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100 results for “AVHRR”
Amplitude of seasonal cycles of vegetation at global scale AVHRR
<p>Using the AVHRR product provided by NOAA, the NDVI time series has been calculated, a periodogram has been made to study different factors that affect the periodicity of the vegetative cycles. In this case, the amplitude of the cycles.</p>
Number of seasonal cycles of vegetation at global scale AVHRR
<p>Using the AVHRR product provided by NOAA, the NDVI time series has been calculated, a periodogram has been made to study different factors that affect the periodicity of the vegetative cycles. In this case, the number of seasonal cycles of vegetation.</p> <p>Value 1: one cycle</p> <p>Value 2: two cycles</p> <p>Value 3: three cycles</p>
MCR LTER: Coral Reef: Optical parameters and SST from SeaWiFS and MODIS, ongoing since 1997 and AVHRR-derived SST from 1985 to 2009
Monthly averages of the Sea Surface Temperature (SST), the Sub-surface chlorophyll-a concentration (Chl), the colored dissolved and detrital organic materials at 443 nm (acdm[443]) and the particulate backscattering coefficient at 443 nm (bbp[443]) around Moorea are obtained or derived from satellite data (SST from AVHRR and MODIS-Aqua; Chl, acdm[443] and bbp[443] from SeaWiFS and MODIS-AQUA). The satellite data are averaged over a 1 month period for geographic areas of 16S-19S/147W-151W (SeaWiFS and MODIS-AQUA) and 15S-20S/145W-155W for AVHRR. This material is based upon work supported by the U.S. National Science Foundation under Grant No. OCE 16-37396 (and earlier awards) as well as a generous gift from the Gordon and Betty Moore Foundation. Research was completed under permits issued by the French Polynesian Government (Délégation à la Recherche) and the Haut-commissariat de la République en Polynésie Francaise (DTRT) (Protocole d'Accueil 2005-2022). This work represents a contribution of the Moorea Coral Reef (MCR) LTER Site.
Perodiogram NDVI Time Series From AVHRR
<p>Vegetation seasonality assessment through remote sensing data is crucial to understand ecosystem responses to climatic variations and human activities at large-scales. Whereas the study of the timing of phenological events showed significant advances, their recurrence patterns at different periodicities has not been widely study, especially at global scale. In this work, we describe vegetation oscillations by a novel quantitative approach based on the spectral analysis of Normalized Difference Vegetation Index (NDVI) time series. A new set of global periodicity indicators permitted to identify different seasonal patterns regarding the intra-annual cycles (the number, amplitude, and stability) and to evaluate the existence of pluri-annual cycles, even in those regions with noisy or low NDVI. Most of vegetated land surface (93.18%) showed one intra-annual cycle whereas double and triple cycles were found in 5.58% of the land surface, mainly in tropical and arid regions along with agricultural areas. In only 1.24% of the pixels, the seasonality was not statistically significant. The highest values of amplitude and stability were found at high latitudes in the northern hemisphere whereas lowest values corresponded to tropical and arid regions, with the latter showing more pluri-annual cycles. The indicator maps compiled in this work provide highly relevant and practical information to advance in assessing global vegetation dynamics in the context of global change.</p>
Monthly Global NDVI at 5 km based on AVHRR product - 1982 to 2019
<p>Long-term monthly NDVI calculated through daily images of AVHRR (<a href="https://developers.google.com/earth-engine/datasets/catalog/NOAA_CDR_AVHRR_NDVI_V5?hl=en">NOAA/CDR/AVHRR/NDVI/V5</a>), which were cloud screened and aggregated by percentile 90th on <a href="https://earthengine.google.com/">Google Earth Engine</a>-GEE. All the images were exported from GEE to R environment where the follow procedures were executed on a pixel-basis:</p> <ol> <li><strong>Outlier removal</strong> based on a temporal moving window, used to identify and remove NDVI outlier values (<em>i.e.</em> greater or less than 1.5 standard deviation calculated on 60-months time window) </li> <li><strong>Smoothing and gap filling</strong> approach implemented by the <a href="https://greenbrown.r-forge.r-project.org/">greenbrown package</a> (TSGFssa method)</li> </ol> <p>The final product is composed by 456 NDVI global images without gaps and for each month was produced an ancillary image indicating:</p> <ul> <li><strong>Disregarded pixels</strong>: -25000 (removed) and -32000 (nodata)</li> <li><strong>Gapfilled pixels</strong>: -20000</li> <li><strong>Smoothed pixels</strong>: all values greater than -20000 indicate the difference between the original and smoothed AVHHR NDVI</li> </ul> <p>To access and visualize the maps use: <a href="http://www.openlandmap.org/">OpenLandMap.org</a></p> <p>Publication in preparation.</p> <p>All files were internally compressed using "COMPRESS=DEFLATE" creation option in GDAL.<br> File naming convention <em>(veg_ndvi_avhrr_p90_5km_s0..0cm_</em>20000101..20000131 <em>_v1.0)</em>:</p> <ul> <li>veg = Vegetation theme</li> <li>ndvi = Normalized Difference Vegetation Index </li> <li>avhrr = NOAA CDR AVHRR V5 product</li> <li>p90 = Percentile 90th to aggregate the daily images</li> <li>5km = 5 km of spatial resolution</li> <li>s0..0cm = land surface as vertical reference</li> <li>20000101..20000131 = from 2000-01-01 to 2000-01-31 (time reference)</li> <li>v1.0 = version number</li> </ul>
