Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
103
datasets available to search
ShareScore release 0.7.1
Dataset results
103 results for “Seamless”
ChinaHighTEMmin: Daily Seamless 1 km Minimum Air Temperature Dataset for China (2003–Present)
<p>ChinaHighTEM is part of a series of long-term, seamless, high-resolution, and high-quality datasets of air pollutants for China (i.e., ChinaHighAirPollutants, CHAP). It is generated from big data sources (e.g., ground-based measurements, satellite remote sensing products, atmospheric reanalysis, and model simulations) using artificial intelligence, taking into account the spatiotemporal heterogeneity of air pollution.</p> <p>Here is the big data-derived seamless (spatial coverage = 100%) daily 1 km (i.e., D1K) <strong>minimum air temperature</strong> (TEMmin) dataset for China <strong>from 2003 to the present</strong>. This dataset exhibits high quality, with a cross-validation coefficient of determination (CV-R<sup>2</sup>) of 0.98 and a root-mean-square error (RMSE) of 1.53 ℃ on a daily basis.</p> <p>If you use the ChinaHighTEMmin dataset in your scientific research, please cite the following reference (Wang et al., SD, 2024):</p> <ul> <li>Wang, M., Wei, J., Wang, X., Luan, Q., and Xu, X. <a href="https://weijing-rs.github.io/publications/Wang_et_al-SD-2024.pdf" target="_blank" rel="noopener">Reconstruction of all-sky daily air temperature datasets with high accuracy in China from 2003 to 2022</a>. <em>Scientific Data</em>, 2024, 11, 1133. https://doi.org/10.1038/s41597-024-03980-z</li> </ul> <p><strong>More CHAP datasets for different air pollutants are available at: </strong><a href="https://weijing-rs.github.io/product.html"><strong>https://weijing-rs.github.io/product.html</strong></a></p>
ChinaHighTEMavg: Daily Seamless 1 km Average Air Temperature Dataset for China (2003–Present)
<p>ChinaHighTEM is part of a series of long-term, seamless, high-resolution, and high-quality datasets of air pollutants for China (i.e., ChinaHighAirPollutants, CHAP). It is generated from big data sources (e.g., ground-based measurements, satellite remote sensing products, atmospheric reanalysis, and model simulations) using artificial intelligence, taking into account the spatiotemporal heterogeneity of air pollution.</p> <p>Here is the big data-derived seamless (spatial coverage = 100%) daily 1 km (i.e., D1K) <strong>average air temperature</strong> (TEMavg) dataset for China <strong>from 2003 to the present</strong>. This dataset exhibits high quality, with a cross-validation coefficient of determination (CV-R<sup>2</sup>) of 0.99 and a root-mean-square error (RMSE) of 1.18 ℃ on a daily basis.</p> <p>If you use the ChinaHighTEMavg dataset in your scientific research, please cite the following reference (Wang et al., SD, 2024):</p> <ul> <li>Wang, M., Wei, J., Wang, X., Luan, Q., and Xu, X. <a href="https://weijing-rs.github.io/publications/Wang_et_al-SD-2024.pdf" target="_blank" rel="noopener">Reconstruction of all-sky daily air temperature datasets with high accuracy in China from 2003 to 2022</a>. <em>Scientific Data</em>, 2024, 11, 1133. https://doi.org/10.1038/s41597-024-03980-z</li> </ul> <p><strong>More CHAP datasets for different air pollutants are available at: </strong><a href="https://weijing-rs.github.io/product.html"><strong>https://weijing-rs.github.io/product.html</strong></a></p>
GlobalHighO₃: Global Daily Seamless 10 km Ground-Level O₃ Dataset over Land (2000–Present)
