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This study introduces a data-to-data (D2D) translation approach that utilizes a conditional adversarial learning framework to generate hypothetical data at the meteorological imager (MI) sensor on the communication, ocean, and meteorological satellite (COMS). The proposed D2D model produces virtual 10-min data from actual 30-min data by exploiting the 10-min temporal resolution (TR) of the advanced MI (AMI) on GEO-KOMPSAT-2A during the overlapping observation period of the two satellites. Specifically, the D2D model uses one visible (VIS) at 0.64 μm channel and four infrared channels (3.8, 6.9, 10.8, and 12.3 μm) from AMI of the actual 30-min data from April 2020 to April 2022 to train and test the model. Subsequently, the D2D model is applied to simulate hypothetical 10-min-TR COMS data using the 30-min COMS observation data from September 2019 to March 2020 during the coexistence period of the two satellites. Regression-calibrated COMS data alleviated the spectral response function differences between MI and AMI sensors. The proposed D2D method exhibits excellent statistical performance, with an average root-mean-square error of 0.056 for the VIS channel and 3.237 K, 1.005 K, 3.251 K, and 3.184 K for 3.8 μm, 6.9 μm, 10.8 μm, and 12.2 μm channels, respectively. The findings of this study are expected to facilitate various types of remote sensing research and applications using long-term data with AMI and AMI-like past MI data.

True-color imagery is essential for an intuitive comprehension of atmospheric data. However, the Geostationary Environment Monitoring Spectrometer (GEMS) of the geostationary Korea multipurpose satellite (GK) 2B lacks green and red bands, which limits its ability to monitor atmospheric environments. To mitigate this issue, we suggest an innovative method of generating virtual GEMS green and red bands using conditional generative adversarial networks with data observed in the blue-green-red (RGB) bands of the Advanced Meteorological Imager sensor, a payload of the GK-2A satellite. The paired datasets of the AMI blue band and the AMI RGB bands were used to train and test the data-to-data (D2D) translation model. Using the GEMS blue band as input data, the D2D model generated GEMS hypothetical radiance data at the green and red bands. Our results show that the D2D model generated hypothetical GEMS green and red bands with outstanding performance. The averaged values of the correlation coefficient, root-mean-square error, and bias between the observed and D2D-generated GEMS blue band were 0.999, 3.450 W/cm2/cm/sr, and −1.858 W/cm2/cm/sr, respectively. This research is expected to significantly contribute to the monitoring and comprehension of atmospheric environments in Asia and potentially improve the GEMS's global ability to monitor air quality. Additionally, the proposed method has the potential to enhance the capabilities of other satellites with limited spectral bands.

Cirrus cloud contributes significantly to earth's radiation budget and the greenhouse effect. The Advanced Himawari Imager (AHI) onboard the Himawari-8 satellite lacks a 1.37 μm band, sensitive to monitoring cirrus clouds. This article proposed a conditional generative adversarial network-based data-to-data translation (D2D) model to generate a hypothetical AHI 1.37 μm band. For training and testing the D2D model, the Geo-Kompsat-2A Advanced Meteorological Imager (AMI) 1.37 μm bands and other highly correlated bands to cirrus from July 24, 2019 to July 31, 2020, were used. The D2D model exhibited a high level of agreement (mean of statistics: correlation coefficient (CC) = 0.9827, bias = 0.0004, and root-mean-square error (RMSE) = 0.0086 in albedo units) between the observed and D2D-generated AMI 1.37 μm bands from validation datasets. The application of the D2D model to the AHI sensor showed that the D2D-generated AHI 1.37 μm band was qualitatively analogous to the observed AMI 1.37 μm band (average of statistics: bias = 0.0026, RMSE = 0.0191 in albedo units, and CC = 0.9158) on the 1st, 15th, and 28th of each month of 2020 in the common observing regions between Korea and Japan. The validation results with the CALIPSO data also showed that the D2D-generated AHI 1.37 μm band performed similarly to the observed AMI 1.37 µm band. Consequently, this article can significantly contribute to cirrus detection and its application to climatology.

