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Given the complex spatiotemporal variability of aerosols, high-frequency satellite observations are essential for accurately mapping their distribution. However, optical remote sensing encounters difficulties in detecting Aerosol Optical Depth (AOD) over cloud-covered regions, creating data gaps that limit comprehensive environmental analysis. This study introduces a spatial gap-filling method for Himawari-8/Advanced Himawari Imager (AHI) hourly AOD data, using a Random Forest (RF) model that integrates meteorological variables and model-based AOD data. Developed and validated over South Korea from 1 January to 31 December 2019, the model effectively improved data coverage from 6% to 100%. The approach demonstrated high performance in blind tests, achieving a root mean square error (RMSE) of 0.064 and a correlation coefficient (CC) of 0.966. Meteorological analysis indicated optimal model performance under cold, dry conditions (RMSE: 0.047, CC: 0.956), compared to humid conditions (RMSE: 0.105, CC: 0.921). Validation against Aerosol Robotic Network (AERONET) ground observations showed that, while the original Himawari-8 data exhibited higher accuracy (RMSE: 0.189, CC: 0.815, n = 346), the gap-filled dataset maintained reasonable precision (RMSE: 0.208, CC: 0.711) and significantly increased the number of valid data points (n = 4149). Furthermore, the gap-filled dataset successfully captured seasonal AOD patterns, with values ranging from 0.245–0.300 in winter to 0.381–0.391 in summer, providing a comprehensive view of aerosol dynamics across South Korea.

The increasing water temperature due to climate change has led to more frequent algae blooms and deteriorating water quality in coastal areas and rivers worldwide. To address this, we developed a deep learning-based model for identifying floating algae blooms using PlanetScope optical images and the Shifted Windows (Swin) Transformer architecture. We created 1,998 datasets from 105 scenes of PlanetScope imagery collected between 2018 and 2023, covering 14 water bodies known for frequent algae blooms. The methodology included data pre-processing, dataset generation, deep learning modeling, and inference result generation. The input images contained six bands, including vegetation indices such as the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI), enhancing the model’s responsiveness to algae blooms. Evaluations were conducted using both single-period and multi-period datasets. The single-period model achieved a mean Intersection over Union (mIoU) between 72.18% and 76.47%, while the multi-period model significantly improved performance, with an mIoU of 91.72%. This demonstrates the potential of our model and highlights the importance of change detection in multi-temporal images for algae bloom monitoring. Additionally, the padding technique proposed in this study resolved the border issue that arises when mosaicking inference results from individual patches, providing a seamless view of the satellite scene.

Forest fires are caused by various climatic and anthropogenic factors. In Republic of Korea, forest fires occur frequently during spring when the humidity is low. During the past decade, the number of forest fire incidents and the extent of the damaged area have increased. Satellite imagery can be applied to assess damage from these unpredictable forest fires. Despite the increasing threat, there is a lack of comprehensive analysis and effective strategies for addressing these forest fires, particularly considering the diverse topography of Republic of Korea. Herein, we present an approach for the automated detection of forest fire damage using Sentinel-2 images of 14 areas affected by forest fires in Republic of Korea during 2019–2023. The detection performance of deep learning (DL), machine learning, and spectral index methods was analyzed, and the optimal model for detecting forest fire damage was derived. To evaluate the independent performance of the models, two different burned areas exhibiting distinct characteristics were selected as test subjects. To increase the classification accuracy, tests were conducted on various combinations of input channels in DL. The combination of false-color RNG (B4, B8, and B3) images was optimal for detecting forest fire damage. Consequently, among the DL models, the HRNet model achieved excellent results for both test regions with intersection over union scores of 89.40 and 82.49, confirming that the proposed method is applicable for detecting forest fires in diverse Korean landscapes. Thus, suitable mitigation measures can be promptly designed based on the rapid analysis of damaged areas.

Soil moisture (SM) is a crucial hydrologic factor that affects the global cycle of energy, carbon, and water, as well as plant growth and crop yield; therefore, an accurate estimate of SM is important for both the global environment and agriculture. Satellite-based SM data have been provided by the National Aeronautics and Space Administration (NASA)’s Soil Moisture Active Passive (SMAP) and the European Space Agency (ESA)’s Soil Moisture and Ocean Salinity (SMOS) satellite missions, but these data are based on passive microwave sensors, which have limited spatial resolution. Thus, detailed observations and analyses of the local distribution of SM are limited. The recent emergence of deep learning techniques, such as rectified linear unit (ReLU) and dropout, has produced effective solutions to complex problems. Deep neural networks (DNNs) have been used to accurately estimate hydrologic factors, such as SM and evapotranspiration, but studies of SM estimates derived from the joint use of DNN and high-resolution satellite data, such as Sentinel-1 and Sentinel-2, are lacking. In this study, we aim to estimate high-resolution SM at 30 m resolution, which is important for local-scale SM monitoring in croplands. We used a variety of input data, such as radar factors, optical factors, and vegetation indices, which can be extracted from Sentinel-1 and -2, terrain information (e.g., elevation), and crop information (e.g., cover type and month), and developed an integrated SM model across various crop surfaces by using these input data and DNN (which can learn the complexity and nonlinearity of the various data). The study was performed in the agricultural areas of Manitoba and Saskatchewan, Canada, and the in situ SM data for these areas were obtained from the Agriculture and Agri-Food Canada (AAFC) Real-time In Situ Soil Monitoring for Agriculture (RISMA) network. We conducted various experiments with several hyperparameters that affected the performance of the DNN-based model and ultimately obtained a high-performing SM model. The optimal SM model had a root-mean-square error (RMSE) of 0.0416 m3/m3 and a correlation coefficient (CC) of 0.9226. This model’s estimates showed better agreement with in situ SM than the SMAP 9 km SM. The accuracy of the model was high when the daily precipitation was zero or very low and also during the vegetation growth stage. However, its accuracy decreased when precipitation or the vitality of the vegetation were high. This suggests that precipitation affects surface erosion and water layer formation, and vegetation adds complexity to the SM estimate. Nevertheless, the distribution of SM estimated by our model generally reflected the local soil characteristics. This work will aid in drought and flood prevention and mitigation, and serve as a tool for assessing the potential growth of crops according to SM conditions.

