Petropoulos et al., Evaluation of the Soil Moisture Operational Estimates from SMOS in Europe: Results Over Diverse
Ecosystems
Evaluation of the Soil Moisture Operational
Estimates from SMOS in Europe: Results Over
Diverse Ecosystems
[A] George P. Petropoulos, [B] Gareth Ireland, [C] Prashant K. Srivastava

Abstract—This study presents the results of an extensive
validation of the Soil Moisture and Ocean Salinity Mission
(SMOS) soil moisture operational product from selected
European sites representative of a variety of climatic,
environmental, biome and seasonal conditions. SMOS soil
moisture estimates were compared against corresponding in-situ
measurements from the CarboEurope observational network.
The agreement between the two datasets was evaluated on the
basis of a series of statistical metrics. In addition, the effect of
variability of site characteristics such as land cover, seasonality
and also that of the Radio Frequency Interference (RFI) effect on
SMOS performances was explored.
In overall, the SMOS soil moisture product estimates agreed
reasonably well with near concurrent CarboEurope in-situ
measurements acquired from the 0-5 cm soil moisture layer.
Significant changes in the SMOS performance were observed
with local adjustments such as land cover and seasonal changes.
The performances were found to be higher over low vegetation
cover and during the autumn season. To deduce the impact of
RFI on SMOS soil moisture, the RFI contaminated pixels were
filtered out from the pooled datasets, as well as from the
seasonally discriminated datasets, which resulted in noticeably
improved performances. This study provides strong supportive
evidence of the potential value of the SMOS soil moisture product
for hydro-meteorological studies.
Index Terms—CarboEurope, MIRAS, Remote Sensing, Soil
Moisture, SMOS
I.
INTRODUCTION
Soil moisture is an important control over several
hydrological and atmospheric processes, playing a
fundamental role in the partitioning of mass and energy fluxes
between the hydrosphere, the biosphere and the atmosphere
[1]. It is the principal control on the exchange of latent and
sensible heat between the land surface and atmosphere
interface through evaporation and transpiration processes [2],
and also affects surface thermal inertia, temperature, and
shortwave albedo, while providing key information about
evaporation, infiltration and runoff [3, 4]. Frequent soil
[A] Dr. G. P. Petropoulos is currently working at the Department of
Geography and Earth Sciences, Aberystwyth, SY23 3BD (email:
gep9@aber.ac.uk).
[B] G. Ireland is also currently working at the Department of Geography
and Earth Sciences, Aberystwyth, SY23 3BD (email: gai2@aber.ac.uk).
[C] Dr. Prashant K Srivastava is currently working as a research scientist at
ESSIC/ NASA GSFC, Hydrological Sciences Laboratory, Greenbelt,
Maryland, USA (Email: prashant.k.srivastava@nasa.gov)
moisture observations at different spatial scales is thus of
crucial importance to many environmental and biogeophysical applications such as flood forecasting [5],
meteorology [6], agricultural applications [7], and global and
regional circulation climate models [8].
Given the importance of soil moisture, various methods
for directly measuring it in the field or under laboratory
conditions have been developed (for a review see [9, 10]). The
most widely adopted method is the use of ground
instrumentation; however such techniques pose a number of
limitations. They are often rather complex, labour-intensive
and are only able to provide localised estimates of soil
moisture, whereas some are also destructive [9]. Notably,
active and passive spaceborne microwave instruments are
regarded as some of the most promising techniques to
overcome such limitations [11, 12, 13, 14, 15]. Microwave
instruments operate in the low frequency microwave region
from 1 to 10 GHz, with the majority at frequencies above 5
GHz (e.g. SMMR; SSM/I; TRMM-TMI; AMSR-E; ASCAT).
However, the low frequency protected microwave range of 1-2
GHz (L-band) is generally preferred to higher frequencies due
to its greater sensitivity to soil moisture [16]. In the last
decade, the advent of Earth Observation (EO) technology has
lead to a launch of dedicated satellites such as the Soil
Moisture and Ocean Salinity (SMOS) or upcoming Soil
Moisture Active and Passive (SMAP) missions [11, 17].
SMOS was launched in November 2009 to provide nearsurface soil moisture with a target accuracy of 4 % [12, 18]. A
large number of SMOS validation studies have been
conducted around the globe [19-21], where the knowledge
gained from such studies can potentially help re-evaluate the
soil moisture retrieval parameters and algorithm structure.
Exploiting this feedback is an essential step to help further
develop the accuracy and applicability of such products [19].
