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Remote Sensing - I

Remote Sensing - I. - Optical Range - Imaging Spectroscopy - Introduction. Spectral Bands versus Spatial Resolutions of selected Earth-Observing Sensors. casi/HySpex/Aisa. TM/ETM xs. APEX. AVIRIS. (from 20000 m). HyMap. DAIS 7915. reflective. aircraft. DAIS 7915. thermal. TIMS.

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Remote Sensing - I

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  1. Remote Sensing - I - Optical Range - Imaging Spectroscopy - Introduction

  2. Spectral Bands versus Spatial Resolutions of selected Earth-Observing Sensors casi/HySpex/Aisa TM/ETM xs APEX AVIRIS (from 20000 m) HyMap DAIS 7915 reflective aircraft DAIS 7915 thermal TIMS (from 3200 m) 300 100 10 1 Detailed assessments, monitoring with infrequent coverage EnMAP Hyperion Large scale assessments, monitoring with frequent coverage Number of spectral bands MODIS MODIS Chris (VNIR only) reflective thermal satellite MERIS low res. sat. MERIS red spatial resolution ASTER full spatial resolution AVHRR HRG xs METEOSAT IKONOS pan Quickbird Geo-Eye pan HRG pan ETM pan TM thermal LISS-1C 1 10 100 1000 10 000 Spatial resolution (GSD in meter)

  3. multispectral hyperspectral N chlorite calcite dolomite alunite gypsum kaolinite • Contiguous bands • maximum identification • high confidence • data base usable • spectral unmixing • Few fixed bands • minimum identification • low confidence • field knowledge and lab- • analysis required appr. 1 km Makhtesh Ramon/Israel color composite of bands 1, 20, 48 Multi- versus Hyperspectral / Potentials

  4. Spectral Signatures in the SWIR Region I TM: Band-Pass Filter of a Multispectral Sensor HS: Contiguous bands of a Hyperspectral Sensor curves offset for clarity HS TM Mg-OH % Reflectance rel. to Halon Al-OH 20.0 C-O 0.0 Wavelength in Microns 2.0 2.05 2.1 2.15 2.2 2.25 2.3 2.35 2.4 2.45 chlorite saponite kaolinite alunite gypsum vegetation dolomite limestone

  5. Each material on the Earth‘s surface has a unique spectral characteristic 100 Rc 90 kaolinite Pigments, Minerals, Man Made Objects 80 Reflectance [%] 70 Shape Position Depth Quantification Identification 60 Rmin 50 D = 1 - Rmin / Rc 40 2.0 2.05 2.1 2.15 2.2 2.25 2.3 2.35 Wavelength in microns Identification – Quantification => Diagnosis

  6. O O O H H H H HH Water Molecule The three fundamental modes of vibration 2 at 6.27 µm 3 at 2.66 µm • 1 at 2.73 µm • 1 refers to the symmetrical O-H stretch • 2 refers to the H-O-H bend 3 refers to the asymmetrical O-H stretch

  7. Detectable Minerals Mineral identification via spectral absorption features is feasible for minerals containing molecules or anions such as H2O, OH-, SO42-, CO32-, NOX, CH4 and more This encompasses the phyllosilicates, most sorosilicates, the hydroxides, some sulphates, the amphiboles and the carbonates.

  8. Model of Hydrothermal Alteration Cross Section Map View of Present Ground Surface Ground Surface at Time of Ore formation Present Ground Surface Propylitic Zone (epidote, calcite, chlorite) Potassic Zone (quartz, sericite, biotite, potassium feldspar) Argillic Zone (quartz, kaolinite, mont- morillonite) Ore Zone (chalcopyrite, molybdenite, pyrite) Limonite from weathered ore Phyllic Zone (quartz, sericite, pyrite) after Lowell & Guilbert (1970)

