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3,788 results for “indices”
Landsat-based Spectral Indices for pan-EU 2000-2022 - Bimonthly predictor (2022-07-01/2022-08-31): Spectral indices
<h2><strong>Data information</strong></h2> <p>This dataset includes widely used indices for 2022-07-01/2022-08-31. It covers key aspects such as vegetation, crops, soil, and water, available bimonthly (i.e. one value per two months).</p> <ul> <li><strong>Normalized Difference Vegetation Index (NDVI)</strong> is used to evaluate vegetation health and biomass. It is calculated as <code>(nir - red) / (nir + red)</code> (<a href="https://www.sciencedirect.com/science/article/pii/0034425779900130?via%3Dihub">Tucker, 1979</a>).</li> <li><strong>Normalized Difference Tillage Index (NDTI)</strong>, also known as Normalized Burn Ratio 2 (NBR2), used in tillage detection, post-fire recovery studies, and soil sealing identification. It is calculated as <code>(swir1 - swir2) / (swir1 + swir2)</code> (<a href="https://www.asprs.org/wp-content/uploads/pers/1997journal/jan/1997_jan_87-93.pdf">Van Deventer et al., 1997</a>).</li> <li><strong>Soil Adjusted Vegetation Index (SAVI)</strong> is more effective in areas with sparse vegetation by minimizing the impact of soil brightness on vegetation sensing. It is calculated as <code>((nir - red) / (nir + red + 0.5)) * 1.5</code> (<a href="https://www.sciencedirect.com/science/article/pii/003442578890106X">Huete, 1988</a>).</li> <li><strong>Fraction of Absorbed Photosynthetically Active Radiation (FAPAR)</strong> provides a more direct measurement of plant productivity. It is calculated as <code>((ndvi - 0.03) * (0.95 - 0.001)) / (0.96 - 0.03) + 0.001</code> (<a href="https://zslpublications.onlinelibrary.wiley.com/doi/abs/10.1002/rse2.74">Robinson et al., 2018</a>).</li> <li><strong>Normalized Difference Water Index (NDWI)</strong> provides insights into water dynamics and climatic characteristics. It is calculated as <code>(nir - swir1) / (nir + swir1)</code> (<a href="https://www.sciencedirect.com/science/article/pii/S0034425796000673">Gao, 1996</a>).</li> <li><strong>Normalized Difference Snow Index (NDSI)</strong> helps identify snowy areas. It is calculated as <code>(green - swir1) / (green + swir1)</code> (<a href="https://ieeexplore.ieee.org/abstract/document/1645275/">Salomonson and Appel, 2006</a>).</li> </ul> <h2><strong>As a part of a Data Cube</strong></h2> <p>This data represents a subset of the <a href="../records/10776892">Time-series of Landsat-based Spectral Indices (EU, 30m) data cube</a>. For a comprehensive overview and full dataset information, please visit the landing page of this data cube using the provided link.</p> <ul> <li>To cite this dataset, refer to the DOI available on the landing page.</li> <li>To access other data layers in the data cube, use the navigation catalog on the landing page as well.</li> </ul> <h2><strong>Support</strong></h2> <p>If you discover a bug, artifact, or inconsistency, or if you have a question, please raise a <a href="https://github.com/AI4SoilHealth/SoilHealthDataCube/issues">Github Issue</a>!</p>
Landsat-based Spectral Indices for pan-EU 2000-2022 - Bimonthly predictor (2022-03-01/2022-04-30): Spectral indices
<h2><strong>Data information</strong></h2> <p>This dataset includes widely used indices for 2022-03-01/2022-04-30. It covers key aspects such as vegetation, crops, soil, and water, available bimonthly (i.e. one value per two months).</p> <ul> <li><strong>Normalized Difference Vegetation Index (NDVI)</strong> is used to evaluate vegetation health and biomass. It is calculated as <code>(nir - red) / (nir + red)</code> (<a href="https://www.sciencedirect.com/science/article/pii/0034425779900130?via%3Dihub">Tucker, 1979</a>).</li> <li><strong>Normalized Difference Tillage Index (NDTI)</strong>, also known as Normalized Burn Ratio 2 (NBR2), used in tillage detection, post-fire recovery studies, and soil sealing identification. It is calculated as <code>(swir1 - swir2) / (swir1 + swir2)</code> (<a href="https://www.asprs.org/wp-content/uploads/pers/1997journal/jan/1997_jan_87-93.pdf">Van Deventer et al., 1997</a>).</li> <li><strong>Soil Adjusted Vegetation Index (SAVI)</strong> is more effective in areas with sparse vegetation by minimizing the impact of soil brightness on vegetation sensing. It is calculated as <code>((nir - red) / (nir + red + 0.5)) * 1.5</code> (<a href="https://www.sciencedirect.com/science/article/pii/003442578890106X">Huete, 1988</a>).</li> <li><strong>Fraction of Absorbed Photosynthetically Active Radiation (FAPAR)</strong> provides a more direct measurement of plant productivity. It is calculated as <code>((ndvi - 0.03) * (0.95 - 0.001)) / (0.96 - 0.03) + 0.001</code> (<a href="https://zslpublications.onlinelibrary.wiley.com/doi/abs/10.1002/rse2.74">Robinson et al., 2018</a>).</li> <li><strong>Normalized Difference Water Index (NDWI)</strong> provides insights into water dynamics and climatic characteristics. It is calculated as <code>(nir - swir1) / (nir + swir1)</code> (<a href="https://www.sciencedirect.com/science/article/pii/S0034425796000673">Gao, 1996</a>).</li> <li><strong>Normalized Difference Snow Index (NDSI)</strong> helps identify snowy areas. It is calculated as <code>(green - swir1) / (green + swir1)</code> (<a href="https://ieeexplore.ieee.org/abstract/document/1645275/">Salomonson and Appel, 2006</a>).</li> </ul> <h2><strong>As a part of a Data Cube</strong></h2> <p>This data represents a subset of the <a href="../records/10776892">Time-series of Landsat-based Spectral Indices (EU, 30m) data cube</a>. For a comprehensive overview and full dataset information, please visit the landing page of this data cube using the provided link.</p> <ul> <li>To cite this dataset, refer to the DOI available on the landing page.</li> <li>To access other data layers in the data cube, use the navigation catalog on the landing page as well.</li> </ul> <h2><strong>Support</strong></h2> <p>If you discover a bug, artifact, or inconsistency, or if you have a question, please raise a <a href="https://github.com/AI4SoilHealth/SoilHealthDataCube/issues">Github Issue</a>!</p>
