Algorithm Theoretical Baseline Document (ATBD) Surface ...hsaf.meteoam.it/documents/ATDD/SSM_ASCAT_DR_ATBD_v0.4.pdf · Algorithm Theoretical Baseline Document (ATBD) Surface Soil

Algorithm TheoreticalBaseline Document (ATBD)

Surface Soil Moisture ASCATData Record Time Series

Doc.No: SAF/HSAF/CDOP2/ATBD/Issue/Revision: 0.4Date: 2016/10/12Page: 1/31

EUMETSAT Satellite Application Facility onSupport to Operational Hydrology and Water Management



H25, H108–H111




Revision History

Revision Date Author(s) Description0.1 2013/08/06 S. Hahn First draft. Adopted content from H25 ATBD.0.2 2016/01/19 S. Hahn Update annex, add product table list.0.3 2016/07/07 S. Hahn Modifications according to review item discrep-

ancies (RIDs) from the data record readiness re-view (DRR) 2016.

0.4 2016/10/12 S. Hahn Modifications according to review from EU-METSAT Secretariat.




Table of Contents1. Executive summary 8

2. Introduction 82.1. Purpose of the document . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82.2. Targeted audience . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82.3. Related H SAF surface soil moisture products . . . . . . . . . . . . . . . . . . . . 8

3. ASCAT on-board Metop 93.1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93.2. Instrument description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93.3. Geometry and coverage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103.4. Product processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

4. Soil moisture retrieval algorithm 124.1. Model assumptions and requirements . . . . . . . . . . . . . . . . . . . . . . . . . 134.2. Model limitations and caveats . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

4.2.1. Vegetation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 144.2.2. Snow . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 144.2.3. Frozen soil . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 144.2.4. Surface water . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 144.2.5. Topographic complexity . . . . . . . . . . . . . . . . . . . . . . . . . . . . 144.2.6. Long-term land cover changes . . . . . . . . . . . . . . . . . . . . . . . . . 15

4.3. Estimated standard deviation of backscatter . . . . . . . . . . . . . . . . . . . . . 154.4. Azimuth and incidence angle dependency of backscatter . . . . . . . . . . . . . . 15

4.4.1. Azimuthal anisotropy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 164.4.2. Incidence angle modelling and normalization . . . . . . . . . . . . . . . . 164.4.3. Vegetation characterization . . . . . . . . . . . . . . . . . . . . . . . . . . 17

4.5. Dry and wet reference estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . 174.5.1. Dry correction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 194.5.2. Wet correction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

4.6. Soil moisture computation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

5. WARP 195.1. Processing steps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

5.1.1. Re-sampling to a Discrete Global Grid (DGG) . . . . . . . . . . . . . . . 205.1.2. Calculation of azimuthal correction coefficients . . . . . . . . . . . . . . . 215.1.3. Calculated ESD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 215.1.4. Estimation of slope and curvature . . . . . . . . . . . . . . . . . . . . . . 215.1.5. Incidence angle normalization . . . . . . . . . . . . . . . . . . . . . . . . . 225.1.6. Calculation of dry and wet reference . . . . . . . . . . . . . . . . . . . . . 235.1.7. Wet correction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 245.1.8. Computation of soil moisture . . . . . . . . . . . . . . . . . . . . . . . . . 25

5.2. Side note on error propagation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 255.3. Limitations and caveats . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 265.4. Processing modes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26




5.5. Input data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 265.6. Level 2 output data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

5.6.1. Surface soil moisture and its noise . . . . . . . . . . . . . . . . . . . . . . 265.6.2. Processing and correction flags . . . . . . . . . . . . . . . . . . . . . . . . 27

6. References 27

Appendices 29

A. Introduction to H SAF 29

B. Purpose of the H SAF 29

C. Products / Deliveries of the H SAF 30

D. System Overview 31




List of Tables2.1. List of soil moisture products produced related to this ATBD. . . . . . . . . . . . 9

List of Figures3.1. ASCAT swath geometry for a Metop minimum orbit height (822 km). The di-

mensions are symmetric with respect to the satellite ground track [1]. . . . . . . 113.2. Functional overview of the ASCAT Level 1 ground processing chain [2]. . . . . . 124.1. Crossover angle concept. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 185.1. Overview of the WARP processing steps. . . . . . . . . . . . . . . . . . . . . . . . 20A.1. Conceptual scheme of the EUMETSAT Application Ground Segment. . . . . . . 29A.2. Current composition of the EUMETSAT SAF Network. . . . . . . . . . . . . . . 30




List of AcronymsASAR Advanced Synthetic Aperture Radar (on Envisat)

ASAR GM ASAR Global Monitoring

ASCAT Advanced Scatterometer

ATBD Algorithm Theoretical Baseline Document

BUFR Binary Universal Form for the Representation of meteorological data

DORIS Doppler Orbitography and Radiopositioning Integrated by Satellite (on Envisat)

ECMWF European Centre for Medium-range Weather Forecasts

Envisat Environmental Satellite

ERS European Remote-sensing Satellite (1 and 2)

ESA European Space Agency

EUM Short for EUMETSAT

EUMETCast EUMETSAT’s Broadcast System for Environment Data

EUMETSAT European Organisation for the Exploitation of Meteorological Satellites

FTP File Transfer Protocol

H SAF SAF on Support to Operational Hydrology and Water Management

Météo France National Meteorological Service of France

Metop Meteorological Operational Platform

NRT Near Real-Time

NWP Near Weather Prediction

PRD Product Requirements Document

PUM Product User Manual

PVR Product Validation Report

SAF Satellite Application Facility

SAR Synthetic Aperture Radar

SRTM Shuttle Radar Topography Mission

TU Wien Technische Universität Wien (Vienna University of Technology)

WARP Soil Water Retrieval Package




WARP H WARP Hydrology

WARP NRT WARP Near Real-Time

ZAMG Zentralanstalt für Meteorologie und Geodynamic (National Meteorological Service ofAustria)




1. Executive summaryThe Algorithm Theoretical Baseline Document (ATBD) provides a detailed description of thealgorithm used to retrieve surface soil moisture information. The concept of the Level 2 surfacesoil moisture retrieval method developed at the Vienna University of Technology (TU Wien)for use with C-band scatterometers is a physically motivated change detection method. Thefirst realization of the concept was based on ERS-1/2 satellite data sets [3–6] and later theapproach was successfully transferred to Advanced Scatterometer (ASCAT) data onboard theMetop-A satellite [7–9]. The soil moisture retrieval algorithm is implemented within a softwarepackage called soil WAter Retrieval Package (WARP) and, for near real-time (NRT) applications,in a software package called WARP NRT. WARP is implemented in IDL/Python, whereasWARP NRT is a software package written in C++. In contrast to WARP, which processesseveral years of Metop ASCAT Level 1b data and produces both a model parameter databaseand soil moisture time series, WARP NRT processes single orbits and produces soil moisture inthe original Level 1b orbit geometry.

