Lab

Earth Observation Lab

About the lab

"We advance terrestrial Earth observation from space"
Our focus is on developing and applying remote sensing methods to better understand global change related to land systems. We map land cover and land use and related spatio-temporal change patterns. Applications extend from agriculture (both cropland and grassland), over forest ecosystems to urban areas. The analysis of long and dense time series from Landsat and Sentinel data is core for our research - from landscape to continental scales. Hyperspectral image analysis is a second cornerstone of the Geomatics Lab.

Featured research (2)

Land cover and land use change monitoring is fundamental for ecosystem services, global biodiversity, food security, and climate change analyses. To provide management relevant information, land cover and land use change analyses need to be carried out at suitable spatial and temporal scales. Remote sensing data are an outstanding source of information on the Earth surface and moderate-to-high spatial resolution observations are routinely used for land cover and land use change monitoring. Ever-increasing archives of satellite data allow to obtain information at the ‘field level’ going back to, as early as, the 1980s. Among others, Copernicus Sentinel-2 missions and NASA/USGS Landsat program provide the most widely accessible satellite observations. While the Sentinel-2 mission offers high revisit time and 10- to 20 m resolution, its observation record is still relatively short. Landsat satellites have been acquiring data every 16 days at 30-m resolution ensuring an uninterrupted data record since the early 1980s. Convergence between technical specifications of both satellite systems therefore creates a unique opportunity for data integration for land cover and land use change analyses, which has been implemented, among others, in the Harmonized Landsat Sentinel (HLS) project and the Sen2like framework. Here we analyzed potential gains and tradeoffs of synergetic use of Sentinel-2 and Landsat collection 2 time series for long-term grassland monitoring. Specifically, i) we quantified the increase in density of clear-sky observations when supplementing 2015-2021 Landsat observations with Sentinel-2 time series; ii) we evaluated whether integrating 2015 2021 Sentinel 2 data into the 1984-2021 Landsat time series enhances quality of the resulting time series for long-term analyses. We implemented our analysis for test sites across Europe characterized by different climate and hence different cloud cover probability. We used two most popular variants of data aggregation to construct annual time series: i) a single annual data point per-pixel representing land cover conditions during a selected phenology period, here the summer, and derived as a maximum value composite; ii) annual sums of monthly composites cumulated over the pixel-specific growing seasons, corresponding to GPP. We compared the utility of Landsat – Sentinel-2 integration for long-term grassland monitoring using all available clear-sky pixels in 2015-2021 Sentinel-2 and 1984-12021 Landsat observations. We compared the frequency of the clear sky acquisitions calculated based on 2015 2021 Landsat time series and combined Landsat – Sentinel-2 data. We integrated Landsat and Sentinel-2 time series for long-term trend analysis through ground cover fraction estimates. We used the Land Use/Cover Area frame Survey (LUCAS) to identify compatible Landsat- and Sentinel-2-specific image endmembers characteristic for temperate grassland ecosystems (i.e., green vegetation, non photosynthetic vegetation and soil) and ran Spectral Mixture Analyses on each available pixel using the corresponding image endmembers. We consolidated both time series, and derived the maximum summer composites and monthly composites used for annual sums prioritizing Sentinel-2 data and pixels with the lowest unmixing RMSE. When needed, we predicted missing monthly data (Lewińska et al., 2021) before calculating annual sums of ground cover fractions, i.e., Cumulative Endmember Fractions (Lewińska et al., 2021, 2020). To limit interference of variance in the length of snow cover, we calculated Cumulative Endmember Fractions only for photosynthetically active periods defined on a per pixel basis and unified across the time series. We compared the resulting 2015-2021 time series by measuring the absolute difference between summer maximum value composites and Cumulative Endmember Fractions (for all three ground cover fractions) calculated for Landsat only and integrated Landsat – Sentinel 2 time series. Finally, we analyzed differences in 1984 2021 long term trends in grassland ground covers calculated using summer maximum value composite and Cumulative Endmember Fractions, based on Landsat time series and integrated Landsat – Sentinel-2 time series. We analyzed trends in all three ground cover fractions excluding the effect of temporal autocorrelation (Ives et al., 2021). Our work evaluates the benefits of Landsat and Sentinel-2 integration for long-term grassland analyses requiring different time series length and data availability. Our findings allow a deeper understanding of the tradeoffs between, on the one hand, enhanced data quality and observation frequency and, on the other hand, efforts of integrating multi-source data from Landsat and Sentinel-2. Moreover, we present here an alternative workflow for integration of Sentinel-2 and Landsat archives for grassland monitoring based on time series of ground cover fractions.
Severe droughts caused unprecedented impacts on grasslands in Central Europe in 2018 and 2019. Yet, spatially varying drought impacts on grasslands remain poorly understood as they are driven by complex interactions of environmental conditions and land management. Sentinel-2 time series offer untapped potential for improving grassland monitoring during droughts with the required spatial and temporal detail. In this study, we quantified drought effects in a major Central European grassland region from 2017 to 2020 using a regression-based unmixing framework. The Sentinel-2-based intra-annual time series of photosynthetic vegetation (PV), non-photosynthetic vegetation (NPV), and soil fractional cover provide easily interpretable quantities relevant for understanding drought effects on grasslands. Fractional cover estimates from Sentinel-2 matched in-situ conditions observed during field visits. The comparison to a multitemporal reference dataset showed the best agreement for PV cover (MAE = 7.2%). Agreement was lower for soil and NPV, but we observed positive relationships between fractional cover from Sentinel-2 and the reference data with MAE = 10.1% and MAE = 15.4% for soil and NPV, respectively. Based on the fractional cover estimates, we derived a Normalized Difference Fraction Index (NDFI) time series contrasting NPV and soil cover relative to PV. In line with meteorological and soil moisture drought indices, and with the Normalized Difference Vegetation Index (NDVI), NDFI time series showed the most severe drought impacts in 2018, followed by less severe, but persisting effects in 2019. Drought-specific metrics from NDFI time series revealed a high spatial variability of onset, duration, impact, and end of drought effects on grasslands. Evaluating drought metrics on different soil types, we found that grasslands on less productive, sandy Cambisols were strongly affected by the drought in 2018 and 2019. In comparison, grasslands on Gleysols and Histosols were less severely impacted suggesting a higher drought resistance of these grasslands. Our study emphasizes that the high temporal and spatial detail of Sentinel-2 time series is mandatory for capturing relevant vegetation dynamics in Central European lowland grasslands under drought.

Lab head

Patrick Hostert
Department
  • Department of Geography

Members (15)

Dirk Pflugmacher
  • Humboldt-Universität zu Berlin
David Frantz
  • Universität Trier
Akpona Okujeni
  • Humboldt-Universität zu Berlin
Philippe Rufin
  • Université Catholique de Louvain - UCLouvain
Andreas Rabe
  • Humboldt-Universität zu Berlin
Jan Knorn
  • Humboldt-Universität zu Berlin
Benjamin Jakimow
  • Humboldt-Universität zu Berlin
Sam Cooper
  • Humboldt-Universität zu Berlin
Dirk Pflugmacher
Dirk Pflugmacher
  • Not confirmed yet