Landsat-based Spectral Indices for pan-EU 2000-2022 - Bimonthly predictor (2022-01-01/2022-02-28): Spectral indices
<h2><strong>Data information</strong></h2> <p>This dataset includes widely used indices for 2022-01-01/2022-02-28. It covers key aspects such as vegetation, crops, soil, and water, available bimonthly (i.e. one value per two months).</p> <ul> <li><strong>Normalized Difference Vegetation Index (NDVI)</strong> is used to evaluate vegetation health and biomass. It is calculated as <code>(nir - red) / (nir + red)</code> (<a href="https://www.sciencedirect.com/science/article/pii/0034425779900130?via%3Dihub">Tucker, 1979</a>).</li> <li><strong>Normalized Difference Tillage Index (NDTI)</strong>, also known as Normalized Burn Ratio 2 (NBR2), used in tillage detection, post-fire recovery studies, and soil sealing identification. It is calculated as <code>(swir1 - swir2) / (swir1 + swir2)</code> (<a href="https://www.asprs.org/wp-content/uploads/pers/1997journal/jan/1997_jan_87-93.pdf">Van Deventer et al., 1997</a>).</li> <li><strong>Soil Adjusted Vegetation Index (SAVI)</strong> is more effective in areas with sparse vegetation by minimizing the impact of soil brightness on vegetation sensing. It is calculated as <code>((nir - red) / (nir + red + 0.5)) * 1.5</code> (<a href="https://www.sciencedirect.com/science/article/pii/003442578890106X">Huete, 1988</a>).</li> <li><strong>Fraction of Absorbed Photosynthetically Active Radiation (FAPAR)</strong> provides a more direct measurement of plant productivity. It is calculated as <code>((ndvi - 0.03) * (0.95 - 0.001)) / (0.96 - 0.03) + 0.001</code> (<a href="https://zslpublications.onlinelibrary.wiley.com/doi/abs/10.1002/rse2.74">Robinson et al., 2018</a>).</li> <li><strong>Normalized Difference Water Index (NDWI)</strong> provides insights into water dynamics and climatic characteristics. It is calculated as <code>(nir - swir1) / (nir + swir1)</code> (<a href="https://www.sciencedirect.com/science/article/pii/S0034425796000673">Gao, 1996</a>).</li> <li><strong>Normalized Difference Snow Index (NDSI)</strong> helps identify snowy areas. It is calculated as <code>(green - swir1) / (green + swir1)</code> (<a href="https://ieeexplore.ieee.org/abstract/document/1645275/">Salomonson and Appel, 2006</a>).</li> </ul> <h2><strong>As a part of a Data Cube</strong></h2> <p>This data represents a subset of the <a href="../records/10776892">Time-series of Landsat-based Spectral Indices (EU, 30m) data cube</a>. For a comprehensive overview and full dataset information, please visit the landing page of this data cube using the provided link.</p> <ul> <li>To cite this dataset, refer to the DOI available on the landing page.</li> <li>To access other data layers in the data cube, use the navigation catalog on the landing page as well.</li> </ul> <h2><strong>Support</strong></h2> <p>If you discover a bug, artifact, or inconsistency, or if you have a question, please raise a <a href="https://github.com/AI4SoilHealth/SoilHealthDataCube/issues">Github Issue</a>!</p>
Landsat-based Spectral Indices for pan-EU 2000-2022 - Bimonthly predictor (2000-11-01/2000-12-31): Spectral indices
<h2><strong>Data information</strong></h2> <p>This dataset includes widely used indices for 2000-11-01/2000-12-31. It covers key aspects such as vegetation, crops, soil, and water, available bimonthly (i.e. one value per two months).</p> <ul> <li><strong>Normalized Difference Vegetation Index (NDVI)</strong> is used to evaluate vegetation health and biomass. It is calculated as <code>(nir - red) / (nir + red)</code> (<a href="https://www.sciencedirect.com/science/article/pii/0034425779900130?via%3Dihub">Tucker, 1979</a>).</li> <li><strong>Normalized Difference Tillage Index (NDTI)</strong>, also known as Normalized Burn Ratio 2 (NBR2), used in tillage detection, post-fire recovery studies, and soil sealing identification. It is calculated as <code>(swir1 - swir2) / (swir1 + swir2)</code> (<a href="https://www.asprs.org/wp-content/uploads/pers/1997journal/jan/1997_jan_87-93.pdf">Van Deventer et al., 1997</a>).</li> <li><strong>Soil Adjusted Vegetation Index (SAVI)</strong> is more effective in areas with sparse vegetation by minimizing the impact of soil brightness on vegetation sensing. It is calculated as <code>((nir - red) / (nir + red + 0.5)) * 1.5</code> (<a href="https://www.sciencedirect.com/science/article/pii/003442578890106X">Huete, 1988</a>).</li> <li><strong>Fraction of Absorbed Photosynthetically Active Radiation (FAPAR)</strong> provides a more direct measurement of plant productivity. It is calculated as <code>((ndvi - 0.03) * (0.95 - 0.001)) / (0.96 - 0.03) + 0.001</code> (<a href="https://zslpublications.onlinelibrary.wiley.com/doi/abs/10.1002/rse2.74">Robinson et al., 2018</a>).</li> <li><strong>Normalized Difference Water Index (NDWI)</strong> provides insights into water dynamics and climatic characteristics. It is calculated as <code>(nir - swir1) / (nir + swir1)</code> (<a href="https://www.sciencedirect.com/science/article/pii/S0034425796000673">Gao, 1996</a>).