The Sentinel 2 MSI Spectral Mixing Space
The Sentinel 2 MSI Spectral Mixing Space
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Sentinel 2 MSI 光谱混合空间
DOI：
10.3390/rs14225748
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发表时间：
2022
期刊：
影响因子：
--
通讯作者：
D. Sousa
中科院分区：
文献类型：
--
作者：
C. Small;D. Sousa
复合光谱特征空间用于描述Sentinel 2多光谱仪器（MSI）光谱在多种景观上的光谱混合特性。通过表征光谱混合的线性度和识别边界光谱端元，可以将之前为Landsat和MODIS传感器开发的基质植被暗（SVD）光谱混合模型扩展到Sentinel 2 MSI传感器。SVD模型的实用之处在于，它能够根据其光谱和物理上最明显的成分的实际丰度来表示各种各样的景观。将特定位置的光谱混合模型与标准化光谱指数相结合，基于物理的SVD模型具有简单性、一致性、包容性和适用性，适用于各种土地覆盖制图应用。在本研究中，从全球光谱多样性热点地区收集的110幅图像块为这种表征提供了基础，并为识别跨越特征空间的光谱端元提供了基础。得到的13,000,000,000个光谱混合空间是有效的3D，其中99%的方差在3个低阶主成分维上。确定了四种物理上不同的光谱混合连续体：雪：雪：冰，礁：水，蒸发岩：水和基质：植被：暗（水或阴影）。前3个连续体表现出复杂的非线性，但地理上占主导地位的基质：植被：黑暗（SVD）连续体在其光谱混合的线性上是明显的。确定了SVD连续体的边界端元谱。在80个景观的子集中，不包括3个非线性混合连续体（珊瑚礁、蒸发岩、冰冻圈），一个3端元（SVD）线性混合模型产生的端元分数估计代表了99%的模型光谱，RMS失拟小于6%。Sentinel 2 MSI传感器确定了两组SVD端元，允许Sentinel 2光谱在全球范围内不混合，并跨时间和空间进行比较。鉴于11D光谱特征空间与统计三维光谱混合空间之间存在明显差异，采用参数（Pearson Correlation）和非参数（Mutual information）度量量化了11个Sentinel 2 MSI光谱波段对该空间信息含量的相对贡献。比较SVD混合空间的线性（主成分）和非线性（均匀流形近似和投影）投影，揭示了物理上可解释的光谱混合连续性和在线性投影中无法解决的地理上不同的光谱特性。
A composite spectral feature space is used to characterize the spectral mixing properties of Sentinel 2 Multispectral Instrument (MSI) spectra over a wide diversity of landscapes. Characterizing the linearity of spectral mixing and identifying bounding spectral endmembers allows the Substrate Vegetation Dark (SVD) spectral mixture model previously developed for the Landsat and MODIS sensors to be extended to the Sentinel 2 MSI sensors. The utility of the SVD model is its ability to represent a wide variety of landscapes in terms of the areal abundance of their most spectrally and physically distinct components. Combining the benefits of location-specific spectral mixture models with standardized spectral indices, the physically based SVD model offers simplicity, consistency, inclusivity and applicability for a wide variety of land cover mapping applications. In this study, a set of 110 image tiles compiled from spectral diversity hotspots worldwide provide a basis for this characterization, and for identification of spectral endmembers that span the feature space. The resulting spectral mixing space of these 13,000,000,000 spectra is effectively 3D, with 99% of variance in 3 low order principal component dimensions. Four physically distinct spectral mixing continua are identified: Snow:Firn:Ice, Reef:Water, Evaporite:Water and Substrate:Vegetation:Dark (water or shadow). The first 3 continua exhibit complex nonlinearities, but the geographically dominant Substrate:Vegetation:Dark (SVD) continuum is conspicuous in the linearity of its spectral mixing. Bounding endmember spectra are identified for the SVD continuum. In a subset of 80 landscapes, excluding the 3 nonlinear mixing continua (reefs, evaporites, cryosphere), a 3 endmember (SVD) linear mixture model produces endmember fraction estimates that represent 99% of modeled spectra with <6% RMS misfit. Two sets of SVD endmembers are identified for the Sentinel 2 MSI sensors, allowing Sentinel 2 spectra to be unmixed globally and compared across time and space. In light of the apparent disparity between the 11D spectral feature space and the statistically 3D spectral mixing space, the relative contribution of 11 Sentinel 2 MSI spectral bands to the information content of this space is quantified using both parametric (Pearson Correlation) and nonparametric (Mutual Information) metrics. Comparison of linear (principal component) and nonlinear (Uniform Manifold Approximation and Projection) projections of the SVD mixing space reveal both physically interpretable spectral mixing continua and geographically distinct spectral properties not resolved in the linear projection.
Sentinel-2 反射率的联合表征：来自流形学习的见解
影响因子：
5
作者：
Sousa, Daniel;Small, Christopher
通讯作者：
Small, Christopher