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Various drought indices have been used for agricultural drought monitoring, such as Standardized Precipitation Index (SPI), Standardized Precipitation Evapotranspiration Index (SPEI), Palmer Drought Severity Index (PDSI), Soil Water Deficit Index (SWDI), Normalized Difference Vegetation Index (NDVI), Vegetation Health Index (VHI), Vegetation Drought Response Index (VegDRI), and Scaled Drought Condition Index (SDCI). They incorporate such factors as rainfall, land surface temperature (LST), potential evapotranspiration (PET), soil moisture content (SM), and vegetation index to express the meteorological and agricultural aspects of drought. However, these five factors should be combined more comprehensively and reasonably to explain better the dryness/wetness of land surface and the association with crop yield. This study aims to develop the Integrated Crop Drought Index (ICDI) by combining the weather factors (rainfall and LST), hydrological factors (PET and SM), and a vegetation factor (enhanced vegetation index (EVI)) to better express the wet/dry state of land surface and healthy/unhealthy state of vegetation together. The study area was the State of Illinois, a key region of the U.S. Corn Belt, and the quantification and analysis of the droughts were conducted on a county scale for 2004–2019. The performance of the ICDI was evaluated through the comparisons with SDCI and VegDRI, which are the representative drought index in terms of the composite of the dryness and vegetation elements. The ICDI properly expressed both the dry and wet trend of the land surface and described the state of the agricultural drought accompanied by yield damage. The ICDI had higher positive correlations with the corn yields than SDCI and VegDRI during the crucial growth period from June to August for 2004–2019, which means that the ICDI could reflect the agricultural drought well in terms of the dryness/wetness of land surface and the association with crop yield. Future work should examine the other factors for ICDI, such as locality, crop type, and the anthropogenic impacts, on drought. It is expected that the ICDI can be a viable option for agricultural drought monitoring and yield management.
Soo-Jin Lee; Nari Kim; Yangwon Lee. Development of Integrated Crop Drought Index by Combining Rainfall, Land Surface Temperature, Evapotranspiration, Soil Moisture, and Vegetation Index for Agricultural Drought Monitoring. Remote Sensing 2021, 13, 1778 .
AMA StyleSoo-Jin Lee, Nari Kim, Yangwon Lee. Development of Integrated Crop Drought Index by Combining Rainfall, Land Surface Temperature, Evapotranspiration, Soil Moisture, and Vegetation Index for Agricultural Drought Monitoring. Remote Sensing. 2021; 13 (9):1778.
Chicago/Turabian StyleSoo-Jin Lee; Nari Kim; Yangwon Lee. 2021. "Development of Integrated Crop Drought Index by Combining Rainfall, Land Surface Temperature, Evapotranspiration, Soil Moisture, and Vegetation Index for Agricultural Drought Monitoring." Remote Sensing 13, no. 9: 1778.
Evapotranspiration (ET) is an important component of the Earth’s energy and water cycle via the interaction between the atmosphere and the land surface. The reference evapotranspiration (ET0) is particularly important in the croplands because it is a convenient and reasonable method for calculating the actual evapotranspiration (AET) that represents the loss of water in the croplands through the soil evaporation and vegetation transpiration. To date, many efforts have been made to retrieve ET0 on a spatially continuous grid. In particular, the Moderate Resolution Imaging Spectroradiometer (MODIS) product is provided with a reasonable spatial resolution of 500 m and a temporal resolution of 8 days. However, the applicability to the local-scale variabilities due to complex and heterogeneous land surfaces in countries like South Korea is not sufficiently validated. Meanwhile, the AI approaches showed a useful functionality for the ET0 retrieval on the local scale but have rarely demonstrated a substantial product for a spatially continuous grid. This paper presented a retrieval of the daily reference evapotranspiration (ET0) over a 500 m grid for croplands in South Korea using machine learning (ML) with satellite images and numerical weather prediction data. In a blind test for 2013–2019, the ML-based ET0 model produced the accuracy statistics with a root mean square error of 1.038 mm/day and a correlation coefficient of 0.870. The results of the blind test were stable irrespective of location, year, and month. This outcome is presumably because the input data of the ML-based ET0 model were suitably arranged spatially and temporally, and the optimization of the model was appropriate. We found that the relative humidity and land surface temperature were the most influential variables for the ML-based ET0 model, but the variables with lower importance were also necessary to consider the nonlinearity between the variables. Using the daily ET0 data produced over the 500 m grid, we conducted a case study to examine agrometeorological characteristics of the croplands in South Korea during the period when heatwave and drought events occurred. Through the experiments, the feasibility of the ML-based ET0 retrieval was validated, especially for local agrometeorological applications in regions with heterogeneous land surfaces, such as South Korea.
