Editors’ Highlights are summaries of recent papers by AGU’s journal editors.
Source: Space Weather
TianQin is a geocentric space-borne gravitational wave detector, which is proposed to detect the gravitational wave by measuring tiny displacements using inter-satellite laser interferometry. However, the space surrounding the orbit and laser links of TianQin is not a vacuum—but filled with plasma, which can bend the laser links and induce pointing accuracy noise in the gravitational wave de
TianQin is a geocentric space-borne gravitational wave detector, which is proposed to detect the gravitational wave by measuring tiny displacements using inter-satellite laser interferometry. However, the space surrounding the orbit and laser links of TianQin is not a vacuum—but filled with plasma, which can bend the laser links and induce pointing accuracy noise in the gravitational wave detection.
Based on a global magnetohydrodynamic model, Zhou et al. [2026] use a ray-tracing method to obtain the laser deflection caused by laser propagation through plasma, and to evaluate the pointing accuracy noise. The result shows that the laser deflection effect caused by large-scale space plasma distribution under quiet to moderate space weather conditions does not represent a fundamental risk to the TianQin mission. However, during severe space weather events, the laser propagation effect could become a considerable noise in the gravitational wave detection.
This work establishes a connection between space weather and gravitational wave detection. Furthermore, this work raises awareness of the impact of space weather on other high-precision electromagnetic wave measurements in space.
Citation: Zhou, S. W, Su, W., Zhou, S. Y., Li, C. F., & Zhang, J. X. (2026). The pointing error due to laser propagation in space plasma for TianQin gravitational wave detection. Space Weather, 24, e2025SW004784. https://doi.org/10.1029/2025SW004784
Editors’ Highlights are summaries of recent papers by AGU’s journal editors.
Source: AGU Advances
The evolution of rivers that split into multiple channels is a scientific challenge in terms of modeling and prediction. On the other hand, these rivers are widespread and play a key role for ecosystems’ life, groundwater recharge, and therefore, water security. They are also extremely sensitive to hydroclimatic changes, leading to shifts in precipitation, erosion and sediment transport.
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The evolution of rivers that split into multiple channels is a scientific challenge in terms of modeling and prediction. On the other hand, these rivers are widespread and play a key role for ecosystems’ life, groundwater recharge, and therefore, water security. They are also extremely sensitive to hydroclimatic changes, leading to shifts in precipitation, erosion and sediment transport.
Zhao et al. [2026] investigate the drivers of river evolution for 97 multithread river reaches worldwide, spanning diverse climates and morphologies. The study reveals the key role of intermittency for river evolution. In particular, higher flow intermittency could lead to more even flow partitioning among threads, therefore impacting hydrology and ecosystems. With flow variability increasing after climate change, rivers are likely to increase their thread count, thus impacting livelihoods and ecosystems.
Two example multithread reaches shown in Landsat images from (b) the Irtysh River (wandering) and (c) the Yukon River (braided). Credit: Zhao et al. [2026], Figure 1(b,c)
Citation: Zhao, F., Ganti, V., Chadwick, A., Greenberg, E., McLeod, J., Liu, Y., et al. (2026). Global hydroclimatic controls on multithread River dynamics. AGU Advances, 7, e2025AV002166. https://doi.org/10.1029/2025AV002166
Editors’ Vox is a blog from AGU’s Publications Department.
As climate change increases the frequency and intensity of flooding, it’s becoming increasingly important to monitor and predict flood hazards at different scales. A new article in Reviews of Geophysics presents a data-driven performance analysis of various space-based sensors that monitor flood hazards. Here, we asked the lead author to give an overview of satellite-based flood monitoring, the benefits and challenges of using satell
As climate change increases the frequency and intensity of flooding, it’s becoming increasingly important to monitor and predict flood hazards at different scales. A new article in Reviews of Geophysics presents a data-driven performance analysis of various space-based sensors that monitor flood hazards. Here, we asked the lead author to give an overview of satellite-based flood monitoring, the benefits and challenges of using satellite-based sensors, and future space-based projects.
Why is it important to monitor the surface waters on Earth?
More than half of the world’s population lives within three kilometers of a freshwater body. When seasonal flooding behaves as anticipated, it provides essential nutrient replenishment to soils and crops. However, extreme flooding disturbs the careful balance of freshwater systems and can cause damaging flooding that disrupts livelihoods.
Climate change is making these extremes more frequent and less predictable, while expanding populations in flood-prone areas amplify the human cost. Continuous monitoring of Earth’s surface waters is essential as it helps us anticipate hazards, evaluate risk, and design interventions that protect the people and places most exposed to hydrologic hazards.
What are the benefits of monitoring flood inundation from space compared to other techniques?
Monitoring flood inundation from space is advantageous due to the wide-scale global coverage that captures important information over large areas. In-situ sensors, such as river gauges, provide valuable data but are limited in spatial coverage and may even fail under significant flood conditions. A single satellite overpass can potentially capture an entire river basin, allowing responders to see where water has spread, which communities are affected, and how the event is evolving.
When did scientists first start using satellites to monitor surface waters?