<p>GlobalHighO<sub>3</sub> is part of a series of long-term, seamless, global, high-resolution, and high-quality datasets of air pollutants over land (i.e., GlobalHighAirPollutants, GHAP). It is generated from big data sources (e.g., ground-based measurements, satellite remote sensing products, atmospheric reanalysis, and model simulations) using artificial intelligence, taking into account the spatiotemporal heterogeneity of air pollution.</p> <p>Here is the first big data-derived gapless (spatial coverage = 100%) daily, monthly, and yearly 10 km (i.e., D10K, M10K, and Y10K) global ground-level maximum daily 8-hour average (MDA8) O<sub>3</sub> dataset over land <strong>from 2000 to the present</strong>. This dataset exhibits high quality, with a cross-validation coefficient of determination (CV-R<sup>2</sup>) of 0.86 and a root-mean-square error (RMSE) of 6.25 ppb on a daily basis.</p> <p><strong>More GHAP datasets for different air pollutants are available at: <a href="https://weijing-rs.github.io/product.html">https://weijing-rs.github.io/product.html</a></strong></p>
Daily 1 km seamless Antarctic sea ice albedo product from VIIRS data
<p><strong>Summary</strong>:<strong> </strong>In the context of climate change, the sea ice albedo feedback mechanism makes the albedo of Antarctic sea ice a crucial element in polar environmental evolution and global climate models. Existing albedo products for Antarctic sea ice are limited, with a coarse spatiotemporal resolution, and numerous data gaps due to persistent cloud cover. This study uses the Multiband Reflectance Iteration (MBRI) algorithm, which retrieves the Antarctic sea ice albedo using reflectance data from the Visible Infrared Imaging Radiometer Suite (VIIRS). Moreover, the study reconstructs the albedo of cloudy-sky pixels by integrating spatiotemporal information and physical models. A new 1 km daily seamless albedo product for Antarctic sea ice has been developed, covering the period from 2012 to 2021. The results, validated against the automatic weather stations data from the Baseline Surface Radiation Network (BSRN), reveal that the proposed product has higher accuracy, finer spatiotemporal resolution, and superior spatial continuity compared to existing products. The algorithms used fully account for the anisotropy of the sea ice surface, and the high spatiotemporal resolution enables this dataset to enable quantitative analysis of both overall and localized Antarctic sea ice changes. This dataset is valuable for studying the radiation balance of Antarctic sea ice and conducting research in climate modeling. Uncertainty of the dataset is available at https://doi.org/10.5281/zenodo.15067607.</p> <p><strong>Spatial resolution</strong>: 1 km</p> <p><strong>Temporal resolution</strong>: daily (2012-2021)</p> <p><strong>Format</strong>: GeoTIFF</p> <p><strong>Projection</strong>: This dataset adopts Sinusoidal projection and is gridded using the MODIS Sinusoidal Tile Grid</p> <p><strong>How to name and use data files</strong>: The dataset has a longitude range of 180°W to 180°E and a latitude range of 50°S to 80°S, covering 53 tiles (v14: h06~h29; v15: 09~h26; V16: h11~h17, h21~h24). The GeoTIFF file contains a band that represents the shortwave sea ice albedo under clear-sky or cloudy-sky conditions, with 16-bit integer values of 0-10000 and a scale factor of 0.0001. The ocean water and Antarctic continent are set to a filling value of -1. The file name is "Antarctic_Sea_Ice_Albedo_ {yyyyddd}_ {hv}.tif ”, where yyyy represents year, ddd represents day of the year, and hv represents the number of the tile. For example, "Antarctic_Sea_Ice_Albedo_2014270_h18v15.tif" represents the sea ice albedo data of the h18v15 area on the 270th day of 2014. </p> <p><strong>Contacts</strong>: Weifeng Hao (haowf@whu.edu.cn), Chao Ma (macwhu@whu.edu.cn)</p> <p> </p> <p> </p>
Two-step fusion method for generating 1 km seamless multi-layer soil moisture with high accuracy in the Qinghai-Tibet plateau
