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Remote Sensing of Soil Moisture

Sebastian Hahn, Mariette Vreugdenhil, Bernhard Bauer-Marschallinger, Wolfgang Wagner

Department of Geodesy and Geoinformation (GEO), TU Wien http://www.geo.tuwien.ac.at/

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Topics

• Introduction

• Microwave Remote Sensing

• Spaceborne Microwave Instruments

• Sampling Requirements

• Retrieval Approaches

• Summary

2

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GEO Department

3

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Microwave Remote Sensing Satellites

4

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Microwave Remote Sensing

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Electromagnetic Spectrum

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Image credit: NASA

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Remote Sensing of Soil Moisture

• Measurement principles

– No direct measurement of possible, only indirect techniques

• Optical to Mid-Infrared (0.4 – 3 m)

– Change of “colour”

– Water absorption bands at 1.4, 1.9 and 2.7 m

• Thermal Infrared (7-15 m)

– Indirect assessment of soil moisture through its effect on the surface energy balance (temperature, thermal inertia, etc.)

• Microwaves (1 mm – 1 m)

– Change of dielectric properties

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Active and Passive Microwave Sensors

• Passive – Passive remote sensing systems record electromagnetic energy that is

reflected or emitted from the surface of the Earth

– Sensors • Microwave radiometers

• Active – Active remote sensors create

their own electromagnetic energy

– Sensors • Altimeters

• Side-looking real aperture radar

• Scatterometer (SCAT)

• Synthetic Aperture Radar (SAR)

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ERS-1

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Active and Passive Sensing of Soil Moisture

• Observables – Passive: Brightness temperature TB = eTs where e is the emissivity and Ts

is the surface temperature – Active: Backscattering coefficient 0; a measure of the reflectivity of the

Earth surface

• Active measurements are somewhat more sensitive to roughness and vegetation structure than passive measurements, but – are not affected by surface temperature (above 0°C) – have a much better spatial resolution

• Despite these differences both active and passive sensors measure essentially the same variables: – Passive and active methods are interrelated through Kirchhoff’s law:

• e = 1 – r where r is the reflectivity

– Increase in soil moisture content • backscatter • emissivity

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Dielectric Properties of Water

• Water is unique amongst naturally abundant material because of its electric dipole

• “Directional” polarisation arises

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Water molecule

The complex dielectric constant of water and its relaxation spectrum

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Soil Scattering • Soil scattering is principally driven by

– Soil dielectric constant

• Soil moisture

• Texture

– Soil surface “roughness”

• Relative to wavelength

• Dependent on soil moisture

11

Graphic by R. Quast, TU Wien

Soil Moisture (m3m-3)

Soil

Die

lect

ric

Co

nst

ant s

oil

Behari (2005) Microwave dielectric behaviour of wet soils, Springer, 164 p.

휀′𝑠𝑜𝑖𝑙

휀′′𝑠𝑜𝑖𝑙

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Penetration Depth & Soil Volume Scattering

• When the penetration depth is large then soil volume scattering effects may become important

• Penetration depth may be large when

– Absorption losses are small

– Wavelenght is long

• For dry sand penetration may be in the range from decimeters (X-band) to several meters (P-band)

12

Graphic by R. Quast, TU Wien

𝛿 ∝𝜆 휀′𝑠𝑜𝑖𝑙𝜋휀′′𝑠𝑜𝑖𝑙

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Spaceborne Microwave Instruments

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Spaceborne Radar Remote Sensing

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Radar Altimeter Range to ground https://sentinel.esa.int/

Weather Radar 3D view of precipitation particles

Radar Scatterometer Directional surface backscatter

Synthetic Aperture Radar 2D surface backscatter

https://sentinel.esa.int/

modified from https://www.nasa.gov/

Metop-SG SCA

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Side-Looking Radars

• Spatial resolution of real-aperture side-looking radars determined by

– Pulse length (cross-track)

