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File: BEC-SMOS-0001-PD.pdf Version: 1.3 Date: 19/09/2014 Page: 1 of 20

SMOS-BEC OCEAN AND LAND PRODUCTS DESCRIPTION

Abstract: This technical note describes the products distributed by the SMOS-BEC team through itsdata visualization and distribution service CP34-BEC http://cp34-bec.cmima.csic.es

http://cp34-bec.cmima.csic.es/http://cp34-bec.cmima.csic.es/

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Title: SMOS-BEC Ocean and Land Products Description.

Contents

1 Introduction 4

2 Ocean Products 5

2.1 SMOS ocean salinity data ltering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

2.1.1 Geophysical lters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

2.1.2 Retrieval lters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

2.1.3 Geometrical lters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62.2 Ocean salinity Level 3 products . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

2.2.1 Binned products . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

2.2.2 Optimal interpolation products . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

2.3 Ocean salinity Level 4 products . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

2.3.1 Fused products using singularity analysis techniques . . . . . . . . . . . . . . . 8

2.4 Ocean salinity reprocessing campaign . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

2.5 Ocean auxiliary data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

2.5.1 Singularity exponents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

2.6 Ocean les structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

2.7 Ocean products list . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

3 Land Products 13

3.1 Soil moisture Level 3 products . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

3.1.1 Soil moisture data ltering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

3.1.2 ISEA land product . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

3.1.3 Binned land products . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

3.2 Soil moisture Level 4 products . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

3.2.1 High resolution soil moisture: delayed . . . . . . . . . . . . . . . . . . . . . . . 17

3.2.2 High resolution soil moisture: near real-time . . . . . . . . . . . . . . . . . . . 17

3.3 Land les structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

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3.4 Land products list . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

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1 INTRODUCTION

The ESA’s Soil Moisture and Ocean Salinity (SMOS) mission is an innovative Earth Observationsatellite launched on November 2009 to remotely sense soil moisture over the land surfaces and seasurface salinity over the oceans ([ Kerr et al., 2010 ], [Font et al., 2010 ]). The SMOS single payloadis the Microwave Imaging Radiometer using Aperture Synthesis (MIRAS), a L-band 2D syntheticaperture radiometer with multiangular and full polarimetric capabilities. It is a completely new typeof instrument, a technological challenge that has required the development of dedicated calibrationand image reconstruction algorithms ( [McMullan et al., 2008 ]). The SMOS Barcelona Expert Center(BEC) is an ESA Expert Support Laboratory dedicated to developing and testing new algorithms toimprove the baseline SMOS Level 2 products. Also the BEC aims at generating higher added-valueproducts of interest for a broad range of users. The SMOS-BEC products for sea surface salinityand soil moisture are generated and distributed through the Production Center of Level 3 and 4

(CP34) since the beginning of the mission in an operational way. In the near future, the inclusion of complementary remotely sensed products is envisaged.

This document describes the products currently created and distributed by the BEC through theCP34.

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2 OCEAN PRODUCTS

2.1 SMOS ocean salinity data lteringThe SMOS data used to compute the Ocean products described in section 2.2 (and in addition totheir derivatives of Level 4) come from Level 2 Ocean Salinity User Data Product (UDP) and OceanSalinity Data Analysis Product (DAP). These UDP and DAP les are generated by ESA and in-clude geophysical parameters, a theoretical estimate of their accuracy, ags, and descriptors for theproduct quality for three di ff erent roughness models (see [DPG, 2012 , section 4.2.2.1] for a detaileddescription of this product). All products developed at BEC are based on the third roughness model[Guimbard et al., 2012 ].

