Lu Zhang, Peng Zhang , Xiuqing Hu, Lin Chen

Slides:



Advertisements
Similar presentations
Preliminary Study of Lunar Calibration for Geostationary Imagers Japanese Multi-functional Transport SATellite-2 (MTSAT-2) incorporates the special device.
Advertisements

Page 1 Study of Sensor Inter-calibration Using CLARREO Jack Xiong, Jim Butler, and Steve Platnick NASA/GSFC, Greenbelt, MD with contributions from.
Resolution.
Spencer Anderson Brent Fogleman Daryl Vonhagel.  Objectives:  C-band (w = 3.8 – 7.5 cm) & X-band (w = 2.4 – 3.8 cm) IFSAR to acquire topographic data.
Shows the data from one orbit This is the page to go to to look at the GUVI data in the form of images.
Remote sensing in meteorology
School of Earth and Space Exploration Existing Lunar Datasets M. S. Robinson School of Earth and Space Exploration Arizona State University.
Satellite Imagery Meteorology 101 Lab 9 December 1, 2009.
Hyperspectral Satellite Imaging Planning a Mission Victor Gardner University of Maryland 2007 AIAA Region 1 Mid-Atlantic Student Conference National Institute.
Satellite Imagery and Remote Sensing NC Climate Fellows June 2012 DeeDee Whitaker SW Guilford High Earth/Environmental Science & Chemistry.
Ben Gurion University Mission scientists (PI's) : Gérard Dedieu & Arnon Kanieli G. Dedieu 1, O. Hagolle 2, A. Karnieli 3, S. Cherchali 2 P. Ferrier 2 and.
Resolution A sensor's various resolutions are very important characteristics. These resolution categories include: spatial spectral temporal radiometric.
Resolution Resolution. Landsat ETM+ image Learning Objectives Be able to name and define the four types of data resolution. Be able to calculate the.
1 Applications of Remote Sensing: SeaWiFS and MODIS Ocean Color Outline  Physical principles behind the remote sensing of ocean color parameters  Satellite.
Remote Sensing Data Acquisition. 1. Major Remote Sensing Systems.
Use of the Moon as a calibration reference for NPP VIIRS Frederick S. Patt, Robert E. Eplee, Robert A. Barnes, Gerhard Meister(*) and James J. Butler NASA.
SATELLITE ORBITS The monitoring capabilities of the sensor are, to a large extent, governed by the parameters of the satellite orbit. Different types of.
ISUAL Design Concept S. Mende. SDR 7 Jun NCKU UCB Tohoku ISUAL Design Concept S. Mende Sprite Example Sprite Image obtained by Berkeley/NCKU 1999.

