Copernicus Global Land Operations · 2018. 12. 19. · In this Validation Report (VR), the new product Sentinel-1 1km Surface Soil Moisture (called SSM1km) is subject to validation

Copernicus Global Land Operations – Lot 1 Date Issued: 29.11.2018 Issue: I1.20

Copernicus Global Land Operations

“Vegetation and Energy” ”CGLOPS-1”

Framework Service Contract N° 199494 (JRC)

VALIDATION REPORT

SURFACE SOIL MOISTURE

COLLECTION 1KM

VERSION 1

Issue I1.20

Organization name of lead contractor for this deliverable: TU Wien

Book Captain: Bernhard Bauer-Marschallinger

Contributing Authors: Stefan Schaufler

Claudio Navacchi


Document-No. CGLOPS1_VR_SSM1km-V1 © C-GLOPS Lot1 consortium

Issue: I1.20 Date: 29.11.2018 Page: 2 of 54

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 29.11.2018

Approval: Roselyne Lacaze Sign Date 03.12.2019

Endorsement: Michael Cherlet Sign Date

Distribution: Public




Change Record

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

06.04.2017 All First version I1.00

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

I1.10 29.11.2018 All Update related to product public release I1.20




TABLE OF CONTENTS

Executive summary .................................................................................................................. 12

1 Background of the document ............................................................................................. 13

1.1 Scope and Objectives............................................................................................................. 13

1.2 Content of the document....................................................................................................... 13

1.3 Related documents ............................................................................................................... 13

1.3.1 Applicable documents ................................................................................................................................ 13

1.3.2 Input ............................................................................................................................................................ 14

1.3.3 Output ......................................................................................................................................................... 14

1.3.4 External documents .................................................................................................................................... 14

2 Review of Users Requirements ........................................................................................... 15

3 Validation Method ............................................................................................................ 17

3.1 The SSM1KM Dataset ............................................................................................................ 17

3.1.1 Soil Moisture Retrieval ................................................................................................................................ 19

3.1.2 Product Value Flags ..................................................................................................................................... 19

3.1.3 Product Masks ............................................................................................................................................ 20

3.1.4 Limitations of the Product .......................................................................................................................... 22

3.2 In-situ Reference Products ..................................................................................................... 23

3.2.1 Used Networks ............................................................................................................................................ 23

3.2.2 Spatial Representativeness ......................................................................................................................... 25

3.3 Auxilliary Products ................................................................................................................ 26

3.4 Dataset Preparations ............................................................................................................. 27

3.4.1 Temporal Resampling ................................................................................................................................. 27

3.4.2 Temporal Matching ..................................................................................................................................... 27

3.4.3 Data Scaling ................................................................................................................................................. 28

3.5 Validation Procedure ............................................................................................................. 28

3.5.1 Temporal Analyses ...................................................................................................................................... 28

3.5.2 Spatial Analyses .......................................................................................................................................... 28

3.5.3 Mask Quality Assessment ........................................................................................................................... 29

4 Results .............................................................................................................................. 30

4.1 Visual Inspection of Datasets ................................................................................................. 30

4.1.1 Example Images Series ................................................................................................................................ 31

4.2 Temporal Analyses ................................................................................................................ 35




4.2.1 Time Series Comparison ............................................................................................................................. 35

4.2.2 Overall Temporal Statistics ......................................................................................................................... 37

4.3 Spatial Analyses .................................................................................................................... 40

4.3.1 Spatial Correlation Time Series ................................................................................................................... 40

4.3.2 Overall Spatial Statistics .............................................................................................................................. 43

4.4 Validation of SSM Masks ....................................................................................................... 44

5 Conclusions ....................................................................................................................... 49

5.1.1 Update 2018: Evaluation Experiments over Italy ....................................................................................... 50

6 Recommendations ............................................................................................................. 51

7 References ........................................................................................................................ 53




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.

Overlaid country borders for orientation. ................................................................................ 18

Figure 2: SSM1km Water Mask over Europe (with country borders for orientation). Pixels in dark

blue masked in the SSM1km data. ......................................................................................... 20

Figure 3: SSM1km Sensitivity Mask over Europe (with country borders for orientation). Pixels in

orange masked in the SSM1km data. .................................................................................... 21

Figure 4: SSM1km Elevation Slope Mask over Europe (with country borders for orientation). Pixels

in orange masked in the SSM1km data. ................................................................................. 22

Figure 5: Overview map on ISMN networks used in the validation. With colored dots for individual

stations. ................................................................................................................................. 25

Figure 6: Detail of , SSM1km at 2015-06-11 over Eastern Europe. White areas are not covered on

this day. ................................................................................................................................. 30

Figure 7: SSM1km at 2015-09-01, with ISMN stations used for validation (colored dots). ............. 32



Figure 10: SSM1km at 2015-09-04, with ISMN stations used for validation (colored dots). ........... 33



Figure 13: SSM1km and soil moisture in Volumetric Soil Moisture [m³/m³] from in-situ over the year

2015. Periods with frozen conditions show no data. ............................................................... 35

Figure 14: As Figure 13. ................................................................................................................ 36

