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Data Details

Surface Reflectance Dataset for the Lower Reaches of the Tarim River Based on Enhanced Super-resolution Generative Adversarial Network (ESRGAN) Generator (2000-2020)


LI Haowen1,2LIU Qi*3LIU Yunfei*2,4JIN Qian2,4XIAO Xufeng1ZHAO Jianping1ZHANG Xiao2GUI Shiyuan1CHEN Yue1LIU Zhixue1
1 Xinjiang University，Urumqi 830046，China2 Xinjiang Institute of Ecology and Geography，Chinese Academy of Sciences，Urumqi 830011，China3 King Abdullah University of Science and Technology，Thuwal 23955-6900，Saudi Arabia4 Cele National Field Scientific Observation and Research Station for Desert Grassland Ecosystems，Cele 848300，China

DOI:10.3974/geodb.2026.09.01.V1

Published:Sep. 2026

Visitors：16       Data Files Downloaded：0      
Data Downloaded： 无      Citations：

Key Words：

Super-resolution reconstruction,ESRGAN,Landsat,Sentinel-2,lower reaches of the Tarim River

Abstract：

The lower reaches of the Tarim River lie between the Taklamakan Desert and Kuruktag Desert. Satellite observations with high spatial resolution and a long temporal span are important for studying ecological and environmental changes in this region. Combining Landsat surface reflectance data at 30 m covering 2000-2020 with Sentinel-2 observations covering 2015-2020, we developed a six-band Landsat multispectral super-resolution reconstruction workflow based on an enhanced super-resolution generative adversarial network (ESRGAN). The workflow takes six bands as inputs: red, green, blue, near-infrared (NIR), shortwave infrared 1 (SWIR1) and shortwave infrared 2 (SWIR2). A generator comprising 16 residual-in-residual dense blocks (RRDBs) performs threefold spatial up sampling to produce the surface reflectance dataset for the lower reaches of the Tarim River. The dataset includes six-band surface reflectance imagery with a 10 m pixel size for five key years: 2000, 2005, 2010, 2015 and 2020. The data are stored as UInt16 with LZW compression, and use a scale factor of 0.0001. Evaluation of the 2020 validation scene showed that ESRGAN outperformed the bicubic interpolation baseline in mean PSNR, global PSNR, mean SSIM and SAM. The reconstructed imagery showed good agreement with Sentinel-2 L2A reference data aligned to a common 10 m grid. The dataset is archived in GeoTIFF (.tif) files, and consists of 5 data files with data size of 17.2 GB.

Foundation Item：

National Natural Science Foundation of China (42361144792); Xinjiang Uygur Autonomous Region (2023TSYCLJ0049); The Field Scientific Observation and Research Station for Oasis Water and Soil Processes and Ecological Security in the Tarim River Headwaters Region, Ministry of Natural Resources (2025BSYK-07, 2025BSYK-05)

Data Citation：

LI Haowen, LIU Qi*, LIU Yunfei*, JIN Qian, XIAO Xufeng, ZHAO Jianping, ZHANG Xiao, GUI Shiyuan, CHEN Yue, LIU Zhixue. Surface Reflectance Dataset for the Lower Reaches of the Tarim River Based on Enhanced Super-resolution Generative Adversarial Network (ESRGAN) Generator (2000-2020)[J/DB/OL]. Digital Journal of Global Change Data Repository, 2026. https://doi.org/10.3974/geodb.2026.09.01.V1.
.

References：


     [1] Drusch, M., Del Bello, U., Carlier, S., et al. Sentinel-2: ESA's optical high-resolution mission for GMES operational services [J]. Remote Sensing of Environment, 2012, 120: 25-36. DOI: 10.1016/j.rse.2011.11.026.
     [2] Lu, Y. G., Qian, Y. R., Bai, L., et al. SWLS: Dataset for Shawan City land cover segmentation [J]. Journal of Xinjiang University (Natural Science Edition in Chinese and English). 2025, 42(4): 434-443. DOI: 10.13568/j.cnki.651094.651316.2024.12.29.0002.
     [3] Wang, X., Yu, K., Wu, S., et al. ESRGAN: Enhanced super-resolution generative adversarial networks [C]. Proceedings of the European Conference on Computer Vision Workshops. Cham: Springer, 2018: 63-79. DOI: 10.1007/978-3-030-11021-5_5.
     [4] Ledig, C., Theis, L., Huszár, F., et al. Photo-realistic single image super-resolution using a generative adversarial network [C]. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Honolulu: IEEE, 2017: 4681-4690. DOI: 10.1109/CVPR.2017.19.
     [5] United States Geological Survey. Landsat collection 2 level-2 science products [EB/OL]. [2026-08-14]. https://www.usgs.gov/landsat-missions/landsat-collection-2-level-2-science-products.
     [6] Zhu, Z., Woodcock, C. E. Object-based cloud and cloud shadow detection in Landsat imagery [J]. Remote Sensing of Environment, 2012, 118: 83-94. DOI: 10.1016/j.rse.2011.10.028.
     [7] Roy, D. P., Kovalskyy, V., Zhang, H. K., et al. Characterization of Landsat-7 to Landsat-8 reflective wavelength and normalized difference vegetation index continuity [J]. Remote Sensing of Environment, 2016, 185: 57-70. DOI: 10.1016/j.rse.2015.12.024.
     [8] European Space Agency. Sentinel-2 MSI level-2A product user guide [EB/OL]. [2026-08-14]. https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-2-msi/product-types/level-2a.
     [9] Wang, Z., Bovik, A. C. Mean squared error: love it or leave it? A new look at signal fidelity measures [J]. IEEE Signal Processing Magazine, 2009, 26(1): 98-117. DOI: 10.1109/MSP.2008.930649.
     [10] Wang, Z., Bovik, A. C., Sheikh, H. R., et al. Image quality assessment: from error visibility to structural similarity [J]. IEEE Transactions on Image Processing, 2004, 13(4): 600-612. DOI: 10.1109/TIP.2003.819861.
     [11] Kruse, F. A., Lefkoff, A. B., Boardman, J. W., et al. The Spectral Image Processing System (SIPS)—Interactive visualization and analysis of imaging spectrometer data [J]. Remote Sensing of Environment, 1993, 44(2-3): 145-163. DOI: 10.1016/0034-4257(93)90013-N.
     [12] Pearson, K. Mathematical contributions to the theory of evolution. III. Regression, heredity, and panmixia [J]. Philosophical Transactions of the Royal Society of London, Series A, 1896, 187: 253-318. DOI: 10.1098/rsta.1896.0007.
     [13] Willmott, C. J., Ackleson, S. G., Davis, R. E., et al. Statistics for the evaluation and comparison of models [J]. Journal of Geophysical Research: Oceans, 1985, 90(C5): 8995-9005. DOI: 10.1029/JC090iC05p08995.
     [14] Xu, S. L., Lai, M. Q., liu, J. Z. Farmland change detection algorithm based on improved BIT [J]. Journal of Xinjiang University (Natural Science Edition in Chinese and English), 2026, 43(2): 144-155. DOI: 10.13568/j.cnki.651094.651316.2025.07.07.0004.
     [15] Li, F. X., Liu, G. P., Du, G. H., et al. Evaluation of geological disaster susceptibility based on information quantity and machine learning in Toksun County, Xinjiang [J]. Journal of Xinjiang University (Natural Science Edition in Chinese and English), 2025, 42(4): 469-484. DOI: 10.13568/j.cnki.651094.651316.2024.12.26.0001.

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