The estimation of biophysical variables from remote sensing data raises important challenges in terms of the acquisition technology and its limitations. In this way, some vegetation parameters, such as chlorophyll fluorescence, require sensors with a high spectral resolution that constrains the spatial resolution while significantly increasing the subpixel land-cover heterogeneity. Precisely, this spatial variability often makes that rather different canopy structures are aggregated together, which eventually generates important deviations in the corresponding parameter quantification. In the context of the Copernicus program (and other related Earth Explorer missions), this article proposes a new statistical methodology to manage the subpixel spatial heterogeneity problem in Sentinel-3 (S3) and FLuorescence EXplorer (FLEX) by taking advantage of the higher spatial resolution of Sentinel-2 (S2). Specifically, the proposed approach first characterizes the subpixel spatial patterns of S3/FLEX using inter-sensor data from S2. Then, a multivariate analysis is conducted to model the influence of these spatial patterns in the errors of the estimated biophysical variables related to chlorophyll which are used as fluorescence proxies. Finally, these modeled distributions are employed to predict the confidence of S3/FLEX products on demand. Our experiments, conducted using multiple operational S2 and simulated S3 data products, reveal the advantages of the proposed methodology to effectively measure the confidence and expected deviations of different vegetation parameters with respect to standard regression algorithms. The source codes of this work will be available at https://github.com/rufernan/PixelS3 .

中文翻译：

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使用Sentinel-2土地覆盖物空间分布的Sentinel-3 / FLEX生物物理产品置信度

从遥感数据估计生物物理变量对采集技术及其局限性提出了重大挑战。这样，某些植被参数（例如叶绿素荧光）需要具有高光谱分辨率的传感器，从而限制了空间分辨率，同时显着增加了子像素土地覆盖的异质性。恰恰是，这种空间变异性通常使相当不同的树冠结构聚集在一起，最终在相应的参数量化中产生重要的偏差。在哥白尼计划（和其他相关的地球探险者任务）的背景下，本文提出了一种新的统计方法，以利用Sentinel-2（S2）的较高空间分辨率来管理Sentinel-3（S3）和荧光发光仪（FLEX）中的子像素空间异质性问题。具体而言，所提出的方法首先使用来自S2的传感器间数据来表征S3 / FLEX的子像素空间模式。然后，进行多变量分析以模拟这些空间模式对与叶绿素有关的估计生物物理变量的误差中的影响，这些误差被用作荧光代理。最后，使用这些模型化的分布来预测S3 / FLEX产品随需应变的置信度。我们的实验是使用多个可操作的S2和模拟的S3数据产品进行的，揭示了所提出方法相对于标准回归算法可有效测量不同植被参数的置信度和预期偏差的优势。这项工作的源代码将在以下位置提供https://github.com/rufernan/PixelS3 。