The quality ags and descriptors from UDP and DAP les allow discarding unreliable Sea Surface

Salinity values. In order to create Level 3 and Level 4 products, three categories of lters are appliedto ocean Level 2 data: geophysical lters, retrieval lters and geometrical lters. Each ltering processis coded using a 7 characters string that appears in the name of the resulting products (string EEEEEEEin section 2.6). The current ltering process, coded as 2013001 , follows the rules described in sections2.1.1, 2.1.2 and 2.1.3

2.1.1 Geophysical lters

These lters are related to the geophysical conditions present in the area (grid point) and the time of measurement [ DPG, 2012 , tables 4-19 to 4-21]. Retrieved salinity in a given gridpoint is discarded if any of the following conditions is accomplished:

• Suspect ice presence (more than 50% of measures having a positive test ice)

• Rain (rain rate larger than 0.01 mm/h)

• High number of outliers (more than 20% of measures)

• Too many measures agged for sunglint or moonglint (10%)

• Salinity is retrieved using a too low number of valid measures (less than 30 brightness tempera-ture valid measures)

• Wind speed is larger then a given threshold (set to 12 m/s)

• Grid point is suspected of being contaminated by RFI (more than 33% of RFI outlier)

2.1.2 Retrieval lters

The iterative retrieval scheme implemented in the L2 processor provides information about its ownreliability. This information is summarized in some retrieval ags stored in Level 2 UDP les. Theconditions used to discard a SSS value in the ltering process are:

• Iterative scheme returns an error

• Retrieved value is outside range (SSS must be positive and lower than 42 psu)

• High retrieval value of sigma (theoretical uncertainty computed for SSS larger than 5 psu)

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• Normalised cost function at the last iteration is below the signicance level (5%)

• Maximum number of iteration (20) reached before convergence using forward model

• Iterative loop ends because Marquardt increment reaches a given threshold (100)

• The total number of available measures is too low (16)

2.1.3 Geometrical lters

It is known that the external parts of the swath provide lower quality data [ Zine et al., 2007 ]. Thus,only measures taken up to 360 km from the satellite track are considered to generate Ocean Salinityproducts.

2.2 Ocean salinity Level 3 products

According to the direction of the SMOS orbit passes, SMOS products can be classied in ascending ,descending and both products. These products are created in a variety of averaging periods: 3 daysand 9 days generated every 3 days, monthly, seasonal (quaterly) and annual. The spatial averaging iscomputed by default in a regular lat-lon grid of 0.25o × 0.25o

2.2.1 Binned products

The binned maps are constructed by simple weighted averaging of the ltered L2 SSS values. Theweight average of Sea Surface Salinity in the cell k is given by the expression [ Boutin et al., 2012 ]:

hSSS i k =N

Xi =1 wi SSS i , where wi =1

R 2i σ2i

N

P j =11

R 2j σ2j

, (1)

σ i is the theoretical uncertainty computed for SSS at a grid point i, R i is the equivalent footprint size(diameter of the equivalent circle) centered on the grid point i and N is the number of grid pointscontained in the cell k.

Each netCDF le contains:

• Sea Surface Salinity

• Number of L2 grid points averaged in each cell

• Variance of these SSS values

• SSS anomaly with WOA 2009

The WOA 2009 used to compute anomaly is linear interpolated to the center of the averaging periodof each product.

2.2.2 Optimal interpolation products

SMOS Level 2 SSS data are optimally interpolated (Objective Analysis) to produce maps of higherconsistency and fewer gaps as compared to the L3 binned products. To reduce the computational

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(a) Binned product

(b) Optimal Interpolated product corresponding to the above binned product

Figure 1: Example of distributed Level 3 products

cost, 0 .25o × 0.25o grid binned L2 data are used to feed the OI algorithm. The OI is performed usingmonthly WOA 2009 data as background eld.

L3 products, as well as L4 products, are validated with near-surface measurements provided by Argoprolers, which allow us to dene several quality metrics. We have found that the implementation of objective analysis signicantly improve data accuracy with respect to binned maps.

The resulting product contains:

• Sea Surface Salinity analysis


• Background eld

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2.3 Ocean salinity Level 4 products

2.3.1 Fused products using singularity analysis techniques

These products are obtained with a singularity analysis based fusion technique. A template variableof good quality (Sea surface temperature, SST, in our case, see section 2.5.1) is used as template torestore the multifractal structure of singularity fronts in a noisy variable (SSS in our case). Furtherinformation on the multifractal structure of ocean scalars can be found in [Turiel et al., 2009 ].

Singularity analysis based fusion can be used not only to improve the signal level, but also to increasethe spatial and time resolution of fused maps, provided that the template (SST for us) has the targetspace and time resolutions following the local relationship

SSS = a × SST + b (2)

where a and b are known as the local slope and intercept coecients respectively.