Nari Kim; Kwangjin Kim; Soobong Lee; Jaeil Cho; Yangwon Lee. Retrieval of Daily Reference Evapotranspiration for Croplands in South Korea Using Machine Learning with Satellite Images and Numerical Weather Prediction Data. Remote Sensing 2020, 12, 3642 .
AMA StyleNari Kim, Kwangjin Kim, Soobong Lee, Jaeil Cho, Yangwon Lee. Retrieval of Daily Reference Evapotranspiration for Croplands in South Korea Using Machine Learning with Satellite Images and Numerical Weather Prediction Data. Remote Sensing. 2020; 12 (21):3642.
Chicago/Turabian StyleNari Kim; Kwangjin Kim; Soobong Lee; Jaeil Cho; Yangwon Lee. 2020. "Retrieval of Daily Reference Evapotranspiration for Croplands in South Korea Using Machine Learning with Satellite Images and Numerical Weather Prediction Data." Remote Sensing 12, no. 21: 3642.
Many process-based models for carbon flux predictions have faced a wide range of uncertainty issues. The complex interactions between the atmosphere and the forest ecosystems can lead to uncertainties in the model result. On the other hand, artificial intelligence (AI) techniques, which are novel methods to resolve complex and nonlinear problems, have shown a possibility for forest ecological applications. This study is the first step to present an objective comparison between multiple AI models for the daily forest gross primary productivity (GPP) prediction using satellite remote sensing data. We built the AI models such as support vector machine (SVM), random forest (RF), artificial neural network (ANN), and deep neural network (DNN) using in-situ observations from an eddy covariance (EC) flux tower and satellite remote sensing data such as albedo, aerosol, temperature, and vegetation index. We focused on the Gwangneung site from the Korea Regional Flux Network (KoFlux) in South Korea, 2006–2015. As a result, the DNN model outperformed the other three models through an intensive hyperparameter optimization, with the correlation coefficient (CC) of 0.93 and the mean absolute error (MAE) of 0.68 g m−2 d−1 in a 10-fold blind test. We showed that the DNN model also performed well under conditions of cold waves, heavy rain, and an autumnal heatwave. As future work, a comprehensive comparison with the result of process-based models will be necessary using a more extensive EC database from various forest ecosystems.
Bora Lee; Nari Kim; Eun-Sook Kim; Keunchang Jang; Minseok Kang; Jong-Hwan Lim; Jaeil Cho; Yangwon Lee. An Artificial Intelligence Approach to Predict Gross Primary Productivity in the Forests of South Korea Using Satellite Remote Sensing Data. Forests 2020, 11, 1000 .
AMA StyleBora Lee, Nari Kim, Eun-Sook Kim, Keunchang Jang, Minseok Kang, Jong-Hwan Lim, Jaeil Cho, Yangwon Lee. An Artificial Intelligence Approach to Predict Gross Primary Productivity in the Forests of South Korea Using Satellite Remote Sensing Data. Forests. 2020; 11 (9):1000.
Chicago/Turabian StyleBora Lee; Nari Kim; Eun-Sook Kim; Keunchang Jang; Minseok Kang; Jong-Hwan Lim; Jaeil Cho; Yangwon Lee. 2020. "An Artificial Intelligence Approach to Predict Gross Primary Productivity in the Forests of South Korea Using Satellite Remote Sensing Data." Forests 11, no. 9: 1000.
This paper describes the development of an optimized corn yield prediction model under extreme weather conditions for the Midwestern United States (US). We tested six different artificial intelligence (AI) models using satellite images and meteorological data for the dominant growth period. To examine the effects of extreme weather events, we defined the drought and heatwave by considering the characteristics of corn growth and selected the cases for sensitivity tests from a historical database. In particular, we conducted an optimization for the hyperparameters of the deep neural network (DNN) model to ensure the best configuration for accuracy improvement. The result for drought cases showed that our DNN model was approximately 51–98% more accurate than the other five AI models in terms of root mean square error (RMSE). For the heatwave cases, our DNN model showed approximately 30–77% better accuracy in terms of RMSE. The correlation coefficient was 0.954 for drought cases and 0.887–0.914 for heatwave cases. Moreover, the accuracy of our DNN model was very stable, despite the increases in the duration of the heatwave. It indicates that the optimized DNN model can provide robust predictions for corn yield under conditions of extreme weather and can be extended to other prediction models for various crops in future work.