Long-term Continuous SIF-informed Photosynthesis Proxy reconstructed with calibrated AVHRR surface reflectance (LCSPP-AVHRR), 1982-2000
<p><strong>Usage Notes</strong>:<br>This is the updated LCSPP dataset (v3.2), reconstructed using the AVHRR record from 1982–2023. Due to Zenodo’s size constraints, LCSPP-AVHRR is divided into two separate repositories. Previously referred to as "LCSIF," the dataset was renamed to emphasize its role as a SIF-informed long-term photosynthesis proxy derived from surface reflectance and to avoid confusion with directly measured SIF signals.</p> <p>Key updates in version 3.2 include:</p> <ul> <li><strong>Improved Calibration</strong>: Enhanced consistency in calibration methods, addressing technical limitations in version 3.1 including applying more stringent quality filtering and snow masks. We also </li> <li><strong>Quality Flags</strong>: New quality flag layer enables users to identify whether a pixel is derived from observed surface reflectance (QA=0), high-quality gap-filled values (QA=1), lower-quality gap-filled based on the mean seasonal cycle (QA=2), or missing entirely (QA=3). We advice the user to rely only on observed and high-quality gap-filled values for their analyses.</li> <li><strong>Extension</strong> to include observations from the year of 2023.</li> </ul> <p>Other LCSPP repositories can be accessed via the following links:</p> <ul> <li>LCSPP-AVHRR v3.2 (2001-2023): <a href="https://doi.org/10.5281/zenodo.11906675" target="_blank" rel="noopener">10.5281/zenodo.11906675</a></li> <li>LCSPP-MODIS v3.2(2001-2023): <a href="https://doi.org/10.5281/zenodo.11658088" target="_blank" rel="noopener">10.5281/zenodo.11658088</a></li> </ul> <p>The user can choose between LCSPP-AVHRR and LCSPP-MODIS for the overlapping period from 2001-2023. The two datasets are generally consistent during this overlapping period, although LCSPP-MODIS shows a stronger greening trend between 2001-2023. For studies exploring the long-term vegetation dynamics, the user can either use only LCSPP-AVHRR or use a blend dataset of LCSPP-AVHRR and LCSPP-MODIS as a sensitivity test. </p> <p>In addition, the updated long-term continuous reflectance datasets (LCREF), used for the production of LCSPP, can be accessed using the following links:</p> <ul> <li>LCREF-AVHRR v3.1 (1982-2023): <a href="https://doi.org/10.5281/zenodo.11905959" target="_blank" rel="noopener">10.5281/zenodo.11905959</a></li> <li>LCREF-MODIS v3.1 (2001-2023): <a href="https://doi.org/10.5281/zenodo.11657458" target="_blank" rel="noopener">10.5281/zenodo.11657458</a></li> </ul> <p>A manuscript describing the technical details is available at <a href="https://arxiv.org/abs/2311.14987" target="_blank" rel="noopener">https://arxiv.org/abs/2311.14987</a>, while detailed the uses and limitations of the dataset. In particular, we note that <strong>LCSPP</strong> <strong>is a reconstruction of SIF-informed photosynthesis proxy and should not be treated as SIF measurements</strong>. Although LCSPP has demonstrated skill in tracking the dynamics of GPP and PAR absorbed by canopy chlorophyll (APARchl), it is not suitable for estimating fluorescence quantum yield.</p> <p>All data outputs from this study are available at 0.05° spatial resolution and biweekly temporal resolution in NetCDF format. Each month is divided into two files, with the first file “a” representative of the 1<sup>st</sup> day to the 15<sup>th</sup> day of a month, and the second file “b” representative of the 16<sup>th</sup> day to the last day of a month.</p> <p><strong>Abstract:</strong></p> <p>Satellite-observed solar-induced chlorophyll fluorescence (SIF) is a powerful proxy for the photosynthetic characteristics of terrestrial ecosystems. Direct SIF observations are primarily limited to the recent decade, impeding their application in detecting long-term dynamics of ecosystem function. In this study, we leverage two surface reflectance bands available both from Advanced Very High-Resolution Radiometer (AVHRR, 1982-2023) and MODerate-resolution Imaging Spectroradiometer (MODIS, 2001-2023). Importantly, we calibrate and orbit-correct the AVHRR bands against their MODIS counterparts during their overlapping period. Using the long-term bias-corrected reflectance data from AVHRR and MODIS, a neural network is trained to produce a Long-term Continuous SIF-informed Photosynthesis Proxy (LCSPP) by emulating Orbiting Carbon Observatory-2 SIF, mapping it globally over the 1982-2023 period. Compared with previous SIF-informed photosynthesis proxies, LCSPP has similar skill but can be advantageously extended to the AVHRR period. Further comparison with three widely used vegetation indices (NDVI, kNDVI, NIRv) shows a higher or comparable correlation of LCSPP with satellite SIF and site-level GPP estimates across vegetation types, ensuring a greater capacity for representing long-term photosynthetic activity.</p>
