A newly developed microwave (MW) land surface temperature (LST) product is used to substitute thermal infrared (TIR)-based LST in the Atmosphere–Land Exchange Inverse (ALEXI) modeling framework for estimating evapotranspiration (ET) from space. ALEXI implements a two-source energy balance (TSEB) land surface scheme in a time-differential approach, designed to minimize sensitivity to absolute biases in input records of LST through the analysis of the rate of temperature change in the morning. Thermal infrared retrievals of the diurnal LST curve, traditionally from geostationary platforms, are hindered by cloud cover, reducing model coverage on any given day. This study tests the utility of diurnal temperature information retrieved from a constellation of satellites with microwave radiometers that together provide six to eight observations of Ka-band brightness temperature per location per day. This represents the first ever attempt at a global implementation of ALEXI with MW-based LST and is intended as the first step towards providing all-weather capability to the ALEXI framework.
The analysis is based on 9-year-long, global records of ALEXI ET generated using both MW- and TIR-based diurnal LST information as input. In this study, the MW-LST (MW-based LST) sampling is restricted to the same clear-sky days as in the IR-based implementation to be able to analyze the impact of changing the LST dataset separately from the impact of sampling all-sky conditions. The results show that long-term bulk ET estimates from both LST sources agree well, with a spatial correlation of 92 % for total ET in the Europe–Africa domain and agreement in seasonal (3-month) totals of 83–97 % depending on the time of year. Most importantly, the ALEXI-MW (MW-based ALEXI) also matches ALEXI-IR (IR-based ALEXI) very closely in terms of 3-month inter-annual anomalies, demonstrating its ability to capture the development and extent of drought conditions. Weekly ET output from the two parallel ALEXI implementations is further compared to a common ground measured reference provided by the Fluxnet consortium. Overall, the two model implementations generate similar performance metrics (correlation and RMSE) for all but the most challenging sites in terms of spatial heterogeneity and level of aridity. It is concluded that a constellation of MW satellites can effectively be used to provide LST for estimating ET through ALEXI, which is an important step towards all-sky satellite-based retrieval of ET using an energy balance framework.
Estimating terrestrial evapotranspiration (ET) on continental to global scales is central to understanding the partitioning of energy and water at the earth surface and for evaluating modeled feedbacks operating between the atmosphere and biosphere. ET is an important flux that links the water, carbon, and energy cycles (Campbell and Norman, 1998). Approximately two-thirds of the precipitation over land is returned to the atmosphere by ET (Baumgartner and Reichel, 1975). Moreover, ET consumes 25–30 % of the net radiation reaching the land surface (Trenberth et al., 2009). ET occurs as a result of atmospheric demand for water vapor and depends on the availability of water and energy. When plants are present, this balancing is controlled by leaf-level stomatal controls, and in agricultural areas the water availability may also be managed on the field scale through irrigation or drainage. The high spatial and temporal variability in the driving mechanisms in combination with possible field-scale management decisions poses a significant challenge to bottom-up modeling of ET at sub-monthly timescales, even on the spatial scales of numerical weather prediction (NWP) models (5-25 km). In order for NWP models to improve the characterization of the surface energy budget, there is a need for timely diagnostic information on ET (Hain et al., 2015). This, in turn, could lead to a more timely and accurate identification of developing droughts (Anderson et al., 2011) which would aid farm-level management decisions as well as regional yield impact predictions.
ET is highly variable in space, so no amount of ground stations can provide
an accurate estimate of the spatial average over larger domains, let alone
the globe. Therefore, approaches have been developed to integrate satellite
data with models to estimate ET from space. Surface energy balance
approaches use surface temperature observations as the main diagnostic to
estimate ET by partitioning the available energy into turbulent fluxes of
sensible heating (
To date, ALEXI has always been implemented with land surface temperature (LST) retrievals from thermal infrared (TIR) imaging radiometers (Anderson et al., 2011). Most applications of ALEXI have utilized data products from geostationary satellites, for example the Geostationary Operational Environmental Satellite (GOES) with coverage over the Americas. More recently it has been applied to records from polar-orbiting satellites to obtain consistent global coverage from a single sensor with short latency. This is based on day–night temperature differences from the Moderate Resolution Imaging Spectroradiometer (MODIS) on the Aqua satellite from NASA's Earth Observing System (EOS) program (Hain and Anderson, 2017). Reliance on TIR effectively limits ET retrievals to clear skies (Rossow et al., 1989), and failure to completely mask cloud-affected observations is shown to limit the precision in TIR-LST (TIR-based LST; Holmes et al., 2016). Continuous daily estimates of ET are generated from clear-sky ALEXI samples through temporal interpolation based on maintaining a normalized flux partitioning metric. In ALEXI this also accounts for daily evaporative losses (Anderson et al., 2007a). Recent work by Alfieri et al. (2017) analyzed measurements from eddy-covariance (EC) towers and found the persistence for energy flux partitioning metrics to be short. In their analysis, they found that a return interval of no more than 5 days is necessary to keep the relative error in daily ET below 20 %.
