Center For Remote Sensing (United States)

Forest disturbances are among the main drivers of global carbon emissions. These disturbances are associated with various human-related and natural drivers, including unsustainable resource extraction, fires, overgrazing and extreme weather events. Such disturbances vary in intensity and type, ranging from stand-replacing disturbances following clear-cuts or windthrow to more scattered disturbance patterns involving standing deadwood resulting from drought-induced mortality or small-scale canopy removal from selective logging. In recent years, multiple Earth observation products have been generated from Landsat and Sentinel missions to monitor such disturbances. These products vary in their methodological approaches and in their global and temporal coverage. However, there are currently no consistent benchmarks with which to evaluate their performance under different disturbance regimes and drivers. This study aims to evaluate and compare the accuracy and operational applicability of satellite-based forest disturbance products. We compared eight large-scale satellite products for detecting various forest disturbances, such as scattered tree mortality, large-scale removal and natural hazards. The disturbance products compared include Global Forest Change (GFC), DIST-ALERT, DeadTrees.Earth and the European Forest Disturbance Atlas (EFDA), amongst others. The products were compared qualitatively and quantitatively using reference data on disturbance events obtained from globally distributed aerial imagery acquired using unmanned aircraft systems (UAS). We use a total of 35 aerial orthomosaics acquired between 2015 and 2024, obtained from the DeadTrees.Earth platform. We identify forest disturbance types and quantify their extent using visual interpretation. This study advances our understanding of the strengths and limitations of current forest disturbance products by systematically assessing their performance across diverse disturbance types and environmental contexts.

The time series of the total electron content (TEC) extracted from the global ionosphere map (GIM) is useful to detect seismo-ionospheric anomalies at a certain region. When the detected anomalies are similar to those repeatedly appearing before large earthquakes in the same region, it might be considered temporal SIPs (seismo-ionospheric precursors) being observed. To discriminate the possible SIPs from global effects (such as solar disturbances, magnetic storms, etc.), a global search on anomalies of the GIM TEC is ideal to be employed. The spatial analysis simultaneously detects anomalies similar to the temporal SIP at each lattice of GIM and finds the distribution or pattern of the detected anomalies of the globe. When the detected anomalies specifically and continuously appear specifically near the monitoring region, we can declare spatial SIPs of the GIM TEC being observed. The distance between northern and southern crests of equatorial ionization anomaly (EIA) in GIM TEC along the earthquake longitude can be used to estimate electric fields associated with the observed SIPs. Meanwhile, radio occultation (RO) observations of FORMOSAT-3/COSMIC (F3C) satellites are useful to examine the vertical electron density structures. Results show that GIM TEC and the electron density at the F2-peak, NmF2, of F3C/RO profiles significantly increase specifically over the epicenter 3-4 days before the quake, which suggests SIPs of the Tohoku earthquake being detected. The EIA crests poleward motion and the F3C RO electron density profiles uplift indicate that the eastward electric fields have been enhanced during the SIP days.

Runways and taxiways constitute a vital component of an airport hub. They are engineered to endure for decades; however, there are reports indicating that they may deteriorate prior to the completion of their designated lifespan, as seen in cases where 30-year designs fail within the first decade. Such deterioration includes premature distress indicators such as rutting, mud pumping, concrete slab sinking, and reflective cracking, often linked to specific material factors such as 100% Ordinary Portland Cement (OPC) design mixes. These issues can be influenced by factors such as subgrade condition, drainage quality, seasonal moisture variations, and geotechnical stability. Furthermore, factors such as ambient temperature and construction workmanship can significantly reduce the design life; if the pavement is not cured properly or poured correctly, it becomes highly susceptible to early-stage failure.Present monitoring practices are predominantly reliant on limited in-situ testing and visual surveys, which often fail to capture the rate of deterioration at a network-wide scale or the root cause of early pavement failure. The advancement of remote sensing technologies, including Synthetic Aperture Radar (SAR) time-series, provides a reliable instrument for the precise monitoring of millimetric-scale deformations across extensive airport areas [1]. However, a comprehensive linkage between SAR observations, pavement failure mechanics, surface distress evolution, and long-term asset management decision making is still at an early stage.In this study, we propose a prototype multi-sensor framework designed for the early detection and characterisation of airport pavement failures. This framework integrates satellite InSAR technology for long-term and seasonal deformation time-series analysis, high-resolution optical imagery for surface distress mapping, and incorporates detailed in-situ pavement investigations. The methodology examines the correlation between seasonality of SAR displacement and ambient temperature, weather, or traffic records to separate consolidation, settlements, and thermoelastic responses. The framework evaluates the utility of these data streams to identify trend profiles that may help characterise the speed of deterioration in "difficult" sections, such as those experiencing mud pumping or sinking bays [2]. The distress metrics and their temporal evolution are extracted from co-registered optical products to track the progression of visible surface damage. In-situ observations serve to validate and provide structural and materials ground truth [3]. The combination of multi-source data facilitates deformation features relating to environmental drivers. These data sources are essential to better understand the pavement states and different scenarios of changes over time. This approach supports asset owners in moving toward data-driven pavement management and optimal budget allocation. Keywords: InSAR Time-Series Analysis; Airport Asset Management; Pavement Deterioration Modelling; Multi-modal Remote Sensing; Infrastructure Resilience References[1] Ferretti, A., Prati, C., & Rocca, F. (2001). Permanent scatterers in SAR interferometry. IEEE Transactions on Geoscience and Remote Sensing, 39(1), 8–20.[2] Gagliardi, V.; Bianchini Ciampoli, L.; Trevisani, S.; D’Amico, F.; Alani, A.M.; Benedetto, A.; Tosti, F. Testing Sentinel-1 SAR Interferometry Data for Airport Runway Monitoring: A Geostatistical Analysis. Sensors 2021, 21, 5769.[3] Asadollahkhan Vali, A. (2022). Airport Pavement Management System: Assessing current condition and estimating remaining life from aircraft demand. Spectrum Research Repository.

