Accurately estimating fractional cover of vegetated ecosystems over large areas is essential for many scientific studies, including climate change, land cover and land use, etc. Taking both accuracy and large spatial coverage into account, different methods have been proposed, such as upscaling from high to low spatial resolution remote sensing images, and harmonized data from varied sources. In this work, a new method, called centroid-based endmember optimization (CEO), is proposed to assist endmember selection for fractional cover estimation using the triangular space method. The basic idea is to retrieve endmembers from a triangular space built on a fine spatial resolution image, then correct and apply them to a coarse spatial resolution image. The method is discussed and tested using Sentinel-2 MSI and Sentinel-3 OLCI images to estimate non-photosynthetic vegetation (NPV), photosynthetic vegetation (PV), and bare soil (BS) fractional cover. With CEO, the fractional cover of an OLCI image can be estimated more accurately, reducing the work of mosaicking MSI images to acquire fractional cover of the same large area. The premises that CEO can be applied effectively are: (1) the acquisition dates of the MSI and OLCI images are close, ensuring a similar land cover; and (2) the spatial overlap between the MSI and OLCI images covers enough NPV, PV, and BS endmembers. When taken the average fractional cover retrieved from an MSI image as truth value, the CEO method reduced the estimation difference to 0.7%, compared to the differences of 8.1% and 6.6% retrieved using uncorrected or incompletely corrected triangular space method of an OLCI image, respectively. In addition to the average fractional cover estimation, the histogram of fractional cover distribution also improved obviously. When applying CEO to a full OLCI image, two full MSI images covering different locations were used for fractional cover validation, which supported a robust estimation result.

中文翻译：

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用于分数覆盖估计的三角空间方法的基于质心的端元优化：在 Sentinel-3 OLCI 图像上映射植被生态系统的分数覆盖


准确估计大面积植被生态系统的覆盖分数对于许多科学研究至关重要，包括气候变化、土地覆盖和土地利用等。考虑到准确性和大空间覆盖范围，人们提出了不同的方法，例如从高尺度升级低空间分辨率遥感图像以及来自不同来源的统一数据。在这项工作中，提出了一种称为基于质心的端元优化（CEO）的新方法，以辅助使用三角空间方法进行分数覆盖估计的端元选择。基本思想是从建立在精细空间分辨率图像上的三角形空间中检索端元，然后对其进行校正并将其应用于粗略空间分辨率图像。使用 Sentinel-2 MSI 和 Sentinel-3 OLCI 图像对该方法进行讨论和测试，以估计非光合植被 (NPV)、光合植被 (PV) 和裸土 (BS) 覆盖率。借助 CEO，可以更准确地估计 OLCI 图像的分数覆盖，从而减少镶嵌 MSI 图像以获取相同大区域的分数覆盖的工作。能够有效应用CEO的前提是：（1）MSI和OLCI影像的采集日期接近，确保土地覆盖相似； (2) MSI 和 OLCI 图像之间的空间重叠覆盖了足够的 NPV、PV 和 BS 端元。当将从 MSI 图像检索到的平均分数覆盖率作为真值时，CEO 方法将估计差异减少到 0.7%，而使用 OLCI 图像的未校正或不完全校正的三角空间方法检索到的差异为 8.1% 和 6.6%，分别。 除了平均分数覆盖估计之外，分数覆盖分布的直方图也明显改善。当将 CEO 应用到完整的 OLCI 图像时，覆盖不同位置的两个完整 MSI 图像用于部分覆盖验证，这支持了稳健的估计结果。