Monthly total freeboard and snow depth over Arctic sea ice from AMSR-E&2 and AVHRR measurements (2003-2020)
<p><strong>[Data description]</strong></p> <p>Monthly total freeboard (Ft), snow depth (hs), and snow depth uncertainty over Arctic sea ice data for January-February-March months of the 2003-2020 period produced by Lee and Shi et al. (2021, manuscript under review) are provided.</p> <p>Both variables are derived from satellite passive infrared and microwave measurements: Total freeboard was obtained from AMSR-E&2 measurements and snow depth was estimated from the AMSR and AVHRR measurements.</p> <p>The uploaded file titled "monthly averaged total freeboard and snow depth (JFM 2003-2020).zip" contains two directories: one for total freeboard and the other for snow depth. Naming convention is "variable_yyyymm.bin" and data format is 32-bit floating point array in shape of 304 x 448 (25 km polar stereographic grid).</p> <p>Here we provide an example Python code to read monthly snow depth of January 2003 using numpy.<br> import numpy as np<br> hs = np.fromfile('hs_200301.bin', dtype=np.float32).reshape(448,304)</p> <p>Geocoordinate tools for the 25 km polar stereographic grid are available at NSIDC website (https://nsidc.org/data/polar-stereo/tools_geo_pixel.html)<br> <br> <strong>[Abbreviations]</strong></p> <p>AMSR: Advanced Microwave Scanning Radiometer<br> AVHRR: Advanced Very High Resolution Radiometer<br> NSIDC: National Snow and Ice Data Center</p>
A downscaled 0.05-Degree Monthly Solar-Induced Chlorophyll Fluorescence Product derived using AVHRR Data in East Asia (1995-2003)
<p>Downscaling techniques offer the opportunity to utilize coarse-spatial-resolution SIF products for investigating carbon cycles and ecological processes at finer resolutions. Here, we generated a new monthly SIF product, DSIF_EA0.05, at a resolution of 0.05° in East Asia from July 1995 to June 2003. The random forest kriging (RFK) approach was employed, incorporating GOME SIF, AVHRR data, ERA5 climate data, and using the optimal explanatory variables. The unit of SIF is mW m<sup>-2</sup> nm<sup>-1</sup> sr<sup>-1</sup>. The selected variables were daily maximum values of air temperature (T<sub>air</sub>), skin temperature (T<sub>skin</sub>), fraction of absorbed photosynthetically active radiation (fPAR), near-infrared reflectance of vegetation (NIRv), downward shortwave radiation (SR<sub>down</sub>), and precipitation. To verify the reliability of DSIF_EA0.05 and the advantages over original GOME SIF, this dataset has been validated with the original GOME SIF, ground gross primary productivity (GPP) data from eight flux sites, and two other SIF products at 1-degree and 0.05-degree resolutions from SCIAMACHY SIF and downscaled SCIAMACHY SIF datasets.</p>
Long-term Continuous SIF-informed Photosynthesis Proxy reconstructed with calibrated AVHRR surface reflectance (LCSPP-AVHRR), 2001-2023
<p><strong>Usage Notes</strong>:<br>This is the updated LCSPP dataset (v3.2), generated using the LCREF-AVHRR record from 1982–2023. Due to Zenodo’s size constraints, LCSPP-AVHRR is divided into two separate repositories. Previously referred to as "LCSIF," the dataset was renamed to emphasize its role as a SIF-informed long-term photosynthesis proxy derived from surface reflectance and to avoid confusion with directly measured SIF signals.</p> <p>Key updates in version 3.2 include:</p> <ul> <li><strong>Improved Calibration</strong>: Enhanced consistency in calibration methods, addressing technical limitations in version 3.1 including applying more stringent quality filtering and snow masks.</li> <li><strong>Quality Flags</strong>: New quality flag layer enables users to identify whether a pixel is derived from observed surface reflectance (QA=0), high-quality gap-filled values (QA=1), lower-quality gap-filled based on the mean seasonal cycle (QA=2), or missing entirely (QA=3). We advice the user to rely only on observed and high-quality gap-filled values for their analyses.</li> <li><strong>Extension</strong> to include observations from the year of 2023.</li> </ul> <p>Other LCSPP repositories can be accessed via the following links:</p> <ul> <li>LCSPP-AVHRR v3.2 (1982-2000): <a href="https://doi.org/10.5281/zenodo.7916850" target="_blank" rel="noopener">10.5281/zenodo.7916850</a></li> <li>LCSPP-MODIS v3.2(2001-2023): <a href="https://doi.org/10.5281/zenodo.11658088" target="_blank" rel="noopener">10.5281/zenodo.11658088</a></li> </ul> <p>The user can choose between LCSPP-AVHRR and LCSPP-MODIS for the overlapping period from 2001-2023. The two datasets are generally consistent during this overlapping period, although LCSPP-MODIS shows a stronger greening trend between 2001-2023. For studies exploring the long-term vegetation dynamics, the user can either use only LCSPP-AVHRR or use a blend dataset of LCSPP-AVHRR and LCSPP-MODIS as a sensitivity test. </p> <p>In addition, the updated long-term continuous reflectance datasets (LCREF), used for the production of LCSPP, can be accessed using the following links:</p> <ul> <li>LCREF-AVHRR v3.2 (1982-2023): <a href="https://doi.org/10.5281/zenodo.11905959" target="_blank" rel="noopener">10.5281/zenodo.11905959</a></li> <li>LCREF-MODIS v3.2 (2001-2023): <a href="https://doi.org/10.5281/zenodo.11657458" target="_blank" rel="noopener">10.5281/zenodo.11657458</a></li> </ul> <p>A manuscript describing the technical details is available at <a href="https://arxiv.org/abs/2311.14987" target="_blank" rel="noopener">https://arxiv.org/abs/2311.14987</a>, while detailed the uses and limitations of the dataset. In particular, we note that <strong>LCSPP</strong> <strong>is a reconstruction of SIF-informed photosynthesis proxy and should not be treated as SIF measurements</strong>. Although LCSPP has demonstrated skill in tracking the dynamics of GPP and PAR absorbed by canopy chlorophyll (APARchl), it is not suitable for estimating fluorescence quantum yield.