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

Prof. Frank S. Marzano

Meeting time: Wednesday h. 15:00

Dip. di Ingegneria dell'Informazione - Via Eudossiana 18 00184 Roma

Tel. 06.44585847


Exam dates

Room and time

ECTS course program

See and use:  Sapienza INFOSTUD system

EODA class schedule

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



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.

PROGRAM (read here)

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.



For those students attending lectures:

- participate to laboratory exercises and seminars
- do the foreseen 3-4 homeworks on EO data analysis during the course

For those students NOT attending lectures:

- answer to 1 oral question during the exam (flexible date to be agreed)
- complete and discuss a report on EO data analysis case study
For the HOMEWORK plan, instructions are available here (please, contact the lecturer in case).


Topics. See  Master thesis and stages.

See also Vademecum.


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


What:     explore Sentinel-1/2/3 data for monitoring Earth targets
Where:   ESA-ESRIN, Via Galilei, Frascati (Roma) – “Tor Vergata” train stop from Termini station
Who:      RSS group @ESRIN: from Progressive Systems
               Dr. G. Rivolta, Dr. G. Sabatino, Dr. R. Cuccu, Dr. B. Abis
Material: SNAP official manual and course slides provided by ESA RSS group

1. Register on ESA-EO Users' Single Sign On (SSO) on https://earth.esa.int/web/guest/general-registration
    fill the EO questionnaire on https://wiki.services.eoportal.org/rss-showQuestionnaire.php?QuestionnaireId=35
    receive instructions to use RSS CloudToolbox service with customized virtual machines (http://eogrid.esrin.esa.int/cloudtoolbox)
    Login to SS=, after registration, at at https://eo-sso-idp.eo.esa.int/idp/umsso20/admin
2. For the teaching material, I recommend reading the Canada Centre textbook that provides a good general basis.
    For learning EO, very useful is: http://www.learn-eo.org/index.php
3. For the Lab and data analysis, we will use the SNAP / STEP tool/platform found on:
    Forum: http://forum.step.esa.int/
    and I recommend you to download it on your PC (about 400 Mb).
    As an image example, you can use a MODIS ans Sentinel2 case study whose HDF files are available in this remote folder
    For a SNAP training, I would suggest to follow and perform the homework described in this document.






F.S. Marzano


Strolling around Earth observation

F.S. Marzano


Earth observation principles and concepts

F.S. Marzano, N. Pierdicca


Modeling radiation for Earth observation

F.S. Marzano, N. Pierdicca


Earth observation sensors and missions

F.S. Marzano, N. Pierdicca


Earth observation applications

F.S. Marzano, N. Pierdicca


Earth observation data processing

F.S. Marzano, N. Pierdicca






Remote sensing foundations

F.S. Marzano


Spaceborne systems

Spaceborne remote sensing

F.S. Marzano

Faccani,_"Satellite meteorology"

Microwave radiometry

Microwave radiometry and applications

F.S. Marzano


Radar meteorolology

Weather radar and applications 1, 2

M. Montopoli

Tiberi,_"Radar Met"

Wind profilers

Wind profilers and boundary layers

F.S. Marzano

Croce,_"Wind profilers"

Lidar systems

Lidar and aerosol observations

F.S. Marzano

Gentile_DOAS, Scipione,_"DOAS" Deberardinis_Lidar, Pichelli,_"Lidar"

Data processing

Data analysis and laboratory

F.S. Marzano