Earth observation satellite imagery is increasingly accessible, and has become a key component for vegetation mapping and monitoring. Sentinel-2 satellites acquire optical images with five days’ revisit frequency, which is an important feature to increase the probability of acquisition with reasonable cloud cover in tropical regions. Regular and reliable satellite observations open perspectives for the monitoring of vegetation properties and biodiversity. Atmospheric correction methods (ACMs) producing bottom-of-atmosphere (BOA) reflectance are critical to ensure temporal consistency of higher-level products and optimal sensitivity to changes in vegetation properties. Still their application in tropical regions remains challenging due to complex atmospheric issues. This study aims at performing ACM inter-comparison in the context of tropical forest monitoring. We produced BOA reflectance for a set of Sentinel-2 acquisitions corresponding to a forested area in Cameroon, using four atmospheric correction methods: Sen2cor, MAJA, Overland and LaSRC. We selected five successive acquisitions with moderate to no cloud cover, and computed a set of spectral indices and spectral diversity metrics in order to compare the consistency of these products through time, under the hypothesis that they should remain stable over a short period. We also assessed the agreement between atmospheric correction methods. Two spatial extents were used for the computation of spectral diversity metrics to assess the robustness of the data-driven processes applied to compute spectral diversity. We found that the choice of an ACM did have a significant impact on BOA reflectance and higher-level products. In the visible domain, Overland and LaSRC produced consistent BOA reflectance values, while MAJA and Sen2Cor showed strong variability which could not be explained by changes in surface properties. This directly influenced the temporal consistency of NDVI. Yet, the influence on the temporal consistency for EVI and NDWI was moderate. Spectral diversity metrics were consistent through time for all methods, but to a lesser degree than vegetation indices. When comparing the mean values over the period considered, vegetation indices were stable across methods, but not diversity metrics. Spatial context changes had an impact on the Shannon index, but not on Bray-Curtis dissimilarity. These results suggest that the choice of ACM has major potential implications for tropical forest monitoring.

地球观测卫星图像越来越容易获得，并已成为植被测绘和监测的关键组成部分。Sentinel-2卫星以5天的重访频率获取光学图像，这是增加热带地区合理云量获取概率的重要特征。定期和可靠的卫星观测为监测植被特性和生物多样性开辟了前景。产生大气底部 (BOA) 反射率的大气校正方法 (ACM) 对于确保更高级别产品的时间一致性和对植被特性变化的最佳敏感性至关重要。由于复杂的大气问题，它们在热带地区的应用仍然具有挑战性。本研究旨在在热带森林监测的背景下进行 ACM 比对。我们使用四种大气校正方法：Sen2cor、MAJA、Overland 和 LaSRC，为一组与喀麦隆森林区域相对应的 Sentinel-2 采集生成了 BOA 反射率。我们选择了五次连续采集，云量适中到无云，并计算了一组光谱指数和光谱多样性指标，以比较这些产品在一段时间内的一致性，假设它们应该在短时间内保持稳定。我们还评估了大气校正方法之间的一致性。两个空间范围用于计算光谱多样性指标，以评估应用于计算光谱多样性的数据驱动过程的稳健性。我们发现 ACM 的选择确实对 BOA 反射率和更高级别的产品产生了重大影响。在可见域中，Overland 和 LaSRC 产生了一致的 BOA 反射率值，而 MAJA 和 Sen2Cor 表现出强烈的可变性，这无法用表面特性的变化来解释。这直接影响了 NDVI 的时间一致性。然而，对 EVI 和 NDWI 的时间一致性的影响是中等的。所有方法的光谱多样性指标随时间保持一致，但程度低于植被指数。在比较所考虑的时期内的平均值时，植被指数在各种方法中是稳定的，但不是多样性指标。空间背景变化对香农指数有影响，但对布雷-柯蒂斯相异度没有影响。