copernicus global land operations · 2019. 4. 29. · copernicus global land operations – lot 1...

Copernicus Global Land Operations – Lot 1

Date Issued: 24.04.2019

Issue: I1.20

COPERNICUS GLOBAL LAND OPERATIONS

”VEGETATION AND ENERGY”

“CGLOPS-1”

Framework Service Contract N° 199494 (JRC)

ALGORITHM THEORETICAL BASIS DOCUMENT

SOIL WATER INDEX

COLLECTION 1KM

VERSION 1

ISSUE I1.20

Organisation name of lead contractor for this deliverable: TU Wien

Book Captain : Bernhard Bauer-Marschallinger

Contributing authors : Christoph Paulik



Issue: I1.20

Document-No. CGLOPS1_ATBD_SWI1km-V1 © C-GLOPS1 consortium

Issue: I1.20 Date: 24.04.2019 Page: 2 of 40

Dissemination Level

PU Public X

PP Restricted to other programme participants (including the Commission Services)

RE Restricted to a group specified by the consortium (including the Commission Services)

CO Confidential, only for members of the consortium (including the Commission Services)



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DOCUMENT RELEASE SHEET

Book Captain: B. Bauer-Marschallinger Date: 24.04.2019 Sign.

Approval: R. Lacaze Date: 26.04.2019 Sign.

Endorsement: M. Cherlet Date: Sign.

Distribution: Public



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CHANGE RECORD

Issue/Revision Date Page(s) Description of Change Release

26.06.2015 All First issue I1.00

I1.00 19.01.2016 All Clarifications according to external

review

I1.01

I1.01 11.04.2016 12-13 Revision of Users Requirements I1.10

I1.10 24.04.2019 All Move to CGLOPS template

Update for public release of SWI1km

V1.0.1

I1.20



Issue: I1.20



TABLE OF CONTENT

Executive Summary ________________________________________________________ 10

1. Background of the Document _____________________________________________ 11

1.1 Scope and Objectives _____________________________________________________ 11

1.2 Evolution Cycles _________________________________________________________ 11

1.3 Content of the Document __________________________________________________ 12

1.4 Related Documents ______________________________________________________ 12

1.4.1 Applicable document ____________________________________________________________ 12

1.4.2 Input __________________________________________________________________________ 12

1.4.3 Output ________________________________________________________________________ 13

1.4.4 Other Documents _______________________________________________________________ 13

2. Review of Users Requirements ____________________________________________ 14

3. Methodology Description ________________________________________________ 16

3.1 Background _____________________________________________________________ 16

3.2 The SCATSAR-SWI Algorithm _______________________________________________ 17

3.2.1 Input data _____________________________________________________________________ 17

3.2.2 Overview ______________________________________________________________________ 19

3.2.3 Data Fusion Parameter Generation (PG) _____________________________________________ 21

3.2.4 SCATSAR-SWI Retrieval (NRT) ______________________________________________________ 27

3.2.5 Output Generation ______________________________________________________________ 31

3.2.6 First Experimental Results _________________________________________________________ 32

3.2.7 Description of Quality and Uncertainty ______________________________________________ 36

3.3 Limitations of the Product _________________________________________________ 36

3.3.1 Timeliness _____________________________________________________________________ 36

3.3.2 Spatial Coverage ________________________________________________________________ 37

3.3.3 Balancing between SCAT and SAR __________________________________________________ 37

3.3.4 Uncertainty Propagation __________________________________________________________ 37

3.3.5 Relation to Soil Depth ____________________________________________________________ 37

3.3.6 Effects from Evapotranspiration ____________________________________________________ 38



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4. References ____________________________________________________________ 39



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LIST OF FIGURES

Figure 1: Main processing steps of the Sentinel-1 SAR processing with SGRT 2.0. Inputs are L1 radar backscatter

images from Sentinel-1 IW with initial 10m sampling. The outputs are SSM images with a 500m sampling. ..... 18

Figure 2: General structure of the SCATSAR-SWI algorithm for the SWI1km production, with offline procedures

of the algorithm in blue, and NRT procedures in green. ....................................................................................... 20

Figure 3: a) Detailed description of the data flows for the offline Parameter Generation (PG) and b) the

SCATSAR-SWI retrieval in near-real-time (NRT). The (static) PG outputs are used during the (daily) NRT SCATSAR-

SWI retrieval. ......................................................................................................................................................... 21

Figure 4: Concept of CDF-matching for two time series at one pixel location, using linearization between data-

to-match values x and target data, yielding matched data values x’. .................................................................. 26

Figure 5: ASCAT, ASAR, CDF-matched ASAR, and SCATSAR-SWI time series for the years 2008-2009 at

56.37°N/9.24°E (as indicated in the location map in Figure 7). ............................................................................ 26

Figure 6: Quicklook image (CEURO extent) of the SWI1km dataset from 2018-08-26. Blue colours represent wet

soil conditions, and brown colours dry conditions. Overlaid by country boundaries and legend for better

orientation. ........................................................................................................................................................... 32

Figure 7: Different SWI products, Leaf Area Index (LAI) from SPOT/Vegetation and CORINE Land Cover over

Denmark. Overview on the study area on the lower left. The upper images show SWI estimates (T-value of 5

days) from Metop ASCAT at 0.1°, Envisat ASAR in Wide Swath Mode (WS) at 1km and the SWI1km. In black, the

SCATSAR correlation mask covers areas where correlation between ASCAT and ASAR soil moisture is low. The

centre lower image shows a decadal mean of LAI at 1km, giving indication on local vegetation status. The lower

right image displays the land cover classes from CORINE 2006 with the most prevalent land cover classes in the

legend. ................................................................................................................................................................... 34

Figure 8: Different SWI products, Leaf Area Index (LAI) from SPOT/Vegetation and CORINE Land Cover over

south-eastern Austria. The upper left image shows SWI estimates (T-value of 10 days) from Metop ASCAT at

0.1°, the upper right image the overview map. The centre images show the ASAR and SWI1km. The lower left

image displays the land cover classes from CORINE 2006 with the most prevalent land cover classes in the

legend. The lower right image shows a decadal mean of LAI at 1km, giving indication on local vegetation status.

