm) and 6 (1.629μm). We validate our result using MODIS RGB image and MOD10 L2 swath (MODIS swath snow cover product). And we use PA (Producer’s Accuracy), UA (User’s Accuracy) and CI (Comparison Index) as validation criteria. The result of our study detect as snow cover in the several regions which are did not detected as snow in MOD10 L2 and detected as snow cover in MODIS RGB image. The result of our study can improve accuracy of other surface product such as land surface reflectance and land surface emissivity. Also it can use input data of hydrological modeling.
Many countries try to launch satellite to observe the Earth surface. As important of surface remote sensing is increased, the reflectance of surface is a core parameter of the ground climate. But observing the reflectance of surface by satellite have weakness such as temporal resolution and being affected by view or solar angles. The bidirectional effects of the surface reflectance may make many noises to the time series. These noises can lead to make errors when determining surface reflectance. To correct bidirectional error of surface reflectance, using correction model for normalized the sensor data is necessary. A Bidirectional Reflectance Distribution Function (BRDF) is making accuracy higher method to correct scattering (Isotropic scattering, Geometric scattering, Volumetric scattering). To correct bidirectional error of surface reflectance, BRDF was used in this study. To correct bidirectional error of surface reflectance, we apply Bidirectional Reflectance Distribution Function (BRDF) to retrieve surface reflectance. And we apply 2 steps for retrieving Background Surface Reflectance (BSR). The first step is retrieving Bidirectional Reflectance Distribution (BRD) coefficients. Before retrieving BSR, we did pre-running BRDF to retrieve BRD coefficients to correct scatterings (Isotropic scattering, Geometric scattering, Volumetric scattering). In pre-running BRDF, we apply BRDF with observed surface reflectance of SPOT/VEGETATION (VGT-S1) and angular data to get BRD coefficients for calculating scattering. After that, we apply BRDF again in the opposite direction with BRD coefficients and angular data to retrieve BSR as a second step. As a result, BSR has very similar reflectance to one of VGT-S1. And reflectance in BSR is shown adequate. The highest reflectance of BSR is not over 0.4μm in blue channel, 0.45μm in red channel, 0.55μm in NIR channel. And for validation we compare reflectance of clear sky pixel from SPOT/VGT status map data. As a result of comparing BSR with VGT-S1, bias is from 0.0116 to 0.0158 and RMSE is from 0.0459 to 0.0545. They are very reasonable results, so we confirm that BSR is similar to VGT-S1. And weakness of this study is missing pixel in BSR which are observed less time to retrieve BRD components. If missing pixels are filled, BSR is better to retrieve surface products with more accuracy. And we think that after filling the missing pixel and being more accurate, it can be useful data to retrieve surface product which made by surface reflectance like cloud masking and retrieving aerosol.
Water vapor is main absorption factor of outgoing longwave radiation. Because increase of water vapor accelerate to become high land surface temperature, it is essential to monitoring the changes in the amount of water vapor and to investigating the causes of such changes. This paper, we monitor variability pattern of Total Precipitable Water (TPW) which observed by satellite. But long-term investigation of climate over Korea peninsula is very difficult due to climatic characteristic in middle latitude of instable atmospheric. El Nino that is one of climate variables appears regularly when compared to the others. Also, precipitation of all climate variables play an important part to analyze variability pattern of water vapor because it is produced by water vapor. Therefore, if we know climatic variability by them, correlation analysis between TPW and climate variables can be improved. In this study, we analyze long-term change of TPW from Moderate-Resolution Imaging Spectroadiometer (MODIS) and precipitation change in middle area of Korea peninsula quantitatively and El Nino was compared to relation of TPW and precipitation. The aim of study is to investigate precipitation and El Nino has an impact on variability pattern of TPW. First, time series analysis is used to calculate TPW and precipitation quantitatively, and anomaly analysis is performed to analyze their correlation. From the results obtained, TPW and precipitation has correlation mostly but the part had inverse correlation was found. We compare it with El Nino of anomaly results. As a result, after El Nino occurred, TPW and precipitation had inverse correlation.
