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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
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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
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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
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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:
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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.
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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.
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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).
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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.
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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
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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:
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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.
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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].
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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.
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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.
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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.
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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.
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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.
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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
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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.
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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.
Hydrological Processes 19: 1549-1566.
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
Global Mode Data for Surface Soil Moisture Retrieval over Oklahoma, USA.
Geoscience and Remote Sensing, IEEE Transactions on 47 (2). IEEE: 468–80.
Paulik, C., Dorigo, W., Wagner, W., & Kidd, R. (2014): Validation of the ASCAT Soil Water
Index using in situ data from the International Soil Moisture Network. International
Journal of Applied Earth Observation and Geoinformation, 30, 1-8
Scipal, K., Holmes, T., De Jeu, R., Naeimi, V., & Wagner, W. (2008): A possible solution for
the problem of estimating the error structure of global soil moisture data sets. Geophysical
Research Letters, 35(24).
Su, C-H., Narsey, SY., Gruber, A., Xaver, A., Chung, D., Ryu, D., Wagner, W. (2014):
Evaluation of Post-Retrieval de-Noising of Active and Passive Microwave Satellite Soil
Moisture. Remote Sensing of Environment 163. Elsevier: 127–39.
Wagner, W., Lemoine, G., Rott. H. (1999a): A Method for Estimating Soil Moisture from ERS
Scatterometer and Soil Data. Remote Sensing of Environment 70 (2). Elsevier: 191–
207.
Wagner, W., Noll, J., Borgeaud, M., Rott, H. (1999b): Monitoring Soil Moisture over the
Canadian Prairies with the ERS Scatterometer. Geoscience and Remote Sensing,
IEEE Transactions on 37 (1). IEEE: 206–16.
Wilkie, C.J., Scott, E. M., Miller, C., Tyler, A. N., Hunter, P. D., Spyrakos,E. (2015): Data
Fusion of Remote-sensing and In-lake chlorophylla Data Using Statistical Downscaling.
Procedia Environmental Sciences, Volume 26, Pages 124-127, ISSN 1878-0296