利用明确求解的地形效应模拟高山积雪的大气顶部辐射率

IF 10.6 1区 地球科学 Q1 GEOGRAPHY, PHYSICAL ISPRS Journal of Photogrammetry and Remote Sensing Pub Date : 2024-08-03 DOI:10.1016/j.isprsjprs.2024.07.017
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引用次数: 0

摘要

高山环境中复杂的辐射传递机制给雪的光学遥感带来了挑战。在解释雪的卫星图像时,地形效应的表示仍然局限于不充分的分析建模。在此,我们开发了一个框架,明确地解决了多重地形反射问题,并通过修正的四流辐射传递理论生成了高山积雪的大气层顶(TOA)辐射率。该框架包括一个大气模块、一个地形模块和一个表面光谱模块,均依赖于近似渐近辐射传递(ART)模型。在地形模块中,多重地形反射的迭代求解采用视角计算算法,该算法可识别相邻斜坡和相关几何角度,从而得出地形反射辐照度。建模的 TOA 辐射率与 Landsat-8/9 OLI、Sentinel-2A/B MSI 和 Terra MODIS 辐射率图像进行了比较。对帕米尔地区几个积雪覆盖的山区进行的实验表明,TOA 辐射率建模结果与卫星观测结果非常吻合,尽管由于复杂的地形和季节性而存在不确定性。利用名为 LargE-Scale Remote Sensing Data and Image Simulation Framework (LESS) 的光线跟踪软件对模型的地形反射辐照度进行了验证,并确认了可靠的建模性能,其值为...。该模型框架通过与雪的固有特性和环境条件的物理联系,更好地解释了高山积雪的视光谱。
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Modeling the top-of-atmosphere radiance of alpine snow with topographic effects explicitly solved

Optical remote sensing of snow is challenged by the complex radiative transfer mechanism in alpine environments. The representation of topographic effects in interpreting satellite imagery of snow is still limited to inadequate analytical modelization. Here we develop a framework that explicitly solves multiple terrain reflections and generates the top-of-atmosphere (TOA) radiance of alpine snow by the modified four-stream radiative transfer theory. This framework comprises an atmosphere module, a terrain module and a surface spectra module relying on the approximate asymptotic radiative transfer (ART) model. In the terrain module, the iterative solution to multiple terrain reflections is facilitated with a viewshed calculating algorithm which identifies adjacent slopes and related geometric angles to derive terrain-reflected irradiance. The modeled TOA radiance is compared with Landsat-8/9 OLI, Sentinel-2A/B MSI and Terra MODIS radiance imagery. Experiments of several snow-covered mountainous regions in the Pamir area reveal that the TOA radiance modeling results agree well with satellite observations with reported R2 0.86, though subject to the uncertainties due to complex topography and seasonality. The modeled terrain-reflected irradiance is verified with the ray-tracing software called LargE-Scale Remote Sensing Data and Image Simulation Framework (LESS), and reliable modeling performance is confirmed as R2 values are 0.90. This model framework allows for better interpreting the apparent spectra of alpine snow through the physically-based linkage with snow’s intrinsic properties and environmental conditions.

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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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