2. Introduction2.1. Purpose of the documentThe Algorithm Theoretical Baseline Document (ATBD) is intended to provide a detailed de-scription of the scientific background and theoretical justification for the algorithms used toproduce the Metop ASCAT soil moisture data sets.

2.2. Targeted audienceThis document mainly targets:

1. Remote sensing experts interested in the retrieval and error characterization of satellitesoil moisture data sets.

2. Users of the remotely sensed soil moisture data sets who want to obtain a more in-depthunderstanding of the algorithms and sources of error.

2.3. Related H SAF surface soil moisture productsIn the framework of the H SAF project several soil moisture products, with different timeliness(e.g. NRT, offline, data records), spatial resolution, format (e.g. time series, swath orbit geom-etry) or the representation of the water content in various soil layers (e.g. surface, root-zone),are generated on a regular basis and distributed to users. A list of all available soil moistureproducts, as well as other H SAF products (such as precipitation or snow) can be looked up onthe H SAF website1. The following Table 2.1 gives an overview of the instances of surface soilmoisture (SSM) products, which are related to this ATBD.

1http://hsaf.meteoam.it/.

http://hsaf.meteoam.it/




Table 2.1: List of soil moisture products produced related to this ATBD.ID Product Name

H25 Metop ASCAT DR2015 SSM time series 12.5 km samplingH108 Metop ASCAT DR2015 EXT SSM time series 12.5 km samplingH109 Metop ASCAT DR2016 SSM time series 12.5 km samplingH110 Metop ASCAT DR2016 EXT SSM time series 12.5 km samplingH111 Metop ASCAT DR2017 SSM time series 12.5 km sampling

3. ASCAT on-board Metop3.1. IntroductionThe European contribution within the framework of the Initial Joint Polar System (IJPS) is theEUMETSAT Polar System (EPS). The space segment of the EPS programme is envisaged tocontain three sun-synchronous Meteorological Operational Platforms (Metop-A, Metop-B andMetop-C) jointly developed by ESA and EUMETSAT. Each satellite has a nominal lifetime inorbit of about 5 years, with a planned 6 month overlap between consecutive satellites. The firstsatellite Metop-A, was launched on 19 October 2006, Metop-B was launched on 17 September2012. Metop-C is planned to be launched approximately in 2018. The Advanced Scatterometer(ASCAT) is one of the instruments carried on-board the series of Metop satellites.

3.2. Instrument descriptionASCAT is a real aperture radar system operating in C-band (VV polarization) and providesday- and night-time measurement capability, which is almost unaffected by cloud cover. Theinstrument design and performance specification is based on the experience of the Scatterom-eter flown on the ERS-1 and ERS-2 satellite. It can basically work in two different modes:Measurement or Calibration. The Measurement Mode is the only mode that generates sciencedata for users. The instrument uses a pulse compression method called “chirp”, at which longpulses with linear frequency modulation were generated at a carrier frequency of 5.255 GHz.After receiving and de-chirping of the ground echoes, a Fourier transform is applied in order torelate different frequencies in the signal to slant range distances. A pre-processing of the noiseand echo measurements is done already on board to reduce the data rate to the ground sta-tions, e.g. various averaging takes place reducing the raw data by a factor of approximately 25.Within each pulse repetition interval, after all pulse echoes have been decayed, the contributionof the thermal noise is monitored in order to perform a measurement noise subtraction duringthe ground processing. A side effect of on board processing is a degree of spatial correlationbetween different and within received echoes, but this is taken into account later on by theLevel 1b processing. The radar pulse repetition frequency (PRF) is approximately 28.26 Hz,which yields to 4.71 Hz for the beam pulse repetition frequency in the sequence fore-, mid- andaft beam [10].The Calibration Mode is used during external calibration campaigns (29 days every 13 months),

when the platform passes over three different ground transponders located in central Turkey.The objective of such calibration campaigns is to verify that the backscatter measurements froma target is correct for the whole incidence angle range. In other words, the absolute and relative




calibration will be monitored and checked. A stable radar cross section and an accurately knownposition on the Earth’s surface are the basic and important requirements for each transponder.After receiving a pulse from ASCAT, the active transponder send a delayed pulse back, which inturn can be recorded by ASCAT. A comparison between the localised transponder position andthe expected data allows to estimate three depointing angles and gain correction values for eachantenna. An east-west distribution with a displacement of approximately 150 km, will allowto obtain transponder measurements from most of the incidence angles for each antenna beam.This high sampling is necessary in order to reconstruct the characteristics of each antenna gainpattern for the whole incidence angle range and achieve an appropriate relative calibration. Sucha calibration set up allows to establish a reference system in order to evaluate and monitor theinstrument performance on regular basis [11].

3.3. Geometry and coverageThe Metop satellites are flying in sun-synchronous 29 day repeat cycle orbit with an ascendingnode at 9:30 p.m. and a minimum orbit height of 822 km. This implies that the satellite willmake a little more than 14 orbits per day. The advanced measurement geometry of ASCATallows twice the coverage, compared to its predecessors flying on ERS-1 and ERS-2. Thus, thedaily global coverage increased from 41% to 82%. The spatial resolution was also improvedfrom 50 km to 25-34 km, still reaching the same radiometric accuracy compared to the ERS-1/2scatterometer.The advanced global coverage capability is accomplished by two sets of three fan-beam an-

tennas, compared to only one set at the ERS satellites. The fan-beams are arranged broadsideand ±45◦ of broadside, thus, allowing to observe three azimuthal directions in each of its two550 km swaths (see Figure 3.1). The swaths are separated from the satellite ground track byabout 360 km for the minimum orbit height. Each point on the Earth’s surface that falls withinone of the two swaths, will be seen by all three antennas and a so-called σ◦ triplet can beobserved. The incidence angles for those two antennas which are perpendicular to the flightdirection intersect the surface between 25◦ and 53.3◦, whereas the other four antennas have anincidence angle range from 33.7◦ to 64.5◦ [1].

3.4. Product processingGlobal data products are often classified into different levels according to their processingprogress. In case of ASCAT the different product levels can be summarized as follows:

• Level 0: Unprocessed raw instrument data from the spacecraft. These are transmitted tothe ground stations in binary form.

• Level 1a: Reformatted raw data together with already computed supplementary informa-tion (radiometric and geometric calibration) for the subsequent processing.

• Level 1b: Calibrated, georeferenced and quality controlled backscatter coefficients in fullresolution or spatially averaged. It includes also ancillary engineering and auxiliary data.