.............................................................................................................................................................................. 35



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LIST OF TABLES

Table 1: Target requirements of GCOS for soil moisture (up to 5cm soil depth) as Essential Climate Variable

(GCOS-154, 2011) .................................................................................................................................................. 15

Table 2: CGLOPS uncertainty levels for SWI product. ............................................................................................ 15

Table 3: Main features of the input (ASCAT SSM/SWI, Sentinel-1 SSM)- and fused output (SWI 1km) products. 17



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ACRONYMS

ASAR

ASCAT

Advanced Synthetic Aperture Radar (Envisat satellite)

Advanced Scatterometer (Metop satellites)

BUFR

CGLS

CL

Binary Universal Form for the Representation of meteorological data

Copernicus Global Land Services

Correlation Layer

CDF Cumulative Distribution Function

DGG Discrete Global Grid

EAPG EUMETSAT ASCAT Product Guide

ECMWF

EODC

European Centre for Medium Range Weather Forecasting

Earth Observation Data Centre for Water Resources Monitoring

EUMETSAT

FSD

European Organisation for the Exploitation of Meteorological Satellites

Fused Surface Soil Moisture Database

GMES Global Monitoring for Environment and Security

IDL

SCATSAR-SWI

SGRT

Interactive Data Language

Scatterometer Synthetic Aperture Radar Soil Water Index

SAR Geophysical Retrieval Toolbox

SSF Surface State Flag

SSM Surface Soil Moisture

SSM/I Special Sensor Microwave Imager

SWI

TU Wien

WARP

WF

Soil Water Index

Technische Universität Wien

Soil Water Retrieval Package

Weighting Functions

WMO World Meteorological Organization

ZAMG Zentralanstalt für Meteorologie und Geophysik



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EXECUTIVE SUMMARY

The Copernicus Global Land Service (CGLS) is earmarked as a component of the Land

service to operate “a multi-purpose service component” that will provide a series of bio-

geophysical products on the status and evolution of land surface at global scale. Production

and delivery of the parameters are to take place in a timely manner and are complemented

by the constitution of long-term time series.

Satellite radar remote sensing has shown its capability to retrieve soil moisture. However, by

today, only scatterometer-based services achieved to provide soil moisture in an operational

manner. Scatterometer-backscatter data from the Metop ASCAT sensors are used

successfully for Surface Soil Moisture (SSM) and Soil Water Index (SWI) monitoring.

Here, a newly developed algorithm for operational soil moisture is presented. The

Copernicus Global Land Service SWI1km (Soil Water Index at 1km) product describes soil

water content on a 1km (1/112°) spatial sampling. It is derived using a data fusion approach

from microwave radar data observed by the Metop A/B/C ASCAT and the Sentinel 1 A/B

CSAR satellite sensors, named SCATSAR (Scatterometer-Synthetic Aperture Radar)

algorithm. It is an evolution of the Metop ASCAT SWI Version3 product as described in

CGLOPS1_PUM_SWIV3-SWI10-SWI-TS.



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1. BACKGROUND OF THE DOCUMENT

1.1 SCOPE AND OBJECTIVES

The current CGLS Soil Water Index (SWI) is based on observations from the ASCAT

sensors on board the Metop-A/B/C satellites that leads to near daily revisit times globally.

This revisit cycle enables the product to describe temporal soil moisture dynamics well, but

comes at the cost of a spatial sampling of 0.1°, which is much coarser than the 1/112° spatial

sampling of most other products distributed through the CGLS. Because of that, the current

SWI product does not facilitate analysis of hydrological patterns on a local level that many

users require. It is also more difficult to use it in combination with other CGLS products. High-

resolution Synthetic Aperture Radar (SAR) data from the 2014-launched European Sentinel-

1 (S-1) mission is used for an evolution of the current SWI_V3 towards the new SWI1km.

This product employs the SCATSAR-SWI data fusion algorithm and will be much more

attractive for local users of the CGLS than the original ASCAT-only based SWI product, as it

combines the high temporal resolution of the ASCAT sensor and the high spatial resolution

of the Sentinel-1 sensor.

1.2 EVOLUTION CYCLES

In Cycle 1, the SCATSAR SWI algorithm was tested with SSM input data from Sentinel-1A

and ASCAT-A/B on a daily basis in near-real-time (NRT) across limited European sites. The

parameters for the data fusion are generated off-line, using SSM time series records from

ASCAT-A/B and, as proxy for Sentinel-1, Envisat ASAR. This substitution was necessary

since, for the parameter retrieval, time series of SAR SSM with a length of at least one year

are needed. The SWI calculation itself is identical to the SWI_V3 product.

In Cycle 2, the operational framework for the SWI1km production was established, extending

the spatial coverage to entire Europe. The parameters for data fusion included SSM from

Sentinel-1A.

In a third stage ("NRT 2019”, producing SWI1km at version 1.0.1), the SWI1km production

was launched over the entire European area, ingesting SSM data streams in NRT from five

satellite radar sensors (Sentinel-1A/B and Metop-A/B/C ASCAT) and disseminated via the

CGLS portal. The parameter baseline for the SCATSAR-SWI fusion model is based on the

years 2015-2018, and static weights are used for the SCAT and SAR input data,

respectively.



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1.3 CONTENT OF THE DOCUMENT

This document is structured as follows:

Section 2 reviews the users’ requirements

Section 3 gives an overview on the status before algorithm evolution, explains in

detail, key to the algorithm, the data fusion approach and finishes with an outlook on

foreseen improvements in the upcoming evolution.

As being part of a technological evolution, and as being on the scientific edge of retrieving

soil moisture from remote sensing data, this document explains generally the fusion concept

and highlights for each element of the algorithm the actual implementation.

1.4 RELATED DOCUMENTS

1.4.1 Applicable document

AD1: Annex I – Technical Specifications JRC/IPR/2015/H.5/0026/OC to Contract Notice

2015/S 151-277962 of 7th August 2015

AD2: Appendix 1 – Copernicus Global land Component Product and Service Detailed

Technical requirements to Technical Annex to Contract Notice 2015/S 151-277962 of 7th

August 2015

AD3: GIO Copernicus Global Land – Technical User Group – Service Specification and

Product Requirements Proposal – SPB-GIO-3017-TUG-SS-004 – Issue I1.0 – 26 May 2015.

1.4.2 Input

Document ID Descriptor

CGLOPS1-SSD

Service Specifications of the Global Component of the

Copernicus Land Service.

CGLOPS1_PUM_SWIV3-

SWI10-SWI-TS

Algorithm theorethical Basis document of Soil Water Index

Version 3.

CGLOPS1_PUM_SWIV3-

SWI10-SWI-TS

Product User Manual of SWI V3, 10-daily SWI and SWI time

series.

CGLOPS1_PUM_LAI1km-

VGT-V1

Product User Manual of LAI Collection 1km V1 derived from

sPOT/VEGETATION



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1.4.3 Output

Document ID Descriptor

CGLOPS1_PUM_SWI1km-V1 Product User Manual summarizing all information about the

Soil Water Index product

GIOGL1_QAR_SWIV3-SWI10 Validation Report describing the results of the scientific

quality assessment of the Soil Water Index

CGLOPS1_QAR_SWI1km-V1 Validation Report describing the results of the scientific

quality assessment of the SWI 1km V1 product

1.4.4 Other Documents

AD E1 Prepare4EODC-Water: Sentinel-1 Pre-processing Chain ATBD

AD E2 SAF/HSAF/CDOP2/ATBD-25/0_4

AD E3 SAF/HSAF/CDOP2/ATBD-16/1_1

AD E4 The Equi7Grid – V13: Grid and Tiling Definition Document – Issue 0.5

https://github.com/TUW-GEO/Equi7Grid/tree/master/docs/doc_files

AD E5 Propagation of Surface Soil Moisture noise to the Soil Water Index

AD E6 The Weighted Soil Water Index – Definition and Derivation

https://github.com/TUW-GEO/Equi7Grid/tree/master/docs/doc_files



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2. REVIEW OF USERS REQUIREMENTS

According to the applicable document [AD2] and [AD3], the user’s requirements relevant for

SWI 1km are as follows:

Definition: soil moisture (ECV T.11): amount of water (m3/m3) contained in soil layers

identified according to their depth measured from top surface.

