Scientists devise way to track space junk as it falls to Earth
Earthquake-detecting seismometers pinpoint sounds of space debris entering atmosphere
Johns Hopkins University
video:
By mapping areas where seismometers in southern California detected sonic booms, researchers at Johns Hopkins University and Imperial College London were able to track the path of the Shenzhou-15 orbital module after it reentered the Earth's atmosphere on April 2, 2024.
view moreCredit: Benjamin Fernando, Johns Hopkins University
Space debris—the thousands of pieces of human-made objects abandoned in Earth’s orbit—pose a risk to humans when they fall to the ground. To locate possible crash sites, a Johns Hopkins University scientist has helped to devise a way to track falling debris using existing networks of earthquake-detecting seismometers.
The new tracking method generates more detailed information in near real-time than authorities have today—information that will help to quickly locate and retrieve the charred and sometimes toxic remains.
“Re-entries are happening more frequently. Last year, we had multiple satellites entering our atmosphere each day, and we don’t have independent verification of where they entered, whether they broke up into pieces, if they burned up in the atmosphere, or if they made it to the ground,” said lead author Benjamin Fernando, a postdoctoral research fellow who studies earthquakes on Earth, Mars, and other planets in the Solar System. “This is a growing problem, and it’s going to keep getting worse.”
The findings are published today in the journal Science.
Fernando and colleague Constantinos Charalambous, a research fellow at Imperial College London, used seismometer data to reconstruct the path of debris from China’s Shenzhou-15 spacecraft after the orbital module entered the Earth’s atmosphere on April 2, 2024. Measuring roughly 3.5 feet wide and more than 1.5 tons, the module was large enough to potentially pose a threat to people, the researchers said.
Space debris entering the Earth’s atmosphere moves faster than the speed of sound and, consequently, produces sonic booms, or shock waves, similar to those produced by fighter jets. As the debris streaks toward the Earth, vibrations from the shockwave trail behind, rumbling the ground and pinging seismometers along the way. Mapping out the activated seismometers allows researchers to follow the debris’ trajectory, determine which direction it’s moving, and estimate where it may have landed.
By analyzing data from 127 seismometers in southern California, the researchers calculated the path and speed of the module. Cruising at Mach 25-30, the module streaked through the atmosphere traveling northeast over Santa Barbara and Las Vegas at roughly 10 times the speed of the fastest jet in the world.
The researchers used the intensity of the seismic readings to calculate the module’s altitude and pinpoint how it broke into fragments. Then, they used trajectory, speed, and altitude calculations to estimate the module was traveling approximately 25 miles north of the trajectory predicted by U.S. Space Command based on measurements of its orbit.
Engulfed in flames, falling debris sometimes produces toxic particulates that can linger in the atmosphere for hours and waft to new parts of the planet as weather patterns change. Knowing the trajectory of the debris will help organizations track where those particulates go and who might be at risk of exposure, the researchers said.
Near-real time tracking will also help authorities quickly retrieve objects that make it to the ground, the researchers said. Such rapid retrievals are especially important because debris can carry harmful substances.
“In 1996, debris from the Russian Mars 96 spacecraft fell out of orbit. People thought it burned up, and its radioactive power source landed intact in the ocean. People tried to track it at the time, but its location was never confirmed,” Fernando said. “More recently, a group of scientists found artificial plutonium in a glacier in Chile that they believe is evidence the power source burst open during the descent and contaminated the area. We’d benefit from having additional tracking tools, especially for those rare occasions when debris has radioactive material.”
Previously, scientists had to rely on radar data to follow an object decaying in low Earth orbit and predict where it would enter the atmosphere. The trouble, the researchers said, is that re-entry predictions can be off by thousands of miles in the worst cases. Seismic data can complement radar data by tracking an object after it enters the atmosphere, providing a measurement of the actual trajectory.
“If you want to help, it matters whether you figure out where it has fallen quickly—in 100 seconds rather than 100 days, for example,” Fernando said. “It’s important that we develop as many methodologies for tracking and characterizing space debris as possible.”