The resulting product contains:

• Sea Surface Salinity analysis


• Local slope coecient ( a from equation 2)• Local intercept coecient ( b from equation 2)

• Local regression coeciet


2.4 Ocean salinity reprocessing campaign

The ESA reprocessing campaign covers all the SMOS data available from January 2010 until December2013 up to L2. The processors used are: L1 Operational Processor L1OP v5.04 and L2 OperationalProcessor L2OSOP v5.50, which are the ones used in the SMOS DPGS operational chain, fromDecember 2011 to date. As a part operational chain, Level 1 data (brightness temperature at antennalevel) bias is corrected by applying an Ocean Target Transformation (OTT) [ Tenerelli and Reul, 2010 ].During the reprocessing campaign, the method to ingest the OTT into the processing chain wasdiff erent from the DPGS operation chain:

• In the DPGS operational chain the OTT is computed once per month, using 6 days of ltereddata from the equatorial Pacic. Then the OTT is used to process data few weeks after its

computation.• In the reprocessing campaign the OTT is computed every 2 weeks in the same region, but the

computation period coincides with the period in which the OTT is applied. This is possiblesince reprocessing is pbviously perfored in delayed mode.

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Figure 2: Fused product from the binned one shown in gure 1(a)

This modication on the OTT validity time (shift backward) leads to more consistent L2 SMOS SSSdata than the DPGS data. In particular, temporal inconsistencies (biases) are much reduced, as shownin gure 3.

So the reprocessed L2 data set is more stable and of higher quality than the DPGS operationalchain data. The L3/L4 reprocessing campaign (coded as 2013001) was performed at SMOS-BEC byltering Level 2 data as described in section 2.1. The maps are built for ascending, descending andboth (ascending+descending) orbits. The products described in sections 2.2 and 2.3 have been alsogenerated with L2 reprocessed data.

It is worth noting that SMOS commissioning phase ended on May 20, 2010. Therefore, it is recom-mended to avoid the use of SMOS data prior to June 2010. Note also that, due to technical problems,SMOS have not acquired reliable measures from December 27, 2010 to January 10, 2011.

2.5 Ocean auxiliary data

2.5.1 Singularity exponents

For any given ocean scalar (SST, SSS, SSH, Chlorophyll Concentration and even Water LeavingRadiances) singularity exponents can be calculated. Singularity exponents are dimensionless measuresof the degree of regularity or irregularity of a function at each of its domain points. They extend theconcept of Holder exponents, such that positive exponents imply that the function is continuous andhas a given number of derivatives, while negative exponents imply that the function is irregular andtherefore experiences transitions, jumps and eventually divergences to innity.

For obtaining singularity exponents we follow the theory explained in [ Turiel et al., 2008a ] and[Turiel et al., 2008b ]. The modulus of the gradient of the scalar is evaluated at each point in the

domain. The resulting eld is projected on a given wavelet at diff

erent resolution scales, such that thedependence of the projection on the resolution scale can be assessed by means of a log-log regression,the slope of which is the singularity exponent.

Singularity exponents derived from regular scalars such as SSS, SST or SSH are lower-bounded at -1,

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(a) Mean (ascending case) (b) Mean (descending case)

(c) Standard deviation (ascending case) (d) Standard deviation (descending case)

Figure 3: Diff erences between SMOS and ARGO for the reprocessed L3 binned maps in the latituderange 60oN-60oS. Each point stands for a 9-days map at 0.25 x 0.25 lat-lon resolution

since they are nite variation functions (see [Turiel and Parga, 2000 ]). They have no upper bound,although values beyond 2 are rare.

It has been veried ([ Turiel et al., 2005 ]; [Isern-Fontanet et al., 2007 ]; [Turiel et al., 2009 ]) that singu-larity exponents derived from SST maps track with remarkable precision the streamlines of the generalcirculation of the ocean. In fact, there is some evidence ( [Isern-Fontanet et al., 2007 ]; [Nieves et al., 2007 ])that di ff erent ocean scalars have the same singularity exponents – what should be expected if singu-larity exponents are the result of ow advection, regardless of the specic process of any particularocean scalar.