</p> <p>All data outputs from this study are available at 0.05° spatial resolution and biweekly temporal resolution in NetCDF format. Each month is divided into two files, with the first file “a” representative of the 1<sup>st</sup> day to the 15<sup>th</sup> day of a month, and the second file “b” representative of the 16<sup>th</sup> day to the last day of a month.</p> <p><strong>Abstract:</strong></p> <p>Satellite-observed solar-induced chlorophyll fluorescence (SIF) is a powerful proxy for the photosynthetic characteristics of terrestrial ecosystems. Direct SIF observations are primarily limited to the recent decade, impeding their application in detecting long-term dynamics of ecosystem function. In this study, we leverage two surface reflectance bands available both from Advanced Very High-Resolution Radiometer (AVHRR, 1982-2023) and MODerate-resolution Imaging Spectroradiometer (MODIS, 2001-2023). Importantly, we calibrate and orbit-correct the AVHRR bands against their MODIS counterparts during their overlapping period. Using the long-term bias-corrected reflectance data from AVHRR and MODIS, a neural network is trained to produce a Long-term Continuous SIF-informed Photosynthesis Proxy (LCSPP) by emulating Orbiting Carbon Observatory-2 SIF, mapping it globally over the 1982-2023 period. Compared with previous SIF-informed photosynthesis proxies, LCSPP has similar skill but can be advantageously extended to the AVHRR period. Further comparison with three widely used vegetation indices (NDVI, kNDVI, NIRv) shows a higher or comparable correlation of LCSPP with satellite SIF and site-level GPP estimates across vegetation types, ensuring a greater capacity for representing long-term photosynthetic activity.</p>
Long-term Continuous Red and Near-infrared Channel Reflectance from AVHRR, 1982-2023 (LCREF-AVHRR)
<p><strong>Usage Notes</strong>:<br>This is the updated LCREF dataset (v3.2) consists of calibrated AVHRR surface reflectance record for the red and near-infrared channel. </p> <p><strong>Key updates in version 3.2 include:</strong></p> <ul> <li><strong>Improved Calibration</strong>: Enhanced consistency in calibration methods, addressing technical limitations in version 3.1 including applying more stringent quality filtering and snow masks.</li> <li><strong>Quality Flags</strong>: New quality flag layer enables users to identify whether a pixel is derived from observed surface reflectance (QA=0), high-quality gap-filled values (QA=1), lower-quality gap-filled based on the mean seasonal cycle (QA=2), or missing entirely (QA=3). We advice the user to rely only on observed and high-quality gap-filled values for their analyses.</li> <li><strong>Extension</strong> to include observations from the year of 2023.</li> </ul> <p>The user can choose between LCREF-AVHRR and LCREF-MODIS for the overlapping period from 2001-2023. The two datasets are generally consistent during this overlapping period, although LCREF-MODIS shows a stronger greening trend between 2001-2023. For studies exploring the long-term vegetation dynamics, the user can either use only LCREF-AVHRR or use a blend dataset of LCREF-AVHRR and LCREF-MODIS as a sensitivity test. </p> <ul> <li>The LCREF-MODIS v3.2 (2001-2023) is available at <a href="https://doi.org/10.5281/zenodo.11657458" target="_blank" rel="noopener">10.5281/zenodo.11657458</a>.</li> </ul> <p>The LCREF-AVHRR dataset was used as the input to generate LCSPP-AVHRR (previously known as LCSIF-AVHRR), and it can also be used to derive temporally consistant records of NDVI, NIRv, kNDVI, and other vegetation indices based on red and NIR surface reflectance variables. The user can access LCSPP products at:</p> <ul> <li>LCSPP-AVHRR v3.2 (1982-2000): <a href="https://doi.org/10.5281/zenodo.7916850" target="_blank" rel="noopener">10.5281/zenodo.7916850</a></li> <li>LCSPP-AVHRR v3.2 (2001-2023): <a href="https://doi.org/10.5281/zenodo.11906675" target="_blank" rel="noopener">10.5281/zenodo.11906675</a></li> <li>LCSPP-MODIS v3.2(2001-2023): <a href="https://doi.org/10.5281/zenodo.11658088" target="_blank" rel="noopener">10.5281/zenodo.11658088</a></li> </ul> <p>A manuscript describing the technical details is available at <a href="https://arxiv.org/abs/2311.14987" target="_blank" rel="noopener">https://arxiv.org/abs/2311.14987</a>, which detailed the uses and limitations of the dataset. All data outputs from this study are available at 0.05° spatial resolution and biweekly temporal resolution in NetCDF format. Each month is divided into two files, with the first file “a” representative of the 1<sup>st</sup> day to the 15<sup>th</sup> day of a month, and the second file “b” representative of the 16<sup>th</sup> day to the last day of a month.