<p>Current remote sensing techniques fail to observe and generate large scale multi-layer soil moisture (SM) due to the inherent features of the satellite sensors. The lack of comprehensive understanding of multi-layer SM hinders the sustainable development of agriculture, hydrology, and food security. In order to overcome the depth barrier of traditional SM assimilation and downscaling methods, we developed a Two-step Multi-layer SM Downscaling (TMSMD) framework by fusing multi-source remotely sensed, reanalysis, and in-situ data through both machine learning and state-of-the-art deep learning models to generate multi-layer SM. The produced multi-layer SM was characterized by high resolution (1 km), high spatio-temporal continuity (cloud-free and daily), and high accuracy (i.e., 3H data). Firstly, the coarse resolution SMAP SM was downscaled to 1 km spatial resolution using LightGBM to weaken the effects of scale mismatch issue and provide high-resolution input for the subsequent calibration. Results indicated that the downscaled SMAP SM remained high consistency with the original SMAP SM product. With the high-resolution inputs, we calibrated the downscaled SMAP SM using multi-layer in-situ SM through state-of-the-art attention-based LSTM. Results demonstrated that the average PCC, RMSE, ubRMSE, and MAE were improved by 22.3%, 50.7%, 26.2%, and 56.7% compared to SMAP L4 SM while 38.5%, 52.1%, 29.5%, and 58.7% compared to downscaled SMAP SM. Further spatio-temporal and comparative analysis confirmed that the multi-layer SM produced by the TMSMD framework had excellent performance in capturing the spatial and temporal dynamics. In conclude, the proposed TMSMD framework successfully generated 3H multi-layer SM data and is promising for accurate assessment and monitoring in agriculture, water resources, and environmental domains.</p> <p> </p> <p>The remaining data will be uploaded soon.</p>
INCA-CH seamless nowcasting system: 1km digital elevation model
<p>Digital elevation model at 1km horizontal resolution used in the INCA-CH seamless nowcasting system (in Swiss coordinates CH03). For INCA-CH parameters see here: <a href="https://zenodo.org/record/6470725">INCA-CH set of data</a></p>
The Long-term, High-accuracy and Seamless Soil Moisture (LHS-SM) dataset over the Qinghai-Tibet Plateau: part 1 (2001-2010)
<p>Soil moisture (SM) is a vital variable in the water-energy cycle and characterizing its spatiotemporal dynamics is crucial for understanding the impacts of climate change. Although substantial efforts have been devoted to derive SM data at fine scale, there is still a research gap in obtaining the long-term, high-accuracy and high-resolution SM data over the Qinghai-Tibet Plateau (QTP) due to its complex topography. Therefore, this study generated the long-term, high-accuracy and seamless soil moisture (LHS-SM) dataset over the QTP during 2001-2020 using a two-step downscaling method. First the daily SM data from the Climate Change Initiative program of the European Space Agency (ESA CCI) was downscaled to 1km utilizing five machine learning approaches. Then a dynamic data merging method that considers the spatiotemporal nonstationary error was applied to derive the final LHS-SM data. Results indicated that LHS-SM data exhibited satisfying accuracy (mean R = 0.55, ubRMSE = 0.049 m³/m³) and certain improvement to the ESA CCI SM data both at station and network scales. The dataset can be used for various regional hydrology, meteorology, ecological analysis and modeling.</p>
ELITE land surface temperature: hourly seamless 0.02° LST over East Asia (2021)