Journal
Science
Article Title
Reentry and disintegration dynamics of space debris tracked using seismic data
Article Publication Date
22-Jan-2026
Watching forests grow from space
image:
Comparison of canopy height dynamics in plantations and secondary forests. (A) Violin plots of canopy height in 2019. (B) The regression slope of annual canopy height for 1986 to 2019. (C) The CV of canopy height during 1986 to 2019 for both plantation (n = 699,195) and secondary forests (n = 300,805). (D and E) Visual examples of plantations (Lon: 108.275, Lat: 22.951) and secondary forests (Lon: 98.023, Lat: 25.280); satellite images from Google Earth (2019). (F) Annual mean canopy height for the selected plantations and (G) secondary forests.
view moreCredit: Journal of Remote Sensing
Forests are central to climate mitigation, yet tracking how fast they grow over decades remains difficult. A new satellite-based approach reconstructs forest canopy height changes across southern China from the 1980s onward. The analysis reveals sustained forest growth, clear differences between plantation and secondary forests, and the dominant role of management in shaping forest structure. The results show that long-term forest development can be monitored consistently from space.
Forest canopy height reflects tree growth, biomass accumulation, and carbon storage potential. While global forest height maps now exist, most capture only a single moment in time, limiting their value for evaluating long-term forest dynamics. Field inventories provide accurate measurements but are expensive, labor-intensive, and difficult to scale across large or mountainous regions. In rapidly changing forest landscapes—especially those shaped by afforestation and plantation management—understanding growth trajectories over decades is essential for sustainable policy and climate planning. Based on these challenges, there is a need to conduct in-depth research into long-term, large-scale forest growth monitoring using consistent satellite observations.
Researchers from the Chinese Academy of Sciences, the University of Copenhagen, and collaborating institutions reported this work in the Journal of Remote Sensing, published (DOI: 10.34133/remotesensing.0810) in December 2025. The study addresses a key challenge in forest science: how to track forest growth continuously over long time periods and vast areas. By combining decades of Landsat satellite data with machine-learning models, the team reconstructed annual canopy height maps for southern China, offering a rare long-term perspective on how forests recover, mature, and respond to human management.
The analysis shows that forests in southern China grew substantially over the past three decades. Average canopy height increased from about 6.4 meters in 1986 to over 10.3 meters in 2019, representing a 61% rise. Areas dominated by taller trees expanded rapidly after 2000, reflecting large-scale afforestation and forest protection efforts. Plantation forests grew faster than secondary forests, with average height gains of about 0.20 meters per year, compared with 0.13 meters per year in secondary forests. However, secondary forests ultimately reached greater heights. The findings highlight that forest age and management practices, rather than climate alone, are the dominant drivers of long-term forest structure change.
To generate a continuous forest growth record, the researchers reconstructed annual canopy height maps at 30-meter resolution from 1986 to 2019. They trained a Random Forest machine-learning model using existing global forest height products and validated the results against national forest inventory data and airborne lidar measurements. Model accuracy remained stable across regions and decades, with average errors of about three meters.
The long-term dataset reveals distinct growth patterns. In the late 1980s and 1990s, landscapes were dominated by short trees. After 2000, canopy heights increased rapidly as planted forests matured. Plantation forests showed pronounced growth cycles linked to harvesting and replanting, while secondary forests displayed steadier, more stable growth. Statistical analyses further showed that forest age was the strongest factor controlling height changes, followed by precipitation and temperature, while soil properties limited maximum attainable height. Together, these results demonstrate that satellite spectral data can reliably capture vertical forest growth over time.
"This study shows that we can now observe how forests grow year by year, not just where they exist," one researcher noted. "By looking back more than 30 years, we can directly see how management decisions shape forest structure and carbon potential. This opens new possibilities for evaluating restoration success and guiding future forest policies."
The team analyzed the full Landsat satellite archive using Google Earth Engine, generating annual cloud-free composites. Multiple vegetation indices were combined with elevation data and processed through a Random Forest model trained on existing canopy height datasets. Predictions were validated using airborne lidar measurements and national forest inventory plots. Trend analysis, regression models, and driver attribution were then applied to quantify forest growth rates and identify the key environmental and management factors influencing canopy height changes.