Geometric properties:

o Pixel size of output data shall be defined on a per-product basis so as to

facilitate the multi-parameter analysis and exploitation.

o The target baseline location accuracy shall be 1/3 of the at-nadir

instantaneous field of view.

o Pixel co-ordinates shall be given for centre of pixel.

Geographical coverage:

o geographic projection: regular lat - lon

o geodetic datum: WGS84

o coordinate position: pixel centre

o window coordinates:

Upper-Left: 180°W - 75°N

Bottom Right: 180°E - 56°S

Ancillary information:

o the per-pixel date of the individual measurements or the start-end dates of the

period actually covered

o quality indicators, with explicit per-pixel identification of the cause of

anomalous parameter result

Accuracy requirements:

o Baseline: wherever applicable the bio-geophysical parameters should meet

the internationally agreed accuracy standards laid down in document

“Systematic Observation Requirements for Satellite-Based Products for

Climate”. Supplemental details to the satellite based component of the

“Implementation Plan for the Global Observing System for Climate in Support

of the UNFCCC. GCOS-#154, 2011”. (Table 1)

o Wherever appropriate the bio-geophysical parameters shall be validated and

compared to CEOS CAL/VAL data sets.

o Target: considering data usage by that part of the user community focused on

operational monitoring at (sub-) national scale, accuracy standards may apply

not on averages at global scale, but at a finer geographic resolution and in any

event at least at biome level.



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Table 1: Target requirements of GCOS for soil moisture (up to 5cm soil depth) as Essential Climate Variable (GCOS-154, 2011)

Variable Horizontal

Resolution

Temporal

resolution

Accuracy Stability

Volumetric soil moisture 50km Daily 0.04 m3/m3 0.01m3/m3/year

GCOS notes that the targets above “are set as an accuracy of about 10 per cent of saturated

content and stability of about 2 per cent of saturated content. These values are judged

adequate for regional impact and adaptation studies and verification and development of

climate models. It is considered premature to consider global scale changes.” It adds that

“stating a general accuracy requirement is difficult for this type of observation, as this

depends not only on soil type but also on soil moisture content itself. The stated numbers

thus should be viewed with some caution”.

Align temporal frequencies of biophysical variables: the rationale to have a 10-

days frequency SWI (SWI10) is to ease the joint use of products by providing them

with the same temporal resolution to end-users.

Re-formatting the historic products: make easier the handling of products by users

working at local and regional scales, interested in obtaining harmonized historic SWI

time series processed with the latest algorithms of both surface soil moisture and

SWI.

Additionally, the Technical User Group of the Copernicus Global Land [AD3] has

recommended uncertainty levels for SWI (Table 2) depending on applications following

EUMETSAT’s specifications.

Table 2: CGLOPS uncertainty levels for SWI product.

Variable Threshold Target Optimal

SWI [10%, 30%] [5% - 15%] [1% - 5%]



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3. METHODOLOGY DESCRIPTION

This section describes the methodology for generating SWI1km product employing the

SCATSAR-SWI data fusion algorithm.

The motivation, the theory on the SCATSAR-SWI data fusion approach, and the details of

the SWI1km retrieval algorithm were published in a research article by Bauer-Marschallinger

et al., 2018a, including also results from an evaluation campaign over Italy.

3.1 BACKGROUND

The SWI, originally developed at TU Wien (Wagner et al., 1999a) and later improved by

other research groups, uses an infiltration model describing the relation between surface-

and profile soil moisture as a function of time. The algorithm is based on a two-layer water

balance model to estimate profile soil moisture from surface soil moisture data, which are

retrieved from the radar backscattering coefficients measured by the ASCAT instrument on

board the Metop satellites. In this model, the water content of the reservoir layer is described

in terms of an index, which is controlled only by the past soil moisture conditions in the

surface layer in a way that the influence of measurements decreases by increasing the time.

The SWI product represents the soil moisture content in the first 1 meter of the soil in relative

units, ranging between wilting level (0%) and field capacity (100%). It is derived from SSM-

values based on a temporal filtering method. The important parameter for modelling the

infiltration into the deeper soil layers - to calculate the SWI - is the characteristic time length,

called T-value (the higher the T-values the smoother the SWI).

The Copernicus Global Land Service provides already the SWI V3. The SWI V3 ingests near

real time surface soil moisture data (NRT SSM) from both ASCAT sensors on board Metop-

A/B/Csatellites as disseminated by EUMETSAT via EUMETCast. It generates SWI data for

eight T-values as well as freeze/thaw information called the surface state flag (SSF). In

addition, a 10-day SWI product (SWI10) is produced which is compatible to the other decadal

products produced in the CGLS. Both of these products are disseminated on a regular 0.1°

grid in netCDF4 image format. Time series for eight different T-values (1, 5, 10, 15, 20, 40,

60 and 100 days) are available on a discrete global grid (DGG) with a spatial resolution of

0.1° on a daily temporal sampling rate. All these products are specified in the respective

Product User Manual (CGLOPS1_PUM_SWIV3-SWI10-SWI-TS).

The SWI1km is an evolution of the existing CGLS SWI product. It uses high-resolution SAR

surface soil moisture (SSM) data generated by the 2014-launched Sentinel-1 satellites and

combines it with the ASCAT scatterometer SSM data. The resulting product achieves both,



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high temporal and spatial resolution, while providing improved reliability and accuracy,

allowing hydrological applications of radar data on a new level. The following table

summarises key characteristics of the in- and output products.

Table 3: Main features of the input (ASCAT SSM/SWI, Sentinel-1 SSM)- and fused output (SWI 1km) products.

Product Data

Level

Data Source Temporal

Resolution

Spatial

Sampling

ASCAT

SSM/SWI

L2 Metop ASCAT 1 day 0.1°

Sentinel-1 SSM L2 Sentinel-1 1.5-4 days 1/112°

SCATSAR-SWI L3 Metop ASCAT + Sentinel-1 1 day 1/112°

3.2 THE SCATSAR-SWI ALGORITHM

3.2.1 Input data

3.2.1.1 SAR SSM

The Sentinel-1 SSM retrieval algorithm was published in a research article by Bauer-

Marschallinger et al. 2018b, including also results from an evaluation campaign over Italy.

The input for the SAR SSM component of the SWI1km is generated in near-real-time by the

cooperation of TU Wien, Zentralanstalt für Meteorolgie und Geophysik (ZAMG), and Earth

Observation Data Centre for Water Resources Monitoring (EODC), building upon the

framework of the Prepare4EODC project [AD E1]. The SSM retrieval algorithm ingests

Sentinel-1 data in Interferometric Wide Swath (IW) mode and is an adaptation of the

TU Wien change detection method most closely akin with the method’s earlier adaptation to

Envisat ASAR Global Monitoring (GM) mode (Pathe et al. 2009). The method scales the

backscatter acquisitions between the dry- and wet-references that correspond respectively to

the soil wilting point and saturation level, yielding relative soil moisture in percent. The

method removes the dependency of the backscatter on the local incidence angle by

normalising the initial backscatter to a reference angle. For the scaling to relative soil

moisture, the necessary model parameters (dry- and wet-reference, SAR incidence angle

slope) are computed from the historic Sentinel-1A/B time series.



