• Korean Journal of Remote Sensing
• /
• v.32 no.4
• /
• pp.383-401
• /
• 2016

In this paper, we described the statistical modeling of crop yields using satellite images, climatic datasets, soil property maps, and fertilizer data for the Midwestern United States during 2001-2012. Satellite images were obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS), and climatic datasets were provided by the Parameter-elevation Regressions on Independent Slopes Model (PRISM) Climate Group. Soil property maps were derived from the Harmonized World Soil Database (HWSD). Our multivariate regression models produced quite good prediction accuracies, with differences of approximately 8-15% from the governmental statistics of corn and soybean yields. The unfavorable conditions of climate and vegetation in 2012 could have resulted in a decrease in yields according to the regression models, but the actual yields were greater than predicted. It can be interpreted that factors other than climate, vegetation, soil, and fertilizer may be involved in the negative biases. Also, we found that soybean yield was more affected by minimum temperature conditions while corn yield was more associated with photosynthetic activities. These two crops can have different potential impacts regarding climate change, and it is important to quantify the degree of the crop sensitivities to climatic variations to help adaptation by humans. Considering the yield decreases during the drought event, we can assume that climatic effect may be stronger than human adaptive capacity. Thus, further studies are demanded particularly by enhancing the data regarding human activities such as tillage, fertilization, irrigation, and comprehensive agricultural technologies.

• Korean Journal of Remote Sensing
• /
• v.29 no.3
• /
• pp.331-335
• /
• 2013

This study detected red tide areas using the existing Moderate-Resolution Imaging Spectroradiometer(MODIS) and Geostationary Ocean Color Imager(GOCI), and then compared the results between results of two sensors. The coasts of Jeollanam-do in the South Sea of Korea were set as the study area based on the red tide data which occurred on Aug. 26th, 2012. This study compared the results of sensors to detect red tides by using a satellite. In the results of analyzing MODIS by limiting it as chlorophyll concentration and the sea surface temperature which is considered to have red tides by the existing researches, it was possible to delete considerable amount of errors compared to the case of detecting red tides by using only chlorophyll while still there were differences from the range of red tides actually observed. In the results of GOCI by using empirical algorithm for detecting red tides, currently used by Korea Institute of Ocean Science & Technology(KIOST), it was possible to obtain more detailed results than MODIS. However, there was an area misjudged as red tides due to the influence of clouds. Also both MODIS and GOCI extracted red tides were not actually occurring, which might be because they were not able to perfectly distinguish red tides from turbid water in coastal areas with high turbidity.

• Korean Journal of Remote Sensing
• /
• v.29 no.3
• /
• pp.275-291
• /
• 2013

Monitoring the global Gross Primary Pproduction (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 ($R^2=0.8164$, $RMSE=0.6126g{\cdot}C{\cdot}m^{-2}{\cdot}d^{-1}$, $bias=-0.0271g{\cdot}C{\cdot}m^{-2}{\cdot}d^{-1}$). 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.

• Journal of the Korean earth science society
• /
• v.26 no.2
• /
• pp.149-159
• /
• 2005

Simultaneous observations of MODIS (Moderate-resolution Imaging Spectroradiometer) onboard the Aqua and Terra satellites and weather station at ground near the Inchon International Airport (37.2-37.7 N, 125.7-127.2 E) during the period from December 2002 to September 2004 have been utilized in order to analyze the characteristics of satellite-observed infrared (IR) and visible data under fog and clear-sky conditions, respectively. The differences $(T_{3.7-11})$ in brightness temperature between $3.75{\mu}m\;and\;11.0{\mu}m$ were used as threshold values for remote-sensing fog (or low clouds) from satellite during day and night. The $T_{3.7-11}$ value during daytime was greater by about 21 K when it was foggy than that when it was clear, but during nighttime fog it was less by 1.5 K than during nighttime clear-sky. The value was changed due to different values of emission of fog particles at the wavelength. Since the near-IR channel at $3.7{\mu}m$ was affected by solar and IR radiations in the daytime, both IR and visible channels (or reflectance) have been used to detect fog. The reflectance during fog was higher by 0.05-0.6 than that during clear-sky, and varied seasonally. In this study, the threshold values included uncertainties when clouds existed above a layer of fog.

