 |
| Data processing workflow incorporating the proposed error correction method. The standard SBAS processing modules are in gray, while the proposed method is highlighted in orange and green. Orange arrows indicate the processing path when prior deformation is available, blue solid arrows represent the path without prior deformation, and black arrows denote the shared steps. |
New adaptive error correction method transforms how researchers monitor land subsidence across vast areas, offering hope for millions living in sinking cities
In China's North China Plain, home to over 200 million people, the ground has been sinking at rates of up to 165 millimeters per year—fast enough to cause visible damage to buildings, roads, and critical infrastructure within just a few years. But thanks to a breakthrough in satellite monitoring technology detailed in a recent paper published in IEEE Geoscience and Remote Sensing Letters, scientists now have a much more accurate way to track this kind of ground movement across vast areas.
The advance comes at a crucial time. Over the last 10 years InSAR has moved from being a niche research curiosity to a global monitoring tool with enormous potential, yet traditional processing methods often struggle with the complexity of real-world terrain and weather conditions. The new adaptive error correction method, developed by researchers at Central South University and China Institute of Geo-Environment Monitoring, promises to make satellite-based ground monitoring both more automated and more reliable.
The Challenge of Seeing Through the Noise
Interferometric Synthetic Aperture Radar (InSAR) is a technique for mapping ground deformation using radar images of the Earth's surface that are collected from orbiting satellites, and can potentially measure millimetre-scale changes in deformation over spans of days to years. By comparing radar images taken at different times, scientists can detect surface movements caused by earthquakes, volcanic activity, groundwater pumping, or mining operations.
But there's a catch: the satellite data contains multiple sources of error that can mask the actual ground movement scientists want to measure. Orbital error (OE) and topography-dependent atmospheric delay (TDAD)—two common error sources that can be modeled, may lead to inaccurate fitting or suppression of deformation signals in differential interferograms, when generic models are used.
Traditional correction methods apply one-size-fits-all approaches that can accidentally remove real deformation signals while trying to eliminate errors. "Some deformation information may be misidentified as errors by conventional error correction models, resulting in ineffective error correction, reduced processing efficiency, and potential inaccuracies in deformation retrieval," the researchers note in their paper.
A Smarter Approach Using Prior Knowledge
The breakthrough lies in what the researchers call "deformation prior information"—essentially using historical knowledge about an area's ground movement patterns to create more intelligent error correction. Instead of applying blanket corrections across an entire region, the new method creates customized "mask files" that distinguish between areas likely experiencing real ground movement and stable areas where apparent movement is probably just measurement error.
The process works in two main ways. First, if historical deformation data exists for a region, researchers can use that information to create masks that protect known deformation areas from overcorrection. Second, for new study areas without prior data, the method can generate preliminary deformation maps from the raw satellite data and use those to guide the error correction process iteratively.
The researchers tested their approach in China's North China Plain, a region plagued by severe land subsidence due to groundwater extraction. The results show that the overall ground displacement ranged from −165.4 mm/yr (subsidence) to 9.9 mm/yr (uplift) with a standard variance of 28.8 mm/yr. More importantly, the new method successfully preserved subsidence patterns while eliminating false deformation signals that traditional methods had struggled with.
The Automation Revolution
The timing couldn't be better. The satellite sector is innovating on all fronts – cheaper and smaller hardware, smarter software, better propulsion, and new ways of networking – making satellites more powerful and numerous than ever before. As the volume of satellite data explodes, manual processing becomes impossible.
Planet Labs owns and operates the largest commercial earth-imagery CubeSat constellation and has developed sophisticated automation for managing over 2,800 daily satellite contacts, achieving greater than 99% uptime. This industrial-scale approach to satellite operations is becoming the norm, making automated data processing essential.
The integration of artificial intelligence (AI) into satellite data analytics is becoming critical, with AI helping to generate detailed environmental insights from satellite imagery. The new adaptive error correction method represents another step toward fully automated satellite monitoring systems that can process vast amounts of data with minimal human intervention.
Global Impact and Future Directions
The implications extend far beyond academic research. Land subsidence is caused by natural and/or anthropogenic processes including subsurface fluid extraction, underground mining, drainage of organic soils, sediment compaction/load in coastal regions and permafrost degradation. Land subsidence, a pervasive geological hazard, manifests in over 150 cities across more than 50 nations, emerging as a global environmental challenge jeopardizing human habitation.
