Acknowledgment to the Reviewers of Journal of Environmental & Earth Sciences in 2025
2026-02-06
From Signal to Insight: The Role of Communication Systems in the Remote Sensing Data Value Chain
Authors
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Yuanfeng Liu
CAAC Central Southern Airport Design & Research Institute (Guangzhou) Co., Ltd., Guangzhou 510405, China
DOI:
https://doi.org/10.30564/jees.v8i5.13254Abstract
High-resolution sensors, satellites in greater numbers, and autonomous flying platforms have ushered in a new era of massive amounts of data, a diversity of modalities, and a sense of urgency in their application due to remote sensing. Although sensing and analytics advances have been extensively investigated, communication systems are taking the broader role in deciding whether remote sensing information can be provided with adequate fidelity, timeliness, and accessibility to create actionable insight. The review uses communication as a value-making part of the remote sensing data value chain, which connects signal acquisition, data transport, processing architectures, and insight generation in an end-to-end view. We combine significant communication architectures of spaceborne, airborne, and hybrid satellite-terrestrial networks, and explain how physical-, link-, and network-layer constraints are passed downstream to affect preprocessing decisions, quality of data products, and real-time utility. The review also looks at the increased integration of communication and computation based on edge and distributed processing, communication-conscious data reduction, and joint optimization schemes that trade off bandwidth, latency, energy, and analytical goals. Lastly, we point to new trends: integrated non-terrestrial networks, software-defined and intelligent communications, and learning-based adaptation; as well as open security, scalability, and interoperability challenges. This article will facilitate clarity in the trade-off in designs and the research focus of creating communication-conscious remote sensing systems by integrating sensing-centric and communications-centric perspectives that will more efficiently transform signals into dependable time-independent insight.
Keywords:
Remote Sensing; Non-Terrestrial Networks; Edge Computing; Communication–Computation Co-Design; Data Value ChainReferences
[1] Dash, J., Ogutu, B.O., 2016. Recent advances in space-borne optical remote sensing systems for monitoring global terrestrial ecosystems. Progress in Physical Geography. 40(2), 322–351.
[2] Chuvieco, E., 2008. Earth Observation of Global Change: The Role of Satellite Remote Sensing in Monitoring the Global Environment. Springer: Dordrecht, The Netherlands.
[3] Dalla Mura, M., Prasad, S., Pacifici, E., et al., 2015. Challenges and opportunities of multimodality and data fusion in remote sensing. Proceedings of the IEEE. 103(9), 1585–1601.
[4] Toth, C., Jóźków, G., 2016. Remote sensing platforms and sensors: A survey. ISPRS: International Society for Journal of Photogrammetry and Remote Sensing. 115, 22–36.
[5] Barsi, Á., Kugler, Z., Juhász, A., et al., 2019. Remote sensing data quality model: From data sources to lifecycle phases. International Journal of Image and Data Fusion. 10(4), 280–299.
[6] Olla, P., Patel, N.V., 2002. A value chain model for mobile data service providers. Telecommunications Policy. 26(9–10), 551–571.
[7] Ma, Z., Xiao, M., Xiao, Y., et al., 2019. High-reliability and low-latency wireless communication for internet of things: Challenges, fundamentals, and enabling technologies. IEEE Internet of Things Journal. 6(5), 7946–7970.
[8] Burleigh, S.C., Cola, T.D., Morosi, S., et al., 2019. From connectivity to advanced internet services: A comprehensive review of small satellites communications and networks. Wireless Communications and Mobile Computing. 2019(1), 6243505.
[9] Wang, P., Zhang, J., Zhang, X., et al., 2019. Convergence of satellite and terrestrial networks: A comprehensive survey. IEEE Access. 8, 5550–5588.
[10] Zhang, P., Qin, Q., Zhang, S., et al., 2024. Near real-time remote sensing based on satellite internet: Architectures, key techniques, and experimental progress. Aerospace. 11(2), 167.
[11] Zhang, B., Wu, Y., Zhao, B., et al., 2022. Progress and challenges in intelligent remote sensing satellite systems. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 15, 1814–1822.