Sea ice is an important factor for understanding Antarctic climate change. Especially, annual change of sea ice shows different trend in Antarctica and Arctic. This different variation need to continuously observe the Polar Regions. Sea Ice Albedo (SIA) and Sea Ice Concentration (SIC) are an indicator of variation on sea ice. In addition, albedo is key parameter to understand the energy budget in Antarctica. This being so, it is important to analyze long-term variation of the two factors for observing of change of Antarctic environment. In this study, we analyzed long-term variability of SIC and SIA to understand the changes of sea ice over Antarctic and researched the relationship with two factors. We used the SIA data at The Satellite Application Facility on Climate Monitoring (CM SAF) and the SIC data provided by Ocean and Sea Ice Satellite Application Facility (OSI-SAF) from 1982 to 2009. The study period was selected to Antarctic summer season due to polar nights. We divided study periods into two terms, Nov-Dec(ND) and Jan-Feb(JF) in order to reflect the characteristics of sea ice area, which minimum extend occurred in September and maximum extend occurred in February. We analyzed the correlation between SIA and SIC. As a results, two variables have a strong positive correlation (each correlation coefficients are 0.91 in Nov-Dec and 0.90 in Jan-Feb). We performed time series analysis using linear regression to understand the spatial and temporal tendency of SIA and SIC. As a results, SIA and SIC have a same spatial trend such as Weddle sea and Ross sea sections show the positive trend and Bellingshausen Amundsen sea shows the negative trend of two factors. Moreover, annual SIA change rate is 0.26% ~ 0.04% yr-1 over section where represent positive trend during two study periods. And annual SIA change rate is - 0.14 ~ - 0.25 % yr-1 of in the other part where represent negative trend during two study periods.
There is a strong need for accurate estimation of radiance from satellite regarding establishing a climate records such as global climate circulation, change and Earth’s atmosphere. It is important that exact radiance measurements from satellite to numerical weather prediction models for climate change detection. Furthermore, accurate measurements from satellite rely on calibration of channel data in terms of the radiometric characteristics. Related to improved calibration and inter-calibration of the sensors, the World Meteorological Organization (WMO) and the Coordination Group for Meteorological Satellite (CGMS) initiated the Global Space-based Inter-Calibration System (GSICS) in 2005, which provide coefficients to the user community to adjust satellite observations. To assess influence of the GSICS corrections and impacts of input parameters changes on satellite products, the coefficients of the GSICS corrections were applied to infrared (IR) data from Communication Ocean and Meteorological Satellite (COMS), which have Meteorological Imager (MI) sensor for meteorological missions. The IR data centered at wavelengths of 10.8 (IR1) and 12.0μm (IR2) from the COMS MI were compared with that of the Infrared Atmospheric Sounding Interferometer (IASI) sensor, which is reference sensor of the GSICS corrections. The IR1 and IR2 data that were corrected by GSICS produced Sea Surface Temperature (SST), which has been influenced by input parameters such as IR data and solar zenith angle. As a result of comparison with in situ measurements, the Global Telecommunication System (GTS) buoy data, COMS IR data that were corrected by the GSICS corrections produced high quality products of SST than original COMS IR data.
Snow is a component of the cryosphere which has played an important role in Earth energy balance. Northern
hemisphere snow cover extent (SCE) has steadily decreased since 1980 and in recently the trend of SCE is sharply
decreased. Because Himalaya region's shows most significant changes except for the Arctic, we analyzed this region for
SCE. We used Moderate Resolution Imaging Spectroradiometer (MODIS) snow product from 2001 to 2011 in august.
Analysis was made by considering some conditions (region, elevation, longitude and climate) which can affect the
changes in SCE. The entire SCE in Himalaya for 11 years has steadily increased(+55,098 km2). Trends for SCE in western
region has increased(+77,781km2), But trend for central and eastern have decreased -3,453 km2, -19,230km2, respectively.
According to elevation increases, the ratio of snow in each study area is increased. In 30°N~35°N SCE shows increased
trend, 27°N~28°N shows decreased trend. In tundra climate, trends for SCE are similar to regional analysis. whereas the
result in tropical climate's trend was increased. these performed result shows different side for change of SCE depending
on each condition. The result of this study were similar to the rapid decline of the northern hemisphere SCE area in
recent. The result of this study can be used to help management to water budget in Central-Asia country located to
Himalayas.