<p>The <strong>E</strong>ssential therma<strong>L</strong> <strong>I</strong>nfrared remo<strong>T</strong>e s<strong>E</strong>nsing (<strong>ELITE</strong>) product suite currently has four types of products, including land surface temperature (LST: clear-sky and all-sky), emissivity (NBE: narrowband emissivity; BBE: broadband emissivity; and spectral emissivity), the component of surface radiation and energy budget (SLUR: surface longwave upwelling radiation; SLDR: surface longwave downward radiation SLDR; SLNR: surface longwave net radiation), and the component of Earth’s radiation budget (OLR; outgoing longwave radiation; RSR: reflected solar radiation). The spatial-temporal resolutions of the ELITE products are mainly determined by the employed satellite data sources. For more information about ELITE products, please refer to the website (<a href="https://elite.bnu.edu.cn">https://elite.bnu.edu.cn</a>).</p> <p>This dataset is the ELITE hourly seamless 0.02 ° LST dataset over East Asia (2016-2021). Firstly, the iTES algorithm is employed to retrieve the Himawari-8/AHI LST. Secondly, the CLDAS LST is corrected to eliminate its system deviation. Finally, the multi-scale Kalman filter is employed to fuse Himawari-8/AHI LST and the bias-corrected CLDAS LST to generate 0.02 ° hourly seamless LST. The in situ validation results show that the root mean square error (RMSE) of the seamless LST is about 3k. The temporal resolution and spatial resolution of this dataset are 1 hour and 0.02°, respectively.</p> <p>This is the seamless LST dataset in 2021. Please<a href="../records/10668883" target="_blank" rel="noopener"> <strong><em>click here</em></strong></a> to download the ELITE LST product in Januray-June, 2022.</p> <p><strong>Dataset Characteristics:</strong></p> <ul> <li>Spatial Coverage: East Asia (0-60°N, 80°E-140°E)</li> <li>Temporal Coverage: 2021</li> <li>Spatial Resolution: 0.02 °</li> <li>Temporal Resolution: one hour</li> <li>Data Format: Geotiff</li> <li>Scale: 0.01</li> </ul> <p><strong>Citation </strong>(Please cite these papers when using the data)<strong>:</strong></p> <ol> <li>Dong, S., Cheng, J., Shi, J., Shi, C., Sun, S., & Liu, W. (2022). A Data Fusion Method for Generating Hourly Seamless Land Surface Temperature from Himawari-8 AHI Data. Remote Sensing, 14, 5170</li> <li>Zhou, S., & Cheng, J. (2020). An Improved Temperature and Emissivity Separation Algorithm for the Advanced Himawari Imager. IEEE Transactions on Geoscience and Remote Sensing, 58(10), 7105-7124.</li> </ol> <p>If you have any questions, please contact Prof. Jie Cheng (Jie_Cheng@bnu.edu.cn).</p>
ELITE land surface temperature: hourly seamless 0.02° LST over East Asia (2020)
<p>The <strong>E</strong>ssential therma<strong>L</strong> <strong>I</strong>nfrared remo<strong>T</strong>e s<strong>E</strong>nsing (<strong>ELITE</strong>) product suite currently has four types of products, including land surface temperature (LST: clear-sky and all-sky), emissivity (NBE: narrowband emissivity; BBE: broadband emissivity; and spectral emissivity), the component of surface radiation and energy budget (SLUR: surface longwave upwelling radiation; SLDR: surface longwave downward radiation SLDR; SLNR: surface longwave net radiation), and the component of Earth’s radiation budget (OLR; outgoing longwave radiation; RSR: reflected solar radiation). The spatial-temporal resolutions of the ELITE products are mainly determined by the employed satellite data sources. For more information about ELITE products, please refer to the website (<a href="https://elite.bnu.edu.cn">https://elite.bnu.edu.cn</a>).</p> <p>This dataset is the 0.02 ° hourly seamless LST dataset over East Asia (2016-2021). Firstly, the iTES algorithm is employed to retrieve the Himawari-8/AHI LST. Secondly, the CLDAS LST is corrected to eliminate its system deviation. Finally, the multi-scale Kalman filter is employed to fuse Himawari-8/AHI LST and the bias-corrected CLDAS LST to generate 0.02 ° hourly seamless LST. The in situ validation results show that the root mean square error (RMSE) of the seamless LST is about 3k. The temporal resolution and spatial resolution of this dataset are 1 hour and 0.02°, respectively.</p> <p>This is the ELITE seamless LST product in 2020. Please <a href="https://zenodo.org/record/8260245"><strong><em>click here</em></strong></a> to download the ELITE LST product in 2019 and <a href="https://zenodo.org/record/8260240"><strong><em>click here</em></strong></a> to download the ELITE LST product in 2021.