Earth and Space Science最新文献

Radio occultation is a well-established remote sensing method that provides reliable estimates of atmospheric profiles of diverse variables, including temperature and pressure. However, as with all indirect methods, radio occultation has some inherent systematic and random error effects, which lead to observational uncertainties. While propagation of uncertainties along the processing chain for individual radio occultation profiles was described in recent studies, this uncertainty information has not yet been carried forward to climatological fields. We close this gap and present an uncertainty propagation procedure that provides uncertainty estimates for aggregated means for climate applications. Estimated random uncertainties, basic and apparent systematic uncertainties and sampling uncertainties (due to the discrete sampling by profiles) are propagated through the aggregation process, resulting in uncertainty estimates for gridded fields. We demonstrate the new procedure for two test months and representative variables, inspecting monthly mean profiles for refractivity, dry temperature and physical temperature measurements. Results show that estimated random uncertainties and residual sampling uncertainties (after sampling bias correction) have similar magnitudes, both decreasing with increasing spatial aggregation sizes and corresponding increasing number of aggregated observations. At small aggregation they are the main contributors to uncertainty in refractivity, and important contributors to uncertainty of temperature. Systematic uncertainty, whose magnitude is independent of the number of profiles, is for refractivity the main source of uncertainty for larger aggregation sizes, and for pressure and dry temperature at all commonly used aggregation sizes. All uncertainty components exhibit pronounced spatial variation over the globe, with polar regions showing the greatest uncertainty.

The active channel of alluvial rivers delineates areas of geomorphic activity over a defined time window. While increasing satellite data availability enables monthly active channel delineations, multi-year analyses often rely on temporal aggregates (e.g., annual medians) to reduce computational costs and intra-annual variability. The potential of monthly information to improve active channels delineation and geomorphic interpretation remains largely unexplored. In this work, we delineated active channels for the Po River (Italy) by aggregating monthly Sentinel-2 classifications of river water and sediment bars into annual frequency maps at 10 m resolution. Annual aggregation mitigated monthly sediment underestimation (12%) but also amplified model overestimation biases (15%). Monthly classification persistence (e.g., classified as active channel for more than N months/year) was then used to reduce these errors and produce active channel areas that closely match those manually delineated from 30 cm orthophotos. The spatiotemporal variability of monthly classifications also show that the active channel area of dynamic reaches can vary ∼50% over the year. These changes revealed areas most prone to water-stage fluctuations, sediment transport, as well as zones seasonally or progressively colonized by vegetation—patterns hidden in single orthophotos or annual medians. Less dynamic reaches, by contrast, showed minimal differences between annual and monthly-based delineation methods. These findings emphasize the importance of adapting temporal aggregation to the river type and process analysed, with sub-annual resolutions better capturing, in dynamic rivers, seasonal and progressive active channel reconfigurations, along with their interaction with sediment and vegetation dynamics.

In the widely adopted description of seismic occurrence, earthquakes are categorized as either background or triggered events. In this work, we present a fully automated, non-parametric algorithm for distinguishing between these two categories, a process known as seismic declustering, based on the widely used nearest-neighbor (NN) metric. We introduce a new measure, the susceptibility index, which identifies an optimal threshold to discriminate between background and triggered events within the NN metric. Through statistical testing on simulated epidemic type aftershock sequence catalogs, we demonstrate that our method yields classification metrics exceeding 90%, outperforming state-of-the art algorithms. Notably, we show that a single threshold is sufficient for reliable discrimination within a given data set. The identification of this threshold requires memory capacity and computational time that scale linearly and quadratically with the data set size, respectively, making the method particurarly suited for large earthquake catalogs. We also apply our method to the relocated Southern California catalog and the GeoNet catalog of New Zealand (NZ). Our method effectively adapts across the different tectonic settings, capturing the variability of background seismicity rates between the shallow crustal events of Southern California and the tectonically diverse seismicity of NZ.

