copernicus global land operations · the processing of the level1-data to ssm data is performed as...

Copernicus Global Land Operations – Lot 1 Date Issued: 19.04.2019 Issue: I1.30

Copernicus Global Land Operations

“Vegetation and Energy” ”CGLOPS-1”

Framework Service Contract N° 199494 (JRC)

PRODUCT USER MANUAL

SURFACE SOIL MOISTURE

COLLECTION 1KM

VERSION 1

Issue I1.30

Organization name of lead contractor for this deliverable: TU Wien

Book Captain: Bernhard Bauer-Marschallinger

Contributing Authors: Christoph Paulik


Document-No. CGLOPS1_PUM_SSM1km-V1 © C-GLOPS Lot1 consortium

Issue: I1.30 Date: 19.04.2019 Page: 2 of 35

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)




Document Release Sheet

Book captain: Bernhard Bauer-Marschallinger Sign Date 19.04.2019

Approval: Roselyne Lacaze Sign Date 23.04.2019

Endorsement: M. Cherlet Sign Date

Distribution: Public




Change Record

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

28.02.2017 all Initial version D1.00

D1.00 06.04.2017 all Added Validation, Chapter 4 I1.00

I1.00 27.07.2017 all Update after external review I1.10

I1.10 29.11.2018 all Update related to products public release I1.20

I1.20 19.04.2019 16, 17,

22 Update to model 2015-2018 (V1.1.1) I1.30




TABLE OF CONTENTS

1 Executive Summary .................................................................................................. 10

2 Background of the document ................................................................................... 11

2.1 Scope and Objectives ................................................................................................... 11

2.2 Content of the document ............................................................................................. 11

2.3 Related documents ...................................................................................................... 11

2.3.1 Applicable documents ..................................................................................................................... 11

2.3.2 Input ................................................................................................................................................ 11

2.3.3 External documents ........................................................................................................................ 12

3 Algorithm ................................................................................................................ 13

3.1 Overview ..................................................................................................................... 13

3.2 Soil Moisture Retrieval ................................................................................................ 13

3.2.1 Product Value Flags ......................................................................................................................... 14

3.2.2 Product Masks ................................................................................................................................. 14

3.3 Limitations of the Product ............................................................................................ 15

3.4 Differences with previous versions ............................................................................... 16

4 Product Description .................................................................................................. 17

4.1 File Naming ................................................................................................................. 17

4.2 File Format .................................................................................................................. 17

4.3 Product Content .......................................................................................................... 18

4.3.1 Data files .......................................................................................................................................... 18

4.3.2 Quicklook ......................................................................................................................................... 25

4.3.3 Tips on netCDF4 files: ...................................................................................................................... 26

4.4 Product Characteristics ................................................................................................ 27

4.4.1 Projection and Grid Information ..................................................................................................... 27

4.4.2 Spatial Extent .................................................................................................................................. 27

4.4.3 Temporal Information ..................................................................................................................... 27

4.5 Data Policies ................................................................................................................ 29

4.6 Contacts ...................................................................................................................... 30

5 Validation ................................................................................................................ 31

5.1 Validation Study 2017 - Europe .................................................................................... 31




5.2 Evaluation Experiments 2018 - Italy.............................................................................. 32

6 References ............................................................................................................... 33

7 Annex: Review of Users Requirements ...................................................................... 34




LIST OF FIGURES

Figure 1: Visual impression of the SSM1km dataset (CEURO extent) from 2018-11-26, as

read from the netCDF4 file and displayed in QGIS. With general encodings of values

and flags. ......................................................................................................................20

Figure 2: SSM1km (CEURO extent) dataset from 2018-11-26, with encodings of values and

flags. Flags are displayed in color for visualization. .......................................................21

Figure 3: Quicklook image (CEURO extent) of the ssm dataset from 2018-11-26. Blue

colours represent wet soil conditions, and brown colours dry conditions. ......................26

Figure 4: Number of processed observations of Sentinel-1A for SSM retrieval over the period

2014/Oct – 2016/Oct. ....................................................................................................28

Figure 5: Sentinel-1A+B revisit and coverage frequency, as specified by ESA on 02/2018. .29




LIST OF TABLES

Table 1: Algorithm differences between successive SSM1km versions ................................16

Table 2: Explanation in version numbering and recommendations for using efficiently the

products ........................................................................................................................17

Table 3: Geographic coverage .............................................................................................18

Table 4: General value encoding of SSM1km data files. .......................................................18

Table 5: Overview of flag values and meanings....................................................................19

Table 6: Global attributes of the SSM1km product. ...............................................................22

Table 7: Description of netCDF attributes for the main ssm data variable .............................23

Table 8: Description of netCDF attributes for coordinate variables (latitudes and longitudes)

......................................................................................................................................24

