This study introduces an aerosol effective height (AEH) retrieval algorithm using O4absorption properties at 477 nm measured by Geostationary Environment Monitoring Spectrometer (GEMS). GEMS, launched in February 2020 on GeoKOMPSAT-2B, monitors air quality across Asia, including O3, NO2, SO2, HCHO, and aerosols. The algorithm employs differential optical absorption spectroscopy (DOAS) to retrieve O4slant column density (SCD) and calculates the O4air mass factor (AMF) by dividing SCD by O4vertical column density (VCD), considering variations due to atmospheric temperature and pressure. AEHs were retrieved from GEMS hyperspectral radiance data during the Asian dust period in 2021. In addition, lidar measurements were used to validate the algorithm.

This study presents a novel approach to evaluate the stability of Antarctic ice shelves by analyzing hydrostatic flexure and estimating the effective thickness near the grounding line. Airborne topographic surveys provided data on surface deflection, which was used to calculate the effective thickness. A comparison with measured thickness data revealed significant differences, indicating the possible presence of extensive crevasses on the ice shelf surface. Visual inspections and MODIS imagery supported these observations. It was found that assuming a constant ice thickness near the grounding line may not provide accurate results, highlighting the need for considering variable ice thickness. The choice of Young's modulus also influenced the estimation of effective thickness. The ratio between effective and measured thicknesses appeared to reflect the presence of local pinning points, which contribute to ice shelf stability. This study emphasizes the importance of understanding hydrostatic flexure and effective thickness in assessing the stability of Antarctic ice shelves, suggesting further research to improve our understanding of ice shelf dynamics and potential stability assessment.

SummaryWe examine the distributions of lightning strikes and wildfires across the Western United States (WUS).Lightning data from the Geostationary Lightning Mapper (GLM) are consistent with the ones from the National Lightning Detection Network (NLDN).We use 10-km spatial and 7-day temporal maximum distances to associate lightnings with wildfires.Only wildfires identified by the United States Forest Service (USFS) with a natural cause are considered for modeling.Considered factors of fire lightning include location, time, precipitation, and lightning area and energy.Location and time variables are significantly more important than the other factors.Albeit a good predictor, precipitation is usually nonzero along with a fire lightning.Data of years 2019–2021 are used for model training and data of 2022 for validation.Validation results show decent predictive performance regarding identification of fire lightning.

Snow cover on Arctic sea ice is crucial to understanding sea ice evolution and the heat budget in the rapidly changing Arctic Ocean. Despite advancements in remote sensing technology, accurately estimating snow depth, grain size, and density on a large scale remains challenging. This study examines the influence of snow density and grain size on passive microwave satellite observation data. Data was collected from December 2012 to February 2016 using AMSR2 brightness temperature data, and snow depth was estimated using ice-tethered ice mass balance (IMB) data from the Cold Regions Research and Engineering Laboratory (CRREL). A two-stream approximation radiative transfer model that accounts for the scattering effect of the snow layer over Arctic sea ice was used. The study found that the slope in the linear relationship between snow density and the brightness temperature indices (such as gradient ratio (GR) and brightness temperature ratio (BTR)) is highly sensitive to the density and grain size of the snow layer. Using this dependence, the temporal changes in physical state of the snow layer was inferred from the observation over the winter season. The radiative transfer model simulations revealed that the grain size of the snow layer decreases during winter as the snow density increases, likely due to the counterbalancing effect of new snow on the temporal variability in grain size increasing caused by snow metamorphism.

Highly short-term forecasting, or nowcasting, of heavy rainfall due to rapidly evolving mesoscale convective systems (MCSs) is particularly challenging for traditional numerical weather prediction (NWP) models. To overcome such a challenge, a growing number of studies have shown significant advantages of using machine learning (ML) modeling techniques with remote sensing data, especially weather radar data, for high-resolution rainfall nowcasting. To improve ML model performance, it is essential first and foremost to quantify the importance of radar variables and identify pertinent predictors of rainfall that can also be associated with domain knowledge. In this study, a set of MCS types consisting of convective cell (CC), mesoscale CC, diagonal squall line (SLD), and parallel squall line (SLP), was adopted to categorize MCS storm cells, following the fuzzy logic algorithm for storm tracking (FAST), over the Korean Peninsula. The relationships between rain rates and over 15 variables derived from data products of dual-polarimetric weather radar were investigated and quantified via five ML regression methods and a permutation importance algorithm. As an applicational example, ML classification models were also developed to predict locations of storm cells. Recalibrated ML regression models with identified pertinent predictors were coupled with the ML classification models to provide early warnings of heavy rainfall. Results imply that future work needs to consider MCS type information to improve ML modeling for nowcasting and early warning of heavy rainfall.

