SBAM (Satellite Based Agricultural Monitoring) is a project funded by Italian Space Agency in the framework of Italian-Kenya cooperation. The project has four main objectives: a) to produce an updated map of the agricultural areas for Kenya based on Landsat 8 and Sentinel 2 imagery; b) to develop an automatic monitoring system able to classify agricultural areas and detect land use changes; c) to develop and deliver to the Kenyan partner of the project a system capable to download and process automatically Landsat8, Sentinel2, MODIS and MSG/SEVIRI images by providing standard products (vegetation indices, statistics, temporal analysis, etc.); d) to provide a tool for assessing changes in the agricultural area stability and crop yield. and study the feasibility of a tool capable to forecast crop yields. The paper is devoted to describe the activity carried out in the field of forecasting crop yield by using biomass estimate based on SAR images. The results obtained by using images acquired by X-band (Cosmo-Skymed), C-Band (Sentinel-1) and L-band (PALSAR) systems on a study area devoted to sugarcane will be described.

Sugarcane biomass estimate based on sar imagery: A radar systems comparison / Laneve, Giovanni; Marzialetti, Pablo; Luciani, Roberto; Fusilli, Lorenzo; Mulianga, Betty. - ELETTRONICO. - 1:(2017), pp. 5834-5837. ((Intervento presentato al convegno IEEE International Geoscience and Remote Sensing Symposium (IGARSS) tenutosi a Fort Worth, TX (USA) nel 23 - 28 Luglio 2017 [10.1109/IGARSS.2017.8128335].

Sugarcane biomass estimate based on sar imagery: A radar systems comparison

Laneve, Giovanni
;
Marzialetti, Pablo
;
Luciani, Roberto
;
Fusilli, Lorenzo
;
2017

Abstract

SBAM (Satellite Based Agricultural Monitoring) is a project funded by Italian Space Agency in the framework of Italian-Kenya cooperation. The project has four main objectives: a) to produce an updated map of the agricultural areas for Kenya based on Landsat 8 and Sentinel 2 imagery; b) to develop an automatic monitoring system able to classify agricultural areas and detect land use changes; c) to develop and deliver to the Kenyan partner of the project a system capable to download and process automatically Landsat8, Sentinel2, MODIS and MSG/SEVIRI images by providing standard products (vegetation indices, statistics, temporal analysis, etc.); d) to provide a tool for assessing changes in the agricultural area stability and crop yield. and study the feasibility of a tool capable to forecast crop yields. The paper is devoted to describe the activity carried out in the field of forecasting crop yield by using biomass estimate based on SAR images. The results obtained by using images acquired by X-band (Cosmo-Skymed), C-Band (Sentinel-1) and L-band (PALSAR) systems on a study area devoted to sugarcane will be described.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1048694
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