Table 9: Description of netCDF attributes for time coordinate variable .................................24

Table 10: Description of netCDF attributes for the grid mapping variable .............................25

Table 11: Target requirements of GCOS for soil moisture (up to 5cm soil depth) as Essential

Climate Variable (GCOS-154, 2011) .............................................................................35




LIST OF ACRONYMS

ASAR Advanced Synthetic Aperture Radar (Envisat satellite)

ASCAT Advanced Scatterometer (MetOp satellites)

ATBD Algorithm Theoretical Base Document

CGLS Copernicus Global Land Service

CSAR C-band Synthetic Aperture Radar (sensors onboard Sentinel-1 satellites)

EC European Commission

EODC Earth Observation Data Centre for Water Resources Monitoring

ERS European Radar Satellite

ESA European Space Agency

GCOS Global Climate Observing System

GDAL Geospatial Data Abstraction Library

IW Interferometric Wide Swath mode (Sentinel-1 CSAR observation mode)

netCDF4 Network Common Data Form, Version 4

PUM Product User Manual

QGIS Quantum Geographic Information System (free and open software)

R&D Research and Development

SAR Synthetic Aperture Radar

SSM Surface Soil Moisture

TU Wien Technische Universität Wien

UTC Coordinated Universal Time

VITO Flemish Institute for Technological Research

VR Validation Report

WGS84 World Geodetic System 84 reference system




1 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 provides a series of bio-

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

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

constitution of long-term time series.

From 1st January 2013, the Copernicus Global Land Service is providing Essential Climate

Variables like the Leaf Area Index (LAI), the Fraction of Absorbed Photosynthetically Active

Radiation absorbed by the vegetation (FAPAR), the surface albedo, the Land Surface

Temperature, the soil moisture, the burnt areas, the areas of water bodies, and additional

vegetation indices, are generated every hour, every day or every 10 days on a reliable and

automatic basis from Earth Observation satellite data.

This Product User Manual describes the Copernicus Global Land SSM (Surface Soil

Moisture) product describing soil moisture of the soil’s topmost 5cm on a 1km (1°/112) spatial

sampling. It is derived from microwave radar data observed by the Sentinel-1 SAR satellite

sensors.




2 BACKGROUND OF THE DOCUMENT

2.1 SCOPE AND OBJECTIVES

The Product User Manual (PUM) is the primary document that users have to read before

handling the products.

It gives an overview of the product characteristics, in terms of algorithm, technical

characteristics, and main validation results.

2.2 CONTENT OF THE DOCUMENT

This document is structured as follows:

Chapter 2 recalls the users requirements, and the expected performance

Chapter 3 summarizes the retrieval methodology

Chapter 4 describes the technical properties of the product

Chapter 5 summarizes the results of the quality assessment

2.3 RELATED DOCUMENTS

2.3.1 Applicable documents

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.

2.3.2 Input

Document ID Descriptor

CGLOPS1_SSD_SSM1km-V1 Service Specifications of the Global Component of

the Copernicus Land Service.

CGLOPS1_ATBD_SSM1km-V1 Algorithm Theoretical Basis Document of the

SSM1km-V1 product

CGLOPS1_VR_SSM1km-V1 Report describing the results of the scientific quality

assessment of the SSM1km-V1 product




2.3.3 External documents

Document ID Descriptor

ED1 Sentinel-1 User Guide,

https://sentinel.esa.int/web/sentinel/user-guides/sentinel-1-sar

https://sentinel.esa.int/web/sentinel/user-guides/sentinel-1-sar




3 ALGORITHM

This section presents a short summary on the SSM1km retrieval algorithm. For more

information, the reader is referred to the CGLOPS1_ATBD_SSM1km-V1document. Also, a

recent peer-reviewed publication (Bauer-Marschallinger et al., 2018) explains in detail the

SSM1km retrieval algorithm, underpinned by its scientific background.

3.1 OVERVIEW

The PUM document provides a detailed description of the Sentinel-1 SSM Version 1 product

(SSM1km-V1, hereafter: SSM1km) characteristics, format, validation activities and

availability. The SSM1km data is retrieved from Sentinel-1 radar backscatter images,

acquired in Interferometric Wade Swath (IW) mode and VV-polarization (see ED1 for details).

This raw satellite data (Level1-data) is jointly provided by the European Space Agency (ESA)

and the European Commission (EC).

The output products are daily images of relative surface soil moisture in percent saturation,

at 1km sampling. The Copernicus SSM1km product in Version 1.1.1 is available for the

European continent per individual location every 3-8 days (January 2015 to October 2016)

and every 1.5-4 days from October 2016 ongoing. A global production is foreseen in a future

release.

