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The Korea Meteorological Administration (KMA) has developed many product algorithms including that for soil moisture (SM) retrieval for the geostationary satellite Geo-Kompsat-2A (GK-2A) launched in December 2018. This was developed through a five-year research project owing to the significance of SM information for hydrological and meteorological applications. However, GK-2A’s visible and infrared sensors lack direct SM sensitivity. Therefore, in this study, we developed an SM algorithm based on the conversion relationships between SM and the temperature vegetation dryness index (TVDI) estimated for various land types in the full disk area using two of GK-2A’s level 2 products, land surface temperature (LST) and normalized difference vegetation index (NDVI), and the Global Land Data Assimilation System (GLDAS) SM data for calibration. Methodologically, various coefficients were obtained between TVDI and SM and used to estimate the GK-2A-based SM. The GK-2A SM algorithm was validated with GLDAS SM data during different periods. Our GK-2A SM product showed seasonal and spatial agreement with GLDAS SM data, indicating a dry-wet pattern variation. Quantitatively, the GK-2A SM showed annual validation results with a correlation coefficient (CC) >0.75, bias <0.1%, and root mean square error (RMSE) <4.2–4.7%. The monthly averaged CC values were higher than 0.7 in East Asia and 0.5 in Australia, whereas RMSE and unbiased RMSE values were <0.5% in East Asia and Australia. Discrepancies between GLDAS and GK-2A TVDI-based SMs often occurred in dry Australian regions during dry seasons due to the high LST sensitivity of GK-2A TVDI. We determined that relationships between TVDI and SM had positive or negative slopes depending on land cover types, which differs from the traditional negative slope observed between TVDI and SM. The KMA is currently operating this GK-2A SM algorithm.
Sumin Ryu; Young-Joo Kwon; Goo Kim; Sungwook Hong. Temperature Vegetation Dryness Index-Based Soil Moisture Retrieval Algorithm Developed for Geo-KOMPSAT-2A. Remote Sensing 2021, 13, 2990 .
AMA StyleSumin Ryu, Young-Joo Kwon, Goo Kim, Sungwook Hong. Temperature Vegetation Dryness Index-Based Soil Moisture Retrieval Algorithm Developed for Geo-KOMPSAT-2A. Remote Sensing. 2021; 13 (15):2990.
Chicago/Turabian StyleSumin Ryu; Young-Joo Kwon; Goo Kim; Sungwook Hong. 2021. "Temperature Vegetation Dryness Index-Based Soil Moisture Retrieval Algorithm Developed for Geo-KOMPSAT-2A." Remote Sensing 13, no. 15: 2990.
This study presents a deep-learning model, using the Conditional Generative Adversarial Nets (CGAN) technique, that can produce daytime visible (VIS) band information, mimicking a narrow band sensor, by combining VIS and infrared (IR) broadband measurements by different sensors. The real-observed datasets of the Geostationary Ocean Color Imager (GOCI) and Meteoritical Imager (MI) sensors onboard the Communication, Ocean, and Meteorological Satellite were used for training and testing our CGAN-model over the Yellow Sea and Bohai Sea. The trained and tested CGAN model was then applied to generate daytime GOCI VIS and near IR (NIR) bands (0.412 m to 0.865 m) using daytime MI VIS (0.675 m), shortwave IR (3.75 m), and longwave IR bands (10.8, and 12.0 m) and the differences between them as input data. GOCI and MI data were collected from January 2017 to December 2018 using 705 images of 256256 pixels for the training and 44 images for the model test. The results are statistically favorable (i.e., bias = 0.013 (in a reflectance unit from 0 to 1), root-mean-square-error = 0.112, mean absolute error = 0.076, agreement index = 0.945, and correlation coefficient (CC) = 0.809 for daytime reflectance in the GOCI VIS 0.49 m band) between the real GOCI VIS band observation and the CGAN-generated simulation. Our CGAN-based model showed high CC and favorable results in the GOCI VIS and NIR bands. Consequently, our study demonstrates the possibility of applying a deep learning technique to improve the temporal resolution for ocean color studies using the GOCI sensor.
Sumin Ryu; Sungwook Hong. Hypothetical Product Generation of Geostationary Ocean Color Imager Bands Over the Yellow Sea and Bohai Sea Using Deep Learning Technique. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2021, 14, 7528 -7543.
AMA StyleSumin Ryu, Sungwook Hong. Hypothetical Product Generation of Geostationary Ocean Color Imager Bands Over the Yellow Sea and Bohai Sea Using Deep Learning Technique. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2021; 14 (99):7528-7543.
Chicago/Turabian StyleSumin Ryu; Sungwook Hong. 2021. "Hypothetical Product Generation of Geostationary Ocean Color Imager Bands Over the Yellow Sea and Bohai Sea Using Deep Learning Technique." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 14, no. 99: 7528-7543.
