DOI: 10.5061/dryad.fqz612k3r
title: Impact of data density and endmember definitions on long-term trends in ground cover fractions across European grasslands

description: Long-term monitoring of grasslands is pivotal for ensuring continuity of
 many environmental services and for supporting food security and
 environmental modelling. Remote sensing provides an irreplaceable source
 of information for studying changes in grasslands. Specifically, Spectral
 Mixture Analysis (SMA) allows for quantification of physically meaningful
 ground cover fractions of grassland ecosystems (i.e., green vegetation,
 non-photosynthetic vegetation, and soil), which is crucial for our
 understanding of change processes and their drivers. However, although
 popular due to straightforward implementation and low computational cost,
 ‘classical’ SMA relies on a single endmember definition for each targeted
 ground cover component, thus offering limited suitability and
 generalization capability for heterogeneous landscapes. Furthermore, the
 impact of irregular data density on SMA-based long-term trends in
 grassland ground cover has also not yet been critically addressed. We
 conducted a systematic assessment of i) the impact of data density on
 long‑term trends in ground cover fractions in grasslands; and ii) the
 effect of endmember definition used in ‘classical’ SMA on pixel- and
 map-level trends of grassland ground cover fractions. We performed our
 study for 13 sites across European grasslands and derived the trends based
 on the Cumulative Endmember Fractions calculated from monthly composites.
 We compared three different data density scenarios, i.e., 1984-2021
 Landsat data record as is, 1984-2021 Landsat data record with the monthly
 probability of data after 2014 adjusted to the pre‑2014 levels, and the
 combined 1984-2021 Landsat and 2015-2021 Sentinel-2 datasets. For each
 site we ran SMA using a selection of site‑specific and generalized
 endmembers, and compared the pixel- and map-level trends. Our results
 indicated no significant impact of varying data density on the long-term
 trends from Cumulative Endmember Fractions in European grasslands.
 Conversely, the use of different endmember definitions led in some regions
 to significantly different pixel- and map-level long‑term trends raising
 questions about the suitability of the ‘classical’ SMA for complex
 landscapes and large territories. Therefore, we caution against using the
 ‘classical’ SMA for remote‑sensing‑based applications across broader
 scales or in heterogenous landscapes, particularly for trend analyses, as
 the results may lead to erroneous conclusions.
description: For the detailed description of the methodology used to derived
 the datasets please refer to the related publication: <br>Lewińska
 K.E., Okujeni A., Kowalski K., Lehnamm F., Radeloff V.C., Leser U.,
 Hostert P., "Impact of data density and endmember definitions on
 long-term trends in ground cover fractions across European
 grasslands". under review in Remote Sensing of Environment, Available
 as preprint: https://doi.org/10.31223/X5D43N
description: # Impact of data density and endmember definitions on long-term trends in
 ground cover fractions across European grasslands --- ## Data description:
 Pixel-level trends in green vegetation (`gv`), non-photosynthetic
 vegetation (`npv`), and soil (`soil`) ground cover fractions derived and
 analyzed in the course of a paper: Lewińska K.E., Okujeni A., Kowalski K.,
 Lehnamm F., Radeloff V.C., Leser U., Hostert P., (2025) Impact of data
 density and endmember definitions on long-term trends in ground cover
 fractions across European grasslands, Remote Sensing of Environment,
 323, https://doi.org/10.1016/j.rse.2025.114736 The datasets are generated
 as GTifs and distributed as three `.tar` archives: * `AR_LND`: comprising
 AR(1) trends derived based on the 1984-2021 Landsat (TM, ETM+, and OLI)
 time series * `AR_LND`_baseline: comprising AR(1) trends derived based on
 1984-2021 Landsat (TM, ETM+, and OLI) with the pixel-level probability of
 cloud-, snow-, and shade-free observation for the 2015-2021 period
 adjusted to the 1984-2014 baseline. * `AR_LND_S2`: comprising AR(1) trends
 derived based on combined 1984-2021 Landsat (TM, ETM+, and OLI) and
 2015-2021 Sentinel 2 (MSI) time series Each archive comprises several
 directories following the `SIT_END` naming convention, with `SIT`
 representing the two or three letter code of a test site and `END`
 representing the endmember set used in the unmixing process. The `SIT`
 codes are: ALP - Apline; BX - Belgium; CR - Crete; DE - Germany; ES -
 Spain; FR - France; IE - Ireland; LX - Luxembourg; PL - Poland; RO -