Figure 15: As Figure 13. ................................................................................................................ 36

Figure 16: As Figure 13. ................................................................................................................ 36

Figure 17: As Figure 13. ................................................................................................................ 37

Figure 18: As Figure 13. ................................................................................................................ 37

Figure 19: Violin plot summarizing Spearman rho values calculated for individual stations over the

six networks. The wider the violin at a vertical position, the higher is the frequency of that rho

value. Horizontal dashed lines in the violins indicate quartile values (q25, median, q75). ...... 38

Figure 20: As Figure 19, but summarizing RMSD values. ............................................................. 39




Figure 21: Spatial Pearson R between in-situ and SSM1km for the year 2015. Days with no data,

frozen conditions, or not enough values per day, show no data. ............................................ 41

Figure 22: As Figure 21. ................................................................................................................ 41

Figure 23: As Figure 21. ................................................................................................................ 42

Figure 24: As Figure 21. ................................................................................................................ 42

Figure 25: As Figure 21. ................................................................................................................ 43

Figure 26: Map over northern Greece, showing the Error of Omission (Underestimate), Error of

Commission (Overestimate) and correct classified pixels by the SSM Water Mask. ............... 45

Figure 27: Confusion matrix of the SSM Water Mask (Prediction) vs CORINE Water classes

(Reference). Values are percent of all 1km pixels in the validation area. Lower-right corner

value shows total agreement in percent. ................................................................................ 46

Figure 28: Confusion matrix of the SSM Sensitivity Mask (Prediction) vs CORINE Urban Fabric

classes (Reference). Values are percent of all 1km pixels in the validation area. Lower-right

corner value shows total agreement in percent. ..................................................................... 47

Figure 29: Map over Austrian area, showing the Error of Omission (Underestimate), Error of

Commission (Overestimate) and correct classified pixels by the SSM Sensitivity Mask. ........ 48




List of Tables

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

Variable (GCOS-154, 2011) ................................................................................................... 16

Table 2: ISMN networks used in the present SSM1km validation campaign with characteristics as

given in the documentation. ................................................................................................... 24

Table 3: CORINE Land Cover classes subsumed as “Urban Fabric” for validation of SSM

Sensitivity Mask ..................................................................................................................... 26

Table 4: CORINE Land Cover classes subsumed as “Water” for validation of SSM Water Mask. . 27

Table 5: Average Temporal rho values and RMSD values for the year 2015 at the six in-situ

networks. ............................................................................................................................... 40

Table 6: Average Spatial R values for the year 2015 at the five suitable in-situ networks. ............. 43




List of Acronyms

AD Applicable Document

ASAR Advanced Synthetic Aperture Radar (Envisat satellite)

ATBD Algorithm Theoretical Base Document

CDF Cumulative Distribution Function

CGLS Copernicus Global Land Service

CORINE Coordination of Information on the Environment program of the EU

CSAR Dual Pol C-Band (5.405GHz) Synthetic Aperture Radar onboard Sentinel-1

EC European Commission

ERS European Radar Satellite

ESA European Space Agency

FAPAR Fraction of Absorbed Photosynthetically Active Radiation

FMI-ARC Finish Meteorological Institute Arctic Research Centre

FZJ Forschungszentrum Jülich

GCOS Global Climate Observing System

GIO GMES/Copernicus Initial Operations

GLDAS Global Land Data Assimilation System

HOAL Hydrological Open Air Laboratory

ISMN International Soil Moisture Network

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

LAI Leaf Area Index

MetOp-ASCAT SWI-V3 Meteorological Operational Satellite Advanced Scatterometer Soil Water

Index Version 3

NASA National Aeronautics and Space Administration

PUM Product User Manual

REMEDHUS Red de Estaciones de Medición de la Humedad def Suelo

RFI Radio Frequency Interference

RMSD Root Mean Square Difference

RMSN Continuous Soil Moisture & Temperature Ground-based Observation Network

S-1 Sentinel-1

SAR Synthetic Aperture Radar

SCATSAR-SWI Scatterometer-Synthetic Aperture Radar Soil Water Index

SMOSMANIA Soil Moisture Observing System - Meteorological Automatic Network

Integrated Application

SRTM90 Shuttle Radar Topography Mission - 90 Meters

SSD Service Specification Document

SSM Surface Soil Moisture

SVP Service Validation Plan

SWI Soil Water Index

TERENO Terrestrial Environmental Observatories

TU Wien Technische Universität Wien




UNFCCC United Nations Framework Convention on Climate Change

VR Validation Report




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 a series of bio-

geophysical products describing the status and evolution of land surface at global scale. Essential

Climate Variables like the Leaf Area Index (LAI), the Fraction of PAR absorbed by the vegetation

(FAPAR), the surface albedo, the Land Surface Temperature, the surface soil moisture (SSM) and

soil water index (SWI), the burnt areas, the areas of water bodies, and additional vegetation

indices, are generated every hour, every day or every 10 days from Earth Observation satellite

data.

Validation and continuous Quality Monitoring constitute the only means of guaranteeing the

compliance of generated products with user requirements:

• The former concerns the new products or new versions of products, which must pass an

exhaustive scientific evaluation before to be implemented operationally.