Soil moisture (SM) is an indicator of the moisture status of the land surface, which is useful for monitoring extreme weather events. Representative global SM datasets include the National Aeronautics and Space Administration (NASA) Soil Moisture Active Passive (SMAP), the Global Land Data Assimilation System (GLDAS), and the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis 5 (ERA5), but due to their low spatial resolutions, none of these datasets well describe SM changes in local areas, and they tend to have a low accuracy. Machine learning (ML)-based SM predictions have demonstrated high accuracy, but obtaining semi-real-time SM information remains challenging, and the dependence of the validation accuracy on the data sampling method used, such as random or yearly sampling, has led to uncertainties. In this study, we aimed to develop an ML-based model for real-time SM estimation that can capture local-scale variabilities in SM and have reliable accuracy, regardless of the sampling method. This study was conducted in South Korea, and satellite image data, numerical weather prediction (NWP) data, and topographic data provided within one day were used as the input data. For SM modeling, 13 input variables affecting the surface SM status were selected: 10- and 20-day cumulative standardized precipitation indexes (SPI10 and SPI20), a normalized difference vegetation index (NDVI), downward shortwave radiation (DSR), air temperature (Tair), land surface temperature (LST), soil temperature (Tsoil), relative humidity (RH), latent heat flux (LE), slope, elevation, topographic ruggedness index (TRI), and aspect. Then, SM models based on random forest (RF) and automated machine learning (AutoML) were constructed, trained, and validated using random sampling and leave-one-year-out (LOYO) cross-validation. The RF- and AutoML-based SM models had significantly high accuracy rates based on comparisons with in situ SM (mean absolute error (MAE) = 2.212–4.132%; mean bias error (MBE) = −0.110–0.136%; root mean square error (RMSE) = 3.186–5.384%; correlation coefficient (CC) = 0.732–0.913), while the AutoML-based SM model tended to have a higher accuracy than the RF-based SM model, regardless of the data sampling method used. In addition, when compared to in situ SM data, the SM models demonstrated the highest accuracy, outperforming both GLDAS and ERA5 SM data and well representing changes in the dryness/wetness of the land surface according to meteorological events (heatwave, drought, and rainfall). The SM models proposed in this study can, thus, offer semi-real-time SM data, aiding in the monitoring of moisture changes in the land surface, as well as short-term meteorological disasters, like flash droughts or floods.

Satellite remote sensing can measure large ocean surface areas, but the infrared-based sea surface temperature (SST) might not be correctly calculated for the pixels under clouds, resulting in missing values in satellite images. Early studies for the gap-free raster maps of satellite SST were based on spatial interpolation using in situ measurements. In this paper, however, an alternative spatial gap-filling method using regression residual kriging (RRK) for the Geostationary Korea Multi-Purpose Satellite-2A (GK2A) daily SST was examined for the seas around the Korean Peninsula. Extreme outliers were first removed from the in situ measurements and the GK2A daily SST images using multi-step statistical procedures. For the pixels on the in situ measurements after the quality control, a multiple linear regression (MLR) model was built using the selected meteorological variables such as daily SST climatology value, specific humidity, and maximum wind speed. The irregular point residuals from the MLR model were transformed into a residual grid by optimized kriging for the residual compensation for the MLR estimation of the null pixels. The RRK residual compensation method improved accuracy considerably compared with the in situ measurements. The gap-filled 18,876 pixels showed the mean bias error (MBE) of −0.001 °C, the mean absolute error (MAE) of 0.315 °C, the root mean square error (RMSE) of 0.550 °C, and the correlation coefficient (CC) of 0.994. The case studies made sure that the gap-filled SST with RRK had very similar values to the in situ measurements to those of the MLR-only method. This was more apparent in the typhoon case: our RRK result was also stable under the influence of typhoons because it can cope with the abrupt changes in marine meteorology.

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.

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.