</li> <li><strong>Normalized Difference Snow Index (NDSI)</strong> helps identify snowy areas. It is calculated as <code>(green - swir1) / (green + swir1)</code> (<a href="https://ieeexplore.ieee.org/abstract/document/1645275/">Salomonson and Appel, 2006</a>).</li> </ul> <h2><strong>As a part of a Data Cube</strong></h2> <p>This data represents a subset of the <a href="../records/10776892">Time-series of Landsat-based Spectral Indices (EU, 30m) data cube</a>. For a comprehensive overview and full dataset information, please visit the landing page of this data cube using the provided link.</p> <ul> <li>To cite this dataset, refer to the DOI available on the landing page.</li> <li>To access other data layers in the data cube, use the navigation catalog on the landing page as well.</li> </ul> <h2><strong>Support</strong></h2> <p>If you discover a bug, artifact, or inconsistency, or if you have a question, please raise a <a href="https://github.com/AI4SoilHealth/SoilHealthDataCube/issues">Github Issue</a>!</p>
Landsat-based Spectral Indices for pan-EU 2000-2022 - Bimonthly predictor (2000-09-01/2000-10-31): Spectral indices
<h2><strong>Data information</strong></h2> <p>This dataset includes widely used indices for 2000-09-01/2000-10-31. It covers key aspects such as vegetation, crops, soil, and water, available bimonthly (i.e. one value per two months).</p> <ul> <li><strong>Normalized Difference Vegetation Index (NDVI)</strong> is used to evaluate vegetation health and biomass. It is calculated as <code>(nir - red) / (nir + red)</code> (<a href="https://www.sciencedirect.com/science/article/pii/0034425779900130?via%3Dihub">Tucker, 1979</a>).</li> <li><strong>Normalized Difference Tillage Index (NDTI)</strong>, also known as Normalized Burn Ratio 2 (NBR2), used in tillage detection, post-fire recovery studies, and soil sealing identification. It is calculated as <code>(swir1 - swir2) / (swir1 + swir2)</code> (<a href="https://www.asprs.org/wp-content/uploads/pers/1997journal/jan/1997_jan_87-93.pdf">Van Deventer et al., 1997</a>).</li> <li><strong>Soil Adjusted Vegetation Index (SAVI)</strong> is more effective in areas with sparse vegetation by minimizing the impact of soil brightness on vegetation sensing. It is calculated as <code>((nir - red) / (nir + red + 0.5)) * 1.5</code> (<a href="https://www.sciencedirect.com/science/article/pii/003442578890106X">Huete, 1988</a>).</li> <li><strong>Fraction of Absorbed Photosynthetically Active Radiation (FAPAR)</strong> provides a more direct measurement of plant productivity. It is calculated as <code>((ndvi - 0.03) * (0.95 - 0.001)) / (0.96 - 0.03) + 0.001</code> (<a href="https://zslpublications.onlinelibrary.wiley.com/doi/abs/10.1002/rse2.74">Robinson et al., 2018</a>).</li> <li><strong>Normalized Difference Water Index (NDWI)</strong> provides insights into water dynamics and climatic characteristics. It is calculated as <code>(nir - swir1) / (nir + swir1)</code> (<a href="https://www.sciencedirect.com/science/article/pii/S0034425796000673">Gao, 1996</a>).</li> <li><strong>Normalized Difference Snow Index (NDSI)</strong> helps identify snowy areas. It is calculated as <code>(green - swir1) / (green + swir1)</code> (<a href="https://ieeexplore.ieee.org/abstract/document/1645275/">Salomonson and Appel, 2006</a>).</li> </ul> <h2><strong>As a part of a Data Cube</strong></h2> <p>This data represents a subset of the <a href="../records/10776892">Time-series of Landsat-based Spectral Indices (EU, 30m) data cube</a>. For a comprehensive overview and full dataset information, please visit the landing page of this data cube using the provided link.</p> <ul> <li>To cite this dataset, refer to the DOI available on the landing page.</li> <li>To access other data layers in the data cube, use the navigation catalog on the landing page as well.</li> </ul> <h2><strong>Support</strong></h2> <p>If you discover a bug, artifact, or inconsistency, or if you have a question, please raise a <a href="https://github.com/AI4SoilHealth/SoilHealthDataCube/issues">Github Issue</a>!</p>
Landsat-based Spectral Indices for pan-EU 2000-2022 - Bimonthly predictor (2022-11-01/2022-12-31): Spectral indices
<h2><strong>Data information</strong></h2> <p>This dataset includes widely used indices for 2022-11-01/2022-12-31. It covers key aspects such as vegetation, crops, soil, and water, available bimonthly (i.e. one value per two months).</p> <ul> <li><strong>Normalized Difference Vegetation Index (NDVI)</strong> is used to evaluate vegetation health and biomass. It is calculated as <code>(nir - red) / (nir + red)</code> (<a href="https://www.sciencedirect.com/science/article/pii/0034425779900130?via%3Dihub">Tucker, 1979</a>).</li> <li><strong>Normalized Difference Tillage Index (NDTI)</strong>, also known as Normalized Burn Ratio 2 (NBR2), used in tillage detection, post-fire recovery studies, and soil sealing identification. It is calculated as <code>(swir1 - swir2) / (swir1 + swir2)</code> (<a href="https://www.asprs.org/wp-content/uploads/pers/1997journal/jan/1997_jan_87-93.pdf">Van Deventer et al., 1997</a>).</li> <li><strong>Soil Adjusted Vegetation Index (SAVI)</strong> is more effective in areas with sparse vegetation by minimizing the impact of soil brightness on vegetation sensing. It is calculated as <code>((nir - red) / (nir + red + 0.5)) * 1.5</code> (<a href="https://www.sciencedirect.com/science/article/pii/003442578890106X">Huete, 1988</a>).</li> <li><strong>Fraction of Absorbed Photosynthetically Active Radiation (FAPAR)</strong> provides a more direct measurement of plant productivity. It is calculated as <code>((ndvi - 0.03) * (0.95 - 0.001)) / (0.96 - 0.03) + 0.001</code> (<a href="https://zslpublications.onlinelibrary.wiley.com/doi/abs/10.1002/rse2.74">Robinson et al., 2018</a>).