Nari Kim; Sang-Il Na; Chan-Won Park; Morang Huh; Jaiho Oh; Kyung-Ja Ha; Jaeil Cho; Yang-Won Lee. An Artificial Intelligence Approach to Prediction of Corn Yields under Extreme Weather Conditions Using Satellite and Meteorological Data. Applied Sciences 2020, 10, 3785 .
AMA StyleNari Kim, Sang-Il Na, Chan-Won Park, Morang Huh, Jaiho Oh, Kyung-Ja Ha, Jaeil Cho, Yang-Won Lee. An Artificial Intelligence Approach to Prediction of Corn Yields under Extreme Weather Conditions Using Satellite and Meteorological Data. Applied Sciences. 2020; 10 (11):3785.
Chicago/Turabian StyleNari Kim; Sang-Il Na; Chan-Won Park; Morang Huh; Jaiho Oh; Kyung-Ja Ha; Jaeil Cho; Yang-Won Lee. 2020. "An Artificial Intelligence Approach to Prediction of Corn Yields under Extreme Weather Conditions Using Satellite and Meteorological Data." Applied Sciences 10, no. 11: 3785.
Satellite-based remote sensing techniques have been widely used to monitor droughts spanning large areas. Various agricultural drought indices have been developed to assess the intensity of agricultural drought and to detect damaged crop areas. However, to better understand the responses of agricultural drought to meteorological drought, agricultural management practices should be taken into consideration. This study aims to evaluate the responses to drought under different forms of agricultural management for the extreme drought that occurred on the Korean Peninsula in 2014 and 2015. The 3-month standardized precipitation index (SPI3) and the 3-month vegetation health index (VHI3) were selected as a meteorological drought index and an agricultural drought index, respectively. VHI3, which comprises the 3-month temperature condition index (TCI3) and the 3-month vegetation condition index (VCI3), differed significantly in the study area during the extreme drought. VCI3 had a different response to the lack of precipitation in South and North Korea because it was affected by irrigation. However, the time series of TCI3 were similar in South and North Korea. These results meant that each drought index has different characteristics and should be utilized with caution. Our results are expected to help comprehend the responses of the agricultural drought index on meteorological drought depending on agricultural management.
Jae-Hyun Ryu; Kyung-Soo Han; Yang-Won Lee; No-Wook Park; Sungwook Hong; Chu-Yong Chung; Jaeil Cho. Different Agricultural Responses to Extreme Drought Events in Neighboring Counties of South and North Korea. Remote Sensing 2019, 11, 1773 .
AMA StyleJae-Hyun Ryu, Kyung-Soo Han, Yang-Won Lee, No-Wook Park, Sungwook Hong, Chu-Yong Chung, Jaeil Cho. Different Agricultural Responses to Extreme Drought Events in Neighboring Counties of South and North Korea. Remote Sensing. 2019; 11 (15):1773.
Chicago/Turabian StyleJae-Hyun Ryu; Kyung-Soo Han; Yang-Won Lee; No-Wook Park; Sungwook Hong; Chu-Yong Chung; Jaeil Cho. 2019. "Different Agricultural Responses to Extreme Drought Events in Neighboring Counties of South and North Korea." Remote Sensing 11, no. 15: 1773.
This paper compares different artificial intelligence (AI) models in order to develop the best crop yield prediction model for the Midwestern United States (US). Through experiments to examine the effects of phenology using three different periods, we selected the July–August (JA) database as the best months to predict corn and soybean yields. Six different AI models for crop yield prediction are tested in this research. Then, a comprehensive and objective comparison is conducted between the AI models. Particularly for the deep neural network (DNN) model, we performed an optimization process to ensure the best configurations for the layer structure, cost function, optimizer, activation function, and drop-out ratio. In terms of mean absolute error (MAE), our DNN model with the JA database was approximately 21–33% and 17–22% more accurate for corn and soybean yields, respectively, than the other five AI models. This indicates that corn and soybean yields for a given year can be forecasted in advance, at the beginning of September, approximately a month or more ahead of harvesting time. A combination of the optimized DNN model and spatial statistical methods should be investigated in future work, to mitigate partly clustered errors in some regions.