In purview of the above, this study aims to evaluate the
accuracy of the SMOS global operational product’s soil
moisture estimates at different European ecosystems. For this
purpose, validated observations from in-situ CarboEurope
ground observational networks acquired nearly concurrently to
SMOS overpass are utilised. In this context, appraisal of
SMOS soil moisture is also investigated with respect to
seasons and land use/land cover (LULC) patterns. The
assessment of the product with respect to these different
aspects is an important step for successful hydrological
modelling, agriculture and water resource management, and
can provide importance assistance to policy and decision
making.
Petropoulos et al., Evaluation of the Soil Moisture Operational Estimates from SMOS in Europe: Results Over Diverse
Ecosystems
II. STUDY SITES & DATASETS
A. CarboEurope Validated Observational Network Data
In-situ soil moisture measurements were acquired from
selected European sites representative of different ecosystem
conditions belonging to the CarboEurope validated
observational network [22]. CarboEurope is part of
FLUXNET, nowadays the largest global network of micrometeorological flux measurement sites [23]. Soil moisture is a
core parameter estimated by each FLUXNET site. Information
on the methodology relating to soil moisture estimation, as
well as the specific instrumentation used at each FLUXNET
site is provided by Running et al. [24]. In this study, in-situ
measurements of soil moisture were acquired during 2011
from twenty two CarboEurope sites from nine countries,
including eight land cover types with varying topographical
characteristics. With regards to the land cover use/type
classifications, the CarboEurope network uses the Plant
Functional Type (PFT) classification for each of their flux
tower sites. For a more detailed description of the
methodology used in inferring PFT see Oleson and Bonan [25]
and Bonan et al. [26].
An overview of the characteristics of the validation sites is
provided in Table 1. Information included in Table 1 was
mainly obtained from the CarboEurope database; however
some was supplied directly as ancillary data which
accompanied the soil moisture in-situ measurements provided
by each site. This information was also verified by the site
managers of specific sites where possible. Each site needed to
contain full day half-hourly soil moisture data for days within
the year 2011 and to also show variability in land cover
use/type between the different sites. This allowed
investigating the influence of different characteristics on the
agreement between the SMOS-predicted and in-situ measured
soil moisture.
B. The SMOS Soil Moisture Level 2 User Data Product
Satellite soil moisture estimates were acquired from the
SMOS Soil Moisture Level 2 User Data Product. The platform
operates in a sun-synchronous orbit at a mean altitude of 758
km and an inclination of 98.44o. SMOS’s onboard instrument
is the Microwave Imaging Radiometer with Aperture
Synthesis (MIRAS), which records emitted energy from the
Earth's surface in the microwave L-band (1.4 GHz). It has a
spatial resolution of 35-50 km and operates through a duskdown orbit with a 3 day repeat cycle at the equator [27, 28].
The aim of the SMOS operational product has been to provide
soil moisture at accuracy better than 0.04 m3 m-3, which
should be achievable over relatively uniform areas.
Herein a total of 198 SMOS soil moisture images (product
version 05) were acquired for selected overpasses during 2011
covering our selected sites. Both ascending and descending
orbits were selected. SMOS soil moisture images were also
collected for dates covering all seasons of the year to facilitate
the analysis of the agreement between the satellite and the insitu data by season. All images were acquired from the EOLiSA portal.
III. METHODOLOGY
The quality of the CarboEurope in-situ data was first
evaluated through information acquired from the in-situ data
providers as discussed in Section II. This was based on the
availability of soil moisture measurements recorded at the
requisite half-hourly time-steps, and also the requirement for
no gaps in the data throughout a selected day. Subsequently,
the in-situ measurements recorded nearest to the time of
satellite overpass were selected for comparisons. The soil
moisture values that corresponded to the date and time of the
satellite overpass were then extracted, and joined to the
relevant SMOS pixel within a Geographic Information System
(GIS). The soil moisture value for each SMOS pixel relating
to the in-situ data point could then be extracted and utilised for
statistical comparisons.
To quantify the level of agreement between the SMOS
retrieved soil moisture and the in-situ measurements from the
ground observational network, a set of statistical metrics were
computed (Table 2). Agreement between the reference and the
SMOS-predicted soil moisture was initially examined for the
pooled datasets. Subsequently, additional comparisons were
performed with the data stratified by land use/cover type and
by seasons. In European climates there are generally four
seasons; spring, summer, autumn and winter, which
correspond to the monthly periods of: March to May, June to
August, September to November and December to February
respectively. SMOS images were thus divided into a specific
season dependent on satellite overpass date and time. In the
next step, the effect of Radio Frequency Interference (RFI) on
all the previously obtained sets of results was assessed.