The minimum brightness temperature (mBT) of seawater in the polar region is an important parameter in algorithms for determining sea ice concentration or snow depth. To estimate the mBT of seawater at 6.925 GHz for the Arctic and Antarctic Oceans and to find their physical characteristics, we collected brightness temperature and sea ice concentration data from the Advanced Microwave Scanning Radiometer 2 (AMSR2) for eight years from 2012 to 2020. The estimated mBT shows constant annual values, but we found a significant difference in the seasonal variability between the Arctic and Antarctic Oceans. We calculated the mBT with the radiative transfer model parameterized by sea surface temperature (SST), sea surface wind speed (SSW), and integrated water vapor (IWV) and compared them with our observations. The estimated mBT represents the modeled mBT emitted from seawater under conditions of 2–5 m/s SSW and SST below 0 °C, except in the Arctic summer. The exceptional summer mBT in the Arctic Ocean was related to unusually high SST. We found evidence of Arctic amplification in the seasonal variability of Arctic mBT.
Young-Joo Kwon; Sungwook Hong; Jeong-Won Park; Seung Kim; Jong-Min Kim; Hyun-Cheol Kim. Spatial and Temporal Variability of Minimum Brightness Temperature at the 6.925 GHz Band of AMSR2 for the Arctic and Antarctic Oceans. Remote Sensing 2021, 13, 2122 .
AMA StyleYoung-Joo Kwon, Sungwook Hong, Jeong-Won Park, Seung Kim, Jong-Min Kim, Hyun-Cheol Kim. Spatial and Temporal Variability of Minimum Brightness Temperature at the 6.925 GHz Band of AMSR2 for the Arctic and Antarctic Oceans. Remote Sensing. 2021; 13 (11):2122.
Chicago/Turabian StyleYoung-Joo Kwon; Sungwook Hong; Jeong-Won Park; Seung Kim; Jong-Min Kim; Hyun-Cheol Kim. 2021. "Spatial and Temporal Variability of Minimum Brightness Temperature at the 6.925 GHz Band of AMSR2 for the Arctic and Antarctic Oceans." Remote Sensing 13, no. 11: 2122.
Fjords in the high Arctic, as aquatic critical zones at the interface of land-ocean continuum, are undergoing rapid changes due to glacier retreat and climate warming. Yet, little is known about the biogeochemical processes in the Arctic fjords. We measured the nutrients and the optical properties of dissolved organic matter (DOM) in both seawater and sediment pore water, along with the remote sensing data of the ocean surface, from three West Svalbard fjords. A cross-fjord comparison of fluorescence fingerprints together with downcore trends of salinity, Cl−, and PO43− revealed higher impact of terrestrial inputs (fluorescence index: ~1.2–1.5 in seawaters) and glaciofluvial runoffs (salinity: ~31.4 ± 2.4 psu in pore waters) to the southern fjord of Hornsund as compared to the northern fjords of Isfjorden and Van Mijenfjorden, tallying with heavier annual runoff to the southern fjord of Hornsund. Extremely high levels of protein-like fluorescence (up to ~4.5 RU) were observed at the partially sea ice-covered fjords in summer, in line with near-ubiquity ice-edge blooms observed in the Arctic. The results reflect an ongoing or post-phytoplankton bloom, which is also supported by the higher levels of chlorophyll a fluorescence at the ocean surface, the very high apparent oxygen utilization through the water column, and the nutrient drawdown at the ocean surface. Meanwhile, a characteristic elongated fluorescence fingerprint was observed in the fjords, presumably produced by ice-edge blooms in the Arctic ecosystems. Furthermore, alkalinity and the humic-like peaks showed a general downcore accumulation trend, which implies the production of humic-like DOM via a biological pathway also in the glaciomarine sediments from the Arctic fjords.
Meilian Chen; Ji-Hoon Kim; Sungwook Hong; Yun Kyung Lee; Moo Hee Kang; Young Keun Jin; Jin Hur. Spectral Characterization of Dissolved Organic Matter in Seawater and Sediment Pore Water from the Arctic Fjords (West Svalbard) in Summer. Water 2021, 13, 202 .
AMA StyleMeilian Chen, Ji-Hoon Kim, Sungwook Hong, Yun Kyung Lee, Moo Hee Kang, Young Keun Jin, Jin Hur. Spectral Characterization of Dissolved Organic Matter in Seawater and Sediment Pore Water from the Arctic Fjords (West Svalbard) in Summer. Water. 2021; 13 (2):202.
Chicago/Turabian StyleMeilian Chen; Ji-Hoon Kim; Sungwook Hong; Yun Kyung Lee; Moo Hee Kang; Young Keun Jin; Jin Hur. 2021. "Spectral Characterization of Dissolved Organic Matter in Seawater and Sediment Pore Water from the Arctic Fjords (West Svalbard) in Summer." Water 13, no. 2: 202.
