operational soil moisture from sar systems: towards sentinel-1 · operational soil moisture from...

ISPRS 2010, July, Vienna, Austria

Operational soil moisture from SAR systems: from ENVISAT towards

SENTINEL-1

Doubkova Marcela, Sabel Daniel, Sebastian Hahn, Pathe Carsten, Wagner Wolfgang


Recent application of soil moisture

Soil moisture in models … contributes to the predictability of precipitation (Koster et al., 2004,

Crow, 2008) … is important for improving numerical weather forecasts (Scipal,

2007) ... can improve performance of hydrological models at small basins

(aprox. 100*100km) (Loumagne, 2001; Crow, 2005; Brocca, 2009; Beck, 2009)


Soil moisture products at coarse scale

Presenter

Presentation Notes

Long satellite history and long history of developing soil moisture product at coarse resolution. There is however need for improved resolution soil moisture at hydrological community There is no operational SM product available..


Recent application of soil moisture

www.ipf.tuwien.ac.at/radar/dv/asar/ www.ipf.tuwien.ac.at/radar/share

The need for higher resolution soil moisture has motivated extensive research within the SAR community

A quasi-operational product from ASAR GM existing at IPF (TU WIEN) based on change detection algorithm

Presenter

Presentation Notes

This was a motivation for development of operational ENVISAT ASAR GM product. Currently, the only existing quazi operational product has been developed at our institute from the ASAR GM data.

http://www.ipf.tuwien.ac.at/radar/dv/asar/index.php


ASAR algorithm Change detection algorithm using

long time series of ASAR backscatter (σ0 )

Normalized to 30 degrees LIA

Scaled between hist. minimum and maximum σ0 values

Algorithm

( ) ( ) ( ) ( )tyxmtyxStyxtyx sdry ,,,,,,,, 00 += σσ

Sm drys /)( 00 σσ −=

)/())30(( 0000drywetdrysm σσσθβσ −−−−=


Soil moisture products at medium scale

1 km Soil moisture product from ENVISAT

1 km/500m Soil moisture product from SENTINEL-1

FUTURE (operational appl.) PRESENT (research)

Type Semi-operational at IPF Operational (at ?)

Band C band C band

Spatial resolution

1 km 1 km

Radiometric resolution

1.2 dB In several magnitude better

Temporal resolution (Europe Global)

10-30 days 2-5 days

3-6 days 6-12 days

Accuracy 15 – 20 % over grassland and agricultural areas

4-8% over grassland and agricultural areas

Dissemination Monthly, demonstrated potential for daily update

Within 180 minutes after sensing

Presenter

Presentation Notes

Currently there is a potential for operational soil moisture. The main requirements: Operational and guaranteed future data availability


A good understanding of the existing ASAR GM SM product algorithm and its errors

A - Set of validation studies with in-situ and remote sensing data

B - Error assessment studies of the ASAR GM

Motivation: towards operational products

High temporal sampling Operational availability Near-real-time availability A good radiometric resolution Free and easy access

(fulfilled with SENTINEL-1)

Presenter

Presentation Notes

The question to be answered when talking about transfering to operational sensor are:


A – Validation studies AWRA landscape model

AWRA model –

independent representation

of soil moisture by landscape model

Up to RAsAw values > 0.8 are found over agricultural areas in south-western and south-eastern Australia

Presenter

Presentation Notes

AWRA – landscape model combined with points and satellite data Simulates water stores and flows in the vegetation, soil and local catchment groundwater system Simple but well-performing lumped models of catchment water balance and seasonal vegetation dynamics. Using daily forcing of rainfall gauge data ET is estimated from a combination of satellite and station data and fractions of deep and shallow vegetation ------------------------ It estimates the active capacity of 20 mm – difference between water storage and field capacity and water storage when Evaporation and transpiration cease The depth varies from 5 to 20 mm