• The latter concerns the operational products to check their quality keeps at the same level

along the time.

Both follow, as much as possible, the guidelines, protocols and metrics defined by the Land

Product Validation (LPV) group of the Committee on Earth Observation Satellite (CEOS) for the

validation of satellite-derived land products.

In this Validation Report (VR), the new product Sentinel-1 1km Surface Soil Moisture (called

SSM1km) is subject to validation experiments. For the year 2015, the dataset is compared to in-

situ data from suitable networks over Europe of the International Soil Moisture Network (ISMN).

The analyses comprise examinations of the spatial and temporal dynamics of the SSM1km and

how it agrees with the reference data. The results show low to moderate agreement in temporal

dynamics of soil moisture data from in-situ stations with the satellite product, but show on the other

hand very high agreement in spatial dynamics. These outcomes confirm initial expectations, as 1)

other soil moisture datasets from remote sensing show also a comparable level of agreement in

temporal dynamics and as 2) the product is anticipated to profit from the high spatial detail of the

Sentinel-1 image data.




1 BACKGROUND OF THE DOCUMENT

1.1 SCOPE AND OBJECTIVES

The document presents the results of the scientific validation of Surface Soil Moisture Collection

1km Version 1 product (named hereafter SSM1km) derived from Sentinel-1 data. This product

provides daily global soil moisture images from October 2014 ongoing (but only valid data for

Europe in Version1). The validation is done for the year of 2015 and comprises analyses of the

temporal and spatial signal quality of the product.

The objective is to check that the product fulfils the requirements of the Copernicus program and

that the data quality meets the needs of the users.

This study period covers 12 months from 01.01.2015 to 31.12.2015. During this period, all input

and validation datasets are available and without large temporal gaps. Geographically, the study is

limited to six areas spread over Europe, where suitable in-situ station networks are available.

1.2 CONTENT OF THE DOCUMENT

This document is structured as follows:

Chapter 2 recalls the users requirements, and the expected performance

Chapter 3 describes the methodology for quality assessment, the metrics and the criteria of

evaluation

Chapter 4 presents the results of the analysis

Chapter 5 summarizes the main conclusions of the study

Chapter 6 makes recommendations based upon the results

1.3 RELATED DOCUMENTS

1.3.1 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.




1.3.2 Input

Document ID Descriptor

CGLOPS1_SSD Service Specifications of the Global Component of

the Copernicus Land Service.

CGLOPS1_SVP Service Validation Plan of the Copernicus Global

Land Service

CGLOPS1_ATBD_SSM1km-V1 Algorithm Theoretical Basis Document of the

Collection 1km Surface Soil Moisture Version 1

product

GIOGL1_QAR_SCATSAR-SWI Quality Assessment Report of the CGLS 1km

SCATSAR SWI

1.3.3 Output


CGLOPS1_PUM_SSM1km-V1 Product User Manual summarizing all information about the

Collection 1km Surface Soil Moisture Version 1 product

1.3.4 External documents


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




2 REVIEW OF USERS REQUIREMENTS

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

SSM1km 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”.

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 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 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”.




3 VALIDATION METHOD

3.1 THE SSM1KM DATASET

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 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.

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.




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. Overlaid country

borders for orientation.




3.1.1 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, further developed for SAR by Pathe et al., 2009 and adapted by Bauer-Marschallinger et al.,

2018, 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 for the production necessary parameters (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 in percent.

3.1.2 Product Value Flags

This linear scaling in the SSM1km algorithm depends on statistically derived reference values for

dry and wet soil at each pixel. In most cases, the 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

flag values (“ExceedingMin”=241 and “ExceedingMax”=242, respectively).

3.1.3 Product Masks

The Sentinel-1 SSM 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 SAR backscatter parameters. Here, the water mask is created

from the 5%-percentile parameter, where each pixel with a value lower than -17dB is classified as

water. In the SSM1km images, those pixels are set automatically to a flag value (=251), since SSM

cannot be retrieved over water. Figure 2 illustrates the water mask over Europe.

Figure 2: SSM1km Water Mask over Europe (with country borders for orientation). Pixels in dark blue

masked in the SSM1km data.

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. A

threshold of 1.2dB is used. This yields a mask that detects urban areas. In the SSM1km images,

those pixels are set automatically to a flag value (=252). Figure 3 illustrates the sensitivity mask

over Europe.




Figure 3: SSM1km Sensitivity Mask over Europe (with country borders for orientation). Pixels in

orange masked in the SSM1km data.

The terrain correction in the pre-processing step does not completely remove errors stemming

from topographic effects. To identify and mask out locations with high topographic complexity, an

analysis of the CGIAR-CSI SRTM 90m v4.1 is carried out and the elevation slope is computed with

GDAL’s gdaldem tools. From this elevation slope, a (binary) slope mask at 500m is produced,

masking pixels with an elevation slope higher than 30% (equivalent to a gradient of 17°). For the

resampling from 90m to 500m, gdalwarp cubic resampling method has been applied. In the

SSM1km images, those pixels are set automatically to a flag value (=253). Figure 4 illustrates the

slope mask over Europe.