Evaluation of RFI on soil moisture performance was deemed
necessary since radiometer signals received in the microwave
are susceptible to RFI [29]. RFI contamination
characterisation was based on the computation of the RFI
fraction term, computed from the N_RFIX and N_RFI_Y as
well as the M_AVAO bands following the equation:
[𝑁_𝑅𝐹𝐼_𝑋+𝑁_𝑅𝐹𝐼_𝑌]
(1)
𝑀_𝐴𝑉𝐴𝑂
Where N_RFI_X is the RFI detected in L2 test X
polarisation (the count of deleted views), N_RFI_Y is the RFI
detected in L2 test Y polarisation (again, the count of deleted
views), and M_AVAO is the total number of views available
(ESA SMOS ATBD, [30]). RFI analysis was conducted
initially for all days of comparison, then by season and LULC.
IV. RESULTS
A. Performance
Monitoring Period
Evaluation
During
the
Complete
Statistical scores for the agreement between the SMOS and insitu reference soil moisture measurements for the pooled
datasets were determined and the main results are presented in
Table 3 and Fig. 1. The influence of various biogeophysical
and satellite related parameters, such as different land cover
and seasonality on the agreement of both datasets were
calculated, and the results are presented in Tables 4 and 5.For
Petropoulos et al., Evaluation of the Soil Moisture Operational Estimates from SMOS in Europe: Results Over Diverse
Ecosystems
the pooled datasets, generally there was a reasonable
agreement between both datasets, although the SMOS product
showed a slight underestimation of the in-situ measurements
(Root Mean Square Error (RMSE) = 0.088 m3 m-3, Mean Bias
Error (MBE) = -0.051 m3 m-3, Mean Standard Deviation
(MSD) = 0.072 m3 m-3).
The results of the comparison analysis over the different
land cover types is shown in Table 4, whilst the relevant
scatter plot analysis and histogram results are provided in
Figures 2 and 3. There is a strong variance in terms of RMSE
dependent on land cover type. The highest agreement between
both soil moisture datasets was found on test sites of the
“dehesa” (pasture land) (RMSE = 0.044 m3 m-3) land cover
type, followed by open shrubland (RMSE = 0.057 m3 m-3) and
olive orchards (RMSE= 0.061 m3 m-3) respectively.
Unexpectedly for homogenous short vegetation cover, results
over the grasslands site showed a relatively high error (RMSE
= 0.099 m3 m-3) and MBE (-0.081 m3 m-3) (Table 4). This may
have been caused by errors associated with the location of the
in-situ soil moisture sensors in the ground (could be buried)
and site characteristics (Table 1). Evidently, the lowest
agreement, as expressed from RMSE, was observed for areas
of denser biomass, such as the broadleaf, coniferous and
mixed forest land cover sites (RMSEs of 0.092/0.096/0.158 m3
m-3 respectively). The MBE values found are suggestive that
soil moisture was underestimated by the SMOS product for all
land cover types, in particular over the mixed forest land cover
types (MBE = -0.155 m3 m-3). For the seasonal analysis (Table
5 and Figures 4 and 5), retrieved and in-situ soil moisture
measurements were compared on a seasonal basis, allowing
for the performance of the algorithm under different climatic
and vegetation cover conditions associated with the different
seasons to be analysed. Highest agreement between both
datasets was found during the autumn period (RMSE = 0.076
m3 m-3, MBE = -0.035 m3 m-3, MSD = 0.067 m3 m-3). The
lowest product performance was recorded during the spring
period (RMSE = 0.099 m3 m-3), followed by winter (RMSE =
0.089 m3 m-3), where both seasons exhibited significant error.
Similarly to the results for the pooled datasets and land cover
type analysis, a systematic soil moisture underestimation was
seen
for
the
four seasons (Table 5), especially during the spring period
(MBE = -0.073 m3 m-3).
B. Performance Evaluation for Days with RFI (<0.2)
RFI has been identified as an important issue influencing
SMOS soil moisture retrieval accuracy. Thus, the effect of RFI
was filtered out in order to analyse the influence of RFI
contamination on product accuracy. Results of the comparison
conducted between SMOS data and CarboEurope in-situ
measurements for days of low RFI are summarised in Tables
3-5, where only those pixels which were considered to be
satisfying the criteria of RFI <0.2 were used for performance
comparisons. As expected, after filtering out the RFI
contaminated pixels, results were much improved (RMSE =
0.062 m3 m-3, MBE = -0.039 m3 m-3, MSD = 0.048 m3 m-3)
(Table 3 and Fig. 1b).
