Satellite Sensing in Climate Change Study: A Review

Year : 2025 | Volume : 14 | Issue : 02 | Page : 1-11
    By

    K Kazi,

  • Milind Shivaji Kadam,

  1. Professor, Department of Mechanical Engineering, Brahmdevdada Mane Institute of Technology, Solapur, Maharashtra, India
  2. Assistant Professor, Department of Mechanical Engineering, Brahmdevdada Mane Institute of Technology, Solapur, Maharashtra, India

Abstract

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Satellite remote sensing has revolutionized our understanding of climate change, providing a global and continuous perspective on Earth’s climate system. This study highlights the crucial role of satellite observations in monitoring key climate variables, such as sea surface temperature, ice sheet extent, vegetation cover, and atmospheric composition. From monitoring greenhouse gas concentrations in the atmosphere to mapping ice sheet thickness and sea level rise, satellite instruments offer invaluable data for tracking climate trends and validating climate models. By leveraging a diverse array of sensors and platforms, satellite data enables the detection of long-term trends, assessment of climate feedback mechanisms, and evaluation of climate model performance. Crucially, satellite observations underpin the development of effective climate mitigation and adaptation strategies by informing policy decisions and supporting sustainable resource management. Satellite remote sensing has become a vital tool for tracking and comprehending climate change, especially in hard-to-reach places like dense jungles and polar regions. Monitoring changes in Earth’s frozen zones has been made possible in large part by satellites such as NASA’s ICESat-2 and ESA’s CryoSat. Significant ice loss in Greenland and Antarctica has been revealed by CryoSat, which uses radar wave reflections to determine ice thickness and contribute to sea level rise worldwide. ICESat-2 improves our knowledge of ice sheet dynamics by using laser altimetry to identify subtle variations in ice height. These findings are essential to comprehending how sea level rise and arctic ecosystems are affected by global warming. Satellites outside of the polar regions track vegetation health, land-use changes, and deforestation in dense rainforests, giving vital information for monitoring carbon emissions and biodiversity loss. To estimate agricultural output and manage food security in the face of changing climate circumstances, remote sensing technologies are used in agriculture to evaluate land-use patterns, crop health, and soil moisture levels. Real-time information on environmental changes is provided via satellite-derived data, which aids in disaster management and urban planning. This information helps policymakers create resilience and climate adaptation plans, especially in areas that are at risk. NASA’s Earth observation data, for example, helps us to understand the effects of climate change and guides adaptation and mitigation plans.

Keywords: Satellite, sensing, climate change, environmental study, CryoSat

[This article belongs to Research & Reviews : Journal of Space Science & Technology ]

How to cite this article:
K Kazi, Milind Shivaji Kadam. Satellite Sensing in Climate Change Study: A Review. Research & Reviews : Journal of Space Science & Technology. 2025; 14(02):1-11.
How to cite this URL:
K Kazi, Milind Shivaji Kadam. Satellite Sensing in Climate Change Study: A Review. Research & Reviews : Journal of Space Science & Technology. 2025; 14(02):1-11. Available from: https://journals.stmjournals.com/rrjosst/article=2025/view=0