</p> <p><strong>Abstract:</strong></p> <p>Satellite-observed solar-induced chlorophyll fluorescence (SIF) is a powerful proxy for the photosynthetic characteristics of terrestrial ecosystems. Direct SIF observations are primarily limited to the recent decade, impeding their application in detecting long-term dynamics of ecosystem function. In this study, we leverage two surface reflectance bands available both from Advanced Very High-Resolution Radiometer (AVHRR, 1982-2023) and MODerate-resolution Imaging Spectroradiometer (MODIS, 2001-2023). Importantly, we calibrate and orbit-correct the AVHRR bands against their MODIS counterparts during their overlapping period. Using the long-term bias-corrected reflectance data from AVHRR and MODIS, a neural network is trained to produce a Long-term Continuous SIF-informed Photosynthesis Proxy (LCSPP) by emulating Orbiting Carbon Observatory-2 SIF, mapping it globally over the 1982-2023 period. Compared with previous SIF-informed photosynthesis proxies, LCSPP has similar skill but can be advantageously extended to the AVHRR period. Further comparison with three widely used vegetation indices (NDVI, kNDVI, NIRv) shows a higher or comparable correlation of LCSPP with satellite SIF and site-level GPP estimates across vegetation types, ensuring a greater capacity for representing long-term photosynthetic activity.</p>
A global historical twice-daily (daytime and nighttime) land surface temperature dataset produced by AVHRR observations from 1981 to 2021 (1981–2000)
<ul> <li>Land surface temperature (LST) is a key variable for monitoring and evaluating global long-term climate change. However, existing satellite-based twice-daily LST products only date back to 2000, which makes it difficult to obtain robust long-term temperature variations. We developed the first global historical twice-daily LST dataset (GT-LST), with a spatial resolution of 0.05°, using Advanced Very High Resolution Radiometer Level-1b Global Area Coverage data from 1981 to 2021.</li> <li>Validation with in situ measurements from Surface Radiation Budget sites showed that the overall root-mean-square errors of GT-LST varied from 2.0 K to 3.9 K. Inter-comparison with a common LST product (i.e., MYD11A1) revealed that the overall root-mean-square-difference was approximately 3.2 K.</li> <li>More details of this dataset can be seen in <em>readme.pdf.</em></li> <li>This dataset provides GT-LST product from 1981 to 2000.</li> </ul>
A global historical twice-daily (daytime and nighttime) land surface temperature dataset produced by AVHRR observations from 1981 to 2021 (2001–2005)
<ul> <li>Land surface temperature (LST) is a key variable for monitoring and evaluating global long-term climate change. However, existing satellite-based twice-daily LST products only date back to 2000, which makes it difficult to obtain robust long-term temperature variations. We developed the first global historical twice-daily LST dataset (GT-LST), with a spatial resolution of 0.05°, using Advanced Very High Resolution Radiometer Level-1b Global Area Coverage data from 1981 to 2021.</li> <li>Validation with in situ measurements from Surface Radiation Budget sites showed that the overall root-mean-square errors of GT-LST varied from 2.0 K to 3.9 K. Inter-comparison with a common LST product (i.e., MYD11A1) revealed that the overall root-mean-square-difference was approximately 3.2 K.</li> <li>More details of this dataset can be seen in <em>readme.pdf.</em></li> <li>This dataset provides GT-LST product from 2001 to 2005.</li> </ul>
A global historical twice-daily (daytime and nighttime) land surface temperature dataset produced by AVHRR observations from 1981 to 2021 (2006–2021)
<ul> <li>Land surface temperature (LST) is a key variable for monitoring and evaluating global long-term climate change. However, existing satellite-based twice-daily LST products only date back to 2000, which makes it difficult to obtain robust long-term temperature variations. We developed the first global historical twice-daily LST dataset (GT-LST), with a spatial resolution of 0.05°, using Advanced Very High Resolution Radiometer Level-1b Global Area Coverage data from 1981 to 2021.</li> <li>Validation with in situ measurements from Surface Radiation Budget sites showed that the overall root-mean-square errors of GT-LST varied from 2.0 K to 3.9 K. Inter-comparison with a common LST product (i.e., MYD11A1) revealed that the overall root-mean-square-difference was approximately 3.2 K.</li> <li>More details of this dataset can be seen in <em>readme.pdf.</em></li> <li>This dataset provides GT-LST product from 2006 to 2021.</li> </ul>
Annual 30-m land use/land cover maps of China for 1980–2015 from the integration of AVHRR, MODIS and Landsat data using the BFAST algorithm
<p>This package supplements the following paper entitled “Annual 30-m land use/land cover maps of China for 1980–2015 from the integration of AVHRR, MODIS and Landsat data using the BFAST algorithm” published with Science China Earth Sciences.</p>