– Antenna size (along-track)

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http://lms.seos-project.eu/learning_modules/

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Synthetic Aperture Radar (SAR)

• Repeated measurements of a target

– The echoes from a stationary target undergo a well-defined systematic frequency shift due to the Doppler effect

• On-board recording of amplitude and phase of echoes

• Post-processing to create a synthetic (virtual) aperture (antenna)

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𝐴𝑙𝑜𝑛𝑔 − 𝑡𝑟𝑎𝑐𝑘 𝑟𝑒𝑠𝑜𝑙𝑢𝑡𝑖𝑜𝑛 = 𝐴𝑛𝑡𝑒𝑛𝑛𝑎 𝑙𝑒𝑛𝑔𝑡ℎ

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ASCAT on-board Metop-A, Metop-B, Metop-C

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Metop

Launch: Metop-A 19 October 2006, Metop-B 17 September 2012, Metop-C 7 November 2018

Orbit: Sun-synchronous orbit

Inclination: 98.7 degrees to the Equator

Repeat Cycle: 29 days

Mean altitude: ~ 817 km

Source: Metop-A, EUMETSAT

Source:http://www.esa.int/Our_Activities/Observing_the_Earth/The_Living_Planet_Programme/Meteorological_missions/MetOp/Facts_figures

Sensor: Advanced Scatterometer (ASCAT) Instrument: Active microwave scatterometer

Frequency: C-band, 5.255 GHz

Polarisation: VV

Antenna: six; 3 (quasi) instantaneous independent measurements

Swath: 2 x 500 km

Main applications: Wind measurements, land and sea ice monitoring, soil moisture, snow properties, soil thawing.

Spatial Resolution: 25 km/ 50 km

Multi-incidence: 25-65°

Daily global coverage: 82 %

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ASCAT vs SCA

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Improvements w.r.t. ASCAT in red Courtesy: ESA/EUMETSAT

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Sentinel-1

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Launches

Sentinel-1A: 3 April 2014

Sentinel-1B: 25 April 2016

Orbit: Polar, Sun-synchronous

Revisit time: Six days (from two satellite constellation)

Altitude: 693 km

Frequency: C-band SAR, 5.405 GHz

Polarisation: depends on mode

Spatial Resolution: depends on mode

Main applications: Monitoring sea ice, oil spills, marine winds & waves, land surface dynamics, land deformation, floods

Operational modes:

• Interferometric wide-swath mode (250 km, 5 x 20 m), VV+VH or HH+HV

• Wave-mode images (100 km intervals; 20 x 20 km and 5 x 5 m), VV or HH

• Strip map mode (80 km swath, 5 x 5 m), VV+VH or HH+HV

• Extra-wide swath mode (400 km, 20 x 40 m), VV+VH or HH+HV

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AMSR2 on-board GCOM-W1

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GCOM-W1 Launch: 18 May 2012

Orbit: Sun-Synchronous

Inclination: Approx. 98 degrees

Repeat Cycle:

Altitude: 700km/ (98.2deg)

Global coverage: 99% every 2 days

Source: GCOM-W1, JAXA

Source: http://suzaku.eorc.jaxa.jp/GCOM_W/w_amsr2/amsr2_body_main.html

Sensor: AMSR2 Scan and rate: Conical scan at 40 rpm

Frequency: six bands (7 GHz to 89 GHz)

Polarisation: V and H

Spatial Resolution: depends on frequency

Antenna: Offset parabola with 2.0m dia.

Swath: 1450 km

Incidence angle: Nominal 55 degrees

Main applications: precipitation, vapor amounts, wind velocity above the ocean, sea water temperature, water levels on land areas, and snow depths

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The Expectation

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“…, L-band [soil moisture] retrievals can be performed

and meet the science requirements. In contrast, C- and

X-band measurements are representative of the top 1 cm or

less of soil. Moderate vegetation (greater than ~3 kg m-2)

attenuates the signal sufficiently at these frequencies to make

the measurements relatively insensitive to soil moisture.”

