Abstract
Grassland plays an important role in German agriculture. The interplay of ecological processes in grasslands secures important ecosystem functions and, thus, ultimately contributes to essential ecosystem services. To sustain, e.g., the provision of fodder or the filter function of soils, agricultural management needs to adapt to site-specific grassland characteristics. Spatially explicit information derived from remote sensing data has been proven instrumental for achieving this. In this study, we analyze the potential of Sentinel-2 data for deriving grassland-relevant parameters. We compare two well-established methods to calculate the aboveground biomass and leaf area index (LAI), first using a random forest regression and second using the soil–leaf-canopy (SLC) radiative transfer model. Field data were recorded on a grassland area in Brandenburg in August 2019, and were used to train the empirical model and to validate both models. Results confirm that both methods are suitable for mapping the spatial distribution of LAI and for quantifying aboveground biomass. Uncertainties generally increased with higher biomass and LAI values in the empirical model and varied on average by a relative RMSE of 11% for modeling of dry biomass and a relative RMSE of 23% for LAI. Similar estimates were achieved using SLC with a relative RMSE of 30% for LAI retrieval, and a relative RMSE of 47% for the estimation of dry biomass. Resulting maps from both approaches showed comprehensible spatial patterns of LAI and dry biomass distributions. Despite variations in the value ranges of both maps, the average estimates and spatial patterns of LAI and dry biomass were very similar. Based on the results of the two compared modeling approaches and the comparison to the validation data, we conclude that the relationship between Sentinel-2 spectra and grassland-relevant variables can be quantified to map their spatial distributions from space. Future research needs to investigate how similar approaches perform across different grassland types, seasons and grassland management regimes.
Highlights
Grasslands make up about 28.51% of the agricultural area in Germany (Destatis 2020) and are, a characteristic landscape element
leaf area index (LAI) models performed with a mean R2 of 0.62 (0.44–0.81) and an normalized the RMSE (NRMSE) of 23% (19–28%; Fig. 5)
The comparison with the field data resulted in a R2 of 79% and a NRMSE of 23% for the LAI
Summary
Grasslands make up about 28.51% of the agricultural area in Germany (Destatis 2020) and are, a characteristic landscape element. In Germany, grasslands are used both intensively and extensively and the management related to both systems requires information on optimal timing for management practices such as fertilization, harrowing, harvesting, or grazing periods. An important parameter that can be derived from remote sensing is the leaf area index (LAI), which resembles the quantification of vegetation foliage per unit of ground area and is related to biosphere–atmosphere interactions such as photosynthesis and evapotranspiration (Chen and Cihlar 1996). The LAI allows insights into the state of vegetation, and renders an important input variable for various modeling approaches, aiming to derive spatially explicit information on parameters that are relevant for grassland management such as soil moisture, yield estimates or fodder quality estimation (Herrmann et al 2005; Löpmeier 1983; Nendel 2014)
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