</p> <p><strong>Dataset Characteristics:</strong></p> <ul> <li>Spatial Coverage: East Asia (0-60°N, 80°E-140°E)</li> <li>Temporal Coverage: 2020</li> <li>Spatial Resolution: 0.02 °</li> <li>Temporal Resolution: one hour</li> <li>Data Format: Geotiff</li> <li>Scale: 0.01</li> </ul> <p><strong>Citation </strong>(Please cite these papers when using the data)<strong>:</strong></p> <ol> <li>Dong, S., Cheng, J., Shi, J., Shi, C., Sun, S., & Liu, W. (2022). A Data Fusion Method for Generating Hourly Seamless Land Surface Temperature from Himawari-8 AHI Data. Remote Sensing, 14, 5170</li> <li>Zhou, S., & Cheng, J. (2020). An Improved Temperature and Emissivity Separation Algorithm for the Advanced Himawari Imager. IEEE Transactions on Geoscience and Remote Sensing, 58(10), 7105-7124.</li> </ol> <p>If you have any questions, please contact Prof. Jie Cheng (Jie_Cheng@bnu.edu.cn).</p> <p> </p>
ELITE land surface temperature: hourly seamless 0.02° LST over East Asia (2018)
<p>The <strong>E</strong>ssential therma<strong>L</strong> <strong>I</strong>nfrared remo<strong>T</strong>e s<strong>E</strong>nsing (<strong>ELITE</strong>) product suite currently has four types of products, including land surface temperature (LST: clear-sky and all-sky), emissivity (NBE: narrowband emissivity; BBE: broadband emissivity; and spectral emissivity), the component of surface radiation and energy budget (SLUR: surface longwave upwelling radiation; SLDR: surface longwave downward radiation SLDR; SLNR: surface longwave net radiation), and the component of Earth’s radiation budget (OLR; outgoing longwave radiation; RSR: reflected solar radiation). The spatial-temporal resolutions of the ELITE products are mainly determined by the employed satellite data sources. For more information about ELITE products, please refer to the website (<a href="https://elite.bnu.edu.cn">https://elite.bnu.edu.cn</a>).</p> <p>This dataset is the 0.02 ° hourly seamless LST dataset over East Asia (2016-2021). Firstly, the iTES algorithm is employed to retrieve the Himawari-8/AHI LST. Secondly, the CLDAS LST is corrected to eliminate its system deviation. Finally, the multi-scale Kalman filter is employed to fuse Himawari-8/AHI LST and the bias-corrected CLDAS LST to generate 0.02 ° hourly seamless LST. The in situ validation results show that the root mean square error (RMSE) of the seamless LST is about 3k. The temporal resolution and spatial resolution of this dataset are 1 hour and 0.02°, respectively.</p> <p>This is the ELITE seamless LST product in 2018. Please <a href="https://zenodo.org/record/8266456"><strong><em>click here</em></strong></a> to download the ELITE LST product in 2017 and <a href="https://zenodo.org/record/8260245"><strong><em>click here</em></strong></a> to download the ELITE LST product in 2019.</p> <p><strong>Dataset Characteristics:</strong></p> <ul> <li>Spatial Coverage: East Asia (0-60°N, 80°E-140°E)</li> <li>Temporal Coverage: 2018</li> <li>Spatial Resolution: 0.02 °</li> <li>Temporal Resolution: one hour</li> <li>Data Format: Geotiff</li> <li>Scale: 0.01</li> </ul> <p><strong>Citation </strong>(Please cite these papers when using the data)<strong>:</strong></p> <ol> <li>Dong, S., Cheng, J., Shi, J., Shi, C., Sun, S., & Liu, W. (2022). A Data Fusion Method for Generating Hourly Seamless Land Surface Temperature from Himawari-8 AHI Data. Remote Sensing, 14, 5170</li> <li>Zhou, S., & Cheng, J. (2020). An Improved Temperature and Emissivity Separation Algorithm for the Advanced Himawari Imager. IEEE Transactions on Geoscience and Remote Sensing, 58(10), 7105-7124.</li> </ol> <p>If you have any questions, please contact Prof. Jie Cheng (Jie_Cheng@bnu.edu.cn).</p> <p> </p>