3.2 SOIL MOISTURE RETRIEVAL

The processing of the Level1-data to SSM data is performed as follows: The Sentinel-1

backscatter value at time , , is terrain-geo-corrected and radiometrically calibrated. The

TU-Wien-Change-Detection model is used to derive soil moisture directly from backscatter

observations. This model was originally developed for the ERS scatterometer by Wagner et

al., 1998, and adapted by Hornacek et al., 2012 to the specific measurement configuration of

Sentinel-1. In this model, long-term backscatter measurements are used to model dry and

wet soil conditions from the radar backscatter signal. Further, the incidence angle

dependency of the backscatter is modelled by the linear slope parameter , which allows a

normalization to a common reference observation angle ( °).

The relative surface soil moisture estimates range between 0% and 100% and are

derived by linearly scaling the angle-normalized backscatter between the

lowest/highest backscatter values at the individual location (

). These

backscatter values correspond to the driest/wettest soil conditions. The difference between

those values is called the soil moisture sensitivity

.

These parameters necessary for the production (dry-reference , wet-reference

, and slope ) are obtained from the Sentinel-1 backscatter long-term time series of

the individual location. The relative surface soil moisture is estimated by:




The soil moisture in the SSM1km represents degree of saturation , but can be

translated from relative (%) to absolute volumetric units (in m³/m³), using porosity

information (also in m³/m³):

The SSM1km value range is from 0% to 100% relative soil moisture, which is encoded in the

files as datatype “byte” with the integer values 0-200, using the scale factor of 0.5. Please

see Figure 1 for illustration, and Table 4 listing the encoding.

3.2.1 Product Value Flags

The Sentinel-1 SSM1km algorithm depends on statistically derived reference values for dry

and wet soil at each pixel. In most cases, Sentinel-1 CSAR radar observations lie between

these two extreme values and the observations can be converted meaningfully to soil

moisture values from 0% to 100% relative soil moisture.

However, in cases of one of the following, the SSM retrieval is ill posed:

extreme dry conditions,

frozen soil,

snow covered soil,

floodings.

Therefore, all SSM retrievals exceeding the local pixel’s dry- and wet-reference are replaced

by the flags “ExceedingMin” (encoded as value=241) and “ExceedingMax” (encoded as

value=242), respectively. Please see Figure 2 for illustration, and Table 4 listing the

encoding.

3.2.2 Product Masks

The Sentinel-1 SSM1km algorithm does not apply, or rather does not make sense, for all

surfaces. It is straightforward to mask out water surfaces to remove misleading values over

the sea, lakes and rivers. This is facilitated with the Sentinel-1 CSAR backscatter

parameters.

A water mask, generated from Sentinel-1 CSAR time series, is directly applied to the product

dataset, as all water pixels are set to the flag value “WaterMask” (encoded as value =251).

The sensitivity S40 is an important measure for the accuracy and reliability of the S-1 SSM

algorithm. Therefore, a sensitivity mask is generated, masking out pixels with low

sensitivity. This yields a mask that detects urban areas, rivers, and dense forests. This mask

is directly applied to the product dataset, setting all pixels with low sensitivity to the flag value

“SensitivityMask” (encoded as value =252).




Highly undulated terrains challenges SAR processing and cause errors in the backscatter

values. Areas with high elevation slope are identified by the slope mask. It indicates pixels

with an elevation slope higher than 30% (equivalent to a gradient of 17°). This mask is

directly applied to the product dataset, setting all pixels with extreme terrain slope to the flag

value “SlopeMask” (encoded as value =253). Please see Figure 2 for illustration, and Table

4 listing the encoding.

3.3 LIMITATIONS OF THE PRODUCT

The current algorithm to retrieve SSM from Sentinel-1 CSAR does not account for vegetation

dynamics. This can lead to biases in the soil moisture, during the vegetation full development

period especially over areas with high density (e.g. forests).

Soil moisture cannot be retrieved over deserts and high vegetation areas like tropical forests.

Although a terrain correction is performed, it does not completely remove the influence of

topography. Especially over high mountain ranges this limitation comes into effect and thus

the SlopeMask is applied.

In addition, no reliable soil moisture measurements can be done during frozen or snow

covered conditions. In this first version, no specific mask or flag is in place for these

conditions (e.g. frozen soil, snow cover, open water). The “ExceedingMin” value flag give

some indication on frozen/snow soil conditions, but it is left to the user to judge whether the

SSM1km data is meaningful or not during the cold period in his region of interest. Users of

SSM1km are advised to use the best auxiliary data available to improve the flagging of snow,

frozen soil and (temporary) standing water.

However, under the following conditions a SSM retrieval from Sentinel-1 should be most

suited:

low to moderate vegetation regimes

unfrozen and no snow

low to moderate topographic variations

Linear artefacts can be seen in the individual SSM images. Although a calibration is

performed by ESA on the three parallel sub-swaths, a number of orbits are still affected by

scalloping (a bias in the backscatter per sub-swath). Although visually un-appealing to the

user, the error is of low magnitude and has only little effect on the temporal signal.

