DOI: 10.5281/zenodo.8403714
title: Ensemble Machine Learning Prediction of Potential FAPAR: Monthly time-series 2021 and Long-Term Comparison with Actual FAPAR

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 raise a GitHub issue: https://github.com/Open-Earth-Monitor/Global_FAPAR_250m/issues


Reference


Hackländer, J., Parente, L., Ho, Y.-F., Hengl, T., Simoes, R., Consoli, D., Şahin, M., Tian, X., Herold, M., Jung, M., Duveiller, G., Weynants, M., Wheeler, I., (2023?) "Land potential assessment and trend-analysis using 2000–2021 FAPAR monthly time-series at 250 m spatial resolution", submitted to PeerJ, preprint available at: https://doi.org/10.21203/rs.3.rs-3415685/v1


 


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 data. In this way users can search files, prepare data analysis etc, without needing to open files. The fields are:



	
generic variable name: pot.fapar = Potential Fraction of Absorbed Photosynthetically Active Radiation
	
variable procedure combination: eml = ensemble machine learning
	
Position in the probability distribution / variable type: m = mean
	
Spatial support: 250m
	
Depth reference: s = surface
	
Time reference begin time: 20210101 = 2021-01-01
	
Time reference end time: 20211231 = 2021-12-31
	
Bounding box: go = global (without Antarctica)
	
EPSG code: epsg.4326 = EPSG:4326
	
Version code: v20230924 = 2023-09-24 (creation date)