Dip. di
Ingegneria dell'Informazione (DIET)
Sapienza Università di Roma
Earth Observation Data Analysis
Master Laurea (Laurea magistrale) in Data Science
6 ECTS (CFU) - 60 hours
Lecture period: March-May (2nd semester), II year
Meeting time: Wednesday h. 15:00
Dip. di Ingegneria dell'Informazione - Via Eudossiana 18 00184 Roma
Tel. 06.44585847
Exam dates |
Room and time |
DIET Department, 3rd floor, Via Eudossiana 18
|
SSD: ING-INF/02
Credits: 6
(CFU)
Teachers: Frank S. Marzano
(DIET, Sapienza) in collaboration with Dr. G. Rivolta and RSS team (ESRIN, ESA)
Calendar:
Second semester (March-May), II year
Offered
to: Master Laurea in Data Science
Evaluation:
Oral exam with grade in x/30 (homework report included)
Prerequisites:
Elements of Calculus and Informatics,
Elements of Physics (not indispensable)
Web site:
http://datascience.i3s.uniroma1.it/it/node/5612
https://cispio.diet.uniroma1.it/marzano/EODataAnalysis.html
The module aims at providing a general background on the remote sensing systems for Earth Observation from space-borne platforms and on data processing techniques. It describes, using a system approach, the characteristics of the system to be specified to fulfil the final user requirements in different domains of application. Remote sensing basics and simple wave-interaction models useful for data interpretation are reviewed together with technical principles of the main remote sensors. The course also provides an overview of the most important applications and bio-geophysical parameters (of the atmosphere, the ocean and the land) which can be retrieved. The most important techniques for data processing and product generation, also by proposing practical exercises using the computer, are analysed together with an overview of the main Earth Observation satellite missions and the products they provide to the final user.
INTRODUCTION. Course presentation. Topics overview. Exam and homeworks. Grouping.
1.
STROLLING AROUND EARTH OBSERVATION (Introducing EO between data science
and its applications). Data science and its paradoxes: Data scientists,
Big data, little data. Earth observation (EO) and data science: Remote
sensing and its applications, EO big data and research support
services. Data scientist for space sciences: EO opportunities for data
scientists, Data scientists skill for EO. Strolling around EO
applications: From atmospheric monitoring to climate analysis, From
natural hazards to geodesy and geophysics, From urban planning to
deforestation surveillance, Fromenvironmental to monumental diagnosis.
2. EARTH OBSERVATION PRINCIPLES AND CONCEPTS (Overview of EO basic methodologies and techniques). Remote sensing basics: Problem definition and its actors, Target, source, receiver, medium and processes, Inverse problems and retrieval techniques. Electromagnetic radiation basics: Wave fields, electromagnetic spectrum and radiant energy, Wave-matter interaction basic processes and Earth atmosphere, Radiative transfer modeling for Earth observation. Earth observation system basics: EO space segment and ground segments. EO electromagnetic sensors. EO user requirements (radiometric, spectral, spatial, temporal). Remote sensing platforms. SatelliteKeplerian orbits (LEO, GEO).
3. MODELING RADIATION FOR EARTH OBSERVATION (Introducing electromagnetic radiation theory for remote sensing). Wave-matter EM interaction mechanisms: Radiation: intensity, irradiance, exitance and received power, Emission: Planck law, approximations and emissivity, Surface interaction electromagnetic parameters, Volume interaction electromagnetic parameters, Wave reflection and refraction. Radiative transfer theory: Integral-differential equation, Formal integral solution and special cases, Application to absorbing and scattering atmospheres, Application to space and ground remote sensing. Radiation backscatter theory: Wadar equation for singlescatterer, Wadar equation for distributed scatterers, Doppler effect and signal statistics.
4. EARTH OBSERVATION SENSORS AND MISSIONS (Introducing EO satellite sensors and missions). Earth observation remote sensors: EO sensor classification and requirements, Passive optical sensors: photocamera principles, Electro-optical sensors: spectroradiometers, interferometers and lidars, Electro-optical sensor scanning systems and geometric distortions, Microwave sensors, imaging radiometers and sounders, Active microwave sensors: altimeters, scatterometers and SARs. Earth observation satellite missions: GEO: EU Meteosat and China Fengyun, LEO: US Aqua and Terra, LEO: US GPM and US/France CALIPSO, LEO: EU MetOP and US Suomi-NPP, LEO: EU Sentinel-1, Sentinel-2 and Sentinel-3, LEO: Italy COSMO-SkyMed and Germany TerraSAR-X, LEO:US DG-High-resolution Worldview
5.
EARTH OBSERVATION APPLICATIONS (Main applications to Earth science and
physicallybased techniques). Information content in remote sensing
observations: Information content in visible and near-infrared remote
sensing, Information content in thermal-infrared and microwave remote
sensing. Remote sensing of Earth sea environment: Sea water spectral
response, transmittance and reflectance, Visible, near-infrared and
thermal-infrared passive remote sensing, Microwave remote sensing:
scatterometry, SAR, altimeter and radiometry. Remote sensing of Earth
atmosphere: Atmospheric response in the visible-infrared reflective and
emissive bands, Profiling radiometric techniques for thermal structure
and gas concentration, Water vapor, clouds and precipitation from
infrared and microwave radiometers. Remote sensing of Earth solid
surface: Vegetation visible-infrared spectral response and retrieval,
Rock and surface humidity visible-infraredspectral response and
retrieval, Radar and radiometric remote sensing of land surface and
emissivity.
6. EARTH OBSERVATION DATA PROCESSING (Introducing EO data processing and retrieval techniques). EO image data processing: Levels of EO data processing, Color perception and synthesis, Image format and data structure, Image analysis: histogram, contrast, slicing, pseudocoloring, filtering, Image geocoding: ground control points and resampling. EO inverse problem and retrieval techniques: Inverse and ill-conditioned problems, Regularization, statistical and neuralnetwork solution methods. EO feature extraction and classification: Image feature classification: unsupervised and supervised approach, Feature extraction and principal component analysis, Statistical Bayesian classification method, Thematic map generation process, Image textureexploitation.
Canada Centre, Fundamentals of remote sensing, 2008 (PDF available)
Elachi and VanZyl, Introduction to physics and techniques of remote sensing, Wiley Intersc., 1987, 2006
Marzano and
Visconti, Eds., Remote sensing of atmosphere and ocean from space, Kluwer Ac. Publ., 2002.
Richards and Jia, Remote sensing digital image analysis: an introduction, Springer Verlag, 2006
For further reading:
Kidder
and Von der Haar, Satellite meteorology, Artech House, 1996
Measures, Laser remote sensing, Springer Verlag, 1984
Sauvageot, Radar meteorology, Artech House, 1991
Ulaby, Moore and Fung, Microwave remote sensing, vol. 1-3, Addison-Wesley, 1982
REFERENCE |
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CHAPTER00 |
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CAHPTER01 |
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CHAPTER02 |
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CHAPTER03 |
F.S. Marzano, N. Pierdicca |
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CHAPTER04 |
F.S. Marzano, N. Pierdicca | |
CHAPTER05 |
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CHAPTER06 |
AVAILABLE |
TRAINER REFERENCE |
AVAILABLE |
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Foundations |
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Spaceborne systems |
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Microwave radiometry |
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Radar
meteorolology |
M. Montopoli | ||
Wind profilers |
F.S. Marzano | ||
Lidar systems |
Gentile_DOAS, Scipione,_"DOAS" Deberardinis_Lidar, Pichelli,_"Lidar" |
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Data processing |
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