Nari Kim; Kyung-Ja Ha; No-Wook Park; Jaeil Cho; Sungwook Hong; Yang-Won Lee. A Comparison Between Major Artificial Intelligence Models for Crop Yield Prediction: Case Study of the Midwestern United States, 2006–2015. ISPRS International Journal of Geo-Information 2019, 8, 240 .
AMA StyleNari Kim, Kyung-Ja Ha, No-Wook Park, Jaeil Cho, Sungwook Hong, Yang-Won Lee. A Comparison Between Major Artificial Intelligence Models for Crop Yield Prediction: Case Study of the Midwestern United States, 2006–2015. ISPRS International Journal of Geo-Information. 2019; 8 (5):240.
Chicago/Turabian StyleNari Kim; Kyung-Ja Ha; No-Wook Park; Jaeil Cho; Sungwook Hong; Yang-Won Lee. 2019. "A Comparison Between Major Artificial Intelligence Models for Crop Yield Prediction: Case Study of the Midwestern United States, 2006–2015." ISPRS International Journal of Geo-Information 8, no. 5: 240.
Warming of the Arctic leads to a decrease in sea ice, and the decrease of sea ice, in turn, results in warming of the Arctic again. Several microwave sensors have provided continuously updated sea ice data for over 30 years. Many studies have been conducted to investigate the relationships between the satellite-derived sea ice concentration (SIC) of the Arctic and climatic factors associated with the accelerated warming. However, linear equations using the general circulation model (GCM) data, with low spatial resolution, cannot sufficiently cope with the problem of complexity or non-linearity. Time-series techniques are effective for one-step-ahead forecasting, but are not appropriate for future prediction for about ten or twenty years because of increasing uncertainty when forecasting multiple steps ahead. This paper describes a new approach to near-future prediction of Arctic SIC by employing a deep learning method with multi-model ensemble. We used the regional climate model (RCM) data provided in higher resolution, instead of GCM. The RCM ensemble was produced by Bayesian model averaging (BMA) to minimize the uncertainty which can arise from a single RCM. The accuracies of RCM variables were much improved by the BMA2 method, which took into consideration temporal and spatial variations to minimize the uncertainty of individual RCMs. A deep neural network (DNN) method was used to deal with the non-linear relationships between SIC and climate variables, and to provide a near-future prediction for the forthcoming 10 to 20 years. We adjusted the DNN model for optimized SIC prediction by adopting best-fitted layer structure, loss function, optimizer algorithm, and activation function. The accuracy was much improved when the DNN model was combined with BMA2 ensemble, showing the correlation coefficient of 0.888. This study provides a viable option for monitoring Arctic sea ice change of the near future.
Jiwon Kim; Kwangjin Kim; Jaeil Cho; Yong Q. Kang; Hong-Joo Yoon; Yang-Won Lee. Satellite-Based Prediction of Arctic Sea Ice Concentration Using a Deep Neural Network with Multi-Model Ensemble. Remote Sensing 2018, 11, 19 .
AMA StyleJiwon Kim, Kwangjin Kim, Jaeil Cho, Yong Q. Kang, Hong-Joo Yoon, Yang-Won Lee. Satellite-Based Prediction of Arctic Sea Ice Concentration Using a Deep Neural Network with Multi-Model Ensemble. Remote Sensing. 2018; 11 (1):19.
Chicago/Turabian StyleJiwon Kim; Kwangjin Kim; Jaeil Cho; Yong Q. Kang; Hong-Joo Yoon; Yang-Won Lee. 2018. "Satellite-Based Prediction of Arctic Sea Ice Concentration Using a Deep Neural Network with Multi-Model Ensemble." Remote Sensing 11, no. 1: 19.
