Exploiting Time Series of Sentinel-1 and Sentinel-2 Imagery to Detect Meadow Phenology in Mountain Regions
Karlsen S R
MetadataShow full item record
A synergic integration of Synthetic Aperture Radar (SAR) and optical time series offers an unprecedented opportunity in vegetation phenology monitoring for mountain agriculture management. In this paper, we performed a correlation analysis of radar signal to vegetation and soil conditions by using a time series of Sentinel-1 C-band dual-polarized (VV and VH) SAR images acquired in the South Tyrol region (Italy) from October 2014 to September 2016. Together with Sentinel-1 images, we exploited corresponding Sentinel-2 images and ground measurements. Results show that Sentinel-1 cross-polarized VH backscattering coefficients have a strong vegetation contribution and are well correlated with the Normalized Difference Vegetation Index (NDVI) values retrieved from optical sensors, thus allowing the extraction of meadow phenological phases. Particularly for the Start Of Season (SOS) at low altitudes, the mean difference in days between Sentinel-1 and ground sensors is compatible with the acquisition time of the SAR sensor. However, the results show a decrease in accuracy with increasing altitude. The same trend is observed for senescence. The main outcomes of our investigations in terms of inter-satellite comparison show that Sentinel-1 is less effective than Sentinel-2 in detecting the SOS. At the same time, Sentinel-1 is as robust as Sentinel-2 in defining mowing events. Our study shows that SAR-Optical data integration is a promising approach for phenology detection in mountain regions.
Showing items related by title, author, creator and subject.
Attarzadeh R; Amini J; Notarnicola C; Greifeneder F (2018)This paper presents an approach for retrieval of soil moisture content (SMC) by coupling single polarization C-band synthetic aperture radar (SAR) and optical data at the plot scale in vegetated areas. The study was carried ...
Marin C; Callegari M; Günther D; Bertoldi G; Marke T; Strasser U; Bruzzone L; Zebisch M; Notarnicola C (2018)In this paper, a novel approach for the retrieval of snow wetness is presented for Sentinel-1 (S-1) data. The approach uses the information on snow proprieties provided by the hydroclimatological model AMUNDSEN and confirmed ...
Estimation of Soil Moisture in Vegetation-Covered Floodplains with Sentinel-1 SAR Data Using Support Vector Regression Holtgrave A; Förster M; Greifeneder F; Notarnicola C; Kleinschmit B (2018)Soil moisture (SM) is a significant parameter influencing various environmental processes in hydrology, ecology, and climatology. SAR-derived remote sensing products can be valuable input features for estimating SM. In the ...