Balancing accuracy and feasibility in diurnal temperature modeling: A comparison of data-driven and physical-based models using geostationary satellite observations

IF 11.1 1区地球科学 Q1 ENVIRONMENTAL SCIENCES

Remote Sensing of Environment Pub Date : 2025-07-21 DOI:10.1016/j.rse.2025.114902

Nur Fajar Trihantoro, Karin J. Reinke, Simon D. Jones

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引用次数: 0

Abstract

The derivation of Diurnal Temperature Cycle (DTC) models from geostationary satellite data plays a critical role in temperature monitoring of the landscape and thermal anomaly applications such as wildfire detection. This study compares the performance of physical-based and data-driven DTC models on 1,305 study sites across Australia, leveraging Himawari-8 AHI middle-infrared (MIR) band 7 data. The physical-based model, GOT09 (based on Göttsche and Olesen study), achieved the highest accuracy, with a mean validation Root Mean Square Error (RMSE) of 2.41 K, but its practical application was limited by a lower model generation rate (48.77%), especially under high cloud cover conditions. Among data-driven methods, the proposed TRI model (named after the first author) balances accuracy and practical feasibility, achieving a validation RMSE of 3.62 K and a generation rate of 85.07%. The TRI model consistently generated reliable DTCs under various environmental conditions, including high cloud cover, outperforming alternative data-driven models such as RW (from Roberts-Wooster study), XIE (from Xie et al. study), and HAL (from Hally et al. study). Additionally, the TRI model maintained reliability across diverse land cover and climate types, showing only minimal variations in performance. The study further highlights strategies for addressing cloud and data availability challenges, proposing methods such as the use of previous day’s DTC or adjusting training data criteria in cloudy conditions. These approaches ensure a continuous temperature background where continuity of measurements is required, such as for wildfire detection. Overall, the research underscores the importance of balancing accuracy and model generation rates in DTC modeling, particularly for real-time applications. Future work could explore hybrid models and additional factors to further improve performance.

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平衡日温度模式的准确性和可行性：利用地球静止卫星观测数据驱动模式和基于物理模式的比较

基于地球同步卫星数据的日温度循环模型在景观温度监测和火灾探测等热异常应用中起着至关重要的作用。本研究利用Himawari-8 AHI中红外（MIR）波段7数据，在澳大利亚1305个研究地点比较了基于物理和数据驱动的DTC模型的性能。基于物理的模型GOT09（基于Göttsche和Olesen的研究）获得了最高的精度，平均验证均方根误差（RMSE）为2.41 K，但其实际应用受到较低的模型生成率（48.77%）的限制，特别是在高云量条件下。在数据驱动方法中，本文提出的TRI模型（以第一作者命名）平衡了准确性和实际可行性，验证RMSE为3.62 K，生成率为85.07%。TRI模型在各种环境条件下始终生成可靠的dtc，包括高云量，优于RW（来自robert - wooster研究）、XIE（来自XIE等人的研究）和HAL（来自haly等人的研究）等替代数据驱动模型。此外，TRI模型在不同的土地覆盖和气候类型中保持了可靠性，仅显示出最小的性能变化。该研究进一步强调了应对云和数据可用性挑战的策略，提出了诸如使用前一天的DTC或在多云条件下调整训练数据标准等方法。这些方法确保了需要连续测量的连续温度背景，例如野火探测。总的来说，研究强调了在DTC建模中平衡精度和模型生成速率的重要性，特别是在实时应用中。未来的工作可能会探索混合模型和其他因素，以进一步提高性能。

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

Remote Sensing of Environment 环境科学-成像科学与照相技术

CiteScore

25.10

自引率

8.90%

发文量

455

审稿时长

53 days

期刊介绍： Remote Sensing of Environment (RSE) serves the Earth observation community by disseminating results on the theory, science, applications, and technology that contribute to advancing the field of remote sensing. With a thoroughly interdisciplinary approach, RSE encompasses terrestrial, oceanic, and atmospheric sensing. The journal emphasizes biophysical and quantitative approaches to remote sensing at local to global scales, covering a diverse range of applications and techniques. RSE serves as a vital platform for the exchange of knowledge and advancements in the dynamic field of remote sensing.