Entekhabi, D., Njoku, E.G., O'Neill, P.E., Kellog, K.H., Crow, W.T., Edelstein, W.N., Entin, J.K., Goodman, S.D., Jackson, T.J., Johnson, J., Kimball, J., Piepmeier, J.R., Koster, R., Martin, N., McDonald, K.C., Moghaddam, M., Moran, S., Reichle, R., Shi, J.C., Spencer, M.W., Thurman, S.W., Tsang, L., & Van Zyl, J. (2010). The Soil Moisture Active Passive (SMAP) mission. Proceedings of the IEEE, 98, 704-716

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MIRAS on-board SMOS

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SMOS – Soil moisture and Ocean Salinity

Launch: 2 November 2009

Orbit: Sun-synchronous

Revisit time: 3 day revisit at equator

Altitude: 758 km

Spatial Resolution: 35 km at centre of FOV

Main applications: Monitoring soil moisture and ocean salinity

The goal of the SMOS mission is to monitor surface soil moisture with an accuracy of 4% m³ m-³ (at 35–50 km spatial resolution).

Source: SMOS, ESA

Sensor: MIRAS (Microwave Imaging Radiometer using Aperture Synthesis instrument)

Passive microwave 2-D interferometric radiometer

Frequency: L-band 1.41 GHz

Polarisation: H & V (polarimetric mode optional)

Antenna: 69 antennas, equally distributed over the 3 arms and the central structure

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SMAP – Soil Moisture Active Passive • Launch: 31 January 2015

• Orbit: near-polar, sun-synchronous orbit

• Revisit time: global coverage within 3 days at the equator and 2 days at boreal latitudes (> 45 degrees N)

• Altitude: 680 km

• Polarisation: depends on instrument

• Spatial Resolution:

– Radiometer: (IFOV): 39 km x 47 km

– Radar: 1-3 km (over outer 70% of swath)

• Rotation rate: 14.6 RPM

• Main applications: weather & climate forecasting, drought, floods & landslides

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Source: SMAP, NASA, https://smap.jpl.nasa.gov/instrument/ Radiometer

• Frequency: 1.41 GHz

• Polarizations: H, V, 3rd & 4th Stokes

• Relative accuracy (30 km grid): 1.3 K

• Data collection:

– High-rate (sub-band) data acquired over land

– Low-rate data acquired globally

Radar (failure in July 2015)

• Frequency: 1.26 GHz

• Polarizations: VV, HH, HV (not fully polarimetric)

• Relative accuracy (3 km grid): 1 dB (HH and VV), 1.5 dB (HV)

• Data collection:

– High-resolution (SAR) data acquired over land

– Low-resolution data acquired globally

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Active and Passive Microwave Missions

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Sampling Requirements

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Soil Moisture

• Definition, e.g.

• Average

Area Depth

dzdxdyzyxDepthArea

),,(1

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Thin, remotely sensed soil layer

Root zone: layer of interest for most applications

Soil profile

)(m Volume Total

)(m VolumeWater 3

3

Air

Water

Solid Particles

Cross-section of a soil

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Scaling Issues

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• The term “scale” refers to a – characteristic length – characteristic time

• The concept of scale can be applied to – Process scale = typical time and length scales at which a process takes

place – Measurement scale = spatial and temporal sampling characteristics of the

sensor system – Model scale = Mathematical/physical description of a process

Ideally: Process = Measurement = Model scale

• Microwave remote sensing offers a large suit of sensors

– Scaling issues must be understood in order to select the most suitable sensors for the application

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Soil moisture scales

• Small-scale land-surface related component

– Topography

– Vegetation

– Soil

• Large-scale atmospheric-forcing related component

– Rainfall

– Evapotranspiration

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Sampling Requirements and Characteristics

