hyperspectral in-situ surface reflectances from hypernets...•first test sites now deployed and...

The HYPERNETS project is funded by the European Union’s Horizon 2020 research and innovation programme under grant agreement No 775983.

This communication represents only the authors’ views. The European Union is not liable for any use that may be made of the information contained therein.

(c) HYPERNETS Consortium, 2018 (RBINS, TARTU, SU, CNR, NPL, GFZ, CONICET)

NPL - Commercial

Hyperspectral in-situ surface

reflectances from HYPERNETS

Pieter De Vis,

Sam Hunt, Clemence Goyens, Morven Sinclair,

Sarah Taylor, Agnieszka Bialek, Chris Maclellan

& HYPERNETS team

(c) HYPERNETS Consortium, 2018 (RBINS, TARTU, SU, CNR, NPL, GFZ, CONICET)(c) HYPERNETS Consortium, 2018 (RBINS, TARTU, SU, CNR, NPL, GFZ, CONICET)(c) HYPERNETS Consortium, 2018 (RBINS, TARTU, SU, CNR, NPL, GFZ, CONICET)NPL - Commercial

Content

• HYPERNETS introduction

• Hypernets_processor + example data

• Uncertainty budget + application

• Conclusions


Calibrationmonitoring LED

Radiometer

Pan & Tilt

HYPERNETSmulti-head

hyperspectralRadiometer

HYPERNETSAutonomous

System

810

1

1

2

1

HYPERNETS water

validation network

phase 1

HYPERNETS land

validation network

phase 11

Validation of surface reflectance at all spectral bands of all optical missions inc.Sentinel-2A&B, Sentinel-3A&B, MODIS-A&T, VIIRS, Landsat-8, Pléiades-2A&B,

PROBA-V, CHRIS, ENMAP, PRISMA, SABIAMAR, etc. ... + nanosats

The Idea

Design a new

“low cost” hyperspectral radiometer

for use in

federated networks of water and land sites

Measure reference surface reflectances for multi-mission satellite validation


[MERIS 3rd reprocessing data validation report, ACRI, 2012]

Data courtesy of PIs (D. McKee, K. Ruddick, D. Siegel, S. Kratzer) and AERONET-OC PIs (G.

Zibordi, G. Schuster, S. Kratzer, B. Gibson), matchup using MERMAID

In situ

The Motivation for automated hyperspectral

Water reflectance 490nm

Sa

telli

te (

ME

RIS

)

AUTOMATED Data acquisition HYPERSPECTRAL Instrument

Sentinel-2A/B spectral response


Instrument development

3 LAND

(VISNIR+SWIR)

4 WATER

The first set of HYPSTAR instruments has been produced and are currently being

tested in the field

Calibrations traceable to SI through NMI-calibrated lamp and reflectance panels

Rugged pc drives the

instrument, pan tilt,

communication with

server, …


Hypernets Processor Introduction

Common processor for the land and water networks to process raw data to surface reflectance, developed as Python module.

There are two main use cases for the hypernets_processor module.

• Automated processing of network data for distribution to users.

• Ad-hoc sequence processing, for example for testing instrument operation in the field.


Hypernets nomenclature

• Scan: a single measurement of the full spectrum

• Series: number of scans (typically 10) made with the same

azimuth and zenith angle

• Sequence: combination of series with different angles

files description

spe raw data from instrument

L0 raw data in netcdf

L1a calibrated scans

L1b calibrated series (scans averaged)

L1c calibrated series with coincident irradiance and radiance

measurements

L2a surface reflectances


From raw to L1a for land and water network


Raw data (VNIR+SWIR)


Land network: L1a


Land network: L1a to L2a


Land network: L1b


Land network: L1a to L2a


Land network: L2A

- Temperature correction currently missing

- BRDF measurements can be made


Uncertainties: MC methodGUM Supplement 1 – Monte Carlo Methods defines three main stages of uncertainty evaluation:

• Formulation: • Define Y, X, f

• Assign PDF

• joint PDF and

correlation matrix S

• Generate sample of

draws 𝑋𝑖 from these

joint PDF

• Propagation• Propagate each draw 𝑋𝑖 through the measurement function to get 𝑌𝑖 = 𝑓(𝑋𝑖)

• Together 𝑌𝑖 give the PDF for 𝑌

• Summarizing• Use the PDF for Y to obtain the expectation of Y, the standard uncertainty u(Y)

and the covariance between the different values in Y.