A – Other Validation studies Goulbourn catchment, Australia

ASAR GM versus in-situ (Hydraprobes station)

capable to show wetting and drying patterns in a high agreement to in-situ data

Average R = 0.75, improves with averaging over several ASAR pixels

apparent wet bias

I. Mladenova, . et al, 2010

Presenter

Presentation Notes

Hydraprobe – based on TDR using lower frequencies. TDR uses the high permittivity of water. TDR - Uses the electromagnetic signal propagated from the center tine of the probe to measure multiple parameters. The voltages measured at the return and the speed of return depends on the electrical properties of the soil


ASAR algorithm Change detection algorithm using

long time series of ASAR backscatter (σ0 )

Normalized to 30 degrees

Scaled between hist. minimum and maximum σ0 values

B - ASAR GM Error analyses

( ) ( ) ( ) ( )tyxmtyxStyxtyx sdry ,,,,,,,, 00 += σσ

Sm drys /)( 00 σσ −=

)/())30(( 0000drywetdrysm σσσθβσ −−−−=


A B - ASAR GM Error analyses

Error 1. noise of GM backscatter 2. uncertainties of the parameter

estimation - β, σdry , σwet Gaussian propagation law – summation of partial derivatives of individual parameters

01.02.1 22

+

+

=∆

SSms

β

Error estimate (%)

Pathe, 2009


RMSE (%) between ASAR and AWRA soil moisture

How well can we define differences between ASAR data and modelled soil moisture using ASAR error assessment?

B - ASAR GM Error analyses

AWRA dataset kindly provided by A. van Dijk,, CSIRO, AUSTRALIA

2

)ˆˆ()( 1

2

−

−=

∑=

n

MSSMjRMSE

n

iASARijASARij

RMSE – deviation from the fit between ASAR GM soil moisture and AWRA modelled soil moisture

Presenter

Presentation Notes

AWRA – landscape model combined with points and satellite data Simulates water stores and flows in the vegetation, soil and local catchment groundwater system Simple but well-performing lumped models of catchment water balance and seasonal vegetation dynamics. Using daily forcing of rainfall gauge data ET is estimated from a combination of satellite and station data and fractions of deep and shallow vegetation ------------------------ It estimates the active capacity of 20 mm – difference between water storage and field capacity and water storage when Evaporation and transpiration cease The depth varies from 5 to 20 mm


A - ASAR GM Error analyses

R=0.7955 R=0.795 RMSE= 2.348


The change detection algorithm performs well Averaging over 2-5 km over ASAR GM increases the

radiometric quality of the product The first results show a good relation between ASAR GM

error and RMSE with modeled data A quasi operational product from ASAR GM can be retrieved

within few hours

CONCLUSION 1

Due to all above points and simplicity of the model, a transfer is seen as relatively straight forward


THANK YOU


Soil moisture products at medium scale

1 km Soil moisture product from ENVISAT

1 km/500m Soil moisture product from SENTINEL-1

FUTURE (operational appl.) PRESENT (research)

Type Semi-operational at IPF Operational (at ?)

Band C band C band

Radiometric resolution

1.2 dB In magnitude better than ENVISAT

Spatial resolution

1 km 1 km

Temporal resolution (Europe Global)

10-30 days 2-5 days

3-6 days 6-12 days

Accuracy 15 – 20 % over grassland and agricultural areas

4-8% over grassland and agricultural areas

Dissemination Monthly, demonstrated potential for daily update

Within 180 minutes after sensing

Presenter

Presentation Notes

Currently there is a potential for operational soil moisture. The main requirements: Operational and guaranteed future data availability


C – Does ASAR GM soil moisture product represent secondary effects of soil moisture on vegetation?

C band (~4.5cm) is able to

partial penetrate vegetation At what vegetation volume

do we no longer sense soil but vegetation?

