TOA RADIANCE SIMULATOR FOR THE NEW HYPERSPECTRAL MISSIONS: STORE (SIMULATOR OF TOA RADIANCE)

Similar documents
UAV-based Remote Sensing Payload Comprehensive Validation System

Potential of Sentinel-2 for retrieval of biophysical and biochemical vegetation parameters

End-to-End Simulation of Sentinel-2 Data with Emphasis on Atmospheric Correction Methods

Quality assessment of RS data. Remote Sensing (GRS-20306)

Motivation. Aerosol Retrieval Over Urban Areas with High Resolution Hyperspectral Sensors

Operational use of the Orfeo Tool Box for the Venµs Mission

CHRIS Proba Workshop 2005 II

Predicting Atmospheric Parameters using Canonical Correlation Analysis

Determining satellite rotation rates for unresolved targets using temporal variations in spectral signatures

Hyperspectral Remote Sensing

Overview of the EnMAP Imaging Spectroscopy Mission

IR-Signature of the MULDICON Configuration Determined by the IR-Signature Model MIRA

Kohei Arai 1 Graduate School of Science and Engineering Saga University Saga City, Japan

Linking sun-induced fluorescence and photosynthesis in a forest ecosystem

ATMOSPHERIC CORRECTION ITERATIVE METHOD FOR HIGH RESOLUTION AEROSPACE IMAGING SPECTROMETERS

GENESIS Generator of Spectral Image Simulations

ENMAP RADIOMETRIC INFLIGHT CALIBRATION

Comparison of Full-resolution S-NPP CrIS Radiance with Radiative Transfer Model

GEOG 4110/5100 Advanced Remote Sensing Lecture 2

Modeling reflectance and transmittance of leaves in the µm domain: PROSPECT-VISIR

Update on Pre-Cursor Calibration Analysis of Sentinel 2. Dennis Helder Nischal Mishra Larry Leigh Dave Aaron

Shallow-water Remote Sensing: Lecture 1: Overview

Infrared Scene Simulation for Chemical Standoff Detection System Evaluation

Interactive comment on Quantification and mitigation of the impact of scene inhomogeneity on Sentinel-4 UVN UV-VIS retrievals by S. Noël et al.

Interactive comment on Quantification and mitigation of the impact of scene inhomogeneity on Sentinel-4 UVN UV-VIS retrievals by S. Noël et al.

SWIR/VIS Reflectance Ratio Over Korea for Aerosol Retrieval

AISASYSTEMS PRODUCE MORE WITH LESS

Crop Types Classification By Hyperion Data And Unmixing Algorithm

Airborne Hyperspectral Imaging Using the CASI1500

Retrieval of Surface Reflectance and LAI Mapping with Data from ALI, Hyperion and AVIRIS

GEOBIA for ArcGIS (presentation) Jacek Urbanski

THE EFFECT OF TOPOGRAPHIC FACTOR IN ATMOSPHERIC CORRECTION FOR HYPERSPECTRAL DATA

Monte Carlo Ray Tracing Based Non-Linear Mixture Model of Mixed Pixels in Earth Observation Satellite Imagery Data

Continued Development of the Look-up-table (LUT) Methodology For Interpretation of Remotely Sensed Ocean Color Data

Using MODTRAN for Atmospheric Parameter Retrieval under the ASCOPE Architecture

Preliminary validation of Himawari-8/AHI navigation and calibration

Analisi di immagini iperspettrali satellitari multitemporali: metodi ed applicazioni

Diffraction gratings. e.g., CDs and DVDs

Kohei Arai 1 1Graduate School of Science and Engineering Saga University Saga City, Japan. Kenta Azuma 2 2 Cannon Electronics Inc.