ELITE land surface temperature: hourly seamless 0.02° LST over East Asia (2019)
<p>The <strong>E</strong>ssential therma<strong>L</strong> <strong>I</strong>nfrared remo<strong>T</strong>e s<strong>E</strong>nsing (<strong>ELITE</strong>) product suite currently has four types of products, including land surface temperature (LST: clear-sky and all-sky), emissivity (NBE: narrowband emissivity; BBE: broadband emissivity; and spectral emissivity), the component of surface radiation and energy budget (SLUR: surface longwave upwelling radiation; SLDR: surface longwave downward radiation SLDR; SLNR: surface longwave net radiation), and the component of Earth’s radiation budget (OLR; outgoing longwave radiation; RSR: reflected solar radiation). The spatial-temporal resolutions of the ELITE products are mainly determined by the employed satellite data sources. For more information about ELITE products, please refer to the website (<a href="https://elite.bnu.edu.cn">https://elite.bnu.edu.cn</a>).</p> <p>This dataset is the 0.02 ° hourly seamless LST dataset over East Asia (2016-2021). Firstly, the iTES algorithm is employed to retrieve the Himawari-8/AHI LST. Secondly, the CLDAS LST is corrected to eliminate its system deviation. Finally, the multi-scale Kalman filter is employed to fuse Himawari-8/AHI LST and the bias-corrected CLDAS LST to generate 0.02 ° hourly seamless LST. The in situ validation results show that the root mean square error (RMSE) of the seamless LST is about 3k. The temporal resolution and spatial resolution of this dataset are 1 hour and 0.02°, respectively.</p> <p>This is the ELITE seamless LST product in 2019. Please <a href="https://zenodo.org/record/8256087"><strong><em>click here</em></strong></a> to download the ELITE LST product in 2018 and <a href="https://zenodo.org/record/8264798"><strong><em>click here</em></strong></a> to download the ELITE LST product in 2020.</p> <p><strong>Dataset Characteristics:</strong></p> <ul> <li>Spatial Coverage: East Asia (0-60°N, 80°E-140°E)</li> <li>Temporal Coverage: 2019</li> <li>Spatial Resolution: 0.02 °</li> <li>Temporal Resolution: one hour</li> <li>Data Format: Geotiff</li> <li>Scale: 0.01</li> </ul> <p><strong>Citation </strong>(Please cite these papers when using the data)<strong>:</strong></p> <ol> <li>Dong, S., Cheng, J., Shi, J., Shi, C., Sun, S., & Liu, W. (2022). A Data Fusion Method for Generating Hourly Seamless Land Surface Temperature from Himawari-8 AHI Data. Remote Sensing, 14, 5170</li> <li>Zhou, S., & Cheng, J. (2020). An Improved Temperature and Emissivity Separation Algorithm for the Advanced Himawari Imager. IEEE Transactions on Geoscience and Remote Sensing, 58(10), 7105-7124.</li> </ol> <p>If you have any questions, please contact Prof. Jie Cheng (Jie_Cheng@bnu.edu.cn).</p> <p> </p>
Seamless integration of conducting hydrogels in daily life: from preparation to wearable application
<p><span>The dataset contains the recent advances in the development of CHs for smart wearable devices. We summarize the synthesis of conducting polymers and the various approaches used to prepare CHs. We also analyze their </span><span>properties </span><span>and discuss the fabrication of specific geometries to improve the performance of the final CH-based wearable device</span><span>s.</span><span>. The studies presented herein contribute to the growing field of wearable devices by highlighting the potential of CHs as versatile and functional material platform</span><span>s. The development and integration of CHs present challenges and opportunities that highlight the need for novel fabrication techniques and advanced materials.</span></p>
Global oceanic seamless POC concentration products derived from MODIS-Aqua
