Ensemble Machine Learning Prediction of Potential FAPAR: Monthly time-series 2021 and Long-Term Comparison with Actual FAPAR

General Description The dataset contains composites at 250 m spatial resolution of (1) monthly potential FAPAR for the year 2021 from ensemble ML model predictions, (2) the model deviance for each prediction, (3) the yearly average of potential FAPAR, (4) the yearly average of actual FAPAR and (5) t...

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Main Authors: Julia Hackländer, Leandro Parente, Davide Consoli, Tomislav Hengl
Format: Article in Journal/Newspaper
Language:unknown
Published: Zenodo 2023
Subjects:
Online Access:https://doi.org/10.5281/zenodo.8404136
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spelling ftzenodo:oai:zenodo.org:8404136 2024-09-15T17:47:47+00:00 Ensemble Machine Learning Prediction of Potential FAPAR: Monthly time-series 2021 and Long-Term Comparison with Actual FAPAR Julia Hackländer Leandro Parente Davide Consoli Tomislav Hengl 2023-10-03 https://doi.org/10.5281/zenodo.8404136 unknown Zenodo https://zenodo.org/communities/oemc-project https://zenodo.org/communities/eu https://doi.org/10.5281/zenodo.8403714 https://doi.org/10.5281/zenodo.8404136 oai:zenodo.org:8404136 info:eu-repo/semantics/openAccess Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode info:eu-repo/semantics/article 2023 ftzenodo https://doi.org/10.5281/zenodo.840413610.5281/zenodo.8403714 2024-07-25T21:52:24Z General Description The dataset contains composites at 250 m spatial resolution of (1) monthly potential FAPAR for the year 2021 from ensemble ML model predictions, (2) the model deviance for each prediction, (3) the yearly average of potential FAPAR, (4) the yearly average of actual FAPAR and (5) the yearly average of the difference between actual and potential (actual minus potential) FAPAR. The dataset is based on the 95th percentile of the monthly aggregated FAPAR derived from 250 m 8 d GLASS V6 FAPAR. Potential FAPAR was predicted by fitting an ensemble ML model using globally distributed training points (cca 3 Mio) and a set of 52 biophysical covariates including several layers related to human pressure. The code for modeling potential FAPAR is openly available at https://github.com/Open-Earth-Monitor/Global_FAPAR_250m. The dataset can be used in many applications like land degradation modeling, land productivity mapping, and land potential mapping. Data Details Time period: January 2021 - December 2021 Type of data: Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) How the data was collected or derived: Derived from 250m 8 d GLASS V6 FAPAR Statistical methods used: Ensemble machine learning Limitations or exclusions in the data: The dataset does not include data for Antarctica. Coordinate reference system: EPSG:4326 Bounding box (Xmin, Ymin, Xmax, Ymax): (-180.00000, -62.0008094, 179.9999424, 87.37000) Spatial resolution: 1/480 d.d. = 0.00208333 (250m) Image size: 172,800 x 71,698 File format: Cloud Optimized Geotiff (COG) format. Support If you discover a bug, artifact, or inconsistency, or if you have a question please use some of the following channels: Technical issues and questions about the code: GitLab Issues General questions and comments: LandGIS Forum Name convention To ensure consistency and ease of use across and within the projects, we follow the standard Open-Earth-Monitor file-naming convention. The convention works with 10 fields that describes important properties of the ... Article in Journal/Newspaper Antarc* Antarctica Zenodo
institution Open Polar
collection Zenodo
op_collection_id ftzenodo
language unknown
description General Description The dataset contains composites at 250 m spatial resolution of (1) monthly potential FAPAR for the year 2021 from ensemble ML model predictions, (2) the model deviance for each prediction, (3) the yearly average of potential FAPAR, (4) the yearly average of actual FAPAR and (5) the yearly average of the difference between actual and potential (actual minus potential) FAPAR. The dataset is based on the 95th percentile of the monthly aggregated FAPAR derived from 250 m 8 d GLASS V6 FAPAR. Potential FAPAR was predicted by fitting an ensemble ML model using globally distributed training points (cca 3 Mio) and a set of 52 biophysical covariates including several layers related to human pressure. The code for modeling potential FAPAR is openly available at https://github.com/Open-Earth-Monitor/Global_FAPAR_250m. The dataset can be used in many applications like land degradation modeling, land productivity mapping, and land potential mapping. Data Details Time period: January 2021 - December 2021 Type of data: Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) How the data was collected or derived: Derived from 250m 8 d GLASS V6 FAPAR Statistical methods used: Ensemble machine learning Limitations or exclusions in the data: The dataset does not include data for Antarctica. Coordinate reference system: EPSG:4326 Bounding box (Xmin, Ymin, Xmax, Ymax): (-180.00000, -62.0008094, 179.9999424, 87.37000) Spatial resolution: 1/480 d.d. = 0.00208333 (250m) Image size: 172,800 x 71,698 File format: Cloud Optimized Geotiff (COG) format. Support If you discover a bug, artifact, or inconsistency, or if you have a question please use some of the following channels: Technical issues and questions about the code: GitLab Issues General questions and comments: LandGIS Forum Name convention To ensure consistency and ease of use across and within the projects, we follow the standard Open-Earth-Monitor file-naming convention. The convention works with 10 fields that describes important properties of the ...
format Article in Journal/Newspaper
author Julia Hackländer
Leandro Parente
Davide Consoli
Tomislav Hengl
spellingShingle Julia Hackländer
Leandro Parente
Davide Consoli
Tomislav Hengl
Ensemble Machine Learning Prediction of Potential FAPAR: Monthly time-series 2021 and Long-Term Comparison with Actual FAPAR
author_facet Julia Hackländer
Leandro Parente
Davide Consoli
Tomislav Hengl
author_sort Julia Hackländer
title Ensemble Machine Learning Prediction of Potential FAPAR: Monthly time-series 2021 and Long-Term Comparison with Actual FAPAR
title_short Ensemble Machine Learning Prediction of Potential FAPAR: Monthly time-series 2021 and Long-Term Comparison with Actual FAPAR
title_full Ensemble Machine Learning Prediction of Potential FAPAR: Monthly time-series 2021 and Long-Term Comparison with Actual FAPAR
title_fullStr Ensemble Machine Learning Prediction of Potential FAPAR: Monthly time-series 2021 and Long-Term Comparison with Actual FAPAR
title_full_unstemmed Ensemble Machine Learning Prediction of Potential FAPAR: Monthly time-series 2021 and Long-Term Comparison with Actual FAPAR
title_sort ensemble machine learning prediction of potential fapar: monthly time-series 2021 and long-term comparison with actual fapar
publisher Zenodo
publishDate 2023
url https://doi.org/10.5281/zenodo.8404136
genre Antarc*
Antarctica
genre_facet Antarc*
Antarctica
op_relation https://zenodo.org/communities/oemc-project
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https://doi.org/10.5281/zenodo.8403714
https://doi.org/10.5281/zenodo.8404136
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op_rights info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
op_doi https://doi.org/10.5281/zenodo.840413610.5281/zenodo.8403714
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