• Atmosphere
• /
• v.21 no.4
• /
• pp.373-389
• /
• 2011

This research focuses on the sensitivity of the WRF(Weather Research and Forecasting) Model according to three different land cover data(USGS(United States Geological Survey), MODIS(Moderate Resolution Imaging Spectroradiometer)30s+USGS, and KLC (Korea Land Cover)) for an event of sea breeze, occurred over the Gangwon Yeongdong region on 13 May 2009. Based on the observation, the easterly into Gangneung, due to the sea-breeze circulation, was identified between 1000 LST and 1640 LST. It did not reach beyond the Taebaek Mountain Range and thus the easterly was not observed near Daegwallyeong. On the other hand, the numerical simulations utilizing land cover data of USGS, MODIS30s+USGS, and KLC showed easterlies beyond the Taebaek Mountain Range up to Daegwallyeong. In addition, rather different penetration distances of each easterly, and different timings of beginning and ending of sea breeze were identified among the simulations. The Bias, MAE(Mean Absolute Error) and RMSE(Root Mean Square Error) of the wind from WRF simulation using MODIS30s+USGS land cover data were the least among the simulations particularly over Gangwon Yeongdong coastal area(Sokcho, Gangneung and Donghae), while those of the wind over the Gangwon Mountain area(Daegwallyeong and Jinbu) from the simulation using KLC land cover data were the least among them. The wind field over Gangwon Yeongdong coastal area from the simulation using USGS land cover data was rather poor among them.

• Korean Journal of Remote Sensing
• /
• v.33 no.4
• /
• pp.401-410
• /
• 2017

Estimation of snow depth using optical image is conducted by using correlation with Snow Cover Fraction (SCF). Various algorithms have been proposed for the estimation of snow cover fraction based on Normalized Difference Snow Index (NDSI). In this study we tested linear, quadratic, and exponential equations for the generation of snow cover fraction maps using data from the Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua satellite in order to evaluate their applicability to the complex terrain of South Korea and to search for improvements to the estimation of snow depth on this landscape. The results were validated by comparison with in-situ snowfall data from weather stations, with Root Mean Square Error (RMSE) calculated as 3.43, 2.37, and 3.99 cm for the linear, quadratic, and exponential approaches, respectively. Although quadratic results showed the best RMSE, this was due to the limitations of the data used in the study; there are few number of in-situ data recorded on the station at the time of image acquisition and even the data is mostly recorded on low snowfall. So, we conclude that linear-based algorithms are better suited for use in South Korea. However, in the case of using the linear equation, the SCF with a negative value can be calculated, so it should be corrected. Since the coefficients of the equation are not optimized for this area, further regression analysis is needed. In addition, if more variables such as Normalized Difference Vegetation Index (NDVI), land cover, etc. are considered, it could be possible that estimation of national-scale snow depth with higher accuracy.

• 한국방재학회:학술대회논문집
• /
• 2011.02a
• /
• pp.219-219
• /
• 2011

순 장파복사량은 지표면으로 입사되는 하강 장파복사량(Downward Longwave Radiation, $R_{ld}$)과 지표면에서 반사되는 상승 장파복사량(Upward Longwave Radiation, $R_{lu}$)의 차이로 정의되는데 이는 에너지 수지 및 농업기상 연구의 중요한 주제 중 하나로서 다루어져 온 순복사량의 중요한 요소이다. 일반적으로 $R_{lu}$의 경우 지표면 온도와 방사율(emissivity)를 이용하여 산출되므로 정확히 추정이 가능하나, $R_{ld}$의 경우 대기 최상층에서 관측되는 방사량과 지표면 근처의 방사량을 함께 고려해야 하므로 실측이 어렵다. $R_{ld}$는 야간 복사계(pyrgeometer)를 이용하여 직접적으로 측정할 수 있지만 관측기기 자체가 구비되어있는 관측소가 적어 매우 드물게 이용된다. 또한 단파 복사 에너지 측정 기기에 비해 비용이 많이들고 종종 관측값이 큰 오차를 가지고 있기 때문에 실무에 적용하기 힘든 단점이 있다. 따라서 기상 관측소에서 얻어지는 증기압과 온도 관측치를 물리식, 경험식 등에 적용하여 산정하게 된다. 현재는 $R_{ld}$의 추정은 관측된 방사량간의 관계를 나타내는 경험식을 기반으로 지표면 근처의 대기 온도와 습도를 이용하여 산출하는 방법이 널리 사용되고 있다. 본 연구에서는 증발산 산정 알고리즘 개발의 시발점으로써 $R_{ld}$를 먼저 구하고 $R_{lu}$를 구하였다. 신뢰성 높은 방법을 이용하여 $R_{ld}$를 구하게 되면 정확도 높은 $R_N$을 구하는 데 기여할 수 있으며, 궁극적으로 보다 정확한 증발산을 산정할 수 있게 된다. $R_{ld}$는 일반적으로 clear sky 조건 하에서의 복사 에너지 플럭스($R_{ldc}$)를 구한 후 구름의 양에 따라 보정한다. 하강 장파복사량의 경우 널리 사용되는 공식 중 하나인 Brutsaert의 공식을 사용하였다. 광릉, 해남에 위치한 플럭스 타워지점에서 실측된 기온과 실제 수증기압을 입력인자로 사용하여 지점별 $R_{ldc}$를 먼저 구하고 Moderate Resolution Imaging Spectroradiometer(MODIS) 영상자료를 이용하여 검증한 뒤 최종적으로 남한지역을 대상으로 순 장파복사량 지도를 작성하였다. 이를 위해 MODIS 07 대기 프로파일 산출물(Atmospheric Profile Product)중 기온 및 이슬점온도를 추출하여 산정식의 입력자료로서 사용하였다. 상승 장파복사량의 경우 MODIS 11 지표면 온도 산출물(Land Surface Temperature product)를 이용하여 산정하였다. 이는 남한 지역의 증발산량 추정 및 에너지 수지 연구를 위한 중요한 기본 자료로서 유용하게 사용될 수 있으리라 사료된다.