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The retrieval method estimates the degree of soil moisture saturation in the top layer

(~topmost 5cm) of the soil from C-band backscatter observations. The method has been

implemented using the SAR Geophysical Retrieval Toolbox (SGRT). An updated version

SGRT 2.0 was developed using the Python programming language and the technical details

are discussed in (AD E1). Figure 1 illustrates the processing lines of SGRT 2.0, which are

conceptually divided into pre-processing, model parameter retrieval, and product generation.

Figure 1: Main processing steps of the Sentinel-1 SAR processing with SGRT 2.0. Inputs are L1 radar backscatter images from Sentinel-1 IW with initial 10m sampling. The outputs are SSM

images with a 500m sampling.

The historic Sentinel-1A/B data for the SWI1km is provided by TU Wien as a SSM image

archive covering the period 2015-2018.



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3.2.1.2 SCAT SSM

The SSM data are generated in near-real time at EUMETSAT based on a soil moisture

retrieval algorithm developed by TU Wien (Naeimi et al. 2009). The SSM retrieval model for

use with C-band Scatterometers is a physically motivated change detection method. The first

realisation of the concept was based on ERS 1/2 satellite data sets (Wagner et al. 1999b)

and later the approach was successfully transferred to Advanced Scatterometer (ASCAT)

data onboard the METOP-A satellite (Bartalis et al. 2007). The retrieval algorithm is

implemented within a software package called Soil Water Retrieval Package (WARP).

Analogous to the SAR SSM, the SCAT SSM represents the degree of saturation of the

topmost soil layer and is given in relative units ranging from 0 (dry) to 1 (wet). The soil

moisture value is complemented by its noise, derived by error propagation of the backscatter

noise (covering instrument noise, speckle and azimuthal effects). A detailed description of

the retrieval algorithm is found in (AD E2). The product used as input to the SCATSAR-SWI

algorithm is the ASCAT SSM image product H16 as described in (AD E3).

The ASCAT-A/B/C SSM data is delivered via satellite downstream as BUFR files to the

Austrian met office ZAMG/EODC.

3.2.2 Overview

The SWI1km product is based on fused SSM data from the individual ASCAT and Sentinel-1

products. The input SSM products (Level 2) are obtained from partners outside the

Copernicus Services, namely EUMETSAT for ASCAT and TU Wien/ZAMG/EODC for

Sentinel-1. The SCATSAR-SWI algorithm itself consists of two components: 1) the offline

Data Fusion Parameter Generation (PG) and the near-real-time (NRT) SWI1km product

retrieval, taking up the input SSM and the data fusion parameters. Figure 2 summarises the

general structure of the SCATSAR-SWI algorithm.

For the PG, historic Envisat ASAR Global Monitoring Mode (GM) data was used as proxy for

Sentinel-1 SAR in Cycle 1, since not enough Sentinel-1 observations are available in the

beginning. The Envisat ASAR data is spanning the period 2004-2012 and was provided by

TU Wien. From Cycle 2 on, Sentinel-1 SAR data was used for the PG.



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Figure 2: General structure of the SCATSAR-SWI algorithm for the SWI1km production, with offline procedures of the algorithm in blue, and NRT procedures in green.



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Figure 3: a) Detailed description of the data flows for the offline Parameter Generation (PG) and b) the SCATSAR-SWI retrieval in near-real-time (NRT). The (static) PG outputs are used during

the (daily) NRT SCATSAR-SWI retrieval.

3.2.3 Data Fusion Parameter Generation (PG)

The Data Fusion Parameter Generation (PG) algorithm component derives static parameters

for the data fusion between SCAT and SAR SSM. This includes the generation of

1. Look-up-tables for the resampling (RLUT) between the Discrete Global Grid (DGG;

ASCAT SSM) and the Equi7Grid (Sentinel-1 SSM).

2. Matching Parameters (MP) to scale the dynamics of SCAT SSM time series to those

of the SAR SSM time series, at each location. Static matching function parameters

are saved and applied to incoming SCAT SSM data prior to SCATSAR-SWI retrieval



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3. Weighting Functions (WF) to account for different accuracy of the local input SSM

estimates from SCAT and SAR. As input parameters, the WF employs error

measures of the individual SSM products and proportionally weights the ASCAT and

Sentinel-1 input SSM.

4. A Correlation Layer (CL) serving as mask and as local quality indicator. The CL gives

the Spearman correlation between the SCAT- and SAR-SSM time series at each

location. With an empirical threshold for minimum correlation, the validity of the grid

location for SCATSAR-SWI retrieval can be determined.

5. Look-up-tables for the resampling (RLUT) between the obtained SCATSAR-SWI

values and the Copernicus GLS 1/112° grid, used for the final SWI1km products.

Figure 3a) gives a schematic overview about the PG procedures and the dataflow.

3.2.3.1 Resampling/reprojecting Look-Up Tables

The spatial reference system for internal processing of SCATSAR-SWI is the Equi7Grid

(Bauer-Marschallinger et al., 2014), which is already used for the SAR processing of

TU Wien. The Equi7Grid is a global spatial reference system optimised for high-resolution

satellite raster data and was developed for resolutions finer than 1km.

The inputs, ASCAT and Sentinel-1 SSM data, are delivered in different spatial grids,

featuring a completely different spatial referencing approach. The ASCAT data is using the

WARP Discrete Global Grid (DGG, [CGLOPS1_PUM_SWIV3-SWI10-SWI-TS]), which is

constructed explicitly for each location by a portioning technique capable to equally space

grid points according to a predefined arc length. Calculations of grid point locations are

directly performed on a spheroid, resulting in equal area cells and equidistance between

neighbouring grid points. Sentinel-1 SSM is using the Equi7Grid, an implicit grid definition,

using for each continent a set of two axis spawning a metric space defined by map

projections, forming a regular, planar, and equidistant raster for the grid locations.

The relations between individual DGG locations (in latitude and longitude) and the

corresponding coordinates in the Equi7Grid (in meters) can be computed with geographic

transformation equations as described in [AD E4]. These relations are linear and can either

be computed on the fly during ASCAT SSM resampling or computed once for every DGG

location and saved then into a resampling look-up-table (RLUT).

The reprojection of obtained SCATSAR-SWI values to the final CGLS 1km grid with a 1/112°

sampling is done in an analogous way.

Implementation:

The relations between DGG and Equi7-Grid locations, and Equi7-Grid locations and CGLS

1km grid, are computed once and saved as look-up-tables that are called in the ASCAT SSM



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resampling procedures during NRT SCATSAR-SWI retrieval and during the SWI1km

production.

3.2.3.2 Correlation Layer

The SCAT and SAR sensors observe in completely different spatial scales. Consequently,

the retrieved SSM products are likely to describe different geophysical processes, when

spatial heterogeneity on the SAR-scale is given. Different land cover may affect the finer

SAR observations but no the coarse SCAT observations. However, if both input SSM time

series feature similar temporal dynamics, it can be assumed that they describe the same

process.

