DOI: 10.5281/zenodo.15052974
title: Advancing Ecosystem Monitoring: Global Annual Maps of Biophysical Vegetation Properties (LAIe, FAPAR, FCOVER) for 2019-2024

description: Disclaimer

This resource is currently being under revision. A preprint is available at: https://doi.org/10.21203/rs.3.rs-6343364/v1

The data is available at three resolutions:



1000 m: this deposition

100 m: see related datasets below

20 m: Only on Google Earth Engine (also 1000 m and 100 m available):



LAI: ee.ImageCollection('/ee-speckerfelix/assets/open-earth/lai_predictions-mlp_20m_v01')

FAPAR: ee.ImageCollection('/ee-speckerfelix/assets/open-earth/fapar_predictions-mlp_20m_v01')

FCOVER: ee.ImageCollection('/ee-speckerfelix/assets/open-earth/fcover_predictions-mlp_20m_v01')

GEE-App: Open GEE App

Visualization Code: Open in GEE Code Editor

Example: Mosaicking / Scaling / Filtering Code:  Open in GEE Code Editor 




Abstract

Environmental restoration projects are crucial for ecosystem recovery and biodiversity conservation but monitoring progress at a global scale poses substantial challenges. Publicly funded satellite missions such as Sentinel-2 have great potential to transform ecosystem monitoring due to their high spatial and temporal resolution if they can be reliably linked to ecosystem characteristics. Here, we present the first global, analysis-ready, decametric maps for three key vegetation biophysical properties on an annual basis, including effective leaf area index (LAIe), fraction of absorbed photosynthetically active radiation (FAPAR), and fractional vegetation cover (FCOVER). We utilize a hybrid retrieval approach of the physically based radiative transfer model PROSAIL to directly estimate biophysical variables from multispectral Sentinel-2 images, making use of multiple observations during the peak of the growing season. All retrievals are aggregated into mean values, standard deviations, and the number of observations taken during this period. The maps are available at 20 m, 100 m, and 1000 m spatial resolution for the years 2019 to 2024, totaling approximately 20 TB of analysis-ready data, and are validated using in-situ data from the Ground-Based Observations for Validation (GBOV). The annual temporal and decametric spatial resolution of these maps provides new opportunities for biodiversity and ecosystem monitoring, enabling more effective assessments of restoration efforts and contributing to the development of standardized global monitoring frameworks.

Description

This data set includes:



Annual Effective Leaf Area Index (LAIe) (2019 - 2024):Includes annual mean (mean of retrievals), variation (standard deviation of retrievals) and count (number of cloud-free observations) for LAI at 1000, and 100 m resolution.

Annual Fractional Vegetation Cover (FCOVER) (2019 - 2024):Includes annual mean (mean of retrievals), variation (standard deviation of retrievals) and count (number of cloud-free observations) for FCOVER at 1000, and 100 m resolution.

Annual fraction of absorbed photosynthetically activate radiation (FAPAR) (2019 - 2024):Includes annual mean (mean of retrievals), variation (standard deviation of retrievals) and count (number of cloud-free observations) for FCOVER at 1000, and 100 m resolution.


Higher-resolution 20-meter maps are only available on Google Earth Engine (see this link).

Data Format and Scaling



Data Types: Mean and standard deviation maps are stored as int16. Count maps are stored as uint8.

NoData Values:



Mean and standard deviation maps: -9999

Count maps: 255



Scaling Factors (applies only for int16 maps):



FAPAR and FCOVER: 0.0001

LAI: 0.001




Children depositions (100 m versions)

To download all at once, you can use the following bash script:  Link GitHub





LAI



Mean



2019

2020

2021

2022

2023

2024



Std / Count



2019

2020

2021

2022

2023

2024





FAPAR



Mean



2019

2020

2021

2022

2023

2024



Std / Count



2019

2020

2021

2022

2023

2024





FCOVER



Mean



2019

2020

2021

2022

2023

2024



Std / Count



2019

2020

2021

2022

2023

2024







Code

The code used to generate the data is available at Zenodo, and at the GitHub repository.

Naming 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 describe important properties of the data. In this way, users can search files, prepare data analysis etc, without needing to open files. The fields are:



Variable name: [lai, fapar, fcover]

Method: rtm.mlp (Radiative transfer model inversion, using multi-layer perceptron regression model)

Position in probability distribution: [mean, std, count]

Spatial resolution: [20m, 100m, 1000]

Depth reference or depth interval s ("s": surface)

Time reference begin: 20190101

Time reference end: 20191231

Bounding box: go (global land without Antarctica)

Coordinate reference system: epsg.4326 World Geodetic System 1984 (WGS 84)

Version Code: v1


Contact

Felix Specker: felix.specker@usys.ethz.ch / speckerfelix@gmail.com

Johan van den Hoogen: johan.vandenhoogen@usys.ethz.ch