In order to provide a more consistent and short return interval for daily ET retrievals on the global scale there is a need for accurate values during cloudy intervals. The approach we take here to address this challenge is to leverage passive microwave (MW) observations. The longer wavelengths (0.1–1 m) make MW observations of the land surface generally less susceptible to scattering and absorption by clouds than observations in the TIR spectral region (except for notable water and oxygen absorption windows; Ulaby et al., 1986). One MW frequency band with a particularly high sensitivity to LST (Prigent et al., 2016) and high tolerance to clouds (Holmes et al., 2016) is Ka band (36-37 GHz). MW radiometers with a Ka-band channel are available from several low-Earth-orbiting satellites that sample at different times of the day. Collectively they can be used to construct a diurnal cycle of brightness temperature for each location on Earth (Holmes et al., 2013b; Norouzi et al., 2012). This diurnal brightness temperature can then be scaled to match the diurnal temperature cycle (DTC) as measured by TIR imagers (Holmes et al., 2015, 2016).
The methodology developed in Holmes et al. (2015) was applied to create an
11-year record of MW-based LST (MW-LST) from various Ka-band sensors (see
Sect. 2). Because this new dataset specifically includes diurnal information,
it presents an opportunity to evaluate use of constellation-based MW-LST in a
TSEB framework for estimating ET. For this purpose, we substituted MW-LST for
MODIS LST in the global implementation of ALEXI as described in Hain and Anderson (2017) and
generated a data record of weekly ET for the time period 2003 to 2013 using
each LST data source. No recalibration of ALEXI was applied in this
experiment to accommodate MW-LST. The only difference between the two
resulting multi-year records of ET estimates are the spectral window (MW Ka
band vs. TIR) and spatial resolution of the LST inputs (0.25
The ALEXI method is a comprehensive set of algorithms to diagnose the
surface energy balance with the aim of retrieving ET (Anderson et al., 2007a; Mecikalski et al., 1999).
ALEXI is based on the TSEB land surface parameterization (Kustas and Norman, 1999; Norman et
al., 1995) in which the partitioning of turbulent fluxes is evaluated for
the soil (
ALEXI couples TSEB with an atmospheric boundary layer model to relate the
morning rise in
The experiment described in this paper is based on a recent global
implementation of the ALEXI model (Hain and Anderson, 2017). This
global ALEXI implementation differs from prior geostationary implementations
in that its analysis is performed at weekly timescales. While a daily system
is in preparation, at present, the global model is executed using 7-day
averages of all inputs on clear-sky days to minimize computational load.
In practice this means taking an average of all needed inputs (at time 1 and 2)
on the clear-sky days in the 7-day period and running ALEXI. As in
prior geostationary implementations the retrieved latent heat estimate at
time 2 is upscaled to a daily flux, conserving a flux ratio metric and using
daily solar radiation retrievals. This accounts for changes in atmospheric
demand while preserving the scaling flux ratio as determined on the
clear-sky days. However, because the scaling flux ratio is held constant
over the 7-day period the output is also reported as 7-day total ET
(mm week
Primary inputs for current global implementation of ALEXI.
MW-LST workflow.
The MODIS instrument on the polar-orbiting Aqua satellite (July 2002 to
present) with an equator overpass time of 13:30 and 01:30 LST provides global
TIR observations with spectral bands suitable for estimating LST. The
specific LST product used for the ALEXI implementation is the MODIS Climate
Modeling Grid 0.05
The MW-LST product is based on vertical polarized Ka-band (36–37 GHz)
brightness temperature (
All available Ka-band observations are combined to create a global record
with up to 8 observations per day for each 0.25
For days with suitable MW observations (a minimum of 4, at least one of
which is close to solar noon) and no
To relate the diurnal cycle in Ka-band brightness temperature to the
composite radiative temperature of the land surface requires a set of DTC
parameters that is equivalent to those derived from
The Ka-band DTC parameters (
The set of time-constant scaling parameters (
Global maps of the time-constant parameters (
Temporal coverage of MW- and IR-based
The continuous 7-day totals are achieved by temporal gap-filling of (clear sky) ET as a fraction of clear-sky latent heat flux to incoming solar radiation (Anderson et al., 2007a). To maximize similarity, the same MODIS cloud mask is applied to the ALEXI-MW implementation so that the mechanics of standard ALEXI can be evaluated under circumstances for which it has previously been developed and validated.