Thus, the SWI1km is only be retrieved at grid locations where SSM from SCAT and SAR are

highly correlated. This is facilitated by the Correlation Layer (CL), serving as a positive grid

point mask for valid SWI retrievals.

For the variable soil moisture, a normal distribution of the values cannot be assumed. Thus,

the Spearman Rank Correlation Coefficient ρ is calculated at each grid point between the

ASCAT and ASAR time series. Additionally, the correlation significance is calculated to

account for the sample size of the investigated time series. The correlation layer holds for

each grid point two values then: The ρ-value and the corresponding significance level.

Implementation:

In Cycle 1, the Spearman correlation coefficient ρ and its significance level p is calculated for

each Equi7Grid point between ASCAT SSM and Envisat ASAR GM SSM between 2007 and

2012. From Cycle 2 on, the correlation coefficients between ASCAT and Sentinel-1 SSM

have been used (in stage NRT 2019 (SWI1km V1.0.1): from the period 2015-2018). In V1.0.1

the minimum correlation is set 0.3, flagging all pixels with lower values.

3.2.3.3 Weighting Functions

The quality of input SSM data from SCAT and SAR can be characterised in terms of their

Signal to Noise (SNR). In the static case, these values are calculated once from the historic

SSM data archives and are summarised as a quality score. This score is used for

Weighting SCAT versus SAR before SWI retrieval.

Estimating overall data quality at this grid point.

The weights at each grid point for SCAT ( and SAR ( sum up to unity, giving

1 SARSCAT ww

They are calculated as weighted mean between the SNR values:



Issue: I1.20



SARSCAT

SCATSCAT

SNRSNR

SNRw

SARSCAT

SARSAR

SNRSNR

SNRw

The setup for a dynamic calculation of the SNR scores needs to be designed, with

application in later product versions.

Per grid point, the weights are combined to a weighting function, giving a weighting value for

each SSM observation of the time series in the Fused Surface Soil Moisture Database

(FSD). This setting is in preparation for a dynamic Weighting Functions (WF). For a static

WF, a simple lookup table assigning a weight to either SCAT or SAR SSM values would be

enough.

Implementation:

Since not enough Sentinel-1 history were available initially, the data fusion weighting was

kept simple and the weights are set equal, giving 21SCATw and

21SARw . Considering the

three to six times higher revisit frequency of ASCAT (A+B) on an average European location,

these weights prioritise the ASCAT SSM over the Sentinel-1. This should account for the

expected higher quality and reliability of the ASCAT data in Cycle 1 and Cycle 2.

In stage NRT 2019 (SWI1km V1.0.1), the static weights for SCAT SSM are set 0.4, and for

SAR SSM to 0.6. With this choice, the SCAT contribution to the SCATSAR-SWI signal is still

higher, due to a higher number of SCAT observations per day, but now more balanced

against the SAR contribution.

The employment of the equations for w_SCAT and w_SAR will be applied in a future version,

expecting a more robust SNR estimation when using longer time series.

3.2.3.4 Matching Parameters

Both SSM products, from SCAT and SAR, are supposed to describe the same physical

parameter, and they are retrieved from radar backscatter data that is recorded by

comparable technologies. However, systematic biases are likely to occur, due to different

complexity at the original scale and sensor- and retrieval- specifics. Fusing ASCAT with

Sentinel-1 SSM should account for systematic differences in order to obtain a consistent

FSD.

Data matching techniques are methods to resolve signal biases and different signal variance

in a dataset containing disparate sources (Su et al, 2014). Here, we use data matching to

produce SSM signals with similar means and dynamics at each grid point.



Issue: I1.20



There is a large set of data matching methods, among which the Cumulative Distribution

Function (CDF) matching is widely used for soil moisture applications (Reichle and Koster

2004, Scipal et al. 2008). CDF-matching helps in overcoming systematic differences in

mean, variance, skewness and kurtosis and is relatively easy to implement at low

computational costs.

CDF matching can be applied to any time series with the following methodology, using some

mathematical simplifications. The SSM time series that should be matched shall be called

ts_tomatch and the SSM time series that it should be matched to shall be called ts_ref:

1. Identification of time bins for which ts_tomatch and ts_ref sets provide valid soil

moisture values and create time-collocated subsets ts_tomatch_tc and ts_ref_tc.

2. Compute CDFs of ts_tomatch_tc and ts_ref_tc.

3. Determine for both CDF-curves the 0, 5, 10, 20, 30, 40, 50, 60, 70, 80, 90, 95 and

100 percentile.

4. Linearize both CDF-curves between the 13 percentile-values.

5. Find piecewise linear fits between the two sets of 12 linear CDF elements.

6. Transform values of whole ts_tomatch_tc with the obtained linear fits.

The (static) CDF matching parameters consist then of a set of

Percentiles

Slope values

Intercept values

which will form the Matching Parameters for each Equi7 Grid location.

Figure 4 shows schematically the matching process between two CDFs, with 10%-

percentiles as piecewise linearization intervals for display simplification.



Issue: I1.20



Figure 4: Concept of CDF-matching for two time series at one pixel location, using linearization between data-to-match values x and target data, yielding matched data values x’.

In Figure 5, exemplary SWI time series from SCAT, SAR and fused SCATSAR-SWI are

plotted. The four time series share the same range and dynamics, resulting from the CDF-

matching. Both individual signals, from SCAT and SAR, are preserved and are integrated in

the SWI1km time series.

Figure 5: ASCAT, ASAR, CDF-matched ASAR, and SCATSAR-SWI time series for the years 2008-2009 at 56.37°N/9.24°E (as indicated in the location map in Figure 7).



Issue: I1.20



Implementation:

In Cycle1, for each grid point, the time-collocated SSM time series from SCAT and SAR from

the period 2007-2012 are used for finding the CDF matching parameters, using Envisat

ASAR to ASCAT SSM.

As Sentinel-1 SSM proved to have a stable and reliable quality, the choice of the direction of

the data matching was switched in Cycle 2. Then SCAT is matched to SAR, allowing also to

rectify problems of the ASCAT SSM around cities and coastal areas (which result from the

coarse SCAT footprint). The obtained parameters are applied to the input ASCAT SSM,

whereas the SAR SSM stays unchanged. In stage NRT 2019 (SWI1km V1.0.1), Sentinel-1

SSM data from the period 2015-2018 was used.

3.2.4 SCATSAR-SWI Retrieval (NRT)

For operational provision of SWI1km, a product retrieval with a design for near-real-time

(NRT) operation is demanded. The NRT SCATSAR-SWI retrieval, as the second component

of the algorithm, ingests SSM inputs from SCAT and SAR provider as well as data fusion

parameters retrieved with the PG procedures as described in the previous section 3.2.3 and

delivers SWI data operationally to the Copernicus Services. Figure 3 b) gives a detailed

overview on the relevant data flows and processing steps.