The worst forest fire in South Korea occurred in April 2000 on the eastern coast. Forest recovery works were conducted until 2005, and the forest has been monitored since the fire. Remote sensing techniques have been used to detect the burned areas and to evaluate the recovery-time point of the post-fire processes during the past 18 years. We used three indices, Normalized Burn Ratio (NBR), Normalized Difference Vegetation Index (NDVI), and Gross Primary Production (GPP), to temporally monitor a burned area in terms of its moisture condition, vegetation biomass, and photosynthetic activity, respectively. The change of those three indices by forest recovery processes was relatively analyzed using an unburned reference area. The selected unburned area had similar characteristics to the burned area prior to the forest fire. The temporal patterns of NBR and NDVI, not only showed the forest recovery process as a result of forest management, but also statistically distinguished the recovery periods at the regions of low, moderate, and high fire severity. The NBR2.1 for all areas, calculated using 2.1 μm wavelengths, reached the unburned state in 2008. The NDVI for areas with low and moderate fire severity levels became significantly equal to the unburned state in 2009 (p > 0.05), but areas with high severity levels did not reach the unburned state until 2017. This indicated that the surface and vegetation moisture conditions recovered to the unburned state about 8 years after the fire event, while vegetation biomass and health required a longer time to recover, particularly for high severity regions. In the case of GPP, it rapidly recovered after about 3 years. Then, the steady increase in GPP surpassed the GPP of the reference area in 2015 because of the rapid growth and high photosynthetic activity of young forests. Therefore, the concluding scientific message is that, because the recovery-time point for each component of the forest ecosystem is different, using only one satellite-based indicator will not be suitable to understand the post-fire recovery process. NBR, NDVI, and GPP can be combined. Further studies will require more approaches using various terms of indices.
Jae-Hyun Ryu; Kyung-Soo Han; Sungwook Hong; No-Wook Park; Yang-Won Lee; Jaeil Cho. Satellite-Based Evaluation of the Post-Fire Recovery Process from the Worst Forest Fire Case in South Korea. Remote Sensing 2018, 10, 918 .
AMA StyleJae-Hyun Ryu, Kyung-Soo Han, Sungwook Hong, No-Wook Park, Yang-Won Lee, Jaeil Cho. Satellite-Based Evaluation of the Post-Fire Recovery Process from the Worst Forest Fire Case in South Korea. Remote Sensing. 2018; 10 (6):918.
Chicago/Turabian StyleJae-Hyun Ryu; Kyung-Soo Han; Sungwook Hong; No-Wook Park; Yang-Won Lee; Jaeil Cho. 2018. "Satellite-Based Evaluation of the Post-Fire Recovery Process from the Worst Forest Fire Case in South Korea." Remote Sensing 10, no. 6: 918.
Observations of urbanization will provide a framework for understanding the biophysical processes caused by artificial land changes. Sejong Multifunctional Administrative City (MAC) is under development since 2006 to decentralize the function of Seoul, the capital city of South Korea. MAC was originally agricultural land and now is rapidly developing to mega-city. Using U.S. Air Force Defense Meteorological Satellite Program’s Operational Linescan System (DMSP-OLS) and Moderate-resolution Imaging Spectroradiometer (MODIS) satellite data, the spatio-temporal characteristics of nighttime light (NTL) emission, normalized difference vegetation index (NDVI), land surface temperature (LST), and surface albedo were investigated in MAC and its adjacent cities. NTL was generally stronger in the presence of vegetation degradation and surface warming conditions. LST was negatively correlated with a growth in vegetation. Those relationships among NTL, LST, and NDVI were shown both in temporal change at MAC and spatial variation of MAC’s adjacent cites. Further, because the ratio value of LST to NDVI was similar in temporal and spatial scales, these two indices can be used as important indicator of urbanization. However, surface albedo is not suitable to represent the temporal transition from rural to urban state because tall buildings can often bring relatively low surface albedo by blocking outgoing radiation.
Sungwook Hong; Yang-Won Lee; Jae-Hyun Ryu; Jong-Min Yeom; WonSik Kim; Jaeil Cho. Satellite-based assessment of rapid mega-urban development on agricultural land. Journal of Agricultural Meteorology 2018, 74, 87 -91.
AMA StyleSungwook Hong, Yang-Won Lee, Jae-Hyun Ryu, Jong-Min Yeom, WonSik Kim, Jaeil Cho. Satellite-based assessment of rapid mega-urban development on agricultural land. Journal of Agricultural Meteorology. 2018; 74 (2):87-91.
Chicago/Turabian StyleSungwook Hong; Yang-Won Lee; Jae-Hyun Ryu; Jong-Min Yeom; WonSik Kim; Jaeil Cho. 2018. "Satellite-based assessment of rapid mega-urban development on agricultural land." Journal of Agricultural Meteorology 74, no. 2: 87-91.
Ji-Won Kim; Kwang-Jin Kim; Soo-Jin Lee; Yeong-Ho Kim; Ji-Hye Ahn; Yang-Won Lee. Prediction of Arctic Sea Ice Concentration of Kara-Barents Seas Using RCM Data with Machine Learning. Journal of Climate Research 2017, 12, 349 -365.