Partial penetration State the problem that C

band may not penetrate


B – Validation studies


38 stations (Time Domain

reflectometry stations) at 5 cm depth

Average correlation: XX Improvement with

averaging over several ASAR GM pixels

C – Other Validation studies Murrumbudgee catchment

Presenter

Presentation Notes

HTDR using lower frequencies. TDR uses the high permittivity of water. TDR - Uses the electromagnetic signal propagated from the center tine of the probe to measure multiple parameters. The voltages measured at the return and the speed of return depends on the electrical properties of the soil Scatterplots Improving with better spatial resolution = this may simulate higher radiometric accuracy of SENTINEL-1


The spatial patterns of R correspond to recurrent vegetation

Local types of recurrent vegetation:

- water responsive grasses - cropping areas

B – Validation studies


Possible explanations for low correlation in western regions

extremely low variance of soil moisture over these areas

missing soil properties information in the AWRA model poor quality of AWRA input precipitation

Distribution of gauge stations available in the estimation of precipitation input for the AWRA system

yy

yy

SSSEESS

R−

=

Variability SS of ASAR GM

? Include ?B – Validation studies

Presenter

Presentation Notes

Total sample variability around that is explained by the linear relationship between Y and X


Drought


Example from the Atlas of our changing environment, 2008


The ASAR GM data are available since 2004. Here we present the ASAR GM data averaged for two different months from 2007 to 2009. The figure demonstrates the interanual variability of soil moisture and the movement of the intertropical convergence zone (ITCZ) that moves from the northern to the southern tropics resulting in regular dry and wet seasons. Evident are also for example a) the larger southern extent of the ITCZ in January 2008 and b) the wet soil moisture conditions in Mozambique in July 2009.


ASAR

GM

AS

CAT

The below average soil moisture conditions in May 2009 in Kenya as represented by ASCAT and ASAR GM soil moisture products and the NDVI vegetation anomaly in following month of June.


2005 2006 2007

MAR

CH

APRI

L

MAR

CH

APRI

L ASAR GM Surface Soil Moisture; Monthly means 2005 - 2007


Hudur

Baidoa

JRC, Mars-Food, 2007


JRC, Mars-Food, 2007

Monitoring agricultural vegetation in Somalia using SPOT VGT Vegetation Index, AFRICOVER and ECMWF Global Meteorological Modelling


Australia – fires 2009

Satellite image of bushfires in southeast Australia taken Feb. 7, 2009. NASA image courtesy the MODIS Rapid Response Team, NASA Goddard Space Fligh

ASCAT SM

ASAR GM SM

Presenter

Presentation Notes

Australia fires and precedent soil moisture conditions


Assimilation into hydrological models


Validation w. modeled data (TOPKAPI model)

SHARE ext. final meeting

Source: Vischel et al., 2008

TOPKAPI (physically-based hydrological model) versus ERS-SCAT SM

R2 between 0.68 and 0.92

Topkapi --- Scatteromter o comparison in two seasons.

Presenter

Presentation Notes

My data use overview will start with hydrological applications and with results from the recently, after SHARE deadline, published article from Mr. Vischel and G. Pegram - Results show a good correspondence between the modelled and remotely sensed soil moisture regression R2 coefficients lying between 0.68 and 0.92 were yielded. This demonstrates the good ability to capture the soil moisture dynamic Suggests possibility to assimiliation to hydrological models

ISPRS 2010, July, Vienna, Austria Source: Scheffler, 2008


J2000 model is a modeling system for the distributed simulation of the water balance in large river basins


Validation w. ASCAT SM (with ASCAT)

SHARE ext. final meeting

Different technologies of ASAR and ASCAT (polarization, resolution, radiometric accuracy)

Very high correlation

Comparison between averaged ASAR SM and ASCAT SM.

Cropland Rangeland Forest

Mean R 0.91 0.74 0.65

Mean RMSE [%]

13.9 17.3 17.3

Mean bias [%]

3.3 14.8 -3.6

Source: Sabel et al., 2008

Presenter

Presentation Notes

The spatial correspondence is quantified on following slide representing results over southeastern Australia According to Daniel’s recent publication This demonstrates the high correlation of ASCAT SM and ASAR GM SM over cropland and rangeland considerable good were results also over forests its important to mention that positive bias was evident

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