  9. Alteration Zones Har Shen Ramon/Israel 70 roof rock 80.0 30 N propylitic zone 60.0 % Reflectance rel. to Halon 15 Talus kaolinitic zone 40.0 potassic zone 20.0 Scale 0.0 500 0 Meters 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0 2.2 2.4 Wavelength in Microns Roof Rock ? (quartz, feldspars, arfvedsonite) Propylitic Zone (Q,Fss, Arf + Fe-O + chlorite, epidote) Kaolinitic Zone (Q, Fss, Arf + kaolinite, chlorite) Potassic Zone (Q, Fss, Arf + K-FSS, Fe-oxides) geol. source: Itamar und Baer (1986)

  10. “The Worst Environmental Disaster” 1943 - 1971Deposit of 6 million tons of sulphide-rich tailings 1972 - 2001Massive spreading of contaminants500 ha forest devasted since 2001Rehabilitation of the mine tailings areas in ~ 50 - 100 yearsExpected completion of the main rehabilitation goals Kam-Kotia Mine/Canada

  11. Vital and Dry Vegetation May 2004 July 2003 Stipa (Stipa tenacissima) - Cabo de Gata Espagna

  12. Soil and Cellulose Spectral Signatures Yellow sands Red sands Cellulose

  13. 100.0 100.0 80.0 80.0 60.0 60.0 40.0 40.0 20.0 20.0 0.0 0.0 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0 2.2 2.4 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0 2.2 2.4 Wavelength in Microns Wavelength in Microns Spectral Characteristics of Surface Coatings cyano- bacteria + algae Fe-patina % Reflectance rel. to Halon fine particle size (<125 µ) cut surface exposed (brown patina) cutsurface exposed surface

  14. Spectral Characteristics of Vegetation 100 leaf pigments cell structure water content 80 chlorophyll-a,b & carotenoid- absorptions H2O-absorption 60 % reflectance relative to halon red edge cellulose 40 a) healthy (green) b) c) stressed d) e) dry (brown) 20 0 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0 2.2 2.4 wavelength in microns measured at leafes of prunus plant Chlorophyll Anthocyanin Carotenoids

  15. Reflectance Spectra of Phytopigments CHL CAR PE PC CHL 440 nm 485 nm 570 nm 624 nm 678 nm 8.0 7.0 6.0 5.0 Reflectance [%] 4.0 3.0 2.0 1.0 0.0 400 450 500 550 600 650 700 750 Wavelengths [ nm ] 1 – (25.9.‘97): 2 µg/l Chl -a 4 – (25.9.‘97): 48 µg/l Chl -a Großer Wummsee Braminsee 2 – (11. 6.‘97): 11 µg/l Chl -a 5 – ( 2.9.‘97): 70 µg/l Chl -a Schwarzer See Braminsee 3 – (11.6.‘97): 34 µg/l Chl -a 6 – (10.6.‘97): 90 µg/l Chl -a Kagarsee Braminsee Phytopigmente: CHL: Chlorophyll CAR: Carotinoide PE: Phycoerythrin PC: Phycocyanin Blue Algae produce 'Microcystin', a liver toxine

  16. Distribution of Cyanobacteria PC = (K(A) – K(C)) 2 – K(B) Cyanobacteria occurrences none frequent plentiful Absorption depth - Goetz (1991) K(A) = band13 at 616 nm K(B) = band14 at 631 nm K(C) = band15 at 646 nm HyMAP data Havel River 30.07.2003

  17. Parameter Extraction • Roof materials • Unknown or contradiction tiles new tiles old concrete aluminium zink copper pvc polyethylene glass / plexiglass bitumen dark/bright/red & tar-paper schist vegetation gravel N facade 400 m other

  18. Summary of Processing Methods • Endmember Assignment: • Classification: • Unmixing (full): • Unmixing (partial): • Quantification: PPI, IEA, N-FINDR, ORASIS, AMEE, ICE, CCA, VCA, LDA, IFA, ICA, DECA, CPMF, SSEE SAM, SFF, BE, MSFM, SIM, MLP, SVM, Tetracoder LSU, NNLS, MESMA, HBM OSP (OBC,SSC,TSC), CEM, MF, MTMF,TCIMF, SPU LS, PLS, SMRA, SMGM, LSU

