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 (𝝈𝟎𝑽𝐻, 𝝈𝟎𝑽𝑉, 𝝈𝟎 𝑽𝐻×VV and 𝝈𝟎𝑽𝐻/VV) and field-measured traits. Our results indicate a good agreement between Sentinel-1 time-integrated signal and maize traits, with 𝑹2 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 𝝈0 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.
MOREL Julien;
BAUP Frédéric;
FRANSSON Johan;
SÖDERSTRÖM Mats;
PARSONS David;
HETTA Mårten;
STEELE-DUNNE Susan;
2026-08-20
TAYLOR & FRANCIS LTD
JRC146352
1651-1913 (online),
https://www.tandfonline.com/doi/full/10.1080/09064710.2026.2713875,
https://publications.jrc.ec.europa.eu/repository/handle/JRC146352,
10.1080/09064710.2026.2713875 (online),
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