</li> <li><strong>Normalized Difference Water Index (NDWI)</strong> provides insights into water dynamics and climatic characteristics. It is calculated as <code>(nir - swir1) / (nir + swir1)</code> (<a href="https://www.sciencedirect.com/science/article/pii/S0034425796000673">Gao, 1996</a>).</li> <li><strong>Normalized Difference Snow Index (NDSI)</strong> helps identify snowy areas. It is calculated as <code>(green - swir1) / (green + swir1)</code> (<a href="https://ieeexplore.ieee.org/abstract/document/1645275/">Salomonson and Appel, 2006</a>).</li> </ul> <h2><strong>As a part of a Data Cube</strong></h2> <p>This data represents a subset of the <a href="../records/10776892">Time-series of Landsat-based Spectral Indices (EU, 30m) data cube</a>. For a comprehensive overview and full dataset information, please visit the landing page of this data cube using the provided link.</p> <ul> <li>To cite this dataset, refer to the DOI available on the landing page.</li> <li>To access other data layers in the data cube, use the navigation catalog on the landing page as well.</li> </ul> <h2><strong>Support</strong></h2> <p>If you discover a bug, artifact, or inconsistency, or if you have a question, please raise a <a href="https://github.com/AI4SoilHealth/SoilHealthDataCube/issues">Github Issue</a>!</p>
Landsat-based Spectral Indices for pan-EU 2000-2022 - Bimonthly predictor (2000-07-01/2000-08-31): Spectral indices
<h2><strong>Data information</strong></h2> <p>This dataset includes widely used indices for 2000-07-01/2000-08-31. It covers key aspects such as vegetation, crops, soil, and water, available bimonthly (i.e. one value per two months).</p> <ul> <li><strong>Normalized Difference Vegetation Index (NDVI)</strong> is used to evaluate vegetation health and biomass. It is calculated as <code>(nir - red) / (nir + red)</code> (<a href="https://www.sciencedirect.com/science/article/pii/0034425779900130?via%3Dihub">Tucker, 1979</a>).</li> <li><strong>Normalized Difference Tillage Index (NDTI)</strong>, also known as Normalized Burn Ratio 2 (NBR2), used in tillage detection, post-fire recovery studies, and soil sealing identification. It is calculated as <code>(swir1 - swir2) / (swir1 + swir2)</code> (<a href="https://www.asprs.org/wp-content/uploads/pers/1997journal/jan/1997_jan_87-93.pdf">Van Deventer et al., 1997</a>).</li> <li><strong>Soil Adjusted Vegetation Index (SAVI)</strong> is more effective in areas with sparse vegetation by minimizing the impact of soil brightness on vegetation sensing. It is calculated as <code>((nir - red) / (nir + red + 0.5)) * 1.5</code> (<a href="https://www.sciencedirect.com/science/article/pii/003442578890106X">Huete, 1988</a>).</li> <li><strong>Fraction of Absorbed Photosynthetically Active Radiation (FAPAR)</strong> provides a more direct measurement of plant productivity. It is calculated as <code>((ndvi - 0.03) * (0.95 - 0.001)) / (0.96 - 0.03) + 0.001</code> (<a href="https://zslpublications.onlinelibrary.wiley.com/doi/abs/10.1002/rse2.74">Robinson et al., 2018</a>).</li> <li><strong>Normalized Difference Water Index (NDWI)</strong> provides insights into water dynamics and climatic characteristics. It is calculated as <code>(nir - swir1) / (nir + swir1)</code> (<a href="https://www.sciencedirect.com/science/article/pii/S0034425796000673">Gao, 1996</a>).</li> <li><strong>Normalized Difference Snow Index (NDSI)</strong> helps identify snowy areas. It is calculated as <code>(green - swir1) / (green + swir1)</code> (<a href="https://ieeexplore.ieee.org/abstract/document/1645275/">Salomonson and Appel, 2006</a>).</li> </ul> <h2><strong>As a part of a Data Cube</strong></h2> <p>This data represents a subset of the <a href="../records/10776892">Time-series of Landsat-based Spectral Indices (EU, 30m) data cube</a>. For a comprehensive overview and full dataset information, please visit the landing page of this data cube using the provided link.</p> <ul> <li>To cite this dataset, refer to the DOI available on the landing page.</li> <li>To access other data layers in the data cube, use the navigation catalog on the landing page as well.</li> </ul> <h2><strong>Support</strong></h2> <p>If you discover a bug, artifact, or inconsistency, or if you have a question, please raise a <a href="https://github.com/AI4SoilHealth/SoilHealthDataCube/issues">Github Issue</a>!</p>
Landsat-based Spectral Indices for pan-EU 2000-2022 - Bimonthly predictor (2000-05-01/2000-06-30): Spectral indices
<h2><strong>Data information</strong></h2> <p>This dataset includes widely used indices for 2000-05-01/2000-06-30. It covers key aspects such as vegetation, crops, soil, and water, available bimonthly (i.e. one value per two months).</p> <ul> <li><strong>Normalized Difference Vegetation Index (NDVI)</strong> is used to evaluate vegetation health and biomass. It is calculated as <code>(nir - red) / (nir + red)</code> (<a href="https://www.sciencedirect.com/science/article/pii/0034425779900130?via%3Dihub">Tucker, 1979</a>).</li> <li><strong>Normalized Difference Tillage Index (NDTI)</strong>, also known as Normalized Burn Ratio 2 (NBR2), used in tillage detection, post-fire recovery studies, and soil sealing identification. It is calculated as <code>(swir1 - swir2) / (swir1 + swir2)</code> (<a href="https://www.asprs.org/wp-content/uploads/pers/1997journal/jan/1997_jan_87-93.pdf">Van Deventer et al., 1997</a>).</li> <li><strong>Soil Adjusted Vegetation Index (SAVI)</strong> is more effective in areas with sparse vegetation by minimizing the impact of soil brightness on vegetation sensing. It is calculated as <code>((nir - red) / (nir + red + 0.5)) * 1.5</code> (<a href="https://www.sciencedirect.com/science/article/pii/003442578890106X">Huete, 1988</a>).