Application of the SCAPE-M atmospheric correction algorithm to the processing of MERIS data over continental water bodies

Testing Hyperspectral Remote Sensing Monitoring Techniques for Geological CO 2 Storage at Natural Seeps

2017 Summer Course on Optical Oceanography and Ocean Color Remote Sensing. Introduction to Remote Sensing

Global and Regional Retrieval of Aerosol from MODIS

Prototyping GOES-R Albedo Algorithm Based on MODIS Data Tao He a, Shunlin Liang a, Dongdong Wang a

Correction and Calibration 2. Preprocessing

DEEP LEARNING TO DIVERSIFY BELIEF NETWORKS FOR REMOTE SENSING IMAGE CLASSIFICATION

UV Remote Sensing of Volcanic Ash

Nonlinear Mixing Model of Mixed Pixels in Remote Sensing Satellite Images Taking Into Account Landscape

Attività cal/val missione FLEX e possibili sinergie con PRISMA

CRISM (Compact Reconnaissance Imaging Spectrometer for Mars) on MRO. Calibration Upgrade, version 2 to 3

Remote Sensing Introduction to the course

CWG Analysis: ABI Max/Min Radiance Characterization and Validation

ABSTRACT 1. INTRODUCTION

Shortwave infrared measurements of the TROPOMI instrument on the Sentinel 5 Precursor mission

Analysis Ready Data For Land

Optical Theory Basics - 2 Atmospheric corrections and parameter retrieval

Sentinel-2 Calibration and Validation : from the Instrument to Level 2 Products

Atmospheric correction of hyperspectral ocean color sensors: application to HICO

Appendix III: Ten (10) Specialty Areas - Remote Sensing/Imagry Science Curriculum Mapping to Knowledge Units-RS/Imagry Science Specialty Area

Lukas Paluchowski HySpex by Norsk Elektro Optikk AS

An Inter-Comparison and Rigorous Analysis of UAV and Manned Airborne Hyperspectral Imagers

Update on S3 SYN-VGT algorithm status PROBA-V QWG 4 24/11/2016

SENTINEL-2 SEN2COR: L2A PROCESSOR FOR USERS

Measuring Ground Deformation using Optical Imagery

A Method Suitable for Vicarious Calibration of a UAV Hyperspectral Remote Sensor

PICSCAR Status Radiometric Calibration Workshop for European Missions

SEVENTH FRAMEWORK PROGRAMME Capacities Specific Programme Research Infrastructures

A Survey of Modelling and Rendering of the Earth s Atmosphere

10/2/2012 Corporate Headquarters 9390 Gateway Dr, Ste 100 Reno, NV p: f:

Laser Beacon Tracking for High-Accuracy Attitude Determination

CHRIS PROBA instrument

REMOTE SENSING OF BENTHIC HABITATS IN SOUTHWESTERN PUERTO RICO

The 4A/OP model: from NIR to TIR, new developments for time computing gain and validation results within the frame of international space missions

Remote Sensing for environment monitoring and surveillance applications

The NIR- and SWIR-based On-orbit Vicarious Calibrations for VIIRS

Introduction to Remote Sensing Wednesday, September 27, 2017

Calculation steps 1) Locate the exercise data in your PC C:\...\Data

MODIS Atmosphere: MOD35_L2: Format & Content

Menghua Wang NOAA/NESDIS/STAR Camp Springs, MD 20746, USA

RADIOMETRIC CROSS-CALIBRATION OF GAOFEN-1 WFV CAMERAS WITH LANDSAT-8 OLI AND MODIS SENSORS BASED ON RADIATION AND GEOMETRY MATCHING

Uncertainties in the Products of Ocean-Colour Remote Sensing

MRO CRISM TRR3 Hyperspectral Data Filtering

ENHANCEMENT OF DIFFUSERS BRDF ACCURACY

Preprocessed Input Data. Description MODIS

Retrieval of crop characteristics from high resolution airborne scanner data

Onshore remote sensing applications: an overview

Improvements to Ozone Mapping Profiler Suite (OMPS) Sensor Data Record (SDR)

A spectral climatology for atmospheric compensation

OMAERO README File. Overview. B. Veihelmann, J.P. Veefkind, KNMI. Last update: November 23, 2007