<p>The dataset integrates seamless POC concentration daily products for the global ocean from 2011 to 2016, derived from MODIS-Aqua‘s XGBoost satellite retrieval products. The POC concentration in the product used int32 to integer data, specifically, the raw POC concentration (mg m-3) is first multiplied by 10000 and then rounded using int32.</p>
Global oceanic seamless POC concentration products derived from MODIS-Aqua
<p>The dataset integrates seamless POC concentration daily products for the global ocean from 2003 to 2010, derived from MODIS-Aqua‘s XGBoost satellite retrieval products. It covers the time span from 2003 to 2010. The POC concentration in the product used int32 to integer data, specifically, the raw POC concentration (mg m-3) is first multiplied by 10000 and then rounded using int32.</p>
Global oceanic seamless POC concentration products derived from MODIS-Terra
<p>The dataset integrates seamless POC concentration daily products for the global ocean from 2001 to 2008, derived from MODIS-Terra‘s XGBoost satellite retrieval products. The POC concentration in the product used int32 to integer data, specifically, the raw POC concentration (mg m-3) is first multiplied by 10000 and then rounded using int32.</p>
Seamless short- to mid-term probabilistic wind power forecasting: Forecasting Results
<div> <div> <div> <div> <div>This dataset contains the predicted time series and the forecast assessment conducted in the paper <strong>Seamless short- to mid-term probabilistic wind power forecasting</strong>.</div> </div> </div> </div> </div>
China Earth Observation Data Cube: The 30m Seamless Annual Leaf-On Landsat Composites from 1985 to 2024
<p>The <strong>30m seamless annual leaf-on Landsat composites from 1985 to 2024</strong> were generated using a comprehensive framework designed to ensure high-quality, consistent data across decades. Starting with preprocessed Level-2 surface reflectance images from multiple Landsat sensors, the dataset is restricted to the Leaf-On season, with rigorous cloud and shadow masking applied based on quality assessment bands. To maintain consistency across sensors, spectral harmonization is conducted, followed by annual composite generation using the medoid method to capture peak vegetation conditions. The resulting composites are structured into a spatially consistent data cube, facilitating efficient analysis and monitoring of vegetation dynamics over time.</p> <p>The band naming convention follows Landsat TM standards, with bands designated as <strong>Blue (B1), Green (B2), Red (B3), NIR (B4), SWIR1 (B5), and SWIR2 (B7)</strong>. Both qualitative and quantitative evaluations were conducted to validate the data quality. Here, we provide 2023 image data covering southwestern forest regions of China as a sample for testing. For access to the full dataset, please visit <strong>Google Earth Engine</strong> at <a href="https://code.earthengine.google.com/6d1ea26ff4463277840eaf6a2662763c">this link</a>, and <strong>Earth Engine App (<a target="_blank">Landsat Yearly Composite Viewer</a>)</strong> at <a href="https://ee-caiyt33-catcd.projects.earthengine.app/view/landsat-yearly-composite-viewer">this link</a>.</p> <p><strong><a target="_blank">The dataset has now been updated to include data up to 2024.</a></strong></p> <p><a target="_blank"><strong>Data citation:</strong> Cai, Y., Li, X., Zhu, P., Nie, S., Wang, C., Liu, X., & Chen, Y. (2025). China Earth Observation Data Cube: The 30m Seamless Annual Leaf-On Landsat Composites from 1985 to 2023. <em>Journal of Remote Sensing</em>. </a><a href="https://doi.org/10.34133/remotesensing.0698">DOI: 10.34133/remotesensing.0698</a></p> <p>For data-related inquiries, please contact Dr. Yaotong Cai at <a href="mailto:caiyt33@mail2.sysu.edu.cn">caiyt33@mail2.sysu.edu.cn</a>.</p>
A seamless global 5 km surface soil moisture product from 1982 to 2021