True-color imagery of satellites provides various atmospheric and surface information for intuitive understanding and visualization. This article presents a conditional generative adversarial network method for generating daytime and nighttime hypothetical visible (VIS) bands of the advanced meteorological imager (AMI) sensor onboard the geostationary Korea multipurpose satellite. The AMI datasets in the form of albedo and brightness temperature (${{\boldsymbol{T}}}_{\boldsymbol{B}}$) were normalized and denormalized between 0 and 1 data-to-data (D2D) translation. The D2D model was trained and tested using data pair of the albedos at AMI VIS bands and AMI infrared (IR) bands or ${{\boldsymbol{T}}}_{\boldsymbol{B}}$ differences between two AMI IR bands. The constructed D2D model showed that the statistical results of bias, root-mean-square-error, and correlation coefficient between the observed and D2D-generated AMI VIS bands during daytime were −0.006 and 0.047 in albedo, and 0.941 for the blue band; −0.007 and 0.05 in albedo and 0.939 for the green band; −0.01 and 0.061 in albedo, and 0.917 for the red band, respectively. The proposed D2D method is being officially used by the Korea meteorological administration. Except for simulating desert areas as clouds at night, the D2D model demonstrated excellent performance, generating hypothetical AMI VIS bands at day and night. Consequently, this article could significantly contribute to the monitoring and understanding of meteorological phenomena over one-third of the Earth. Additionally, the method can be extended to other geostationary weather satellites, including Himawari-8, Fengyun-4A, meteosat third generation, and geostationary operational environmental satellites.

This paper compared the predictive performance of different regression models for trend component estimation in the spatial downscaling of coarse resolution satellite data using area-to-point regression kriging in the context of the sensitivity to input data errors. Three regression models, linear regression, random forest, and support vector regression, were applied to trend component estimation. An experiment on downscaling synthetic Landsat data with different noise levels demonstrated that a regression model with higher explanatory power and residual correction led to the highest predictive performance only when the input coarse resolution data were assumed to be error-free. Through an experiment on spatial downscaling of coarse resolution monthly Advanced Microwave Scanning Radiometer-2 soil moisture products with significant errors, we found that the higher explanatory power of regression models did not always lead to better predictive performance. The residual correction and normalization of trend components also degraded the predictive performance. Using trend components as a final downscaling result showed the best performance in both experiments as the input errors increased. As the predictive performance of spatial downscaling results is susceptible to input errors, the findings of this study should be considered to evaluate downscaling results and develop advanced spatial downscaling methods.

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 to 0.865 μm) using daytime MI VIS (0.675 μm), shortwave IR (3.75 μm), and longwave IR bands (10.8 and 12.0 μm) and the differences between them as input data. GOCI and MI data were collected from January 2017 to December 2018 using 705 images of 256 × 256 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-μm 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.

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 (CGANs) model to generate virtual nighttime visible imagery using infrared (IR) multiband satellite observations and the brightness temperature difference between the two IR bands in the communication, ocean, and meteorological satellite. For the summer daytime case study with visible band imagery, our multiband CGAN model showed better statistical results [correlation coefficient (CC) = 0.952, bias = -1.752 (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 IR bands (CC = 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.

This paper investigates the potential of cloud-free virtual optical imagery generated using synthetic-aperture radar (SAR) images and conditional generative adversarial networks (CGANs) for early crop mapping, which requires cloud-free optical imagery at the optimal date for classification. A two-stage CGAN approach, including representation and generation stages, is presented to generate virtual Sentinel-2 spectral bands using all available information from Sentinel-1 SAR and Sentinel-2 optical images. The dual-polarization-based radar vegetation index and all available multi-spectral bands of Sentinel-2 imagery are particularly considered for feature extraction in the representation stage. A crop classification experiment using Sentinel-1 and -2 images in Illinois, USA, demonstrated that the use of all available scattering and spectral features achieved the best prediction performance for all spectral bands, including visible, near-infrared, red-edge, and shortwave infrared bands, compared with the cases that only used dual-polarization backscattering coefficients and partial input spectral bands. Early crop mapping with an image time series, including the virtual Sentinel-2 image, yielded satisfactory classification accuracy comparable to the case of using an actual time-series image set, regardless of the different combinations of spectral bands. Therefore, the generation of virtual optical images using the proposed model can be effectively applied to early crop mapping when the availability of cloud-free optical images is limited.