Monthly Global NDVI at 5 km based on MODIS and AVHRR products - 1982 to 2019
<p>Long-term monthly NDVI calculated through daily images of AVHRR (<a href="https://developers.google.com/earth-engine/datasets/catalog/NOAA_CDR_AVHRR_NDVI_V5?hl=en">NOAA/CDR/AVHRR/NDVI/V5</a>) and MOD09GA.006 (<a href="https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MOD09GA">MODIS/006/MOD09GA</a>), which were cloud screened and aggregated by percentile 90th on <a href="https://earthengine.google.com/">Google Earth Engine</a>-GEE. All the images were exported from GEE to R environment (MOD09GA data were downsampled to 5km using the average), where the follow procedures were executed on a pixel-basis:</p> <ol> <li><strong>Outlier removal</strong> based on a temporal moving window, used to identify and remove NDVI outlier values (<em>i.e.</em> greater or less than 1.5 standard deviation calculated on 60-months time window) </li> <li><strong>Smoothing and gap filling</strong> approach implemented by the <a href="https://greenbrown.r-forge.r-project.org/">greenbrown package</a> (TSGFssa method)</li> <li><strong>Temporal harmonization</strong> using MOD09GA NDVI as target variable to train a model <em>[modis.ndvi ~ poly(avhrr.ndvi, 2) + max.temp + min.temp]</em> able to predict the MODIS NDVI before 2000</li> </ol> <p>The final product is composed by 456 NDVI global images without gaps, which were harmonized, from 1982 to 1999, to match with the expected MODIS NDVI values from 2000 onwards. For each month, an auxiliary image indicates which pixels were:</p> <ul> <li><strong>Disregarded</strong>: value 0 (nodata)</li> <li><strong>Gapfilled</strong>: value 1</li> <li><strong>smoothed</strong>: value 2</li> </ul> <p>To access and visualize maps use: <a href="http://www.openlandmap.org/">OpenLandMap.org</a></p> <p>Publication in preparation.</p> <p>All files were internally compressed using "COMPRESS=DEFLATE" creation option in GDAL.<br> File naming convention <em>(veg_ndvi_avhrr.mod09ga_p90_5km_s0..0cm_20000101..20000131_v1.0.tif)</em>:</p> <ul> <li>veg = Vegetation theme</li> <li>ndvi = Normalized Difference Vegetation Index </li> <li>avhrr.mod09ga = NOAA CDR AVHRR V5 and MOD09GA version 6 products</li> <li>p90 = Percentile 90th to aggregate the daily images</li> <li>5km = 5 km of spatial resolution</li> <li>s0..0cm = land surface as vertical reference</li> <li>20000101..20000131 = from 2000-01-01 to 2000-01-31 (time reference)</li> <li>v1.0 = version number</li> </ul>
AVHRR NDVI Time Series
<p>Vegetation seasonality assessment through remote sensing data is crucial to understand ecosystem responses to climatic variations and human activities at large-scales. Whereas the study of the timing of phenological events showed significant advances, their recurrence patterns at different periodicities has not been widely study, especially at global scale. In this work, we describe vegetation oscillations by a novel quantitative approach based on the spectral analysis of Normalized Difference Vegetation Index (NDVI) time series. A new set of global periodicity indicators permitted to identify different seasonal patterns regarding the intra-annual cycles (the number, amplitude, and stability) and to evaluate the existence of pluri-annual cycles, even in those regions with noisy or low NDVI. Most of vegetated land surface (93.18%) showed one intra-annual cycle whereas double and triple cycles were found in 5.58% of the land surface, mainly in tropical and arid regions along with agricultural areas. In only 1.24% of the pixels, the seasonality was not statistically significant. The highest values of amplitude and stability were found at high latitudes in the northern hemisphere whereas lowest values corresponded to tropical and arid regions, with the latter showing more pluri-annual cycles. The indicator maps compiled in this work provide highly relevant and practical information to advance in assessing global vegetation dynamics in the context of global change.</p>
40-year monthly mean AVHRR GAC Land Surface Temperature data for the Pan-Arctic region (Pan-Arctic AVHRR LST)
<p><em>This data collection contains 40 years of monthly mean daytime AVHRR Global Area Coverage (GAC) land surface temperature (LST) data. This dataset covers the 1981-2020 perdiod and covers the whole globe above 50° latitude. The spatial extent of the dataset is the following : (-180°, 50°N) ; (180°, 90°N)</em></p> <p><strong>Dataset description:</strong></p> <p>The LST monthly mean composites are computed from daily daytime LST files, that were generated from the EUMETSAT AVHRR PyGAC FDR (https://navigator.eumetsat.int/product/EO:EUM:DAT:0862) as described in Dupuis et al. (2024). These daily LST files contain only cloud-free pixels and pixels with sufficient quality regarding satellite zenith angle and error margin from the radiative transfer modelling. The probabilistic cloud mask from the CLARA-A3 (https://navigator.eumetsat.int/product/EO:EUM:DAT:0874) dataset has been used. The LST monthly means do not contain any water masks, as potential users might have different requirements regarding water masks. The dataset has been validated against in situ data from the SURFRAD (https://gml.noaa.gov/grad/surfrad/overview.html), ARM (https://arm.gov/capabilities/observatories/nsa) and KIT (https://www.imk-asf.kit.edu/english/skl_stations.php) networks.