[12] Feng, D., Lai, L., Luo, J., et al., 2021. Ultra-reliable and low-latency communications: Applications, opportunities and challenges. Science China Information Sciences. 64(2), 120301.
[13] Hui, M., Zhai, S., Wang, D., et al., 2025. A review of LEO satellite communication payloads for integrated communication, navigation, and remote sensing: Opportunities, challenges, future directions. IEEE Internet of Things Journal. 12 (12), 18954–18992.
[14] Drake, D.A., Quist, G.M., 2007. Remote sensing and communication system. US7292143B2. 16 November 2007.
[15] Siddiqi, A., Baber, S., de Weck, O., et al., 2020. Error and uncertainty in earth observation value chains. In Proceedings of IGARSS 2020 IEEE International Geoscience and Remote Sensing Symposium, Waikoloa, HI, USA, 26 September–2 October 2020; pp. 3158–3161.
[16] Kumar, G.K., Sankar, P., 2025. Artificial intelligence powered satellite communications and Sentinel satellite constellations: An overview and future perspectives. SN Computer Science. 6(6), 735.
[17] Zhao, G., Imran, M.A., Pang, Z., et al., 2018. Toward real-time control in future wireless networks: Communication-control co-design. IEEE Communications Magazine. 57(2), 138–144.
[18] Sabins Jr., F.F., Ellis, J.M., 2020. Remote Sensing: Principles, Interpretation, and Applications, 4th ed. Waveland Press: Lake Zurich, IL, USA.
[19] Chi, M., Plaza, A. Benediktsson, J.A., et al., 2016. Big data for remote sensing: Challenges and opportunities. Proceedings of the IEEE. 104(11), 2207–2219.
[20] Khanal, S., Kushal, K.C., Fulton, J. P., et al., 2020. Remote sensing in agriculture—Accomplishments, limitations, and opportunities. Remote Sensing. 12(22), 3783.
[21] Navalgund, R., Jayaraman, V., Kumar, A.S.K., et al., 1996. Remote sensing data acquisition, platforms and sensor requirements. Journal of the Indian Society of Remote Sensing. 24(4), 207–237.
[22] Sapsanis, C., Sophocleous, M.A., Andreou, A.G., et al., 2022. Trade-offs in sensor systems design: A tutorial. IEEE Sensors Journal. 22(11), 10040–10061.
[23] Milani, L., 2019. Atmospheric Remote Sensing and Radiopropagation: From Numerical Modeling to Spaceborne and Terrestrial Applications [PhD Thesis]. Università degli Studi di Roma La Sapienza: Rome, Italy.
[24] Lee, C.A., Gasster, S.D., Plaza, A., et al., 2011. Recent developments in high performance computing for remote sensing: A review. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 4(3), 508–527.
[25] Patanè, G., Spagnuolo, M. (Eds.), 2016. Heterogeneous Spatial Data: Fusion, Modeling, and Analysis for GIS Applications. Morgan & Claypool Publishers: Cham, Switzerland.
[26] Al Mamun, M., Jia, X., Ryan, M., 2009. Adaptive data compression for efficient sequential transmission and change updating of remote sensing images. In Proceedings of 2009 IEEE International Geoscience and Remote Sensing Symposium, Cape Town, South Africa, 12–17 July 2009.
[27] Zhu, C., Shu, L., Hara, T., et al., 2014. A survey on communication and data management issues in mobile sensor networks. Wireless Communications and Mobile Computing. 14(1), 19–36.
[28] Dai, H.-N., Wong, R.C.-W., Wang, H., et al., 2019. Big data analytics for large-scale wireless networks: Challenges and opportunities. ACM Computing Surveys. 52(5), 1–36.
[29] Zhang, Z., Zhu, L., 2023. A review on unmanned aerial vehicle remote sensing: Platforms, sensors, data processing methods, and applications. Drones. 7(6), 398.
[30] Liang, S., Wang, J. (Eds.), 2019. Advanced Remote Sensing: Terrestrial Information Extraction and Applications. Academic Press: Cambridge, MA, USA.