Live fuel moisture (LFM), defined as the ratio between water in the fresh biomass out of the dry biomass, is a vital measurement of vegetation water content and flammability. In this study, we investigated the dynamics of in-situ measurement of LFM at all the active sites in California, USA and revealed the difference between evergreen forest and shrub/scrub, the two dominant land cover types in California's fire-prone regions. We found that LFM of evergreen forest responses to soil moisture increase later than shrub/scrub, due to a later occurrence of major precipitation, a lower air temperature, and the different plant physiology. The comparison between SMAP L-band radiometer soil moisture and LFM showed that the lag between the rise in soil moisture and the response from LFM was much longer in evergreen forest. Compared with the evergreen forest, LFM of shrub/scrub was more sensitive to the inter-annual variability of soil moisture due to plant physiology and air temperature.

Live fuel moisture (LFM) is a key fire risk indicator and is commonly used in fire behavior models. We compared eight widely used vegetation indices derived from MODIS reflectance and SMAP soil moisture on their capability to estimate LFM at 38 sites in Southern California, USA, a fire-prone Mediterranean ecosystem. Seasonality, inter-annual variability, or both were considered in our multivariate regression model construction. We found that VARI, a vegetation index calculated utilizing the reflectance from blue, green, and red bands performed the best among all remotely sensed moisture level indicators. SMAP soil moisture did not yield a good model performance mostly due to the great number of missing soil moisture retrievals in the data series. Frequency of LFM sampling also affected the model performance. The time lead of SMAP soil moisture to vegetation response can be a potential remotely-sensed predictor of LFM or an input to biophysical process model to retrieve LFM.

Wildfires are a major ecological disturbance in Southern California and often lead to great destruction along the Wildland-Urban Interface. Live fuel moisture has been used as an important indicator of wildfire risk in measurements of vegetation water content. However, the limited field measurements of live fuel moisture in both time and space have affected the accuracy of wildfire risk estimations. Traditional estimation of live fuel moisture using remote sensing data was based on vegetation indices, indirect proxies of vegetation water content and subject to influence from weather conditions. In this study, we investigated the feasibility of estimating live fuel moisture using vegetation indices, Soil Moisture Active Passive L-band soil moisture data and the modeled vegetation water content using a non-linear model based on VIs and the stem factor associated with remote sensing moisture data products. The stem factor describes the peak amount of water residing in stems of plants and varies by land cover. We also compared the outcomes from regression models and recurrent neural network using the same independent variables. We found the modeled vegetation water content outperformed vegetation indices and the L-band soil moisture observations, suggesting a nonlinear relationship between live fuel moisture and the remotely sensed vegetation signatures. We discuss our results which will improve the predictability of live fuel moisture.

Intertidal flats which are located between land and ocean are productive and rapidly changing places. But these places are now facing many environmental challenges related to climate change and human-induced impacts. Topographic change related to sedimentation or erosion in intertidal flats is the most evident sign of the environmental changes. The intertidal flats usually have small topographic variations (less than 5m) and experience ebb and flood tides every day. Thus, the conventional SAR interferometric techniques (repeat-pass InSAR) cannot be applied for generating DEMs in these intertidal flats. In this study, we developed a long-baseline single-pass airborne interferometric SAR (InSAR) system that has small ambiguity height and collected airborne InSAR data in several intertidal flats, the west coast of Korean peninsula. The constructed topographies using the long-baseline airborne InSAR system were compared with TanDEM-X DEM obtained during long-baseline mission phase.

Anthropogenic impacts on ecosystems are prevalent and pose significant risks to vulnerable systems. In particular, wetland ecosystems are often found in urban centers with dense populations and rapid changes. Wetlands provide opportunities to study impacts of rapid urbanization and expansion, climate effects and pollution. The outcomes of human activities directly impact wetlands through coupled human-natural processes, including activities such as agriculture, oil extraction and resulting land cover changes, as well as indirect effects across the entire global biosphere. Here we examine human impacts of the last several years on the La Ballona Wetland Complex in Los Angeles, California, and discuss how remote sensing techniques and modeling can provide better understanding not only for this important wetland area in a megacity but for other wetland areas in other parts of the world.

Intertidal flats are productive and rapidly changing places. However, these places are facing many environmental challenges related to climate change and human-induced impacts. Topographic change due to sedimentation or erosion in intertidal flats can be the key indicator for recognizing these environmental changes. The intertidal flats usually have small topographic variations (less than 5m) and high-moistured soil surfaces (greater than 50%). The conventional SAR interferometric techniques cannot be used for generating DEMs in these intertidal flats. In this study, we developed a long-baseline airborne interferometric SAR (InSAR) system that has small ambiguity height and collected airborne InSAR data in Jebu intertidal flat, west coast of Korean peninsula. The constructed topographies using the long-baseline airborne InSAR were compared with TanDEM-X DEM (acquired during long-baseline mission phase) and GPS-RTK measurements.