Figure 4: SSM1km Elevation Slope Mask over Europe (with country borders for orientation). Pixels in

orange masked in the SSM1km data.

For more information on the retrieval, the reader is referred to the CGLOPS1_ATBD_SSM1km-V1

document. Also, a recent peer-reviewed publication (Bauer-Marschallinger et al., 2018) explains in

detail the SSM1km retrieval algorithm, underpinned by its scientific background.

For more information on the data formats and the user recommendations, the reader is referred to

the CGLOPS1_PUM_SSM1km-V1 document.

3.1.4 Limitations of the Product

The current algorithm to retrieve SSM from Sentinel-1 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 vegetation 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. At the moment (at this product 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

SSM algorithm at the moment.

A more detailed discussion of the limitations can be found in the CGLOPS1_ATBD_SSM1km

document and in the recent publication of Bauer-Marschallinger et al. (2018).

3.2 IN-SITU REFERENCE PRODUCTS

In situ data from the International Soil Moisture Network (ISMN) are used as a reference. The

ISMN provides a harmonized repository of in situ soil moisture observations (Website). This

harmonization makes it feasible to use in situ data from a wide array of networks. The data quality

of stations can vary widely and is not guaranteed to be consistent in time for any station. Wild

animals as well as farmers or other natural phenomena can lead to sensor failure or shifts in the

location which invalidate the sensor calibration. These errors are partly addressed by various

quality and consistency checks that were recently introduced by Dorigo et al. (2013) which aim to

automatically detect and flag jumps, plateaus and other unrealistic behavior in the data. Also,

checks for realistic maximum and minimum values are performed.

3.2.1 Used Networks

For the validation of the SSM1km, the ISMN have been browsed for networks holding data meeting

the following requirements:

Location in the SSM1km coverage area, for V1 this is (greater) Europe

Observations during the validation period, here the whole year 2015.

Measurement of surface-, or near-surface soil moisture

Featuring a spatial dense network configuration, allowing the spatial analyses.

This requirements yield the following selection of networks (Table 2). The RSMN stations are

excluded from the spatial analyses, as their mutual distances are too large and disparate. For the

same reason, the east part of SMOSMANIA is excluded from the spatial analyses.

https://ismn.geo.tuwien.ac.at/




Table 2: ISMN networks used in the present SSM1km validation campaign with characteristics as

given in the documentation.

Network Name Location Climatic + Surface

Character.

Main Soil

Characteristics

Altitude Suitable

Stations

Temporal

Analyses

Spatial

Analyses

Link

FMI-ARC Northern

Finland

Boreal climate, boreal

forests, wetland,

tundra sites

not specified ~180m 28 Yes Yes Website

HOAL Central

Austria

Humid temperate

climate; agricultural

use

Cambisols,

Plenosols

270m –

325m

24 Yes Yes Website

REMEDHUS Central

Spain

Continental, semi-arid

Mediterranean climate

“Sandy (71%)” 700m-

900m

19 Yes Yes Website

RSMN Romania Oceanic to

Continental temperate

climate

not specified ca. 100m –

ca. 300m

19 Yes No Website

SMOSMANIA Southern

France

Mediterranean climate “diverse” max: 538m 27 Yes West part Website

TERENO - FZJ Western

Germany

Humid temperate

climate; agricultural

use

“representative” ca. 100m –

ca. 200m

10 Yes Yes Website

Figure 5 shows the sites of the validation ISMN networks, as groups of colored dots.

http://fmiarc.fmi.fi/

http://hoal.hydrology.at/index.php?id=2

http://campus.usal.es/~hidrus/

http://assimo.meteoromania.ro/

https://www.hymex.org/

http://teodoor.icg.kfa-juelich.de/overview-de




Figure 5: Overview map on ISMN networks used in the validation. With colored dots for individual

stations.

3.2.2 Spatial Representativeness

When comparing remotely sensed and in situ data, one must consider spatial representativeness.

This means how well an in situ observation, which is essentially a point measurement, represents

the data gathered by the much larger satellite footprint. This is also different for each station and

depends on topography, soil types and microclimates.

For this validation, all datasets are kept with their native product samplings. However, results from

this validation need always be understood in reflection of the large difference of scales. In the case

of the present validation study of 1km data, this so-called representativeness error is assumed not

negligible and thus validation results need to be handled with care.

The comprehensive validation of remotely sensed soil moisture at kilometric scale is hampered by

the lack of comparable extensive datasets with similar spatial resolution. Model output at kilometric

scale describing soil moisture, even with limited extent, could provide a more accurate assessment

of the temporal and spatial SSM signal quality. Thus, future assessments of the temporal and the

spatial quality are intended to be complemented by comparisons to suitable future high-resolution

model output data.




3.3 AUXILLIARY PRODUCTS

Soil moisture can only be estimated meaningfully when the soil is not frozen or snow-covered. To

avoid statistics flawed by frozen conditions, the GLDAS (Global Land Data Assimilation System)

Noah Land Surface Model is consulted for identifying the frozen periods at the validation sites.

