Rice plays an important role in Italy and particularly in Piemonte Region. It heavily impacts on waters resources determining critical situations related to irrigation management. This work, stimulated by the Agriculture Office of Piemonte Region Administration, tries to point out the potentialities of freely available satellite data to describe both agronomic and water dynamics of rice during its phenological season. SAR (Synthetic Aperture Radar) measurements from Sentinel-1 mission, proved to be effective in describing water dynamics and structure variations of crop. Temporal profiles the back-scattered signal (sima0) were used to describe submersion phases and structural changes of crops. Differently, optical data from Sentinel-2 and Landsat 8 missions, were jointly used to monitor crop health and water content after plants emersion. Spectral indices (NDVI, NDWI, GRVI) time series were used for this purpose. Results, for the 2016 year, demonstrate that this integrated approach can well describe the main rice crop agronomic phases.
MONITORING RICE CROPS IN PIEMONTE (ITALY): TOWARDS AN OPERATIONAL SERVICE BASED ON FREE SATELLITE DATA
CORVINO, GIANMARCO;Lessio, A.;Borgogno Mondino, E.
2018-01-01
Abstract
Rice plays an important role in Italy and particularly in Piemonte Region. It heavily impacts on waters resources determining critical situations related to irrigation management. This work, stimulated by the Agriculture Office of Piemonte Region Administration, tries to point out the potentialities of freely available satellite data to describe both agronomic and water dynamics of rice during its phenological season. SAR (Synthetic Aperture Radar) measurements from Sentinel-1 mission, proved to be effective in describing water dynamics and structure variations of crop. Temporal profiles the back-scattered signal (sima0) were used to describe submersion phases and structural changes of crops. Differently, optical data from Sentinel-2 and Landsat 8 missions, were jointly used to monitor crop health and water content after plants emersion. Spectral indices (NDVI, NDWI, GRVI) time series were used for this purpose. Results, for the 2016 year, demonstrate that this integrated approach can well describe the main rice crop agronomic phases.File | Dimensione | Formato | |
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