This approach offers a scalable tool for monitoring forest growth, carbon accumulation, and management outcomes worldwide. With similar satellite archives available globally, the method can be applied to other regions undergoing reforestation or plantation expansion. As higher-resolution satellite and lidar data become more accessible, long-term forest monitoring from space could directly inform carbon accounting, biodiversity conservation, and climate strategies. Ultimately, the ability to watch forests grow from space may transform how societies manage and value forest ecosystems.
###
References
DOI
Original Source URL
https://spj.science.org/doi/10.34133/remotesensing.0810
Funding information
This work was supported by the National Key Research and Development Program of China for Young Scientists (2023YFF1305700), the National Natural Science Foundation of China (42371129), the National Natural Science Fund for Excellent Young Scientists (Overseas), the National Key Research and Development Program of China (2022YFF1300700), the Science and Technology Innovation Program of Hunan Province (2024RC1067), the International Partnership Program of Chinese Academy of Sciences (CAS) (092GJHZ2022029GC), and the CAS Interdisciplinary Team (JCTD-2021-16). M.B. acknowledges support from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement no. 947757 TOFDRY).
About Journal of Remote Sensing
The Journal of Remote Sensing, an online-only Open Access journal published in association with AIR-CAS, promotes the theory, science, and technology of remote sensing, as well as interdisciplinary research within earth and information science.
Journal
Journal of Remote Sensing
Subject of Research
Not applicable
Article Title
Tracking Southern China’s Forest Growth from Space
Research Article | Evaluation of ten satellite-based and reanalysis precipitation datasets on a daily basis for Czechia (2001–2021)
image:
Current and Future States of Observation Systems
view moreCredit: Big Earth Data
A new study published in Big Earth Data provides a comprehensive evaluation of the accuracy of widely used satellite-based and reanalysis precipitation datasets, offering critical guidance for hydrological, climate, and environmental applications in Central Europe.
Citation
Paluba, D., Bližňák, V., Müller, M., & Štych, P. (2025). Evaluation of ten satellite-based and reanalysis precipitation datasets on a daily basis for Czechia (2001–2021). Big Earth Data, 1–30. https://doi.org/10.1080/20964471.2025.2592444
Abstract
This study assesses the accuracy of ten satellite-based and reanalysis precipitation datasets available in Google Earth Engine (GEE) using in-situ rain gauge measurements across Czechia, Central Europe, from 2001 to 2021. The gauge-adjusted GSMaP dataset (GSMaPGA) was the most accurate dataset overall (Pearson’s correlation coefficient r = 0.79), followed by ERA5-Land (r = 0.75), with both showing superior performance for rainy days above 1 mm of precipitation. In contrast, CHIRPS, GLDAS, and PERSIANN-CDR showed the weakest performance (r ≈ 0.41–0.42). All datasets overestimated precipitation on days with no or with very light rain (≤1 mm/day) and underestimated it during heavy rainfall events ( >5 mm/day). ERA5-Land systematically overestimated annual precipitation by 15–35%, while GSMaPGA showed slight underestimation by 0.5–9%. Although absolute errors generally increased with elevation, GSMaPGA showed the smallest elevation-related biases, highlighting the importance for gauge-adjustment. Part of the observed spatial and seasonal biases may be explained by the combination of coarse spatial resolution and the challenges of capturing short-lived summer convective storms over complex terrain. Overall, GSMaPGA is recommended for most applications due to its superior accuracy, while ERA5-Land is suitable for long-term studies because of its long historical record extending back to the 1950s.
Keywords
Precipitation, reanalysis, Google Earth Engine, time series, Czechia
Big Earth Data is an interdisciplinary Open Access journal which aims to provide an efficient and high-quality platform for promoting the sharing, processing and analyses of Earth-related big data, thereby revolutionizing the cognition of the Earth’s systems. The journal publishes a wide range of content, including Research Articles, Review Articles, Data Notes, Technical Notes, and Perspectives. It is now included in ESCI (IF=3.8, Q1), Scopus (CiteScore=9.0, Q1), Ei Compendex, GEOBASE, and Inspec. Starting from 2023, Big Earth Data has announced a new award series for authors: Best and Outstanding Paper Awards.
Journal
Big Earth Data
Method of Research
Data/statistical analysis
Subject of Research
Not applicable
Article Title
Evaluation of ten satellite-based and reanalysis precipitation datasets on a daily basis for Czechia (2001–2021)
No comments:
Post a Comment