Green bands in satellite remote sensing play an important role in monitoring water and vegetation information. Due to the lack of observed green band, the Geostationary Operational Environmental Satellite (GOES-16) Advanced Baseline Imager (ABI) sensor uses a synthetic one. This study presents an ABI green band generation method using the Pix2Pix based on conditional generative adversarial networks (CGANs) and convolutional neural network techniques with data observed in the visible range of the GOES-16/ABI sensor. Our model was constructed from the radiance data sets in the red, blue, and green bands of the Advanced Himawari Imager (AHI) onboard Himawari-8/9 satellites from August 27, 2018 to May 1, 2019, and applied to generate a GOES-16 ABI green band using the ABI blue band radiance data. A comparison between the AHI and the Pix2Pix-generated AHI green bands displayed high accuracy, evaluated through bias = 0.120, root mean square error (RMSE) = 0.983 in digital number (DN) units, and correlation coefficient (CC) = 0.999. Furthermore, comparison between the Pix2Pix-generated and synthetic ABI green bands resulted in a good agreement (bias = 1.029 and RMSE = 2.892 in DN units, CC = 0.993). The statistical comparison between the green band, and red or blue band resulted in the exceptional performance of the Pix2Pix-generated ABI green band compared to the synthetic ABI green band. Consequently, our Pix2Pix-based model can be effectively used to generate nonexistent green band of ABI sensor and be applied in a variety of scientific applications requiring green band.
Jeong-Eun Park; Goo Kim; Sungwook Hong. Green Band Generation for Advanced Baseline Imager Sensor Using Pix2Pix With Advanced Baseline Imager and Advanced Himawari Imager Observations. IEEE Transactions on Geoscience and Remote Sensing 2020, PP, 1 -9.
AMA StyleJeong-Eun Park, Goo Kim, Sungwook Hong. Green Band Generation for Advanced Baseline Imager Sensor Using Pix2Pix With Advanced Baseline Imager and Advanced Himawari Imager Observations. IEEE Transactions on Geoscience and Remote Sensing. 2020; PP (99):1-9.
Chicago/Turabian StyleJeong-Eun Park; Goo Kim; Sungwook Hong. 2020. "Green Band Generation for Advanced Baseline Imager Sensor Using Pix2Pix With Advanced Baseline Imager and Advanced Himawari Imager Observations." IEEE Transactions on Geoscience and Remote Sensing PP, no. 99: 1-9.
The Advanced Dvorak Technique (ADT) uses geostationary satellite data to estimate tropical cyclone (TC) intensity owing to the difficulty in directly observing a TC’s internal structure. This study presents a new relationship (Hong and Ryu scale) between the current intensity (CI) number and estimated maximum wind speed (MWS) of TCs over the northwestern Pacific region; the CI number is the TC intensity index retrieved from the ADT. The Soil Moisture Active Passive (SMAP) with the L-band (1.4 GHz) microwave radiometer, is used to calibrate and produce the new Hong and Ryu scale for the ADT algorithm. Japan Meteorological Agency (JMA) best track MWS data, SMAP sea surface wind speed estimates, and ADT’s TC intensity data between 2015–2018 are spatiotemporally collocated for the calibration process. The CI number is derived from the Korea Meteorological Administration (KMA) operational ADT which uses the Koba scale to convert to the MWS for validation against the MWS of the best track. The conversion relationships between CI number and SMAP MWS, and between SMAP MWS and MWS of the best track a derived, and the MWS of two ADTs with the Koba and Hong and Ryu scales are then estimated using the same CI numbers with TC intensity data between 2015–2018. Finally, the MWS of the ADT with the Koba scale and the new ADT with the proposed Hong and Ryu scale are independently validated on best track data from 2013–2014. The MWS root mean square error (RMSE) is 4.39 m/s for the new ADT using the Hong and Ryu scale, which is lower than 4.77 m/s RMSE of the ADT using the Koba scale. Hence, the ADT using the Hong and Ryu scale can modestly improve the accuracy of TC intensity analysis in the northwestern Pacific region.
Sumin Ryu; Sung-Eun Hong; Jun-Dong Park; Sungwook Hong. An Improved Conversion Relationship between Tropical Cyclone Intensity Index and Maximum Wind Speed for the Advanced Dvorak Technique in the Northwestern Pacific Ocean Using SMAP Data. Remote Sensing 2020, 12, 2580 .
AMA StyleSumin Ryu, Sung-Eun Hong, Jun-Dong Park, Sungwook Hong. An Improved Conversion Relationship between Tropical Cyclone Intensity Index and Maximum Wind Speed for the Advanced Dvorak Technique in the Northwestern Pacific Ocean Using SMAP Data. Remote Sensing. 2020; 12 (16):2580.
Chicago/Turabian StyleSumin Ryu; Sung-Eun Hong; Jun-Dong Park; Sungwook Hong. 2020. "An Improved Conversion Relationship between Tropical Cyclone Intensity Index and Maximum Wind Speed for the Advanced Dvorak Technique in the Northwestern Pacific Ocean Using SMAP Data." Remote Sensing 12, no. 16: 2580.
The visible band of satellite sensors is of limited use during the night due to a lack of solar reflection. This study presents an improved conditional generative adversarial networks (CGAN) model to generate virtual nighttime visible imagery using infrared multiband satellite observations and the brightness temperature difference between the two infrared bands in the Communication, Ocean and Meteorological Satellite. For daytime case studies with visible band imagery, our multiband CGAN model showed better statistical results (correlation coefficient = 0.952, bias = -7.752 (in a digital number (DN) unit from 0 to 255, converted from reflectance from 0 to 1), and root-mean-square-error (RMSE) = 26.851 DN) than the single-band CGAN model using a pair of visible and infrared bands (correlation coefficient = 0.916, bias = -4.073 DN, and RMSE = 35.349 DN). The proposed multiband CGAN model performed better than the single-band CGAN model, particularly, in convective clouds and typhoons, because of the sounding effects from the water vapor band. In addition, our multiband CGAN model provided detailed patterns for clouds and typhoons at twilight. Therefore, our results could be used for visible-based nighttime weather analysis of convective clouds and typhoons, using data from next-generation geostationary meteorological satellites.