Creep resistance and fracture tolerance are considered two essential aspects to look at for material selection and optimization in high-temperature applications. Materials with extensive industrial applications must inherit adequate immune against creep such that operating temperatures can be raised to maximize performance ensuring a long service life of the equipment. The creep study of cupronickel (CuNi) alloy has become crucial because cupronickel alloys are enticing candidates for high-temperature applications. Nevertheless, the fundamental understanding of creep deformation in cupronickel alloys is still obscure. Herein, we executed classical Molecular Dynamics (MD) simulations to reveal the nature of creep characteristics of cupronickel alloy at a molecular level. In particular, we investigated the thermal and mechanical properties of nanocrystalline (NC) Cu, Ni, and Cu0.5Ni0.5 alloy. As the Cu content in the alloy is raised from 0 to 100%, the steady-state creep rate (SSCR) exhibits a ∼12% increment. Based on our MD simulations, we found that the SSCR of Cu0.5Ni0.5 alloy speeds up dramatically under elevated stress, temperature, and decreasing grain size. Our MD results also reveal that at low stress, the governing creep process is the confluence of lattice diffusion, grain boundary diffusion, and grain boundary sliding, while at high stress, the governing creep mechanism turns into dislocation dominated. Admittedly, our computational analysis will help acquire fundamental insights into the creep mechanism of NC cupronickel alloy, which is crucial for the successful usage of this alloy in corrosive, high stress, and high-temperature applications.

Parameterization of interatomic forcefields is a necessary first step in performing molecular dynamics simulations. This is a non-trivial global optimization problem involving quantification of multiple empirical variables against one or more properties. We present EZFF, a lightweight Python library for parameterization of several types of interatomic forcefields implemented in several molecular dynamics engines against multiple objectives using genetic-algorithm-based global optimization methods. The EZFF scheme provides unique functionality such as the parameterization of hybrid forcefields composed of multiple forcefield interactions as well as built-in quantification of uncertainty in forcefield parameters and can be easily extended to other forcefield functional forms as well as MD engines.

Accurate cloud-masking procedures to distinguish cloud-free pixels from cloudy pixels are essential for optical satellite remote sensing. Many studies on satellite-based cloud-detection have been performed using the spectral characteristics of clouds in terms of reflectance and temperature. This study proposes a cloud-detection method using reflectance in four bands: 0.56 μm, 0.86 μm, 1.38 μm, and 1.61 μm. Methodologically, we present a conversion relationship between the normalized difference water index (NDWI) and the green band in the visible spectrum for thick cloud detection using moderate-resolution imaging spectroradiometer (MODIS) observations. NDWI consists of reflectance at the 0.56 and 0.86 μm bands. For thin cloud detection, the 1.38 and 1.61 μm bands were applied with empirically determined threshold values. Case study analyses for the four seasons from 2000 to 2019 were performed for the sea surface area of the Yellow Sea and Bohai Sea. In the case studies, the comparison of the proposed cloud-detection method with the MODIS cloud mask (CM) and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation data indicated a probability of detection of 0.933, a false-alarm ratio of 0.086, and a Heidke Skill Score of 0.753. Our method demonstrated an additional important benefit in distinguishing clouds from sea ice or yellow dust, compared to the MODIS CM products, which usually misidentify the latter as clouds. Consequently, our cloud-detection method could be applied to a variety of low-orbit and geostationary satellites with 0.56, 0.86, 1.38, and 1.61 μm bands.

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 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.

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.

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.

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.

Extensive sea ice over Arctic regions is largely involved in heat, moisture, and momentum exchanges between the atmosphere and ocean. Some previous studies have been conducted to develop statistical models for the status of Arctic sea ice and showed considerable possibilities to explain the impacts of climate changes on the sea ice extent. However, the statistical models require improvements to achieve better predictions by incorporating techniques that can deal with temporal variation of the relationships between sea ice concentration and climate factors. In this paper, we describe the statistical approaches by ordinary least squares (OLS) regression and a time-series method for modeling sea ice concentration using satellite imagery and climate reanalysis data for the Barents and Kara Seas during 1979–2012. The OLS regression model could summarize the overall climatological characteristics in the relationships between sea ice concentration and climate variables. We also introduced autoregressive integrated moving average (ARIMA) models because the sea ice concentration is such a long-range dataset that the relationships may not be explained by a single equation of the OLS regression. Temporally varying relationships between sea ice concentration and the climate factors such as skin temperature, sea surface temperature, total column liquid water, total column water vapor, instantaneous moisture flux, and low cloud cover were modeled by the ARIMA method, which considerably improved the prediction accuracies. Our method may also be worth consideration when forecasting future sea ice concentration by using the climate data provided by general circulation models (GCM).

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.

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.

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.

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.

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.

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.

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.

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.