Journals
2026 EN
Podkowa A. · Nghiem S. V. · Kugler Z.
+1 more
Abstract 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.
Journals
2026 EN
Cheshm Siyahi V. · Kudryavtsev V. · Chapron B.
+1 more
Abstract The Surface Water and Ocean Topography (SWOT) mission provides unprecedented high‐resolution simultaneous observations of both sea surface height anomalies and sea surface roughness. Specifically, it enables more precise analysis of strong internal waves. Off the Amazon Shelf, in the Indonesian Seas, and near the Mascarene Ridge, internal wave signatures range from 3 to 50 km in wavelength. Using a three‐layer model to describe upper ocean stratification, SWOT measurements of sea surface heights are converted into thermocline displacements, which can reach amplitudes of up to 80 m. Simultaneous measurements of changes in sea surface roughness and height offer new insights into the mechanisms behind internal wave detection through precise radar intensity measurements. In fact, SWOT data can be analyzed with a modulation transfer function that connects radar intensity contrasts to the divergence of surface currents derived from sea surface height measurements. Based on these observations, a SWOT‐based modulation transfer function is developed as a function of the amplitude and wavenumber of internal waves, as well as local wind conditions. The highest radar intensity contrasts occur when internal waves propagate in the same direction as the wind, indicating resonant conditions between short wind waves and internal waves. These findings open new possibilities for extracting valuable information about Brunt–Väisälä frequency profiles from satellite observations of internal waves.
Journals
2026 EN
Dapremont Angela M. · Klima Rachel L. · Wilk Kierra A.
+11 more
Abstract The Lunar Trailblazer smallsat mission High‐resolution Volatiles and Minerals Moon Mapper (HVM 3 ) science instrument was designed to acquire targeted spectral image cubes of the lunar surface at visible to shortwave infrared (VSWIR) wavelengths (0.6–3.6 μm) in an effort to understand the distribution, abundance, and form (OH, H 2 O, ice) of lunar water, as well as the lunar water cycle. The Lunar Trailblazer mission end was declared in July 2025. Here, we describe the formulation and testing of VSWIR spectral parameters in preparation for previously anticipated returned data from HVM 3 using global image cubes and mosaic data from the Moon Mineralogy Mapper (M 3 ) imaging spectrometer, HVM 3 's predecessor, and the Deep Impact spacecraft. We expand upon the existing M 3 global spectral parameter library, test the efficacy of presented parameters individually and alongside existing M 3 spectral parameters, provide examples of quantitative thresholds intended to indicate robust mineral detections, and discuss the spectral parameter limitations. We demonstrate that newly formulated and existing parameters capture lunar mineral diversity well and serve as a reliable indicator of lunar surface hydration, making them useful for existing and future scientific analysis using lunar orbital remote sensing data sets.
Journals
2026 EN
Nierula Kai · Keil Sabrina · Shutin Dmitriy
+2 more
Abstract Passive seismic ambient noise interferometry (ANI) has shown potential for lunar seismic exploration, offering the capability to detect near‐surface subsurface structures critical for future lunar mission, such as near‐surface ice deposits and lava tubes, without the need for active seismic sources. Performing ANI on the Moon can be realized with a multi‐agent system, in which a network of individual rovers either carry or deploy seismic receivers. However, these systems have inherent uncertainties in localization and timing. Additionally, methods used to extract dispersion curves from cross‐correlations are fundamentally limited in achievable time–frequency resolution, which we demonstrate for the continuous wavelet transform (CWT). Quantifying how these factors propagate into Rayleigh wave velocity estimates is essential for accurate detection of lunar subsurface features. In this study, analytical error formulas are derived and validated through Monte Carlo simulations using passive seismic data from the Apollo 17 lunar seismic profiling experiment. Results indicate that velocity uncertainties due to localization errors remain around an acceptable2.2 % $2.2\,\%$ for realistic positional standard deviations of0.9 m $0.9\,$ at the receiver distance of56.9 m $56.9\,$ as in the Apollo 17 Lunar Seismic Profiling Experiment. Timing errors induced by clock instabilities are negligible. However, uncertainties in seismic travel‐time estimations are significantly dominated by the resolution limits imposed by the CWT. The developed analytical uncertainty model thus provides a critical foundation for designing autonomous lunar seismic networks for future lunar missions.
