The objective of this paper is to present the multi-orbit (MO) surface soil
moisture (SM) and angle-binned brightness temperature (TB) products for the
SMOS (Soil Moisture and Ocean Salinity) mission based on a new multi-orbit
algorithm. The Level 3 algorithm at CATDS (Centre Aval de Traitement des
Données SMOS) makes use of MO retrieval to enhance the robustness and
quality of SM retrievals. The motivation of the approach is to make use of
the longer temporal autocorrelation length of the vegetation optical depth
(VOD) compared to the corresponding SM autocorrelation in order to enhance
the retrievals when an acquisition occurs at the border of the swath. The
retrieval algorithm is implemented in a unique operational processor
delivering multiple parameters (e.g. SM and VOD) using multi-angular
dual-polarisation TB from MO. A subsidiary angle-binned TB product is
provided. In this study the Level 3 TB V310 product is showcased and compared
to SMAP (Soil Moisture Active Passive) TB. The Level 3 SM V300 product is
compared to the single-orbit (SO) retrievals from the Level 2 SM processor
from ESA with aligned configuration. The advantages and drawbacks of the
Level 3 SM product (L3SM) are discussed. The comparison is done on a global
scale between the two datasets and on the local scale with respect to in situ
data from AMMA-CATCH and USDA ARS Watershed networks. The results obtained
from the global analysis show that the MO implementation enhances the number
of retrievals: up to 9 % over certain areas. The comparison with the
in situ data shows that the increase in the number of retrievals does not
come with a decrease in quality, but rather at the expense of an increased
time lag in product availability from 6
Surface soil moisture (SM) is a control physical parameter for many hydrological processes like infiltration, runoff, precipitation and evaporation (Koster et al., 2004). Estimates of SM are needed for many applications concerned with monitoring droughts (Keyantash and Dracup, 2002), floods (Brocca et al., 2010; Lievens et al., 2015), weather forecast (Drusch, 2007; de Rosnay et al., 2013), climate (Jung et al., 2010) and agriculture (Guérif and Duke, 2000). It is identified among the 50 Essential Climate Variables (ECVs) for the Global Climate Observing System (GCOS). It has also been selected for the creation of decadal (10 years) time series from remote sensing in the ESA Climate Change Initiative (CCI) project (Hollmann et al., 2013).
SM can be obtained from several Earth observation (EO) techniques ranging from visible to microwave wavelengths using active (Ulaby et al., 1996) and passive (Kerr and Njoku, 1990) instruments. Retrieval of SM from passive microwave sensors is a challenging task because features like surface heterogeneity (water surfaces and land use), vegetation cover (vegetation density and distribution), climatic conditions (freezing and snow), acquisition configurations (angle, frequency and polarisation) and topography (multiple scattering) need to be carefully considered while upscaling to the sensor coarse resolution. Several approaches like regression models (Njoku et al., 2003; Wigneron et al., 2004; Saleh et al., 2006), statistical and contextual methods (Verhoest et al., 1998), neural networks (Liu et al., 2002; Rodríguez-Fernández et al., 2015), and radiative-transfer-based approaches (Kerr and Njoku, 1990; Wigneron et al., 2007; Owe et al., 2008; O'Neill et al., 2015) have been developed to retrieve SM based on the sensor frequency, acquisition modes and richness of information (multi-angular, full polarisation and active). The Soil Moisture and Ocean Salinity (SMOS) mission of ESA (Kerr et al., 2001, 2010) with contributions from Centre National d'Etudes Spatiales (CNES) in France and Centro para el Desarrollo Tecnológico Industrial (CDTI) in Spain is the first Earth observation mission dedicated to SM mapping. The SMOS Level 2 (L2) SM retrieval algorithm (Kerr et al., 2012) minimizes the squared differences between L-MEB (Wigneron et al., 2007) forward simulations of multi-angular dual-polarisation TB and corresponding SMOS measurements using the Levenberg–Marquardt optimisation algorithm to retrieve physical parameters, mainly SM and VOD.