Grounding line estimation has substantial importance of determining vulnerability of ice mass in the Polar Regions. In this study, high resolution digital elevation model is utilized to estimate and update grounding line of Thwaites Glacier in West Antarctica in 2011. Estimation is done by comparing the calculated bottom topography of ice with the bathymetry model from Operation IceBridge. The bottom topography is computed by subtracting ice thickness which is also estimated from surface elevation assuming hydrostatic equilibrium. The updated grounding line infers its retreat of up to 5km inland when compared with the grounding line in 2009. The retreat is conspicuous over the bathymetric trough. Conceivable error sources are still present such as ice column density, bathymetry accuracy and firn height. Nonetheless, high resolution digital elevation model shows high practicability of estimating grounding line.

Doppler velocity can be derived by calculating Doppler shift anomalies between predicted and estimated Doppler centroids. The predicted Doppler centroid is calculated based on a geometric model of satellite assuming that the target is not moving. The estimated Doppler centroid can be directly extracted from the raw SAR signal data by applying the average cross-correlation coefficient method. It is known that wind-generated ocean waves can significantly contribute to Doppler velocity due to the correlation between orbital motions of the waves and (tilt and hydrodynamic) modulated radar cross sections, in addition to what sea surface current contributes. In this study, the characteristics of Doppler velocities under hurricane conditions were investigated using RADARSAT-1 ScanSAR raw data. Five different hurricanes (Hurricane Dean, Hurricane Ivan, Hurricane Kyle, Hurricane Lili, and Typhoon Xangsane) and sequential acquisitions of two hurricanes (Hurricane Kyle and Hurricane Lili) were selected to study the contribution of wind-induced waves to Doppler velocities and compared with in situ measurements of drifting buoys. The results show that hurricane-generated seas and associated winds and waves appear to be different from ordinary sea state. This leads to lower estimates of Doppler velocities than expected and much closer to sea surface current velocities.

TanDEM mission enables the generation of high resolution digital elevation model over fast changing surfaces such as the Antarctic ice sheets. This study assesses the surface elevation over the marginal region of Thwaites Glacier in West Antarctica. The absolute height of SAR interferometry was derived by utilizing CryoSat-2 radar altimeter over the same region with minimizing temporal differences between the datasets. Then, the derived heights are compared with the airborne laser and radar altimetry data from the Operation IceBridge. The results show that surface and coherence are two main factors causing height offsets between the datasets, however, each effect must be separated for more accurate analysis.

The coastal sea surface temperature (SST) is one of the important oceanic environmental factors in determining the change of marine environments and ecological activities. In order to extract coastal SST, we developed ship- and airborne thermal infrared remote sensing systems. The acquired thermal infrared images are radiometrically calibrated using an atmospheric radiative transfer model as well as a thermometer/hygrometer sensor and geometrically calibrated using a GPS/IMU sensor. In this study, the ship- and airborne SST data were compared and verified with spaceborne SST data and in-situ data. The comparison and validation results showed that the ship and air-borne thermal infrared remote sensing could be used in generating SST map, especially in mapping fine-scale coastal features.

The surface heights of the marginal region of ice sheets are of interest among most glaciologist and remote sensing studies of glaciers. There have been a number of difficulties in estimating accuracy surface information in the marginal regions due to rough surfaces and high slope angles. In this study, the surface heights of the marginal region of Thwaites Glacier are calculated in high resolution using SAR interferometry of TanDEM-X. The calculated surface heights are compared after the absolute offset correction using CryoSat-2 radar altimeter. The results show that the general surface patterns are highly correlated, however, the RMS heights are larger than expected. The possible explanations can be related to the footprint size of CryoSat-2 and snow penetration depth due to different radar frequencies. Besides, absolute phase offset needs to be calculated accurately. In general, the possibility of utilizing TanDEM-X and CryoSat-2 data in the marginal regions is expected to be promising.

Various polarimetric analyses were carried out in order to verify the break-up of the Thwaites Ice Tongue. The hypothesis was that the observed crack on the ice tongue must be filled with sea ice if completely broken. Although the recently published paper asserted that the ice tongue had been completely broken, we found that it is too early to come into the conclusion. The depolarization ratio is mainly used to compare and distinguish between certain features of the ice surface and entropy, as well as mean alpha angle, is also computed as complementary information.