3.2.4.1 SCAT Resampling

During the daily data uptake, ASCAT SSM and information for the Surface State Flags

(SSF), saved as EUMETSAT BUFR files, are decoded, resampled, and tiled to the Equi7Grid

format, yielding time series for each Equi7Grid location. The sampling distance is 500m and

the tile extent is 600km. All ASCAT data are saved as one netCDF4-file per Equi7Grid tile.

SSF values (at the sampling of ASCAT data, 12.5km) are used during SWI calculation for

filtering for frozen conditions.

All operations involving the spatial referencing, resampling, tiling and pixel location

identification are carried out with Equi7Grid Python software (developed by TU Wien), using

the Resampling look-up table (RLUT) for coordinate conversion.

This setup using netCDF4 files ensures an efficient data handling as it is optimised for

product retrieval from high-resolution satellite imagery. Redundancy from the significant

down-sampling of ASCAT data does not come into effect since the data layers are

compressed using the LZW-algorithm. During Parameter Generation (PG) procedures, time

series access at individual grid points is necessary. This is done with pixel location indices in

respect to Equi7Grid tiles. Consequently, it is not necessary to read in the netCDF files

completely. For the recursive formulation, which is used for the daily processing, an

additional denominator factor layer is saved in the same format as the SSM data.



Issue: I1.20



3.2.4.2 SCAT Data Matching

During the regular data uptake, incoming ASCAT SSM images are matched to local

Sentinel-1 dynamics using the Matching Parameters (MP) for each 500m Equi7Grid location,

as described in Section 3.2.3.4.

3.2.4.3 SCATSAR-SWI Calculation

The moisture contained within a soil profile is mainly derived from precipitation via the

process of infiltration. The SWI algorithm is based on a simple model describing this

infiltration process. It is calculated from temporally irregular scatterometer measurements

using a continuous formulation. Wagner et al. (1999a) proposed the first version of the

retrieval algorithm with SWI being calculated for time tn integrating SSM data over a

preceding time period T using the formula:

nin

i

T

tt

n

i

T

tt

i

n ttfor

e

etSSM

tSWIin

in

(Eq. 1)

In which nt is the observation time of the current measurement and it are the observation

times of the previous measurements. These times are given in Julian days. In (Eq. 1) all

SSM observations made before nt are summed up and exponentially weighted. The factor

T determines how fast the weights become smaller and how strongly SSM observations

taken in the past influence the current SWI. If e.g. the T value is 5 and a SSM measurement

was taken 10 days before nt then this observation would receive the weight

Using a T value of 20 in the same case would increase the weight to

.

This formulation represents a simple two-layer water balance model, with the first layer as

the soil surface layer accessible to C-band scatterometers and the second layer as the part

of the profile that extends downwards from the bottom of the soil surface layer. The second

layer is assumed to serve as a reservoir that is connected to the atmosphere via the first

layer. The moisture conditions in the first layer, which is in direct contact with the

atmosphere, is temporally highly dynamic. With increasing profile depth in the second layer,

the temporal dynamic of the moisture conditions is decreasing, as the amount of water stored

in the reservoir depends on the infiltration of water added to the first layer during precipitation

events. Therefore the water content of the reservoir layer is solely controlled by the past

moisture conditions in the surface layer and thus the precipitation history. Here, more recent

precipitation events have a stronger impact on the moisture conditions in the reservoir, which



Issue: I1.20



is accounted for by the exponential weighting function in Eq. 1. The link between the

remotely sensed surface layer s and the reservoir is described by (Ceballos et al. 2005):

)()()(

ttCdt

tdL s

, (Eq. 2)

where t is time, L the depth of the reservoir layer and C an area-representative pseudo-

diffusivity constant. If C is assumed constant and CLT / , then the differential equation

(Eq. 2) can be solved as follows:

tdT

ttt

Tt

t

s

exp)(1

)( (Eq. 3)

The calculation of the SWI values using the approach introduced by (Wagner et al. 1999a)

requires the availability of historic SSM time series data. A computational adaption of this

SWI algorithm has been proposed by (Albergel et al, 2008). Using a recursive reformulation

of the SWI, the calculation can be started at any time or during an ongoing process. The SWI

at time t for a given characteristic time length T can be derived using the formula:

))()()(()()( 11 nTnnTnTnT tSWItSSMtgaintSWItSWI (Eq. 4)

The )( nT tgain factor at time t is derived using the formulation:

T

ntnt

etgain

tgaintgain

nT

nTnT 1

)(

)()(

1

1

. (Eq. 5)

In Eq(4) and Eq(5), n refers to the latest, and 1n to the previous SSM measurement.

Calculation of SWI based on recursive formulation can start, as new input SSM data are

available from scatterometer measurements. However, in SWI processor, the SWI output

product is provided in daily intervals. According to Eq(1), it can be shown that

)()( nTcT tSWItSWI , where ct is the SWI calculation time and nt is the time of last

available SSM measurement. The calculation of SWI is started at any time with following

values:



Issue: I1.20



SWIT(t)=SSM(t), gainT(t)=1, qflagT(t)=1, tn-1=t

The SWI Quality Flag (qflagT(t)) is identical to the one from the ASCAT SWI V3, as described

in [CGLOPS1_PUM_SWIV3-SWI10-SWI-TS], giving indication of the temporal density of

input SSM measurements used for the SWI calculation.

There is an alternative formulation for the recursive SWI calculation, as described in [AD E5]:

)(

)()()()(

1

11

nT

nnnTnT

tden

tSSMtSWItSWItSWI , (Eq.6)

shifting the indexing by 1 and using the denominator parameter )( 1nT tden instead of the

gain parameter )( 1nT tgain :

)(

1)(

1

1

nT

nTtgain

tden . (Eq.7)

In the SCATSAR-SWI algorithm, the SCAT SSM and SAR SSM values are weighted

individually. Thus, Eq.1 becomes with )( ntw as weight for )( ntSSM and n total observations:

nin

i

T

ttn

i

i

n

i

T

tt

ii

n

w

T ttfor

etw

etSSMtwin

tSWIin

in

)(

)()1(

(Eq.8)

For SWI retrieval in NRT, the recursive calculation of the weighted SWI is needed. As

described in [AD E6], the weighted SWI w

TSWI is calculated as

)(

)()2)(()1)(()(

11

11112

1

nTn

nnnTninin

n

w

Tn

w

Ttdenw

twintSSMtdenwtSWItSWI (Eq.9)

where nw and 1 nw represent the sums of weights of in and in 1 previous

observations, respectively.



Issue: I1.20



Implementation:

Once per day of production, new SSM data from ASCAT and Sentinel-1 and observation

times in the previous 24 hours are registered. Times and locations with a corresponding

ASCAT SSF indicating frozen conditions, are flagged and disregarded from further

calculations. For each (valid) time step, and for each grid point with new data, SSM values

are converted to SWI values. Since the weights of SCAT and SAR are set equal in Cycle 1,

Eq.6 is applied for SWI calculation. From Cycle 2 on, Eq.9 is applied.

3.2.4.4 SCATSAR-SWI Masking

In stage NRT 2019 (SWI1km V1.0.1), the mask for water surfaces (sea, lakes, major rivers)

from the Sentinel-1 SSM input is forwarded and used in the fused SWI1km product as flag

value (251, with flag meaning WaterMask). Also, a mask for complex topography is

forwarded to the SWI1km as flag value (253, with flag meaning SlopeMask).

