Integrated Remote Sensing Studio, Department of Forest Resources Management, University of British Columbia, 2424 Main Mall, Vancouver, BC V6T 1Z4, Canada.
Sensors (Basel). 2022 Nov 26;22(23):9205. doi: 10.3390/s22239205.
Surface reflectance is an essential product from remote sensing Earth observations critical for a wide variety of applications, including consistent land cover mapping and change, and estimation of vegetation attributes. From 2000 to 2017 the Earth Observing-1 Hyperion instrument acquired the first satellite based hyperspectral image archive from space resulting in over 83,138 publicly available images. Hyperion imagery however requires significant preprocessing to derive surface reflectance. SUREHYP is a Python package designed to process batches of Hyperion images, bringing together a number of published algorithms and methods to correct at sensor radiance and derive surface reflectance. In this paper, we present the SUREHYP workflow and demonstrate its application on Hyperion imagery. Results indicate SUREHYP produces flat terrain surface reflectance results comparable to commercially available software, with reflectance values for the whole spectral range almost entirely within 10% of the software's over a reference target, yet it is publicly available and open source, allowing the exploitation of this valuable hyperspectral archive on a global scale.
地表反射率是遥感地球观测的重要产品，对各种应用至关重要，包括一致的土地覆盖制图和变化以及植被属性的估算。从 2000 年到 2017 年，地球观测一号 Hyperion 仪器从太空获取了首个基于卫星的高光谱图像档案，共提供了超过 83138 张可公开获取的图像。然而，Hyperion 图像需要进行大量预处理才能得出地表反射率。SUREHYP 是一个 Python 软件包，用于处理 Hyperion 图像批处理，汇集了许多已发表的算法和方法，以校正传感器辐射亮度并得出地表反射率。在本文中，我们介绍了 SUREHYP 的工作流程，并展示了它在 Hyperion 图像上的应用。结果表明，SUREHYP 生成的平坦地形地表反射率结果与商业可用软件相当，整个光谱范围内的反射率值几乎全部在参考目标的 10%以内，但它是公开可用的且开源的，允许在全球范围内利用这一有价值的高光谱档案。