[31] Ehala, J., Kaugerand J, Pahtma R, et al., 2017. Situation awareness via Internet of things and in-network data processing. International Journal of Distributed Sensor Networks. 13(1), 95–100. DOI: https://doi.org/10.1177/1550147716686578
[32] Zhao, F., Guibas, L.J., 2004. Wireless Sensor Networks: An Information Processing Approach. Morgan Kaufmann: San Francisco, CA, USA.
[33] Xu, Y., Chen, X., Ying, M., et al., 2025. Integrated communication and remote sensing in LEO satellite systems: Protocol, architecture and prototype. IEEE Transactions on Wireless Communications. 25, 1609–1623.
[34] Davarian, F., Asmar, S., Angert, M., et al., 2020. Improving small satellite communications and tracking in deep space—A review of the existing systems and technologies with recommendations for improvement. Part II: Small Satellite Navigation, Proximity Links, and Communications Link Science. IEEE Aerospace and Electronic Systems Magazine. 35(7), 26–40.
[35] Jawhar, I., Mohamed, N., Al-Jaroodi, J., et al., 2017. Communication and networking of UAV-based systems: Classification and associated architectures. Journal of Network and Computer Applications. 84, 93–108.
[36] Ippolito Jr., L.J., 2017. Satellite Communications Systems Engineering: Atmospheric Effects, Satellite Link Design and System Performance. John Wiley & Sons: Chichester, UK.
[37] Caponi, L., Chiti, F., Fantacci, R., 2007. Performance evaluation of a link adaptation technique for high speed wireless communication systems. IEEE Transactions on Wireless Communications. 6(12), 4568–4575.
[38] Dhabliya, D., Soundararajan, R., Selvarasu, P., et al., 2022. Energy-efficient network protocols and resilient data transmission schemes for wireless sensor networks—An experimental survey. Energies. 15(23), 8883.
[39] Vijay, Siddiqui, S.T., Ritu, et al., 2022. Intertwine connection‐based routing path selection for data transmission in mobile cellular networks and wireless sensor networks. Wireless Communications and Mobile Computing. 2022(1), 8398128.
[40] Yang, J., Li, D., Jiang, X., et al., 2020. Enhancing the resilience of low earth orbit remote sensing satellite networks. IEEE Network. 34(4), 304–311.
[41] Al Mamun, M.A., Li, M., Pramanik, B.K., 2024. Development of delay-tolerant networking protocols for reliable data transmission in space networks: A simulation-based approach. IEEE Access. 12, 36677.
[42] Ganesan, D., Estrin, D., Heidemann, J., 2003. Dimensions: Why do we need a new data handling architecture for sensor networks? ACM Sigcomm Computer Communication Review. 33(1), 143–148.
[43] Rohith, G., Sutha, G.L., 2022. Super-Resolution for Remote Sensing Applications Using Deep Learning Techniques. Cambridge Scholars Publishing: Necastle upon Tyne, UK.
[44] Dritsas, E., Trigka, M., 2025. Remote sensing and geospatial analysis in the big data era: A survey. Remote Sensing. 17(3), 550.
[45] Ahmad, R., 2024. Smart remote sensing network for disaster management: An overview. Telecommunication Systems. 87(1), 213–237.
[46] Shen, C.-C., Srisathapornphat, C., Jaikaeo, C., 2001. Sensor information networking architecture and applications. IEEE Personal Communications. 8(4), 52–59.
[47] Lippitt, C.D., Stow, D.A., Riggan, P.J., 2016. Application of the remote-sensing communication model to a time-sensitive wildfire remote-sensing system. International Journal of Remote Sensing. 37(14), 3272–3292.
[48] Franconi, N., Sabogal, S., George, A., et al., 2020. A novel RF Architecture for Simultaneous Communication, Navigation, and Remote Sensing with Software-Defined Radio. In Proceedings of the 34th Annual Conference on Small Satellites, Logan, UT, USA, 3–8 August 2020.
[49] Wu, Z., Sun, J., Zhang, Y., et al., 2021. Recent developments in parallel and distributed computing for remotely sensed big data processing. Proceedings of the IEEE. 109(8), 1282–1305.