The fraction of days in a year where a clear-sky MODIS-based
Tower measurements of latent heat flux obtained using the eddy-covariance
technique are commonly used for ground truthing of remote sensing and
model-based ET estimates (Baldocchi et al., 2001).
Harmonized Fluxnet data are distributed in so-called synthesis datasets.
They include the original observations at a 30 min observation time and
aggregate values per day, week and month. For this work, we used the
synthesis 2015 Tier 1 data as accessed in July 2016
(
Based on these daily data, we computed the 7-day averages matching the window length of ALEXI. If not all days within a window have valid data, that window is disregarded. Overall, eddy-covariance observations of ET were available from 68 flux towers with at least 1 year of observations within the time period of this study.
Location of flux-tower sites used in the analysis (see also Table 2):
Although both MW and IR sets are available globally, the main analysis of this paper is focused on the domain encompassing Africa and Europe. This is because only in that region is the scaling of MW-LST to TIR-based LST currently supported by data (see Sect. 2.3.3). However, temporal comparisons (e.g., correlations) are much less affected by the mean absolute value of the MW-LST product. Because of the limited availability of flux-tower data, we include all available stations from across the globe which allows us to double the amount of stations available for the analysis compared to only the sites in Europe and Africa.
Within the main focus domain of this study we further highlight 11 climate-based
domain subsets (see also Fig. 3, bottom-right panel):
West-African Sahel, arid; West-African Sahel, semi-arid; Guinean coast, dry subhumid; Central Africa, humid; Horn of Africa, arid; Southern Africa, semi-arid (large bias in Fig. 4); Southern Africa, arid (large bias in Fig. 4); Iberia, semi-arid; Germany, continental humid; European Russia, continental humid, boreal forest (large bias in
Fig. 4); France, humid.
These regions are selected to represent a wide variety of seasonal variation
in precipitation and climate class and are based on the work of Trambauer
et al. (2014). Rather than attempting to cover the entire
domain with these subsets, we selected smaller subsets in order to visualize
the local deviations between MW and IR products that might otherwise be
averaged out. We also added regions in Europe and several regions that
showed a large bias in Fig. 4.
Multi-year mean of clear-sky
Mean monthly ET as estimated with ALEXI-IR over 2003–2011, and monthly
means of its MODIS-based
Cumulative annual and seasonal fluxes are compared in terms of their
relative difference – RD (%), calculated following Eq. (5):
The temporal agreement of the weekly ET estimates is further compared
relative to the flux-tower observations that serve as a common reference.
For this assessment, MW- and IR-based ET estimates are again compared in
terms of
The mean average
The general agreement in mean
Figure 5 provides a more detailed comparison between the MW and IR products
for the domain subsets as described in Sect. 2.5. For each domain subset, it
shows the mean monthly total ET and the associated monthly means of
To provide some additional spatial and temporal context for these observations, the 3-month total MW and TIR ET (averaged over 2003–2011) are shown in Fig. 6 for December–January–February (DJF), March–April–May (MAM), June–July–August (JJA) and September–October–November (SON). This shows that the cold season overestimation of MW-based ET, seen in the European regions, is present not only in Europe but also in eastern and Southern Africa in SON. The underestimation of MW-based ET in summer is not as pronounced in terms of its relative difference. The apparent difference in timing, seen in the Sahel and Iberian regions, shows up across the southern border of the Sahara – MW ET is higher in MAM, and TIR ET is higher in JJA. The spatial correlation between MW and IR is higher in SON (96 %) and DJF (97 %) compared to the periods MAM (83 %) and JJA (84 %). Despite these localized differences, the transect averages are remarkably similar showing the general success of scaling MW-LST to TIR-LST (Sect. 2.3.3).
As Fig. 4, but now averaged by season.
Time-series correlation of flux-tower ET observations with three
alternative satellite-based ET estimates (ALEXI-IR, ALEXI-MW, ALEXI-IR
Continued.
ENF: evergreen needleleaf forest, DBF: deciduous broadleaf forest, EBF: evergreen broadleaf forest, Mosaic: cropland–natural-vegetation mosaic.
Comparison of anomaly in 3-month ET totals as calculated from ALEXI-IR and ALEXI-MW for selected regions (see Fig. 3 for definition of regions).
