The Sentinel-2 data by European Space Agency were recently made available for free. Their technical features suggest synergies with Landsat-8 dataset by NASA (National Aeronautics and Space Administration), especially in the agriculture context were observations should be as dense as possible to give a rather complete description of macro-phenology of crops. In this work some preliminary results are presented concerning geometric and spectral consistency of the two compared datasets. Tests were performed specifically focusing on the agriculture-devoted part of Piemonte Region (NW Italy). Geometric consistencies of Sentinel-2 and Landsat-8 datasets were tested “absolutely” (in respect of a selected reference frame) and “relatively” (one in respect of the other) by selecting, respectively, 160 and 100 well distributed check points. Spectral differences affecting at-the-ground reflectance were tested after images calibration performed by dark object subtraction approach. A special focus was on differences affecting derivable NDVI and NDWI spectral indices, being the most widely used in the agriculture remote sensing application context. Results are encouraging and suggest that this approach can successfully enter the ordinary remote sensing-supported precision farming workflow

Preliminary Tests and Results Concerning Integration of Sentinel-2 and Landsat-8 OLI for Crop Monitoring

LESSIO, ANDREA;FISSORE, VANINA;BORGOGNO MONDINO, Enrico Corrado
Last
2017-01-01

Abstract

The Sentinel-2 data by European Space Agency were recently made available for free. Their technical features suggest synergies with Landsat-8 dataset by NASA (National Aeronautics and Space Administration), especially in the agriculture context were observations should be as dense as possible to give a rather complete description of macro-phenology of crops. In this work some preliminary results are presented concerning geometric and spectral consistency of the two compared datasets. Tests were performed specifically focusing on the agriculture-devoted part of Piemonte Region (NW Italy). Geometric consistencies of Sentinel-2 and Landsat-8 datasets were tested “absolutely” (in respect of a selected reference frame) and “relatively” (one in respect of the other) by selecting, respectively, 160 and 100 well distributed check points. Spectral differences affecting at-the-ground reflectance were tested after images calibration performed by dark object subtraction approach. A special focus was on differences affecting derivable NDVI and NDWI spectral indices, being the most widely used in the agriculture remote sensing application context. Results are encouraging and suggest that this approach can successfully enter the ordinary remote sensing-supported precision farming workflow
2017
3
4
1
16
http://www.mdpi.com/2313-433X/3/4/49
data integration; sensitivity analysis; Sentinel-2; Landsat-8 OLI; precision agriculture
Lessio, Andrea; Fissore, Vanina; BORGOGNO MONDINO, Enrico Corrado
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2318/1651302
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