The L-MEB radiative transfer model is based on the optical depth
single-scattering albedo (
Passive microwave sensors have a high revisit frequency: 1 day for Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E) (Njoku and Entekhabi, 1996) and 2–3 days for SMOS and SMAP. In this study the multi-orbit (MO), multi-angular and dual-channel horizontal and vertical (H/V) operational retrieval algorithm implemented at the CATDS (Centre Aval de Traitement des Données SMOS) by CNES is presented. Retrieval using temporal series is becoming increasingly common in operational EO retrieval algorithms for optical and to some extent microwave technologies. Some examples in the optical domain are the correction of aerosol impact for visible images (Hagolle et al., 2008, 2015), cloud detection (Hagolle et al., 2010) and the use of MO for land cover classification (Inglada and Mercier, 2007). The previous methodologies are being implemented for high-end level 2-A and level 3 products for the Copernicus Sentinel-2 mission. The use of MO in the radar community is a standard approach. The SM retrievals from ERS (European Remote Sensing), Advanced Scatterometer (ASCAT), RADARSAT-2 and Sentinel-1 are based on a change detection algorithm (Wagner et al., 1999, 2013; Naeimi et al., 2009). Similarly, Mattia et al. (2006) introduced a priori surface parameters and multi-temporal synthetic aperture radar (SAR) data to reduce the impact of vegetation and soil roughness in SM retrieval from SAR. Recently, a generalisation of change detection to multiple regression using cumulative distribution function (CDF) transformations was applied to RADARSAT-2 time series data and validated over the Berambadi watershed, South India (Tomer et al., 2015). In microwave radiometry, Konings et al. (2016) presented a time series retrieval of vegetation optical depth based on AQUARIUS L-band acquisitions.
Here a detailed presentation of the products and retrieval algorithm of an inter-comparison between the SMOS SO (single-orbit) and the SMOS MO (multi-orbit) operational products is given. More specifically, the objective of this paper is to present the daily L3 SM and TB V310 products and associated algorithms and to compare the SMOS MO level 3 retrievals to the level 2 single-orbit operational retrievals obtained using V600 L1 ESA-SMOS products. Since the SMOS mission launch in November 2009, this is the first reprocessing to have an aligned version of the processors from Level 1 up to Level 3, enabling a direct comparison of the products. In the next sections the MO retrieval SM algorithm and the L3 TB are presented. The datasets used for the assessment, the results of the comparison and conclusions are presented.
The Level 3 SM (L3SM) processor consists of a set of several algorithms. The
forward model in L3SM uses the same physically based forward models as the
ESA SMOS Level 2 SM processor, but in a MO retrieval context. A short summary
of the main features of this processor is provided here and a detailed
description can be found in Kerr et al. (2012). The SMOS L2 retrieval can be
divided into two main components:
The first component is a physical model that computes TB at the antenna
reference frame forced by ancillary data (land classification and soil
properties) and physical parameters (skin or near-surface temperature and
soil temperature). The selected physical model for the SMOS mission is L-MEB
from Wigneron et al. (2007). The main features of the L-MEB physical model
implementation in the SMOS operational processor are as follows:
Effective scattering albedo is considered. SM and VOD are jointly retrieved over nominal (bare soil and low
vegetation) surfaces using angular signature and polarisation information. Dual polarisation is used. Full polarisation data are only used to take
into account the Faraday rotation and geometric rotation to transform
modelled TB from the top of atmosphere (TOA) to the antenna reference frame. The mean antenna pattern (Kerr et al., 2012) is used in the iterative
retrieval algorithm. The mean weighting function expresses the average
contributions for all angular acquisitions. The Surface heterogeneity is considered through aggregated TB contributions
from Dynamic changes in surface state (freezing, rainfall, etc.) are
considered through the use of ancillary weather data from ECMWF reanalysis products. Since the mission launch, many improvements have been implemented in the
operational processing model. Some examples include, for instance, the improved
parametrization of the forest albedo in Rahmoune et al. (2014) or the choice
of dielectric mixing models in Mialon et al. (2015).
Number of TB records across the swath for a period of 8 days – from 18 to 25 May 2010 – over the area of La Plata, Argentina.