Ji-Hye Kim; Sumin Ryu; Jaehoon Jeong; Damwon So; Hyun-Ju Ban; Sungwook Hong. Impact of Satellite Sounding Data on Virtual Visible Imagery Generation Using Conditional Generative Adversarial Network. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2020, 13, 4532 -4541.
AMA StyleJi-Hye Kim, Sumin Ryu, Jaehoon Jeong, Damwon So, Hyun-Ju Ban, Sungwook Hong. Impact of Satellite Sounding Data on Virtual Visible Imagery Generation Using Conditional Generative Adversarial Network. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2020; 13 (99):4532-4541.
Chicago/Turabian StyleJi-Hye Kim; Sumin Ryu; Jaehoon Jeong; Damwon So; Hyun-Ju Ban; Sungwook Hong. 2020. "Impact of Satellite Sounding Data on Virtual Visible Imagery Generation Using Conditional Generative Adversarial Network." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 13, no. 99: 4532-4541.
Many previous studies have attempted to distinguish fog from clouds using low-orbit and geostationary satellite observations from visible (VIS) to longwave infrared (LWIR) bands. However, clouds and fog have often been misidentified because of their similar spectral features. Recently, advanced meteorological geostationary satellites with improved spectral, spatial, and temporal resolutions, including Himawari-8/9, GOES-16/17, and GeoKompsat-2A, have become operational. Accordingly, this study presents an improved algorithm for detecting daytime sea fog using one VIS and one near-infrared (NIR) band of the Advanced Himawari Imager (AHI) of the Himawari-8 satellite. We propose a regression-based relationship for sea fog detection using a combination of the Normalized Difference Snow Index (NDSI) and reflectance at the green band of the AHI. Several case studies, including various foggy and cloudy weather conditions in the Yellow Sea for three years (2017–2019), have been performed. The results of our algorithm showed a successful detection of sea fog without any cloud mask information. The pixel-level comparison results with the sea fog detection based on the shortwave infrared (SWIR) band (3.9 μm) and the brightness temperature difference between SWIR and LWIR bands of the AHI showed high statistical scores for probability of detection (POD), post agreement (PAG), critical success index (CSI), and Heidke skill score (HSS). Consequently, the proposed algorithms for daytime sea fog detection can be effective in daytime, particularly twilight, conditions, for many satellites equipped with VIS and NIR bands.
Han-Sol Ryu; Sungwook Hong. Sea Fog Detection Based on Normalized Difference Snow Index Using Advanced Himawari Imager Observations. Remote Sensing 2020, 12, 1521 .
AMA StyleHan-Sol Ryu, Sungwook Hong. Sea Fog Detection Based on Normalized Difference Snow Index Using Advanced Himawari Imager Observations. Remote Sensing. 2020; 12 (9):1521.
Chicago/Turabian StyleHan-Sol Ryu; Sungwook Hong. 2020. "Sea Fog Detection Based on Normalized Difference Snow Index Using Advanced Himawari Imager Observations." Remote Sensing 12, no. 9: 1521.
Most infrared satellite remote sensors have a higher spatial resolution than microwave satellite sensors. Microwave satellite remote sensing has proven successful for the retrieval of soil moisture (SM) information. In this study, we propose a SM retrieval algorithm based on temperature vegetation dryness index (TVDI), a function of land surface temperature (LST), and the normalized difference vegetative index (NDVI) provided by Moderate Resolution Imaging Spectroradiometer (MODIS) data. We implemented the LST correction with elevation effect. Conversion relationships between TVDI and SM content for a variety of land types were obtained from spatial and temporal collocation of TVDI and Global Land Data Assimilation System (GLDAS) SM content for 2014. From the comparison with the GLDAS SM for 2015, the proposed TVDI-based SM algorithm showed good performance with CC = 0.609, bias = −0.035 m3/m3, and root-mean-square-error (RMSE) = 0.047 m3/m3, while the Soil Moisture Active Passive (SMAP) and Soil Moisture and Ocean Salinity (SMOS) SMs present CC = 0.637 and 0.741, bias = 0.042 and 0.010 m3/m3, and RMSE = 0.152 and 0.103 m3/m3, respectively. For the in situ SM measurements of the Korea Rural Development Administration (RDA), the proposed TVDI-based SM algorithm yielded CC = 0.556, bias = −0.039 m3/m3, and RMSE = 0.051 m3/m3 excluding the winter season. Consequently, the proposed SM algorithm could contribute to complementing the low spatial resolutions of microwave satellite SM products and low temporal resolutions of GLDAS SM products.
Young-Joo Kwon; Sumin Ryu; Jaeil Cho; Yang-Won Lee; No-Wook Park; Chu-Yong Chung; Sungwook Hong. Infrared Soil Moisture Retrieval Algorithm Using Temperature-Vegetation Dryness Index and Moderate Resolution Imaging Spectroradiometer Data. Asia-Pacific Journal of Atmospheric Sciences 2020, 56, 275 -289.