Air temperature (Ta) plays important role for the circulation of energy and water between the surface and atmosphere. Ta
was accurately measured from ground observation stations. However, the number of ground observation stations is
limited, and Ta is influenced from temporal and spatial change. In this study, Ta was estimated using satellite data from
April 2011 to March 2012 in the Northeast Asia where consist of the various ecosystem. States of surface and
atmosphere were considered through Normalized Difference Water Index (NDWI) and the differences of brightness
temperature values of 11μm (TBB1) and 12μm (TBB2). Dataset was divided into nine cases that had seasonal
characteristics according surface states (NDWI) and atmosphere states (TBB1-TBB2). Ta was acquired from 174 ground
observation stations, and multiple regression equation of each case was consisted of LST, NDVI, TBB1-TBB2. The
weighting region was set to be within 8.33% of total density from boundary area of cases in order to reduce the errors
that can occur due to the small value. The weighting was applied as distance from the nearest four points. The spatial
representativeness of estimated Ta was determined as 9 by 9 window size. R-squared of estimated Ta from satellite was
0.94, RMSE was 2.98 K, Bias was 0.56 K.
Surface of the earth temperature of the earth caused phenomenon that rise and is global warming as greenhouse
gas concentration into waiting by continuous discharge of greenhouse gas increases since passing industrial
revolution. While gravity about climate fluctuation is risen worldwide, place that can diminish successively
biggest surface of the earth change by global warming is high latitude area of polar regions. This study observed
distribution of vegetation to confirm change of tundra-taiga boundary. Tundra-taiga boundary is used to observe
the transfer of vegetation pattern because it is very sensitive to human activity, natural disturbances and climate
change. The circumpolar tundra-taiga boundary could observe reaction about some change. Reaction and
confirmation about climate change were definite than other place. This study used Leaf Area Index(LAI) 8-Day
data in August from 2000 to 2009 that acquire from Terra satellite MODerate resolution Imaging
Spectroradiometer(MODIS) sensor and used Köppen Climate Map, Global Land Cover 2000 for reference data.
This study conducted analysis of spatial distribution in low density vegetated areas and inter-annual / zonal
analysis for using the long period data of LAI. Change of LAI was confirmed by analysis based on boundary
value of LAI in study area. Development of vegetation could be confirmed by area of grown
vegetation(730,325km2 ) than area of reduced vegetation(22,372km2 ) in tundra climate. Also, area was
increased with the latitude 64°N~66° N as the center and around the latitude 62° N through area analysis by
latitude. Vegetation of tundra-taiga boundary was general increase from 2000 to 2009. While area of reduced
vegetation was a little, area of vegetation growth and development was increased significantly.
Monitoring the global gross primary production (GPP) is relevant to understanding the global carbon cycle and
evaluating the effects of interannual climate variation on food and fiber production. GPP, the flux of carbon into
ecosystems via photosynthetic assimilation, is an important variable in the global carbon cycle and a key process in land
surface-atmosphere interactions. The Moderate-resolution Imaging Spectroradiometer (MODIS) is one of the primary
global monitoring sensors. MODIS GPP has some of the problems that have been proven in several studies. Therefore
this study was to solve the regional mismatch that occurs when using the MODIS GPP global product over Korea. To
solve this problem, we estimated each of the GPP component variables separately to improve the GPP estimates. We
compared our GPP estimates with validation GPP data to assess their accuracy. For all sites, the correlation was close
with high significance (R2 = 0.8164, RMSE = 0.6126 g·C·m-2·d-1, bias = -0.0271 g·C·m-2·d-1). We also compared our
results to those of other models. The component variables tended to be either over- or under-estimated when compared to
those in other studies over the Korean peninsula, although the estimated GPP was better. The results of this study will
likely improve carbon cycle modeling by capturing finer patterns with an integrated method of remote sensing.
Keywords: VEGETATION, Gross Primary Production, MODIS.
Global warming and climatic changes due to human activities impact on marine and terrestrial
ecosystems, which feedbacks to climate system. These negative feedbacks amplify or accelerate again
global climate change. In particular, life cycle of vegetation sensitively vary according to global
climate change. This study attempts to analyze quantitatively vegetation change in Korea peninsula
using harmonic analysis. Satellite data was extracted from SPOT/VEGETATION S10 MVC
(Maximum Value Composite) NDVI (Normalized Difference Vegetation Index) products during 10
years (1999 to 2008) around Korea peninsula. This NDVI data set was pre-processed to correct noise
pixels cause by cloud and ground wetness. Variation of vegetation life cycle was analyzed through
amplitudes and phases of annual harmonic components (first harmonic components) per year for two
land cover types (cropland and forest). The results clearly show that the peak of vegetation life cycle
in Korea peninsula is brought forward to early. Especially, it represents that the phases over low
latitudes area between 32.8°N and 38°N steadily decrease every year both forest and cropland. The
study estimated that phase values moved up approximately 0.5 day per year in cropland and 0.8 day per year in forest.
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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