Journals
2026 EN
Ramachandran Rahul · Bugbee Kaylin · BernabeMoreno Juan
Abstract This paper introduces the sixth paradigm of scientific discovery: accelerated knowledge discovery (AKD). This paradigm is defined by the full integration of artificial intelligence (AI) into the research workflow as a tool augmenting cognitive capabilities of human scientists. AKD emerges from the convergence of advanced AI models, autonomous agentic systems, and human‐AI collaboration. AKD accelerates the research cycle by reducing the time from conceptualization to discovery. It automates labor‐intensive tasks such as literature review, hypothesis generation, experimental design, data analysis, modeling, simulation, and manuscript drafting. In well‐defined domains, AKD can transform the scientific method into a continuously adaptive cycle, where outputs from each phase inform the next. These closed‐loop scientific workflows shorten discovery timelines and reduce overhead. In addition to the scientific speed up, AKD targets an increase in the quality of research allowing for more systematic discovery of knowledge. However, AKD's success depends on principled, trustworthy design. This requires a holistic approach that emphasizes explainability, reproducibility, robustness, adaptability, and transparency. Key requirements include alignment with open science principles, required human oversight, scientific accountability, and rigorous provenance tracking. Human researchers must remain ultimately responsible for scientific integrity, ethical reasoning, and interpretation, with AI serving as an augmentative partner. Although the proposed approach applies to any domain focused on scientific discovery, this paper highlights AKD's potential to advance NASA's science mission, given the agency's vast data assets, complex objectives, and interdisciplinary challenges. By integrating NASA's data, foundation models, scalable computing, and knowledge frameworks, AKD can accelerate discovery and foster innovation across its scientific portfolio.
Journals
2026 EN
Battaglia Alessandro · Manconi Francesco · Recupero Massimiliano
+9 more
Abstract The WIVERN (Wind Velocity Radar Nephoscope) mission significantly enhances the global tropical cyclone observing system. Operating from a 500 km near‐polar orbit, the 3 m diameter conically scanning antenna provides an 800 km swath. The radar operated at 94 GHz (3 mm wavelength) provides high‐resolution observations with a vertical resolution of 600 m and horizontal resolution finer than 1 km. With quasi‐daily global coverage, WIVERN measures in‐cloud tropical cyclone winds from 1 km above the surface to the upper troposphere. Simulations of the Weather Research and Forecasting model with 1.5 km grid spacing were carried out for Hurricane Milton (2024) to serve as a testbed to demonstrate the potential capabilities of the WIVERN mission and its associated data products. The high‐resolution simulation successfully reproduces the hurricane's trajectory and intensification, capturing a remarkable 78‐knot increase in maximum sustained wind speed during the 24‐hr period from 7 October to 8 October. End‐to‐end simulations demonstrate that WIVERN: (a) can provide a three‐dimensional view of the horizontal wind inside cyclones, in particular capturing the vertical wind shear, the upper level divergence and the in‐cloud circulations inside the anvil produced by the hurricane convective towers, and some of the inflow and outflows in the lower layers of the atmosphere; (b) in presence of close‐in‐time overpasses, has the potential to detect the intensification of cyclone by estimating the maximum winds in the inner core; (c) can profile the tropical cyclone ice mass as a function of the distance from the eye, which will help shed light on the anvil formation and dissipation mechanisms.
Journals
2026 EN
Angulo Victor · Scilla Dario · Rodriguez Jorge L.
+3 more
Abstract Developed by the King Abdullah University of Science and Technology (KAUST) to support research in atmospheric science and remote sensing, KAUSTSat represented Saudi Arabia's (and the Middle East's) first research‐focused hyperspectral CubeSat mission for Earth observation. The primary payload consisted of the Simera HyperScape50, a miniaturized hyperspectral sensor operating in the visible to near‐infrared range. The sensor was equipped with a custom continuous variable filter that collected imagery at 30 m spatial resolution with a 120 km swath. The HyperScape50 allowed for up to 32 user‐defined spectral bands to be selected per acquisition from a total of 442 programmable channels between 442 and 884 nm, including a panchromatic band. These capabilities enable detailed observations of vegetation, soil, coastal zones, and other surface features relevant to applications in agriculture, biodiversity, resource management, and disaster response. In this paper, we provide an overview of the mission architecture, sensor design, acquisition strategy, and data structure. The hyperspectral data sets acquired over the 14‐month mission lifetime will also be presented, with a particular focus on the Arabian Peninsula and RadCalNet calibration sites. The KAUSTSat mission serves as a demonstration case of the viability of academic‐driven CubeSat platforms for delivering targeted, high‐quality environmental data, and represents a valuable reference for future small satellite Earth observation programs.