AMA StyleYoung-Joo Kwon, Sumin Ryu, Jaeil Cho, Yang-Won Lee, No-Wook Park, Chu-Yong Chung, Sungwook Hong. Infrared Soil Moisture Retrieval Algorithm Using Temperature-Vegetation Dryness Index and Moderate Resolution Imaging Spectroradiometer Data. Asia-Pacific Journal of Atmospheric Sciences. 2020; 56 (2):275-289.
Chicago/Turabian StyleYoung-Joo Kwon; Sumin Ryu; Jaeil Cho; Yang-Won Lee; No-Wook Park; Chu-Yong Chung; Sungwook Hong. 2020. "Infrared Soil Moisture Retrieval Algorithm Using Temperature-Vegetation Dryness Index and Moderate Resolution Imaging Spectroradiometer Data." Asia-Pacific Journal of Atmospheric Sciences 56, no. 2: 275-289.
The variations in the Arctic sea ice thickness (SIT) due to climate change have both positive and negative effects on commercial human activities, the ecosystem, and the Earth’s environment. Satellite microwave remote sensing based on microwave reflection signals reflected by the sea ice surface has been playing an essential role in monitoring and analyzing the Arctic SIT and sea ice concentration (SIC) during the past decades. Recently, passive microwave satellites incorporating an L-band radiometer, such as soil moisture and ocean salinity (SMOS) and soil moisture active passive (SMAP), have been used for analyzing sea ice characteristics, in addition to land and ocean research. In this study, we present a novel method to estimate thin SIT and sea ice roughness (SIR) using a conversion relationship between them, from the SMAP and SMOS data. Methodologically, the SMAP SIR is retrieved. The SMAP thin SIT and SMOS SIR are estimated using a conversion relationship between thin SIT data from SMOS data and SMAP-derived SIR, which is obtained from the spatial and temporal collocation of the SMOS thin SIT and the SIR retrieved from SMAP. Our results for the Arctic sea ice during December for four consecutive years from 2015 to 2018, show high accuracy (bias = −2.268 cm, root mean square error (RMSE) = 15.919 cm, and correlation coefficient (CC) = 0.414) between the SMOS-provided thin SIT and SMAP-derived SIT, and good agreement (bias = 0.03 cm, RMSE = 0.228 cm, and CC = 0.496) between the SMOS-estimated SIR and SMAP-retrieved SIR. Consequently, our study could be effectively used for monitoring and analyzing the variation in the Arctic sea ice.
Suna Jo; Hyun-Cheol Kim; Young-Joo Kwon; Sungwook Hong. Circumpolar Thin Arctic Sea Ice Thickness and Small-Scale Roughness Retrieval Using Soil Moisture and Ocean Salinity and Soil Moisture Active Passive Observations. Remote Sensing 2019, 11, 2835 .
AMA StyleSuna Jo, Hyun-Cheol Kim, Young-Joo Kwon, Sungwook Hong. Circumpolar Thin Arctic Sea Ice Thickness and Small-Scale Roughness Retrieval Using Soil Moisture and Ocean Salinity and Soil Moisture Active Passive Observations. Remote Sensing. 2019; 11 (23):2835.
Chicago/Turabian StyleSuna Jo; Hyun-Cheol Kim; Young-Joo Kwon; Sungwook Hong. 2019. "Circumpolar Thin Arctic Sea Ice Thickness and Small-Scale Roughness Retrieval Using Soil Moisture and Ocean Salinity and Soil Moisture Active Passive Observations." Remote Sensing 11, no. 23: 2835.
Midwave infrared (MWIR) band of 3.75 μm is important in satellite remote sensing in many applications. This band observes daytime reflectance and nighttime radiance according to the Earth’s and the Sun’s effects. This study presents an algorithm to generate no-present nighttime reflectance and daytime radiance at MWIR band of satellite observation by adopting the conditional generative adversarial nets (CGAN) model. We used the daytime reflectance and nighttime radiance data in the MWIR band of the meteoritical imager (MI) onboard the Communication, Ocean and Meteorological Satellite (COMS), as well as in the longwave infrared (LWIR; 10.8 μm) band of the COMS/MI sensor, from 1 January to 31 December 2017. This model was trained in a size of 1024 × 1024 pixels in the digital number (DN) from 0 to 255 converted from reflectance and radiance with a dataset of 256 images, and validated with a dataset of 107 images. Our results show a high statistical accuracy (bias = 3.539, root-mean-square-error (RMSE) = 8.924, and correlation coefficient (CC) = 0.922 for daytime reflectance; bias = 0.006, RMSE = 5.842, and CC = 0.995 for nighttime radiance) between the COMS MWIR observation and artificial intelligence (AI)-generated MWIR outputs. Consequently, our findings from the real MWIR observations could be used for identification of fog/low cloud, fire/hot-spot, volcanic eruption/ash, snow and ice, low-level atmospheric vector winds, urban heat islands, and clouds.
Yerin Kim; Sungwook Hong. Deep Learning-Generated Nighttime Reflectance and Daytime Radiance of the Midwave Infrared Band of a Geostationary Satellite. Remote Sensing 2019, 11, 2713 .