AMA StyleJi-Won Kim, Kwang-Jin Kim, Soo-Jin Lee, Yeong-Ho Kim, Ji-Hye Ahn, Yang-Won Lee. Prediction of Arctic Sea Ice Concentration of Kara-Barents Seas Using RCM Data with Machine Learning. Journal of Climate Research. 2017; 12 (4):349-365.
Chicago/Turabian StyleJi-Won Kim; Kwang-Jin Kim; Soo-Jin Lee; Yeong-Ho Kim; Ji-Hye Ahn; Yang-Won Lee. 2017. "Prediction of Arctic Sea Ice Concentration of Kara-Barents Seas Using RCM Data with Machine Learning." Journal of Climate Research 12, no. 4: 349-365.
Kwang Jin Kim; Soo Jin Lee; Yeong Ho Kim; Ji Won Kim; Kyung Ja Ha; Yang Won Lee; Kwang Jin; Soo Jin; Yeong Ho; Ji Won; Kyung Ja; Yang Won. Satellite-based Ensemble of Rain Rate using the EBMA for Gamma-distributed Data. Journal of Korean Society for Geospatial Information Science 2017, 25, 25 -33.
AMA StyleKwang Jin Kim, Soo Jin Lee, Yeong Ho Kim, Ji Won Kim, Kyung Ja Ha, Yang Won Lee, Kwang Jin, Soo Jin, Yeong Ho, Ji Won, Kyung Ja, Yang Won. Satellite-based Ensemble of Rain Rate using the EBMA for Gamma-distributed Data. Journal of Korean Society for Geospatial Information Science. 2017; 25 (4):25-33.
Chicago/Turabian StyleKwang Jin Kim; Soo Jin Lee; Yeong Ho Kim; Ji Won Kim; Kyung Ja Ha; Yang Won Lee; Kwang Jin; Soo Jin; Yeong Ho; Ji Won; Kyung Ja; Yang Won. 2017. "Satellite-based Ensemble of Rain Rate using the EBMA for Gamma-distributed Data." Journal of Korean Society for Geospatial Information Science 25, no. 4: 25-33.
Yeong-Ho Kim; 부경대학교지구환경시스템과학부공간정보공학전공석사과정; Kwang-Jin Kim; Soo-Jin Lee; Ji-Won Kim; Yang-Won Lee. Deep Learning-based Retrieval of Daily 500-m Soil Moisture for South Korea. Journal of the Korean Cartographic Association 2017, 17, 109 -121.
AMA StyleYeong-Ho Kim, 부경대학교지구환경시스템과학부공간정보공학전공석사과정, Kwang-Jin Kim, Soo-Jin Lee, Ji-Won Kim, Yang-Won Lee. Deep Learning-based Retrieval of Daily 500-m Soil Moisture for South Korea. Journal of the Korean Cartographic Association. 2017; 17 (3):109-121.
Chicago/Turabian StyleYeong-Ho Kim; 부경대학교지구환경시스템과학부공간정보공학전공석사과정; Kwang-Jin Kim; Soo-Jin Lee; Ji-Won Kim; Yang-Won Lee. 2017. "Deep Learning-based Retrieval of Daily 500-m Soil Moisture for South Korea." Journal of the Korean Cartographic Association 17, no. 3: 109-121.
Kwangjin Kim; 부경대학교 지구환경시스템과학부 공간정보시스템공학전공; Kyung-Ja Ha; Yang-Won Lee. Production of Daily Mean Temperature Ensemble from the CMIP GCM Using EBMA. Journal of Climate Research 2017, 12, 199 -213.
AMA StyleKwangjin Kim, 부경대학교 지구환경시스템과학부 공간정보시스템공학전공, Kyung-Ja Ha, Yang-Won Lee. Production of Daily Mean Temperature Ensemble from the CMIP GCM Using EBMA. Journal of Climate Research. 2017; 12 (2):199-213.
Chicago/Turabian StyleKwangjin Kim; 부경대학교 지구환경시스템과학부 공간정보시스템공학전공; Kyung-Ja Ha; Yang-Won Lee. 2017. "Production of Daily Mean Temperature Ensemble from the CMIP GCM Using EBMA." Journal of Climate Research 12, no. 2: 199-213.
