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ShareScore release 0.9.0
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3 results for “Leaf imagery”
Global leaf chlorophyll content (LCC) product from MODIS imagery (2000-2020)
<p>The spatial and temporal distribution of leaf chlorophyll content (LCC) is critical for understanding the capacity of vegetation photosynthesis. Here, a global 8-day leaf chlorophyll content (LCC) dataset at 500-m resolution was generated from MODIS data using a matrix system with two pairs of vegetation indices. </p> <p><strong>The following paper should be cited when using the data:</strong></p> <p>Xu, M., Liu, R., Chen, J.M., Liu, Y., Wolanin, A., Croft, H., He, L., Shang, R., Ju, W., Zhang, Y., He, Y., Wang, R., 2022. A 21-year time-series of global leaf chlorophyll content maps from MODIS imagery. IEEE Trans. Geosci. Remote Sens. <a href="https://doi.org/10.1109/TGRS.2022.3204185">https://doi.org/10.1109/TGRS.2022.3204185</a>.</p> <p>Detailed description of data organization can be found in the uploaded document "1Readme.docx".</p> <p>The whole dataset of MODIS LCC product is from 2000 to 2020. <strong>Due to the data volume limitation of Zenodo, currently the data from 2000-2010 can be downloaded through Google Drive sharing link:</strong></p> <p><a href="https://drive.google.com/drive/folders/11eXetjsAB_ZjFqGs8SXEWzd6byr_8LoW?usp=sharing">https://drive.google.com/drive/folders/11eXetjsAB_ZjFqGs8SXEWzd6byr_8LoW?usp=sharing</a></p> <p><strong>Related dataset: </strong>Mingzhu, X., Liu, R., Chen, J. M., Shang, R., & Liu, Y. (2022). Global leaf chlorophyll content product from MERIS imagery (GLOBMAP MERIS LCC) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.10467919">https://doi.org/10.5281/zenodo.10467919</a></p> <p>For any other questions, please send email to Mingzhu Xu (<a href="mailto:xumzhu@gmail.com">xumzhu@gmail.com</a>).</p>
Data from: Evaluating UAV captured RGB and multispectral imagery as a proxy for visual rating of leaf spot in cultivated peanut
Open the record for dataset details and reuse information.
Global leaf chlorophyll content product from MERIS imagery (GLOBMAP MERIS LCC)
<p>Leaf chlorophyll content (LCC) is an indicator of plant physiological function and is an important parameter in estimating the carbon and water fluxes of terrestrial ecosystems. Here, we produced a new global 7-day LCC product (GLOBMAP MERIS LCC) at 300-m resolution from 2003.01 to 2012.03 using a neural network model based on radiative transfer model simulations from CCI MERIS surface reflectance data. It shows an improvement over the previous MERIS LCC product in capturing LCC seasonal variations in different plant functional types.</p> <p><strong>The following paper should be cited when using the data:</strong><br>Xu, M., Liu, R., Chen, J.M., Shang, R., Liu, Y., Qi, L., Croft, H., Ju, W., Zhang, Y., He, Y., Qiu, F., Li, J., Lin, Q., 2022. Retrieving global leaf chlorophyll content from MERIS data using a neural network method. Isprs J. Photogramm. Remote Sens. 192, 66–82. <a href="https://doi.org/10.1016/j.isprsjprs.2022.08.003">https://doi.org/10.1016/j.isprsjprs.2022.08.003</a>.</p> <p><strong>Due to the data volume limitation of Zenodo, the data we uploaded here are an example tile (h59v10) at 300-m resolution and a global 0.05° composition of multi-year average LCC with a 7-day interval</strong><strong>. The original dataset of GLOBMAP MERIS LCC product can be downloaded through Google Drive sharing link:</strong></p> <p><a href="https://drive.google.com/drive/folders/1HNlBI2hHeUAW-o7HCgLOxbUEh1ak4AX2?usp=sharing">https://drive.google.com/drive/folders/1HNlBI2hHeUAW-o7HCgLOxbUEh1ak4AX2?usp=sharing</a></p> <p><strong>Data format description:<br></strong>Data type: int16<br>Projection: GCS_WGS_1984<br>Scaling factor: 0.1<br>Unit: ug/cm2</p> <p><strong>Related dataset: </strong>Mingzhu Xu, Ronggao Liu, Jing M. Chen, Yang Liu, & Rong Shang. (2021). Global leaf chlorophyll content (LCC) product from MODIS imagery (2000-2020) (Version V1) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.5805575">https://doi.org/10.5281/zenodo.5805575</a></p> <p>For any other questions, please send email to Mingzhu Xu (<a href="mailto:xumzhu@gmail.com">xumzhu@gmail.com</a>).</p>
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International Brain Laboratory public data
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OpenNeuro
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