• Level 2: Geo-referenced measurements converted to geophysical parameters, at the samespatial and temporal resolution as the Level 1b data.




left swath right swath

satelliteground track

25◦ 53.5◦33.7◦

64.5◦antennalookangles antennaincidenceangles

forebeam forebeam

midbeam midbeam

aftbeam aftbeam

45◦90◦135◦

41 nodes/12.5 km gridSZR21 nodes/25 km gridSZO

550 km360 km

Figure 3.1: ASCAT swath geometry for a Metop minimum orbit height (822 km). The dimen-sions are symmetric with respect to the satellite ground track [1].

• Level 3: Geophysical products derived from Level 2 data, which are either re-sampled orgridded points.

The raw data arriving at the ground processing facility are passed into the Level 1 groundprocessor (see Figure 3.2). This data driven processing chain generates radiometrically calibratedLevel 1b backscatter coefficients. It also includes an external calibration processing chain in orderto support the localization and normalisation process of the measurement power echoes. Beforesatellite launch, it is only possible to estimate parameters characterising the expected instrumentperformance, since some of them depend on in-flight conditions. But once the satellite is his orbit,these parameters can be assessed during a calibration campaign. The first intermediate productgenerated just before the swath node generation and spatial averaging steps, is called Level 1bfull resolution product. The main geophysical parameter in this product is the normalizedbackscatter coefficient σ◦. Along each antenna beam 256 σ◦ values are projected on the Earth’ssurface. The footprint size is about 10 × 20 km of various shapes and orientations, dependingon the Doppler pattern over the surface [12]. The radiometric accuracy and the inter-beamradiometric stability is expected to be lass then 0.5 dB peak-to-peak, whereas the georeferencingaccuracy is about 4 km. In addition a number of quality flags are computed associated withevery individual σ◦ sample along each antenna beam.

The other two products generated within the Level 1b processing are averaged σ◦ values at




Figure 3.2: Functional overview of the ASCAT Level 1 ground processing chain [2].

two different spatial grids. A two-dimensional Hamming window function centered at every gridnode is used in both cases for spatial filtering. This weighting function is based on a cosinefunction, which will basically attenuate the contribution of values with increasing distance.The window width is deciding the magnitude of spatial resolution and which values contributeto the weighted result. This means, values beyond the window width are disregarded. Thespatial resolution is then defined as the distance when the window function reaches 50% of itspeak intensity. This spatial averaging is used in the along- and across-track direction, withthe objective to generate a set of σ◦ triplets for each grid node of each swath at the desiredradiometric resolution. The first product generated with this approach is called the nominalproduct and has a spatial resolution of 50 km for each grid point. The grid spacing is 25 kmwith 21 nodes at each swath per line. The higher resolution product has a spatial resolutionranging from 25 to 34 km. This variability is the result of a trade-off between spatial resolutionand the desired radiometric accuracy, which lead to an alternating Hamming window widthacross the swath for the different beams. In this case the grid spacing is only 12.5 km, with 41nodes at each swath per line.

4. Soil moisture retrieval algorithmThe TU Wien change detection algorithm is from a mathematical point of view less complexthan a radiative transfer model and can be inverted analytically. Therefore, soil moisture canbe estimated directly from the Scatterometer measurements without the need for an iterativeadjustment process. As a result, it is also quite straight forward to perform an error propagationto estimate the retrieval error for each location on the land surface [8]. A disadvantage of the




change detection model is that it is a lumped representation of the measurement process. Thedifferent contributions to the observed total backscatter from the soil, vegetation, and soil-vegetation-interaction effects cannot be separated as would be the case for radiative transfermodelling approaches. It also means that it is necessary to calibrate its model parameters usinglong backscatter time series to implicitly account for land cover, surface roughness, and manyother effects.

4.1. Model assumptions and requirementsThe basic assumptions of the TU Wien change detection method are:

1. The relationship between the backscattering coefficient σ◦ expressed in decibels (dB) andthe surface soil moisture content is linear.

2. The backscattering coefficient σ◦ depends strongly on the incidence angle θ. The rela-tionship is characteristic by roughness conditions and land cover, but is not affected bychanges in the soil moisture content.

3. At the spatial scale of the Scatterometer measurements roughness and land cover are stablein time.

4. When vegetation grows, backscatter may decrease or increase, depending on whether theattenuation of the soil contribution is more important than the enhanced contributionfrom the vegetation canopy, or vice versa. Because the relative magnitude of these effectsdepends on the incidence angle, the curve σ◦ (θ) changes with vegetation phenology overthe year. This effect can be exploited to correct for the impact of vegetation phenologyin the soil moisture retrieval by assuming that there are distinct incidence angles (θd andθw) and where the backscattering coefficient is stable despite seasonal changes in aboveground vegetation biomass for dry and wet conditions.

5. Vegetation phenology influences backscatter σ◦ on a seasonal scale. Local short-termfluctuations are suppressed at the scale of the scatterometer measurements.

In order to be able to apply the TU Wien soil moisture change detection algorithm, thefollowing requirements needs to be fulfilled:

1. Regular measurements (every 1-3 days),

2. Backscatter measurements from various incidence angles (approx. 20◦-60◦)

3. Sufficiently long time period (> 2 years).

If all of these requirements are met, it is possible to derive robust model parameters, such asthe characterization of the incidence angle vs backscatter behaviour, correction coefficients forazimuthal effects and estimation of the dry and wet reference.




4.2. Model limitations and caveats4.2.1. Vegetation

The influence of vegetation on the backscatter signal is currently accounted for using a sea-sonal correction. The so-called cross-over angle concept (section 4.4.3) allows to determine thedry/wet reference at incidence angles where the backscattering coefficient is stable despite sea-sonal changes in the above ground biomass. Inter-annual variability is not accounted for at themoment.In densely vegetated areas (such as Tropical rainforests) the C-band microwaves are not able

to penetrate to the ground. Hence, a soil moisture signal from the soil surface is not containedin the backscatter measurements, which is typically characterized by a very low signal variation.However, it is possible to determine these cases with the error propagation, since the signalsensitivity plays an important role.

4.2.2. Snow

Depending on the physical parameters of the snow layer (i.e. liquid water content, roughnessof the air-snow interface, depth and layering of the snow pack, grain size and shape) differentscattering mechanisms characterize the overall backscatter signal. Snow scattering phenomenaare not treated in the TU Wien model and snow covered periods needs to be masked based onauxiliary information.

4.2.3. Frozen soil

At temperatures below 0◦C microwave backscatter drops, because of the inability of the soilwater molecules to align themselves to the external electromagnetic field. The effect of freezingis more complex in case of vegetation, because plants have adaptive mechanisms to withstandthe cold winter weather. Like for snow, handling backscatter from soil freezing periods is notcovered by the TU Wien model and affected time periods needs to be masked based on auxiliaryinformation.