• Sampling requirements driven by

– High temporal variability of soil moisture

– Spatial resolution is of secondary concern

• Preference is for long-term, temporally dense data

– Wide swath width

– 100 % duty cycle

– No conflicting modes

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Small-scale land surface related soil moisture field

Large-scale atmospheric-driven soil moisture field

Te

mp

ora

l S

am

plin

g

Spatial Resolution

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Daily Coverage of ASCAT and ASAR (ENVISAT)

• Metop ASCAT

– 2 swath with each 500 km

– 25 km resolution

– 100 % duty cycle

– 82 % daily global coverage

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• ASAR Global Monitoring Mode

– 405 km swath

– 1 km resolution

– Potentially 100 % duty cycle

– Background mission

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Daily Coverage of ASAR Wide Swath and Image Mode

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ASAR Wide Swath Mode

• 450 km swath

• 150 m

• Max. 30 % duty cycle

–20 min for descending orbit

–10 min for ascending orbit

ASAR Imaging Mode

• 100 km swath

• 30 m resolution

• Max. 30 % duty cycle

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Spatio-Temporal Sampling of ASCAT Daily global ASCAT coverage achieved by METOP-A and METOP-B constellation

Wagner et al. (2013) The ASCAT soil moisture product: A review of its specifications,

validation results, and emerging applications, Meteorologische Zeitschrift, 22(1), 5-33.

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In-Situ Soil Moisture Time Series

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Mean (red) and station (black) in-situ soil moisture time series. REMEDHUS network in Spain. © University of Salamanca

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Satellite versus In Situ SM Data over HOAL

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CRNS: Cosmic Ray Neutron Sensor HOAL: Catchment average of 31 TDT measurements ASCAT: 25 km ASCAT soil moisture retrievals S-1: 1 km Sentinel-1 soil moisture retrievals Hydrological Open Air Laboratory (HOAL) in Petzenkirchen, Austria

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Soil Moisture from Models, In Situ and Satellites

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Field-Scale Model

Global Satellite (ASCAT Test Product)

Global Models

Local In Situ

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Comparison of Short-Term Anomalies

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Field-Scale Model

Global Satellite (ASCAT Test Product)

Field-Scale Model

Global Model

Local In Situ

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Comparison Against Mean Seasonal Signals

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Information Content of SM Retrievals

• Microwave sensors can provide information about spatio-temporal soil moisture trends

– Information about absolute values comes from external data sets

• Absolute values in soil moisture retrievals driven strongly by

– Used soil moisture maps

• Soil porosity, texture, etc.

– Surface roughness parameterization

• Not a geometric concept - use of “effective roughness” values - roughness depend on soil moisture

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Schneeberger et al. (2004) Topsoil structure influencing soil water retrieval by microwave radiometry, Vadose Zone Journal, 3(4), 1169-1179.

Air-to-Soil Transition Model

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Retrieval Approaches

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Retrieval of Geophysical Variables from EO Data

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Empirical models Semi-empirical models Theoretical models

Lookup tables and neural networks Least-square matching Direct inversion

Soil moisture, water surface,

biomass etc.

Backscatter, radiance, brightness temperature etc.

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Microwave Signals from Vegetation

• Except for dense forest canopies, backscatter from vegetation is due to surface-, volume- and multiple scattering

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Surface scattering (attenuated by

vegetation canopy)

Volume scattering Surface-volume interaction

0000ninteractiosurfacevolumetotal

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Model Calibration

• No model is perfect Calibration is needed

– Per-pixel calibration

– Global model parameters

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“All natural systems models are to some degree lumped, and use

effective parameters to characterize these spatial-

temporal processes.” Jasper Vrugt

http://math.lanl.gov/~vrugt/research/

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SMOS and SMAP

The mission goal of SMOS and SMAP is to provide absolute soil moisture retrievals with an accuracy of 0.04 m3m-3.