This correspondence of singularity exponents can be exploited to reduce the e ff ects of noise andartefacts on a given scalar map using the information conveyed by the singularity exponents derivedfrom a diff erent, higher-quality map. We have implemented a numerical algorithm capable of usingthe singularity exponents of one scalar eld to improve the quality of a di ff erent scalar eld.

The singularity exponent product distributed has been generated from the daily OSTIA SST product(downloadable at MyOcean webpage http://www.myocean.eu ). The algorithm described above to-gether with the OSTIA SST product are used to derive our L4 product, which outperforms the SMOSSSS L3 products. The resolution of the product has been degraded to one fourth of degree to match

that of SSS L3 maps used to generate the L4 product.Singularity exponent maps are also useful for front identication, eddy tracking and assessing mesoscaleactivity. For such reason we distribute them here along with the other products (for instance, toproduce the SMOS L4 SSS products descibed in 2.3.1 using OSTIA SST products as template).

http://www.myocean.eu/http://www.myocean.eu/

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Figure 4: Singularity exponents derived from OSTIA SST maps at 0 .25o resolution

2.6 Ocean les structure

The resulting Level 3 and Level 4 products are distributed in netCDF format and the name of eachle follows the layout:

BEC_AAAAAA_B_CCCCCCCCCCCCCCC_DDDDDDDDDDDDDDD_EEEEEEE_FFF_GGG.nc

Where each eld of the lename is as follows:

• AAAAAA: is the product’s name:

– BINNED: Binned product

– OI : Optimal Interpolation product

– L4 SST: Fused product using singularity analysis techniques derived from SST

– EXPSST: Singularity Exponents

• B: Indicates the orbit composition of the product.

– A for products composed by ascending orbits

– D for products composed by descending orbits

– B for products composed by both types of orbits

• CCCCCCCCCCCCCCC: Starting UTC time (YYYYMMDDThhmmss) of the rst L2 product usedto create the L3/L4 product. This is an inherited value in products not derived directly from

Level 2 orbits.• DDDDDDDDDDDDDDD: Ending UTC time (YYYYMMDDThhmmss) of the last L2 product used to

create the L3/L4 product. This is an inherited value in products not derived directly from Level2 orbits (Optimal Interpolation and L4 products).

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Spatial resolution Type Generation Rate Averaging Period Product Orbit passes Code

0.25 degrees Reprocessed / Near Real Time

3 days

3 days

binned

ascending XXXBIN003D025Adescending XXXBIN003D025D

both XXXBIN003D025B

9 days

ascending XXXBIN009D025A

descending XXXBIN009D025Dboth XXXBIN009D025B

optimal interpolatedascending XXXOI 009D025Adescending XXXOI 009D025D

both XXXOI 009D025B

fused using singularity analysisascending XXXFUT009D025Adescending XXXFUT009D025D

both XXXFUT009D025B

monthly 1 natura l month

binnedascending XXXBIN001M025Adescending XXXBIN001M025D

both XXXBIN001M025B

optimal interpolatedascending XXXOI 001M025Adescending XXXOI 001M025D

both XXXOI 001M025B

fused using singularity analysisascending XXXFUT001M025Adescending XXXFUT001M025D

both XXXFUT001M025B

quaterly seasonal

binnedascending XXXBIN003M025Adescending XXXBIN003M025D

both XXXBIN003M025B

optimal interpolatedascending XXXOI 003M025Adescending XXXOI 003M025D

both XXXOI 003M025B

fused using singularity analysisascending XXXFUT003M025Adescending XXXFUT003M025D

both XXXFUT003M025B

annual annual

binnedascending XXXBIN001Y025Adescending XXXBIN001Y025D

both XXXBIN001Y025B

optimal interpolatedascending XXXOI 001Y025Adescending XXXOI 001Y025D

both XXXOI 001Y025B

fused using singularity analysisascending XXXFUT001Y025Adescending XXXFUT001Y025D

both XXXFUT001Y025B

Table 1: Ocean products distributed by BEC. Three rst letters of the code indicated as XXX are NRTfor near real time products and REP for reprocessed products. Code string is necessary to automaticallydownload a given product using getBEC tool

• EEEEEEE: Internal code that designates the ltering applied. This is an inherited value in productsnot derived directly from Level 2 orbits.