Product performance was improved over the majority of
land cover types after filtering out days of high RFI (Table 4
and Fig. 2b and 3). The ”dehesa” land cover type showed the
greatest decrease in RMSE (decreased by 0.011 m3 m-3) where
all other land covers showed comparable decreases between
the range of 0.07 – 0.10 m3 m-3. The majority of land cover
types also showed an improvement in MBE and MSD, with
only the coniferous forest and open shrubland land cover types
showing a decline in MBE (Table 4). A better product
performance was also exhibited for all four seasons when RFI
was filtered out (Table 5, Figures 4b and 5). Spring, summer,
autumn and winter all displayed a decrease in RMSE values,
with spring showing the greatest reduction in error from 0.099
to 0.086 m3 m-3 (an improvement of 0.013 m3 m-3).
Furthermore, all seasons showed an improvement in MSD
results; however, only one of the four showed an improved
MBE (spring) (Table 5). The performance metrics indicated
that the closest agreement between satellite derived and in-situ
measured soil moisture was found for the autumn season.
Overall, the results demonstrated that removal of RFI flag
information had a positive effect on product accuracy and
performances
V. DISCUSSION
In this study an extensive validation of the SMOS soil
moisture product was conducted using in-situ measurements
acquired from CarboEurope network sites, representative of
different ecosystem, climatic and environmental conditions as
reference. Results suggested that in general, soil moisture
retrievals by the SMOS product are in the appropriate range of
values, yet with an overall tendency to underestimate ground
measurements. The reduction in important flag information
such as RFI leads to notably higher product accuracies and a
reduction in the underestimation. The results of this study
showed slightly lower agreement in terms of soil moisture
accuracy in comparison to those reported in other analogous
validation experiments performed on a smaller catchment
scale [21, 31, 32].
The differences in accuracy between the SMOS retrieved
and in-situ soil moisture datasets for this study in comparison
to the smaller scale studies discussed above [21, 31, 32] can
possibly be attributed to the higher number and density of
ground measurements within such studies. Thus an increase in
the spatial mismatch between the in-situ datasets and SMOS
spatial resolution, which is quite coarser in nature and
incorporates a much more heterogeneous surface, is more
evident. Indeed, the vast differences in spatial resolution when
the utilisation of coarser resolution sensors are adopted in such
studies create a number of challenges with regards to
limitations in the spatial representativeness of flux tower
measurements. Flux towers measure soil moisture in the order
of several metres surrounding the instrument location, where
land cover are generally uniform [20]. A coarse resolution
sensor represents a much larger spatial footprint which
possibly incorporates a more fragmented and heterogonous
landscape not represented by the uniformity of the flux tower
measurements. Thus, to overcome such issues, area-averaged
in-situ measurements derived from a higher density of ground
points could provide a better representation of heterogeneous
land surfaces encapsulated within a remotely sensed footprint,
Petropoulos et al., Evaluation of the Soil Moisture Operational Estimates from SMOS in Europe: Results Over Diverse
Ecosystems
significantly reducing errors related to spatial discrepancy.
The results obtained in this study are in much closer
agreement to those reported by validation studies of SMOS
accuracy conducted on a larger scale [33-35].
Land cover impacts the variation of soil moisture because
of increasing transpiration losses and rainfall interception.
Furthermore, the type of land cover also influences the
vegetation attenuation and scattering albedo which can affect
the overall soil moisture retrieval [36]. The overall analysis
conducted in the present study indicated highest product
accuracies within the “dehesa” (pasture) and open shrub land
cover types. This is possibly due to these sites being
considered as homogeneous low vegetation cover; hence
lesser attenuation and surface roughness effects on the
microwave signal in comparison to other categories [37-39].
Furthermore, open shrubland and olive orchard land cover
types include a large percentage of bare soil or open ground,
nullifying the effect of attenuation from vegetation cover on
the microwave signal and improving the product accuracy. On
the other hand, results suggested that coniferous, broadleaf
and mixed forest show less agreement between the SMOS
product and CarboEurope in-situ measurements. This may be
due to strong attenuation of the microwave signal by dense
canopies to crown, understory and litter, as confirmed by other
studies [20, 40]. As litter is usually present in vegetation
canopies which are not (or rarely) ploughed: non-agricultural
canopies, natural cover, forests, etc. influence optical
thickness and thus attenuation of soil emissions [41, 42]. The
effect of intercepted water by the standing vegetation canopy
due to rainfall has also shown to be very significant, having a
negative effect on soil moisture retrieval [43].