Recent studies show the problem is widespread and accelerating. The subsidence area with a rate greater than 50 mm/yr was 265.41 km2 until 2010 in Beijing alone, while infrastructure projects like high-speed railways face ongoing challenges from ground movement.
The technology's potential applications go well beyond subsidence monitoring. The satellite data services market was valued at US$ 11.98 billion in 2024 and is expected to reach US$ 67.02 billion by 2033, growing at a CAGR of 22.69% from 2025-2033, driven partly by environmental monitoring applications.
The Road Ahead
Despite these advances, challenges remain. While there are several promising technologies that could shape environmental monitoring in the future, the slow pace of technological development suggests that the year 2025 may not see any of the breakthrough successes the industry is hoping for. Issues like data integration, affordability, and infrastructure development still need work.
However, the momentum is clearly building. By 2025, digital twin adoption is expanding from engineering into operations: continuous digital models of entire constellations and ground networks are kept in sync with live telemetry, enabling real-time monitoring and scenario analysis. The global satellite spectrum monitoring market is expected to reach an estimated $6.3 billion by 2031 with a CAGR of 8.2% from 2025 to 2031.
The researchers' new adaptive error correction method represents an important step toward making satellite-based ground monitoring both more accurate and more automated. As cities around the world grapple with subsidence, flooding, and other ground movement challenges, tools like this could prove essential for protecting infrastructure and lives.
For the millions of people living in areas prone to ground subsidence—from California's Central Valley to the Netherlands to the North China Plain—more accurate satellite monitoring could mean the difference between reactive disaster response and proactive risk management. And as the technology becomes more automated and accessible, it promises to democratize access to sophisticated environmental monitoring capabilities that were once available only to well-funded research institutions.
The next time you see news about a sinking city or damaged infrastructure due to ground movement, remember that high above, a constellation of satellites is watching—and thanks to advances like adaptive error correction, they're getting better at separating the signal from the noise.
Sources
[1] Wang, Y., Feng, G., Guo, H., Wang, Y., Xiong, Z., & Jiang, H. (2025). An Adaptive Error Correction Method for InSAR Data Processing Guided by Deformation Prior Information. IEEE Geoscience and Remote Sensing Letters, 22, 4009905. [Direct access via IEEE Xplore]
[2] U.S. Geological Survey. (n.d.). InSAR—Satellite-based technique captures overall deformation "picture". https://www.usgs.gov/programs/VHP/insar-satellite-based-technique-captures-overall-deformation-picture
[3] Wikipedia. (2025, June 1). Interferometric synthetic-aperture radar. https://en.wikipedia.org/wiki/Interferometric_synthetic-aperture_radar
[4] TRE ALTAMIRA. (2019, January 31). InSAR Interferometric Synthetic Aperture Radar. https://site.tre-altamira.com/insar/
[5] U.S. Geological Survey. (n.d.). Interferometric Synthetic Aperture Radar (InSAR). https://www.usgs.gov/centers/land-subsidence-in-california/science/interferometric-synthetic-aperture-radar-insar
[6] Cigna, F., & Tapete, D. (2020). How satellite InSAR has grown from opportunistic science to routine monitoring over the last decade. Nature Communications, 11, 3863. https://www.nature.com/articles/s41467-020-17587-6
[7] NASA JPL. (2020, May 27). Interferometry | Get to Know SAR – NASA-ISRO SAR Mission (NISAR). https://nisar.jpl.nasa.gov/mission/get-to-know-sar/interferometry/
[8] GeoKinesia. (2024, October 3). Interferometric Synthetic-Aperture Radar (InSAR). https://geokinesia.com/technology/interferometric-synthetic-aperture-radar/
[9] NASA Earthdata. (2025, February 28). InSAR On Demand. https://www.earthdata.nasa.gov/learn/gis/storymaps/insar-demand
[10] European Space Agency. (n.d.). InSAR Principles: Guidelines for SAR Interferometry Processing and Interpretation (ESA TM-19). https://www.esa.int/About_Us/ESA_Publications/InSAR_Principles_Guidelines_for_SAR_Interferometry_Processing_and_Interpretation_br_ESA_TM-19