The GLDAS Noah Land Surface Model (Rodell et al. 2004) is produced by NASA. It includes 4 soil

layers with the bottom depth at 0.1, 0.4, 1 and 2 m respectively and simulates over 30 land surface

parameters in total. Temperature data is available with a temporal sampling of 3 hours on a global

0.25° grid.

For quality assessment of the SSM masks, the CORINE Land Cover 2012 was used. The raster

data was obtained from the EC via their website, projected and resampled to the space if the SSM

product with the GDAL nearest neighbor method. For the SSM Sensitivity Mask, which is supposed

to filter the area for urban areas, four relevant classes of CORINE (relevant considering SAR

sensitivity) are used as reference, subsumed under the name “Urban Fabric” (see Table 3). For the

SSM Water Mask, thirteen relevant classes of CORINE are used as reference, subsumed under

the name “Water” (see Table 4).

Table 3: CORINE Land Cover classes subsumed as “Urban Fabric” for validation of SSM Sensitivity

Mask

Surface Class Name Hierarchical Class Identifier

(Level 3) Value in Dataset

Continuous urban fabric 1.1.1 1

Discontinuous urban fabric 1.1.2 2

Industrial or commercial units 1.2.1 3

Road and rail networks and associated

land 1.2.2 4




Table 4: CORINE Land Cover classes subsumed as “Water” for validation of SSM Water Mask.

Surface Class Name Hierarchical Class Identifier

(Level 3) Value in Dataset

Moors and heathland 3.2.1 27

Beaches, dunes, and sand plains 3.3.1 30

Glaciers and perpetual snow 3.3.5 35

Inland marshes 4.1.1 36

Peatbogs 4.1.2 37

Salt marshes 4.2.1 38

Salines 4.2.2 39

Intertidal flats 4.2.3 40

Water courses 5.1.1 41

Water bodies 5.1.2 42

Coastal lagoons 5.2.1 43

Estuaries 5.2.2 44

Sea and ocean 5.2.3 45

NO DATA . 255

3.4 DATASET PREPARATIONS

3.4.1 Temporal Resampling

The SSM1km datasets are natively with daily timestamp. The reference in-situ datasets offer

generally a much higher sampling and are thus down-sampled for this validation to daily mean

values. Furthermore, potential diurnal jamming of in-situ soil moisture sensors through e.g.

temperature effects are removed.

3.4.2 Temporal Matching

For the temporal analyses, the individual (daily sampled) datasets are checked at each validation

location for no-data-values. Then both time series are matched, reducing them to days where both

time series have valid data. This allows the calculation of correlation coefficient- and RMSD-values

as measures of agreement.




3.4.3 Data Scaling

Since the SSM1km is in relative units and the validation products are in absolute units, a data

scaling was necessary to facilitate a valid comparison. Here, this was done with Cumulative

Distribution Function (CDF) -matching (Liu et al. 2011) of the SSM1km data to the local in-situ soil

moisture, yielding absolute soil water content in m³/m³, ranging commonly from 0.0 – 0.5. CDF-

matching matches the distributions of two time series and allows consequently direct comparison.

It is widely used for soil moisture applications (Reichle and Koster 2004, Scipal et al. 2008).

3.5 VALIDATION PROCEDURE

The validation follows the procedure described in the CGLOPS1_SVP document, section 4.1.8. In

particular for the SSM1km product, the following analyses are performed.

3.5.1 Temporal Analyses

For assessment of temporal signal quality, the SSM images are converted to time series. These

SSM1km time series are then compared with data from overlapping stations of the selected ISMN

networks, (which was monitoring in 2015). For the comparison, basic statistical measures are

calculated after scaling the SSM1km data (for unit conversion to absolute values). The Spearman

correlation coefficient (rho) and the Root Mean Square Difference (RMSD) are computed. They are

presented together with the time series in section 4.2.

3.5.2 Spatial Analyses

For assessment of spatial signal quality of the high resolution SSM, in-situ data from the ISMN

stations are re-gridded to the grid of the SSM1km images and then validated with basic spatial

statistics and spatial correlation analyses (Wilks, 2011; von Storch and Zwiers, 2001), yielding

RMSD and spatial Pearson correlation coefficient (R). Of course, these analyses can only be

carried out over spatially dense in-situ networks, dropping networks where the stations

geographically scattered.

For each selected network, the time series data of the in-situ stations is transformed to a daily

image series spanning the validation period. These daily images, shaped in the same format as the

SSM1km data, are then spatially correlated with the overlapping sections of SSM1km on the

corresponding day, yielding a time series of correlation coefficients. With this procedure, the spatial

agreement between the satellite products and local ground station data is estimated. However, due

to the small number of in-situ locations, and thus often leaving gaps in the 1km raster, these results

should be seen only as first estimate of the spatial signal quality of the SSM1km.




3.5.3 Mask Quality Assessment

A comparison of the SSM1km masks with corresponding classes of the CORINE 2012 land cover

dataset is done (Section 3.3). The amount of correctly masked and unmasked area is computed

over the entire validation area. In other words, it is measured how often the masks correctly mask

areas where soil moisture cannot be retrieved. Quantitative results are presented as confusion

matrices, displaying the errors of commission and omission in percent of total pixel counts in the

validation area. The validation area comprises the European continent, including sea areas that in

the range of 100km to land pixels.




