Processing of AVHRR data over Europe and North-Africa in the TIMELINE project

Corinne FreyWith contributions from Martin Bachmann, Thomas Ruppert, Andreas Dietz, and Fa. Brockmann Consult

Team Dynamik der Landoberfläche - Abteilung Landoberfläche

Deutsches Fernerkundungsdatenzentrum (DFD)

Deutsches Zentrum für Luft- und Raumfahrt (DLR)

TIMe Series Processing of Medium Resolution Earth Observation Data assessing Long -Term Dynamics In our Natural Environment

• 30 – year time series with AVHRR land and atmosphere products• Enable change detection analyses and the identification of geoscientific phenomenons

and trends• Enhancing our ability to automatically process mass data• Simple user-access / download possibilities for the time series

TIMELINE

Develop AVHRR time series of geo-scientific

variables in the context of ‚global change‘

L1b/L2L0 L3

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AVHRR data at DFD

• Data since the early 80ies• Three AVHRR sensors: AVHRR/1: 4

bands, AVHRR/2: five bands, AVHRR/3: six bands

19791982

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19911994

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20092012

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HRPT scenes at DFD noaa-19noaa-18noaa-17noaa-16noaa-15noaa-14noaa-12noaa-11noaa-10noaa-9noaa-8noaa-7noaa-6

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Data consolidation: comparison with Dundee

DLR archive• Green: OP+BE antenna• Rot: OP antenna

Other archives• Blue: Dundee• Black: NASA CLASS

Coverage of selected orbits with TIMELINE area at minimum 5%.

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Input data

NOAA-AVHRR HRPT/LAC

Aux dataRaster dataRe-analyisis

Params

TOA/BOA reflectance, and

brightness temperature

Interpre-tation

variables

Landproducts

Atm. products

Scientific tasks

Generation of interpretation

variables

Evaluation / Validation

Processing workflows

Land processors

Snow and Ice

Vegetation

Fire

Surface Temp

Pre-Processing

Calibration Navigation

Atm. Processors and corrections

AtmcorrWater mask

Cloud prod. Cloud mask

Users Free online access

TIMELINE – The project

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TIMELINE variables and processing scenario

Type Variables

Radiative variablesReflectanceBrightness temperatureAlbedoLand Surface Temperature

Land surfaceVegetation variables (NDVI, LAI, FAPAR, FVC)Burnt areas„Hot Spots“Water mask

Cryosphere Snow and Ice over land, Sea Ice

Atmosphere

Cloud coverageThermodynamic cloud phaseCloud top temperatureCloud heightCloud optical depthPrecipitation potential

Pre processing

Apollo NG processor

Atmospheric Correction Processor

Water mask processor

NDVI processor

FAPAR processor

Snow / Sea Ice processor

LAI processor

FVC

Surface Temperature

processorHot Spot processor Albedo processor Burnt Area

processor

L0 StitchingLo Stitching

Pre Processing

Stage 1 processing

Stage 2 processing

Stage 3 processing

L1b-Preprocessing (L1b-Pre): Responding to extended user requirements

TIMELINE L1b product

Online

System correction and base calibration

Offline

Adaptation of navigation parameter

Online

System correction and base calibration

Calibration site extraction

CF, ACDD, and EOP-conform metadata

Chip matching

Quality layer generation

Orthorectification

Data quality checks

Offline: Just calibration sites

Conversion from technical albedo to

apparent TOA reflectance

Spectral normalization

Radiometric harmonisation

Generation of harmonisation factors

NetCDF + xlm-File + Quicklooks

Harmonisation factors

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Base AVHRR preprocessing TIMELINE AVHRR preprocessing

L1b-Pre: Geometry

   

Not produced

Poor quality

Reduced quality

Good quality

Lat/Lon-correction

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3-step vector definition

Quality assessmentResulting shifts

b) Chip-matching: A generic correction tool for imprecise geolocation

Newton-Raphson

Schematic overview of Newton-Raphson method

Example showing an uncorrected RGB of an orbit segment and a colour coded shift distance “before and after” the correction

c) Ortho: A generic correction tool for location errors due to relief impact

a) SeaSpace TerraScan: Application of orbit model with coastline matching

Clouds act as limiting factor, therefore:

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L1b-Pre: Consistency issues

Band Variance

Channel 1 -2% - +7%

Channel 2 -6% - +2%

NDVI -20% - +15 %

Spectral response of channel 1 Resulting variance in measurements

a) Screening und flagging

b) Sensor inconsistencies

Relative differences between AVHRR TOA radiances due to different spectral responseVariance is given with NOAA-19 as reference:

L1b-Pre: Final product

• „Technical Albedo“• In Orbit-Projection• Additional bands

• Quality layer• Reflective• TIR

• Lat / Lon• Sensor zenith and -

azimuth• Sun zenith and -azimuth• Elevation• Local time

• Nominal NOAA OSPO calibration

• Quicklooks

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NOAA-16 Gain Offset

2011260 23.543 32.523

2011261 24.257 33.346

2011262 23.832 33.643

2011263 23.963 33.245

2011264 26.258 32.654

211265 22.378 31.214

211266 23.472 31.532

2011267 23.832 33.643

2011268 23.963 33.245

2011269 26.258 32.654

2011270 26.258 32.654

NOAA-17 Gain Offset

2011260 23.543 32.523

2011261 24.257 33.346

2011262 23.832 33.643

2011263 23.963 33.245

2011264 26.258 32.654

211265 22.378 31.214

211266 23.472 31.532

2011267 23.832 33.643

2011268 23.963 33.245

2011269 26.258 32.654

2011270 26.258 32.654

NOAA-18 Gain Offset

2011260 23.543 32.523

2011261 24.257 33.346

2011262 23.832 33.643

2011263 23.963 33.245

2011264 26.258 32.654

211265 22.378 31.214

211266 23.472 31.532

2011267 23.832 33.643

2011268 23.963 33.245

2011269 26.258 32.654

2011270 26.258 32.654

Normalized apparent TOA reflectances / AUX data + Harmonisation factors

= TIMELINE L1b - Product

Format: NetCDF with CF (Climate and Forecast)- und Dataset Discovery NetCDF Attribute Convention (ACDD)- conform metadata

Watermask- and snowmask - processors

Band 1 Band 2

Preliminary cloud mask

Static water mask

Final L2 water mask

a) Water mask processor

• Calculation of preliminary cloudmask from L1b data• Static water mask is used to derive dynamic thresholds for

band 2 (0.72-1.00 µm) • Use of the Normalized Difference Water Index (NDWI)• Thresholds are applied for each single orbit-segment

Wasser-maske

SPARC B_Score

LSTECM

WF

b4 - LSTECM

WF

Mint: detected snow pixels in the L2 productt

Score combination

b) Snow and ice processor

Round robin with: ESA GlobSnow algorithm, MODIS snowmap, Canadian SPARC, APOLLO Snow-detection algorithm

Example SPARC• Scores are derived from reflectances and temperature differences.

The sum of all scores is used as indicator for the probability of snow.

Round robin• Analysis of the influence of view

angle, water vapour, and LST on split window algorithms

• Comparison of five difference SW algorithms in terms of accuracy and sensitivity of input parameters

SurfTemp: Derivation of Land Surface Temperatures

Wan and Dozier 1996 *

Jimenez-Munoz 2008 *

Ulivieri 1994*

Price 1984 *

Becker & Li 1990 *

Accuracy Sensitivity

* New parameter sets

Emissivity

TIMELINE products:• Band 4, band 5• View angle, lat/lon• FVC• Water mask, snow mask, cloud mask

AUX data:• ECMWF water vapour• Land use classification

LST Uncertainty Quality flags

LST – L2 Product

Under develop-

ment

Hot Spot: Overview

preliminary version of automated contextual algorithm

testing

preliminary results

Enhancement of the algorithm

Legend

No Fire

Fire

Legend

No Fire

Fire

Contextual algorithm works better

Limits• Saturation in channel-3 (desert, sun

glint)• Low contextual information at 1 km

resolution • Omission errors:

• Obscuration by clouds and smoke• Low intensity fires• Acquisition times

Aims and prerequisites• To be applied to all AVHRR sensors• Usage of channel 3 and channel 3 and 4

difference

Round robin• Multi-threshold versus contextual• Testing of algorithms in different ecosystems

• Atmospheric correction using a look-up table (LUT) approach.• Correction parameters are being derived and saved for x atmospheric states.

• Uses aux data for atmospheric state• ERA-Interim• Aerosol-climatology

• Each pixel is being treated individually• Generic methodology

TAC – TIMELINE Atmospheric Correctionby Brockmann Consult

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Thank you for attention

Time for questions