Lastly, some images are affected by Radio Frequency Interference (RFI) stemming from

ground based C-band transmitting systems. Unfortunately, the RFI cannot be removed by

ESA or by the SSM1km algorithm at the moment.

A more detailed discussion of the limitations can be found in the

CGLOPS1_ATBD_SSM1km-V1 document and in Bauer-Marschallinger et al. (2018).




3.4 DIFFERENCES WITH PREVIOUS VERSIONS

The differences between successive versions of the SSM 1km product are explained in the

table below.

Table 1: Algorithm differences between successive SSM1km versions

Versions Algorithm Differences

1.0 Initial version with model reference period 2015-2017

1.1 Model reference period 2015-2018




4 PRODUCT DESCRIPTION

4.1 FILE NAMING

The Surface Soil Moisture 1km version 1 products follow the following naming standard:

c_gls_SSM1km_<YYYYMMDDHHMM>_CEURO_S1CSAR_<VX.Y.Z>.nc

where

<YYYYMMDDHHMM> gives the temporal location of the file. YYYY, MM, DD, HH,

and MM denote the year, the month, the day, the hour, and the minutes, respectively.

Note that HHMM is always 0000 (0h UTC).

<AREA> gives the geographic coverage of the file. In this first version, <AREA> is

CEURO, denoting continental Europe (Table 3).

<VX.Y.Z> shows the processing line version used to generate these SSM products.

The version denoted as M.m.r (e.g. 1.1.1), with ‘M’ representing the major version

(e.g. V1), ‘m’ the minor version (starting from 0) and ‘r’ the production run number

(starting from 1) (Table 2)

Table 2: Explanation in version numbering and recommendations for using efficiently the

products

Versions Differences Recommendations

Major Significant change to the algorithm.

Do not mix various major versions in the

same applications, unless it is otherwise

stated.

Minor Minor changes in the algorithm Can be mixed in the same applications, but

require attention or modest modifications

Run Fixes to bugs and minor issues. Later

run automatically replaces former Consider it as a drop-in replacement

4.2 FILE FORMAT

The SSM products are delivered as a set of files:

An internally compressed, multiband Network Common Data Form version 4

(netCDF4) with metadata attributes compliant with version 1.6 of the Climate &

Forecast conventions (CF V1.6) containing the following full-resolution bands:

o ssm: surface soil moisture

o ssm_noise: surface soil moisture noise




An xml file containing the metadata conforms to INSPIRE2.1.

A quicklook in a coloured geo-tiff format. The quicklook sub-sampled to 25% in both

directions from the ssm layer.

For a more user-friendly view of the XML contents, please download the XSL transformation

file from https://land.copernicus.eu/global/sites/cgls.vito.be/files/xml/c_gls_ProductSet.xsl.

Then place the XSL file in the same folder as the XML file and open the XML file in your

favourite web browser

Each product covers the geographic area indicated by the bounding box coordinates in the

following table.

Table 3: Geographic coverage

Region Coordinates

CEURO / continental Europe Longitude: 11°W to 50°E

Latitude: 72°N to 35°N

4.3 PRODUCT CONTENT

4.3.1 Data files

The digital values are not stored in its physical dimension (e.g. relative soil moisture in

percent), but they are encoded into unsigned byte data type. The physical values (PhyVal)

are retrieved from the Digital Numbers (DN) through the following equation, with

scale_factor, add_offset given in Table 4.

PhyVal = DN * Scale_factor + add_offset

As an example, a digital ssm value of 50 indicates a relative surface soil moisture of 25%.

Table 4: General value encoding of SSM1km data files.

Variable Type Min physical value

Max physical value

Max digital (DN) value

Scale Factor

Add_offset Unit No-Data Value

ssm Unsigned byte 0 100 200 0.5 0 % 255

ssm_noise Unsigned byte 0 100 200 0.5 0 % 255

The digital value 255 is used to indicate where the SSM is not retrieved (no-data). Flagged

pixels, for which the above encoding (scaling) doesn’t apply, are summarized in Table 5.

https://land.copernicus.eu/global/sites/cgls.vito.be/files/xml/c_gls_ProductSet.xsl




Table 5: Overview of flag values and meanings

Flag value Name Description

241 ExceedingMin Exceeding local pixel’s dry extreme

242 ExceedingMax Exceeding local pixel’s wet extreme

251 WaterMask Masked water surface

252 SensitivityMask Masked for low sensitivity S40

253 SlopeMask Masked for extreme slope (topography)

An example SSM1km image over Europe (with added country borders) is shown in Figure 1.

