Federica Silvestri Effetti della turbolenza atmosferica su FSO

Frank S. Marzano, Nazzareno Pierdicca, Giovanni Laneve, Maria Marsella

Roma - May 13, 2015

2 CRAS research activity in EO and remote sensing

CRAS activity in Earth observation (EO)

Center for Research in Aerospace Sapienza (CRAS)

• Earth observation research topics – Electromagnetic physical-statistical interaction models

– Inversion algorithms for geophysical mapping and retrieval

– Space mission and payload design and development

• EO applications for a changing planet Agriculture

– Flood area discrimination

– Soil moisture retrieval

– Crop monitoring and classification

Agrometeorology

– Precipitation mapping and retrieval from space

– Rain monitoring from ground

3 CRAS research activity in EO and remote sensing

EO4 agriculture: mapping soil moisture

New (Sentinel-1, SMAP) and future (SAOCOM) satellite radars have shorter revisit time which makes it possible to implement multitemporal algorithm for soil moisture retrieval

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SMCSMC

SAR

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SARSAR

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Pipeline processing of Sentinel 1 images to produce

soil moisture maps by a multitemporal algorithm Unprecedent spatial resolution

from satellite (order of km) suitable for agriculture

Radar, optical and GIS data fusion and product integration

HR soil moisture

4 CRAS research activity in EO and remote sensing

EO4 for agriculture: mapping flooded areas

RGB color composite of CSK SAR images Red: December, 20 2009 Green: December, 30 2009 Blue: December, 31 2009

Massaciuccoli

lake

Ambiguous radar signature of flood under vegetation (bright rather than dark)

5 CRAS research activity in EO and remote sensing

EO4 for disaster management: cases

Electromagnetic model inversion and data processing algorithms aiming at: Mapping flood and other changes from spaceborne SAR’s (COSMO SkyMed)

Multitemporal

color

composite of

CSK

acquisitions

during

snowfall

event in

Rome (2012)

Debris and inundation after tsunami, Japan

Jan. 25, 2010 Flood

Water

body

Albania,

Mapping earthquake damage/change

by VHR imagery (Aphorism FP7)

Mapping volcanic ash from

microwave radiometry and radar (FP7)

6 CRAS research activity in EO and remote sensing

Ground truth

UC

CL1

CL2

CL3

WL1

CL5

WL2

CL4

WB1

CL6

CL7

CL8+CL9+CL10

UL1

Image Classification

By integrating images from different sensors (optical, SAR) by suitable algorithms (e.g., Support Vector Machine) land use maps comparable to those used by local administration for agriculture management are feasible

EO4 for mapping land use at regional scale

7 CRAS research activity in EO and remote sensing

ASI has planned to fund research/training projects on topics of interest for both

Italian and Kenyan institutions. The list includes: programs of higher education,

space sensors development, satellite data acquisition, telemedicine, remote sensing.

One of the projects funded by ASI, aims to obtain a

SYSTEM IMPLEMENTATION AND CAPACITY BUILDING FOR SATELLITE BASED

AGRICULTURAL MONITORING AND CROP STATISTICS IN KENYA (SBAM)

Partners: IMAA-CNR (Italy), University of Maryland

(USA), Physics Dept - University of Nairobi.

DIAEE (Sapienza University of Rome)

EO4 for Africa: crop monitoring in Kenya

8 CRAS research activity in EO and remote sensing

(Left) Land cover map of Kenya provided by Africover. Green areas correspond to the agricultural areas of the country according to visual classification of the Landsat images acquired before the year 2000 (mostly 1995).

(Right) Comparison between a Landsat 5 (above) of April 1995 and a Landsat 8 image of April 2013 (bottom) for preliminarily showing changes in the Africover land cover map (grey areas correspond to agricultural areas) (see orange ellipse and VHR image on the left).

Land-change monitoring by remote sensing in SBAM

Total agricultural areas lost in South-

east Kenya: 476,952.21 ha

(4769.5221 km²)

EO4 for Africa: land cover maps in Kenya

9 CRAS research activity in EO and remote sensing

Methodology The University of Maryland in collaboration with NASA and the United States Department of Agriculture (USDA) have developed a satellite-based Global Agricultural Monitoring System (GLAM) (Becker-Reshef, 2010), which has become a primary tool for USDA crop analysts to forecast crop yield and production (based on MODIS satellite imagery at 250 m spatial resolution). • In the SBAM project the GLAM concept will be

transferred to OLI/Landsat 8 (http://landsat.usgs.gov/) images and, in the near future, to the Sentinel - 2 data

• Method calibration and validation will be supported by experiments and ground truth collection

EO and agricultural monitoring system

EO4 for agrometeorology: rain mapping

10 CRAS research activity in EO and remote sensing

Surface rain intensity (Radar national mosaik)

METEOSAT brightness temperature

(4 channels: IR 8.7, IR 10.8, IR 12.0, IR 13.4 μm)

RAIN ESTIMATION

MUPM

(Multiple-Univariate

Probability Matching)

Regional-scale surface

rain intensity

MICRA Output (mm/h)

Microwave Infrared Combined Retrieval

Algorithm (MICRA: Marzano et al., 2004,

2007; Cimini et al., 2014): regional and

continental scale satellite-based rain

mapping for drought/flood management

EO4 for agrometeorology: precip monitoring

CRAS meteorological portable radar at X band: • rain monitoring and nowcasting for

agriculture decision support (Marzano et al., 2004, 2007, 2010; Montopoli et al., 2012)

• hail monitoring and mapping for crop management (Marzano et al., 2007)

11 CRAS research activity in EO and remote sensing

Colosseum