AMA StyleYerin Kim, Sungwook Hong. Deep Learning-Generated Nighttime Reflectance and Daytime Radiance of the Midwave Infrared Band of a Geostationary Satellite. Remote Sensing. 2019; 11 (22):2713.
Chicago/Turabian StyleYerin Kim; Sungwook Hong. 2019. "Deep Learning-Generated Nighttime Reflectance and Daytime Radiance of the Midwave Infrared Band of a Geostationary Satellite." Remote Sensing 11, no. 22: 2713.
Visible (VIS) bands, such as the 0.675 μm band in geostationary satellite remote sensing, have played an important role in monitoring and analyzing weather and climate change during the past few decades with coarse spatial and high temporal resolution. Recently, many deep learning techniques have been developed and applied in a variety of applications and research fields. In this study, we developed a deep-learning-based model to generate non-existent nighttime VIS satellite images using the Conditional Generative Adversarial Nets (CGAN) technique. For our CGAN-based model training and validation, we used the daytime image data sets of reflectance in the Communication, Ocean and Meteorological Satellite / Meteorological Imager (COMS/MI) VIS (0.675 μm) band and radiance in the longwave infrared (10.8 μm) band of the COMS/MI sensor over five years (2012 to 2017). Our results show high accuracy (bias = −2.41 and root mean square error (RMSE) = 36.85 during summer, bias = −0.21 and RMSE = 33.02 during winter) and correlation (correlation coefficient (CC) = 0.88 during summer, CC = 0.89 during winter) of values between the observed images and the CGAN-generated images for the COMS VIS band. Consequently, our CGAN-based model can be effectively used in a variety of meteorological applications, such as cloud, fog, and typhoon analyses during daytime and nighttime.
Kimoon Kim; Ji-Hye Kim; Yong-Jae Moon; Eunsu Park; Gyungin Shin; Taeyoung Kim; Yerin Kim; Sungwook Hong. Nighttime Reflectance Generation in the Visible Band of Satellites. Remote Sensing 2019, 11, 2087 .
AMA StyleKimoon Kim, Ji-Hye Kim, Yong-Jae Moon, Eunsu Park, Gyungin Shin, Taeyoung Kim, Yerin Kim, Sungwook Hong. Nighttime Reflectance Generation in the Visible Band of Satellites. Remote Sensing. 2019; 11 (18):2087.
Chicago/Turabian StyleKimoon Kim; Ji-Hye Kim; Yong-Jae Moon; Eunsu Park; Gyungin Shin; Taeyoung Kim; Yerin Kim; Sungwook Hong. 2019. "Nighttime Reflectance Generation in the Visible Band of Satellites." Remote Sensing 11, no. 18: 2087.
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.
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.
Heavy summer rainfall is a primary natural disaster affecting lives and properties in the Korean Peninsula. This study presents a satellite-based rainfall rate retrieval algorithm for the South Korea combining polarization-corrected temperature (PCT) and scattering index (SI) data from the 36.5 and 89.0 GHz channels of the Advanced microwave Scanning Radiometer 2 (AMSR-2) onboard the Global Change Observation Mission (GCOM)-W1 satellite. The coefficients for the algorithm were obtained from spatial and temporal collocation data from the AMSR-2 and groundbased automatic weather station rain gauges from 1 July - 30 August during the years, 2012-2015. There were time delays of about 25 minutes between the AMSR-2 observations and the ground raingauge measurements. A new linearly-combined rainfall retrieval algorithm focused on heavy rain for the PCT and SI was validated using ground-based rainfall observations for the South Korea from 1 July - 30 August, 2016. The validation presented PCT and SI methods showed slightly improved results for rainfall > 5 mm h-1 compared to the current ASMR-2 level 2 data. The best bias and root mean square error (RMSE) for the PCT method at AMSR-2 36.5 GHz were 2.09 mm h-1 and 7.29 mm h-1, respectively, while the current official AMSR-2 rainfall rates show a larger bias and RMSE (4.80 mm h-1 and 9.35 mm h-1, respectively). This study provides a scatteringbased over-land rainfall retrieval algorithm for South Korea affected by stationary front rain and typhoons with the advantages of the previous PCT and SI methods to be applied to a variety of spaceborne passive microwave radiometers.
Young-Joo Kwon; Hayan Shin; Hyunju Ban; Yang-Won Lee; Kyung-Ae Park; Jaeil Cho; No-Wook Park; Sungwook Hong. A scattering-based over-land rainfall retrieval algorithm for South Korea using GCOM-W1/AMSR-2 data. Asia-Pacific Journal of Atmospheric Sciences 2017, 53, 385 -392.
AMA StyleYoung-Joo Kwon, Hayan Shin, Hyunju Ban, Yang-Won Lee, Kyung-Ae Park, Jaeil Cho, No-Wook Park, Sungwook Hong. A scattering-based over-land rainfall retrieval algorithm for South Korea using GCOM-W1/AMSR-2 data. Asia-Pacific Journal of Atmospheric Sciences. 2017; 53 (3):385-392.