  19. EeteS – End to End Simulation Tool Onboard Calibration Non-linearity Dark Current Sensor Data (DN) L1 Processor Absolute Calibration EnMAP Scene Simulator Radiometric Module Forward Simulation L2 Processors BackwardSimulation Spectral Module Co-registration Spatial Module Atmospheric Correction Atmospheric Module Orthorectification Input Data (Reflectance) Output Data (Reflectance)

  20. Simulations for Optimum Band Design & SNR full resolution radiance resampledradiance retrievedEnMAP spectra SNR 50 SNR 100 SNR 200 resampled field spectrum full resolution field spectrum Spectral Response Function MODTRAN noisyresampled radiance MODTRAN Noise Model Spectral Response Function

  21. ESA - Sentinel-2 Series Two satellites; Launched 2013/2015 Main Objective: GMES Support Main Parameters: GSD: 10 m (bands 2,3,4,8); 20 m (bands 5,6,7,8a,11,12); 60 m (bands 9,10) Swath width: 290 km Repetitivity: 10 days New bands for corrections and retrieval Water Vapor Retrieval based on bands 8a and 9 centered at 865 nm and 940 nm Aerosol Retrieval bands 2, 4 and 12 centered at 490 nm, 665 nm and 2190 nm Cirrus Clouds Detection based on bands 4, 7 and 10 centered at 670 nm, 790 nm and 1380 nm

  22. LDCM and Sentinel-2 approved nominal Filtersincl. feature producing materials pan 8 LDCM 6 4 9 2 5 1 3 7 Sentinel 10 7 8a 6 8 11 3 9 4 5 12 2 1 70 60 Fe2+3+ C-O Al-OH Vege- tation 50 40 Reflectance [%] Bands used to correct for 30 water vapor aerosol cirrus clouds 20 10 0 1.7 2.3 1.2 1.3 1.4 2.1 2.2 1.6 1.8 1.9 2.0 1.1 1.5 1.0 0.8 0.9 0.5 0.6 0.7 0.4 Wavelength in Microns

  23. LDCM and Sentinel-2 bands 4 – 8a LDCM 1.4 8 8a 7 6 4 5 1.3 Sentinel 1.2 1.1 1.0 Vegetation 0.9 0.8 Spectral Rad. [W / m**2 * nm * sr] 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0 0.7 0.8 0.9 1.0 0.6 Wavelength in Microns

  24. History and Current Status Commissioning Phase Launch and Early Orbit Phase Decommissioning Phase Mission Preparation Phase Operational Phase 5 years Phase B Phase C Phase D Phase A Launch 2015 (PSLV/India) Present Status • 2005 Phase A study accomplished • 2006 Start of phase B • 2007 End of phase B • 2008 Start of phase C/D • 2010 CDR Ground Segment • 2012 (June) CDR Space Segment • 2015 Launch date 24

  25. Project Partners Status 2011 Scientific Principal Investigator GFZ-Potsdam Project Management DLR Agency Space Segment Kayser-Threde - Spectrometer OHB Bremen - Bus Technology Core Science Team ECST Ground Segment DLR-Oberpfaffenhofen

  26. Main Sensor/Orbit Parameters

  27. Instrument Optics Unit - Main Elements star trackers VNIR detector field splitter slit assembly VNIR optics Redundant SWIR-FPA telescope optics SWIR detector calibration unit SWIR optics diffuser/ telescope entrance baffle solar ca. entrance baffle Nominal SWIR-FPA source: KT

  28. EnMAP - Bench Clean Room (KT) 28

  29. Tasks – EnMAP Core Science Team (ECST) SP1 M1 M2 SP2 M3 M4 SP3 M5 S2 S1 S3 W3 W5 W4 W1 W2 A3 A1 A2 A4 A5 A6

  30. EnMAP Box • License free and platform independent processing environment • Optimized for EnMAP/hyperspectral processing Humboldt Uni Berlin, 2012

  31. User Portal http://www.enmap.org/ 31

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