• Journal of the Korean Society of Environmental Restoration Technology
• /
• v.19 no.1
• /
• pp.73-83
• /
• 2016

Forest degradation reduces ecosystem services provided by forest and could lead to change in composition of species. In North Korea, there has been significant forest degradation due to conversion of forest into terrace fields for food production and cut-down of forest for fuel woods. This study analyzed the phenological changes in North Korea, in terms of vegetation and moisture in soil and vegetation, from March to Octorber 2013, using MODIS (MODerate resolution Imaging Spectroradiometer) images and indexes including NDVI (Normalized Difference Vegetation Index), NDSI (Normalized Difference Soil Index), and NDWI (Normalized Difference Water Index). In addition, marginal farmland was derived using elevation data. Lastly, degraded terrace fields of 16 degree was analyzed using NDVI, NDSI, and NDWI indexes, and marginal farmland characteristics with slope variable. The accuracy value of land cover classification, which shows the difference between the observation and analyzed value, was 84.9% and Kappa value was 0.82. The highest accuracy value was from agricultural (paddy, field) and forest area. Terrace fields were easily identified using slope data form agricultural field. Use of NDVI, NDSI, and NDWI is more effective in distinguishing deforested terrace field from agricultural area. NDVI only shows vegetation difference whereas NDSI classifies soil moisture values and NDWI classifies abandoned agricultural fields based on moisture values. The method used in this study allowed more effective identification of deforested terrace fields, which visually illustrates forest degradation problem in North Korea.

• Asian Journal of Atmospheric Environment
• /
• v.12 no.2
• /
• pp.165-177
• /
• 2018

Particulate matter (PM) measurements are important in air quality, public health, epidemiological studies and decision making for short and long-term policies implementation. However, only few cities in the word have advance air quality-monitoring networks able to provide reliable information of PM leaves in the ambient air, trends and extent of the pollution. In Colombia, only major cities measure PM concentrations. Available measurements from Bogota, Medellin and Bucaramanga show that PM concentration are well above World Health Organization guidelines, but up to now levels and trends of PM in other cities and regions of the country are not well known. Satellite measurements serve as an alternative approach to study air quality in regions were surface measurements are not available. The aim of this study is to perform a spatial and temporal assessment of PM in the ambient air of Colombia. We used Aerosol optical depth (AOD) retrieved by the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite of NASA and surface measurements from the air quality networks of Bogota, Medellin and Bucaramanga. In a first step, we estimated the correlation between MODIS-AOD and monthly average surface measurements (2000 to 2015) from these three cities, obtaining correlation coefficient R values over 0.4 for the cities under study. After, we used AOD and $PM_{10}$ measurements to study the temporal evolution of PM in different cities and regions. Finally, we used AOD measurements to identify cities and regions with the highest AOD levels in Colombia. All the methods presented in this paper may serve as an example for other countries or regions to identify and prioritize locations that require the implementation of more accurate air quality measurements.

• Proceedings of the KSRS Conference
• /
• v.1
• /
• pp.236-238
• /
• 2006

An algorithm for detection of yellow sand aerosols has been developed with infrared bands from Moderate Resolution Imaging Spectroradiometer (MODIS) and Multi-functional Transport Satellite-1 Replacement (MTSAT-1R) data. The algorithm is the hybrid algorithm that has used two methods combined together. The first method used the differential absorption in brightness temperature difference between $11{\mu}m$ and $12{\mu}m$ (BTD1). The radiation at 11 ${\mu}m$ is absorbed more than at 12 ${\mu}m$ when yellow sand is loaded in the atmosphere, whereas it will be the other way around when cloud is present. The second method uses the brightness temperature difference between $3.7{\mu}m$ and $11{\mu}m$ (BTD2). The technique would be most sensitive to dust loading during the day when the BTD2 is enhanced by reflection of $3.7{\mu}m$ solar radiation. We have applied the three methods to MTSAT-1R for derivation of the yellow sand dust and in conjunction with the Principle Component Analysis (PCA), a form of eigenvector statistical analysis. As produced Principle Component Image (PCI) through the PCA is the correlation between BTD1 and BTD2, errors of about 10% that have a low correlation are eliminated for aerosol detection. For the region of aerosol detection, aerosol index (AI) is produced to the scale of BTD1 and BTD2 values over land and ocean respectively. AI shows better results for yellow sand detection in comparison with the results from individual method. The comparison between AI and OMI aerosol index (AI) shows remarkable good correlations during daytime and relatively good correlations over the land.