The image includes all Sentinel-1 observations from the day 2018-11-26, consisting of six

individual satellite overpasses on that day.

The same dataset is displayed in Figure 2 with masks and flags in colours, highlighting the

encoding of the values and flags (water mask, slope mask, etc.).




Figure 1: Visual impression of the SSM1km dataset (CEURO extent) from 2018-11-26, as read

from the netCDF4 file and displayed in QGIS. With general encodings of values and flags.




Figure 2: SSM1km (CEURO extent) dataset from 2018-11-26, with encodings of values and

flags. Flags are displayed in color for visualization.

The netCDF files contain a number of metadata attributes

on the file-level (Table 6);

on the level of the data variable (Table 7);

at the level of the coordinate variables for latitude (‘lat’) and longitude (‘lon’) (Table 8);

at the level of the coordinate variable for time dimension (‘time’), holding one value for

the reference or nominal date (Table 9);

at the level of the grid mapping (spatial reference system) variable (‘crs’) (see Table

10)




Table 6: Global attributes of the SSM1km product.

Attribute name Description Example

Conventions Version of the CF-Conventions used CF-1.6

archive_facility Institution that archives the product VITO

copyright Copyright notice, to be used when referring to the data source of this product in publications

Copernicus Service information 2019

geospatial_lat_max Minimum Latitude +72

geospatial_lat_min Maximum Latitude +35

geospatial_lon_max Minimum Longitude +50

geospatial_lon_min Maximum Longitude -11

history Describes the lineage 2019-04-08 -- Near Real Time Production

identifier Unique identifier for the product urn:cgls:global:ssm_v1_1km:SSM1km_201904010000_CEURO_S1CSAR_V1.1.1

institution Institution that produced the product TU Wien

model_parameter_baseline Reference years for the SSM model 2015 - 2018

long_name Extended product name Surface Soil Moisture

orbit_type Orbit type of the orbiting platform(s) LEO

parent_identifier Identifier of the product collection for the product in Copernicus Global Land Service metadata catalogue

urn:cgls:global:ssm_v1_1km

platform Name(s) of the orbiting platform(s) used. One of “Sentinel-1A”, “Sentinel-1B” and “Sentinel-1 A+B”

Sentinel-1 A+B

pixel_size Size of a single pixel (in square) 0.0089285714 degrees

processing_level Product processing level L2

processing_mode

Processing mode used when generating the product. One of Near-Real Time, Consolidated, Offline or Reprocessing

Near Real Time

product_version Version of the product (VM.m.r) V1.1.1

references Published references that describe the data or methods used to produce it.

https://land.copernicus.eu/global/products/ssm

region_name Name of the geographic region covered

CEURO

sensor Name(s) of the sensor(s) used CSAR

source The method of production of the original data

Derived from EO radar observations

time_coverage_end End date and time of the temporal coverage of the input data

2019-04-01T23:59:59Z

time_coverage_start Start date and time of the temporal coverage of the input data.

2019-04-01T00:00:00Z

title A description of the contents of the file Daily Surface Soil Moisture 1km: CEURO 2019-04-01T00:00:00Z




Table 7: Description of netCDF attributes for the main ssm data variable

Attribute Description Examples values

CLASS Dataset type DATA

_FillValue Single value used to represent missing or undefined data and to pre-fill memory space in case a non-written part of data is read back.

255

_Unsigned Hint to processing software that an unsigned integer data type is used

true

coordinates References the coordinate variables time lat lon

flag_values Values imprinted in the dataset, indicating flags and masks of the measurement, as defined by flag_meanings

241, 242, 251, 252, 253

flag_meanings Meanings of the corresponding item of flag_values

ExceedingMin, ExceedingMax, WaterMask, SensitivityMask,

SlopeMask

grid_mapping Reference to the grid mapping variable crs

long_name A descriptive name that indicates a variable’s content. This name is not standardized.

Surface Soil Moisture

missing_value Single value used to represent missing or undefined data, for applications following older versions of the standards.

255

scale_factor Multiplication factor for the variable’s contents that must be applied in order to obtain the physical values.

0.5

units Units of the variable’s content, conforming to Unidata’sudunits library.

%

valid_range

Smallest and largest values for the variable.

Missing data is to be represented by one or several values outside of this range.