Targeted information: absolute soil moisture

Accuracy metric: root mean square error (RMSE) in m3m-3

Retrieval Approach: Iterative inversion of semi-empirical models

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Launch 2009

Launch 2015

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Working Hypothesis for ASCAT Soil Moisture Retrieval

• Information about absolute soil moisture content comes from soil maps, not the satellite

• ASCAT data are not fundamentally different to SMOS or SMAP. Nonetheless, for ASCAT we have always stressed that the information content lies in the relative variation of the observations

– This has resulted in a disparate treatment of ASCAT and SMOS data in the literature

• ASCAT data have often been referred to as soil moisture index

• ASCAT users approached the problem with less expectations

• ASCAT soil moisture data are represented in degree of saturation

– Unit 0-1 or 0-100 %

– Dry and wet reference values are extracted from multi-year time series

– Conversion to absolute values possible if soil porosity and soil moisture residual content are known

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TU Wien Backscatter Model

• Motivated by physical models and empirical evidence

– Formulated in decibels (dB) domain

– Linear relationship between backscatter (in dB) and soil moisture

– Empirical description of incidence angle behaviour

– Seasonal vegetation effects cancel each other out at the "cross-over angles"

• dependent on soil moisture

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ERS Scatterometer measurements

Incidence angle behaviour is determined by vegetation and roughness roughness

Changes due to soil moisture variations

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What Has Happened So Far?

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TU Wien Model: Remarkably Stable Since 1998

• In its core algorithms, the TU Wien model is still the same as in 1998 • Algorithmic improvements have dealt with

– Calibration/model parameter estimation – Azimuthal effects – Temporal behaviour of 𝜎0 𝜃 – Error propagation

• Algorithmic progress has been slower than wished-for due to – Rapidly growing user community

• Already thousands of direct and indirect users

– Constraints imposed by operational nature of data service – Need to improve software (IDL Python)

• Performance, tractability, quality control, modularisation, versioning, …

– Lack of high-quality reference data – Lack of in-biased validation techniques – Major shortcomings of available theoretical models

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Advanced Scatterometer (ASCAT) onboard Metop

• Sensor characteristics

– Active microwave scatterometer

– Frequency: C-band, 5.255 GHz

– Polarisation: VV

– Spatial Resolution: 25 km/ 50 km

– Antennas: 2 x 3

– Swath: 2 x 500 km

– Multi-incidence: 25-65°

– Daily global coverage: 82 %

• Main applications

– Wind measurements, soil moisture, sea ice, freeze/thaw, vegetation dynamics

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TU Wien Change Detection Approach

• Formulated in 1996-1998 out of the need to circumvent the lack of adequate backscatter models

– Accounts indirectly for surface roughness and land cover

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tt

tttm

drywet

drys 00

00

)(

)()(

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Functional Behaviour

• Mimics a semi-empirical backscatter model with a strong surface-volume interaction term

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Mixing model with fraction of

non-transparent (nt) and

transparent (tr) vegetation

Bare soil scattering 𝑠0

modelled with Improved Integral

Equation Method I2EM

Interaction term enhanced soil

moisture contributions

0 = 1 − 𝑓𝑛𝑡 𝑡𝑟𝑐𝑜𝑠

2 1 − 𝑒

−2𝑡𝑟𝑐𝑜𝑠 + 𝑠

0 𝑒−

2𝑡𝑟𝑐𝑜𝑠 + 2𝑅0𝑡𝑟 𝑡𝑟𝑒

−2𝑡𝑟𝑐𝑜𝑠 + 𝑓𝑛𝑡

𝑛𝑡 𝑐𝑜𝑠

2

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Cross-over angle calibration & Dry/wet reference

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Sensitivity to Soil Moisture

• The sensitivity describes the signal response to soil moisture changes and depends strongly on land cover

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Metop ASCAT Surface Soil Moisture Climatology

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Vegetation Optical Depth

• Using the Water Cloud model we can now retrieve VOD from the TU Wien backscatter model formulation as well