The second component of the retrieval algorithm is an iterative
optimisation scheme that minimises a Bayesian cost function constructed from
the observed and the modelled TBs in order to retrieve the physical parameter
values. Preprocessing and post-processing steps are implemented to filter
the input and output data for undesired effects like the decrease in quality
due to spatial sampling or radio frequency interferences (RFIs) (Oliva et
al., 2012; Richaume et al., 2014). The physical approach at Level 3 MO is the same as that of Level 2 SO. In
fact the core processing uses the same implementation of the L-MEB radiative
transfer model. The main difference in Level 3 is the use of several orbits,
rather than one, to retrieve SM and VOD. This has an impact first on the
post-processing steps for selecting the orbits and second on the
optimisation scheme to retrieve the parameters. Since the Level 2 retrieval
is a multi-parameter retrieval, the Level 3 is thus a multi-orbit
multi-parameter retrieval. The reasons that motivated the use of the MO
approach are the following:
The angular sampling and radiometric accuracy at the border of the swath
are reduced. Figure 1 shows the cumulative number of records for several
descending orbits. The asterisk in each panel represents the same location in
the La Plata region in South America. The orange regions inside the orbits
observed on 18, 20 and 23 May 2010 depict the mild decrease in the number of
TB measurements (15–35) during the instrument calibration phases. However,
most important is the low number of TB measurements (35) observed on 21 May
when the point of interest is at the border of the swath. A low number of TB
measurements spanning a narrow range of incidence angles generates failures
in the iterative retrieval of SM and VOD. The use of MO can help improve the
number of successful retrievals at the border of the swath. The VOD is expected to vary slowly in time and thus to be highly
correlated between two consecutive ascending or descending orbits or over a
short period of time (a few days). In fact, at L band the VOD is mainly
correlated to vegetation water content (Jackson and Schmugge, 1991), which is
expected to vary slowly in time compared with temporal variability in SM. Other general motivations for Level 3 products are to provide a global
gridded product, in contrast to swath-based products and to provide
fixed-angle-binned TB products. The 25
The selection of orbits is needed to select TBs at high latitudes where a
sub-daily revisit is available and to generate the time series dataset on the
EASE-Grid 2.0 as input to the MO retrieval. The following criteria are
applied for the selection of revisits:
Ascending and descending orbits are processed separately since the impact
of RFI (Oliva et al., 2012) and sun corrections (Khazâal et al., 2016)
between ascending and descending orbits are very different. TB products are filtered at high latitudes where more than one revisit per
day occurs (latitudes above 60
At this level the acquisitions for a given day for ascending and descending
orbits are separately stored in a three-dimensional matrix accounting for
snapshots, longitude and latitude. A snapshot is an image associated to the
acquisition of SMOS during a given integration time (epoch). Snapshots have
different epochs and polarisation following a preprogrammed acquisition
sequence. From this product a fixed-angle-binned TB product is generated as
presented in Sect. 3. The product is also used in the next processing steps
of L3SM MO.
For each retrieval and over each node a 7-day period is considered in
which three revisits are selected from the complete list of revisits
(Fig. 2). The first coincides
with the central date (date of main product). The two others correspond to
selected dates either before (previous 3.5 days) or after (3.5 days
posterior) the considered date. Like in the previous processing step, the
selection is done based on minimum distance from the swath centre for each
node.
Selection of revisit orbits for the multi-orbit retrieval at SMOS CATDS.
Observed TBs at the antenna reference frame from the precedent, actual and
succeeding dates are assembled for each node. The forward algorithm is
run to generate the modelled TB for each of the TB dataset records. The
ancillary data and parameters are independently considered for each record. A
Bayesian cost function that includes the aforementioned MO observed TB and
modelled TB is then constructed. This is achieved by incorporating in the
retrieval approach a temporal autocorrelation function for the VOD. The cost
function is as follows:
It is important to note that three SM values are retrieved simultaneously at
each node: SM
Where
When
Figure 3 shows the shape of the correlation function for the two correlation lengths used in the processing. The green curve corresponds to the forested surfaces and the blue one to the nominal surfaces (bare soil and low vegetation).
Autocorrelation functions for vegetation optical depth (VOD) for different correlation lengths (green shows forested surfaces and blue shows nominal surfaces).
The parameter values namely (SM
The objective of this algorithm is to generate a product containing
fixed-angle full-polarisation brightness temperatures at top of atmosphere
(TOA) but with the polarisations expressed in the ground reference frame
(horizontal and vertical components) over the EASE-Grid 2.0. The main input
for this algorithm is the snapshot dataset mentioned in the previous section.