Direct radiative forcing of aerosol

Rochester Institute of Technology, 54 Lomb Memorial Drive, Rochester, NY USA 14623; ABSTRACT 1. INTRODUCTION

RECENT ADVANCES IN THE SCIENCE OF RTTOV. Marco Matricardi ECMWF Reading, UK

S2 MPC Data Quality Report Ref. S2-PDGS-MPC-DQR

GEOG 4110/5100 Advanced Remote Sensing Lecture 4

Overview of MOLI data product (MOLI: Multi-footprint Observation Lidar and Imager)

Study on LAI Sampling Strategy and Product Validation over Non-uniform Surface. Lingling Ma, Xiaohua Zhu, Yongguang Zhao

Hyperspectral CHRIS Proba imagery over the area of Frascati and Tor Vergata: recent advances on radiometric correction and atmospheric calibration

A MAXIMUM NOISE FRACTION TRANSFORM BASED ON A SENSOR NOISE MODEL FOR HYPERSPECTRAL DATA. Naoto Yokoya 1 and Akira Iwasaki 2

Algorithm Theoretical Basis Document (ATBD) for ray-matching technique of calibrating GEO sensors with Aqua-MODIS for GSICS.

IWPSS, 26 th March 2013

Transcription:

TOA RADIANCE SIMULATOR FOR THE NEW HYPERSPECTRAL MISSIONS: STORE (SIMULATOR OF TOA RADIANCE) Malvina Silvestri Istituto Nazionale di Geofisica e Vulcanologia

In the frame of the Italian Space Agency (ASI) Hyperspectral Mission PRISMA (Precursore IperSpettrale della Missione Applicativa), the Istituto Nazionale di Geofisica e Vulcanologia (INGV) is coordinating the scientific project ASI-AGI (Analisi Sistemi Iperspettrali per le Applicazioni Geofisiche Integrate) to develop specific algorithms and products for various geophysical applications. In the ASI-AGI project a PRISMA-like data simulator to test the specific algorithms during the developing phases of the PRISMA mission has been developed. STORE (Simulator of TOa RadiancE) is an image satellite sensor simulator developed to generate a simulated hyperspectral data.

Why an hyperspectral simulator It allows the generation of realistic satellite images based on detailed models of the surface, the atmosphere, and the sensor; it also can be used to study the effect of system parameters on an output measure, such as classification accuracy or other applications; it allows to assess the capacity of existing and future satellite sensors to meet the user requirements for hypothetical applications The sensed image depends on the interaction among the source, the atmosphere and the target Path radiance Diffuse irradiance Adjacency effects Direct irradiance

In order to properly simulate by means simulator tool the hyperspectral images, with a spectral range from 0.4 to 2.5 m, several inputs have to be used. The confidence of the simulated images depends on the capability to constrain the input data. During the design phase, we have considered inputs related to the earth-atmosphere-sensor geometry and spectral response function as baseline for obtaining a suitable simulated image. The study does not consider parameters related to sensor technical characteristics such as Signal-to-Noise Ratio (SNR) of sensor and the electro-mechanical noise due to sensor functionalities. The following inputs have to be used to obtain at-sensor radiances in different channels of the sensor: Radiative Transfer Model, MODTRAN, that is the main core of STORE; this code offers the possibility to simulate under different atmospheric and surface conditions the interaction among sensor and target; Atmospheric features in terms of visibility, water vapour amounts, aerosol types and depth, etc, to correctly model the atmosphere's optical properties Surface characterization/field campaigns in terms of surface temperature, land surface spectral response (known also as albedo or surface reflectance) and topography (or altitude) to allow to describe the image viewed by the sensor; Sensor information/srf in terms of satellite attitude (considering the geometry among source, sensor and target), day and time of acquisition, spectral response function (SRF), spectral channels resolution and distribution to proper define the correct acquisition geometry.