• VOD is a measure of how much the soil moisture signal is taken away by the vegetation layer

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𝜏 =𝑐𝑜𝑠𝜃

2𝑙𝑛

Δ𝜎0𝑓𝑜𝑟 𝑏𝑎𝑟𝑒 𝑠𝑜𝑖𝑙

𝛥𝜎0 𝑓𝑜𝑟 𝑣𝑒𝑔𝑒𝑡𝑎𝑡𝑖𝑜𝑛 𝑐𝑜𝑣𝑒𝑟𝑒𝑑 𝑠𝑜𝑖𝑙

Vreugdenhil et al. (2016) Analysing the Vegetation Parameterisation in the TU-Wien ASCAT Soil Moisture Retrieval, IEEE Transactions on Geoscience and Remote Sensing, 54(6), 3513-3531.

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Optical Depth Estimated from Sensitivity

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𝑐𝑜𝑠

2𝑙𝑛

∆𝜎𝑠0

Δ𝜎0

Theoretical sensitivity for a bare soil

Observed sensitivity

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Limitations & Caveats

• Soil moisture retrieval is not possible over

– Urban areas, concrete and rock

– Water bodies and inundation

– Frozen or snow covered soil

– Under forests and dense shrubs

• Soil moisture data quality varies in space and time because of

– Vegetation water content and structure

– Sub-surface scattering in dry areas

– Topographic effects

– Temperature dependency (for passive only)

• Data quality described using uncertainty estimates (from error propagation) and advisory flags

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Sentinel-1 – A Game Changer • C-band SAR satellite in

continuation of ERS-1/2 and ENVISAT

• High spatio-temporal coverage

– Spatial resolution 20-80 m

– Temporal resolution < 3 days over Europe and Canada

• with 2 satellites

• Excellent data quality

• Highly dynamic land surface processes can be captured

– Impact on water management, health and other applications could be high if the challenges in the ground segment can be overcome

Solar panel and SAR antenna of Sentinel-1

launched 3 April 2014. Image was acquired by

the satellite's onboard camera. © ESA

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Sentinel-1 Data Volume

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Data-volume estimates (Single Polarization, raw format, excl. annotation)

Product Type Data rate

[MB/s] Data acq. per orbit

Data volume per orbit

Data volume per day

Data volume per year

Data volume 7.5 years

Data volume 20 years

IW L1 SLC 127.554 3.75 min 28.7 GB 419.0 GB 152.9 TB 1.1 PB 3.1 PB

IW L1 GRD-HR 32.418 15 min 29.2 GB 426.0 GB 156.0 TB 1.2 PB 3.1 PB

IW L1 GRD-MR 5.190 15 min 4.7 GB 68.2 GB 25.0 TB 186.7 TB 497.8 TB

IW L1 BRW 0.007 15 min 6.3 MB 92.0 MB 33.6 GB 251.8 GB 671.5 GB

Total - - 62.6 GB 913.3 GB 333.9 TB 2.5 PB 6.7 PB

Average Revisit time for two

Sentinel-1 satellites © ESA

~ 6 days

~ 3 days

Comparable in size to the

complete ASAR data volume

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Earth Observation Data Centre @ TU Wien

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24/7 Operations

& Rolling Archive

Petabyte-Scale

Disk Storage

Supercomputer

Tape Storage

Virtual Machines

(VMs)

VSC-3 Rank 85 of the World‘s most

powerful computers (11/2014)

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x y ti

me

Optimisation of Sentinel-1 Processing System

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• Fast parallel processing

• Optimized data formatting

• Fast access in time and

spatial domain

• Efficient data archiving

Equi7 Grid Bauer-Marschallinger et al (2014)

Optimisation of global grids for high-

resolution remote sensing data,

Computers & Geosciences, 72, 84-93.