4.2.4. Surface water

Backscatter characteristics are primarily controlled by the roughness of the water surface, whichis related to the short penetration depth of C-band microwaves (< 1-2 mm). A calm watersurface acts like a mirror scattering almost the complete signal into the forward direction. Wavescaused by near surface winds increase the backscatter looking at up- and downwind direction, andreduces the signal normal to the wind direction. As a result, open water can have a disturbinginfluence on the retrieval of soil moisture, if the area covered is large compared to the overallfootprint size. Locations with (temporary) standing water needs to be treated carefully.

4.2.5. Topographic complexity

A high variability of backscatter can be observed in mountainous areas which is not necessar-ily coupled with soil moisture changes. Hence, the surface topography directly influences thescattering behavior, which is not considered in the TU Wien model. A reduced soil moistureretrieval performance can be expected in topographic complex areas.




4.2.6. Long-term land cover changes

Long-term land cover changes are not treated in the TU Wien model at the moment. Forexample, if urbanization or desertification take place on a larger scale, the long-term backscattersignal can be influenced.

4.3. Estimated standard deviation of backscatterThe Estimated Standard Deviation (ESD) of backscatter is derived from the measurements ofthe for and aft beam. This is based on the following observation: all three beams observe thesame region and due to the observation geometry the for and aft beam have the same incidenceangle. Thus, as long as there are no azimuthal effects, the measurements of the for and aft beamare comparable, i.e. statistically speaking, they are instances of the same distribution. Hencethe expectation of the difference

δ := E[σ◦f − σ◦a

](1)

should be 0, and its variance should be twice the variance of one of the beams (i.e Var [δ] =2 ·Var [σ◦]). This can be derived using error propagation

Var [δ] ≈ Var[σ◦f

]·(∂δ

∂σ◦f

)2

+ Var [σ◦a] ·(∂δ

∂σ◦a

)2+ 2 · Cov(σ◦f , σ◦a) ·

(∂δ

∂σ◦f

)·(∂δ

∂σ◦a

)+ . . .

The first two terms should dominate, whereas the third term is 0 under the assumption ofi.i.d. (independent and identically distributed random variables).


]· (1)2 + Var [σ◦a] · (−1)2 + 0 (2)

Under the assumption of homoscedasticity (a vector of random variables is homoscedastic ifall random variables in the sequence or vector have the same finite variance) the variances areequal and the equation reads


]+ Var [σ◦a] = 2 ·Var [σ◦] (3)

The previous equation can be re-written to find the final formula for the ESD

Var [σ◦] = Var [δ]2 (4)

ESD [σ◦] =

√Var [δ]

2 (5)

4.4. Azimuth and incidence angle dependency of backscatterThe backscattering coefficient changes as a function of the observed surface properties, mostimportantly surface dielectric properties, roughness and vegetation. However, the overall mag-nitude also strongly depends on the measurement geometry, i.e. the incidence angle θ as well




as the azimuth angle φ. Only measurements based on the same observation geometry can becompared directly.

4.4.1. Azimuthal anisotropy

In some dedicated regions azimuthal effects on the backscattering coefficient are very strong.This azimuthal anisotropy is accounted for by applying a polynomial correction term to thebackscatter signal as suggested by [13]. For ASCAT on board Metop, the azimuth angle un-der which a location is seen depends on the beam (for-, mid- or aft-beam), the swath (left orright) and the orbit direction (ascending or descending), resulting in twelve different azimuthconfigurations (i.e. φi with i ∈ [1, 12]). For each of these configurations the incidence angle vsbackscatter dependency is modeled as a second order polynomial pi (θ). The coefficients of thesepolynomials are determined by fitting the model to all observations falling into the respectiveconfiguration category. Furthermore, an overall model p0 (θ) is fitted to all observations, result-ing in a total of 3 × 13 = 39 coefficients. The following Equation 6 describes the application ofthe azimuthal correction

σ◦bi,ac = σ◦bi(θ) + p0 (θ)− pi (θ) (6)

where subscript bi stands for the beam (i.e. for, mid, aft) considered at the current configu-ration and the subscript ac denotes the backscatter corrected for azimuthal anistropy. The lastterm in Equation 6 corrects the incidence angle vs. backscatter dependency for the individualcharacteristics of a certain configuration pi (θ) and references it to an overall average behaviorp0 (θ).

4.4.2. Incidence angle modelling and normalization

A second order polynomial is also used for the incidence angle normalization (Equation 7), witha reference incidence angle defined as θr = 40. Such a formulation can be considered as asecond-order Taylor polynomial with an expansion point at θr. Taylor polynomials at a givenpoint are finite order truncation of its Taylor series, which completely determines the functionin some neighbourhood of the point. The reference incidence angle of 40◦ has been chosen inorder to minimize interpolation errors [3].

σ◦θ (t) = σ◦r (t) + σ′r (t) · (θ − θr) + 12 · σ

′′r (t) · (θ − θr)2 (7)

In Equation 7 the zero-order coefficient σ◦r (t) represents the normalized backscatter at thereference incidence angle θr, and the first- and second-order coefficients and are referred to asslope σ′r and curvature σ′′r parameters, characterizing surface roughness and the phenologicalcycle of the vegetation. The TU Wien model assumes that vegetation changes have a directimpact on the sensitivity of the backscatter signal (e.g. for sparse vegetation, the backscattervs. incidence angle relationship tends to drop off rapidly, while for fully grown vegetation, itbecomes less steep, almost horizontal in the case of rain forest), whereas changes in soil moistureare expected to have a minimal influence on the sensitivity (or in other words the incidence anglebehaviour stays the same, except for on offset).Slope and curvature are determined as the coefficients of a straight line fitted to the so called

local slopes σ′l. A local slope is estimated by the first derivative of the backscatter vs incidence




angle dependency and are computed as difference quotients between for and mid beam, as wellas aft and mid beam.

σ′l (θl) = ∆σ◦

∆θ (8)

Specifically, each backscatter triplet (σ◦f , σ◦m, σ◦a) taken at their respective incidence angle (θf ,θm, θa) yields to local slope estimates.

σ′fm

(θm + θf

2

)=σ◦m − σ◦fθm − θf

(9)

σ′am

(θm + θa

2

)= σ◦m − σ◦aθm − θa

(10)

These local slopes are taken as estimates of the second derivative of the following equation:

σ′θ = σ′r + σ′′r · (θ − θr) (11)

The number of local slopes used for fitting a regression line, in order to determine slope andcurvature at the reference incidence angle, are selected on a time-window basis.