Date of download: 6/1/2016 Copyright © 2016 SPIE. All rights reserved. Spatial (angular) resolution versus field angle for design with toroidal grating.
Orbits and Sensors Multispectral Sensors. Satellite Orbits Orbital parameters can be tuned to produce particular, useful orbits Geostationary Sun synchronous.
2015 GSICS Annual Meeting, Deli India March 16~20, 2015 Xiuqing Hu National Satellite Meteorological Center, CMA Yupeng Wang, Wei Fang Changchun Institute.
Lunar Calibration based on SELENE/SP data
Lunar Radiance Calibration ABI/AHI Solar Reflective Bands
NOAA VIIRS Team GIRO Implementation Updates
Lunar observation data set preparation
A Probabilistic Nighttime Fog/Low Stratus Detection Algorithm
Wang Ling, Hu Xiuqing, Chen Lin
Deep Convective Clouds (DCC) BRDF Characterization Using PARASOL Bidirectional Observations Bertrand Fougnie CNES.
DCC method implementation in FY3/MERSI and FY2
The ROLO Lunar Calibration System Description and Current Status
Verifying the DCC methodology calibration transfer
An Overview of MODIS Reflective Solar Bands Calibration and Performance Jack Xiong NASA / GSFC GRWG Web Meeting on Reference Instruments and Their Traceability.
Aqua MODIS Reflective Solar Bands (RSB)
FY2-IASI and FY3C-IASI towards Demo
Calibration and Performance MODIS Characterization Support Team (MCST)
Lunar data preparation for FY-2
Update on Advancing Development of the ROLO Lunar Calibration System
MTF Evaluation for FY-2G Based on Lunar
GOES-R Hyperspectral Environmental Suite (HES) Requirements
CALIBRATION over the Moon An introduction to « POLO »
Toru Kouyama Supported by SELENE/SP Team HISUI calibration WG
Data Preparation for ASTER
Deep Convective Cloud BRDF characterization using PARASOL
Using the Moon for Sensor Calibration Inter- comparisons
The Successor of the TOU
Lunar reflectance model based on SELENE/SP data
Instrument Considerations
Effort toward Characterization of Selected Lunar Sites for the Radiometric Calibration of Solar Reflective Bands Fangfang Yu1, Xi Shao1, Xiangqian Wu2.
Changchun Institute of Optics Fine Mechanics and Physics
S-NPP Visible Infrared Imaging Radiometer Suite (VIIRS) Lunar Calibration using GSICS Implementation of the ROLO model (GIRO) for Reflective Solar Bands.
National Satellite Meteorological Centre
Moving toward inter-calibration using the Moon as a transfer
Lu Zhang, Peng Zhang , Xiuqing Hu, Na Xu, Lin Chen,Ronghua Wu
Definition of a benchmark for the GIRO
13-16 November 2017, Xi’an, China Peng ZHANG
Lu Zhang, Peng Zhang , Xiuqing Hu, Lin Chen
TanSat/CAPI Calibration and validation
Lunar Observation Activities with a Small Satellite and a Planetary Exploration Satellite. Hodoyoshi-1 Hayabusa-2 Toru Kouyama, AIST
Deep Convective Clouds (DCC) BRDF Characterization Using PARASOL Bidirectional Observations Bertrand Fougnie CNES.
An introduction of FY2 and its Lunar calibration
Moving toward inter-calibration using the Moon as a transfer
Preliminary SCIAMACHY Lunar observations as intercalibration source
Lu Zhang, Peng Zhang , Xiuqing Hu, Lin Chen
Remote sensing in meteorology
Na XU Xiuqing HU Lin CHEN Ling SUN NSMC CMA
Gravitation and Satellites
Earth, sun and moon.
Introduction of FY historical dataset reprocessing
Toward a synergy between on-orbit lunar observations
Presentation transcript:

Lu Zhang, Peng Zhang , Xiuqing Hu, Lin Chen Lunar Model Establishment based on SELENE/SP with incorporated into lunar DEM Lu Zhang, Peng Zhang , Xiuqing Hu, Lin Chen National Satellite Meteorological Center (NSMC)

Outline Introduction The observation geometry of SP re-calculation Model working

Introduction The advantage of lunar calibration. The photometric stability of lunar surface had a characteristic time of 1*109 years [Kieffer, 1997]. Lunar reflectance is not strongly variegated in color. Lunar is surrounded by a black field.

Introduction Lunar model development The data from Earth-based observation(Atmosphere). The data from Earth orbit satellite(lack). The data from lunar orbit satellite. The Moon mineralogy mapper (M3) on Chandrayaan-1(geo-location, not covered entire lunar surface). The  Interference imaging spectrometer(IIM) on Chang’E-1(not covered entire lunar surface). The Spectral Profiler(SP) on SELENE.

Model Recalculating incidence angle , emission angle, phase angle.

Model The Reflectance of SP Data Screening Classifying The establishment of model

Model Data Screening Incidence angle (<90 ° ) Phase angle (<180 °) Emission angle (<90 ° ) EMD (Empirical Mode Decomposition) The data with noise is less than a threshold value

Model

Model Classifying: VIS band, 512-1010nm. Method:K-SVD( unsupervised learning method)

working Lunar reflectance 512.6nm 998.5nm

Working 1 °×1° in equator of lunar is about 900km^2 the SP’s Spatial resolution for 100 km altitude. Along track 562 m. Cross track 400 m. Near the equator the data is enough. Near the pole the data is not enough.

The end