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References

  1. Kazi K, Gaikwad A, Chendke A, Mulani N, Sarika M. Submersible pump theft indicator. Int Eng J Res Dev. 2020 Apr;5(4):1–5. DOI: 10.17605/OSF.IO/4N5ZJ5(4): 5.
  2. Thies B, Bendix J. Satellite based remote sensing of weather and climate: Recent achievements and future perspectives. Meteorol Appl. 2011;18:262–95. doi:10.1002/met
  3. Mulani AO, Patil RM, Liyakat KK. Discriminative appearance model for robust online multiple target tracking. Telematique. 2023 Jan 1; 22(1): 24–43.
  4. Hotkar PR, Kulkarni V, Kamble P, Kazi KS. Implementation of low power and area efficient carry select adder. Int J Res Eng Sci Manag. 2019; 2(4): 183–4.
  5. Digambar KN, Subhash JS, Ganpati PC, Heena MS, Rafiq DK. Design of vehicle system using CAN protocol. Int J Res Appl Sci Eng Technol. 2020; 8(5): 1979–84.
  6. Liyakat KK. Lessar methodology for network intrusion detection. Sch Res J Humanit Sci Engl Lang. 2017; 4(24): 6853–61.
  7. Argonda UA. Review paper for design and simulation of a patch antenna by using HFSS. Int J Trends Sci Res Dev. 2018; 2(2): 158–60.
  8. Kumar S, Rai M, Kumar R, Ghosh J. Analysis and design of capacitive coupled wideband microstrip antenna in C and X band. Int J Eng Adv Technol. 2013 Apr; 2(4): 836–840.
  9. Liyakat KK. Model for agricultural information system to improve crop yield using IoT. J Open Source Dev. 2022; 9(2): 16–24.
  10. Shirdale MY, Kazi K, Kazi K. Coplanar capacitive coupled probe fed microstrip antenna for C and X band. Int J Adv Res Comput Commun Eng. 2016; 5(4): 661–3.
  11. Aavula R, Deshmukh A, Mane VA, Chavhan GH, Liyakat KK. Design and implementation of sensor and IoT-based remembrance system for closed one. Telematique. 2022; 21(1): 2769–78.
  12. Nikita S, Sanika C, Nagveni K, Sakshi K, Kazi KS. Announcement system in bus. J Image Process Intell Remote Sens. 2022 Oct; 2(6): 1–10.
  13. Kamuni MS, Devasani TP, Nalla LR, Liyakat KK. Fruit quality detection using thermometer. J Image Process Intell Remote Sens. 2022 Sep; 2(5): 2292–311.
  14. Kumtole S, Suryawanshi P, Pawar R, Sayyed A, Liyakat KK. Automatic wall painting robot. J Image Process Intell Remote Sens. 2018 Jan;102022; 2(6): 11–22.
  15. Akansha K. Email security. J Image Process Intell Remote Sens. 2022 Oct; 2(6): 295–320.
  16. Kapse MM, Patel NR, Narayankar SK, Malvekar SA, Liyakat KK. Smart grid technology. Int J Inf Technol Comput Eng. 2022 Oct; 2(6): 10–7.
  17. Vaijnath SP, Siddheshwar MP, Pradip MS, Liayakat DK. Smart safety device for women. Int J Aquat Sci. 2022 Jan 1; 13(1): 556–60.
  18. Maithili W. Smart watch system. Int J Inf Technol Comput Eng (IJITC). 2022; 2(6): 1–9.
  19. Swami D, Thamake SS, Ubale NS, Lokhande PV, Liyakat KK. Sending notification to someone missing you through smart watch. Int J Inf Technol Comput Eng. 2022 Sep; 2(8): 19–24.
  20. Liyakat KK, Warhade NS, Pol RS, Jadhav HM, Mulani AO. Yarn quality detection for textile industries using image processing. J Algebraic Stat. 2022 Jul 26; 13(3): 3465–72.
  21. Pol RS, Deshmukh AB, Jadhav MM, Liyakat KK, Mulani AO. iButton-based physical access authorization and security system. J Algebraic Stat. 2022 Aug 3; 13(3): 3822–9.
  22. Liyakat KK, Mane VA, Paradeshi KP, Kadam DB, Pandyaji KK. Development of pose invariant face recognition method based on PCA and artificial neural network. J Algebraic Stat. 2022 Jul 31; 13(3): 3676–84.
  23. Kutubuddin DK. Development of machine learning-based epileptic seizure prediction using Web of Things (WoT). Neuro Quantology. 2022; 20(8): 9394–409.
  24. Kadam BD, et al. Implementation of carry select adder (CSLA) for area, delay and power minimization. Telematique. 2022; 21(1): 5461–74.
  25. Kazi KSL. IoT-based weather prototype using WeMos. J Control Instrum Eng. 2023; 9(1): 10–22.
  26. Kosgiker GM. Machine learning-based system, food quality inspection and grading in food industry. Int J Food Nutr Sci. 2018; 11(10): 723–30.
  27. Vahida, et al. Deep learning, YOLO and RFID based smart billing handcart. J Commun Eng Syst. 2023; 13(1): 1–8.
  28. Mamdyal M, Sandupatia M, et al. GPS tracking system. Int J Adv Res Sci Commun Technol. 2022; 2(1): 2492–529. Available from: https://ijarsct.co.in/A7317.pdf
  29. Patil RM. Modelo de apariencia discriminatorio para un sólido seguimiento en línea de múltiples objetivos. Telematique. 2023; 22(1): 24–43.
  30. Karale AA, et al. Smart billing cart using RFID, YOLO and deep learning for mall administration. Int J Instrum Innov Sci. 2023; 8(2): 1–9.