4.4.3. Vegetation characterization

Typically, a distinct backscatter signal can be observed over a range of incidence angles. Thecharacteristic behaviour is mainly influenced by vegetation, soil roughness, dielectric propertiesand land cover. The dominant scattering mechanisms of vegetation are volume scattering inthe vegetation canopy and surface scattering from the underlying soil surface [14]. A distinctlydifferent behaviour can normally be observed between volume and surface scattering. Thebackscattering coefficient rapidly decreases with the incidence angle, with the exception of veryrough surfaces [15]. By taking into consideration the model assumptions (e.g. roughness and landcover are stable), a simple concept can be formulated explaining the backscatter incidence anglerelationship in relation to vegetation and soil moisture changes. Such a concept is outlined inFigure 4.1, which shows the backscatter signal over the incidence angle for two different states ofvegetation (dormant and fully developed) and soil moisture (dry and wet soil). It is noticeablethat for both soil conditions there is a crossover angle indicating that backscatter is ratherstable despite vegetation growth. If these cross-over angles are chosen correctly, it is possibleto compensate for vegetation effects. In the current formulation of the TU Wien backscattermodel, θd and θw are set to 25◦ and 40◦, respectively.

4.5. Dry and wet reference estimationThe estimation of the dry and wet backscatter reference is based upon the average of the extremelowest (driest) and highest (wettest) measurements for a given location that have ever beenrecorded. An explicit uncertainty range, defined as a 95% 2-sided confidence interval, is used toseparate the extreme low and high values [9]. The definition of the confidence interval considersthe noise of dry and wet reference, which has been estimated using error propagation:

Confidence Interval = ±1.96 · σ◦n (θ) (12)




Incidence angle [degree]

Back

scatter [dB

]

fully developed vegetation

fully developed vegetation

dormant vegetation

dormant vegetation

wet soil

dry soil

Figure 4.1: Crossover angle concept.

The value 1.96 represents the 97.5 percentile of the standard normal distribution, which isoften rounded to 2 for simplicity. After separation of the extreme observations, a transforma-tion to the presumed cross-over angles is needed. The cross-over angels are distinct incidenceangles θd and θw, where the backscattering coefficient is stable despite seasonal changes in aboveground vegetation biomass for dry and wet conditions, respectively. Rearranging Equation 7will transform the normalized backscatter values to the respective dry and wet cross-over angles:

σ◦d (t) = σ◦r (t) + σ′r (t) · (θd − θr) + 12 · σ

′′r (t) · (θd − θr)2 (13)

σ◦w (t) = σ◦r (t) + σ′r (t) · (θw − θr) + 12 · σ

′′r (t) · (θw − θr)2 (14)

Once the number of extreme measurements for the lower (Nb) and upper (Nu) limits areknown and the transformation to respective cross-over angles is completed, the dry and wetreferences are calculated as the mean value.

Cd = 1Nb·Nb∑j=1

σ◦d,j (15)

Cw = 1Nu·Nu∑j=1

σ◦w,j (16)

Knowing the dry and wet references at their cross-over angles, an inverse transformation to




the normalisation angle is necessary to obtain the final references.

σdr (t) = Cd − σ′r (t) · (θd − θr)−12 · σ

′′r (t) · (θd − θr)2 (17)

σwr (t) = Cw − σ′r (t) · (θw − θr)−12 · σ

′′r (t) · (θw − θr)2 (18)

4.5.1. Dry correction

In the current formulation of the algorithm no dry correction is involved.

4.5.2. Wet correction

A problem related to the correct estimation of the wet reference is that in some regions trulysaturated conditions are never captured simply due to the prevailing climate. Hence, a correc-tion needs to be applied simulating wet conditions allowing to estimate a wet reference. Theapplication of the correction is described in section 5.1.7.

4.6. Soil moisture computationSoil moisture computation is a change detection algorithm which compares the observed normal-ized backscatter to the highest (wettest) and lowest (driest) values ever observed at a certainlocation. Under the assumption of a linear relationship between normalized backscatter andsurface soil moisture, the latter can be estimated using:

Sd (t) = σ◦r (t)− σdr (t)σwr (t)− σdr (t) · 100 (19)

The estimated surface soil moisture represents the topmost soil layer (< 5 cm) and is givenin degree of saturation (Sd), ranging from 0% (dry) to 100% (wet). Sd, given in %, can beconverted into (absolute) volumetric units m3m−3 with the help of soil porosity information.

Θ (t) = p · Sd (t)100 (20)

where Θ is absolute soil moisture in m3m−3, p is porosity in m3m−3. As it can be seen inEquation 20, the accuracy of soil porosity is as much as important as the relative soil moisturecontent.

5. WARPThe TU Wien soil moisture retrieval algorithm is implemented within a software package calledsoil WAter Retrieval Package (WARP), which processes several years of Metop ASCAT Level 1bdata and produces both a model parameter database and soil moisture time series data records.An flowchart of the WARP processing steps is shown in Figure 5.1.




Figure 5.1: Overview of the WARP processing steps.

5.1. Processing stepsThe following subsections describe the implementation of the individual processing steps inWARP.

5.1.1. Re-sampling to a Discrete Global Grid (DGG)

The task of re-sampling is to interpolate scatterometer measurements, given in the orbit grid,to a fixed Earth grid. For this purpose a Discrete Global Grid (DGG) has been developed byTU Wien and is called WARP 5 grid . The WARP 5 grid contains 3264391 grid points (GP)with an equal spacing of 12.5 km in longitude and latitude. Each of the grid points is identifiedby a unique grid point index (GPI). The result of the re-sampling is a time series of interpolatedmeasurements at each GP over land. The WARP 5 is discussed in more detail in [16].The three beams on each side of ASCAT are indicated as for, mid and aft beam. For each

point in the orbit grid, all GPs within an 18 km radius are determined by a nearest-neighbour




(NN) search, from which the interpolated values for the backscatter for each of the three beams(and other such as incidence angle) are obtained as weighted average, with weighting coefficientscomputed according to the Hamming window function:

w (x) = 0.54 + 0.46 · cos(

2π · δxd

)(21)

whereby δx denotes the distance between the actual GPI and the orbit grid points in thediameter d of the search radius. The Hamming window has been chosen for interpolation,because it is also used in the creation of the Level 1b product.The result of the re-sampling step for each of the GPIs is a time series tsgpi, containing Ngpi

records.

tsgpi [i] = σ◦b,i, θb,i, φb,i, ti 1 ≤ i ≤ Ngpi (22)

each consisting of a time stamp ti and measurement triples for backscatter σ◦b,i, incidenceangle θb,i and azimuth angle φb,i. The subscript b ∈ {f,m, a} distinguishes between the for, midand aft beam. Note that the following processing steps are applied for each GPI individuallyand based on the re-sampled time series.

5.1.2. Calculation of azimuthal correction coefficients

As described in section 4.4.1 in some dedicated areas azimuthal effects on the backscatteringcoefficient are very strong. In this step correction coefficients are computed based on a polyno-mial fit estimated for each individual look configuration (12 combinations possible: 2 swaths,3 beams and 2 orbit directions), which is subtracted by a polynomial fit calculated from alllook configurations. This way, the individual incidence angle vs backscatter characteristics areadjusted to a mean relationship canceling static azimuthal effects.The correction coefficients are stored for each grid point and always used to correct the re-

sampled backscatter beforehand.

