An enhanced model for obtaining at-sensor brightness temperature for UAVs incorporating meteorological features and its application in urban thermal environment

IF 10.5 1区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Sustainable Cities and Society Pub Date : 2024-11-15 DOI:10.1016/j.scs.2024.105987

Rong Jin, Lihua Zhao, Peng Ren, Haitang Wu, Xue Zhong, Mingyi Gao, Zichuan Nie

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引用次数: 0

Abstract

Land surface temperatures (LSTs) play a crucial role in characterizing the outdoor thermal environment. Estimating LSTs relies on brightness temperatures (T_bs) as the raw data source. Despite manufacturers offering a linear DN-T_b model for uncooled thermal infrared (TIR) cameras to convert digital numbers (DNs) into T_bs, discrepancies arise for LST retrieval with Unmanned Aerial Vehicles (UAVs) due to temperature dependence. This study addressed this challenge by initially identifying key environmental parameters through an orthogonal experiment in a climatic chamber. Subsequently, an approach for an enhanced DN-T_b model considering environmental parameters was devised based on UAV campaigns and validated with ground-measured temperatures from various underlying surfaces. The results show that the camera temperature (T_cam) and air temperature (T_a) were identified as the primary parameters impacting the DN-T_b conversion. The enhanced DN-T_b models incorporating T_cam, T_a, or bivariate improved T_b accuracy with errors below 20%, compared to potential errors exceeding 30% in the original model. The root mean square error (RMSE) of the optimal enhanced DN-T_b models decreased from 5.97°C to 1.44°C. Compared with the purely data-derived empirical regression, our models significantly decreased errors and increased robustness, imperative to improve the reliability of evaluating urban thermal environments utilizing a low-cost UAV platform.

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结合气象特征的无人机传感器亮度温度增强模型及其在城市热环境中的应用

陆地表面温度（LST）在描述室外热环境特征方面起着至关重要的作用。估算 LST 依赖于作为原始数据来源的亮度温度 (Tbs)。尽管制造商为非制冷型热红外（TIR）相机提供了一个线性 DN-Tb 模型，可将数字（DN）转换为 Tbs，但由于温度依赖性，无人机（UAV）在检索 LST 时仍存在差异。本研究通过在气候室中进行正交实验，初步确定了关键环境参数，从而解决了这一难题。随后，根据无人飞行器活动设计了一种考虑到环境参数的增强 DN-Tb 模型方法，并用地面测量的各种底层表面温度进行了验证。结果表明，相机温度（Tcam）和空气温度（Ta）是影响 DN-Tb 转换的主要参数。包含 Tcam、Ta 或双变量的增强 DN-Tb 模型提高了 Tb 精度，误差低于 20%，而原始模型的潜在误差超过 30%。最佳增强型 DN-Tb 模型的均方根误差（RMSE）从 5.97°C 降至 1.44°C。与纯粹的数据经验回归相比，我们的模型显著降低了误差并提高了稳健性，这对于提高利用低成本无人机平台评估城市热环境的可靠性势在必行。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Sustainable Cities and Society Social Sciences-Geography, Planning and Development

CiteScore

22.00

自引率

13.70%

发文量

810

审稿时长

27 days

期刊介绍： Sustainable Cities and Society (SCS) is an international journal that focuses on fundamental and applied research to promote environmentally sustainable and socially resilient cities. The journal welcomes cross-cutting, multi-disciplinary research in various areas, including: 1. Smart cities and resilient environments; 2. Alternative/clean energy sources, energy distribution, distributed energy generation, and energy demand reduction/management; 3. Monitoring and improving air quality in built environment and cities (e.g., healthy built environment and air quality management); 4. Energy efficient, low/zero carbon, and green buildings/communities; 5. Climate change mitigation and adaptation in urban environments; 6. Green infrastructure and BMPs; 7. Environmental Footprint accounting and management; 8. Urban agriculture and forestry; 9. ICT, smart grid and intelligent infrastructure; 10. Urban design/planning, regulations, legislation, certification, economics, and policy; 11. Social aspects, impacts and resiliency of cities; 12. Behavior monitoring, analysis and change within urban communities; 13. Health monitoring and improvement; 14. Nexus issues related to sustainable cities and societies; 15. Smart city governance; 16. Decision Support Systems for trade-off and uncertainty analysis for improved management of cities and society; 17. Big data, machine learning, and artificial intelligence applications and case studies; 18. Critical infrastructure protection, including security, privacy, forensics, and reliability issues of cyber-physical systems. 19. Water footprint reduction and urban water distribution, harvesting, treatment, reuse and management; 20. Waste reduction and recycling; 21. Wastewater collection, treatment and recycling; 22. Smart, clean and healthy transportation systems and infrastructure;