Pearson's correlation between anomaly in 3-month ET totals as estimated
by ALEXI-IR and ALEXI-MW, calculated at 0.25
Anomaly in seasonal ET compared to multi-year mean (2003–2011, see Fig. 6) as retrieved by ALEXI-IR and ALEXI-MW. The first two columns show the anomalies for 2008 and the two right-hand columns show them for 2011.
The effect of spatial resolution in the satellite product on Pearson
correlation (
The effect of switching from TIR to MW-LST as input to ALEXI on
Pearson correlation (
Because the long-term mean of MW-LST is calibrated to match a TIR reference
(see Sect. 2.3.3), a comparison in terms of anomalies is the real test of its
performance in the ALEXI framework, especially in areas that are water
limited (see Fig. 7). Of the subsets in water-limited regions, the Horn of
Africa (
In energy-limited areas when ET is fully determined based on the meteorological forcing data, the effect of LST inputs is minimal. This is apparent in the Tropical region, where MW and ALEXI-IR have a correlation of 0.99 in Central Africa (region D). Figure 8 shows a map of the correlation between 3-month anomalies of MW- and IR-based ALEXI ET.
Seasonal anomalies are calculated by taking the seasonal total ET for a given year and subtracting its corresponding long-term mean seasonal total (2003–2011 period, as shown in Fig. 6). Examples of this are shown for a dry year (2008) and a wet year (2011), see Fig. 9. Overall the two sets of anomalies agree very well – the MW ALEXI appears to identify roughly the same areas with anomalous high or low ET. The agreement is better in the wet year than in the dry year.
The availability of eddy-covariance observations of ET from 68 flux towers
allows for a more detailed grid-level analysis of temporal agreement. Even
at the 0.05
When 0.05
The following analysis compares MW and IR both at 0.25
It is interesting to investigate what drives the difference in temporal
correlation at individual sites. The second row in Fig. 11 shows how the same
data as presented in Fig. 11a, but now marks the individual sites based on geographic domain, climate or spatial agreement.
The first panel splits the sites by geographic
region. Europe and Africa (blue) is where MW-LST was calibrated with MSG
SEVIRI and the North-American sites (green) is where MODIS ALEXI-IR has been
calibrated with GOES data (see Sect. 2). Between these two groups of stations
the relative improvement in
Six of the 68 sites have a markedly higher US-Ton and US-Var (PET/ Zambia, Savannas; ZM-Mon; water limited (PET/ ES-LgS, woody savannas (
The station in Sudan (SD-Dem) is the only of these six stations that is in a
water-limited region (arid desert climate) and has low spatial bias. Despite
the low bias, the station ET estimates are 2.5 times satellite estimates, so
it could be that the near-station land use is not representative of the wider
area.
The final station that shows a large advantage in
In contrast to these sites, there are two sites where the ALEXI-MW
outperforms ALEXI-IR in terms of correlation with in situ sites despite
being in a relatively arid climate with large spatial bias: US-SRG and US-NR1.
For US-NR1,
This paper shows that a newly developed MW-LST product can be used to
effectively substitute TIR-based LST in a two-source energy balance approach
to estimate coarse-resolution ET (
Because the long-term (7-year mean) diurnal features of MW-LST are
calibrated to TIR-LST, it is perhaps not surprising that the long-term bulk
ET estimates agree with a spatial correlation of 92 % for total ET in the
Europe–Africa domain. A comparison with biases in the input datasets of
The two parallel ALEXI implementations are further compared at the maximum
temporal resolution of the current global ALEXI output (7 days) and relative
to a common ground measured reference provided by the Fluxnet consortium.
The 68 stations that were available for this analysis represent a wide range
of land cover characteristics and climate conditions. Overall, they indicate
a close match in both performance metrics (
Based on the analyses presented in this paper, we outline the following
roadmap for an all-sky implementation of ALEXI-MW. First of all, there is a
need for global observation-based calibration of MW-LST with MODIS LST to
reduce biases as identified at the high incidence angles of the MSG domain
and avoid the need for extrapolation of scaling parameters. Second,
MW-LST could be used to improve the TIR cloud mask by attributing anomalous
TIR-based
The ALEXI-IR data are available from NASA SPoRT (MSFC). ALEXI-MW is an intermediate research product available upon request. Time series of ALEXI-MW and ALEXI-IR covering the site locations and time period of this paper are available upon request from the corresponding author. The flux-tower data are publicly available through the Fluxnet community as detailed in Sect. 2.3.
The authors declare that they have no conflict of interest.
We thank the reviewers for their insightful comments on this paper. This work was funded by NASA through the research grant “The Science of Terra and Aqua” (13-TERAQ13-0181). Edited by: Bob Su Reviewed by: Xuelong Chen and Carlos Jimenez