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Summary

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CONCERNS

• Coarse spatial resolution

– 25-50 km for current operational data services

• Only thin surface layer is sensed

– A few centimetres under growing conditions

• Does not penetrate dense vegetation

WHY IT STILL WORKS

• Temporal Stability

– Soil moisture dynamics can be compared across spatial scale

• Dense temporal sampling

– Allows to predict profile soil moisture content

• Retrieval accuracy best over agricultural areas and grasslands

Specific Concerns about Satellite SM Data

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Conclusions

• The latest generation of microwave satellites offers a high spatio-temporal coverage suitable for monitoring

– Soil moisture and other highly dynamic land surface processes

• Impact on water management, drought monitoring and other applications could be high if the challenges in the ground segment can be overcome

– Restrictions in the distribution and I/O operations due to bandwidth limitations

– High infrastructure costs

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Earth Observation is not the only discipline

facing Big Data challenges!

As in other economic sectors, novel IT

technologies will fundamentally change how

the earth observation sector is working

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References - Books

• Jensen, J.R. (2006): Remote Sensing of the Environment: An Earth Resource Perspective (2nd Edition). Pearson, ISBN:978-0131889508

• Tipler, P.A. (2000): Physik. Spektrum Akademischer Verlag, ISBN: 978-3860251225

• F. T. Ulaby, R. K. Moore, and A. K. Fung, Microwave Remote Sensing: Active and Passive. Vol. III - Volume Scattering and Emission Theory, Advanced Systems and Applications. Artech House, Inc.

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References - Technical Report

• ASCAT Product Guide, Tech. Rep. Doc. No: EUM/OPS-EPS/MAN/04/0028, v5, 2015.

• Algorithm Theoretical Baseline Document (ATBD) Soil Moisture Data Records, Metop ASCAT Soil Moisture Time Series, Tech. Rep. Doc. No: SAF/HSAF/CDOP3/ATBD, v0.7, 2018.

• Algorithm Theoretical Baseline Document (ATBD) Soil Moisture NRT, Metop ASCAT Soil Moisture Orbit, Tech. Rep. Doc. No: SAF/HSAF/CDOP2/ATBD, v0.4, 2016.

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References - Articles

• Z. Bartalis, K. Scipal, and W. Wagner, Azimuthal anisotropy of scatterometer measurements over land, vol. 44, no. 8, pp. 2083–2092.

• Z. Bartalis, W. Wagner, V. Naeimi, S. Hasenauer, K. Scipal, H. Bonekamp, J. Figa, and C. Anderson, Initial soil moisture retrievals from the METOP-a advanced scatterometer (ASCAT), vol. 34.

• J. Figa-Saldana, J. J. W. Wilson, E. Attema, R. Gelsthorpe, M. R. Drinkwater, and A. Stoffelen, The Advanced Scatterometer (ASCAT) on the Meteorological Operational (MetOp) Platform: A follow on for European Wind Scatterometers, Canadian Journal of Remote Sensing, vol. 28, no. 3, pp. 404–412, 2002.

• R. V. Gelsthorpe, E. Schied, and J. J. W. Wilson, ASCAT - MetOp’s Advanced Scatterometer, ESA Bull. ISSN 0376-4265, vol. 102, pp. 19–27, 2000.

• V. Naeimi, K. Scipal, Z. Bartalis, S. Hasenauer, and W. Wagner, An improved soil moisture retrieval algorithm for ERS and METOP scatterometer observations, vol. 47, no. 7, pp. 1999–2013.

• W. Wagner, G. Lemoine, and H. Rott, A method for estimating soil moisture from ERS scatterometer and soil data, vol. 70, no. 2, pp. 191–207.

• W. Wagner, J. Noll, M. Borgeaud, and H. Rott, Monitoring soil moisture over the Canadian prairies with the ERS scatterometer, vol. 37, pp. 206–216.

• W. Wagner, G. Lemoine, M. Borgeaud, and H. Rott, A study of vegetation cover effects on ERS scatterometer data, vol. 37, no. 2II, pp. 938–948.

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