Aerosol-cloud layer detection algorithm of the DQ-1/ACDL

IF 11.4 1区地球科学 Q1 ENVIRONMENTAL SCIENCES

Remote Sensing of Environment Pub Date : 2025-10-15 DOI:10.1016/j.rse.2025.115068

Feiyue Mao , Weiwei Xu , Zengxin Pan , Lin Zang , Ge Han , Linxin Dai , Xiuqing Hu , Weibiao Chen , Wei Gong

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Abstract

Satellite lidar plays a unique role in observing the global vertical distribution of aerosols and clouds. CALIPSO (Apr 2006–Aug 2023) pioneered such observations, and China's Aerosol and Carbon Detection Lidar (ACDL) on board the DQ-1 satellite (Apr 2022-) continues this mission. Consequently, it is crucial to develop aerosol and cloud products of ACDL. Particularly, detecting the vertical and horizontal extent of aerosol and cloud layers is one of the most challenging tasks. In this study, we developed an ACDL layer detection algorithm based on the Two-Dimensional Multiscale Hypothesis Testing (2D-MHT) methodology. Notably, we proposed an approach for the uncertainty estimation in lidar return signals from the background atmosphere, enabling successful layer detection for ACDL. The results demonstrate that our algorithm not only accurately identifies layers within ACDL measurements, but also provides the probability that a specific signal bin belongs to a layer. This probability enables users to customize layer definitions, a feature not available in other lidar products that typically rely on threshold-based methods. Furthermore, the ACDL layer products offer higher horizontal resolution and detect 53.0 % more layers globally compared to the CALIPSO V4.51 merged layer product in June 2022. These findings underscore the significant potential of our algorithm and ACDL layer products for advancing atmospheric and climate research.

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DQ-1/ACDL的气溶胶-云层检测算法

卫星激光雷达在观测全球气溶胶和云的垂直分布方面起着独特的作用。CALIPSO（2006年4月- 2023年8月）是此类观测的先驱，中国DQ-1卫星上的气溶胶和碳探测激光雷达（ACDL）（2022年4月-）继续这项任务。因此，开发ACDL的气溶胶和云产品至关重要。特别是，探测气溶胶和云层的垂直和水平范围是最具挑战性的任务之一。在本研究中，我们开发了一种基于二维多尺度假设检验（2D-MHT）方法的ACDL层检测算法。值得注意的是，我们提出了一种基于背景大气的激光雷达返回信号的不确定性估计方法，使ACDL的层探测成功。结果表明，该算法不仅可以准确地识别ACDL测量中的层，而且可以提供特定信号盒属于某一层的概率。这种可能性使用户能够自定义层定义，这是其他通常依赖于基于阈值方法的激光雷达产品所不具备的功能。此外，与2022年6月发布的CALIPSO V4.51合并层产品相比，ACDL层产品提供了更高的水平分辨率，在全球范围内检测的层数增加了53.0%。这些发现强调了我们的算法和ACDL层产品在推进大气和气候研究方面的巨大潜力。

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.