[50] Zhang, Q., Xu, L., Huang, J., et al., 2025. Distributed satellite information networks: Architecture, enabling technologies, and trends. Science China Information Sciences. 68(9), 190301.
[51] Yang, A., Liu, J., Yang, B., et al., 2025. Balancing communication overhead and accuracy in compression integration: A survey. The Journal of Supercomputing. 81(8), 964.
[52] Ballotta, L., Schenato, L., Carlone, L., 2020. Computation-communication trade-offs and sensor selection in real-time estimation for processing networks. IEEE Transactions on Network Science and Engineering. 7(4), 2952–2965.
[53] Park, J., Samarakoon, S., Elgabli, A., et al., 2021. Communication-efficient and distributed learning over wireless networks: Principles and applications. Proceedings of the IEEE. 109(5), 796–819.
[54] Fortino, G., Galzarano, S., Gravina, R., et al., 2015. A framework for collaborative computing and multi-sensor data fusion in body sensor networks. Information Fusion. 22, 50–70.
[55] He, Y., Yu, G., Luo, H., et al., 2023. Integrated sensing, computation, and communication: System framework and performance optimization. IEEE Transactions on Wireless Communications. 23(2), 1114–1128.
[56] Kibria, M.G., Nguyen, K., Villardi, G.P., et al., 2018. Big data analytics, machine learning, and artificial intelligence in next-generation wireless networks. IEEE Access. 6, 32328–32338.
[57] Ahmad, I., Shahabuddin, S., Malik, H., et al., 2020. Machine learning meets communication networks: Current trends and future challenges. IEEE Access. 8, 223418–223460.
[58] Iqbal, R., Doctor, F., More, B., et al., 2020. Big data analytics and computational intelligence for cyber–physical systems: Recent trends and state of the art applications. Future Generation Computer Systems. 105, 766–778.
[59] Matera, F., Settembre, M., Rago, A., et al., 2024. Terrestrial and non-terrestrial networks for integrated sensing and communication. In Proceedings of 2024 IEEE International Symposium on Systems Engineering (ISSE), Perugia, Italy, 16–19 October 2024; pp. 1–5.
[60] Geraci, G., López-Pérez, D., Benzaghta, M., et al., 2022. Integrating terrestrial and non-terrestrial networks: 3D opportunities and challenges. IEEE Communications Magazine. 61(4), 42–48.
[61] Xu, S., Wang, X.-W., Huang, M., 2018. Software-defined next-generation satellite networks: Architecture, challenges, and solutions. IEEE Access. 6, 4027–4041.
[62] Baillieul, J., Antsaklis, P.J., 2007. Control and communication challenges in networked real-time systems. Proceedings of the IEEE. 95(1), 9–28.
[63] Zhang, D., Ren, L., Shafiq, M., et al., 2022. A lightweight privacy-preserving system for the security of remote sensing images on IoT. Remote Sensing. 14(24), 6371.
[64] Kodheli, O., Lagunas, E., Maturo, N., et al., 2020. Satellite communications in the new space era: A survey and future challenges. IEEE Communications Surveys & Tutorials. 23(1), 70–109.
[65] Koufos, K., EI Haloui, K., Dianati, M., et al., 2021. Trends in intelligent communication systems: Review of standards, major research projects, and identification of research gaps. Journal of Sensor and Actuator Networks. 10(4), 60.
[66] Wang, J., Varshney, N., Gentile, C., et al., 2022. Integrated sensing and communication: Enabling techniques, applications, tools and data sets, standardization, and future directions. IEEE Internet of Things Journal. 9(23), 23416–23440.
[67] Rathore, M.M.U., Paul, A., Ahmad, A., et al., 2015. Real-time big data analytical architecture for remote sensing application. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 8(10), 4610–4621.
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How to Cite
Liu, Y. (2026). From Signal to Insight: The Role of Communication Systems in the Remote Sensing Data Value Chain. Journal of Environmental & Earth Sciences, 8(5), 154–168. https://doi.org/10.30564/jees.v8i5.13254
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Copyright © 2026 Yuanfeng Liu
This is an open access article under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) License.
