通过将物理约束条件与深度学习框架相结合来估算 AVHRR 雪盖分数

IF 10.6 1区 地球科学 Q1 GEOGRAPHY, PHYSICAL
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引用次数: 0

摘要

准确的雪盖信息对于研究全球气候和水文至关重要。虽然深度学习对雪覆盖率(SCF)检索进行了创新,但其实际应用效果仍然有限。这种局限性源于它对适当训练数据的依赖性和更高级可解释性的必要性。为了克服这些挑战,研究人员开发了一种新型深度学习框架模型,该模型通过与渐近辐射传递(ART)模型耦合,基于先进的甚高分辨率辐射计(AVHRR)表面反射率数据来检索北半球的雪盖率,命名为 ART-DL SCF 模型。新模型使用大地遥感卫星 5 号雪覆盖图像作为参考 SCF,将 ART 模型的雪面反照率检索作为物理约束纳入相关的雪识别参数。使用 Landsat 参考 SCF 的综合验证结果显示,RMSE 为 0.2228,NMAD 为 0.1227,偏差为 -0.0013。此外,二元验证显示总体准确率为 90.20%,遗漏误差和误差均低于 10%。值得注意的是,引入物理约束既提高了模型的准确性和稳定性,又缓解了低估问题。与无物理约束的模型相比,ART-DL SCF 模型的均方根误差和最大允许误差分别显著降低了 4.79 个百分点和 5.35 个百分点。这些精度明显高于欧洲航天局(ESA)目前可用的 SnowCCI AVHRR 产品。此外,该模型还具有很强的时空通用性,在森林地区表现良好。本研究提出了一种结合深度学习的 SCF 检索物理模型,可更好地服务于全球气候、水文和其他相关研究。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Estimating AVHRR snow cover fraction by coupling physical constraints into a deep learning framework

Accurate snow cover information is crucial for studying global climate and hydrology. Although deep learning has innovated snow cover fraction (SCF) retrieval, its effectiveness in practical application remains limited. This limitation stems from its reliance on appropriate training data and the necessity for more advanced interpretability. To overcome these challenges, a novel deep learning framework model by coupling the asymptotic radiative transfer (ART) model was developed to retrieve the Northern Hemisphere SCF based on advanced very high-resolution radiometer (AVHRR) surface reflectance data, named the ART-DL SCF model. Using Landsat 5 snow cover images as the reference SCF, the new model incorporates snow surface albedo retrieval from the ART model as a physical constraint into relevant snow identification parameters. Comprehensive validation results with Landsat reference SCF show an RMSE of 0.2228, an NMAD of 0.1227, and a bias of −0.0013. Moreover, the binary validation reveals an overall accuracy of 90.20%, with omission and commission errors both below 10%. Significantly, introducing physical constraints both improves the accuracy and stability of the model and mitigates underestimation issues. Compared to the model without physical constraints, the ART-DL SCF model shows a marked reduction of 4.79 percentage points in the RMSE and 5.35 percentage points in MAE. These accuracies were significantly higher than the currently available SnowCCI AVHRR products from the European Space Agency (ESA). Additionally, the model exhibits strong temporal and spatial generalizability and performs well in forest areas. This study presents a physical model coupled with deep learning for SCF retrieval that can better serve global climatic, hydrological, and other related studies.

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来源期刊
ISPRS Journal of Photogrammetry and Remote Sensing
ISPRS Journal of Photogrammetry and Remote Sensing 工程技术-成像科学与照相技术
CiteScore
21.00
自引率
6.30%
发文量
273
审稿时长
40 days
期刊介绍: The ISPRS Journal of Photogrammetry and Remote Sensing (P&RS) serves as the official journal of the International Society for Photogrammetry and Remote Sensing (ISPRS). It acts as a platform for scientists and professionals worldwide who are involved in various disciplines that utilize photogrammetry, remote sensing, spatial information systems, computer vision, and related fields. The journal aims to facilitate communication and dissemination of advancements in these disciplines, while also acting as a comprehensive source of reference and archive. P&RS endeavors to publish high-quality, peer-reviewed research papers that are preferably original and have not been published before. These papers can cover scientific/research, technological development, or application/practical aspects. Additionally, the journal welcomes papers that are based on presentations from ISPRS meetings, as long as they are considered significant contributions to the aforementioned fields. In particular, P&RS encourages the submission of papers that are of broad scientific interest, showcase innovative applications (especially in emerging fields), have an interdisciplinary focus, discuss topics that have received limited attention in P&RS or related journals, or explore new directions in scientific or professional realms. It is preferred that theoretical papers include practical applications, while papers focusing on systems and applications should include a theoretical background.
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