Using Google Earth imagery and 2019-2022 Sentinel-2 datasets, we developed a two-stage classification framework to obtain the annual global dataset of solar photovoltaic panels at 20-meter resolution from 2019 to 2022.To classify the global solar photovoltaic panels, we applied the global zoning method of IPCC AR6 WGI to define the main-zoning, and used 4 degree × 4 degree grids to create the sub-zoning. Then, we extracted the solar photovoltaic panels in each sub-zoning, and stored the result data in TIFF format.The number of each file corresponds to the ID in the attribute table of the sub-zoning-ID file, and users can download and use the corresponding file based on the sub-zoning-ID. After the ID, 2019, 2020, 2021, and 2022 respectively represent four years.The folder of the annual global PV dataset is named after the year, and each file is named as "sub zoning ID_year".The dataset has been published in Scientific Data, please cite: Li, A., Liu, L., Li, S. et al. Global photovoltaic solar panel dataset from 2019 to 2022. Sci Data 12, 637 (2025).  https://doi.org/10.1038/s41597-025-04985-y

Using Google Earth imagery and 2019-2022 Sentinel-2 datasets, we developed a two-stage classification framework to obtain the annual global dataset of solar photovoltaic panels at 20-meter resolution from 2019 to 2022.To classify the global solar photovoltaic panels, we applied the global zoning method of IPCC AR6 WGI to define the main-zoning, and used 4 degree × 4 degree grids to create the sub-zoning. Then, we extracted the solar photovoltaic panels in each sub-zoning, and stored the result data in TIFF format.The number of each file corresponds to the ID in the attribute table of the sub-zoning-ID file, and users can download and use the corresponding file based on the sub-zoning-ID. After the ID, 2019, 2020, 2021, and 2022 respectively represent four years.The folder of the annual global PV dataset is named after the year, and each file is named as "sub zoning ID_year".The dataset has been published in Scientific Data, please cite: Li, A., Liu, L., Li, S. et al. Global photovoltaic solar panel dataset from 2019 to 2022. Sci Data 12, 637 (2025).  https://doi.org/10.1038/s41597-025-04985-y

采用最优分区方法生产的2013—2022年度华北平原地区30米分辨率冬小麦分类产品。已发表于Big Earth Data (https://doi.org/10.1080/20964471.2024.2363552)

采用最优分区方法生产的2013—2022年度华北平原地区30米分辨率冬小麦分类产品。已发表于Big Earth Data (https://doi.org/10.1080/20964471.2024.2363552)

DMSP-OLS provides the longest observations of NTL information, from 1992 to 2013, an unparalleled dataset for studying historical artificial lights. Version 4 of the DMSP-OLS Nighttime Lights Time Series is widely used ( Image and data processing by NOAA's National Geophysical Data Center. DMSP data collected by US Air Force Weather Agency ). However, it suffers from three main problems: inter-annual inconsistency, saturation, and blooming effect.We used a  series of methods to mitigate the impact and improve data quality. After processing, we get consistent and corrected nighttime light dataset (CCNL).The version 1 products span the globe from 75N latitude to 65S. The products are produced in 30 arc resolution and are made available in GeoTIFF format. Pixel Unit: 'DN'(Digital Number).Each GeoTIFF filename has 4 filename fields that are separated by an underscore "_". A filename extension follows these fields. The fields are described below using this example filename:CCNL_DMSP_1992_V1Field 1: CCNL(Consistent and Corrected Nighttime Light dataset)Field 2: Platform "DMSP"Field 3: Year “1992”Field 4: version “V1”

DMSP-OLS provides the longest observations of NTL information, from 1992 to 2013, an unparalleled dataset for studying historical artificial lights. Version 4 of the DMSP-OLS Nighttime Lights Time Series is widely used ( Image and data processing by NOAA's National Geophysical Data Center. DMSP data collected by US Air Force Weather Agency ). However, it suffers from three main problems: inter-annual inconsistency, saturation, and blooming effect.We used a  series of methods to mitigate the impact and improve data quality. After processing, we get consistent and corrected nighttime light dataset (CCNL).The version 1 products span the globe from 75N latitude to 65S. The products are produced in 30 arc resolution and are made available in GeoTIFF format. Pixel Unit: 'DN'(Digital Number).Each GeoTIFF filename has 4 filename fields that are separated by an underscore "_". A filename extension follows these fields. The fields are described below using this example filename:CCNL_DMSP_1992_V1Field 1: CCNL(Consistent and Corrected Nighttime Light dataset)Field 2: Platform "DMSP"Field 3: Year “1992”Field 4: version “V1”