The Correlation Layer (CL), carrying the Spearman correlation coefficient and significance

from the correlation analysis between SCAT and SAR is used for masking the SWI1km. The

threshold of minimum correlation for considering the fused SWI as a valid estimate is set to

rho=0.3. Pixels with lower correlation are set to the flag value of 252 (with flag meaning

SensitivityMask). This approach was anticipated in first experiments for masking

homogenous and non-artificial areas, excluding cities, rivers, beaches, hills and rocks (see

examples in Figure 7 and Figure 8).

Masking for frozen conditions is performed during the SCATSAR-SWI calculation, setting the

pixel to no-data (255) in case of frozen/snow-covered soil at observation time. It uses the

freeze-thaw information included in the ASCAT SSM product (using the SSF). Therefore, the

freeze-thaw masking is done at a coarser resolution than the output product (at 0.1°

sampling). A migration of the SSF masking to 1km scale is foreseen in upcoming product

updates.

3.2.5 Output Generation

After retrieval of SWI values, geo-referenced images carrying the calculated values are

generated. In order to fulfil the requirements of the CGLS, a resampling to a geographical

reference system with coordinates as latitude and longitude is carried out. This is done with

software of the Equi7Grid, which transforms the data from its metric grid to the geographic

grid using a simple set of linear functions, stored as look-up-tables.

Further specifications of the format of the output products are described in detail in the

Product User Manual [CGLOPS1_PUM_SWI1km-V1].



Issue: I1.20



3.2.6 First Experimental Results

The quality assessment of SWI1km product is on-going. Results will be published in a

Validation Report later in 2019. Preliminary results for the SWI1km at version 1.0.1 show

improvements over urban areas coastal regions, and irrigated agriculture, compared to the

(coarser resolution) global daily Soil Water Index 12.5km version 3 product.

Figure 6 shows an example (quicklook) image of the SWI1km V1.0.1 for the date 2018-08-

26, illustrating also the coverage of the CEURO production window.

Figure 6: Quicklook image (CEURO extent) of the SWI1km dataset from 2018-08-26. Blue colours represent wet soil conditions, and brown colours dry conditions. Overlaid by country

boundaries and legend for better orientation.

A detailed evaluation campaign of the SCATSAR-SWI data over the Italian area is published

in a research article by Bauer-Marschallinger et al., 2018a.



Issue: I1.20



A First SWI1km processing was carried out in 2015, using Metop-A ASCAT and Envisat

ASAR Wide Swath (WS) mode data, allowing the analyses discussed in the below section.

At that time, not enough Sentinel-1 SAR data was available for the creation of a consistent

SSM dataset. However, the examples below (Figure 7 and Figure 8; using Envisat ASAR

data) illustrate the benefits of the fused product over the individual SWI products from SCAT

or SAR. It should be noted that these results stem from early experiments and feature mostly

scatterometer data due to its higher data density. Although the effective resolution is coarser

than 1km, these results show already the benefit of the data fusion.

Figure 7 illustrates the shortcomings of separate SWI products from scatterometers and

SARs. It compares the SWI maps (T = 5 days) derived from Metop ASCAT (0.1° resolution),

Envisat ASAR in Wide Swath Mode (aggregated to 1km) with a first prototype of fused

SCATSAR-SWI data (SWI1km) and relates them to decadal means of Leaf Area Index (LAI)

from SPOT/Vegetation [CGLOPS1_PUM_LAI1km-VGT-V1] and CORINE Land Cover.

Envisat ASAR was chosen as proxy SAR data source for Sentinel-1, since they share main

sensor properties. The ASCAT-derived SWI image shows soil water conditions on a coarse

regional scale and gives no insight into local patterns. Furthermore, mixed ocean-land pixels

near the coast degrade the quality of these pixels. The SWI image from ASAR shows a

certain degree of data noise. Most harmful, it carries a patchy structure (visible in the west)

because of insufficient and irregular coverage by the SAR sensor. However, the SWI1km

product profits from both sources as it combines their assets while it reduces their

drawbacks: both, the regional and the local spatial signals are preserved, and yield a smooth

high-resolution image. A threshold of rho=0.5 in the Correlation Layer (CL) was used for

masking out pixels over land, where low product quality is expected, as explained in Section

3.2.3.2.

A similar experiment performed over agricultural areas in south-eastern Austria (Figure 8)

reveals that the SWI1km product resolves spatial patterns that can be associated with

vegetation and land cover patterns. These fine scale patterns are not visible in the ASCAT

product. However, the SWI1km image exhibits striping artefacts due to limited SAR data

availability. Here, a threshold of rho=0.6 in the Correlation Layer (CL) was used for masking.

The experimental SWI-product from ASAR alone suffers substantially from poor data quality

and low observation frequency.



Issue: I1.20



Figure 7: Different SWI products, Leaf Area Index (LAI) from SPOT/Vegetation and CORINE Land Cover over Denmark. Overview on the study area on the lower left. The upper images show SWI estimates (T-value of 5 days) from Metop ASCAT at 0.1°, Envisat ASAR in Wide

Swath Mode (WS) at 1km and the SWI1km. In black, the SCATSAR correlation mask covers areas where correlation between ASCAT and ASAR soil moisture is low. The centre lower

image shows a decadal mean of LAI at 1km, giving indication on local vegetation status. The lower right image displays the land cover classes from CORINE 2006 with the most prevalent

land cover classes in the legend.



Issue: I1.20



Figure 8: Different SWI products, Leaf Area Index (LAI) from SPOT/Vegetation and CORINE Land Cover over south-eastern Austria. The upper left image shows SWI estimates (T-value of

10 days) from Metop ASCAT at 0.1°, the upper right image the overview map. The centre images show the ASAR and SWI1km. The lower left image displays the land cover classes from CORINE 2006 with the most prevalent land cover classes in the legend. The lower right image

shows a decadal mean of LAI at 1km, giving indication on local vegetation status.



Issue: I1.20



3.2.7 Description of Quality and Uncertainty

A Quality Flag (QFLAG) is calculated for each T-value and describes the input SSM data

density of each SWI value. When no SSM input is available, or when the SSF indicates

frozen soil, the QFLAG is decreasing over time for an individual pixel. If the QFLAG becomes

lower than specified threshold, the pixel is set the flag value of 254 (with the flag meaning

LowQFLAG). The minimum thresholds were empirically identified during evaluation

experiments and are as follows: 35%, 40%, 45%, 50%, 53%, 55%, 60%, 65%, 70%, for the

T-values 2, 5, 10, 15, 20, 40, 60, 100, respectively. If the QFLAG is higher than those values,

the pixels carry the SWI value as normal. Then the user can use the QFLAG information

(which is included as a layer in the SWI1km product) for further masking if required for the

application or over the area-of-interest.

An error model for the SCATSAR data fusion approach is not implemented yet, but is

foreseen in future versions.

3.3 LIMITATIONS OF THE PRODUCT

The SWI1km product is at the forefront of current remote sensing soil moisture research.