4 RESULTS

4.1 VISUAL INSPECTION OF DATASETS

Figure 6 shows SSM1km from 2015-06-11 for a section over Eastern Europe. First, one can see

that Sentinel-1 had two passages over that area on that day. One going from north to south

(descending orbit) in the east section of the image, and one going from south to north (ascending

orbit) in the west section of the image. The resulted SSM1km image shows the surface soil

moisture conditions over the sensed areas, here in brown (dry) and blue (wet) colours. One can

see that the product resolves fine spatial patterns, corresponding to the magnitude of land cover

features or meteorological phenomena, and thus suits applications at the kilometric scale.

Figure 6: Detail of Figure 1, SSM1km at 2015-06-11 over Eastern Europe. White areas are not covered

on this day.




4.1.1 Example Images Series

The following images (Figure 7 to Figure 12) show six consecutive days of SSM1km data over

Europe, with locations of the ISMN stations. This sequence intends to visualize firstly, the satellite’s

coverage pattern and secondly, how often a location is covered by the product. High latitude areas

are more often sensed by the Sentinel-1 then low-latitude areas.

All images are projected onto Plate Carrée, as in the [CGLOPS1_PUM_SSM1km-V1], and all soil

moisture images show relative units (%) and are plotted in this colour legend:




Figure 7: SSM1km at 2015-09-01, with ISMN stations used for validation (colored dots).















4.2 TEMPORAL ANALYSES

For each station of the six selected networks, the RMSD and Spearman Rho between the in-situ

and the satellite data were calculated. In the next section 4.2.1, the time series at the location with

the best performance (while also covering most of the validation period) are displayed to give the

reader an impression of the signals and the temporal coverage. Section 4.2.2 summarizes the

results per network from all stations.

4.2.1 Time Series Comparison

In general, the time series show the different level of variation among the networks. While HOAL,

SMOSMANIA, and TERENO-FZJ appear to be in areas with large absolute soil moisture

dynamics, the variance at FMI-ARC, REMEDHUS and RMSN stations is smaller.

In many cases, and as expected, the SSM1km can reflect the soil moisture levels, but miss short-

time events, portrayed by asymmetric peaks in the in-situ time series, which in turn appear capable

of capturing those events.

4.2.1.1 FMI-ARC (Finland)

Figure 13: SSM1km and soil moisture in Volumetric Soil Moisture [m³/m³] from in-situ over the year

2015. Periods with frozen conditions show no data.




4.2.1.2 HOAL (Austria)

Figure 14: As Figure 13.

4.2.1.3 REMEDHUS (Spain)


4.2.1.4 RSMN (Romania)





4.2.1.5 SMOSMANIA (France)


4.2.1.6 TERENO-FZJ (Germany)


4.2.2 Overall Temporal Statistics

The individual results from one network can differ greatly, as the summarizing violin plot for the

correlation coefficients in Figure 19 shows. Overall, the level of agreement is low, especially for

FMI-ARC, and more for RHEMEDUS. Highest agreement is found for many stations at TERENO-

FZJ, and some at RSMN, HOAL, SMOSMANIA and FMI-ARC. For RHEMEDUS, signal patterns

indicating irrigation activities are detected (as in Figure 15 from mid of June ongoing), which is

likely to be completely missed by satellite data when the irrigation is applied to a small area. For

SMOSMANIA, the most significant difference is that the average soil moisture levels during spring

and during summer are mismatching. In spring, the SSM1km underestimates the in-situ data, in

summer it overestimates it (Figure 17). This seasonal bias shows typical indications for vegetation

dynamics that are superposing the soil moisture signal. Nevertheless, low-frequency peaks and

lows are (relatively) captured by the satellite product.




Figure 19: Violin plot summarizing Spearman rho values calculated for individual stations over the

six networks. The wider the violin at a vertical position, the higher is the frequency of that rho value.

Horizontal dashed lines in the violins indicate quartile values (q25, median, q75).

Violin plots in Figure 20 summarize the RMSD values for the six networks. Here the HOAL, the

TERENO-FZJ and the SMOSMANIA show a higher level of differences, whereas the other

networks feature relatively low RMSD values. This could be explained by the general lower level of

soil moisture variation at the latter three networks, where general drier conditions predominate.




Figure 20: As Figure 19, but summarizing RMSD values.

The reasons for the high RMDS values are potentially numerous: Many stations show periods of

disagreement and thus yield a low correlation coefficient. This can be the result of the inferior

measurement frequency of the SSM1km of 3-6 days, as it misses often rainfall events, and

consequently soil moisture peaks just by lack of frequent measurements. On the other hand, the

errors, or more common biases, in the in-situ time series can influence the validation eminently (as

the seasonal bias at SMOSMANIA). Finally, the representativeness error should be considered, as

SSM1km and in-situ observe at different scales and thus capture different phenomena. In general,

a comparison of remote sensing data to point-data from in-situ stations is vague as it bears

inherently uncertainties through the representativeness errors. This remains an open issue for

further validation campaigns and demands for model soil moisture data kilometric scale. Table 5

lists the average values over all stations per network.




