A history reconstructed time series (1992-2011) of annual global NPP-VIIRS-like nighttime light data through a Super-resolution U-Net model

The dataset is an extend time series of the Version 2 annual NPP-VIIRS nighttime light composites. Based on the super-resolution deep learning technology, the dataset using DMSP-OLS NTL data and Landsat NDVI to reconstruct the NPP-VIIRS NTL data in the early years (1992-2011). The dataset could be easily updated by downloading the version 2 annual NPP-VIIRS NTL data in later years from the academic sector at the Colorado School of Mines (https://eogdata.mines.edu/products/vnl/). The dataset has good spatial pattern and temporal consistency, and is highly consistent with NPP-VIIRS NTL data. It provide a longer time period compared to existing products. Compared with the annual NPP-VIIRS NTL data in 2012, the dataset shows a good consistency at the pixel, city, province and nation levels with R2 of 0.617, 0.747, 0.874, and 0.964, respectively, while the R2 of the NPP-VIIRS-like NTL product provide by Chen (2020) is 0.512, 0.694, 0.86, and 0.966, respectively. The dataset contains (1) simulated NPP-VIIRS NTL time series from the DMSP-OLS NTL data (1992-2011) based on a U-Net based super resolution method; and (2) the version 2 annual NPP-VIIRS NTL data (2012-2020), which were obtained from the academic sector at the Colorado School of Mines (https://eogdata.mines.edu/products/vnl/). The pixels of which value less than zero were set to zero. The dataset are 15 arc second grids, spanning -180 to 180 degrees longitude and -65 to 75 degrees latitude. Each zip file contains an NTL image in geotiff format. The filename of each image is "SRUNet_NPP_VIIRS_V2_Like_yyyy", where "yyyy" is the year of the image. The simulated NPP-VIIRS NTL data converted from the DMSP-OLS NTL data in 2012 and 2013 are also uploaded, which named "simulated_NPP_VIIRS_V2_yyyy".

本数据集为NPP-VIIRS年度夜光合成产品V2版本的扩展时间序列数据集。依托超分辨率深度学习技术，本数据集利用DMSP-OLS夜光（NTL, Nighttime Light）数据与Landsat归一化植被指数（NDVI, Normalized Difference Vegetation Index）数据，重构了1992-2011年早期的NPP-VIIRS夜光数据。用户可通过科罗拉多矿业学院（Colorado School of Mines）学术数据平台（https://eogdata.mines.edu/products/vnl/）下载后续年份的V2版年度NPP-VIIRS夜光数据，以快速完成本数据集的更新。该数据集具备优异的空间格局与时间一致性，且与NPP-VIIRS夜光数据高度匹配；相较于现有同类产品，本数据集拥有更长的时间序列跨度。以2012年的年度NPP-VIIRS夜光数据为参照，本数据集在像元、城市、省级及国家级尺度上均表现出良好的一致性，对应的决定系数（R²）分别为0.617、0.747、0.874和0.964；而Chen（2020）提出的类NPP-VIIRS夜光产品的对应R²值则分别为0.512、0.694、0.86和0.966。本数据集包含两部分内容：(1) 基于U-Net超分辨率方法，由DMSP-OLS夜光数据（1992-2011年）重构得到的模拟NPP-VIIRS夜光时间序列；(2) 从科罗拉多矿业学院学术数据平台获取的2012-2020年V2版年度NPP-VIIRS夜光数据。数据中所有数值小于0的像元均被置为0。本数据集采用15角秒网格分辨率，覆盖经度范围为-180°至180°，纬度范围为-65°至75°。每个压缩包均包含一幅GeoTIFF格式的夜光影像。单幅影像的文件命名格式为"SRUNet_NPP_VIIRS_V2_Like_yyyy"，其中"yyyy"代表影像对应的年份。此外，本数据集还上传了由2012、2013年DMSP-OLS夜光数据转换得到的模拟NPP-VIIRS夜光数据，其文件命名格式为"simulated_NPP_VIIRS_V2_yyyy"。