<p><span>Soil moisture (SM) is an essential climate-sensitive variable that exhibits high spatial and temporal variability. Long-term SM data records (> 30 years) can benefit a range of climate change-related applications. A four-decade global 5-km daily SM product has been generated from 1982 to 2021, as part of the Global Land Surface Satellite (GLASS) products suite. This product (GLASS-AVHRR SM) was derived mainly from the GLASS-AVHRR albedo and LST products, the ERA5-Land reanalysis SM product, and auxiliary datasets, using an attention-based deep learning model. The GLASS-AVHRR SM product has the advantages of long-term coverage, spatial and temporal integrity, reliable accuracy and consistency.</span></p> <p><span>The data values in the GLASS-AVHRR SM product represent the volumetric water content of the uppermost soil layer (0–5 cm). The files are stored in geographic projection and provided in Geo Tiff format, with "No Data" values set to -9999.</span></p>
A global daily seamless 9-km Vegetation Optical Depth (VOD) product from 2010 to 2021
<p> </p> <p><strong>(I) DESCRIPTION</strong>:</p> <p>· A <strong>global daily seamless 9-km Vegetation Optical Depth (VOD)</strong> product is generated through gap-filling and spatiotemporal fusion model. This daily products start <strong>from Jan 01, 2010 to Jul 31, 2021 </strong>(about 20GB memory after uncompressing all zip files).</p> <p>· To further validate the effectiveness of these products, three verification ways are employed as follow: 1) Time series validation; 2) Simulated missing-region validation; And 3) Data comparison validation.</p> <p>· It is important to note that the original data contain missing dates, and these corresponding gaps are also present in our dataset.</p> <p><strong>(II) DATA FORMATTING AND FILE NAMES </strong></p> <p>For the convenience of our readers, we have two formats of data available for download.</p> <p><strong>1) MAT file (Version v1)</strong></p> <p>Data from 2010 to 2021 are stored separately into folders for the corresponding years, with each folder containing daily `.mat` files. The naming convention for the data is “YYYYXXZZ,” where YYYY is the 4-digit year, XX is the 2-digit month, and ZZ is the 2-digit date. The geographic scope is global and the grid size is 4000*2000.</p> <p>MATFILES (.mat): The folders with matfiles contain individual files for:</p> <p>1. Vegetation Optical Depth: VOD_seamless_9km_ YYYYXXZZ.mat</p> <p>2. Latitude/Longitude: VOD_9km_Coordinates.mat</p> <p><strong>2) NetCDF file (Version v2)</strong></p> <p>The year-by-year daily data from 2010 to 2021 are stored in the ‘.nc’ files for the corresponding years. The daily data within each year into one NetCDF file. <span>The variable names are named as VOD_xxxxyydd, where xxxx represents the year, yy represents the month, and dd represents the day. The longitude variable is named “lon” with a dimension of 4000×1, and the latitude variable is named “lat” with a dimension of 2000×1. </span></p> <p><span>It should be noted that these NetCDF files are saved using the netCDF4 library in Python, with the dimension order being (lat, lon). When reading these NetCDF files in MATLAB, the default data dimension order is (lon, lat). Therefore, it is necessary to transpose the variables to match the correct dimension order.</span></p>
A Reconstructed Global Daily Seamless TROPOMI SIF dataset at a 0.05-degree Resolution (SDSIF) using the ML approach
<p>To enhance the spatial and temporal resolutions and continuities of the TROPOMI SIF, a global daily seamless SIF product at 0.05-degree resolution (namely, SDSIF) from May 2018 to December 2020 was generated based on the ML approach using TROPOMI SIF, MODIS reflectance, and ERA5 reanalysis datasets. This dataset has been validated with the original TROPOMI SIF and the long-term tower-based SIF from five flux sites, which verified the reliability of SDSIF and the advantages over original TROPOMI SIF.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.