Sea surface temperature (SST) is an important parameter in understanding atmosphere–ocean circulation processes and monitoring global climate change. In addition to in situ observations of SST, a series of satellite-borne instruments provide global coverage of SST through infrared and microwave remote sensing. This study was the first application of the ensemble Bayesian model averaging (EBMA) method to the blending of satellite SST products to minimize inherent uncertainties and improve the validation statistics. Monthly SST products from moderate resolution imaging spectroadiometer, Advanced Very High Resolution Radiometer and Advanced Microwave Scanning Radiometer-EOS were used as ensemble members. The mean bias and root-mean-square error (RMSE) of the EBMA method were better than those of the individual members or generic methods such as ensemble mean and median. This is because the weighting scheme adjusted by the expectation–maximization algorithm was based on the suitability of each member derived from training procedures. The errors of EBMA in our experiment had almost no spatial and temporal autocorrelation with regard to the latitude and month, which implies that the EBMA method can serve as a viable option for blending of satellite SST, although more experiments are necessary to determine its feasibility in more detail.
Kwangjin Kim; Min Yoon; Jaeil Cho; Sungwook Hong; Hongjoo Yoon; Heesook Mo; Yang-Won Lee. Blending of satellite SST products using ensemble Bayesian model averaging (EBMA). Remote Sensing Letters 2016, 7, 827 -836.
AMA StyleKwangjin Kim, Min Yoon, Jaeil Cho, Sungwook Hong, Hongjoo Yoon, Heesook Mo, Yang-Won Lee. Blending of satellite SST products using ensemble Bayesian model averaging (EBMA). Remote Sensing Letters. 2016; 7 (9):827-836.
Chicago/Turabian StyleKwangjin Kim; Min Yoon; Jaeil Cho; Sungwook Hong; Hongjoo Yoon; Heesook Mo; Yang-Won Lee. 2016. "Blending of satellite SST products using ensemble Bayesian model averaging (EBMA)." Remote Sensing Letters 7, no. 9: 827-836.
Soo-Jin Lee; Jaeil Cho; Sungwook Hong; Kyung-Ja Ha; Hanlim Lee; Yang-Won Lee. On the relationships between satellite-based drought index and gross primary production in the North Korean croplands, 2000–2012. Remote Sensing Letters 2016, 7, 790 -799.
AMA StyleSoo-Jin Lee, Jaeil Cho, Sungwook Hong, Kyung-Ja Ha, Hanlim Lee, Yang-Won Lee. On the relationships between satellite-based drought index and gross primary production in the North Korean croplands, 2000–2012. Remote Sensing Letters. 2016; 7 (8):790-799.
Chicago/Turabian StyleSoo-Jin Lee; Jaeil Cho; Sungwook Hong; Kyung-Ja Ha; Hanlim Lee; Yang-Won Lee. 2016. "On the relationships between satellite-based drought index and gross primary production in the North Korean croplands, 2000–2012." Remote Sensing Letters 7, no. 8: 790-799.
Intensive deforestation due to human activities has been occurring in the Amazon over the last several decades, leading to a projected decrease in precipitation due to reduced evapotranspiration (ET) according to the prediction by climate model experiments. Such hydrological and climatic changes are closely related to the drying of soil moisture (SM) as a source of atmospheric water vapour via evaporation. We used a satellite-observed index, temperature-vegetation dryness index (TVDI), to assess the impact of deforestation on SM during the dry season. Thirteen-year (2002-2014) data for three representative areas (forest, deforesting and deforested) in the Rondônia, southwest (SW) of Amazon were used to evaluate the relative changes in SM corresponding to the extent of deforestation. We found the increase in dryness in the deforested Amazon using the moderate resolution imaging spectroradiometer (MODIS) satellite sensor. Furthermore, given that the impact of forest removal on surface SM can be distinguished from the associated changes in precipitation and vegetation conditions, it is found that the relative proportion of deforested areas is linearly correlated with that of SM. The results from this study are useful to validate climate model simulations of deforestation and to improve our understanding on the biophysical controls of Amazon deforestation.
Jaeil Cho; Jae-Hyun Ryu; Pat J.-F. Yeh; Yang-Won Lee; Sungwook Hong. Satellite-based assessment of Amazonian surface dryness due to deforestation. Remote Sensing Letters 2015, 7, 71 -80.
AMA StyleJaeil Cho, Jae-Hyun Ryu, Pat J.-F. Yeh, Yang-Won Lee, Sungwook Hong. Satellite-based assessment of Amazonian surface dryness due to deforestation. Remote Sensing Letters. 2015; 7 (1):71-80.