Journals
2026 EN
Zampieri Jair · Sabbione Juan I. · Velis Danilo R.
Abstract NASA's InSight mission investigates the interior structure of Mars. The data is characterized by multiple non‐seismic signals with varying attributes, including high‐energy instrumental noise, known as glitches, which frequently exhibit large linear polarization. Short‐duration, high‐frequency spikes often accompany these glitches, further complicating the detection of actual seismic event signals. In contrast, marsquakes signals are typically low in amplitude and can exhibit weak or poorly defined polarization, particularly at high frequencies. Thus, detecting marsquakes remains challenging even under generally low‐noise conditions. To address these issues, we developed an efficient denoising strategy that targets the nighttime periods of reduced noise levels. The method involves two key stages: despiking and deglitching. First, spikes are detected by analyzing the first derivative of the trace's energy, identifying outliers that deviate significantly from the surrounding signal, and then applying a polynomial replacement. After despiking, the method uses a rotation‐based filtering technique to concentrate the energy of the glitches into a single component through moving windows along the traces. The algorithm returns the rotated traces to their original orientation and subtracts the filtered components containing the glitches from the original data. This process produces denoised traces and noticeably mitigates glitches and spikes, preserving only ambient noise and potential Martian seismic events. This automated approach enhances the signal‐to‐noise ratio and facilitates subsequent event detection, offering a scalable solution for processing large data sets. We processed the nighttime hours of 3 months of data to compare the results of automatic detections between raw and denoised traces using an STA/LTA algorithm.
Journals
2026 EN
Austin T. J. · O'Rourke J. G. · Nelson D. M.
Abstract A large volume of scientifically and historically important data from the early exploration of Venus is not widely available to researchers. Our work is focused on ameliorating this problem with the archival of geospatial data including radar, topography, and radiometry. These surface observations date back to 1967, and we include data from the Haystack Observatory, Goldstone Observatory, Arecibo Observatory, Pioneer Venus Orbiter, Venera 9/10 Orbiters, Venera 15/16 Orbiters, and the Galileo flyby. The methods involved in this archival varied with each data set, ranging from as simple as converting images to different formats or as complex as scanning and georeferencing physical media. Finally, we produced a global radar basemap of Venus that combines the best available coverage from multiple data sets. The increased spatial, temporal, and wavelength coverage afforded by these observations will prove useful for mission planning and mapping/surveying efforts at Venus. We also identify several geospatial data sets outside this work that have not yet been adequately preserved and hope efforts can later be expanded to include in situ, atmospheric, and magnetospheric data sets.
Journals
2026 EN
Dubyagin S. · Ganushki. · Sicard A.
+6 more
Abstract We present the new probabilistic model of the electron fluxes designed to assess the risks of the spacecraft surface charging for missions with near‐equatorial orbits in the inner magnetosphere. It is a second model developed within a frame of the European Space Agency's activity “Plasma Environment Modeling in the Earth's Magnetosphere” (PEMEM). The first model PEMEM Percentile (Dubyagin et al., 2024, https://doi.org/10.1029/2023JA032026 ) has a robust though somewhat simple functionality. Addressing the PEMEM Percentile weaknesses, we test a novel approach to incorporating the dependence on geomagnetic activity in probabilistic models. The model is based on Van Allen Probes particle data. The model is driven by the auroral electrojet (AE) index from a period in the past corresponding to the expected solar cycle phase during a mission lifetime. The main model inputs are the spacecraft orbit, the time interval of AE‐index to drive the model, and the confidence levels. For given confidence levels, the model outputs the worst‐case 1–100 keV integrated electron flux and corresponding differential flux spectrum. The model can output these parameters separately for the eclipse and sunlit parts of the orbit. While investigating the response of the electron flux to the AE‐index variations, we have found that lower energy electrons reveal the highest correlation with the AE‐index averaged over the substorm time scale, while higher energy electrons show a higher correlation with AE on the storm time scale. The transition between these two regimes occurs at∼ 30 ${\sim} 30$ keV energy and has a complex dependence on radial distance and MLT.