Therefore, the implemented algorithm is designed in a plain but robust style, limited to the

components that are mature and ready for operations. It is however designed in a way to be

extensible with the foreseen improvements.

The limitations explained in the below paragraphs apply to all SWI products, the SWI1km

however comes with additional limitations due to its data fusion approach. The weighting

function between the S-1 and ASCAT retrievals is currently implemented in a static way. So,

if the relative data quality of the retrievals is not stable over time, then these static data fusion

approach needs to be revised. S-1 data availability strongly influences the effective

geophysical spatial resolution of the product so in areas where S-1 observations are scarce

the resolution might be coarser than the 1km sampling (some 2-10 km).

3.3.1 Timeliness

The timeliness of the SWI1km product is unclear while writing this document, since, in

contrast to the well-established ASCAT products, the Sentinel-1 SAR SSM product suffers

from some erratic timeliness irregularities, which needs further investigations as the S-1

product is provided by external providers outside of the Copernicus Services. For the time

being (at stage NRT 2019), the timeliness of the SWI1km production has a delay of 2 days,

giving enough time for a most consistent and complete data from the input Sentinel-1 SSM

NRT-stream.



Issue: I1.20



3.3.2 Spatial Coverage

The SWI algorithm cannot be applied in regions for which a surface soil moisture extraction

from ASCAT C-band scatterometer or S-1 CSAR data is not meaningful. This especially

applies to high altitude permafrost regions, glaciers, ice caps, dense forests (e.g. tropical

rainforest) and deserts.

In the current stage (NRT 2019) the production is carried out over the European area.

Extension to other continents is foreseen for upcoming updates (first extension will be

Africa).

3.3.3 Balancing between SCAT and SAR

The data qualities and densities of the input SSM products have a considerable impact on

the final SWI1km. If they are stable, but considerably different to each other, the weighting

functions and the correlation layer are efficient tools to balance between the inputs

accordingly and adjust the output mask. If they are highly variable, static fusion parameters

will lead to erroneous SWI values and might bring an overall bad product performance.

Dynamic fusion parameters should therefore be developed, evaluated and if beneficial,

integrated in upcoming versions.

In the early phase of Sentinel-1, mainly 2015 to 2016, the SAR data played a subordinate

role as input to parameter estimation and SWI1km retrieval, leading to an effective resolution

much coarser than 1km. With reaching fully operational status (with two satellites S-1A+B) in

October 2016, the S-1 SAR component has a greater impact on the fused SWI1km product

and the effective resolution is much closer to 1km.

3.3.4 Uncertainty Propagation

No direct uncertainty propagation estimation for the SCATSAR-SWI is carried out (as it is

done for the ASCAT SWI V3), due to missing experience with the interactions of errors in

ASCAT- and Sentinel-1 SSM estimations. However, the fusion parameters are eligible

measures to account for uncertainties in the inputs and are foreseen to be used in Cycle 2.

More precisely, the Correlation Layer (CL) and the SNR values (used for the Weighting

Functions (WF)) indicate the reliability of the SCATSAR-SWI. This could be comprised as

static “Reliability Layer”, giving an indication on the reliability of values at a specific pixel.

3.3.5 Relation to Soil Depth

The SWI retrieval method does not account for soil texture, which determines the relation

between the T-value and soil depth (de Lange, et al. 2008). Therefore, eight T-values (2, 5,

10, 15, 20, 40, 60, 100) are provided per product to give more possibility to the users for



Issue: I1.20



selecting the best matching data. Contrary to the ASCAT-based SWI V3, the top-most T-

value is 2 (instead of 1), allowing a more consistent product from the fused database. Paulik

et al., 2014 analysed statistically the relationship between T-values and soil depth over in-

situ locations.

3.3.6 Effects from Evapotranspiration

Evapotranspiration is not considered when calculating the SWI, which can lead to artificially

high root zone soil moisture estimates when precipitation time and satellite observation time

coincide and the rainfall evaporates instead of filtrating into deeper soil layers. In areas with a

high ground water table, the SWI method does not work particularly for the deeper layers

affected by the ground water.



Issue: I1.20



4. REFERENCES

Albergel, C., Rüdiger, C., Pellarin, T., Calvet, J.-C., Fritz, N., Froissard, F., Suquia, D.,

Petitpa, A., Piguet, B., Martin, E. (2008): From near-surface to root-zone soil moisture

using an exponential filter: an assessment of the method based on in-situ observations

and model simulations. Hydrol. Earth Syst. Sci. 12: 1323-1337.

Bartalis, Z., Wagner, W., Naeimi, V., Hasenauer S., Scipal, K., Bonekamp, H., Figa, J.,

Anderson, C. (2007): Initial Soil Moisture Retrievals from the METOP-A Advanced

Scatterometer (ASCAT). Geophysical Research Letters 34 (20). American Geophysical

Union: L20401.

Bauer-Marschallinger, B., Sabel, D., Wagner, W. (2014): Optimisation of Global Grids for

High-Resolution Remote Sensing Data. Computers & Geosciences, Volume 72, 84-93,

Elsevier.

Bauer-Marschallinger, B., Paulik, C., Hochstöger, S., Mistelbauer, T., Modanesi, S., Ciabatta,

L., Massari, C., Brocca, L. and Wagner, W., (2018a). Soil moisture from fusion of

scatterometer and SAR: Closing the scale gap with temporal filtering. Remote Sensing,

10(7), p.1030.

Bauer-Marschallinger, B., Freeman, V., Cao, S., Paulik, C., Schaufler, S., Stachl, T.,

Modanesi, S., Massari, C., Ciabatta, L., Brocca, L. and Wagner, W., (2018b). Toward

Global Soil Moisture Monitoring With Sentinel-1: Harnessing Assets and Overcoming

Obstacles. IEEE Transactions on Geoscience and Remote Sensing, (99), pp.1-20.

Ceballos, A., Scipal, K., Wagner, W., Martinez-Fernandez, J. (2005): Validation of ERS

scatterometer-derived soil moisture data in the central part of the Duero Basin, Spain.

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Dee, DP, Uppala, S. (2009): Variational Bias Correction of Satellite Radiance Data in the

ERA-Interim Reanalysis. Quarterly Journal of the Royal Meteorological Society 135

(644). Wiley Online Library: 1830–41.

de Lange, R., et al. (2008). "Scatterometer-Derived Soil Moisture Calibrated for Soil Texture

With a One-Dimensional Water-Flow Model." Geoscience and Remote Sensing, IEEE

Transactions on 46(12): 4041-4049.

Reichle, R. H., & Koster, R. D. (2004): Bias reduction in short records of satellite soil

moisture. Geophysical Research Letters, 31(19).



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Naeimi, V., K. Scipal, Z. Bartalis, S. Hasenauer and W. Wagner (2009), An improved soil

moisture retrieval algorithm for ERS and METOP scatterometer observations, IEEE

Transactions on Geoscience and Remote Sensing, Vol. 47, pp. 555-563..

Pathe, C., Wagner W., Sabel, S., Doubkova, M., Basara. JB. (2009): Using ENVISAT ASAR

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