Estimating Grassland Parameters from Sentinel-2: A Model Comparison Study
Autor: | Heike Bach, Katja Kowalski, Jürgen Pickert, Marcel Schwieder, Patrick Hostert, Julia Bartsch, Kira Pfoch, Marion Buddeberg |
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Rok vydání: | 2020 |
Předmět: |
0106 biological sciences
Geography Planning and Development Spatial distribution Atmospheric sciences 01 natural sciences Grassland Ecosystem services Atmospheric radiative transfer codes Empirical model ddc:550 Earth and Planetary Sciences (miscellaneous) Ecosystem Leaf area index Instrumentation geography Biomass (ecology) geography.geographical_feature_category 04 agricultural and veterinary sciences Soil–leaf-canopy radiative transfer model 550 Geowissenschaften 040103 agronomy & agriculture Spatial ecology 0401 agriculture forestry and fisheries Environmental science Sentinel-2 Grassland biomass 010606 plant biology & botany |
Zdroj: | PFG – Journal of Photogrammetry, Remote Sensing and Geoinformation Science. 88:379-390 |
ISSN: | 2512-2819 2512-2789 |
Popis: | 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. |
Databáze: | OpenAIRE |
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