• FFF: Grid size of the product in a lat-lon grid multiplied by 100

• GGG: Version number of the le starting at 001

2.7 Ocean products list

The list of ocean products distributed by CP34 is summarized in table 1

In order to automatically download a given type of product, a Linux-based tool named getBEC isoff ered to users. Registered users can download this tool from http://cp34-bec.cmima.csic.es/bec-tools/

http://cp34-bec.cmima.csic.es/bec-tools/http://cp34-bec.cmima.csic.es/bec-tools/http://cp34-bec.cmima.csic.es/bec-tools/http://cp34-bec.cmima.csic.es/bec-tools/

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3.1.2 ISEA land product

Daily maps of soil moisture, optical thickness and dialectric constant (real and imaginary part) are

constructed from level 2 UDP products with neither spatial nor temporal averaging. Ascending anddescending orbits are processed separately. The resulting product contains:

• Latitude

• Longitude

• Grid point ID (ISEA grid point identier)

• Soil Moisture value ( m 3 /m 3 )

• Data Qualiyty Index value for the soil moisture estimate. It is a measure of the standard

deviation error in the estimate ( m3

/m3

)• Optical thickness at the nadir direction ( Np)

• Data Qualiyty Index value for the optical thickness estimate ( Np)

• Real part of retrieved dielectric constant

• Data Quality Index value for the real part of retrieved dielectric constant

• Imaginary part of retrieved dielectric constant

• Data Qualiyty Index value for the imaginary part of retrieved dielectric constant

3.1.3 Binned land products

Daily soil moisture maps in EASE-ML 25km grid are constructed by DQX-weighted averaging. Theaveraging of soil moisture in the cell k is computed following the expression:

hSM i k =N

Xi =1 wi SM i , where wi =1

DQX 2iN

P j =11

DQX 2j

. (3)

The averaging of the associatd DQX ( hDQX i k ) is computed as:

1hDQX i 2k

=N

Xi=1 1DQX 2i . (4)The averaged spatial variance of the soil moisture estimates ( V ark ) is computed as:

V ark =

N

Pi =11

DQX 2i

N

Pi =11

DQX 2i

2

−N

Pi =11

DQX 4i

N

Xi =1SM 2i

DQX 2i− hSM i 2k

N

Xi =11

DQX 2i ! (5)

Ascending and descending orbits are processed separately. These products are created in a varietyof generation rates and averaging periods: 1 and 3 days -generated daily-, 9 days -generated every 3days-, monthly, seasonal (quaterly) and annual (see Table 2).

The elds given per grid cell are:

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Figure 6: SMOS soil moisture L3 3-days binned maps. The plots show the soil moisture evolutionduring the Colorado in September 2013. Heavy rain was received from 11 to 16 of September.

Figure 7: SMOS soil moisture L3 monthly binned maps. The plots show the mean values of Septemberfor the four years of the mission in the same region where inundation happened.

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• Soil Moisture ( hSM i k of equation 3)

• DQX (hDQX i k of equation 4)

• Variance of SM averaged in each cell ( V ark of equation 5)

• Number of L2 soil moisture estimates used in the computation ( N of equation 3)

3.2 Soil moisture Level 4 products

Soil moisture is a key state variable that links the Earth’s water, energy and carbon cycles, and itsvariations a ff ect the evolution of weather and climate over continental regions. The ESA SMOS is therst satellite mission ever designed to measuring this variable, and its accurate observations of soilmoisture are helping to improve our understanding of water and energy uxes interactions between the

atmosphere, the soil surface and subsurface at a global scale. However, its spatial resolution (on theorder of 40 km) prevents SMOS data from being applied in small scale applications, such as on-farmwater management, ood prediction or meso-scale weather forecasting.

Figure 8: Disaggregated SMOS soil moisture map at 1 km spatial resolution over the Iberian Peninsula,from July 7, 2012 (6 A.M.) using the proposed algorithm. Empty areas in the image correspond toclouds masking MODIS observations or quality-ltered SMOS TB.