</p> <p><strong>Data & File Overview:</strong></p> <p>Short description: AVHRR GAC LST daytime monthly mean composites: daily land surface temperature data are averaged to monthly composites for every afternoon and mid-day satellite (10 different satellites).</p> <ul> <li>File List: This dataset contains monthly daytime land surface temperature (LST) data for the AVHRRs onboard NOAA and MetOp satellites. </li> <li>Filename: Pan_Arctic_LST_avhrr_XXXXX_YYYYMM_DAY__***.nc, where XXXXX represents the satellite identifier, YYYYMM the monthly timestamp (YYYY=year, MM=month) and *** the timestamp of the file generation.</li> <li>Relationship between files: Each file covers a one-month period and is recorded by a different satellite.</li> </ul> <p>Satellite identifiers:<br><em>AVN07 : NOAA 7</em><br><em>AVN09 : NOAA 9</em><br><em>AVN11 : NOAA 11</em><br><em>AVN14 : NOAA 14</em><br><em>AVN16 : NOAA 16</em><br><em>AVN18 : NOAA 18</em><br><em>AVN19 : NOAA 19</em><br><em>AVMEA : MetOp-A</em><br><em>AVMEB : MetOp-B</em><br><em>AVMEC : MetOp-C</em></p> <p><strong>Data specific information:</strong></p> <p>The LST files are available as a gridded product in the WGS84 coordinate reference system and are distributed as NetCDF files. The dataset covers the pan-Arctic region (-180°, 90°, 180°, 50°) at a spatial resolution of 0.05°x0.05° pixel size.<br>Each *.nc file contains one variable (LST) with three dimensions (time, lat, lon) and five coordinates (time, lat, lon, band and spatial_ref).</p> <p>- spatial_ref (): stores the spatial information, such as the coordinate reference system (CRS) and WKT string.<br>- time (time): stores the timestamp, here the month and the year of the monthly mean. The timestamp is the same for all pixels belonging to the same composite.<br>- lat (lat): stores the latitude of each pixel<br>- lon (lon): stores the longitude of each pixel<br>- band (): empty inherited layer </p> <p> </p> <p><strong>Credit:</strong></p> <p>To use this data please cite this dataset and the respective journal publication:</p> <p>Dupuis, S., Göttsche, F.-M., & Wunderle, S. (2024). Temporal stability of a new 40-year daily AVHRR land surface temperature dataset for the pan-Arctic region. <em>The Cryosphere, 18</em>(12), 6027-6059. <a href="https://doi.org/10.5194/tc-18-6027-2024" target="_blank" rel="nofollow noopener">https://doi.org/10.5194/tc-18-6027-2024</a></p> <p> </p> <div> <div><span>@Article</span><span>{</span><span>tc-18-6027-2024</span><span>,</span></div> <div><span>AUTHOR</span><span> = </span><span>{</span><span>Dupuis, S. and G\"ottsche, F.-M. and Wunderle, S.</span><span>}</span><span>,</span></div> <div><span>TITLE</span><span> = </span><span>{</span><span>Temporal stability of a new 40-year daily AVHRR land surface temperature dataset for the pan-Arctic region</span><span>}</span><span>,</span></div> <div><span>JOURNAL</span><span> = </span><span>{</span><span>The Cryosphere</span><span>}</span><span>,</span></div> <div><span>VOLUME</span><span> = </span><span>{</span><span>18</span><span>}</span><span>,</span></div> <div><span>YEAR</span><span> = </span><span>{</span><span>2024</span><span>}</span><span>,</span></div> <div><span>NUMBER</span><span> = </span><span>{</span><span>12</span><span>}</span><span>,</span></div> <div><span>PAGES</span><span> = </span><span>{</span><span>6027--6059</span><span>}</span><span>,</span></div> <div><span>URL</span><span> = </span><span>{</span><span>https://tc.copernicus.org/articles/18/6027/2024/</span><span>}</span><span>,</span></div> <div><span>DOI</span><span> = </span><span>{</span><span>10.5194/tc-18-6027-2024</span><span>}</span></div> <div><span>}</span></div> </div> <p> </p> <p><strong>Information about funding sources that supported the collection of the data:</strong><br>Dr. Alfred Bretscher Fund (University of Bern)</p> <p> </p>
Daily surface all-wave net radiation over global land (1981—2019) from AVHRR data
<p>Surface net radiation, representing surface radiation energy balance, is closely related to several land processes, such as evapotranspiration, photosynthesis, and turbulent and conductive heat fluxes. Reanalysis products can provide a long-term surface net radiation; however, their coarse spatial resolution and large uncertainties hinder us from well applicating the data at a regional scale. Satellite products also include surface net radiation retrievals with high accuracy. The short time span of satellite products (i.e., GLASS product) makes these satellite products not suitable for long-term climate change study. Therefore, we used a deep learning method to upscale in situ measurements collected from global-distributed sites to generate a daily surface net radiation product with 0.05° spatial resolution from AVHRR data (1981-2019). </p> <p>After comprehensive validation, the RMSE of AVHRR net radiation product was ~26 Wm<sup>-2</sup>, which is generally better than some current reanalysis and satellite products.</p>
MUSES Leaf Area Index (LAI) Derived from AVHRR Data Monthly Global 0.05º Geographic Grid Since 1981