Table 5: Average Temporal rho values and RMSD values for the year 2015 at the six in-situ networks.

Network Used Stations Temporal

Correlation rho

RMSD

FMI-ARC 28 0.28 0.035

HOAL 24 0.41 0.093

REMEDHUS 19 0.00 0.051

RSMN 19 0.35 0.033

SMOSMANIA 27 0.17 0.078

TERENO-FZJ 10 0.39 0.086

Overall, the temporal analyses unfold considerable statistical differences of the in-situ data with the

SSM1km product for the validation period. However, this is in line with similar studies comparing

in-situ to other remote sensing products. As investigated in [GIOGL1_QAR_SCATSAR-SWI] for

the HOAL network for example, the SSM1km shows comparable results as the SCATSAR-SWI,

which is based on a fused radar dataset, generated from Sentinel-1 and MetOp ASCAT input,

whereas the latter product alone shows slightly higher correlation coefficients. Though featuring a

much higher measurement frequency, and thus capturing more successfully soil moisture peaks,

also the ASCAT SWI V3 soil moisture differ considerably from in-situ data. We stress this instance,

because the validation of remote sensing products with ground truth in general does not promise

full agreement.

4.3 SPATIAL ANALYSES

4.3.1 Spatial Correlation Time Series

Five networks featuring stations are considered geographically dense so that the mutual distances

are low and similar. For these networks, the spatial correlation analysis was performed in order to

estimate the agreement in spatial patterns of in-situ soil moisture with SSM1km data. The following

figures show the temporal evolution of (spatial) Pearson R over the individual networks. If there a

less than six locations/pixels with valid observations per day available, the result for this day is

omitted.




4.3.1.1 FMI-ARC (Finland)

Figure 21: Spatial Pearson R between in-situ and SSM1km for the year 2015. Days with no data,

frozen conditions, or not enough values per day, show no data.

4.3.1.2 HOAL (Austria)





4.3.1.3 REMEDHUS (Spain)


4.3.1.4 SMOSMANIA (France)





4.3.1.5 TERENO-FZJ (Germany)


4.3.2 Overall Spatial Statistics

Contrary to the temporal signals, the spatial signals of in-situ soil moisture and SSM1km appear to

agree well. For FMI-ARC and REMEDHUS, the agreement is near to perfect (close to 1) over the

whole validation period, meaning the (relative) spatial patterns coincide through time. This holds

also for the other networks, but at lesser degree. A slight trend to higher agreement during the

summer months can be anticipated, indicating a good reflection of soil moisture patterns during the

full-vegetation period, which is somewhat surprising as the retrieval error of the SSM1km tends to

increase with vegetation density Table 6 lists the average R values for the validation period.

Table 6: Average Spatial R values for the year 2015 at the five suitable in-situ networks.

Network Used Stations Spatial

Correlation R

FMI-ARC 28 0.96

HOAL 24 0.75

REMEDHUS 19 0.92

SMOSMANIA 12 0.84

TERENO-FZJ 10 0.67




Overall, the high correlation coefficients from the spatial analyses confirm the believed high spatial

signal quality of the Sentinel-1 images at the kilometric scale. The results suggest that the

SSM1km reflects well the variation of soil moisture in space. In reflection to results from

comparable remote sensing products as the SCATSAR-SWI or the ASCAT SWI V3 (see in

[GIOGL1_QAR_SCATSAR-SWI]), the present results are very promising, as improvements in

describing spatial variances of soil moisture are achieved.

4.4 VALIDATION OF SSM MASKS

The SSM1km product undergoes masking for water pixels, low SSM sensitivity and high

topographic slope (where Sentinel-1 SAR data quality is low) (see section 3.1.2). These masks are

important to the SSM1km and necessary for the reliability of the data. Only at locations with certain

local surface characteristics, the algorithm for soil moisture retrieval from radar observations is

robust.

The SSM Water Mask is compared against the water classes of CORINE (Figure 27). One can see

that the reference water pixels are almost all predicted as water pixels (15.79% of total), but also

that some water pixels are classified as non-water (3.20% of total), while practically no non-water

pixels are predicted as water (0.01% of total) The results for the SSM Water Mask are considered

as satisfying, but let recognize also room for improvement, as an underestimation of water bodies

is evident. Figure 26 maps the geographic distribution of the underestimation for northern Greece,

showing that shorelines to sea and lakes are too tight, reducing the area of water bodies on their

outline. The misinterpretation stems from ambiguous SAR signals at mixed water/land-pixels.

Here, a substantial improvement could be reached when the SSM Water Mask is not calculated at

the 500m pixel spacing, but on the much more accurate initial 10m pixel spacing.




Figure 26: Map over northern Greece, showing the Error of Omission (Underestimate), Error of

Commission (Overestimate) and correct classified pixels by the SSM Water Mask.




Figure 27: Confusion matrix of the SSM Water Mask (Prediction) vs CORINE Water classes

(Reference). Values are percent of all 1km pixels in the validation area. Lower-right corner value

shows total agreement in percent.