The GEO-ring constellation of atmospheric composition missions was established to deliver continuous, diurnally resolved observations of trace gases and aerosols at regional scales. Since the launch of GK2B in 2020, the constellation has expanded to include TEMPO in 2023 and Sentinel-4 in 2025, with additional missions planned. This growing number of operating sensors emphasizes the need for cross-mission consistency and long-term radiometric stability across the GEO-ring framework.As the first GEO-ring sensor, the Geostationary Environment Monitoring Spectrometer (GEMS) onboard GK-2B has been operating for more than five years under harsh space environmental conditions. Over this period, the optical and electronic components of the sensor have experienced degradation of up to 30%, particularly at shorter wavelengths around 300 nm. Given that this sensor has undergone the longest period of degradation, its characterization and correction provide a valuable basis for other GEO-ring sensors with similar instrumental characteristics. In this regard, this study presents post-launch calibration and evaluation methodologies for GEMS with a focus on Level 1 products, including solar irradiance and Earth reflectance. Calibration updates address long-term degradation along with angular dependence and systematic biases of the onboard solar diffuser.For validation of the updates, GEMS is inter-calibrated with both geostationary and low Earth orbit sensors, including the Advanced Meteorological Imager (AMI) onboard the twin satellite GK2A, as well as the Tropospheric Monitoring Instrument (TROPOMI) and Ozone Mapping and Profiler Suite (OMPS). Each reference instrument provides unique strengths in spatial, spectral, and temporal coverage, enabling a comprehensive assessment of GEMS performance. The validation results indicate that the updated GEMS reflectance exhibits spectral biases within 5%, except at wavelengths below 320 nm, where straylight effects dominate.These results demonstrate that the applied calibration and inter-calibration strategies effectively improve the radiometric consistency of GEMS Level 1 products. Building on these approaches, this work highlights the importance of in-flight calibration of Level 1 products for accurate Level 2 retrievals and long-term GEO-ring consistency.

This study uses satellite altimeter data from the new AVISO dataset to investigate the coupling between sea level variability in the Sicily Channel and the Ionian Sea. The dataset spans the last three decades (1993–2024) and provides high spatial resolution coverage of the Mediterranean Sea (1/16°, or approximately 7 km). We analyze the variability of the sea surface height through Empirical Orthogonal Function and Singular Value Decomposition techniques applied to the Absolute Dynamic Topography. While the dominant modes of long-term variability reflect the known dynamics of the North Ionian Gyre, the singular value analysis allows us to identify a coherent spatial structure extending from the Sicily Channel to the Northern Ionian Sea. This provides the first observation-based, robust evidence of a dynamical coupling between the two basins, indicating that in the last thirty years the Northern Ionian Gyre is part of a broader, dynamically connected regional system integrating flows from the Sicily Channel. These findings are consistent with previous work, based on a hindcast simulation covering 1980–2010, in which we highlighted the key role of the Atlantic Ionian Stream in shaping interannual to decadal variability in the Northern Ionian Sea. Here, we extend the analysis to the present day, providing the most up-to-date, observation-based assessment of the regional dynamics.

Soil moisture and saturation are crucial hydrological variables for understanding the soil’s condition and modeling improvement. The National Water Model (NWM), a large-scale model, simulates the hydrologic cycle across the Contiguous United States, Hawaii, and Puerto Rico. The study’s objective was to evaluate the NWM’s performance in estimating and forecasting soil moisture in Puerto Rico from the year 2021 to 2023. The datasets used included in situ stations, model outputs, and remotely sensed data from the Soil Moisture Active Passive (SMAP) mission. Then, we used Volumetric bias (Vbias), Mean Absolute Error (MAE), and Kling–Gupta Efficiency (KGE) to measure performance. The analysis assimilation results showed that three stations in each dataset had an inversely predominant error equal to 25% or less. This low error was reflected in the obtained Vbias and MAE results. Meanwhile, the KGE analysis indicated that the NWM achieves low to moderate soil moisture performance, with better agreement against SMAP than in situ observations. However, the forecasted datasets did not produce satisfactory results. Short-range forecasts exhibited negative KGE values, highlighting the importance of data assimilation, the persistent influence of bias, and scale mismatch. Although the NWM’s primary focus is streamflow forecast, these findings highlight the potential application of the model beyond its primary focus.