Introduction
Earth's atmospheric, biophysical, and hydrological processes
are closely coupled (Walko et al., 2000; Trenberth et al., 2007) and respond
to altered climate forcing manifested by changes in key environmental
variables (Meehl et al., 2007). Integrated and consistent measurements of
Earth system environmental variables at the global scale are needed for
advancing our understanding of interconnected Earth systems (Trenberth et
al., 2007) and for addressing critical global-change-related questions
including global water cycle intensification (Huntington, 2006; Wild et
al., 2008; Déry et al., 2009), arctic amplification, and feedbacks to
climate change (Smith et al., 2005; Grosse et al., 2011), and the primary
drivers behind global vegetation changes (Zhu et al., 2016).
Complementary to optical–thermal infrared (IR) and active microwave remote
sensing, spaceborne passive microwave radiometers allow for measurements of
global environmental variables at a relatively coarse spatial resolution
(∼ 5 to 100 km) but with relatively high temporal fidelity
(∼ daily for higher latitudes ≥ 45∘ N) and with reduced
constraints from variable solar illumination, clouds, and atmosphere aerosol
contamination effects (Ulaby et al., 2014). While lower-frequency (e.g.,
L-band) sensors, including the ESA Soil Moisture and Ocean Salinity (SMOS)
and NASA Soil Moisture Active Passive (SMAP) missions, are generally
considered optimal for detecting soil and surface water signals under
moderate to high vegetation biomass conditions (Kerr et al., 2001; Entekhabi
et al., 2010), higher-frequency sensors, such as AMSR-E (Koike et al., 2004)
and AMSR2 (Imaoka et al., 2012), provide simultaneous multichannel (C- to
W-band) Tb observations with variable sensitivity to surface
water, soil, vegetation, and atmosphere conditions (Njoku et al., 2003; Jones
et al., 2010). The combined observations allow for the distinguishing of
individual land parameter signals from background noise. However, the major
AMSR-E and AMSR2 (hereafter denoted as AMSR-E/2) algorithms have largely
focused on single-parameter retrievals, including the NASA and JAXA standard
soil moisture products (Njoku et al., 2003; Koike et al., 2004). In contrast,
the University of Montana (UMT) global Land Parameter Data Record version 1
(LPDR v1) was developed to exploit AMSR-E multifrequency Tb
observations for global daily mapping of multiple synergistic land parameters
related to the status and storage of water in the atmosphere, vegetation, and
soil (Jones et al., 2010; Jones and Kimball, 2010). The LPDR v1 database has
been applied for a variety of environmental studies, including quantifying
surface water inundation impacts on tundra methane emissions (Watts et
al., 2014), boreal wildfire disturbance and recovery assessments (Jones et
al., 2013), evaluating hydroclimatic controls on vegetation phenology (Alemu
and Henebry, 2013; Guan et al., 2014), biodiversity modeling and prediction
(Waltari et al., 2014), and vector-borne disease risk assessments (Chuang et
al., 2012). The LPDR v1 has also served as a baseline for evaluating other
AMSR-E algorithm retrievals (Mladenova et al., 2014) and refinements (Jang et
al., 2014; Du et al., 2014). The LPDR v1 encompasses the AMSR-E record
(2002–2011), while similar observations from AMSR2 enable potential LPDR
continuity (Du et al., 2014).
In this investigation, the version 2.0 UMT Land Parameter Data Record
(henceforth denoted as LPDR) was generated by incorporating recent algorithm
improvements (Du et al., 2015, 2016a), new algorithm refinements, and an
extended AMSR-E/2 satellite record. The key satellite microwave land
parameter retrievals derived from this study include daily maximum and
minimum surface air temperature (Tmx and Tmn),
atmosphere precipitable water vapor (PWV), vegetation optical depth (VOD),
surface fractional open water cover (FW), and volumetric soil moisture (VSM).
Surface air temperature, defined as air temperature at approximately 2 m
of height in this study and used as a global warming indicator (Hansen and
Lebedeff, 1987; Jones et al., 1999), integrates key information on the
thermal state of the land–atmosphere interface (Jones et al., 2010). PWV
represents the total water content of the atmosphere column within the
satellite sensor field of view (Bedka et al., 2010) and is strongly
interactive with temperature and climate (Held and Soden, 2000; Wentz et
al., 2007). The VOD parameter represents the slant-path opacity of the
intervening vegetation layer to land surface microwave emissions; VOD is
microwave frequency dependent and sensitive to changes in canopy biomass
water content, including woody and foliar elements (Shi et al., 2008; Jones
et al., 2011; Liu et al., 2011). The FW parameter is an important
hydrological and biogeochemical variable (Watts et al., 2012), while
large-scale mapping of FW dynamics has been used for studying high-latitude
ecosystems, wetlands, and carbon-cycle-related feedbacks to climate change
(Van Huissteden et al., 2011; McVicar et al., 2012; Lupascu et al., 2014).
Another key parameter is surface soil moisture, which governs the exchanges
of water, energy, and carbon between the soil and atmosphere (Entekhabi et
al., 2010); soil moisture is defined in this study as the volume of water in
a given volume of soil. The relative depth of soil moisture sensitivity is
dependent on microwave frequency and land surface conditions but is
generally limited to the top (∼ 1 cm depth) soil layer using moderate-frequency (e.g., C-, X-band) Tb retrievals from the AMSR-E/2
sensors.
The goals of this study were to (a) provide an enhanced data record over
prior (v1) LPDR releases in terms of both retrieval accuracy and temporal
coverage, (b) generate consistent retrievals from AMSR-E and AMSR2 suitable
for long-term evaluations of key land parameters important to ecosystem
processes, and (c) facilitate LPDR utility for the Earth science community by
providing detailed descriptions of algorithm structure, retrieval accuracy
and product performance, and data format specifications. The LPDR methods,
data processing, global performance, and uncertainty assessments are presented
below.