0, 200




Table 8: Description of netCDF attributes for coordinate variables (latitudes and longitudes)

Attribute Description Example

CLASS Dataset type DIMENSION_SCALE

standard_name

A standardized name that references a description of a variable’s content in CF-Convention’s standard names table. Note that each standard_name has corresponding unit (from Unidata’s udunits).

longitude

long_name A descriptive name that indicates a variable’s content. This name is not standardized. Used when a standard name is not available (FDOB layers).

longitude

units Units of a the variable’s content, taken from Unidata’s udunits library.

degrees_east

axis Identifies latitude, longitude, vertical, or

time axes. X

valid_range Smallest and largest valid values for the variable. -11, 50

Table 9: Description of netCDF attributes for time coordinate variable


CLASS Dataset type DIMENSION_SCALE

standard_name

A standardized name that references a description of a variable’s content in CF-Convention’s standard names table. Note that each standard_name has corresponding unit (from Unidata’s udunits).

time

long_name A descriptive name that indicates a variable’s content. This name is not standardized. Required when a standard name is not available.

time

units Units of a the variable’s content, taken from Unidata’s udunits library.

days since 1970-01-01 12:00:00

axis Identifies latitude, longitude, vertical, or

time axes. T

calendar Specific calendar assigned to the time variable, taken from the Unidata’s udunits library.

standard




Table 10: Description of netCDF attributes for the grid mapping variable


GeoTransform Six coefficients for the affine transformation from pixel/line space to coordinate space, as defined in GDAL’s GeoTransform

-11 0.008928571428571428 0 72 0 -0.008928571428571428

grid_mapping_name Name used to identify the grid mapping latitude_longitude

inverse_flattening

Used to specify the inverse flattening (1/f) of the ellipsoidal figure associated with the geodetic datum and used to approximate the shape of the Earth

298.257223563

long_name A descriptive name that indicates a variable’s content.

CRS definition

longitude_of_prime_meridian

Specifies the longitude, with respect to Greenwich, of the prime meridian associated with the geodetic datum

0.0

semi_major_axis

Specifies the length, in metres, of the semi-major axis of the ellipsoidal figure associated with the geodetic datum and used to approximate the shape of the Earth

6378137.0

spatial_ref Spatial reference system in OGC’s Well-Known Text (WKT) format

GEOGCS["WGS 84",DATUM["WGS_1984",… AUTHORITY["EPSG","4326"]]

4.3.2 Quicklook

The quicklook is a mean of giving a first impression of the dataset (e.g. when browsing) and

should not be used in analysis.

The quicklook file is a coloured GeoTIFF image showing soil moisture, sub-sampled to 25%

in both directions. Its filename follows a similar naming scheme as the main netCDF4 data

file:

c_gls_SSM1km_QL_<YYYYMMDDHHMM>_<AREA>_S1CSAR_<VX.Y.Z>.tiff

Its colour coding illustrates wet and dry conditions and neglects flags and masks. An

example quicklook and its colour coding are shown in

Figure 3.

http://www.gdal.org/gdal_datamodel.html




Figure 3: Quicklook image (CEURO extent) of the ssm dataset from 2018-11-26. Blue colours

represent wet soil conditions, and brown colours dry conditions.

4.3.3 Tips on netCDF4 files:

1. Download and install netCDF4 library from Unidata.

The library comes with useful tools to discover the organization and contents of the netCDF4

files. For example, you can use the “ncdump” line command to view the hierarchical

organization of the files (using -h option for header information only, as below):

ncdump -h c_gls_SSM1km_201411080000_CEURO_S1CSAR_V1.1.1.nc

2. Download a viewer called Panoply from the NASA panoply website.

This tool is more user friendly oriented than the command-line based tools.

3. Another option is to use the GDAL library from http://www.gdal.org/.

http://www.unidata.ucar.edu/downloads/netcdf/index.jsp

http://www.hdfgroup.org/

http://www.hdfgroup.org/

https://www.giss.nasa.gov/tools/panoply/

http://www.gdal.org/




This library is often bundled with open source geospatial software, such as QGIS, and comes

with useful tools that could be used to inspect and convert the contents of the netCDF4 files

into another format. For example, you can use the ‘gdalinfo’ tool to obtain a description of the

ssm variable (data layer) in the netCDF4 file

gdalinfo NETCDF:c_gls_SSM1km_201411080000_CEURO_S1CSAR_V1.1.1.nc:ssm

In QGIS, the same command is available from menu Raster – Miscellaneous – Information.

4.4 PRODUCT CHARACTERISTICS

4.4.1 Projection and Grid Information

The product is displayed in a regular latitude/longitude grid (plate carrée) with the ellipsoid

WGS 1984 (Terrestrial radius=6378km). The resolution of the grid is 1°/112 or approximately

1km at equator.

The CEURO or continental Europe window hence includes 6832 columns by 4144 rows.

4.4.2 Spatial Extent

The spatial extent of the product is specified in Table 3.

This first version of the product holds data only for the European continent on grounds of the

Sentinel-1 mission’s observation scenario (see also Figure 4 below). For areas outside

Europe, not enough observations are currently available to generate stable SSM retrieval

parameters.

