High resolution and high cadence time series of land surface categories, land use land cover, and land use land cover changes

De Keersmaecker, W., Zanaga, D., Lesiv, M. ORCID: https://orcid.org/0000-0001-9846-3342, Fritz, S. ORCID: https://orcid.org/0000-0003-0420-8549, Senaras, C., Singh Rana, A., Bischke, B., Helber, P., et al. (2023). High resolution and high cadence time series of land surface categories, land use land cover, and land use land cover changes. 10.5281/zenodo.7924341.

Full text not available from this repository.
Project: Project: Advancing the State-of-the-Art for Rapid and Continuous Land Monitoring (RapidAI4EO, H2020 101004356)


A prototype of monthly, 10 m resolution land surface categories, land use land cover (LULC) cover, and LULC change maps derived from Sentinel-2 data over three areas within Belgium, Portugal, and Sicily for the period 2018-2020. The LULC and LULC change maps were independently validated by IIASA. All products were generated within the framework of the RapidAI4EO project, funded from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 101004356.

The data description can be found below. The validation report of the LULC and LULC change maps can be found in validation_LULC.pdf and validation_change.pdf, respectively, and the validation dataset can be found in Lesiv et al. (2023).

Data description

Increasing the cadence of the land cover updates from the typical (multi-)annual to monthly cadence poses several challenges. First, several land cover types are difficult to discriminate without any knowledge of temporal dynamics. For instance, croplands are characterized by a dynamic of vegetation growth and a harvest period (i.e. cycles of bare soil, sparsely vegetated and vegetated periods). This contrasts with grasslands that often lack the harvest period resulting in a bare soil cover. Without this temporal information, it is difficult to distinguish a vegetated cropland field from grassland. Second, phenological changes may introduce a large intra-class variability and thus also confusion between classes. For example, the shedding of leaves during autumn or wilting of herbaceous vegetation in dry summer periods introduces spectral variability within land cover classes.

To overcome these challenges, we developed a workflow with two main phases. The first phase aims to map land surface categories (LSC) at a monthly resolution. The next phase uses the resulting monthly LSC probability time series to classify land cover.

Item Type: Data
Additional Information: Creative Commons Attribution 4.0 International
Research Programs: Advancing Systems Analysis (ASA)
Advancing Systems Analysis (ASA) > Novel Data Ecosystems for Sustainability (NODES)
Depositing User: Luke Kirwan
Date Deposited: 08 Aug 2023 07:35
Last Modified: 08 Aug 2023 07:35
URI: https://pure.iiasa.ac.at/18988

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