roccopancieramesiano sept25 2013

Towards Global Remote Sensing of Soil Moisture:

Rocco Panciera

Wednesday, September 25, 13

The last 10 years• 2003: Master, University of Trento,

Distributed hydrological modeling

• 2004 – 2009: PhD, University of Melbourne, Passive microwave remote sensing of soil moisture,

• 2009 – 2010: Research Fellow, University of Melbourne, Passive and ac9ve microwave remote sensing of soil moisture

• 2011 – present: Super Science Fellowship, ARC SAR remote sensing of soil moisture

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AcIvity Overview

Instrument Development

Field Experiments Research

• Soil moisture monitoring system

• Airborne SyntheIc Aperture Radar (SAR)

• NAFE’05• NAFE’06• SMAPEx-‐1• SMAPEX-‐2• SMAPEx-‐3

• …

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Research Overview

Remote sensing of Land surface

Soil Moisture Land Cover VegetaAon Biomass

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Research Overview

Remote sensing of Land surface

Soil Moisture Land Cover VegetaAon Biomass

LiDAR

SAR

PassiveMicrowave

OpIcal/IR

SAR SAR

OpIcal/IR

SAR:SyntheIc Aperture Radar

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(MANY)Field Experiments

2011

Soil Moisture AcIve Passive Experiment (SMAPEx)

Dec 2010

AMSR-‐E ValidaIon 2004

NaIonal Airborne Field Experiments (NAFE)

20062005

Jul 2010

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• Hydraprobe Data AcquisiIon System (HDAS)

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• Hydraprobe Data AcquisiIon System (HDAS)

Soil moisture (vol) VegetaIon height (cm)VegetaIon Type

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• Polarimetric L-‐band Imaging SAR (PLIS)

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• Polarimetric L-‐band Imaging Sca`erometer (PLIS)

SAR SensiIvity:Soil moistureSurface roughnessVegetaIon structureVegetaIon water contentVegetaIon height

Flight path

3km

3km

15°15°

45°

45°

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Airborne Field Experiments

• Soil Moisture AcIve Passive Experiments (SMAPEx)

~40km

Soil moisture Sampling

Surface roughness & vegetaIon sampling

Panciera, R., Walker, J.P, Jackson, T J., Ryu, D., Gray, D., Monerris, A., Yardley, H., Tanase, M., Rudiger, C. et al.,“The Soil Moisture Ac9ve Passive Experiments (SMAPEx): Towards Soil Moisture Retrieval from the SMAP Mission”, IEEE Transac9ons of Geoscience and Remote Sensing, 51(9), 2013.

Passive AcIve

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Airborne Field Experiments11


Soil Moisture from SAR

• SensiIvity of SAR to soil moisture (Mv)

Bare soil

Canola ~ 140cm height

Wheat ~ 50cm height

-‐20.0000

-‐15.0000

-‐10.0000

-‐5.0000

0 2 4 6 8 10

SAR dB

Days

SAR HH-‐pol MvSAR VV-‐pol

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IrrigaAon



• SensiIvity of SAR to soil moisture

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RoughSurface

Smoothsurface

Surface RMS [cm]



• Time-‐series approach: Backsca`er dynamic over short periods solely due to soil moisture changes

Snapshotapproach

Time-‐seriesapproach

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• Time series approach Using 1km ALOS PALSAR data in Australia

Satalino, G., Maja, F., Balenzano A., Panciera, R., Walker, J.P, “Soil Moisture Maps from Ime series of PALSAR-‐1 scansar data over Australia”, Proceedings of IEEE Interna9onal Geoscience and Remote Sensing Symposium 2013 (IGARSS 2013), 21-‐26 July, Melbourne, Australia.

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Surface roughness from LiDAR

Turner, R., Panciera, R., Tanase, M., Lowell, K., Hacker, J., Walker, P., J.,” Es9ma9on of Soil Surface Roughness of Agricultural Soils using Airborne LiDAR”, Remote Sensing of Environment, In review, 2013.

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Soil Moisture from Passive microwave

• Algorithm development for ESA’s SMOS for Australian condiIons

Uncalibrated parameter “b” Calibrated parameter “b”

(Jackson and Schmugge, 1991)

Wheat/barley

pastures

Panciera, R., Walker, J.P., Kalma, J.D., Kim E.J., Saleh, K., Wigneron, J.-‐P., “Evalua9on of the SMOS L-‐MEB passive microwave soil moisture retrieval algorithm”. Remote Sensing of Environment, 113(2): p. 435-‐444, 2009.

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Soil Moisture from AcIve/Passive microwave

• Downscaling algorithm development for NASA’s SMAP mission

Airborne Simulated SMAP

AcIve/passiveDownscaling to

9km

Downscaling error KPassive

AcIve

Passive

AcIve

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RMSE = 1.5 – 5.8 KSMAP target = 2.4K


Soil Moisture from Passive & OpIcal/NIR

• Downscaled SMOS + MODIS 1km soil moisture productJanuary 2-‐14, 2011

Tropical Cyclone Oswald

Piles, M., Camps, A. , Vall-‐llossera, M., Corbella, I. Panciera, R., Rudiger, C., Kerr, Y. and Walker, J., “Downscaling SMOS-‐derived soil moisture using MODIS visible/infrared data”, Accepted for publica9on in IEEE Transac9on on Geoscience and Remote Sensing, TGRS-‐2010-‐00403.R1, 2010.

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Land cover from SAR & opIcal • Supervised land cover classificaIon using Cosmos-‐SkyMed & Landsat

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Overall classifica9on Accuracy (OA)

Landsat5, 2 imagesOA = 93%

Cosmo-‐SkyMed, 8 images, HH and HV: OA

= 80%


Forest Biomass from SAR and LiDAR

Tanase, M, R. Panciera, K. Lowell, C. Aponte, J. M. Hacker, J. P. Walker, “Forest Biomass Es9ma9on at High Spa9al Resolu9on: Radar vs. Lidar sensors”, accepted for publica9on, IEEE Geoscience and Remote Sensing Le_ers;

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CollaboraIons• Consiglio Nazionale della Ricerca, Italy

– AcIve microwave & land cover mapping• Jet Propulsion Laboratory, Pasadena

– AcIve Microwave (SMAP mission)

• United States Department of Agriculture – AcIve/passive microwave (SMAP mission)

• European Space Agency – Passive microwave (SMOS mission)

• Australian Defence Science and Technology OrganisaAon (DSTO) – Airborne SAR development/calibraIon

• Barcelona SMOS Expert Centre – SMOS/MODIS soil moisture product

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roccopancieramesiano sept25 2013

Education