Federica Silvestri Effetti della turbolenza atmosferica su FSO Frank S. Marzano, Nazzareno Pierdicca, Giovanni Laneve, Maria Marsella Roma - May 13, 2015
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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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