A precise characterization of surface spectral response represents a reliable parameter to feed the simulator, in particular on the proper definition of the surface spectral albedo used to generate the simulate TOA radiances. Piano delle Concazze on Mt. Etna volcano represents a suitable natural laboratory to provide validation for surface parameters such ground reflectance

Test Site - HYPERION, Etna area, 26/06/2012-198 bands with spectral range from 0.4 to 2.4 µm and 30m spatial resolution - Co-registered DEM (credits: INGV) Rifl VNIR Rifl Pixel adiacenti VNIR DEM ASPECT Rifl SWIR Rifl Pixel adiacenti SWIR SLOPE

Reflectance Reflectance input data derived by: - HYPERION, Etna Field campaign, 26/06/2012 - Atmospheric correction on HYPERION data - 2003 and 2009 ground truths 1,0 0,9 0,8 0,7 0,6 2001-2009 Mean reflectance 2009 Ground truth 2003 Ground truth 0,5 0,4 0,3 0,2 0,1 0,0 0,4 0,6 0,8 1,0 1,2 1,4 1,6 1,8 2,0 2,2 2,4 Wavelenght [micron] Curves show a similar trend, shapes and values with the exception of bands characterized by strong atmospheric absorption features

Test case: - HYPERION, Etna area, 26/06/2012-198 bands with spectral range from 0.4 µm to 2.4 µm and 30m spatial resolution - Co-registered DEM (credits: INGV) - Atmospheric features: Mid-Latitude Summer / more info MODTRAN - Sensor information: simulated SRF Rifl VNIR Rifl Pixel adiacenti VNIR DEM ASPECT Rifl SWIR Rifl Pixel adiacenti SWIR SLOPE

HYPERION VNIR Real data VNIR-Simulated data Correlation: low correlation areas Correlation between real and simulated all VNIR and SWIR bands

The incoming TOA radiance collected by the hyperspectral sensor is acquired by each channel according to the SRF, that describes the sensitivity of the channel regarding the energy in a certain range of the electromagnetic spectrum. The SRF is not always available from the manufacturer of the camera, especially in the first phases of instrument design. For this reason we have decide to test simulator with a SRF as a Gaussian f( j ) VNIR SWIR

(µm) Main differences are due to atmospheric contribute: real data for Hyperion Modtran default value for simulation (µm)

Watt /(m2*sr*micron) 50 Piano delle Concazze Radiance 45 VNIR SWIR VNIR SWIR 40 35 30 HYPERION_26 PRISMA_SWIR SPECIM_SWIR Spectro meter Name Spectral Range PRISMA PRISMA EO-1 Hyperion 400-1010 nm 920-2505 nm 400 2500 nm ImSpect or V10E /Specim 400-1000 nm ImSpect or N25E /Specim 970-2500 nm Spectral Resoluti on 12 nm 12 nm 10 nm 2.8 nm 10 nm 25 Spectral bands 66 171 220 di 242 504 239 20 15 10 5 0 0,9 1,1 1,3 1,5 1,7 1,9 2,1 2,3 2,5 Lamba (µm) (micron)

Comparison among radiance spectra HYPERION real data (right) and PRISMA-like simulated (left); black color HYPERION spectra, red color PRISMA-like data.

Comparison among radiance spectra HYPERION real data (right) and PRISMA-like simulated (left); the simulation has be done with a visibility of 100 km; black color HYPERION spectra, red color PRISMA-like data.

A scheme for simulating the at-sensor radiance channels hyperspectral sensor is presented. Such scheme is quite useful in understanding the at-sensor signals in the diverse atmosphere surface scenario before the launch of a satellite. First check on simulation using HYPERION real data and HYPERION simulated data. A PRISMA-like simulation has been done using a SRF simulated Simulated data has been compared with airborne hyperspectral sensor (SPECIM) and PRISMA-like simulated data. Analysing the simulation results it is important to remark the role of the atmospheric parameterizations This study does not consider parameters related to sensor technical characteristics such as Signal-to-Noise Ratio (SNR) of sensor and the electro-mechanical noise due to sensor functionalities and a simulated SRF has been considered.