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Generation of long-term InSAR ground displacement time-series through a novel multi-sensor data merging technique: The case study of the Shanghai coastal area
Research article (ISPRS Journal of Photogrammetry and Remote Sensing, 2019) · cited 76× · AI/ML
Generation of long-term InSAR ground displacement time-series through a novel multi-sensor data merging technique: The case study of the Shanghai coastal area
Summary
Generation of long-term InSAR ground displacement time-series through a novel multi-sensor data merging technique: The case study of the Shanghai coastal area is a scholarly article[1].
Key Facts
Generation of long-term InSAR ground displacement time-series through a novel multi-sensor data merging technique: The case study of the Shanghai coastal area's instance of is recorded as scholarly article[2].
References
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Use these citations when quoting this entity in research, articles, AI prompts, or wherever provenance matters. We aggregate Wikidata + Wikipedia + authoritative open-data sources; the stitched, scored, cross-referenced view is what 4ort.xyz contributes.
APA4ort.xyz Knowledge Graph. (2026). Generation of long-term InSAR ground displacement time-series through a novel multi-sensor data merging technique: The case study of the Shanghai coastal area. Retrieved May 24, 2026, from https://4ort.xyz/entity/generation-of-long-term-insar-ground-displacement-time-series-through-a-novel-multi-sensor-data-merging-technique-the-ca
MLA“Generation of long-term InSAR ground displacement time-series through a novel multi-sensor data merging technique: The case study of the Shanghai coastal area.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/generation-of-long-term-insar-ground-displacement-time-series-through-a-novel-multi-sensor-data-merging-technique-the-ca.
BibTeX@misc{4ortxyz_generation-of-long-term-insar-ground-displacement-time-series-through-a-novel-multi-sensor-data-merging-technique-the-ca_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{Generation of long-term InSAR ground displacement time-series through a novel multi-sensor data merging technique: The case study of the Shanghai coastal area}}, year = {2026}, url = {https://4ort.xyz/entity/generation-of-long-term-insar-ground-displacement-time-series-through-a-novel-multi-sensor-data-merging-technique-the-ca}, note = {Accessed: 2026-05-24}}
LLM promptAccording to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): Generation of long-term InSAR ground displacement time-series through a novel multi-sensor data merging technique: The case study of the Shanghai coastal area — https://4ort.xyz/entity/generation-of-long-term-insar-ground-displacement-time-series-through-a-novel-multi-sensor-data-merging-technique-the-ca (retrieved 2026-05-24)