One key research line at SMOS-BEC is the development of data fusion algorithms to provide down-scaled SMOS-based soil moisture information resolving the dynamics within 100 m to 1 km catchments.Accurate knowledge of the soil moisture status at these scales is essential to understand how to manage

and utilise soil water -one of the Earth’s scarcest and most valuable natural resource- to its maximumpotential.

An innovative downscaling approach for SMOS has been developed, which combines MODIS Visi-ble/Infrared data with SMOS brightness temperatures into high-resolution soil moisture maps. To

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date, validation results from comparison with in situ data over a selected suite of representative sitessupport the use of this technique; high resolution soil moisture maps are shown to nicely reproducesoil moisture dynamics at 1 km without a signicant degradation of the root-mean-squared error withrespect to the SMOS L2 product [ Piles et al., 2011 ], [Sanchez-Ruiz et al., 2014 ], [Piles et al., 2014 ].

This algorithm has been implemented at SMOS-BEC facilities and high resolution soil moisture mapsover the Iberian Peninsula are being distributed: maps from the rst three years of SMOS in orbit areavailable (delayed mode) and two near real-time maps are daily generated corresponding to ascendingand descending overpasses with a delay of less than 12 hours. These maps are already being used assupporting information for forest brigades within the Catalonia region.

3.2.1 High resolution soil moisture: delayed

A data set of soil moisture maps covering the Iberian Peninsula at 1km spatial resolution since January

2011 up to the most recent processing date is provided. It contains two maps per day, corresponding toSMOS ascending (6 A.M.) and descending (6 P.M.) passes. Maps are obtained using the downscalingalgorithm in [Piles et al., 2014 ], which combines the brightness temperature measurements from ESASMOS, with Land Surface Temperature and NDVI (Normalized Di ff erence Vegetation Index) datafrom Aqua MODIS day passes. The latest released SMOS data is available at SMOS-BEC facilities;MODIS version 5 MYD11A1 products are freely distributed by the U.S. Land Processed DistributedActive Archive Center ( http://www.lpdaac.usgs.gov ).

3.2.2 High resolution soil moisture: near real-time

Soil moisture maps covering the Iberian Peninsula at 1km of spatial resolution are provided in near realtime (delay < 12 h). Two maps per day are generated, corresponding to SMOS ascending (6 A.M.) anddescending (6 P.M.) passes. Maps are obtained using the downscaling algorithm in [Piles et al., 2011 ],which combines the brightness temperature measurements from ESA SMOS, with Land Surface Tem-perature and NDVI (Normalized Di ff erence Vegetation Index) data from Terra/Aqua MODIS daypasses. The use of MODIS Terra LST is prefered. Nevertheless, downscaled maps using LST yieldbroadly consistent results in [Piles et al., 2014 ]. Hence, Aqua is used when Terra LST is not available(i.e. masked by clouds). SMOS latest released data in near-time time is available at SMOS-BECfacilities; MODIS data in near real-time is kindly provided by LATUV ( http://www.latuv.uva.es ),Valladolid University.

3.3 Land les structureSMOS BEC Land products are distributed in netCDF format with the following naming convention:

BEC_AAAAAA_B_CCCCCCCCCCCCCCC_DDDDDDDDDDDDDDD_EEEEEEE_FFF_GGG.nc,

where each eld of the lename is as follows:

• AAAAAA: is the product’s name:

– BIN SM: L3 Soil Moisture products

– HDE SM: L4 high resolution delayed soil moisture products

– HNR SM: L4 high resolution near real time soil moisture products

http://www.lpdaac.usgs.gov/http://www.latuv.uva.es/http://www.latuv.uva.es/http://www.lpdaac.usgs.gov/

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• B: indicates the orbit composition of the product.

– A for ascending orbits

– D for descending orbits• CCCCCCCCCCCCCCC: starting UTC time (YYYYMMDD hhmmss) of the half-orbit used to create

the product.

• DDDDDDDDDDDDDDD: ending UTC time (YYYYMMDD hhmmss) of the half-orbit used to createthe product.