4.4.3 Temporal Information

The SSM1km product is a daily composite, available from Jan 2015 onwards and updated

daily with new data from the Sentinel-1A and -1B satellites acquired between 00:00UTC

23:59 UTC. The part “YYYYMMDDHHMM” of the product file name gives the date of the

representative or nominal day at 00:00UTC, to be considered in analysis and time series

construction.

Sentinel-1A and -1B have a fixed observation pattern bound to the satellite’s orbit.

Consequently, Sentinel-1 senses the Earth’s surface differently often and the SSM1km data

density is not uniform. Figure 4 shows the number of observations (revisit) from Sentinel-1A

that was processed to SSM until October 2016. Areas with more observations have a higher

temporal observation frequency. The inclusion of Sentinel-1B does not alter this pattern.




Figure 4: Number of processed observations of Sentinel-1A for SSM retrieval over the period

2014/Oct – 2016/Oct.

From the observation scenario, the satellite sensor does not capture the whole area in 24h.

Consequently, portions of the daily SSM1km file are filled with the no-data-value (255). The

temporal frequency of valid SSM values depends on the individual location, and on the

period:

2015 - Oct 2016: per location one measurement each 3-8 days over Europe.

Oct 2016 – ongoing: per location one measurement each 1.5-4 days over

Europe.

Figure 5 shows the revisit and coverage frequency of the Sentinel-1 mission when operating

with two satellites, as specified by ESA. With the inclusion the Sentinel-1B data starting in

October 2016, the coverage frequency over Europe reduces effectively to 1.5-4 days,

depending on the individual location in relation to the non-uniform coverage pattern (Figure

4).




Figure 5: Sentinel-1A+B revisit and coverage frequency, as specified by ESA on 02/2018.

There are ascending and descending satellite overpasses. Especially in areas with high

latitude, more than one overpass per 24h is possible. Here, simply the latest SSM estimate

per day is selected, which is commonly the afternoon overpass around 18h local time.

4.5 DATA POLICIES

EU law1 grants free and open access to Copernicus Sentinel Data and Service Information,

which includes Global Land Service products, for the purpose of the following use in so far as

it is lawful:

a) reproduction;

b) distribution;

c) communication to the public;

d) adaptation, modification and combination with other data and information;

e) any combination of points (a) to (d).

EU law allows for specific limitations of access and use in the rare cases of security concerns, protection of third party rights or risk of service disruption.

1 European Commission, Regulation (EU) No 377/2014 and Commission Delegated Regulation (EU) No

1159/2013.




By using Service Information the user acknowledges that these conditions are

applicable to him/her and that the user renounces to any claims for damages against

the European Union and the providers of the said Data and Information. The scope of

this waiver encompasses any dispute, including contracts and torts claims that might

be filed in court, in arbitration or in any other form of dispute settlement.

Where the user communicates to the public on or distributes the original SSM1km products,

he/she is obliged to refer to the data source with (at least) the following statement (included

as the copyright attribute of the netCDF4 file):

Copernicus Service information [Year]

With [Year]: year of publication

Where the user has adapted or modified the products, the statement should be:

Contains modified Copernicus Service information [Year]

For complete acknowledgement and credits, the following statement can be used:

“The product was generated by the Global component of the Land Service of Copernicus, the

Earth Observation programme of the European Commission. The research leading to the

current version of the product has received funding from various European Commission

Research and Technical Development programs. This product has been generated from

Sentinel-1 data distributed by ESA.”

The user accepts to inform Copernicus about the outcome of the use of the above-mentioned

products and to send a copy of any publications that use these products to the scientific &

technical support contact specified in the next section.

4.6 CONTACTS

The SSM1km products are available through the Copernicus Global Land Service website at

the address: https://land.copernicus.eu/global/products/ssm

Accountable Contact: European Commission, Directorate-General Joint Research Centre,

Italy

Email address: [email protected]

Scientific & Technical support contact:

https://land.copernicus.eu/global/contactpage.

https://land.copernicus.eu/global/products/ssm

mailto:[email protected]

https://land.copernicus.eu/global/contactpage




5 VALIDATION

5.1 VALIDATION STUDY 2017 - EUROPE

A validation study for the SSM1km product over Europe was carried out in April 2017 and

reported in CGLOPS1_VR_SSM1km-V1. The satellite product describing in daily global

images the surface soil moisture (in relative units (%)) was compare against in-situ

measurements (in absolute values (m³/m³)) from field station networks located at six

European sites in Austria, Finland, France, Germany, Romania and Spain, totalling 127

stations.

The quality of the SSM1km was assessed in the temporal and in the spatial domain,

meaning that the quality of the temporal signal and the spatial signal was analyzed

individually. In addition, the masks for the SSM1km were investigated in their ability to

identify locations where SSM1km retrieval algorithm is not applicable.