The algorithm consists of four steps: (a) filtering, (b) interpolation,
(c) reference frame transformation and (d) angle binning. However, note that
before being projected to a ground reference frame, the data are processed in
the instrument reference frame. Thus, TBs are labelled TB
The filtering eliminates brightness temperatures that are impacted by
anthropogenic effects (such as RFIs), or spurious effects (such as sun
impact). The filtering criteria, shown in Table 1, are similar to those for
L3 MO SM and L2 SO retrievals. A detailed description of the filtering
criterion is provided in the SMOS L2 ATBD (Algorithm Theoretical Basis Document). The reader can refer to
Khazaal et al. (2016) for a more detailed evaluation of the impact of sun
corrections and Richaume et al., 2014 and Soldo et al., 2014 for the impact
of RFIs. All filtering criteria should be met, otherwise the acquisition is
discarded. In case a cross polarisation is discarded, the associated
List of applied filtering criterion used on brightness temperature products prior to interpolation.
ATB is the radiometric accuracy of SMOS TB, ST1 is the first Stokes
parameter,
The acquisition sequence of SMOS is shown in Table 2. At each epoch an
acquisition can be co-polarised (
Acquisition sequences of SMOS in full polarisation mode (capital letters are used for pure acquisition).
The weighting function accounts for the two following elements:
The TB acquisitions have different accuracy
levels since the integration time is longer when only co-polarisation is
acquired (pure acquisition) when compared to the case where combined cross
and co-polarisation are acquired. The time span between two acquisitions in
the same mode is not constant. Acquisitions closer in time are considered
more reliable than farther ones, taking into consideration that the synthetic
antenna weighting function rotates and that the incidence angle
changes.
The time interpolation function of TB at time
In this step, the TBs are transformed from the antenna reference frame (
Transformation from antenna (S) to ground reference frame (G).
The inverse of the rotation matrix is used to transform the TB data from
antenna to ground reference frame:
The accuracies of the TB data are then computed by propagating the accuracies
using the matrix below:
This step consists in averaging the TOA TBs at fixed-angle intervals using an
arithmetic mean. The selected incidence angle bins, shown in Table 3, are
designed to also cover the SMAP acquisition angle (40
Selected incident angle bins.
All TB values outside the interval defined by mean (TB)
The CATDS Level 3 user data products (CLF3UA/D) are MO soil moisture
retrieval products. They contain 1-day global maps of geophysical parameters
(SM, VOD, imaginary and real part of the dielectric constant, etc.) retrieved
as described above, processing parameters (percentage of forest cover, choice
of physical model, etc.) and quality indicators (probability of RFI, goodness
of fit between modelled TB from L-MEB and observed TB
The mean forest cover provides the percentage of forest cover, taking into
account the mean antenna pattern. It is obtained by convoluting the ECOCLIMAP
(Masson et al., 2003) forest cover using the SMOS antenna weighting function at
a resolution of 4
The ESA L2 Soil Moisture User Data Product (SMUDP; Kerr et al., 2012), which is a SO retrieval product, is used in this study for
comparison purposes. This product is a half-orbit swath-based dataset of
physical variables (SM, VOD, dielectric constant imaginary and real parts,
etc.), processing parameters (percentage of forest cover, type of surface
model, etc.) and quality indicators (probability of RFI,
The main characteristics and differences between the L2SM SO retrieval and L3SM MO retrieval products are summarised in Table 4.
Main characteristics of the SMOS Level 3 and Level 2 SM products.
Properties of the in situ sites used for the evaluation.
The SMOS CATDS full-polarisation angle-binned daily brightness temperature
products (CDF3TA/D) version 310, were downloaded from the same database as
the L3 MO SM. These products consist of global 1-day maps of
full-polarisation TB over fixed-angle bins with their associated accuracies.
