A generalized framework for drought monitoring across Central European grassland gradients with Sentinel-2 time series
Creators: name: Kowalski, Katja
name: Okujeni, Akpona
name: Hostert, Patrick
Publisher: name: Humboldt-Universität zu Berlin
Year: 2023
Subjects: subject: Photosynthetic vegetation
subject: PV
subject: Non-photosynthetic vegetation
subject: NPV
subject: Soil
subject: Fractional cover
subject: Spectral unmixing
subject: Support vector regression
subject: Time series
subject: LUCAS
subject: Standardized Precipitation Evapotranspiration Index
subject: Soil moisture
subject: Climate change
subject: 550 Geowissenschaften

description: Fractional cover time series of photosynthetic vegetation (PV), non-photosynthetic vegetation (NPV), and soil from remote sensing provide essential detail to understand how grasslands are affected by recent and future drought periods in the 21st century. In this regard, Sentinel-2A/B offer frequent large-area observations, which have not yet been fully exploited for a spatially continuous drought monitoring of highly dynamic Central European grasslands. In this study, we developed a generalized drought monitoring framework for Central European grasslands linking Sentinel-2 data, field survey information, and spectral unmixing. We first implemented a consistent and repeatable strategy to obtain a grassland spectral library supported by the Europe-wide Land Use/Cover Area frame statistical Survey (LUCAS) and multitemporal Sentinel-2 data. Our library captured the spectral variability of PV, NPV, and soil cover from 12 grassland areas distributed along typical environmental and land use gradients of Central Europe. We trained a generalized regression-based unmixing model with synthetic data generated from the spectral library and compared fractional cover estimates to a multitemporal reference dataset. PV, NPV, and soil were estimated with good accuracy, achieving MAEs of 6.54%, 13.7%, and 12.2%, respectively. Local unmixing models trained on area-specific library subsets were overall outperformed by the generalized model highlighting the value of a comprehensive grassland library for generalized spectral unmixing. Based on fractional cover time series from 2017 to 2021, we calculated time series of the grassland-specific Normalized Difference Fraction Index (NDFI) capturing proportions of NPV and soil relative to PV. Comparison of annual growing season drought metrics derived from the NDFI to annual meteorological drought statistics from the Standardized Precipitation Evapotranspiration Index (SPEI) as well as the Soil Moisture Index (SMI) revealed widespread drought impacts on grasslands during the persistent drought period in Central Europe from 2018 to 2020. While impacts on grasslands overall closely followed meteorological and soil drought conditions, regionally varying drought metrics underline that local to regional environmental and hydrological conditions shaped the drought response of Central European grasslands. Our study emphasizes the value of combining Sentinel-2 data, field survey information, and spectral unmixing to enable drought monitoring across grassland gradients of Central Europe with Sentinel-2 time series.