[11] Viridien. (n.d.). InSAR Satellite Monitoring. https://www.viridiengroup.com/expertise/satellite-mapping/insar-satellite-monitoring
[12] TS2 Space. (2025). Ground Control Goes Cloud: The Digital Overhaul of Satellite Operations (2025–2030). https://ts2.tech/en/ground-control-goes-cloud-the-digital-overhaul-of-satellite-operations-2025-2030/
[13] DefenseScoop. (2025, April 14). Space Force on track to deliver modernized space monitoring software in 2025. https://defensescoop.com/2025/04/14/space-force-atlas-ioc-2025/
[14] Globe Newswire. (2025, May 13). Satellite Spectrum Monitoring Market Report 2025: Revenues Projected to Reach US$6.3 Billion by 2031. https://www.globenewswire.com/news-release/2025/05/13/3079824/28124/en/Satellite-Spectrum-Monitoring-Market-Report-2025-Revenues-Projected-to-Reach-US-6-3-Billion-by-2031.html
[15] TS2 Space. (2025). Latest News and Developments in Satellites (2024–2025). https://ts2.tech/en/latest-news-and-developments-in-satellites-2024-2025/
[16] Ground Control. (2021, December 21). Satellite IoT & Tracking | Iridium & Viasat Airtime. https://www.groundcontrol.com/
[17] SpaceNews. (2024, December 27). 2025 will be a year of slow but steady progress for climate monitoring satellites. https://spacenews.com/2025-will-be-a-year-of-slow-but-steady-progress-for-climate-monitoring-satellites/
[18] Globe Newswire. (2025, July 3). Satellite Data Services Market Set to Surpass Valuation of US$ 67.02 Billion By 2033. https://www.globenewswire.com/news-release/2025/07/03/3109999/0/en/Satellite-Data-Services-Market-Set-to-Surpass-Valuation-of-US-67-02-Billion-By-2033-Astute-Analytica.html
[19] Digital Commons USU. (2021). Autonomous Monitoring of a Diverse Ground Station Network. https://digitalcommons.usu.edu/smallsat/2021/all2021/109/
[20] The Register. (2025, May 28). Eviden unveils satellite monitoring tool. https://www.theregister.com/2025/05/28/eviden_unveils_satellite_monitoring_tool/
[21] Strategies in Satellite Ground Segment Conference. (2025). Strategies in Satellite Ground Segment 2025. https://www.ssgsconference.com
[22] Chen, J., et al. (2020). Recent Ground Subsidence in the North China Plain, China, Revealed by Sentinel-1A Datasets. Remote Sensing, 12(21), 3579. https://www.mdpi.com/2072-4292/12/21/3579
[23] Liu, S., et al. (2022). Spatiotemporal heterogeneity of land subsidence in Beijing. Scientific Reports, 12, 11354. https://www.nature.com/articles/s41598-022-16674-6
[24] Zhu, L., et al. (2016). Imaging Land Subsidence Induced by Groundwater Extraction in Beijing (China) Using Satellite Radar Interferometry. Remote Sensing, 8(6), 468. https://www.mdpi.com/2072-4292/8/6/468
[25] Chaussard, E., et al. (2016). Time-varying land subsidence detected by radar altimetry: California, Taiwan and north China. Scientific Reports, 6, 28160. https://www.nature.com/articles/srep28160
[26] Wu, Q., et al. (2020). Regional-scale monitoring of underwater and dry ground subsidence in high phreatic areas of North China Plain. PLOS ONE, 15(8), e0237878. https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0237878
[27] Zhang, Y., et al. (2024). Risk assessment of land subsidence based on GIS in the Yongqiao area, Suzhou City, China. Scientific Reports, 14, 14547. https://www.nature.com/articles/s41598-024-62108-w
[28] Wang, L., et al. (2022). Land subsidence and its affecting factors in Cangzhou, North China Plain. Frontiers in Environmental Science, 10, 1053362. https://www.frontiersin.org/journals/environmental-science/articles/10.3389/fenvs.2022.1053362/full
[29] Cui, Z., et al. (2025). Land subsidence along the Beijing-Tianjin high-speed railway before and after the South-to-North water diversion project with multi-source monitoring datasets. Frontiers in Earth Science, 12, 1372105. https://www.frontiersin.org/journals/earth-science/articles/10.3389/feart.2024.1372105/full
[30] Liu, Y., et al. (2025). Ground subsidence evolution and groundwater variation in the middle route of south to north water diversion project. Scientific Reports, 15, 1447. https://www.nature.com/articles/s41598-025-04307-7
[31] Li, Y., et al. (2024). Urbanization and land subsidence: multi-decadal investigation combined SBAS-InSAR and multi-factors in Shanghai, China. Geocarto International, 39(1), 2391056. https://www.tandfonline.com/doi/full/10.1080/10106049.2024.2391056
No comments:
Post a Comment