National-scale monitoring of grassland vitality – combining spectral databases, machine learning regression modeling, and multidecadal Landsat/Sentinel-2 time series
Authors: Okujeni Akpona , Kowalski Katja , Hostert Patrick 
Published: February 2023

Drought periods will become more frequent, more severe, and longer in the coming decades in many regions of the world as a consequence of climate change. In Central Europe, grassland vegetation substantially deteriorated immediately in response to extreme droughts in recent years with major impacts on livestock farming. A deeper understanding of the grassland response to drought under different environmental and land management characteristics is required for adapting to future droughts. Fractional cover time series of photosynthetic vegetation (PV), non-photosynthetic vegetation (NPV), and soil from remote sensing provide essential information to characterize grassland dynamics and impacts on grassland vitality during drought periods based on continuous, physically meaningful variables across larger areas.Based on the methodological developments in our previous studies (Kowalski et al. 2022, Kowalski et al. 2023), we here present a regression modeling framework that enabled the retrieval of a consistent, multidecadal time series of PV, NPV, and soil fractional cover from a data cube comprising all available Landsat and Sentinel-2 imagery for Germany. Fractional cover time series retrieval relied on spatially and temporally generalized regression models. The generalization step relevant for applying models across space (i.e. entire Germany), time (i.e. 1984 &#8211; 2021), and sensors (i.e. Landsat 5, 7 & 8, Sentinel-2A & 2B) was based on synthetic training data generated from a global spectral library which integrated laboratory and image-based spectral measurements. Investigation of the multidecadal feature spaces of the Landsat and Sentinel-2 sensor families confirmed the compatibility and global applicability of the spectral library as a training source in our regression modeling framework. The application of the generalized regression models to the data cube produced consistent time series of PV, NPV, and soil fractional cover independent from the underlying sensor. This was confirmed by comparing pairs of estimated cover fractions from different sensors for similar dates (&#177; 2 days) relative to each other and to ground reference fractions. We further demonstrate the value of the multidecadal fractional time series as a means for drought monitoring in temperate grasslands. Periods of anomalous vegetation browning could be consistently linked to meteorological and soil moisture drought in the past four decades. Our study demonstrates the value of integrating spectral measurements from various sources, including image-based data and existing in-situ networks, as a means for consistent grassland fractional cover time series retrieval based on generalized regression models and multisensor data cubes.ReferencesKowalski, K., Okujeni, A., Brell, M., Hostert, P., 2022. Quantifying drought effects in Central European grasslands through regression-based unmixing of intra-annual Sentinel-2 time series. Remote Sensing of Environment 268, 112781. https://doi.org/10.1016/j.rse.2021.112781
Kowalski, K., Okujeni, A., Hostert, P., 2023. A generalized framework for drought monitoring across Central European grassland gradients with Sentinel-2 time series. Remote Sensing of Environment 286, 113449. https://doi.org/10.1016/j.rse.2022.113449