5.1.3. Calculated ESD

This step initializes the error propagation in the soil moisture retrieval algorithm. ESD iscomputed as

δi = σ◦f,i − σ◦a,i (23)

ESD [σ◦] =

√Var [δi]

2 (24)

after strong outliers (defined as ≥ 3× IQR) are masked out of the distribution of δ.

5.1.4. Estimation of slope and curvature

The slope and curvature parameters are determined as the coefficients of a straight line fittedto the so called local slopes (see Equation 11). Local slopes are estimates of the first derivativeof the backscatter vs. incidence angle dependency, and are computed as difference quotientsbetween for and mid beam, and aft and mid beam, respectively.




In order to increase the precision of the estimates, the fit for a given day is computed fromthe local slopes not only for a day, but from a time window of several days centred at the dayunder question. The width of the window is crucial for the quality of the estimates, since awindow that is too short will lead to poor precision of the estimates, while a time window thatis too long will tend to smooth local details or even average measurements taken from differentvegetation periods.In order to (i) address the issue of time window length and (ii) to compute noise estimates for

the parameters (which are difficult to obtain by error propagation in this case), a Monte Carloapproach has been implemented [8] the original time series is contaminated with independent andidentically distributed (i.i.d.) Gaussian noise, resulting in 50 noisy instances of the original timeseries. Also, for each of these instances, a window width in a range of 2–12 is randomly assigned.Now, for a subset of 27 equidistant days, the slope and curvature parameters are estimated foreach of the 50 training sets, and their final values and corresponding noise estimates are obtainedas mean and variance, respectively, of the empirical distribution of the 50 values. Finally, a fullcomplement of 366 parameter and noise values (one per day) is obtained from the 27 computedones by cubic spline interpolation (this approach has been chosen in order to save processingtime).The slope and curvature day-of-year parameter needs to be converted into time series by

mapping the time ti to the respective day of year.

σ′r,i = σ′r (doy (ti)) (25)

σ′′r,i = σ′′r (doy (ti)) (26)

5.1.5. Incidence angle normalization

The incidence angle normalization is computed by inverting Equation 7 and using the alreadyestimated slope σ′r and curvature σ′′r parameter. In order to make use of the slope and curvatureparameter, they need to be converted into a time series from their original day-of-year format.As a result, a backscatter measurement taken at an arbitrary incidence angle θ the correspondingvalue at the reference angle θr will be calculated.Letting

x =[σ◦θ,b,i, σ

′r,i, σ

′′r,i

](27)

wet get

f (x) : σ◦r,b,i = σ◦θ,b,i − σ′r,i ·∆θb,i −12 · σ

′′r,i · (∆θb,i)

2 (28)

with ∆θb,i = θb,i − θr.If we assume that the errors of the normalized backscatter, slope and curvature are uncorre-

lated, the covariance matrix of x is simply

Covx =

ESD [σ◦]2 0 00 Var [σ′r]i 00 0 Var [σ′′r ]i

(29)




The Jacobian J of f (x) is obtained as

J =(δf

δx

)=[1,−∆θb,i,−

12 · (∆θb,i)

2]

(30)

Thus, the noise variance of the normalized backscatter for beam b is

Var [σ◦r ]b,i = ESD [σ◦]2 + Var[σ′r]i · (∆θb,i)

2 + 14 ·Var

[σ′′r]i · (∆θb,i)

4 (31)

Finally, the three beams (now having been shifted to a common reference angle) are averaged

σ̄◦r,i = 13 ·

∑b∈{f,m,a}

σ◦r,b,i (32)

The corresponding variance is given by

Var [σ̄◦r ]i = 19 ·

∑b∈{f,m,a}

Var [σ◦r ]b,i (33)

As can be seen, averaging over the three beams has the effect that the variance of the noisedue to instrument noise, speckle and azimuthal effects is lowered by a factor of three. It does,however, not lower the error due to the lack of fit of the slope model [4].

5.1.6. Calculation of dry and wet reference

For a given grid point, the dry σdr and wet σwr reference are the historically lowest and highestnormalized backscatter values, respectively, measured at this location at a given day d. The dryand wet references are stored as arrays indexed by the day of year, just as slope and curvature.Hence, also in this case the parameters needs to be transformed to time series.

σdr,i = σdr (doy (ti)) (34)

σdr,i = σwr (doy (ti)) (35)

In the TU Wien backscatter model it is assumes that the vegetation (i.e., backscatter vs. inci-dence angle) curves for dormant and full vegetation intersect, and that the point of intersectiondepends on the soil moisture conditions: the intersection points for the driest and wettest condi-tions are called dry and wet crossover angles, respectively. The wet crossover angle θw assumedto be at 40◦ (which is identical to the reference incidence angle θr) and the dry crossover angleθd is located at 25◦. Both parameters have been determined empirically [4]. The importance ofthe crossover angle concept lies in the fact that the crossover angles, vegetation has no effect onbackscatter [5].

In order to determine the lowest backscatter value irrespective of the vegetation conditions,the normalized backscatter measurements are first shifted to the dry crossover angle using Equa-tion 36.

σ◦d,i = σ̄◦r,i + σ′r,i · (∆θd) + 12 · σ

′′r,i · (∆θd)

2 (36)

with ∆θd = (θd − θr), and corresponding noise estimate




Var [σ◦d]i = Var [σ̄◦r ]i + Var[σ′r]i · (∆θd)

2 + 14 ·Var

[σ′′r]i · (∆θd)

4 (37)

From the resulting empirical distribution, an explicit uncertainty range, defined as a 95%2-sided confidence interval, is used to separate the extreme low and high values. The average ofthe Nb smallest values is used as an estimate of the lowest backscatter value at the dry crossoverangle.

Cd = 1Nb·Nb∑j=1

σ◦d,∏{j} (38)

whereby∏

is a permutation that sorts the time series in ascending order w.r.t. the backscattervalues. Since the normalized backscatter values have different noise variances (depending on theday and incidence angle), there exists no simple general expression for the noise variance of theaverage, but we have (assuming the noise contributions of the measurements are uncorrelated)

Var[Cd]

= 1N2b

·Nb∑j=1

Var [σ◦d]∏{j} (39)

Finally, for each day t, Cd has to be shifted back to the reference incidence angle along itscorresponding vegetation curve, in order to obtain σ◦d,i.