Detailed computation was described above in Sect. 3. The product also
contains auxiliary data like the geometric angles, Faraday angles, length of
major semi-axis and length of minor semi-axis. Quality flags are also
provided in the product. The TB
The SMAP mission from NASA was launched in January 2015. It operates like
SMOS in L-band using a radiometer and a radar (that was operational for about
80 days). It has a local overpass time at 18:00 UTC and 06:00 UTC for ascending and descending orbits, respectively, but the
acquisitions are not necessarily synchronous with SMOS. In this study we use
the SMAP TB derived from the radiometer acquisitions. The SMAP L3B_SM_P
product is downloaded from the National Snow and Ice Data Center (NSIDC)
website (O'Neil et al., 2016). The SMAP L3 TB is used as input for the SM
retrievals and it is corrected for water contribution and atmospheric
effects. It is provided on the EASE-Grid 2.0 with a 36
In this study, the SMOS SM products are evaluated against in situ SM from two
networks with spatially distributed SM data at the footprint scale (USDA
Watershed and AMMA-CATCH).The in situ soil moisture data from probes
installed near the surface are used. These sites provide a soil moisture
reading, representative of the first 5
The AMMA long-term observing system (AMMA-CATCH, 1996 and 2005) includes
three mesoscale sites located in Niger, Benin and Mali that are
representative of the West African ecoclimatic gradient (Cappelaere et
al., 2009; Mougin et al., 2009). The AMMA-CATCH soil moisture network is a
well-established network in terms of satellite product assessment (de Rosnay
et al., 2009; Pellarin et al., 2009; Louvet et al., 2015). The Niger and
Benin sites are selected for this study. The Niger site, centred at
13.645
The United States Department of Agriculture (USDA) Agricultural Research
Service monitors a network of watersheds across the US using a high number of
instruments. Surface soil moisture (5
In order to compare the SMOS TB product to SMAP TB, the SMOS daily product
was averaged following the same interpolation procedure as the one suggested
in the SMAP mission. The method consists of using an inverse distance
weighting for all the SMOS EASE 2.0 25
Global comparison is done over the EASE-Grid 2.0 25 bilinear, if more than two soil moisture retrievals are available; linear, if two soil moisture retrievals are available; nearest point, if one soil moisture retrieval is available.
The L2 SO SM is also filtered at high latitude where several soil moisture
retrievals are available. The selection criterion is minimum distance from
the swath centre, the same as for the L3 MO SM algorithm.
No interpolation is used after the extraction of the SM time series. The
comparison is based on the following statistical indicators:
mean bias: (in situ – retrieved soil moisture) ( standard error of the estimate (SEE) ( Pearson correlation coefficient ( RMSE ( the empirical cumulative distribution function (Cox and Oakes, 1984).
Figures 5a, b and 6a, b show the comparison between the SMOS L3 TB and SMAP
L3 TB at a 40
The 3-month average maps of SMOS L3 TB at 40
Distribution of bias between SMAP and SMOS L3 TB for pixels with
less than 0.01 (1 %) water fraction for
January–February–March
Global map of the mean forest-cover percentage used in the SMOS
L2 SO and L3 MO soil moisture retrievals
Statistics of the in situ vs. SMOS L3SM and L2SM for ascending orbits.
Statistics of the in situ vs. SMOS L3SM and L2SM for descending orbits.
Based on the aforementioned evaluation methodology, the L3SM MO retrievals are compared to those of L2SM SO on the global scale over the 2010–2015 period. The auxiliary maps of mean forest cover percentage (Fig. 7a) and average RFI probabilities (Fig. 7b) for 2011 are provided as complementary information. These maps are obtained from the L3SM product.
Figure 8a and b show the mean number of successful retrievals per year (2010–2015) obtained from L3SM and L2SM, respectively. White (blank) pixels in panel (a) show the areas where no successful soil moisture retrieval is available. These pixels are mostly located in areas of dense vegetation (Congo), areas that are seasonally inundated (Amazon Basin) and/or areas with high RFI (South-East Asia and the Middle East). From Fig. 10a it is clear that the coverage area of the L3SM product is higher in these areas.
Figure 9a and b show the difference (MO–SO) in the number of successful soil
moisture retrievals between the L3SM and L2SM products. The general behaviour
shows a systematic increase in the number of retrievals of the MO with
respect to the SO retrievals. The number of retrievals moderately increases
in desert and plain areas (10–20 retrievals per year per orbit). The
increase is much higher for forested areas. The L2SM showed a higher number
of successful retrievals in the area between 62–70
The mean soil moisture from L3SM and L2SM for ascending orbits is provided in Fig. 10a and b. These figures show that the soil moisture spatial patterns are very similar between the SO and MO SM retrievals. The coverage of the multi-orbit product is higher, as already shown in the previous figures. Nevertheless, some discrepancies can be observed from the difference map (Fig. 10c). The L3SM MO soil moisture values are generally higher than those of L2SM SO. This is most visible in forested areas (Fig. 7a), and this is consistent with climatic conditions over these areas. They are also higher in areas with high RFI pollution (Fig. 7b). This generally leads to a decrease in the value of the retrieved soil moisture values. Thus, the higher L3SM can be due to the positive impact of using multiple dates during RFI prone periods.