Chicago/Turabian StyleYoung-Joo Kwon; Hayan Shin; Hyunju Ban; Yang-Won Lee; Kyung-Ae Park; Jaeil Cho; No-Wook Park; Sungwook Hong. 2017. "A scattering-based over-land rainfall retrieval algorithm for South Korea using GCOM-W1/AMSR-2 data." Asia-Pacific Journal of Atmospheric Sciences 53, no. 3: 385-392.
Satellite remote sensing provides significant information for the monitoring of natural disasters. Recently, on a global scale, floods have been increasing both in frequency and in magnitude. In order to map the inundation area, flooding events are investigated using unique RGB composite imagery based on the MODIS surface reflectance (MOD09GA) data obtained from the Terra satellite, which is used to visualize and analyze these events. This study proposes using an RGB combination of MODIS band 6 (1.64 μm), band 5 (1.24 μm), and band 2 (0.86 μm) data from the visible and the near-infrared spectral ranges to map flood events. The flooding events that were investigated in this study occurred on 25 October 2015 along the Pampanga River in the Philippines, and on 28 July 2016 along the Poyang and Dongting Lakes in China. In the case of the Pampanga River, the inundated areas were estimated with surface reflectance (R) thresholds of 0.0 ≤ R6 ≤ 0.102, 0.0 ≤ R5 ≤ 0.138, and 0.03 ≤ R2 ≤ 0.148 for MODIS bands 6, 5, and 2, respectively, which were determined using Otsu’s method. The total inundated area was estimated to be 487.75 km2. This estimate was indirectly compared with the results obtained from SENTINEL-1A Synthetic Aperture Radar (SAR) data. The total inundated area on 26 October 2015 for the case of the Pampanga River was estimated to be 486.37 km2 using histogram analysis based on Otsu’s method. For the flooding case in China, the total estimated inundated area using MODIS RGB imagery on 28 July 2016 and SAR on 3 August 2016 was 1148.25 km2 and 1110.096 km2, respectively. In addition, RGB imagery results using MODIS 6-5-2 bands were supported by the refractive index retrieval along the inundation area. A threshold of 1.6 for the real part of the complex refractive index allows for the discrimination between the flooded and non-flooded areas using the Hong and ASH approximations. This study shows that the RGB composite techniques using advanced sensors with more bands and higher spatio-temporal resolutions, and supported by the refractive index retrieval method, are useful for estimating flood events.
Hyun-Ju Ban; Young-Joo Kwon; Hayan Shin; Han-Sol Ryu; Sungwook Hong. Flood Monitoring Using Satellite-Based RGB Composite Imagery and Refractive Index Retrieval in Visible and Near-Infrared Bands. Remote Sensing 2017, 9, 313 .
AMA StyleHyun-Ju Ban, Young-Joo Kwon, Hayan Shin, Han-Sol Ryu, Sungwook Hong. Flood Monitoring Using Satellite-Based RGB Composite Imagery and Refractive Index Retrieval in Visible and Near-Infrared Bands. Remote Sensing. 2017; 9 (4):313.
Chicago/Turabian StyleHyun-Ju Ban; Young-Joo Kwon; Hayan Shin; Han-Sol Ryu; Sungwook Hong. 2017. "Flood Monitoring Using Satellite-Based RGB Composite Imagery and Refractive Index Retrieval in Visible and Near-Infrared Bands." Remote Sensing 9, no. 4: 313.
This paper investigates the benefits of integrating coarse resolution satellite-derived precipitation estimates with quasi-point rain gauge data for generating a fine spatial resolution precipitation map product. To integrate the two precipitation data sources, a geostatistical downscaling and integration approach is presented that can account for the differences in spatial resolution between data from different supports and adjusts inherent errors in the coarse resolution precipitation estimates. First, coarse resolution precipitation estimates are downscaled at a fine spatial resolution via area-to-point kriging to allow direct comparison with rain gauge data. Second, the downscaled precipitation estimates are integrated with the rain gauge data by multivariate kriging. In particular, errors in the coarse resolution precipitation estimates are adjusted against rain gauge data during this second stage. In this study, simple kriging with local means (SKLM) and kriging with an external drift (KED) are used as multivariate kriging algorithms. For comparative purposes, conditional merging (CM), a frequently-applied method for integrating rain gauge data and radar precipitation, is also employed. From a case study with Tropical Rainfall Measuring Mission (TRMM) 3B43 monthly precipitation products acquired in South Korea from May–October in 2013, we found that the incorporation of TRMM data with rain gauge data did not improve prediction performance when the number of rain gauge data was relatively large. However, the benefit of integrating TRMM and rain gauge data was most striking, regardless of multivariate kriging algorithms, when a small number of rain gauge data was used. These results indicate that the coarse resolution satellite-derived precipitation product would be a useful source for mapping precipitation at a fine spatial resolution if the geostatistical integration approach is applied to areas with sparse rain gauges.
No-Wook Park; Phaedon C. Kyriakidis; Sungwook Hong. Geostatistical Integration of Coarse Resolution Satellite Precipitation Products and Rain Gauge Data to Map Precipitation at Fine Spatial Resolutions. Remote Sensing 2017, 9, 255 .