</li> <li><strong>Fraction of Absorbed Photosynthetically Active Radiation (FAPAR)</strong> provides a more direct measurement of plant productivity. It is calculated as <code>((ndvi - 0.03) * (0.95 - 0.001)) / (0.96 - 0.03) + 0.001</code> (<a href="https://zslpublications.onlinelibrary.wiley.com/doi/abs/10.1002/rse2.74">Robinson et al., 2018</a>).</li> <li><strong>Normalized Difference Water Index (NDWI)</strong> provides insights into water dynamics and climatic characteristics. It is calculated as <code>(nir - swir1) / (nir + swir1)</code> (<a href="https://www.sciencedirect.com/science/article/pii/S0034425796000673">Gao, 1996</a>).</li> <li><strong>Normalized Difference Snow Index (NDSI)</strong> helps identify snowy areas. It is calculated as <code>(green - swir1) / (green + swir1)</code> (<a href="https://ieeexplore.ieee.org/abstract/document/1645275/">Salomonson and Appel, 2006</a>).</li> </ul> <h2><strong>As a part of a Data Cube</strong></h2> <p>This data represents a subset of the <a href="../records/10776892">Time-series of Landsat-based Spectral Indices (EU, 30m) data cube</a>. For a comprehensive overview and full dataset information, please visit the landing page of this data cube using the provided link.</p> <ul> <li>To cite this dataset, refer to the DOI available on the landing page.</li> <li>To access other data layers in the data cube, use the navigation catalog on the landing page as well.</li> </ul> <h2><strong>Support</strong></h2> <p>If you discover a bug, artifact, or inconsistency, or if you have a question, please raise a <a href="https://github.com/AI4SoilHealth/SoilHealthDataCube/issues">Github Issue</a>!</p>
Landsat-based Spectral Indices for pan-EU 2000-2022 - Bimonthly predictor (2000-03-01/2000-04-30): Spectral indices
<h2><strong>Data information</strong></h2> <p>This dataset includes widely used indices for 2000-03-01/2000-04-30. It covers key aspects such as vegetation, crops, soil, and water, available bimonthly (i.e. one value per two months).</p> <ul> <li><strong>Normalized Difference Vegetation Index (NDVI)</strong> is used to evaluate vegetation health and biomass. It is calculated as <code>(nir - red) / (nir + red)</code> (<a href="https://www.sciencedirect.com/science/article/pii/0034425779900130?via%3Dihub">Tucker, 1979</a>).</li> <li><strong>Normalized Difference Tillage Index (NDTI)</strong>, also known as Normalized Burn Ratio 2 (NBR2), used in tillage detection, post-fire recovery studies, and soil sealing identification. It is calculated as <code>(swir1 - swir2) / (swir1 + swir2)</code> (<a href="https://www.asprs.org/wp-content/uploads/pers/1997journal/jan/1997_jan_87-93.pdf">Van Deventer et al., 1997</a>).</li> <li><strong>Soil Adjusted Vegetation Index (SAVI)</strong> is more effective in areas with sparse vegetation by minimizing the impact of soil brightness on vegetation sensing. It is calculated as <code>((nir - red) / (nir + red + 0.5)) * 1.5</code> (<a href="https://www.sciencedirect.com/science/article/pii/003442578890106X">Huete, 1988</a>).</li> <li><strong>Fraction of Absorbed Photosynthetically Active Radiation (FAPAR)</strong> provides a more direct measurement of plant productivity. It is calculated as <code>((ndvi - 0.03) * (0.95 - 0.001)) / (0.96 - 0.03) + 0.001</code> (<a href="https://zslpublications.onlinelibrary.wiley.com/doi/abs/10.1002/rse2.74">Robinson et al., 2018</a>).</li> <li><strong>Normalized Difference Water Index (NDWI)</strong> provides insights into water dynamics and climatic characteristics. It is calculated as <code>(nir - swir1) / (nir + swir1)</code> (<a href="https://www.sciencedirect.com/science/article/pii/S0034425796000673">Gao, 1996</a>).</li> <li><strong>Normalized Difference Snow Index (NDSI)</strong> helps identify snowy areas. It is calculated as <code>(green - swir1) / (green + swir1)</code> (<a href="https://ieeexplore.ieee.org/abstract/document/1645275/">Salomonson and Appel, 2006</a>).</li> </ul> <h2><strong>As a part of a Data Cube</strong></h2> <p>This data represents a subset of the <a href="../records/10776892">Time-series of Landsat-based Spectral Indices (EU, 30m) data cube</a>. For a comprehensive overview and full dataset information, please visit the landing page of this data cube using the provided link.</p> <ul> <li>To cite this dataset, refer to the DOI available on the landing page.</li> <li>To access other data layers in the data cube, use the navigation catalog on the landing page as well.</li> </ul> <h2><strong>Support</strong></h2> <p>If you discover a bug, artifact, or inconsistency, or if you have a question, please raise a <a href="https://github.com/AI4SoilHealth/SoilHealthDataCube/issues">Github Issue</a>!</p>
Landsat-based Spectral Indices for pan-EU 2000-2022 - Bimonthly predictor (2022-05-01/2022-06-30): Spectral indices
