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Landscape heterogeneity around flux measurement stations investigated through Sentinel-2 and PROBA-V satellite imagery

Book Contribution - Book Abstract Conference Contribution

This study proposed the exploitation of Sentinel-2 and Proba-V imagery to assess the heterogeneity in the surroundings of EC stations through the multitemporal analysis of NDVI. The observations by these platforms allow computation of NDVI at 10 m (Sentinel-2) and 100 m (Proba-V) spatial resolutions. Such levels of spatial detail allowed the comparison of pixel values within two relevant geographic extents: the footprint of EC stations and the extent of satellite imagery cells commonly used to force flux modelling. The satellite platforms considered for this study exhibit different but complementary strengths. Proba-V allowed fast processing over a large number of stations in order to screen or rank EC stations as function of the spatial heterogeneity. The fine spatial resolution of Sentinel-2 allowed more in-depth spatial analysis. Three methods were implemented to explore the spatial heterogeneity with Sentinel-2 NDVI: the analysis of simple NDVI histogram, spatial heterogeneity index (SHI) and fitting a semi-variogram. The analysis with Proba-V resulted in a ranking of flux tower stations on the basis of NDVI Interquartile Range. This study was conducted in the framework of a research programme aiming at improving estimates of net primary productivity across different biomes. The methods appeared to complement each other: histograms were simple to interpret and offered a clear view on the spread and shape of data (often bi-model in cropped sites); SHI plots enable the visualization of heterogeneity changes throughout a period of time and semi-variograms allow the analysis at different distances and orientations.
Book: Conference on Remote Sensing for Agriculture, Ecosystems, and Hydrology, XX, SEP 10-13, 2019, Berlin, GERMANY
Number of pages: 13
Publication year:2018
Keywords:P1 Proceeding
Authors from:Higher Education
Accessibility:Closed