Descriptif ED1819-SU11 Surfaces - Historique des versions

Ridel le 17 septembre 2018 à 12:18

2018-09-17T12:18:09Z

← Version précédente		Version du 17 septembre 2018 à 12:18
Ligne 5 :		Ligne 5 :
	- salle à venir		- salle à venir

-	Characterizing natural surfaces: remote observations, modeling and inversion	+	<SPAN STYLE="background-color: #ffffcc;font-weight:bold"> Characterizing natural surfaces: remote observations, modeling and inversion</span>

	Remote sensing of planetary surfaces by satellites or spacecraft provides important clues on their properties, at global scale or/and high spatial resolution. They help following their structural and temporal evolution over days or years which may impact human activities and conditions of living or provide unique knowledge on unexplored extraterrestrial surfaces. Radiances originating from various depths in the near surface and measured over a large range of wavelengths, directions and timescales are modulated by its structure and composition. Heat and radiative transfer models have been developed to infer these properties from radiances.		Remote sensing of planetary surfaces by satellites or spacecraft provides important clues on their properties, at global scale or/and high spatial resolution. They help following their structural and temporal evolution over days or years which may impact human activities and conditions of living or provide unique knowledge on unexplored extraterrestrial surfaces. Radiances originating from various depths in the near surface and measured over a large range of wavelengths, directions and timescales are modulated by its structure and composition. Heat and radiative transfer models have been developed to infer these properties from radiances.

	This course aims at delivering skills in multi-wavelengths remote sensing and modeling of natural surfaces and at training participants in data inversion of satellite/spacecraft data from visible to the thermal/microwave infrared domain. It will be delivered in the form of 10h (5 x 2h) of main lessons, merged with a training session (15h=5*3h). This will consist of characterizing the properties of a specific surface among various solar system bodies such as the Moon, icy satellites of Saturn, etc, using databases from NASA missions mainly. This training includes data manipulation, model study (sensitivity analysis,...) and data inversion based on Bayesian inference under Python environment.		This course aims at delivering skills in multi-wavelengths remote sensing and modeling of natural surfaces and at training participants in data inversion of satellite/spacecraft data from visible to the thermal/microwave infrared domain. It will be delivered in the form of 10h (5 x 2h) of main lessons, merged with a training session (15h=5*3h). This will consist of characterizing the properties of a specific surface among various solar system bodies such as the Moon, icy satellites of Saturn, etc, using databases from NASA missions mainly. This training includes data manipulation, model study (sensitivity analysis,...) and data inversion based on Bayesian inference under Python environment.

Ridel le 17 septembre 2018 à 12:16

2018-09-17T12:16:46Z

← Version précédente		Version du 17 septembre 2018 à 12:16
Ligne 1 :		Ligne 1 :
-	- durée total des cours : 30 h<BR>	+	- durée total des cours : 25 h<BR>
-	- ~~5 matinées (9h30~~-~~12h30)~~ et ~~5 après-midis (14h~~-~~17h) du 7 au 11 janvier 2018 au laboratoire~~ [http://www.apc.univ-paris7.fr/APC_CS/fr/acces-contact ~~APC~~]~~,<BR> salles 302A.~~	+	- avril ou juin 2019, mise à jour à venir rapidement<BR>
		+	- 15h de travaux pratiques en salle informatique et 10h de cours magistral
		+	- batiment [http://www.apc.univ-paris7.fr/APC_CS/fr/acces-contact Condorcet] de l'Université Paris Diderot
		+	- salle à venir

	Characterizing natural surfaces: remote observations, modeling and inversion		Characterizing natural surfaces: remote observations, modeling and inversion

-	Remote sensing of planetary surfaces by satellites or spacecraft provides important clues on their properties, at global scale or/and high spatial resolution. They help following their structural and temporal evolution over days or years which may impact human activities and conditions of living or provide unique knowledge on unexplored extraterrestrial surfaces. Radiances originating from various depths in the near surface and measured over a large range of wavelengths, directions and timescales are modulated by its structure and composition. Heat and radiative transfer models have been developed to infer these properties from radiances. This course aims at delivering skills in multi-wavelengths remote sensing and modeling of natural surfaces and at training participants in data inversion of satellite/spacecraft data from visible to the thermal/microwave infrared domain. It will be delivered in the form of ~~12h~~ (4 x 3h) of main lessons, merged with a training session (15h=5*3h). This will consist of characterizing the properties of a specific surface among various solar system bodies such as the Moon, icy satellites of Saturn, etc, using databases from NASA missions mainly. This training includes data manipulation, model study (sensitivity analysis,...) and data inversion based on Bayesian inference under Python environment.	+	Remote sensing of planetary surfaces by satellites or spacecraft provides important clues on their properties, at global scale or/and high spatial resolution. They help following their structural and temporal evolution over days or years which may impact human activities and conditions of living or provide unique knowledge on unexplored extraterrestrial surfaces. Radiances originating from various depths in the near surface and measured over a large range of wavelengths, directions and timescales are modulated by its structure and composition. Heat and radiative transfer models have been developed to infer these properties from radiances.
		+
		+	This course aims at delivering skills in multi-wavelengths remote sensing and modeling of natural surfaces and at training participants in data inversion of satellite/spacecraft data from visible to the thermal/microwave infrared domain. It will be delivered in the form of 10h (5 x 2h) of main lessons, merged with a training session (15h=5*3h). This will consist of characterizing the properties of a specific surface among various solar system bodies such as the Moon, icy satellites of Saturn, etc, using databases from NASA missions mainly. This training includes data manipulation, model study (sensitivity analysis,...) and data inversion based on Bayesian inference under Python environment.