The accurate co-registration of geospatial data is necessary to answer questions that cross-cut disciplines and are key to understanding fundamental questions about our Solar System. To address this need and provide an updated product for Mars that is tied to a common reference frame, we have photogrammetrically controlled Thermal Emission Imaging System (THEMIS) daytime and nighttime infrared IR images. Using this improved image position knowledge, we generated orthorectified daytime and nighttime IR mosaics of Mars at 100 m per pixel for the ±65° latitude region of Mars. The updated spacecraft position and pointing information for the images is also released as SPICE kernels. The co-registration between individual THEMIS images achieves sub-pixel precision, and the average accuracy with which we know the position of any feature within the THEMIS controlled products is approximately 200 m horizontally. A globally controlled image set, with quantified accuracy and precision, is necessary to facilitate exploration and discovery for all bodies in the Solar System. Controlling THEMIS data allows multi-instrument science to be performed with significantly higher confidence as precise co-registration, and the accuracy knowledge of that registration, is necessary for analyses designed to extract information from the subtle differences between multiple images. A global image mosaic of Mars where uncertainties in the absolute image position are well characterized serves a wide range of purposes, including landing site evaluations, providing an accurate base to which high-resolution images (e.g., CTX and HiRISE) can be tied, and enables the fusion of multiple data types within a single framework.

The NASA Soil Moisture Active Passive Mission (SMAP) satellite passive microwave radiometry (PMR) capability is demonstrated for measurements of river stage, river discharge, and lake level with in situ gauging data in the Lower Mekong Basin (LMB). Five river gauging locations with distinct characteristics in the Mekong River system and a location for the Tonle Sap Lake were selected. The SMAP PMR method was validated with forward-split, backward-split, and full-record approaches. Results from the three different validations were consistent and well compared with in situ gauging data at all the locations. Both the narrowest (42-m width, Songkhram River) and the widest river (1,735-m width, Mekong River) achieved high correlation values ≥0.9 and Nash-Sutcliffe Efficiencies >0.8. The SMAP PMR observations of rivers and lake captured seasonal and interannual patterns of river change corresponding to flood and drought conditions. The synergy of SMAP with satellite Ka-band PMR and Soil Moisture and Ocean Salinity (SMOS) data over multiple decades identified flood and drought events, and abrupt changes in river flows in the LMB corresponding to the operations of the two largest dams, Xiaowan and Nuozhadu, on the Lancang (upper Mekong) River. After these two dams went into operation, wet-season flow stage in the lower Mekong River did not again reach the 2.33-year flood stage, and dry-season water level dropped below the lowest stage recorded in the 2015 exceptional drought year. The PMR method enables river and lake monitoring with global coverage on a daily to nearly daily basis over decades.

Accurate aerosol optical depth (AOD) prediction remains challenging due to complex aerosol-radiation interactions and highly variable spatio-temporal patterns. Three critical scientific issues motivate this work: understanding whether and how physical principles can enhance deep learning predictions, identifying which aerosol properties most strongly govern AOD variations, and improving the prediction of extreme AOD events critical for air quality management. Herein, utilizing MERRA-2 reanalysis data (1980–2024) over the Huaihe River Basin in eastern China, a Physics-Guided deep learning framework is presented for Aerosol Optical Depth (AOD) prediction. The model proposed integrates Convolutional Neural Networks (CNN), Long Short-TermMemory (LSTM) networks, and multi-head attention mechanisms to capture both spatio-temporal features and physical relationships of aerosol properties. Three key aspects are involved: First, a hybrid deep learning model is developed and evaluated, which combines CNNs for spatial correlation extraction, bidirectional LSTM for temporal dependency modeling, and multi-head attention for feature interaction learning. Second, a comprehensive feature importance analysis is conducted by examining the relationships between different aerosol properties (mass concentration, scattering coefficient, and Ångström exponent) and AOD prediction, offering physical insights into the model's decision-making process. Third, a specialized approach is proposed for extreme AOD event prediction, focusing on early detection and accurate forecasting of high-AOD episodes. Overall, the results demonstrate the model's efficacy in capturing both regular AOD variations and extreme events, with the Physics-Guided architecture showing superior performance compared to traditional methods. This integrated approach enhances AOD prediction accuracy and deepens insights into aerosol-radiation interactions, thereby improving atmospheric monitoring and air quality forecasting. While MERRA-2 has inherent temporal delays, this framework provides valuable capabilities for historical trend analysis, numerical model validation, and can be readily adapted for real-time applications through transfer learning with satellite observations.