Seasonality is one of the major controls on soil moisture
dynamic and its variability can have very important influences
on the overall performance of the sensors soil moisture
retrieval. Previous analogous studies providing product
comparisons at the annual scale have shown that soil moisture
estimates are driven to a certain extent by the seasonal cycle
[1, 16, 44]. However, in disagreement with these studies,
product accuracies were greater during the autumn months,
which showed highest correlation values and lowest RMSE
rates compared to all other seasons. This may be ascribed to
the phenological changes of vegetation throughout the year,
leading to other seasons generally having denser plant
canopies compared to autumn. In fact the autumn results (with
RFI) are better than any other seasons. One possible reason
behind this could be lesser pixel contamination by RFI.
Furthermore, low vegetation attenuation by vegetation
canopies in autumn, which is higher in other seasons, could be
another possible source of increased performance. Notably,
Kurum et al. [41, 42] indicates that canopy density plays a
crucial role in vegetation scattering albedo which makes the
performance of the retrieval algorithm poorer than over nonvegetated areas. Thus, less canopy cover during autumn in
comparison to other seasons may be a possible reason for the
improved performance [45].
Another possible explanation for the relative difference in
the seasonal performance accuracies displayed by the SMOS
product may be attributed in part to the precipitation input and
enhanced evapotranspiration losses which ultimately influence
the soil water balance [46, 47]. Qiu et al., [44] achieved
similar results in seasonality accuracies from active (ASCAT)
and passive (AMSR-E) microwave soil moisture estimates,
where the autumn period showed greater accuracies. The
winter period showed low agreement between both datasets,
possibly due to the inactivity of vegetation during this period.
Another point of concern during winter is the way cold areas
are processed by the satellite. Soil appears suddenly dry when
it freezes and vegetation becomes transparent when frozen,
whereas snow cover is more complex. When dry, snow is
almost transparent and SMOS is sensitive to the relatively
warm soil underneath. However, when the snow is wet, it is
rather opaque. All the intermediary cases make retrieval in
transition areas very difficult and the product is prone to be
erroneous [18, 44].
RFI has already proven to be a major source of disturbance
in the natural microwave emissions observed by satellites. It
affects the electromagnetic radiation emitted from an external
source [48]. Hence, RFI is a major drawback on SMOS soil
moisture retrieval [28]. These disturbances generally degrade
or limit the quality of data and sometime lead to a total loss of
data. Thus, evaluating if the selection and removal of RFI
contaminated pixels would improve product accuracy was an
important step in the validation of the SMOS product. As
expected, after filtering out the RFI contaminated pixels,
results were much improved as indicated by higher correlation
values in nearly all RFI <0.2 cases. Globally, RFI affects soil
moisture retrieval results at various levels. In Europe for
instance, probability of RFI interference is high, and results
are generally degraded. Thus the use of flags for filtering out
RFI is suggested for greater product accuracies [1, 49]. The
OZNET network in Australia, which presents the best
correlation values for SMOS, does not seem to be affected by
RFI, and over the US the probability of RFI is low [19, 20].
The non-uniform geographical distribution of RFI
contamination is based on the fact that ninety-nine percent of
the RFI sources are over land and their distribution depends on
whether or not the countries enforce International
Telecommunication Union (ITU) regulations. For instance,
Asia and Europe together hold 86% of all global RFI sources
[50], whereas most of America, Australia and a large part of
Africa have none or very few sources of pollution. Detecting
and flagging contaminated observations, and contacting
national authorities to localise and eliminate point sources
emitting in the protected band are thus present on-going
challenges in Europe [50, 51].
VI. CONCLUSIONS
This study is one of few which aim to validate the
performance of the SMOS Soil Moisture Level 2 User Data
Product in the operational retrieval of soil moisture estimation
over diverse European ecosystems.