<h2><strong>Data information</strong></h2> <p>This dataset includes widely used indices for 2022-05-01/2022-06-30. It covers key aspects such as vegetation, crops, soil, and water, available bimonthly (i.e. one value per two months).</p> <ul> <li><strong>Normalized Difference Vegetation Index (NDVI)</strong> is used to evaluate vegetation health and biomass. It is calculated as <code>(nir - red) / (nir + red)</code> (<a href="https://www.sciencedirect.com/science/article/pii/0034425779900130?via%3Dihub">Tucker, 1979</a>).</li> <li><strong>Normalized Difference Tillage Index (NDTI)</strong>, also known as Normalized Burn Ratio 2 (NBR2), used in tillage detection, post-fire recovery studies, and soil sealing identification. It is calculated as <code>(swir1 - swir2) / (swir1 + swir2)</code> (<a href="https://www.asprs.org/wp-content/uploads/pers/1997journal/jan/1997_jan_87-93.pdf">Van Deventer et al., 1997</a>).</li> <li><strong>Soil Adjusted Vegetation Index (SAVI)</strong> is more effective in areas with sparse vegetation by minimizing the impact of soil brightness on vegetation sensing. It is calculated as <code>((nir - red) / (nir + red + 0.5)) * 1.5</code> (<a href="https://www.sciencedirect.com/science/article/pii/003442578890106X">Huete, 1988</a>).</li> <li><strong>Fraction of Absorbed Photosynthetically Active Radiation (FAPAR)</strong> provides a more direct measurement of plant productivity. It is calculated as <code>((ndvi - 0.03) * (0.95 - 0.001)) / (0.96 - 0.03) + 0.001</code> (<a href="https://zslpublications.onlinelibrary.wiley.com/doi/abs/10.1002/rse2.74">Robinson et al., 2018</a>).</li> <li><strong>Normalized Difference Water Index (NDWI)</strong> provides insights into water dynamics and climatic characteristics. It is calculated as <code>(nir - swir1) / (nir + swir1)</code> (<a href="https://www.sciencedirect.com/science/article/pii/S0034425796000673">Gao, 1996</a>).</li> <li><strong>Normalized Difference Snow Index (NDSI)</strong> helps identify snowy areas. It is calculated as <code>(green - swir1) / (green + swir1)</code> (<a href="https://ieeexplore.ieee.org/abstract/document/1645275/">Salomonson and Appel, 2006</a>).</li> </ul> <h2><strong>As a part of a Data Cube</strong></h2> <p>This data represents a subset of the <a href="../records/10776892">Time-series of Landsat-based Spectral Indices (EU, 30m) data cube</a>. For a comprehensive overview and full dataset information, please visit the landing page of this data cube using the provided link.</p> <ul> <li>To cite this dataset, refer to the DOI available on the landing page.</li> <li>To access other data layers in the data cube, use the navigation catalog on the landing page as well.</li> </ul> <h2><strong>Support</strong></h2> <p>If you discover a bug, artifact, or inconsistency, or if you have a question, please raise a <a href="https://github.com/AI4SoilHealth/SoilHealthDataCube/issues">Github Issue</a>!</p>
Landsat-based Spectral Indices for pan-EU 2000-2022 - Bimonthly predictor (2000-01-01/2000-02-28): Spectral indices
<h2><strong>Data information</strong></h2> <p>This dataset includes widely used indices for 2000-01-01/2000-02-28. It covers key aspects such as vegetation, crops, soil, and water, available bimonthly (i.e. one value per two months).</p> <ul> <li><strong>Normalized Difference Vegetation Index (NDVI)</strong> is used to evaluate vegetation health and biomass. It is calculated as <code>(nir - red) / (nir + red)</code> (<a href="https://www.sciencedirect.com/science/article/pii/0034425779900130?via%3Dihub">Tucker, 1979</a>).</li> <li><strong>Normalized Difference Tillage Index (NDTI)</strong>, also known as Normalized Burn Ratio 2 (NBR2), used in tillage detection, post-fire recovery studies, and soil sealing identification. It is calculated as <code>(swir1 - swir2) / (swir1 + swir2)</code> (<a href="https://www.asprs.org/wp-content/uploads/pers/1997journal/jan/1997_jan_87-93.pdf">Van Deventer et al., 1997</a>).</li> <li><strong>Soil Adjusted Vegetation Index (SAVI)</strong> is more effective in areas with sparse vegetation by minimizing the impact of soil brightness on vegetation sensing. It is calculated as <code>((nir - red) / (nir + red + 0.5)) * 1.5</code> (<a href="https://www.sciencedirect.com/science/article/pii/003442578890106X">Huete, 1988</a>).</li> <li><strong>Fraction of Absorbed Photosynthetically Active Radiation (FAPAR)</strong> provides a more direct measurement of plant productivity. It is calculated as <code>((ndvi - 0.03) * (0.95 - 0.001)) / (0.96 - 0.03) + 0.001</code> (<a href="https://zslpublications.onlinelibrary.wiley.com/doi/abs/10.1002/rse2.74">Robinson et al., 2018</a>).</li> <li><strong>Normalized Difference Water Index (NDWI)</strong> provides insights into water dynamics and climatic characteristics. It is calculated as <code>(nir - swir1) / (nir + swir1)</code> (<a href="https://www.sciencedirect.com/science/article/pii/S0034425796000673">Gao, 1996</a>).</li> <li><strong>Normalized Difference Snow Index (NDSI)</strong> helps identify snowy areas. It is calculated as <code>(green - swir1) / (green + swir1)</code> (<a href="https://ieeexplore.ieee.org/abstract/document/1645275/">Salomonson and Appel, 2006</a>).</li> </ul> <h2><strong>As a part of a Data Cube</strong></h2> <p>This data represents a subset of the <a href="../records/10776892">Time-series of Landsat-based Spectral Indices (EU, 30m) data cube</a>. For a comprehensive overview and full dataset information, please visit the landing page of this data cube using the provided link.</p> <ul> <li>To cite this dataset, refer to the DOI available on the landing page.</li> <li>To access other data layers in the data cube, use the navigation catalog on the landing page as well.</li> </ul> <h2><strong>Support</strong></h2> <p>If you discover a bug, artifact, or inconsistency, or if you have a question, please raise a <a href="https://github.com/AI4SoilHealth/SoilHealthDataCube/issues">Github Issue</a>!</p>
COM-DROUGHTS Drought Indices