Measuring and mapping vegetation structure is essential for understanding the functioning of terrestrial ecosystems and for informing environmental policies. Recent years have seen a growing demand for high-resolution data on vegetation structure, driving their prediction at fine resolutions (1–30 m) at state, continental, and global spatial extents by combining satellite data with machine learning. As these initiatives expand, it is crucial to actively discuss the quality and usability of these products. Here, we briefly summarize current efforts to map vegetation structure and show that continental-to-global canopy height models (CHMs) exhibit significant errors in canopy heights compared to national airborne laser scanning (ALS) data. We recommend that regions with abundant ALS data, such as Europe, prioritize using ALS-based canopy height metrics rather than relying on less accurate predictions from satellite products. Despite variations in ALS data characteristics, such as temporal inconsistencies and differences in acquisition characteristics and classification accuracy, the generation of spatially contiguous canopy height products in raster format at fine spatial resolution is necessary and feasible. This requires coordinating efforts for data and survey harmonization, developing standardized processing pipelines and continent-wide ALS products, and ensuring free access for research and environmental policy. We show that ALS data now cover most of Europe, with newer surveys achieving higher point densities, improving their suitability for vegetation mapping. Beyond numerous applications in forestry, ecology, and conservation, such data sets are crucial for calibrating future Earth Observation missions, making them essential for producing reliable and accurate global, fine-resolution vegetation structure data.

The fifth generation of ECMWF atmospheric reanalyses data (ERA5), operational observed data, S-band radar data and Large Eddy Simulation (LES) tool were employed to investigate the evolution of local small-scale convection in Ziyang City of Sichuan Province on 11 April 2022 in spring. The meso-β-scale vortex and 700-hPa wind shear in the Sichuan Basin horizontally merged and formed the Southwest China vortex. The local convection induced heavy rainfall, hail and thunderstorm in the northeast of the organizing meso-β-scale vortex. The well-developed meso-β-scale vortex became the center of Southwest China vortex. The horizontal scale and time scale of the meso-β-vortex were 20–50 km and 2–3 hr, respectively. The newly formed convections over the northeast of the meso-β-scale vortex developed in the middle troposphere and quickly separated from the main body of the meso-β-scale vortex with the guided airflow. The convective organization at the northeastern forepart of the meso-β-scale vortex was reproduced in LES results. The strong convection and meso-β-scale vortex were basically activated in the same period. The significant isobaric surfaces for strong convection formation were from 750 to 500 hPa. And cold air in the middle and lower troposphere rarely affected the convective process. Local convections were mainly triggered by strong vertically meridional wind shear.

The limits of detection for earthquake surface deformation in the spatial domain have improved with advances in remote sensing imagery data availability, resolution, and analysis. Sub-pixel correlation and digital elevation model (DEM) differencing from sub-meter, earthquake-spanning satellite optical imagery has enhanced surface rupture mapping and deformation measurements. However, knowledge of measurement accuracy and uncertainty is limited. To address this, we construct orthophotos and digital elevation models (DEMs) from repeat high resolution (∼0.5 m) satellite optical imagery along two sections of the Garlock fault, California with clear fault geomorphology and differing land cover. We deform later sets of DEMs and images with synthetic earthquakes containing both diffuse and discrete horizontal and vertical displacements. Sub-pixel image correlation and DEM differencing demonstrate how vegetation degrades recovered displacement accuracy. In barren land cover, horizontal displacements are detectable to an expected ∼1/10th-pixel size. With shrubs, trees, and grass, detectable displacements increase to >1/2-pixel size, and filtering results by correlation score and using elevation values as input rather than image values improves accuracy. Vertical displacement detection thresholds remain lower in vegetation, at >1-pixel size. Higher slope angles degrade displacement recovery, worsened by vegetation. Diminishing seasonal separation improves accuracy over vegetated regions, though not to the level achieved in barren environments. These results will inform research and operational efforts on the utility of high resolution satellite optical imagery for detecting deformation in varied land cover. Furthermore, they reveal where alternative measurements, such as from LiDAR or radar interferometry, are required to mitigate the effects of vegetation and capture fine-scale crustal deformation.