Dr. Dong Yang Profile

Dr. Dong Yang

at China Academy of Space Technology

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Publications (2)

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Proceedings Article | 12 March 2021 Paper

Hyperspectral and multispectral remote sensing of aerosols based on surface spectral reconstruction by PCA

Weizhen Hou, Zhengqiang Li, Dong Yang, Siheng Wang, Hua Xu

Proceedings Volume 11763, 117631X (2021) https://doi.org/10.1117/12.2586309

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A series of studies of hyperspectral remote sensing had been carried out to develop a hyperspectral remote sensing technique for aerosol retrieval in the previous works, including the theoretical framework, information content analysis and application to the real data, in which a hyperspectral inversion algorithm was developed to simultaneously retrieved the aerosol and surface properties, and the surface reflectance spectra were decomposed into different principal components, thus only several weighting coefficients of principal components (PCs) were needed to be retrieved. In this study, based on the optimal estimation (OE) framework, we extend the OE-based hyperspectral inversion algorithm to multispectral remote sensing, and the synthetic multispectral intensities of Polarized Scanning Atmospheric Corrector (PSAC) centered in 410, 443, 555, 670, 865, 1610 and 2250 nm are used to test the inversion framework. Principal component analysis (PCA) has been conducted for the spectral dataset of 4 typical surface types with 7 channels of PSAC, in which the PC’s contribution and spectra, the spectral reconstruction results and constraints of PC’s weighting coeffects are discussed. Unified Linearized Vector Radiative Transfer Model (UNL-VRTM) is used as the forward model, and 1% Gaussian distribution errors has been added to the simulated radiance at the top of the atmosphere for multispectral inversion test. The iterative process of multispectral normalized intensities and the reconstructed surface reflectance during the OE iteration are investigated, and the normalized cost function values are well convergent. This study can provide key support to the development of OE-based inversion algorithms for multispectral remote sensing

Proceedings Article | 5 November 2020 Paper

Statistical-based retrieval of solar-induced chlorophyll fluorescence at proximal and airborne scales using (imaging) spectroscopy data

Siheng Wang, Dong Yang, Rong Yang, Ting Li, Hezhi Sun

Proceedings Volume 11566, 115660A (2020) https://doi.org/10.1117/12.2575939

KEYWORDS: Feature extraction, Vegetation, Spectroscopy, Remote sensing, Imaging spectroscopy, Statistical modeling, Spectral resolution

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Solar-induced chlorophyll fluorescence (SIF) is a weak optical signal emitted by chlorophyll under natural illumination. SIF ranges from 600 nm to 800 nm and is assumed as a direct proxy for actual photosynthesis. Due to recent advances in spectroscopy and retrieval techniques, SIF can be retrieved from hyperspectral remote sensing data. Statistical-based approach, typically the singular value decomposition (SVD) method, is one of the two practical strategies for SIF retrieval. A statistical-based approach collects SIF-free measurements of Fraunhofer Lines as training dataset, extracts their spectral features by a statistical approach and then applies the extracted features in the forward SIF retrieval model. In this paper, we first evaluated the performance of the SVD approach in SIF retrieval at proximal scale. Good consistency was found between diurnal SIF cycles given by the SVD method and a 3-FLD method, with SVD-based SIF values higher than those given by 3-FLD. We then applied the SVD method on HyPlant imaging spectroscopy airborne data. Spatial distribution of SIF was successfully depicted using the SVD method. SIF was in a good spatial accordance with NDVI, but the former exhibited a stronger heterogeneity. For both proximal and airborne scales, the in-filling of the Fraunhofer Lines by SIF was successfully detected by the SVD method. However, whether SVD could induce a systematic error should be further studied. It can be concluded that a statistical-based SIF retrieval method is a reasonable alternative to traditional O₂-lines-based methods, especially when synchronous SIF-free spectrum or pixelwise atmospheric correction is unavailable.

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