<h3><strong>Data Description:</strong></h3> <p>The COM-DROUGHTS Drought Indices dataset consists of daily time series of three drought indicators for meteorological, agricultural, and hydrological drought for 52 lower lying catchments (mean altitude <1500 m asl) in Switzerland for the period 1991-2099. The time series were derived from the newest transiently simulated climate and hydrological scenarios (CH2018, Hydro-CH2018) for Switzerland. Time series are provided on catchment-scale (aggregated).</p> <p>Following Stagge et al. 2015, all years have been harmonized to 365 days by excluding the last day of the year in case of leap years.</p> <p>The time series data is available for 3 representative concentration paths (RCPs, see e.g., Moss et al. 2010 / van Vuuren et al. 2011):<br>- RCP2.6: 8 GCM-RCM model chains<br>- RCP4.5: 16 GCM-RCM model chains<br>- RCP8.5: 20 GCM-RCM model chains</p> <p>Descriptive information is available for:<br>- catchment and climate characteristics (<em>catchment_climate_characteristics.csv</em>)<br>- model calibration and validation statistics (<em>catchment_calibration_validation.csv</em>)<br>- GCM-modelchains and RCP-scenarios (<em>models_scenario_info.csv</em>)</p> <h3><strong>Drought Indices:</strong></h3> <p><strong>1) Meteorological drought: SPI3</strong><br>- represented by the Standardized Precipitation Index (McKee et al. 1993) on a three-monthly basis<br>- derived from the CH2018 climate scenarios for Switzerland (daily mean precipitation aggregated on catchment-area).<br><strong>2) Agricultural drought: ET/PET</strong><br>- represented by the relative evapo(transpi)ration (ET/PET; see e.g., Fuhrer and Jasper 2009) <br>- derived from daily time series of evapotranspiration (ET) and potential evapotranspiration (PET) (output of the Hydro-CH2018 Runoff ensemble / hydrological model simulations; see Muelchi et al. 2021)).<br><strong>3) Hydrological drought: M7Q</strong><br>- represented by the 7-day average streamflow (M7Q)<br>- derived from daily runoff time series (Hydro-CH2018 Runoff ensemble / hydrological model simulations; see Muelchi et al. 2021).</p> <h3><strong>Data Organization:</strong></h3> <p>- Dataset provided as .zip-File<br>- Data files are provided in <em>.csv</em>-Format<br>- A <em>README.txt</em> describes folder, data structure and file-naming convention.<br>- info-files with keys and information on catchments and GCM-RCM chains</p> <p><strong>Drought indices data is structured as follows:</strong><br><strong>> <em><strong>F</strong>older:</em></strong> INDEX_NAME<br>--> containing all data-files: <br>1 File per GCM_RCM model chain containing daily time series for all 52 catchments and index (variations)</p> <p>File-Naming Convention: [INDEX_NAME]_[SCENARIO]_[GCM_RCM_RESOLUTION_SCENARIO].csv<br><br>Example: <em>SPI/SPI_RCP26_KNMI-RACMO_HADGEM_EUR44_RCP26.csv</em></p> <h3><strong>Further reading & methodological details:</strong></h3> <p>The following paper based on this dataset provides more detailed information on the datasets, the underlying methods and associated datasets and literature for further reading. </p> <div> <div>von Matt, C. N., Muelchi, R., Gudmundsson, L., and Martius, O.: Compound droughts under climate change in Switzerland, Nat. Hazards Earth Syst. Sci., 24, 1975–2001, https://doi.org/10.5194/nhess-24-1975-2024, 2024.</div> <div><a href="https://nhess.copernicus.org/articles/24/1975/2024/nhess-24-1975-2024.html">https://nhess.copernicus.org/articles/24/1975/2024/nhess-24-1975-2024.html</a></div> </div> <h3><strong>Funding:</strong></h3> <p>The COM-DROUGHTS project was funded by the Swiss Federal Office for the Environment (FOEN).</p> <h3><strong>References:</strong></h3> <p><strong>1</strong><strong>)</strong> CH2018 Project Team, 2018. CH2018 - Climate Scenarios for Switzerland. National Centre for Climate Services. https://doi.org/doi: 10.18751/Climate/Scenarios/CH2018/1.0<br><strong>2)</strong> Fuhrer, J., Jasper, K., 2009. Bewässerungsbedürftigkeit von Acker- und Grasland im heutigen Klima. Agrarforschung 10, 396–401.<br><strong>3)</strong> McKee, T., Doesken, N., Kleist, J., 1993. THE RELATIONSHIP OF DROUGHT FREQUENCY AND DURATION TO TIME SCALES. Eight conference on applied climatology, 17-22 January 1993, Anaheim, California.<br><strong>4)</strong> Moss, R. H., Edmonds, J. A., Hibbard, K. A., Manning, M. R., Rose, S. K., van Vuuren, D. P., Carter, T. R., Emori, S., Kainuma, M., Kram, T., Meehl, G. A., Mitchell, J. F. B., Nakicenovic, N., Riahi, K., Smith, S. J., Stouffer, R. J., Thomson, A. M., Weyant, J. P., and Wilbanks, T. J.: The next generation of scenarios for climate change research and assessment, Nature, 463, 747–756, https://doi.org/10.1038/nature08823, 2010.<br><strong>5)</strong> Muelchi, R., Rössler, O., Schwanbeck, J., Weingartner, R., Martius, O., 2021. An ensemble of daily simulated runoff data (1981–2099) under climate change conditions for 93 catchments in Switzerland (Hydro-CH2018-Runoff ensemble). Geoscience Data Journal 9, 46–57. https://doi.org/10.1002/gdj3.117<br><strong>6) </strong>Stagge, J. H., Tallaksen, L. M., Gudmundsson, L., Van Loon, A. F., and Stahl, K.: Candidate Distributions for Climatological Drought Indices (SPI and SPEI), International Journal of Climatology, 35, 4027–4040, https://doi.org/10.1002/joc.4267, 2015.<br><strong>7)</strong> van Vuuren, D. P., Edmonds, J., Kainuma, M., Riahi, K., Thomson, A., Hibbard, K., Hurtt, G. C., Kram, T., Krey, V., Lamarque, J.-F., Masui, T., Meinshausen, M., Nakicenovic, N., Smith, S. J., and Rose, S. K.: The representative concentration pathways: an overview, Climatic Change, 109, 5, https://doi.org/10.1007/s10584-011-0148-z, 2011.</p>
Zonal Statistics of Climate Indicators from ERA5-Land for Brazilian Municipalities, 2023