AMA StyleNo-Wook Park, Phaedon C. Kyriakidis, Sungwook Hong. Geostatistical Integration of Coarse Resolution Satellite Precipitation Products and Rain Gauge Data to Map Precipitation at Fine Spatial Resolutions. Remote Sensing. 2017; 9 (3):255.
Chicago/Turabian StyleNo-Wook Park; Phaedon C. Kyriakidis; Sungwook Hong. 2017. "Geostatistical Integration of Coarse Resolution Satellite Precipitation Products and Rain Gauge Data to Map Precipitation at Fine Spatial Resolutions." Remote Sensing 9, no. 3: 255.
Mt. Baekdu is a volcano near the North Korea-Chinese border that experienced a few destructive eruptions over the course of its history, including the well-known 1702 A.D eruption. However, signals of unrest, including seismic activity, gas emission and intense geothermal activity, have been occurring with increasing frequency over the last few years. Due to its close vicinity to a densely populated area and the high magnitude of historical volcanic eruptions, its potential for destructive volcanic activity has drawn wide public attention. However, direct field surveying in the area is limited due to logistic challenges. In order to compensate for the limited coverage of ground observations, comprehensive measurements using remote sensing techniques are required. Among these techniques, Differential Interferometric SAR (DInSAR) analysis is the most effective method for monitoring surface deformation and is employed in this study. Through advanced atmospheric error correction and time series analysis, the accuracy of the detected displacements was improved. As a result, clear uplift up to 20 mm/year was identified around Mt. Baekdu and was further used to estimate the possible deformation source, which is considered as a consequence of magma and fault interaction. Since the method for tracing deformation was proved feasible, continuous DInSAR monitoring employing upcoming SAR missions and advanced error regulation algorithms will be of great value in monitoring comprehensive surface deformation over Mt. Baekdu and in general world-wide active volcanoes.
Jung-Rack Kim; Shih-Yuan Lin; Hye-Won Yun; Ya-Lun Tsai; Hwa-Jung Seo; Sungwook Hong; Yunsoo Choi. Investigation of Potential Volcanic Risk from Mt. Baekdu by DInSAR Time Series Analysis and Atmospheric Correction. Remote Sensing 2017, 9, 138 .
AMA StyleJung-Rack Kim, Shih-Yuan Lin, Hye-Won Yun, Ya-Lun Tsai, Hwa-Jung Seo, Sungwook Hong, Yunsoo Choi. Investigation of Potential Volcanic Risk from Mt. Baekdu by DInSAR Time Series Analysis and Atmospheric Correction. Remote Sensing. 2017; 9 (2):138.
Chicago/Turabian StyleJung-Rack Kim; Shih-Yuan Lin; Hye-Won Yun; Ya-Lun Tsai; Hwa-Jung Seo; Sungwook Hong; Yunsoo Choi. 2017. "Investigation of Potential Volcanic Risk from Mt. Baekdu by DInSAR Time Series Analysis and Atmospheric Correction." Remote Sensing 9, no. 2: 138.
This article presents a geostatistical approach for downscaling precipitation products from passive microwave satellites with geostationary Meteorological Satellite observations. More precisely, the Advanced Microwave Scanning Radiometer 2 (AMSR2) precipitation (daily level 3 product) with 0.25° spatial resolution and the Communication, Ocean and Meteorological Satellite (COMS) infrared (IR) data with 5 km spatial resolution were used for the downscaling experiment over the Korean peninsula. Brightness temperature data observed at COMS IR 1 and water vapour channels were incorporated for downscaling via area-to-point residual Kriging with non-linear regression. The evaluation results with densely sampled Automatic Weather Station data revealed that incorporating the COMS IR observations with the AMSR2 precipitation showed similar error statistics to those of the AMSR2 precipitation because of the limitations of IR observations themselves and the inherent errors of the AMSR2 precipitation product over land. However, the area-based evaluation using information entropy indicated that incorporating the COMS observations resulted in more detailed spatial variation in the final product than direct downscaling of the AMSR2 precipitation. In addition, local precipitation patterns could be captured when there were regions with missing precipitation values in the AMSR2 product. Consequently, the downscaling result is useful for understanding the local precipitation patterns with an accuracy similar to that provided by microwave satellite observations. It is also suggested that the spatial variability in the downscaling result and errors in input low-resolution data should be considered when downscaling coarse resolution data using fine resolution auxiliary variables.
No-Wook Park; Sungwook Hong; Phaedon Kyriakidis; Woojoo Lee; Sang-Jin Lyu. Geostatistical downscaling of AMSR2 precipitation with COMS infrared observations. International Journal of Remote Sensing 2016, 37, 3858 -3869.
AMA StyleNo-Wook Park, Sungwook Hong, Phaedon Kyriakidis, Woojoo Lee, Sang-Jin Lyu. Geostatistical downscaling of AMSR2 precipitation with COMS infrared observations. International Journal of Remote Sensing. 2016; 37 (16):3858-3869.
Chicago/Turabian StyleNo-Wook Park; Sungwook Hong; Phaedon Kyriakidis; Woojoo Lee; Sang-Jin Lyu. 2016. "Geostatistical downscaling of AMSR2 precipitation with COMS infrared observations." International Journal of Remote Sensing 37, no. 16: 3858-3869.