• EEEEEEE: internal code

– NOMINAL: for L3 product indicates that the nominal lter (described in section 3.1.1) hasbeen applied to L2 product

– AQUA1 : for L4 product indicates that LST data at 1km spatial resolution from AQUA hasbeen used

– TERR1 : for L4 product indicates that LST data at 1km spatial resolution from TERRAhas been used

• FFF: grid indicator

– 025 : Indicates that EASE-ML grid of 25 km is considered

– 4H9: ISEA grid resolution

– IBE: Indicates that the product is provided for the Iberian Peninsula

• GGG: version number of the le starting at 001

3.4 Land products list

The list of land products is summarized in table 2.

In order to automatically download a given type of product, a Linux-based tool named getBEC isoff ered to users. Registered users can download this tool from http://cp34-bec.cmima.csic.es/bec-tools/

http://cp34-bec.cmima.csic.es/bec-tools/http://cp34-bec.cmima.csic.es/bec-tools/http://cp34-bec.cmima.csic.es/bec-tools/http://cp34-bec.cmima.csic.es/bec-tools/

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Spatial resolution Type Product Generation Rate Averaging Period Orbit passes Code

0.25 degrees Reprocessed / Near Real Time binned

1 days1 days

ascending XXXSMB001D025Adescending XXXSMB001D025D

3 days ascending XXXSMB003D025A

descending XXXSMB003D025D

3 days 9 days ascending XXXSMB009D025Adescending XXXSMB009D025D

monthly 1 natural month ascending XXXSMB001M025A

descending XXXSMB001M025D

annual annual ascending XXXSMB001Y025A

descending XXXSMB001Y025D

ISEA Reprocessed / Near Real Time single value 1 days 1 days ascending XXXSMB001D4H9A

descending XXXSMB009D4H9D

1kmNear Real Time High Resolution 1days 1days

ascending XXXSMH001DIBEAdescending XXXSMH001DIBED

Delayed High Resolution 1days 1days ascending XXXSMH001DIBEA

descending XXXSMH001DIBED

Table 2: Land products distributed by BEC. Three rst letters of the code indicated as XXX are NRTfor near real time products, DEL for delayed products and REP for reprocessed products. Code stringis necessary to automatically download a given product using getBEC tool

References

[DPG, 2012] (2012). SMOS Level 2 and Auxiliary Data Products Specications SO-TN-IDR-GS-0006 .INDRA. version 6.1.

[Boutin et al., 2012] Boutin, J., Martin, N., Yin, Y., Font, J., Reul, N., and Spurgeon, P. (2012).First assessment of SMOS data over open ocean: Part II-sea surface salinity. IEEE Trans. Geosci.

Remote Sens., vol. 50, no. 5. pp. 1662-1675 .

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[Isern-Fontanet et al., 2007] Isern-Fontanet, J., Turiel, A., Garćıa-Ladona, E., and Font, J. (2007).

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[Kerr et al., 2010] Kerr, Y., Waldteufel, P., Wigneron, J.-P., Delwart, S., Cabot, F., Boutin, J., Escori-huela, M.-J., Font, J., Reul, N., Gruhier, C., Juglea, S., Drinkwater, M., Hahne, A., Martin-Neira,M., and Mecklenburg, S. (2010). The smos mission: new tool for monitoring key elements of theglobal water cycle. Proceedings of the IEEE , 98(5):666–687.

[McMullan et al., 2008] McMullan, K. D., Brown, M., Martin-Neira, M., Rits, W., Ekholm, S., Marti,J., and Lemanczyk, J. (2008). Smos: The payload. Geoscience and Remote Sensing, IEEE Trans-actions on , 46(3):594–605.

[Nieves et al., 2007] Nieves, V., Llebot, C., Turiel, A., Solé, J., Garćıa-Ladona, E., Estrada, M., andBlasco, D. (2007). Common turbulent signature in sea surface temperature and chlorophyll maps.Geophysical Research Letters , 34(23):1944–8007.

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[Turiel et al., 2009] Turiel, A., Nieves, V., Garćıa-Ladona, E., Font, J., Rio, M.-H., and Larnicol, G.(2009). The multifractal structure of satellite sea surface temperature maps can be used to obtainglobal maps of streamlines. Ocean Science , 5(4):447–460.

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