Time-integrated sentinel-1 backscattering coefficient for monitoring of forage maize development and growth
a pilot study in Sweden
Julien Morel (Swedish University of Agricultural Sciences, European Commission - Joint Research Centre)
Frédéric Baup (Université de Toulouse, Université Paul Sabatier, IRD Institut de Recherche pour le Developpement, Centre National de la Recherche Scientifique (CNRS), Institut National de Recherche Pour L’Agriculture, L’Alimentation et L’Environnement (INRAE), CNES Centre National d'Etudes Spatiales)
Johan E.S. Fransson (Linnaeus University - Växjö)
Mats Söderström (Swedish University of Agricultural Sciences)
David Parsons (Swedish University of Agricultural Sciences)
Mårten Hetta (Swedish University of Agricultural Sciences)
Susan Steele-Dunne (TU Delft - Civil Engineering & Geosciences)
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Abstract
The potential of dense Sentinel-2 time series to serve as a basis for operational crop monitoring systems is hindered by cloud cover, especially at high latitudes. Sentinel-1 data can overcome this limitation, but similar to optical data, are prone to saturation, i.e. when changes in vegetation biomass are not reflected in the remote sensing signal. Time-integration is a strategy commonly used in optical remote sensing to mitigate saturation effects. In this pilot study, we tested whether this approach can also improve the relationship between Sentinel-1 backscatter and maize traits, using Sentinel-1 A and B backscatter data. Our test site consisted of a forage maize experimental field in Sweden. Evaluated plant traits included the number of leaves, the phenological stage, the leaf area index, the dry matter yield and the dry matter content. Linear and logistic models were adjusted between time-integrated values of the backscattering coefficient ((Formula presented.), (Formula presented.), (Formula presented.) and (Formula presented.)) and field-measured traits. Our results indicate a good agreement between Sentinel-1 time-integrated signal and maize traits, with (Formula presented.) of 0.97, 0.93, 0.94, 0.95 and 0.86 for phenological stage, leaf number, leaf area index, dry matter yield and dry matter content, respectively, and systematically outperformed models built with non-cumulative (Formula presented.) values. Our findings also indicate that the time-integrated models perform equally well with data acquired from a single Sentinel-1 satellite. These results, if confirmed for a wider range of geographical extent and management conditions, could pave the way for a remote sensing-based, weather-independent and saturation-insensitive decision support tool.
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