MC method for HYPERNETS

• MC method implemented in NPL uncertainty propagation python package

• Correlations w.r.t. wavelength taken into account

• Gaussian PDF are assumed

• Draw samples from joint PDF using Cholesky decomposition method:

• First generate uncorrelated samples 𝑍𝑖 from gaussian

with mean X and std u(X)

• Calculate Cholesky decomposition 𝑅: 𝑅𝑇𝑅 = 𝑆 𝑋

• Correlate samples: 𝑋𝑖 = 𝑋 + 𝑅𝑇(𝑍𝑖−𝑋)


MC method for HYPERNETS

L1A L2A

𝑌 calibrated (ir)radiances surface reflectances

𝑋 • Gains and non-linear from the calibration files

• DN and darks in the raw data

coincident radiances and

irradiances from L1C file

𝑓𝑓1 = 𝑔𝑎𝑖𝑛 𝜆 ∗

𝐷𝑁 𝜆 − 𝑑𝑎𝑟𝑘 𝜆

𝑐𝑛𝑜𝑛−𝑙𝑖𝑛𝑒𝑎𝑟 𝐷𝑁 𝜆 ∗𝑡𝑖𝑛𝑡1000

+ 0 𝑓4 = 𝜋𝐿

𝐸

PDF • Many (16) contributions to the PDF of the gain

and its correlation matrix from lab instrument

calibration (e.g. lamp, distance, panel …)

• Uncertainty of DN calculated from std

between scans

• Further contributions to be added (e.g.

temperature correction)

Gaussian with uncertainties

and correlation matrices

from L1C product


Uncertainty budget

Three different types of uncertainties:• Random uncertainties (from std between scans)

• Systematic uncertainties (from calibration coefficient unc)

• Correlated systematic between rad and irr (cancel out in L2)

• Independent systematic

Propagated from product to product using MC

Each product has for each relevant variable:• Uncertainties for each scan/series for each of the three types

• One correlation matrix for each of the three types


Land network: L1b uncertainty


Land network: L2 uncertainty


Correlation matrices (L2A)


Uncertainty application: TOA vicarious calibration of S2

• No suitable test data available yet

we use simulated data based on ASD data taken at Gobabeb

• Compare to Sentinel-2A overpass at

roughly the same time of day

• Before the HYPERNETS data can be compared to satellite observations, we apply the following steps:

• Read/select satellite data and HYPERNETS data for appropriate angle

• Apply the atmospheric correction based on radiative transfer modelling

• Atmospheric parameters taken from RadCalNet at time of overpass

• Convolve the TOA spectrum with the satellite spectral response function

• Propagate uncertainties

• Compare


Vicarious calibration uncertainties

• Metrological approach is used to propagate uncertainties through a given measurement function using MC.

• Measurement functions for RT model and spectral integration are defined in python using NPL packages

• Three uncertainty components propagated:• Random uncertainties from L2A product• Systematic uncertainties from L2A product (including

covariance matrix)• Uncertainties on atmospheric parameters from RadCalNet

(systematic)


Results


Uncertainty Results


Conclusions

• HYPERNETS is developing a network of hyperspectral

instruments for multi-mission satellite validation

• First test sites now deployed and providing data

• Hyperspectral surface reflectance measurements will be

available over a range of water and land surface types and

can be used for BRDF measurements

• Detailed uncertainty budget available for every product

based on Monte Carlo uncertainty propagation

• Measurement function and uncertainty budget will contain

additional contributions after lab characterization of next batch

hyperspectral in-situ surface reflectances from hypernets...•first test sites now deployed and...

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