  31. Liyakat KSL. Arduino-based weather monitoring system. J Switching Hub. 2023; 8(3): 24–9.
  32. Gund VD, et al. PIR sensor-based Arduino home security system. J Instrum Innov Sci. 2023; 8(3): 33–7.
  33. Liyakat SS. IoT-based alcohol detector using Blynk. J Electron Des Technol. 2024; 1(1): 10–15.
  34. Kazi K. Complications with malware identification in IoT and an overview of artificial immune approaches. Res Rev J Immunol. 2024; 14(1): 54–62.
  35. Liyakat KKS. Nanotechnology in battlefield: A study. J Nanosci Nanoeng Appl. 2024; 14(2): 18–30.
  36. Kosgiker GM. Satellite sensing for sea level monitoring: A transformative approach to understanding climate change. J Microw Eng Technol. 2025; 12(1): 33–41.
  37. Liyakat KKS. e-Skin applications in healthcare and robotics: A study. J Adv Robot. 2025; 12(1): 13–21.
  38. Liyakat KKS. TensorFlow-based big data analytics for IoT networks: A study. Int J Data Struct Stud. 2025; 3(1): 31–8.
  39. Liyakat KKS. VHDL programming for secure true random number generators in IoT security. Res Rev Electron Commun Eng. 2025 Mar; 2(1): 38–47.
  40. Patil JM, Velapure AS, Khadake SB. The intersection of nanotechnology and IoT: New era of connectivity. Int J Appl Nanotechnol. 2025; 11(1): 9–17.
  41. Tamboli DA, Sawant VA, MH M, Sathe S. AI-driven-IoT (AIIoT) based decision-making – KSK approach in drones for climate change study. Proc 4th Int Conf Ubiquitous Comput Intell Inf Syst (ICUIS); ). 2024. p.; 1735–44. doi:10.1109/ICUIS64676.2024.10866450.
  42. Kazi K. Modelling and simulation of electric vehicle for performance analysis: BEV and HEV electrical vehicle implementation using Simulink for e-mobility ecosystems. In: Lakshmi D, Nagpal N, Kassarwani N, Varthanan G, Siano P, editors. E-Mobility in Electrical Energy Systems for Sustainability. Hershey (PA): IGI Global Scientific Publishing; 2024. p. 295–320. Available from: https://doi.org/10.4018/979-8-3693-2611-4.ch014
  43. Kazi KSL. KK approach to increase resilience in Internet of Things: a T-cell security concept. In: Almaiah MA, Salloum S, editors. Cryptography, biometrics, and anonymity in cybersecurity management. Hershey (PA): IGI Global Scientific Publishing; 2025. p. 199–228. Available from: https://doi.org/10.4018/979-8-3693-8014-7.ch010.
  44. Kazi KSL. Hydrogen energy: adaptation and challenges. In: Mabrouki J, editor. Obstacles facing hydrogen green systems and green energy. Hershey (PA): IGI Global Scientific Publishing; 2025. p. 205–36. Available from: https://doi.org/10.4018/979-8-3693-8980-5.ch013.
  45. Kazi KSL. IoT technologies for the intelligent dairy industry: A new challenge. In: Thandekkattu SG, Vajjhala NR, editors. Designing Sustainable Internet of Things Solutions for Smart Industries. Hershey (PA): IGI Global; 2024. p. 321–50. doi:10.4018/979-8-3693-5498-8.ch012.
  46. Odnala S, Shanthy R, Bharathi B, Pandey C, Rachapalli A, Liyakat KKS. Artificial intelligence and cloud-enabled e-vehicle design with wireless sensor integration. SSRN Electron J. 2025. https://doi.org/10.2139/ssrn.5107242
  47. Neeraja P, Kumar RG, Kumar MS, Liyakat KKS, Vani MS. DL-based somnolence detection for improved driver safety and alertness monitoring. Proc IEEE Int Conf Comput Power Commun Technol (IC2PCT); ). 2024. p.; 589–94. doi:10.1109/IC2PCT60090.2024.10486714. Available from: https://ieeexplore.ieee.org/document/10486714
  48. Nerkar PM, Dhaware BU. Predictive data analytics framework based on heart healthcare system (HHS) using machine learning. J Adv Zool. 2023; 44(Spl-2): 3673–86. Available from: https://jazindia.com/index.php/jaz/article/view/1695
  49. Khadake SB, Chounde AB, Suryagan AA, MH M, Khadatare MR. AI-driven-IoT (AIIoT) based decision making system for high-blood pressure patient healthcare monitoring. Proc Int Conf Sustain Commun Netw Appl (ICSCNA); ). 2024. p.; 96–102. doi:10.1109/ICSCNA63714.2024.10863954
  50. Kazi KSL. KK approach to increase resilience in Internet of Things: A T-cell security concept. In: Darwish D, Charan K, editors. Analyzing Privacy and Security Difficulties in Social Media: New Challenges and Solutions. Hershey (PA): IGI Global; 2024. p. 87–120. doi:10.4018/979-8-3693-9491-5.ch005.

Regular Issue Subscription Review Article
Volume 14
Issue 02
Received 26/05/2025
Accepted 28/05/2025
Published 21/06/2025
Publication Time 26 Days

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