Chicago/Turabian StyleJaeil Cho; Jae-Hyun Ryu; Pat J.-F. Yeh; Yang-Won Lee; Sungwook Hong. 2015. "Satellite-based assessment of Amazonian surface dryness due to deforestation." Remote Sensing Letters 7, no. 1: 71-80.
Jaeil Cho; Yang-Won Lee; Ho-Sang Lee. The effect of precipitation and air temperature on land-cover change in the Sahel. Water and Environment Journal 2015, 29, 439 -445.
AMA StyleJaeil Cho, Yang-Won Lee, Ho-Sang Lee. The effect of precipitation and air temperature on land-cover change in the Sahel. Water and Environment Journal. 2015; 29 (3):439-445.
Chicago/Turabian StyleJaeil Cho; Yang-Won Lee; Ho-Sang Lee. 2015. "The effect of precipitation and air temperature on land-cover change in the Sahel." Water and Environment Journal 29, no. 3: 439-445.
Dae-Sun Kim; Kwang-Jin Kim; Jaeil Cho; Sung-Rae Chung; Yang-Won Lee. A Comparative Study on the Generation of COMS Level 3 Product for Sea Surface Temperature. Journal of Climate Research 2015, 10, 43 -56.
AMA StyleDae-Sun Kim, Kwang-Jin Kim, Jaeil Cho, Sung-Rae Chung, Yang-Won Lee. A Comparative Study on the Generation of COMS Level 3 Product for Sea Surface Temperature. Journal of Climate Research. 2015; 10 (1):43-56.
Chicago/Turabian StyleDae-Sun Kim; Kwang-Jin Kim; Jaeil Cho; Sung-Rae Chung; Yang-Won Lee. 2015. "A Comparative Study on the Generation of COMS Level 3 Product for Sea Surface Temperature." Journal of Climate Research 10, no. 1: 43-56.
Jihye Ahn; Sungwook Hong; Jaeil Cho; Yang-Won Lee. Downscaling of AMSR2 Sea Ice Concentration Using a Weighting Scheme Derived from MODIS Sea Ice Cover Product. Korean Journal of Remote Sensing 2014, 30, 687 -701.
AMA StyleJihye Ahn, Sungwook Hong, Jaeil Cho, Yang-Won Lee. Downscaling of AMSR2 Sea Ice Concentration Using a Weighting Scheme Derived from MODIS Sea Ice Cover Product. Korean Journal of Remote Sensing. 2014; 30 (5):687-701.
Chicago/Turabian StyleJihye Ahn; Sungwook Hong; Jaeil Cho; Yang-Won Lee. 2014. "Downscaling of AMSR2 Sea Ice Concentration Using a Weighting Scheme Derived from MODIS Sea Ice Cover Product." Korean Journal of Remote Sensing 30, no. 5: 687-701.
Measuring accurately Sea Surface temperature is important for many marine applications and monitoring the global climate system. Many instruments are used for the measuring the SST. The SST delivered from satellite have the advantages that are a broad scope and consistent detection. But SST products show the different value because of different of retrieval algorithm and sensor. To reduce the uncertain, SST data ensemble is carried out using the Bayesian model averaging(BMA). BMA is method of the weighted average using the posterior probability distribution. And the means and variances of the posterior probabilities are estimated using Expectation-Maximization(EM) algorithm. The estimated mean of the posterior probability is used as the weight for the weighted average. SST data of Aqua/MODIS, Terra/MODIS and NOAA/AVHRR was used as ensemble member. SST data Envisat/AATSR was used as reference data for calculating the posterior probability and validation data. To make the monthly ensemble SST, their provided monthly SST data was used. one-leave-out-cross validation that is one of the statistical validation method is used for validating the ensemble SST. The 12 cases, except for the data of one month per the case, was made and excepted month was used validation period. And we compared with the ensemble mean and median. As the result, ensemble BMA showed the lowest RMSE.
Kwangjin Kim; Yang-Won Lee. Create the ensemble sea surface temperature using the Bayesian model averaging. SPIE Remote Sensing 2014, 9240, 924011 .
AMA StyleKwangjin Kim, Yang-Won Lee. Create the ensemble sea surface temperature using the Bayesian model averaging. SPIE Remote Sensing. 2014; 9240 ():924011.
Chicago/Turabian StyleKwangjin Kim; Yang-Won Lee. 2014. "Create the ensemble sea surface temperature using the Bayesian model averaging." SPIE Remote Sensing 9240, no. : 924011.