Ridel le 17 septembre 2018 à 12:11

2018-09-17T12:11:12Z

← Version précédente		Version du 17 septembre 2018 à 12:11
Ligne 1 :		Ligne 1 :
		+	- durée total des cours : 30 h<BR>
		+	- 5 matinées (9h30-12h30) et 5 après-midis (14h-17h) du 7 au 11 janvier 2018 au laboratoire [http://www.apc.univ-paris7.fr/APC_CS/fr/acces-contact APC],<BR> salles 302A.
		+
	Characterizing natural surfaces: remote observations, modeling and inversion		Characterizing natural surfaces: remote observations, modeling and inversion

	Remote sensing of planetary surfaces by satellites or spacecraft provides important clues on their properties, at global scale or/and high spatial resolution. They help following their structural and temporal evolution over days or years which may impact human activities and conditions of living or provide unique knowledge on unexplored extraterrestrial surfaces. Radiances originating from various depths in the near surface and measured over a large range of wavelengths, directions and timescales are modulated by its structure and composition. Heat and radiative transfer models have been developed to infer these properties from radiances. This course aims at delivering skills in multi-wavelengths remote sensing and modeling of natural surfaces and at training participants in data inversion of satellite/spacecraft data from visible to the thermal/microwave infrared domain. It will be delivered in the form of 12h (4 x 3h) of main lessons, merged with a training session (15h=5*3h). This will consist of characterizing the properties of a specific surface among various solar system bodies such as the Moon, icy satellites of Saturn, etc, using databases from NASA missions mainly. This training includes data manipulation, model study (sensitivity analysis,...) and data inversion based on Bayesian inference under Python environment.		Remote sensing of planetary surfaces by satellites or spacecraft provides important clues on their properties, at global scale or/and high spatial resolution. They help following their structural and temporal evolution over days or years which may impact human activities and conditions of living or provide unique knowledge on unexplored extraterrestrial surfaces. Radiances originating from various depths in the near surface and measured over a large range of wavelengths, directions and timescales are modulated by its structure and composition. Heat and radiative transfer models have been developed to infer these properties from radiances. This course aims at delivering skills in multi-wavelengths remote sensing and modeling of natural surfaces and at training participants in data inversion of satellite/spacecraft data from visible to the thermal/microwave infrared domain. It will be delivered in the form of 12h (4 x 3h) of main lessons, merged with a training session (15h=5*3h). This will consist of characterizing the properties of a specific surface among various solar system bodies such as the Moon, icy satellites of Saturn, etc, using databases from NASA missions mainly. This training includes data manipulation, model study (sensitivity analysis,...) and data inversion based on Bayesian inference under Python environment.

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2018-09-17T12:02:35Z

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Characterizing natural surfaces: remote observations, modeling and inversion

Remote sensing of planetary surfaces by satellites or spacecraft provides important clues on their properties, at global scale or/and high spatial resolution. They help following their structural and temporal evolution over days or years which may impact human activities and conditions of living or provide unique knowledge on unexplored extraterrestrial surfaces. Radiances originating from various depths in the near surface and measured over a large range of wavelengths, directions and timescales are modulated by its structure and composition. Heat and radiative transfer models have been developed to infer these properties from radiances. This course aims at delivering skills in multi-wavelengths remote sensing and modeling of natural surfaces and at training participants in data inversion of satellite/spacecraft data from visible to the thermal/microwave infrared domain. It will be delivered in the form of 12h (4 x 3h) of main lessons, merged with a training session (15h=5*3h). This will consist of characterizing the properties of a specific surface among various solar system bodies such as the Moon, icy satellites of Saturn, etc, using databases from NASA missions mainly. This training includes data manipulation, model study (sensitivity analysis,...) and data inversion based on Bayesian inference under Python environment.