Search results for "COSMO-SkyMed"

showing 5 items of 5 documents

Coupling SAR X-band and optical data for NDVI retrieval: model calibration and validation on two test areas

2013

Sustainability of modern agro-hydrology requires the knowledge of spatial and temporal variability of vegetation biomass to optimize management of land and water resources. Diversely from optical imaging, temporal resolution of active sensors, such as SAR, is not limited by sky cloudiness; thus, they may be combined with optical imageries to provide a more continuous monitoring of land surfaces. Several new SAR missions (e.g., ALOS-PALSAR, COSMO-SkyMed 1 and 2, TerraSAR-X, TerraSAR-X2, Sentinel 1) acquiring at X-, C- and L-bands and dual polarization capability, are characterized by a short revisit time (from 12 h to ~10 days) and high spatial resolution (<20 m). These satellites could prov…

Synthetic aperture radarL bandMeteorologyBackscatterCloud covermedia_common.quotation_subjectSettore ICAR/02 - Costruzioni Idrauliche E Marittime E IdrologiaContinuous monitoringRadar backscatteringNormalized Difference Vegetation IndexNDVI cross-polarized backscattering DEIMOS-1 COSMO-SkyMed Landsat 7 SCL-offGeographySkyTemporal resolutionSettore AGR/08 - Idraulica Agraria E Sistemazioni Idraulico-ForestaliSettore ICAR/06 - Topografia E Cartografiamedia_commonRemote sensingvegetation index
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Vegetation index retrieval by coupling optical and SAR images

2012

Monitoring spatial and temporal variability of Vegetation Indices (VIs) is important to manage land and water resources, with significant impact on the sustainability of modern agriculture Although algorithms based on optical data give accurate products, cloud cover dramatically reduces the temporal resolution of these outputs. The launch of new Synthetic Aperture Radar (SAR) constellations such as COSMO-Skymed opened new opportunities to develop agro-hydrological applications. Indeed, these satellites may represent a suitable source of data for operational applications due to their high spatial and temporal resolutions (10 m in StripMap PingPong acquisition mode, best revisit time with 4 s…

Synthetic aperture radarMeteorologyBackscatterCloud coverSettore ICAR/02 - Costruzioni Idrauliche E Marittime E IdrologiaX bandLand coverNormalized Difference Vegetation IndexGeographyTemporal resolutionSettore AGR/08 - Idraulica Agraria E Sistemazioni Idraulico-ForestaliImage resolutionSettore ICAR/06 - Topografia E CartografiaRemote sensingNDVI crossed-polarized backscattering DEIMOS COSMO-Skymed
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Investigating the Relationship between X-Band SAR Data from COSMO-SkyMed Satellite and NDVI for LAI Detection

2013

Monitoring spatial and temporal variability of vegetation is important to manage land and water resources, with significant impact on the sustainability of modern agriculture. Cloud cover noticeably reduces the temporal resolution of retrievals based on optical data. COSMO-SkyMed (the new Italian Synthetic Aperture RADAR-SAR) opened new opportunities to develop agro-hydrological applications. Indeed, it represents a valuable source of data for operational use, due to the high spatial and temporal resolutions. Although X-band is not the most suitable to model agricultural and hydrological processes, an assessment of vegetation development can be achieved combing optical vegetation indices (V…

Synthetic aperture radarMeteorologyCOSMO-SkyMedCloud coverSettore ICAR/02 - Costruzioni Idrauliche E Marittime E IdrologiaX bandLand coverRadar backscatteringNormalized Difference Vegetation IndexLAIcross-polarized backscatteringTemporal resolutionDEIMOS-1General Earth and Planetary SciencesEnvironmental scienceSettore AGR/08 - Idraulica Agraria E Sistemazioni Idraulico-ForestaliNormalized Difference Vegetation Index (NDVI)lcsh:QNormalized Difference Vegetation Index (NDVI); LAI; cross-polarized backscattering; DEIMOS-1; COSMO-SkyMedLeaf area indexlcsh:ScienceImage resolutionSettore ICAR/06 - Topografia E CartografiaRemote sensingRemote Sensing
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Time series of Cosmo-SkyMed data for landcover classification and surface parameter retrieval over agricultural sites

2012

This paper reports on the results of an Italian project aimed at investigating the use of X-band COSMO-SkyMed (CSK) SAR data for applications in agriculture and hydrology. Existing classification and retrieval algorithms have been tailored to CSK data and time series of crop, leaf area index and soil moisture maps have been retrieved and assessed through the comparison with in situ data collected over three agricultural sites. In addition, the CSK-derived surface parameters have been integrated into crop growth and hydrologic models and the resulting improvements have been assessed. Results indicate that multi-temporal dual-polarized CSK data are very well-suited for agricultural crop class…

Synthetic aperture radarSeries (mathematics)Contextual image classificationbusiness.industryCOSMO-SkyMedHydrological modellingSettore ICAR/02 - Costruzioni Idrauliche E Marittime E IdrologiaVegetationCOSMO-SkyMed; SAR; X-bandHydrology (agriculture)AgricultureEnvironmental scienceSettore AGR/08 - Idraulica Agraria E Sistemazioni Idraulico-ForestaliSAR COSMO-SkyMed X-bandX-bandLeaf area indexbusinessSettore ICAR/06 - Topografia E CartografiaRemote sensingSAR
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On the use of multi-temporal series of COSMO-SkyMed data for LANDcover classification and surface parameter retrieval over agricultural sites

2011

The objective of this paper is to report on the activities carried out during the first year of the Italian project “Use of COSMO-SkyMed data for LANDcover classification and surface parameters retrieval over agricultural sites” (COSMOLAND), funded by the Italian Space Agency. The project intends to contribute to the COSMO-SkyMed mission objectives in the agriculture and hydrology application domains.

retrieval algorithmsContextual image classificationbusiness.industryCOSMO-SkyMedCOSMO-SkyMed classification retrieval algorithmsClassificationData modelingStatistical classificationHydrology (agriculture)AgricultureClassification; COSMO-SkyMed; retrieval algorithmsEnvironmental scienceTerrain mappingbusinessRetrieval algorithmRemote sensing
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