σdr,i = Cd − σ′r,i · (∆θd)−12 · σ

′′r,i · (∆θd)

2 (40)

The noise is given by

Var[σdr

]i

= Var[Cd]−Var

[σ′r]i · (∆θd)

2 − 14 ·Var

[σ′′r]i · (∆θd)

4 (41)

This is the final estimate of the noise variance for the dry reference. The estimates of the wetreference

σwr,i = Cw − σ′r,i · (∆θw)− 12 · σ

′′r,i · (∆θw)2 (42)

where ∆θw = θw − θr and its corresponding noise

Var [σwr ]i = Var [Cw]−Var[σ′r]i · (∆θw)2 − 1

4 ·Var[σ′′r]i · (∆θw)4 (43)

are obtained in a completely analogue fashion, but instead the Nu values are averaged at thewet crossover angle θw in order to compute Cw.It is worth mentioning that due the selection of the crossover angles, which are fixed to 25◦

for σdr and 40◦ for σwr globally, the dry reference is changing over time, whereas σwr is constant(i.e. it does not depend on the day). This is because the wet crossover angel is equal to thereference angle, and thus ∆θw = 0.

5.1.7. Wet correction

The application of wet correction is based on an external climate data set [17], since scatterom-eter measurements alone are not sufficient to locate these regions. The wet correction is done in




two steps: first the lowest level of the wet reference is set to -10 dB and subsequently, in regionswith rarely saturated soil moisture conditions σwr values are raised until a sensitivity (defined asσwr − σdr ) of at least 5 dB has been reached [9].

5.1.8. Computation of soil moisture

Under the assumption of a linear relationship between normalized backscatter and surface soilmoisture, the latter can be estimated using:

Sd,i =σ̄◦r,i − σdr,iσwr,i − σdr,i

· 100 (44)

By proceeding with the derivation of the error propagation, we obtain the following noiseestimate of the soil moisture

Var [Sd]i = Var [σ̄◦r ]i ·1(

σwr,i − σdr,i)2 · 1002

+ Var[σdr

]i·

σ̄◦r,i − σwr,i(σwr,i − σdr,i

)2

2

· 1002

+ Var [σwr ]i ·

σ̄◦r,i − σdr,i(σwr,i − σdr,i

)2

2

· 1002

(45)

5.2. Side note on error propagationLet x = [x1, · · · , xp] be a p-dimensional observation vector. x is assumed to be an instance ofp-dimensional random variable, with known covariance matrix Σx. We are interested in howthe covariance transforms under a mapping: Rp → Rq,y = f (x), i.e., given x and f , we wouldlike to know the covariance of y, Σy, If f is a linear mapping of the form y = Ax+b, A ∈ Rq×p,b ∈ Rq, then the covariance transforms like

Σy = AΣxAT (46)

whereby AT denotes the transpose of A.If, on the other hand, f is a non-linear mapping, we first linearise it by replacing its first order

Taylor approximation about the operation point x0:

y = f (x) ≈ f (x0) +(δf

δx

)(x− x0) (47)

whereby(δfδx

)is the Jacobian J of f . Putting everything together we finally obtain

Σy =(δf

δx

)Σx

(δf

δx

)T(48)




for the variance of y under the mapping f . Equation 48 is the workhorse of the WARP errorpropagation scheme.

5.3. Limitations and caveatsThe soil moisture algorithm is applied on a global basis (i.e. for each grid point over land),but in certain situations, for example when dense forest, snow cover, frozen soil, open wateror topographic complex area dominates the satellite footprint, the retrieval of soil moisture isheavily impacted or not possible at all. In addition, not each case (e.g. frozen soil, snow cover,open water) can be modeled by the error propagation and thus, it is even more importantto reliably mask affected measurements. Advisory and processing flags indicate such criticalcases, but it is left to the user to judge whether the soil moisture data is meaningful or not.Users are advised to to use the best auxiliary data available to improve the flagging of snow,frozen soil and (temporally) standing water. However, under the following conditions a surfacesoil moisture retrieval from Metop ASCAT should be most suited: low to moderate vegetationregimes, unfrozen and no snow, low to moderate topographic variations, no wetlands and coastalareas.

5.4. Processing modesThe software package WARP can operate in two modes: full re-processing and extension. Thedifference between the two modes is that in latter case no new model parameters are computedand new Level 1b are re-sampled, normalized and transformed to soil moisture based on pre-existing model parameters. These model parameters have to be derived from the same Level 1b(i.e. same calibration, resolution, etc.) used for the extension.

5.5. Input dataThe input data is described in detail in the Product User Manual (PUM) [18].

5.6. Level 2 output dataThe following parameters are part of the Level 2 output data. A more detailed list with datatype and data format can be found in the Product User Manual (PUM) [18].

5.6.1. Surface soil moisture and its noise

The surface soil moisture estimate represents the topmost soil layer (< 5 cm) and is given indegree of saturation (Sd), ranging from 0% (dry) to 100% (wet). Sd can be converted into(absolute) volumetric units with the help of soil porosity information (see Equation 20).In extreme cases, the interpolated backscatter at θr may exceed the dry or the wet backscatter

reference. In these cases, the value is less than 0% or more than 100%. When this happens, thevalues are artificially set to 0% and 100% respectively. At the same time, the relevant correctionflags are set.




5.6.2. Processing and correction flags

These flags indicate several conditions of interest. Processing flags are set to flag the reason fora soil moisture value not being provided in the product and correction flags are set to flag asoil moisture value provided in the product, but that has been modified after the retrieval fordifferent reasons, according to quality criteria based on the data itself.

6. References[1] J. Figa-Saldana, J. J. W. Wilson, E. Attema, R. Gelsthorpe, M. R. Drinkwater, and A. Stof-

felen, “The Advanced Scatterometer (ASCAT) on the Meteorological Operational (MetOp)Platform: A follow on for European Wind Scatterometers,” Canadian Journal of RemoteSensing, vol. 28, no. 3, pp. 404–412, 2002.

[2] “ASCAT Product Guide,” Tech. Rep. Doc. No: EUM/OPS-EPS/MAN/04/0028, v5, 2015.

[3] W. Wagner, G. Lemoine, M. Borgeaud, and H. Rott, “A study of vegetation cover effectson ERS scatterometer data,” vol. 37, no. 2II, pp. 938–948.

[4] W. Wagner, G. Lemoine, and H. Rott, “A method for estimating soil moisture from ERSscatterometer and soil data,” vol. 70, no. 2, pp. 191–207.

[5] W. Wagner, J. Noll, M. Borgeaud, and H. Rott, “Monitoring soil moisture over the canadianprairies with the ERS scatterometer,” vol. 37, pp. 206–216.

[6] K. Scipal, W. Wagner, M. Trommler, and K. Naumann, “The global soil moisture archive1992-2000 from ERS scatterometer data: first results,” vol. 3. IEEE, pp. 1399–1401.

[7] Z. Bartalis, W. Wagner, V. Naeimi, S. Hasenauer, K. Scipal, H. Bonekamp, J. Figa, andC. Anderson, “Initial soil moisture retrievals from the METOP-a advanced scatterometer(ASCAT),” vol. 34.