<p>The MUltiscale Satellite remotE Sensing (MUSES) product suite includes products with different spatial and temporal resolutions for parameters such as Normalized Difference Vegetation Index (NDVI), Near-Infrared Reflectance of Vegetation (NIRv), Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fractional Vegetation Coverage (FVC), Gross Primary Production (GPP), Net Primary Production (NPP). For more information about the MUSES products, please refer to this website (<a href="https://muses.bnu.edu.cn/">https://muses.bnu.edu.cn/</a>).</p> <p>This dataset is the MUSES global LAI product at 0.05º spatial resolution and monthly temporal resolution. The MUSES LAI product is provided on Geographic grid and spans from 1981 to 2019 (continuously updated). It was generated from time-series Land Long-Term Data Record (LTDR) Advanced very high resolution radiometer (AVHRR) daily surface reflectance product (Version 4) using general regression neural networks (GRNNs) (Xiao <em>et al</em>., 2014; Xiao <em>et al.</em>, 2016). The MUSES LAI product is spatially complete and temporally continuous.</p> <p><strong>Dataset Characteristics:</strong></p> <ul> <li>Spatial Coverage: 180º W – 180º E, 90º S – 90º N</li> <li>Temporal Coverage: 1981 – 2019</li> <li>Spatial Resolution: 0.05º (approximately 5 km)</li> <li>Temporal Resolution: 1 month</li> <li>Projection: Geographic</li> <li>Data Format: HDF</li> <li>Scale: 0.01</li> <li>Valid Range: 0 – 1000</li> </ul> <p><strong>Citation </strong>(Please cite this paper whenever these data are used)<strong>:</strong></p> <ol> <li>Xiao Zhiqiang, Jinling Song, Hua Yang, Rui Sun and Juan Li. (2022). A 250 m resolution global leaf area index product derived from MODIS surface reflectance data. <em>International Journal of Remote Sensing</em>, 43(4), 1199-1225.</li> <li>Xiao Zhiqiang, <em>et al</em>. (2014). Use of General Regression Neural Networks for Generating the GLASS Leaf Area Index Product From Time-Series MODIS Surface Reflectance. <em>IEEE Transactions on Geoscience and Remote Sensing</em>, 52, 209-223.</li> <li>Xiao Zhiqiang, <em>et al</em>. (2016). Long-time-series global land surface satellite leaf area index product derived from MODIS and AVHRR surface reflectance. <em>IEEE Transactions on Geoscience and Remote Sensing</em>, 54, 5301-5318.</li> <li>Xiao Zhiqiang, <em>et al</em>. (2017). Evaluation of four long time-series global leaf area index products. <em>Agricultural and Forest Meteorology</em>, 246, 218-230.</li> </ol> <p>If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).</p>
MUSES Leaf Area Index (LAI) Derived from AVHRR Data 8-Day Global 0.05º Geographic Grid Since 1981
<p>The MUltiscale Satellite remotE Sensing (MUSES) product suite includes products with different spatial and temporal resolutions for parameters such as Normalized Difference Vegetation Index (NDVI), Near-Infrared Reflectance of Vegetation (NIRv), Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fractional Vegetation Coverage (FVC), Gross Primary Production (GPP), Net Primary Production (NPP). For more information about the MUSES products, please refer to this website (<a href="https://muses.bnu.edu.cn/">https://muses.bnu.edu.cn/</a>).</p> <p>This dataset is the MUSES global LAI product at 0.05º spatial resolution and 8-day temporal resolution. The MUSES LAI product is provided on Geographic grid and spans from 1981 to 2019 (continuously updated). It was generated from time-series Land Long-Term Data Record (LTDR) Advanced very high resolution radiometer (AVHRR) daily surface reflectance product (Version 4) using general regression neural networks (GRNNs) (Xiao <em>et al</em>., 2014; Xiao <em>et al.</em>, 2016). The MUSES LAI product is spatially complete and temporally continuous.</p> <p><strong>Dataset Characteristics:</strong></p> <ul> <li>Spatial Coverage: 180º W – 180º E, 90º S – 90º N</li> <li>Temporal Coverage: 1981 – 2019</li> <li>Spatial Resolution: 0.05º (approximately 5 km)</li> <li>Temporal Resolution: 8 days</li> <li>Projection: Geographic</li> <li>Data Format: HDF</li> <li>Scale: 0.01</li> <li>Valid Range: 0 – 1000</li> </ul> <p><strong>Citation </strong>(Please cite this paper whenever these data are used)<strong>:</strong></p> <ol> <li>Xiao Zhiqiang, Jinling Song, Hua Yang, Rui Sun and Juan Li. (2022). A 250 m resolution global leaf area index product derived from MODIS surface reflectance data. <em>International Journal of Remote Sensing</em>, 43(4), 1199-1225.</li> <li>Xiao Zhiqiang, <em>et al</em>. (2014). Use of General Regression Neural Networks for Generating the GLASS Leaf Area Index Product From Time-Series MODIS Surface Reflectance. <em>IEEE Transactions on Geoscience and Remote Sensing</em>, 52, 209-223.</li> <li>Xiao Zhiqiang, <em>et al</em>. (2016). Long-time-series global land surface satellite leaf area index product derived from MODIS and AVHRR surface reflectance. <em>IEEE Transactions on Geoscience and Remote Sensing</em>, 54, 5301-5318.</li> <li>Xiao Zhiqiang, <em>et al</em>. (2017). Evaluation of four long time-series global leaf area index products. <em>Agricultural and Forest Meteorology</em>, 246, 218-230.</li> </ol> <p>If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).</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.