For the SSM Sensitivity mask, which is effectively masking fabric land covers and comprises

mainly cities and settlements, the results are presented in Figure 28. Here, one can see that many

pixels that are “urban fabric” in the reference (3.91%), are not predicted in the Sensitivity Mask

(only 0.63%), meaning that many urban areas are missed (3.27%). Also, a relatively large amount

of non-fabric pixels is classified as fabric (1.46%).




Figure 28: Confusion matrix of the SSM Sensitivity Mask (Prediction) vs CORINE Urban Fabric

classes (Reference). Values are percent of all 1km pixels in the validation area. Lower-right corner

value shows total agreement in percent.

The results for the Sensitivity Mask, which is supposed to identify urban areas, are not as

satisfying as for the Water Mask, as it does both kind of errors: Masking pixels that should not be

masked (1.46% of total pixels), and allowing pixels, that should be masked (3.27%). However, a

closer look on the comparison with the CORINE land cover reveals the nature of the

underestimation (Figure 29). The city centers, thus areas with dense constructions, are well

captured, whereas the suburban areas or smaller settlements are mostly missed. However, the

used CORINE data is may not be appropriate here, as mixed areas (suburban buildings and green

areas) are classified as urban fabric. Considering SAR, the basis of the SSM1km, the main goal of

removing cities is widely achieved through the Sensitivity Mask, albeit future improvements are

recommended.




Figure 29: Map over Austrian area, showing the Error of Omission (Underestimate), Error of

Commission (Overestimate) and correct classified pixels by the SSM Sensitivity Mask.

The SSM Slope Mask was not validated here, because it is derived from SRTM90 that is already

examined by many studies (e.g. by Mukherjee et al., 2013).




5 CONCLUSIONS

A validation study for the SSM1km product was carried out. 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, totaling 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 accords 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 over 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 in Table 1 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 (as in Table 1) 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.

5.1.1 Update 2018: Evaluation Experiments over 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. 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 RECOMMENDATIONS

The SSM1km product was successfully set up in Version1, as the software developed for 1)

processing the raw satellite SAR data, 2) generating the parameters for the retrieval algorithm, and

3) producing the soil moisture data generates meaningful output, fully harnessing state-of-the-art

radar data. The scientific algorithm developed for Envisat ASAR was successfully adopted to

Sentinel-1 CSAR, yielding data with superior quality and visual appearance compared to the

preceding ASAR, which is even based on a four times longer data record. Concerning data

processing, one potential improvement of the software would be the extension of the quality

checking procedures. Identification and fixing of erroneous raw data is not comprehensively done

by ESA, and affords for automatic checks on raw images.

From the present validation, and from other in-house investigations, the product shows excellent

quality considering the spatial signal. It proves to reflect reliably soil moisture patterns at the

kilometric scale. This is unprecedented for an extensive dataset with continental coverage and

gives full satisfaction.

In the time domain, the satellite data does not agree well with ground station data. The generic

accuracy goal of 0.04 m³/m³ is achieved only at two out of six validation sites. Yet, we see this

relaxed, because hardly any other remote sensing product achieves this goal throughout its extent.

Moreover, a validation against a comprehensive soil moisture model at kilometric scale would

depict a more comprehensive assessment, which is unfortunately not possible yet.

So far, the mediocre correlation results are somewhat expected, as the observation frequency of

Sentinel-1A is relatively low (and not uniform over the area). Here, the inclusion of Sentinel-1B

promises an instant improvement, doubling the revisit rate and reducing the spatial heterogeneity

in the coverage. The increase in frequency goes along with a denser data record, allowing more

reliable estimation of the soil moisture retrieval parameters. Consequently, the temporal dynamics

should be recorded more accurately. The application of a dynamic vegetation correction, as

already implemented for the ASCAT soil moisture retrievals, would potentially remove seasonal

biases stemming from vegetation-induced signal disturbances.

The SSM1km masks appear to successfully identify locations where the soil moisture algorithm is

applicable and thus provides valuable information to the users. Also, the sub-pixel masking during

the SAR image resampling removes efficiently signals from non-soil surfaces. A freeze/thaw

masking for cold periods could further improve the usability, suggesting omitting soil moisture

values over frozen or snow-covered soil. Currently, the “ExceedingMin” flag gives some indication

on local frozen- or snow-conditions.

In summary, we recommend to disseminate the SSM1km to the community, as it would

complement meaningfully the available product palette. While having mediocre temporal signal

quality, it provides surpassing information in the spatial domain. As a pre-operational product, it

needs to come along with 1) a proper description of the caveats (the aforementioned low frequency




and seasonal biases) and 2) a set of masks, guiding the users where to use the data. The

suggested improvements and a longer data record would further enhance the product. Finally, the

derivative 1km product SCATSAR-SWI, which combines assets of the Sentinel-1 CSAR and

MetOp ASCAT while remedying their individual shortcomings, directly profits from the

dissemination and maintenance of the SSM1km product.




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Documents

Copernicus Global Land Operations · 2018. 12. 19. · In this Validation Report (VR), the new product Sentinel-1 1km Surface Soil Moisture (called SSM1km) is subject to validation