[8] V. Naeimi, “Model improvements and error characterization for global ERS and METOPscatterometer soil moisture data,” phD dissertation.

[9] V. Naeimi, K. Scipal, Z. Bartalis, S. Hasenauer, and W.Wagner, “An improved soil moistureretrieval algorithm for ERS and METOP scatterometer observations,” vol. 47, no. 7, pp.1999–2013.

[10] R. V. Gelsthorpe, E. Schied, and J. J. W. Wilson, “ASCAT - MetOp’s Advanced Scat-terometer,” ESA Bull. ISSN 0376-4265, vol. 102, pp. 19–27, 2000.

[11] J. J. W. Wilson, C. Anderson, M. A. Baker, H. Bonekamp, J. F. Saldana, R. G. Dyer, J. A.Lerch, G. Kayal, R. V. Gelsthorpe, M. A. Brown, E. Schied, S. Schutz-Munz, F. Rostan,E. W. Pritchard, N. G. Wright, D. King, and U. Onel, “Radiometric Calibration of theAdvanced Wind Scatterometer Radar ASCAT Carried Onboard the METOP-A Satellite,”IEEE Transactions on Geoscience and Remote Sensing, vol. 48, pp. 3236–3255, 2010.

[12] R. D. Lindsley, C. Anderson, J. Figa-Saldana, and D. G. Long, “A Parameterized ASCATMeasurement Spatial Response Function,” IEEE Transactions on Geoscience and RemoteSensing, pp. 1–10, 2016.




[13] Z. Bartalis, K. Scipal, and W. Wagner, “Azimuthal anisotropy of scatterometer measure-ments over land,” vol. 44, no. 8, pp. 2083–2092.

[14] A. K. Fung, Microwave scattering and emission models and their applications. ArtechHouse.

[15] F. T. Ulaby, R. K. Moore, and A. K. Fung, Microwave Remote Sensing: Active and Passive.Vol. III - Volume Scattering and Emission Theory, Advanced Systems and Applications.Artech House, Inc.

[16] “WARP 5 Grid,” Tech. Rep. v0.3, 2013.

[17] M. Kottek, J. Grieser, C. Beck, B. Rudolf, and F. Rubel, “World map of the köppen-geigerclimate classification updated,” vol. 15, pp. 259–263.

[18] “Product User Manual (PUM) Soil Moisture Data Records, Metop ASCAT Soil MoistureTime Series,” Tech. Rep. Doc. No: SAF/HSAF/CDOP2/PUM, v0.3, 2016.




AppendicesA. Introduction to H SAFH SAF is part of the distributed application ground segment of the “European Organization forthe Exploitation of Meteorological Satellites (EUMETSAT)”. The application ground segmentconsists of a Central Application Facilities located at EUMETSAT Headquarters, and a networkof eight “Satellite Application Facilities (SAFs)”, located and managed by EUMETSAT MemberStates and dedicated to development and operational activities to provide satellite-derived datato support specific user communities (see Figure A.1):

Figure A.1: Conceptual scheme of the EUMETSAT Application Ground Segment.

Figure A.2 here following depicts the composition of the EUMETSAT SAF network, with theindication of each SAF’s specific theme and Leading Entity.

B. Purpose of the H SAFThe main objectives of H SAF are:

a) to provide new satellite-derived products from existing and future satellites with sufficienttime and space resolution to satisfy the needs of operational hydrology, by generating, cen-tralizing, archiving and disseminating the identified products:

• precipitation (liquid, solid, rate, accumulated);• soil moisture (at large-scale, at local-scale, at surface, in the roots region);




Figure A.2: Current composition of the EUMETSAT SAF Network.

• snow parameters (detection, cover, melting conditions, water equivalent);

b) to perform independent validation of the usefulness of the products for fighting against floods,landslides, avalanches, and evaluating water resources; the activity includes:

• downscaling/upscaling modelling from observed/predicted fields to basin level;• fusion of satellite-derived measurements with data from radar and raingauge networks;• assimilation of satellite-derived products in hydrological models;• assessment of the impact of the new satellite-derived products on hydrological applica-tions.

C. Products / Deliveries of the H SAFFor the full list of the Operational products delivered by H SAF, and for details on their charac-teristics, please see H SAF website hsaf.meteoam.it. All products are available via EUMETSATdata delivery service (EUMETCast2), or via ftp download; they are also published in the H SAFwebsite3.All intellectual property rights of the H SAF products belong to EUMETSAT. The use of

these products is granted to every interested user, free of charge. If you wish to use theseproducts, EUMETSAT’s copyright credit must be shown by displaying the words “copyright(year) EUMETSAT” on each of the products used.

2http://www.eumetsat.int/website/home/Data/DataDelivery/EUMETCast/index.html3http://hsaf.meteoam.it

http://www.eumetsat.int/website/home/Data/DataDelivery/EUMETCast/index.html

http://hsaf.meteoam.it




D. System OverviewH SAF is lead by the Italian Air Force Meteorological Service (ITAF MET) and carried on bya consortium of 21 members from 11 countries (see website: hsaf.meteoam.it for details)Following major areas can be distinguished within the H SAF system context:

• Product generation area

• Central Services area (for data archiving, dissemination, catalogue and any other central-ized services)

• Validation services area which includes Quality Monitoring/Assessment and HydrologicalImpact Validation.

Products generation area is composed of 5 processing centres physically deployed in 5 differentcountries; these are:

• for precipitation products: ITAF CNMCA (Italy)

• for soil moisture products: ZAMG (Austria), ECMWF (UK)

• for snow products: TSMS (Turkey), FMI (Finland)

Central area provides systems for archiving and dissemination; located at ITAF CNMCA(Italy), it is interfaced with the production area through a front-end, in charge of productcollecting. A central archive is aimed to the maintenance of the H SAF products; it is alsolocated at ITAF CNMCA.Validation services provided by H SAF consists of:

• Hydrovalidation of the products using models (hydrological impact assessment);

• Product validation (Quality Assessment and Monitoring).

Both services are based on country-specific activities such as impact studies (for hydrologicalstudy) or product validation and value assessment. Hydrovalidation service is coordinated byIMWM (Poland), whilst Quality Assessment and Monitoring service is coordinated by DPC(Italy): The Services activities are performed by experts from the national meteorological andhydrological Institutes of Austria, Belgium, Bulgaria, Finland, France, Germany, Hungary, Italy,Poland, Slovakia, Turkey, and from ECMWF.

Documents

Algorithm Theoretical Baseline Document (ATBD) Surface ...hsaf.meteoam.it/documents/ATDD/SSM_ASCAT_DR_ATBD_v0.4.pdf · Algorithm Theoretical Baseline Document (ATBD) Surface Soil