Mean number of successful SM retrievals per year (2010–2015) for
ascending orbits from L3SM MO
Global map of the difference in the mean number of SM successful
retrievals per year over the 2011–2015 period
(L3SM
Mean soil moisture map over 2011–2015 for ascending orbits from
CATDS L3SM MO
The statistics for the comparison of L2SM SO and L3SM MO with in situ
networks is shown in Tables 3 and 4 for ascending and descending orbits,
respectively. The number of retrievals is systematically higher for the L3SM
than the L2SM as expected from the global analysis. Note that, contrary to the
global analysis, the in situ analysis is done without any grid interpolation
by considering the closest node. Tables 6 and 7 show the statistics for the
on-site comparison for ascending and descending orbits, respectively. The
skills are of similar magnitudes for the LW and Niger sites and the lowest
skill is obtained for the Benin site in descending overpasses. No site showed
a lower number of successful retrievals for L3SM. The bias values are not much
improved by the L3SM. They seem to increase at the majority of the sites. The
comparison shows a slight negative bias for the two datasets. The absolute
value of bias is less than 0.04
More in-depth analysis can be obtained by inspecting the time series of soil moisture. Figures 11 and 12 show the time series for the selected sites for the period 2010–2016 and for ascending and descending overpasses. The Niger and Benin sites present a very pronounced seasonal signal typical of the Sahelian sites. Over these sites the L3SM shows consistently lower soil moisture than L2SM for high soil moisture values. The L3SM is closer in this case to the site data. The time series for LW show that the SMOS data closely follow the behaviour of the soil moisture dynamics over this site. One of the reasons for this is that the rainfall events are well separated, enabling the remote sensing data to capture the dynamics of physical processes (e.g. infiltration and evaporation) on a coarse scale. Thus, the exponential behaviour typical of a drying soil is well depicted.
Time series for the validation sites for the ascending (06:00 UTC) overpasses.
Time series for the validation sites for the descending (18:00 UTC) overpasses.
Figures 13 and 14 show the CDF of the in situ, L2SM and L3SM data for ascending and descending orbits. From these figures it can be concluded that the SMOS soil moisture is drier than the 5 cm in situ data across the different values of soil moisture. This can be explained by the SMOS penetration depth with respect to the depth of the installation of the in situ sensors. Nevertheless, the shape of the distribution function, describing the extreme and seasonal cycles, is well captured in most cases. The Niger site's Sahelian climate is well captured, with a high probability of low soil moisture values and a small number of extreme values. The differences between the L2SM and L3SM data are mainly observed for the Benin and LW sites. When comparing Figs. 13 and 14, small differences can be noted between ascending and descending orbits.
Cumulative distribution function (CDF) for the validation sites for ascending overpasses.
Cumulative distribution function (CDF) for the validation sites for descending overpasses.
The main datasets can be accessed as follows:
MIR_CLF31A / D: SMOS-CATDS Level 3 1-day soil moisture maps for
ascending (06:00 UTC) and descending (18:00 UTC) orbits version 300, link:
MIR_CDF3TA / D: SMOS-CATDS Level 3 1-day fixed-angle bin full-polarisation
brightness temperatures maps for ascending (06:00 UTC) and descending
(18H00) orbits version 310, link:
The level 3 daily maps of soil moisture and brightness
temperatures are presented in this paper. A multi-orbit soil moisture
retrieval algorithm for SMOS data is used to obtain the soil moisture
product. The main feature of the algorithm is the use of MO and of temporal
autocorrelation of optical vegetation depth in the cost function. The
algorithm is implemented operationally at CATDS. The processing chain
delivers gridded products over the EASE 2.0 grid at 25
The authors declare that they have no conflict of interest.
The SMOS L3SM products were obtained from the Centre Aval de Traitement des Données SMOS (CATDS), operated for the “Centre National d'Etudes Spatiales” (CNES, France) by IFREMER (Brest, France). This study was supported by the CNES “Terre, Océan, Surfaces Continentales, Atmosphère” program. The authors would like to thank the USDA ARS Hydrology and Remote Sensing Laboratory, AMMA-CATCH project for the in situ datasets. Edited by: D. Carlson Reviewed by: M. Schwank and one anonymous referee