Impact Assessment of Remotely Sensed Soil Moisture on Ecosystem - - PowerPoint PPT Presentation

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Impact Assessment of Remotely Sensed Soil Moisture on Ecosystem - - PowerPoint PPT Presentation

Nov 02, 2023 144 likes •361 views

Impact Assessment of Remotely Sensed Soil Moisture on Ecosystem Carbon Fluxes Across Europe Willem W. Verstraeten, Frank Veroustraete, Wolfgang Wagner, Tom Van Roey, Walter Heyns, Sara Verbeiren, Corn J. van der Sande, Jan Feyen Method

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Impact Assessment of Remotely Sensed Soil Moisture

  • n Ecosystem Carbon Fluxes

Across Europe

Willem W. Verstraeten, Frank Veroustraete, Wolfgang Wagner, Tom Van Roey, Walter Heyns, Sara Verbeiren, Corné J. van der Sande, Jan Feyen

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SLIDE 2

Gross primary productivity

Method

  • Use of the ERS scatterometer derived estimates of the relative

profile soil moisture content as input into the production efficiency model C-Fix

  • C-Fix

Developed by VITO fAPAR from satellite-NDVI

  • Applied over Europe
  • Validated over

EUROFLUX sites

Verstraeten et al. (2006) Ecological Modelling Soil moisture content Soil respiration

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SLIDE 3

The Remote Sensing Perspective

  • Remote sensing can deliver spatially and temporally complete

data

Land cover information Geophysical parameters, e.g. biomass, soil moisture, …

  • Data could be used in different ways

Validation of model components Initialisation, assimilation, …

  • Good Practice Guidance for Land Use, Land Use Change and

Forestry (IPCC, 2003)

Different remote sensing techniques are discussed, but

No concrete advise of how to use them No real incentives to use them

  • In practice, remote sensing is hardly used for GHG accounting
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SLIDE 4

Reasons for Slow Adoption of Remote Sensing

  • Problems with

Satellite/sensor continuity Relevant information? Retrieval accuracy

Retrieval is often a mathematically ill-defined problem High-level of abstraction needed for model formulation Lack of suitable reference data Retrieval errors generally not well known

  • "Interfaces" between GHG models and remote sensing data

Models parameterisation often not adapted to remote sensing data Data assimilation rather complex

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SLIDE 5

European C-Band Scatterometers

  • 2 ERS scatterometers
  • λ = 5.7 cm
  • VV Polarization
  • Resolution: 50 / (25) km
  • Daily coverage ~ 40%
  • 1991-2008
  • 3 METOP scatterometers (ASCAT)
  • λ = 5.7 cm
  • VV Polarization
  • Resolution: 50 / 25 km
  • Daily coverage ~ 80%
  • 2006 - 2020
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SLIDE 6

Soil Moisture Retrieval

  • Change detection

Accounting for effects of vegetation phenology

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SLIDE 7

Quality of Surface Soil Moisture I

  • Comparison with modelled surface soil moisture data

South-west France RMSE error ~0.06 m3m-3

Pellarin et al. (2006) Geophysial Research Letters

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SLIDE 8

Quality of Surface Soil Moisture II

  • Comparison with

AMSR-E surface soil moisture product

  • R. de Jeu (Univ.

Amsterdam)

  • M. Owe (NASA)

Year 2006

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SLIDE 9

Quality of Surface Soil Moisture III

  • Internal error assessment through error propagation and Monte

Carlo simulations

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SLIDE 10

ASCAT Surface Soil Moisture Anomalies

Bartalis et al. (2007) Geophysical Research Letters

Date: 15-21 March 2007

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SLIDE 11

Soil Water Index (SWI)

  • SWI rests upon simple differential model for describing the exchange of

soil moisture between surface layer (Θs) and the “reservoir” (Θ)

  • T … characteristic time

Thin, remotely sensed soil layer with Θs Root zone with Θ : layer of interest for most applications Soil profile

( )

s

T dt d Θ − Θ = Θ 1

( ) ( )

t d T t t t T t

t s

′ ⎥ ⎦ ⎤ ⎢ ⎣ ⎡ ′ − − ′ Θ = Θ

∫

∞ −

exp 1

SWI is the discrete version of this integral

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SLIDE 12

Global SWI Dynamics (1992-2000 Mean)

Closed Forest Cover Azimuthal Effects Frozen Soil/Snow Cover Dry Soil Wilting Point Wet Soil Field Capacity

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Soil Moisture Network

  • Gravimetric field measurements
  • f soil moisture
  • 48 000 data points
  • Accuracy ≈ 5 % vol.

for 0-100 cm layer

Russland & Ukraine Illinois China Indien Location of in-situ soil moisture stations

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SLIDE 14

SWI versus LPJ modelled Soil Moisture

  • Cooperation with PIK, Germany

Dieter Gerten and Wolfgang Lucht

Correlation R Wagner et al. (2003) Journal of Geophysical Research

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SLIDE 15

Results for Climate Classes

Gerten et al. (2005) Geophysical Research Letters

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SWI in C-Fix - Hyytiala, Finland

  • 4.00
  • 2.00

0.00 2.00 4.00 6.00 8.00 30 60 90 120 150 180 210 240 270 300 330 360 390 Julian Day [-] NEP [gC m-2 d-1] EUROFLUX NEP C-Fix PWL NEP C-Fix FWL NEP

C-Fix model result with ERS derived SWI Year: 1997

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SWI in C-Fix - Loobos, The Netherlands

  • 3.00

0.00 3.00 6.00 9.00 12.00 15.00 30 60 90 120 150 180 210 240 270 300 330 360 390 Julian Day [-] NEP [gC m

  • 2 d
  • 1]

EUROFLUX NEP C-Fix PWL NEP C-Fix FWL NEP C-Fix model result with ERS derived SWI Year: 1997

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SLIDE 18

Difference in NEP with and without SWI

SWI decreases NEP SWI increases NEP

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Conclusions

  • The use of remotely sensed soil moisture data in C-Fix had a

strong impact of modelled NEP

Some countries shift from being a sink to a source

  • Error structures are different in models and remote sensing data

A good agreement suggests a high quality of both methods Poor agreement requires further analysis

  • Remote sensing of soil moisture: What is next?

Different AMSR-E soil moisture data have become available ASCAT soil moisture will become operational in 2008 SMOS will be launched in 2008

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SLIDE 20

Nation Nation-

  • Wide Airborne Laser Scanning

Wide Airborne Laser Scanning