The temporal analyses presented poor to medium performances of the product when

compared against in-situ data, albeit the quality of the in-situ data itself was not questioned.

While there is acceptable agreement with stations in Austria, Germany, Romania and

Finland, little agreement was found with data from Spain and France. Due to the temporal

frequency of the SSM1km, which that is eminently inferior to the one of the in-situ

observations, the temporal signal is inevitably of lower quality. Many events, leading to peaks

in the in-situ data, are missed by the satellite product. This is considered as the most

impacting drawback of the SSM1km. However, the overall level and long-term development

of soil moisture are mostly captured. Putting the present study in relation to similar validation

campaigns, the obtained correlation results undercut only little the results from other

remotely sensed soil moisture data

The spatial analyses show in contrast a much higher agreement, with almost coincident soil

moisture patterns in the data from in-situ and SSM1km in the Spain-, Finland- and France-

validation sites. Daily soil moisture maps agree very well, meaning that in the network area,

the relative soil moisture differences at a time are well captured by the satellite data. This is

not unexpected, as this is in accordance with high quality of the Sentinel-1 image data due to

its spatial detail. The product shows higher potential to describe the variation of soil moisture

in space than other remotely sensed datasets.

The SSM Water Mask and the SSM Sensitivity Mask show good results when compared

against land cover data. The Sensitivity Mask performs well and manages to identify city

areas, but leaves some room for improvement. The SSM Water Mask does not miss any

water pixel, practically, but only overestimates the water area slightly, which is rather a

safeguard to the SSM product.

Overall, the SSM1km meets the expectations as it achieves to reflect the spatial dynamics of

soil moisture at the kilometric scale. On that matter, the product shows more potential than

comparable products. However, the performance overt time against time series data from in-




situ point measurements is rather weak and leaves room for improvement. When using the

average RMSD values, the goal of 0.04 m³/m³ from the GCOS requirements (Table 11) is

reached only at two out of six validation sites. As expected, the product does not deliver

information that can be used reliably to determine the absolute soil moisture at the point

scale. A true validation of the SSM1km would demand an extensive reference dataset at the

kilometric scale, which was not available at the time of this study. The goal for the product’s

stability cannot be validated in Version 1, as one year of data is not sufficient.

However, the outlook is promising, as the parameter database for the Sentinel-1 SSM

retrieval is growing with time since mission start and thus is ever-improving. The longer the

available time series, the more robust are the parameters for soil moisture estimation.

Furthermore, a potential inclusion of Sentinel-1B data will improve the observation frequency

by the factor two and promises a much higher dynamic in the temporal signal. Consequently,

peaks and lows of the soil moisture variation would be captured more successfully, yet

addressing the product’s most impacting shortcoming.

5.2 EVALUATION EXPERIMENTS 2018 - ITALY

A recent peer-reviewed publication (Bauer-Marschallinger et al., 2018) explains first in detail

the SSM1km retrieval algorithm and then investigates its performance over Italy and its

neighbours. Over that area, which comprises challenging terrains and heterogeneous land

cover, a consistent set of model parameters and product masks was generated, unperturbed

by coverage discontinuities. An evaluation of therefrom generated SSM1km data, involving

in-situ measurements and a 1km Soil Water Balance Model (SWBM) over Umbria, yielded

high agreement over plains and agricultural areas, with low agreement over forests and

strong topography. These results are in alignment with the previous study over Europe in

2017 [CGLOPS1_VR_SSM1km-V1]. Also here, positive biases during the growing season

were detected. However, excellent capability to capture small-scale soil moisture changes as

from rainfall or irrigation is evident from this evaluation.




6 REFERENCES

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

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

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

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

Hornáček, M., Wagner, W., Sabel, D., Truong, H.-L., Snoeij, P., Hahmann, T., Diedrich, E.,

Doubková, M., 2012. Potential for high resolution systematic global surface soil

moisture retrieval via change detection using Sentinel-1. Sel. Top. Appl. Earth Obs.

Remote Sens. IEEE J. Of 5, 1303–1311.

Wagner, W., 1998. Soil moisture retrieval from ERS scatterometer data. European

Commission, Joint Research Centre, Space Applications Institute.




7 ANNEX: REVIEW OF USERS REQUIREMENTS

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

S1-SSM are:

Definition:

o Moisture content in the top 5 cm of the soil

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 baseline datasets pixel size shall be provided, depending on the final

product, at resolutions of 100m and/or 300m and/or 1km.

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: lat long

o geodetical datum: WGS84

o pixel size: 1/112° - accuracy: min 10 digits

o coordinate position: pixel centre

o global window coordinates:

Upper Left: 180°W-75°N

Bottom Right: 180°E, 56°S

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 11)

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.




Table 11: 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 50 km Daily 0.04 m3/m

3 0.01 m

3/m

3/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”.

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