 Romania; SA - Sardinia; SE - Sweden; UK - United Kingdom. The endmember
 END sets area: ATL - ; SA - Mediterranean ; SE - Boreal; TEM -
 Continental. When the `END` code is the same as the `SIT` code this
 signifies the 'local' set of endmembers i.e. collected on the
 specific site. Other codes represent regional ets of endmembers i.e.,
 generalized over a specific biogeographical region. for SE and SA sites
 the local and regional endmembers are identical. Each `SIT_END` folder
 further comprises between one and four subfolder following the
 `Xxxxx_Yyyyy` naming convention, where `xxxx` and `yyyy` give,
 correspondingly, X and Y coordinates of a tile in a FORCE data cube. We
 used the standard definition for the data cube coordinates given in:
 https://force-eo.readthedocs.io/en/latest/howto/datacube.html Finally,
 each `Xxxxx_Yyyyy` tile comprises three GTiffs with AR(1) trends in green
 vegetation (`gv`), non-photosynthetic vegetation (`npv`), and soil
 (`soil`) ground cover fractions, `gv_AR.tif`, `npv_AR.tif`, and
 `soil_AR.tif`, respectively. Each `*_AR.tif` comprises 4 bands: band 1:
 AR(1) trend in respective ground cover fraction based on 1984-2021 time
 series of annual Cumulative Endmember Fractions band 2: intercept of the
 AR(1) trend in band 1 band 3: pixel-level P-value of the AR(1) trend in
 band 1 band 4: strength of temporal autocorrelation in the 1984-2021 time
 series. All data are available at 30-m resolution and in the LAEA
 projection defined as: ``` gdalsrsinfo -v 'PROJCS["ETRS89 / LAEA
 Europe",GEOGCS["ETRS89",DATUM["European_Terrestrial_Reference_System_1989",SPHEROID["GRS 1980",6378137,298.257222101,AUTHORITY["EPSG","7019"]],TOWGS84[0,0,0,0,0,0,0],AUTHORITY["EPSG","6258"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AUTHORITY["EPSG","4258"]],PROJECTION["Lambert_Azimuthal_Equal_Area"],PARAMETER["latitude_of_center",52],PARAMETER["longitude_of_center",10],PARAMETER["false_easting",4321000],PARAMETER["false_northing",3210000],UNIT["metre",1,AUTHORITY["EPSG","9001"]],AUTHORITY["EPSG","3035"]]' Validate Succeeds PROJ.4 : '+proj=laea +lat_0=52 +lon_0=10 +x_0=4321000 +y_0=3210000 +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs ' OGC WKT : PROJCS["ETRS89 / LAEA Europe", GEOGCS["ETRS89", DATUM["European_Terrestrial_Reference_System_1989", SPHEROID["GRS 1980",6378137,298.257222101, AUTHORITY["EPSG","7019"]], TOWGS84[0,0,0,0,0,0,0], AUTHORITY["EPSG","6258"]], PRIMEM["Greenwich",0, AUTHORITY["EPSG","8901"]], UNIT["degree",0.0174532925199433, AUTHORITY["EPSG","9122"]], AUTHORITY["EPSG","4258"]], PROJECTION["Lambert_Azimuthal_Equal_Area"], PARAMETER["latitude_of_center",52], PARAMETER["longitude_of_center",10], PARAMETER["false_easting",4321000], PARAMETER["false_northing",3210000], UNIT["metre",1, AUTHORITY["EPSG","9001"]], AUTHORITY["EPSG","3035"]] ``` ## Methodology For detailed description of the methodology used in pre-processing and processing of the shared data, please see the linked publication: Lewińska K.E., Okujeni A., Kowalski K., Lehnamm F., Radeloff V.C., Leser U., Hostert P., (2025) Impact of data density and endmember definitions on long-term trends in ground cover fractions across European grasslands, Remote Sensing of Environment, 323, https://doi.org/10.1016/j.rse.2025.114736 ## Sharing & Access information CC license: CC 00 Data are available at datadryad.org as: 10.5061/dryad.fqz612k3r The data were derived based on freely available Landsat surface reflectance Level 2, Tier 1 (Collection 2) scenes from 1984 through 2021 (Thematic Mapper (TM): doi.org/10.5066/P918ROHC;, Enhanced Thematic Mapper (ETM+): https://doi.org/10.5066/P9TU80IG, and Operational Land Imager (OLI): https://doi.org/10.5066/P975CC9B) and Sentinel-2 TOA reflectance Level-1C (pre Collection-1: https://doi.org/10.5270/S2_-d8we2fl). All used satellite data are in the public domain thus adhere to the CC0 licence. ## Disclaimer: The authors accept no responsibility for errors or omissions in this work and shall not be liable for any damage caused by these. ## Funding Information We gratefully acknowledge support from the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – Project-ID 414984028 – SFB 1404 FONDA. ## Please cite the data as: Lewińska K.E., Okujeni A., Kowalski K., Lehnamm F., Radeloff V.C., Leser U., Hostert P., Impact of data density and endmember definitions on long-term trends in ground cover fractions across European grasslands., Dryad dataset, 10.5061/dryad.fqz612k3r and Lewińska K.E., Okujeni A., Kowalski K., Lehnamm F., Radeloff V.C., Leser U., Hostert P., (2025) Impact of data density and endmember definitions on long-term trends in ground cover fractions across European grasslands, Remote Sensing of Environment, 323, https://doi.org/10.1016/j.rse.2025.114736 ---