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5 results for “hyperspectral remote sensing”
OHS data provided by Competition in Hyperspectral Remote Sensing Image Intelligent Processing Application
<p>Images from Chinese Orbita Hyperspectral Satellites (OHS) provided by <em>the Competition in Hyperspectral Remote Sensing Image Intelligent Processing Application</em> are shared. All the images have been radiometric calibrated and atmospheric corrected by the author.</p> <p>Paper: J. He, J. Li, Q. Yuan, H. Shen, and L. Zhang, "Spectral Response Function-Guided Deep Optimization-Driven Network for Spectral Super-Resolution," <em>IEEE Transactions on Neural Networks and Learning Systems (IEEE TNNLS)</em>, 2021.</p> <p>More information about the author can be found at https://jianghe96.github.io/</p> <p>If this dataset is helpful please cite as:</p> <pre>@article{he2021spectral, title={Spectral Response Function-Guided Deep Optimization-Driven Network for Spectral Super-Resolution}, author={He, Jiang and Li, Jie and Yuan, Qiangqiang and Shen, Huanfeng and Zhang, Liangpei}, journal={IEEE Transactions on Neural Networks and Learning Systems}, year={2021}, }</pre>
A dataset of ground-based vertical profile observations of aerosol, NO2 and HCHO from the hyperspectral vertical remote sensing network in China (2019-2023)
<p>Vertical <span>profile </span>observations of atmospheric composition are crucial for understanding the generation, evolution, and transport of regional air pollution. However, existing technological limitations and costs have resulted in a scarcity of vertical profil<span>e</span> data. This study <span>introduces </span>a high-<span>time-</span>resolution (approximately 15 minutes) dataset of vertical <span>profile </span>observations of atmospheric composition (aerosols, NO2, and HCHO) conducted using passive remote sensing technology across 32 sites in seven major regions of China from 2019 to 2023. The study meticulously documents the vertical distribution, seasonal <span>variations and </span>diurnal <span>pattern</span> of these pollutants, revealing long-term trends in atmospheric composition across various regions of China. This dataset provides essential scientific evidence for regional environmental management and policy-making. Its sharing <span>would </span>facilitate the scientific community <span>in </span>explor<span>ing</span> of source-receptor relationships, investigating the impacts of atmospheric composition on regional and global climate <span>and </span>feedback mechanisms.</p>
Data from: Hyperspectral imaging has a limited ability to remotely sense the onset of beech bark disease
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Hyperspectral Remote-sensing reflectance and Chlorophyll-a concentration from Lakes in North Germany
<p><span>Hyperspectral Remote-sensing reflectance data collected by the </span><span>Leibniz Institute of Freshwater Ecology and Inland Fisheries </span><span>(</span><span>IGB</span><span>) within the</span><span> <span>Connectivity and synchronisation of lake ecosystems in space and time (CONNECT</span></span><span> project</span><span>)</span><span>. </span></p> <p><span>This data set is limited to records for </span><span>one field sampling campaign in 26.07.2019</span><span>. </span></p> <p><span>Field data collection and processing:</span></p> <p><span>Ogashawara, I.; Kiel, C.; Jechow, A.; Kohnert, K.;<span> </span>Ruhtz, T.; Grossart, H.-P.; Hölker, F.; Nejstgaard, J.C.; Berger, S.A.; Wollrab, S. The Use of Sentinel-2 for Chlorophyll-a Spatial Dynamics Assessment: A Comparative Study on Different Lakes in Northern Germany. Remote Sensing, v. 13, 1542, 2021. doi:10.3390/rs13081542 </span></p> <p><span>Data specification</span></p> <p><span>Station name – name of the station</span></p> <p><span>lat, lon - geographical latitude/longitude coordinates in decimal degrees</span></p> <p><span>chl-a</span><span> - </span><span>chlorophyll-a concentration in μg/L</span><span> </span></p> <p><span>Rrs</span><span> 380… Rrs 779 </span><span>- Remote-sensing reflectance (Rrs, units 1/sr). The sequential numbering (</span><span>380 - 779</span><span>) denote</span><span>s the</span><span> wavelength in nm. </span></p> <p> </p>
Cross-Scene Hyperspectral Remote Sensing Wetland image data
<p>Two representative study areas in China, i.e., Yancheng and Huanghekou (i.e, Yellow River Estuary) wetlands, are selected.<br> For Yancheng wetland, there are two HSIs acquired by the Advanced Hyperspectral Imager (AHSI) aboard on China's Ziyuan1-02D (ZY1-02D) and GaoFen-5 (GF-5) satellites, respectively. For Huanghekou wetland, there are also two HSIs acquired by the AHSI aboard on China's ZY1-02D satellite in June 28, 2020 and September 29, 2021, respectively.</p>
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