<p>Climate indicators are used in several statistical models for many research areas and are specially important for modelling Climate Sensitive Diseases (CSD) incidence. Those models usually adopts a lattice structure, where its data is aggregated at administrative boundaries (e.g. disease incidence), but climate indicators are usually presented in a continuous regular grid format.</p> <p>To make climate indicators compatible with lattice structures, zonal statistics may be adopted. Zonal statistics are descriptive statistics calculated using a set of cells that spatially intersects a given spatial boundary. For each boundary in a map, statistics like average, maximum value, minimum value, standard deviation, and sum are obtained to represent the cell's values that intersect the boundary.</p> <p>This dataset present zonal statistic of climate indicators computed from Copernicus ERA5-Land daily aggregates for the Brazilian municipalities, for the year of 2023.</p>
National price indices of materials and labor in Spain
<p>The national materials and labor price index in Spain provides a comprehensive measure of the costs associated with construction and industry in the country. This unique index reflects fluctuations in the prices of a wide range of materials, such as glass, chemicals, wood, aluminum, copper, steel materials, plastic products, spotlights and luminaires, ceramics and others, as well as the labor costs associated with the workforce in these sectors. For each material we have a month-by-month index starting from 2012 until a couple of months in 2023, the data until 2021 were validated by the INE<a href="https://www.ine.es/index.htm">(Instituto Nacional de Estadística)</a></p>
ARISE-SAI_1.5 : CESM2 Extreme Precipitation and Temperature Indices
<p>Assessing Responses and Impacts of Solar climate intervention on the Earth system with Stratospheric Aerosol Injection (ARISE-SAI) is a set of simulations carried out with the Community Earth System Model, version 2 with the Whole Atmosphere Community Climate Model, version 6 (CESM2(WACCM6)) that aims at simulating a plausible deployment of solar climate intervention of stratospheric aerosol injection to enable community assessment of responses of the Earth system.</p> <p>This dataset uses the first set of simulations, called ARISE-SAI-1.5, that utilized the middle-of-the-road SSP2-4.5 emission scenario, and targetted a global mean surface air temperature near 1.5°C above the pre-industrial value. ARISE-SAI-1.5 is described in Richter et al. (2022). Selected data are available at Richter & Visioni (2022a,b).</p> <p>The files contained here contain processed annual daily extremes of surface temperature (TREFHT) and total precipitation (PRECT) from the ARISE-SAI-1.5 simulations and companion SSP245 simulations. Indices are those recommended by the WCRP Expert Team on Climate Change Detection Indices, Zhang et al. 2011). Methods to calculate the indices are also described in Tye et al. (2022).</p> <p><strong>Precipitation Indices</strong></p> <p>PRCPTOT, SDII, RX1D, RX5D, R10mm, R20mm, CDD, CWD, P95TOT, P99TOT</p> <p><strong>Temperature Indices</strong></p> <p>TNN, TNX, FD, TR, TN90, TN10, TN90p, TN10p, TXX, TXN, ID, SU, TX90, TX10, TX10p, TX90p, WSDI</p> <p>Where T?10 is the number of days below an annual 10th percentile threshold and T?90 is the number of days above an annual 90th percentile threshold (i.e. around 30 days per year).</p> <p>T?10p as defined by ETCCDI is the frequency of days below the rolling 5-day average climatological day of year 10th percentile. This threshold is also used for the cold spell duration index (CSDI), or consecutive days that are cool for the season.</p> <p>T?90p as defined by ETCCDI is the frequency of days above the rolling 5-day average climatological day of year 90th percentile. This threshold is also used for the warm spell duration index (WSDI), or consecutive days that are warm for the season.</p>
Dataset on Physics-Based Indicators for Optimizing Phase Change Material Effectiveness in Building Design
<p>This research dataset includes the results as well as the EnrgyPlus models developed to investigate and validate newly proposed indicators to quantify the effectiveness of phase change materials in buildings.</p>
Dataset for "Implementation of disequilibrium chemistry to spectral retrieval code ARCiS and application to 16 exoplanet transmission spectra. Indication of disequilibrium chemistry for HD 209458b and WASP-39b"
<p>This is the supplemental materials for the Astronomy & Astrophysics publication "Implementation of disequilibrium chemistry to spectral retrieval code ARCiS and application to 16 exoplanet transmission spectra. Indication of disequilibrium chemistry for HD 209458b and WASP-39b". Please refer to "README.md" for details.</p>
Indices of the supply of Chinese Higher Education
<p>The higher education indices for 31 Chinese provinces proposed by Borsi, Valerio Mendoza, and Comim (2021). </p>
Mapping Building BioData.pt Indicators against the performance and impact assessment frameworks for research infrastructures of OECD, ESFRI and RI-PATHS project
<p>"Buiding BioData.pt" indicators observed in international frameworks for performance and impact assessment of research infrastructures, namely, OECD, ESFRI and RI-PATHS.</p>
Relevance of "Building BioData.pt" indicators identified in ESFRI (E), OECD (O) and RI-PATHS (R) impact assessment frameworks
<p>The relevance of the indicators maintained by the "Building BioData.pt" project was assessed against the objectives of different organizations/initiatives: 1) Strategic objectives of BioData.pt; 2) Objectives of the Portuguese Roadmap for Research Infrastructures; 3) Objectives of ELIXIR; 4) Objectives of EOSC; 5) Sustainable Development Goals of the United Nations.</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.