The value of monitoring surface water from space was first realized in the early 1970s, following the launch of Landsat 1. Soon after launch, it captured imagery of the devastating 1973 Mississippi River floods, producing one of the first flood maps made from space (Figure 1). By the early 2000s, NASA’s MODIS sensors were providing global coverage at a daily frequency. Today, multiple global flood monitoring systems are in place, including the European Union’s Copernicus Emergency Management Service, which maps floods using Sentinel-1 synthetic aperture radar (SAR), and NOAA’s VIIRS Flood Mapping system.
Figure 1. Imagery from the start of the Landsat 1 mission illustrating the extent of the Mississippi River flooding of 1973 (EROS History Project). The Earth Resources Technology Satellite 1 (ERTS-1) was renamed Landsat 1 in 1975. Credit: USGS
What are the three types of satellite-based sensors that your review focuses on?
Our review examines three families. Multispectral (optical and thermal) sensors capture reflected sunlight or emitted heat. Microwave sensors, including SAR, passive microwave radiometers, and GNSS Reflectometry (GNSS-R), can observe through clouds and at night but involve trade-offs between resolution and coverage. Finally, altimetric sensors measure water surface elevation with high precision but only along narrow tracks. Each family has distinct strengths and weaknesses that lend themselves to use in combination for comprehensive flood inundation monitoring.
What are some of the challenges of using satellite-based sensors to monitor flooding?
The fundamental problem is that floods and satellite observations are mismatched in time and space. Optical sensors often capture clouds rather than the floodwater beneath. Cloud-penetrating sensors like SAR can miss flood peaks if their orbital schedule doesn’t align with the event, and dense vegetation can obstruct floodwater from both optical and shorter-wavelength radar. Sensors with high temporal resolution typically deliver data at coarse spatial resolutions, sometimes tens of kilometers per pixel. These trade-offs form what we describe as the “iron triangle” of Earth observation: temporal resolution, spatial resolution, and cost. A sensor can typically be optimized for two, but rarely all three. Occasionally, the timing and conditions of a flood align well with sensors whose strengths are complementary across the iron triangle, yielding the kind of multi-sensor view shown in Figure 2.
Figure 2. Sentinel‐2 MSI True Color Image with Sentinel‐1 SAR derived flood‐extent superimposed on top. The top right circle highlights the missing SAR‐derived information, whereas the bottom circle highlights the missing optical information. Credit: Campo et al. [2026], Figure 5
What are some upcoming space-based sensor projects that could advance the field of hydrology?
Several are already reshaping the field. NISAR, a joint NASA–ISRO radar satellite launched in 2025, carries an L-band sensor designed to penetrate vegetation canopy, providing new insights into flooding beneath vegetation. Sentinel-1D, launched in late 2025, has restored the Sentinel-1 constellation to full two-satellite capacity, halving the revisit time. Landsat Next, a planned three-satellite constellation with 26 spectral bands and a six-day revisit, would provide valuable hydrologic data at both high temporal and spectral resolutions. However, recent budget pressures have introduced uncertainty about its final scope. Finally, the HydroGNSS mission from ESA will use GNSS-R to monitor hydrologically linked Essential Climate Variables.
Editor’s Note: It is the policy of AGU Publications to invite the authors of articles published in Reviews of Geophysics to write a summary for Eos Editors’ Vox.
Citation: Campo, C. (2026), Can any single satellite keep up with the world’s floods?, Eos, 107, https://doi.org/10.1029/2026EO265016. Published on 20 April 2026.
This article does not represent the opinion of AGU, Eos, or any of its affiliates. It is solely the opinion of the author(s).
Editors’ Highlights are summaries of recent papers by AGU’s journal editors.
Source: AGU Advances
Aerosols are tiny particles suspended in the air. They can cool the climate by making clouds brighter and longer-lasting. Scientists rely on satellite observations to measure the aerosol-cloud interaction, but distinguishing human impacts from natural weather patterns remains a challenge.
Christensen et al. [2026] reveal that the Amazon River itself creates cloud patterns that mimic the si
Aerosols are tiny particles suspended in the air. They can cool the climate by making clouds brighter and longer-lasting. Scientists rely on satellite observations to measure the aerosol-cloud interaction, but distinguishing human impacts from natural weather patterns remains a challenge.
Christensen et al. [2026] reveal that the Amazon River itself creates cloud patterns that mimic the signatures of pollution. Using 15 years of satellite data, researchers found that the temperature difference between the cool river and the warm land drives a local “river breeze” circulation. This natural process creates clouds with smaller and more numerous water droplets, which exhibit very similar features that satellites look for to identify pollution. Consequently, clean clouds over the river can appear polluted in satellite datasets. These findings highlight the critical need to account for local geography and natural weather patterns to accurately assess how human activities are influencing Earth’s climate.
Citation: Christensen, M. W., Varble, A. C., Tai, S.-L., Wind, G., Meyer, K., Holz, R., et al. (2026). The Amazon River-breeze circulation limits detection of aerosol-cloud interactions in warm clouds. AGU Advances, 7, e2025AV002188. https://doi.org/10.1029/2025AV002188
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