利用SDGSAT-1卫星研究极地冰雪表面温度的方法及其潜在应用

IF 11.4 1区地球科学 Q1 ENVIRONMENTAL SCIENCES

Remote Sensing of Environment Pub Date : 2025-06-12 DOI:10.1016/j.rse.2025.114868

Chenlie Shi , Ninglian Wang , Yuwei Wu , Quan Zhang , Zhenxiang Fang

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引用次数: 0

摘要

高空间分辨率的冰/雪表面温度（IST）数据为极地研究提供了突出的优势，如海冰铅的识别、冰架表面融化和冰融区变化的监测。作为第一颗致力于可持续发展目标的卫星，SDGSAT-1配备了30米热红外波段，在极地地区的精细尺度过程监测中具有很大的前景。本文从10种广泛使用的分窗算法（Split-Window Algorithms, SWAs）中选择了SDGSAT-1的最优IST检索算法，重点考虑对发射率的低灵敏度和高绝对检索精度两个关键标准。灵敏度分析发现，4个swa （PR1984、VI1991、UL1994和Enter2019）对发射率和传感器等效噪声的灵敏度较低，因此可以进行后续验证。模拟结果表明，4个SWAs的总体不确定度均小于0.2 K，其中PR1984的冷偏较小，为- 0.16 K。原位IST数据验证表明，4个SWAs的总体不确定度小于1.7 K，偏差约为- 1 K， PR1984的偏差和RMSE较大。4种SWAs之间的相互比较以及与MODIS IST的交叉验证也表明，PR1984与其他3种算法相比存在冷偏差，而VI1991、UL1994和Enter2019的准确性相似。考虑到Enter2019算法稳定性好，对地表发射率敏感性低，IST检索精度高，是VIIRS传感器的官方地表温度检索算法，应用广泛且得到认可，本研究推荐Enter2019作为SDGSAT-1的最佳IST检索算法。此外，通过海冰引线识别、冰谷监测和地热泉提取3个代表性应用案例，验证了SDGSAT-1热红外数据在极地冰雪面精细监测中的应用能力。

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Methodology and potential applications of ice/snow surface temperature over polar regions using SDGSAT-1 satellite

High spatial resolution Ice/snow Surface Temperature (IST) data provides prominent advantages for polar research, such as identification of sea ice lead, monitoring of surface melting on ice shelves and variations of polynyas. As the first satellite dedicated to sustainable development goals, SDGSAT-1 is equipped with 30 m thermal infrared bands, making it highly promising for monitoring fine scale process in polar regions. In this study, an optimal IST retrieval algorithm for SDGSAT-1 was selected from ten widely used Split-Window Algorithms (SWAs), with emphasis on two key criteria: low sensitivity to emissivity and high absolute retrieval accuracy. Sensitivity analysis identified four SWAs (PR1984, VI1991, UL1994, and Enter2019) exhibited low sensitivity to emissivity and sensor equivalent noise, and thereby for subsequent validation. Evaluation using simulated data showed that the overall uncertainty of four SWAs was less than 0.2 K, with PR1984 exhibiting a slight cold Bias of −0.16 K compared to the other three SWAs. Validation using in-situ IST data indicated that the overall uncertainty for four SWAs was less than 1.7 K, with a Bias of approximately −1 K, and PR1984 showed larger Bias and RMSE. Intercomparisons among the four SWAs and cross-validation with MODIS IST also demonstrated that PR1984 had a cold Bias compared to the other three algorithms, while VI1991, UL1994, and Enter2019 showed similar accuracy. Considering that Enter2019 has stability and low sensitivity to surface emissivity, high IST retrieval accuracy, and is widely applied and well recognized as the official land surface temperature retrieval algorithm for the VIIRS sensor, this study recommends Enter2019 as the optimal IST retrieval algorithm for SDGSAT-1. Additionally, three representative application cases—identification of sea ice leads, polynya monitoring, and extraction of geothermal springs, demonstrated the application capacity of SDGSAT-1 thermal infrared data in refined monitoring of polar ice/snow surface.

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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.