The overall performance indicates a generally good
agreement between in-situ measurements of soil moisture and
the SMOS retrieved soil moisture. With regards to the results
stratified by land cover type and seasonality, the SMOS
algorithm performed better over short vegetation cover
(“dehesa”, olive orchards and cropland, RMSE – 0.044, 0.061,
Petropoulos et al., Evaluation of the Soil Moisture Operational Estimates from SMOS in Europe: Results Over Diverse
Ecosystems
0.057 m3 m-3) and during the autumn season (RMSE – 0.076
m3 m-3). The filtering out of the RFI contaminated pixels
resulted in a significant improvement in overall product
accuracy, with improvement in RMSE of the pooled datasets
by ~30%. It should be noted that the removal of RFI resulted
in a minimum improvement of ~5% in all comparison
scenarios.
Studies such as this are important steps in the validation of
operational satellite products and are vital for the future
development of SMOS’s operational capacity on a global
scale. Stratifying the validation by various parameters has
allowed for an insight into errors or limitation associated with
the SMOS algorithm relating to its use over certain land cover
types or topographies. Furthermore, efforts from studies such
as this also substantiates the fact that RFI removal is an
imperative step in SMOS data pre-processing before data
acquired from this sensor can be applied effectively. Further
work should look to expand SMOS soil moisture operational
product validation at a wide range of settings, including intercomparisons to other similar products.
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Dr. George P. Petropoulos received his PhD in 2008 from
the University of London, UK, specialising in Earth
Observation Modelling. He is currently a Senior Lecturer in
Remote Sensing & GIS in the Department of Geography &
Earth Sciences (DGES) at Aberystwyth University, UK.
His research work focuses on exploiting Earth Observation
(EO) data alone or synergistically with land surface process
models for computing key state variables of the Earth's
energy and water budget, including energy fluxes and soil
surface moisture. He is also conducting research on the
application of EO technology to land cover mapping and its
changes occurred from either anthropogenic activities (e.g.
urbanization, mining activity) or natural hazards (mainly
floods and fires). He is author/co-author of +38 peerreviewed journal articles, +75 international conferences and
of +19 book chapters, and editor of a book published by
Taylor & Francis.
Gareth Ireland received his B.Sc.
degree in Physical Geography at
Aberystwyth University, Wales,
before subsequently receiving
his M.Sc. degree in GIS and
Remote Sensing from the
same institution. He currently
works as a researcher in the
Department of Geography and
Earth
Sciences
at
Aberystwyth
University. His research interests focus
on remote sensing, and in particular the
use of satellite imagery for the study of land surface
interactions and of land use/cover changes occurred from
both natural hazards and/or anthropogenic activities.
Dr. Prashant K Srivastava received the
B.Sc. degree in Agriculture Sciences
from Banaras Hindu University
(BHU), and M.Sc. degree in
Environmental
Sciences
from
Jawaharlal Nehru University (JNU),
India. He has worked as an Assistant
professor for few years in India and
then moved to Department of Civil Engineering, University
of Bristol, for his PhD sponsored and funded by British
High Commission, U.K. under the Commonwealth
Scholarship and Fellowship Plan (CSFP). His Ph.D.
research focused on the soil moisture retrieval algorithm
development for SMOS satellite and mesoscale modelling
for hydrological applications. Dr. Prashant is currently
working as a research scientist with ESSIC/NASA GSFC,
Hydrological Sciences laboratory on SMAP satellite soil
moisture retrieval algorithm development and its
applications. He has been a recipient of many awards
including University of Maryland Postdoctoral Fellowship,
USA, Commonwealth fellowship, U.K., CSIR (twice),
MHRD and UGC fellowships from India. He has published
Petropoulos et al., Evaluation of the Soil Moisture Operational Estimates from SMOS in Europe: Results Over Diverse
Ecosystems
several peer reviewed papers and edited two books for
remote sensing and computational intelligence community
published by Springer.
Figure 1: Agreement between CarboEurope in-situ and
SMOS-derived soil moisture (m3 m-3) for all days
divided into two groups (a) No RFI threshold (b) RFI
threshold.
Figure 2: Agreement between CarboEurope in-situ and
SMOS-derived soil moisture (m3 m-3) with land cover
types for (a) No RFI threshold (b) RFI threshold.
Figure 3: Mean soil moisture error by land cover type. Blue
bars illustrate the mean error when no RFI threshold
is applied and red bars illustrate the mean error when
an RFI threshold of <0.2 is applied to the data.
Figure 4: Agreement between CarboEurope in-situ and
SMOS-derived soil moisture (m3 m-3)
with
seasonality (a) No RFI threshold (b) RFI threshold.
Figure 5: Mean soil moisture error by seasonality. Blue bars
illustrate the mean error when no RFI threshold is
applied and red bars illustrate the mean error when an
RFI threshold of <0.2 is applied to the data.