Methods
LPDR v1 algorithm and refinements
In the LPDR v1 algorithms, the satellite-observed microwave emission from
land overlying a non-scattering atmosphere is theoretically described by
three components representing the upward emission of the atmosphere, land
surface upward emission attenuated by the atmosphere, and the downward
atmosphere emission reflected by the land surface and attenuated by
atmosphere (Wang and Manning, 2003; Jones et al., 2010). Atmosphere effects
are mainly determined by air temperature and the optical depth of oxygen,
cloud liquid water, and atmosphere water vapor (Wentz and Meissner, 2000;
Jones et al., 2010). The land surface upward microwave emission is
represented as the overall emission from a mix of land surface features,
including open water, vegetation, and soil (Mo et al., 1982; Jones et
al., 2010). The AMSR-E/2 frequencies have variable sensitivity to land and
atmosphere properties, and the frequency-dependent optical depth of
vegetation or atmospheric layers determines the degree to which measured
microwave emissions originate from the soil, vegetation, or atmosphere
(Jones, 2016). The C- and X-band AMSR-E/2 measurements are generally used for
inferring soil moisture under vegetation and atmosphere layers, while higher
Tb frequencies (> 18 GHz) show relatively greater
sensitivity to atmospheric properties (Njoku et al., 2003). In addition, open
water may significantly impact the measured microwave emissions at all
AMSR-E/2 frequencies due to the high dielectric constant of water (Jones et
al., 2010; Du et al., 2016b). Based on the above theory and considerations,
the LPDR v1 algorithms utilize observations at relatively high frequencies
(> 18 GHz) to estimate PWV and FW and then apply the inferred
information to derive the X-band VOD and VSM retrievals. The two-step
retrieval process is detailed as follows: first, effective surface
temperature (Ts), Tmx and Tmn, FW, and
PWV are obtained using an iterative algorithm approach that incorporates H-
and V-polarized 18.7 and 23.8 GHz Tb data and several
temperature-insensitive microwave indices (Jones et al., 2010). In this step,
a simplified land emission model that considers constant dry soil emissivity
is adopted for facilitating the inversion process. The X-band VOD is then
obtained by inverting the land–water microwave emissivity slope index, and
surface (∼ 0–1 cm depth) VSM is acquired after correcting for X-band
atmosphere, FW, and vegetation effects (Jones et al., 2010). More detailed
descriptions of the LPDR v1 algorithms are provided elsewhere (Jones et
al., 2010). Recent refinements based on the LPDR v1 algorithm framework were
carried out separately using AMSR-E or AMSR2 Tb observations,
including (a) an empirical calibration of the AMSR2 PWV retrieval based on
similar observations from the Atmospheric Infrared Sounder (AIRS; Du
et al., 2015), (b) a refined AMSR2 estimation of Tmx and
Tmn that considers terrain and latitude effects (Du et
al., 2015), and (c) an improved AMSR-E VSM retrieval using a weighted
averaging strategy and dynamic selection of vegetation-scattering albedos (Du
et al., 2016a).
LPDR retrieval algorithms
The latest (v2) LPDR algorithms were developed based on the available
algorithm framework and improvements (Sect. 2.1). For generating a consistent
LPDR product, the available algorithm refinements were adapted for both
AMSR-E and AMSR2 portions of the combined, calibrated Tb record
(Sect. 3.1). The final regression formulas for estimating PWV are described
below, which follow from Du et al. (2015) but use different regression
coefficients; for the satellite ascending (PM) overpass, the empirical
calibration resulted in
PWVPM=-4.06+0.22Ts+Avdav23-av180.47+0.26exp(-H)-1.63logΔTb(89.0)ΔTb(36.0),
and for the descending (AM) overpass it was
PWVAM=1.06+0.27Ts+Avdav23-av180.48+0.21exp(-H)-1.63logΔTb(89.0)ΔTb(36.0).
The PWV estimate is derived by a weighted sum of Ts (∘C),
atmosphere optical depth Avd estimated from the 23.8 and
18.7 GHz Tb polarization difference ratios, a cloud
correction term ΔTb(89.0)ΔTb(36.0), and surface elevation H (km). The terms av18 and
av23 are empirically derived water vapor absorption coefficients
(Jones et al., 2010). The regression formulas for estimating Tmx
and Tmn are given as
Tmn=3.55+0.69Ts+11.86Tc-6.67Tc2-0.14(abs(Lat))+2.74γcos(t)+1.83⋅logFW+1.0,Tmx=7.49+0.79Ts-5.71Tc+11.45Tc2-0.14(abs(Lat))+2.20γcos(t)+1.75⋅logFW+1.0,
where Ts is the effective surface temperature and Tc is
the frequency-dependent vegetation transmissivity, which is Tc=exp(-VOD); t=2πω-π; ω=doyn; γ=sign(Lat)(1-abs(abs(Lat)-45)45) in which doy is the
day of year, n is the total days in a year, and Lat is the geographic
latitude. FW is the fractional proportion (%) of standing water cover
within a grid cell and is used for minimizing open water impacts on the
temperature retrievals.
In addition to the above updates, we performed additional FW calibration for
improving the VSM retrievals in this study. As described above, the iterative
retrieval algorithm proposed in Jones et al. (2010) and revised in Du et
al. (2015) assumes dry soil conditions for estimating FW, VOD, and atmosphere
properties. Consequently, the FW retrieval is likely to be affected by a soil
moisture signal when the simplified dry soil assumption is not fully
satisfied. Therefore, an empirical calibration of AMSR-E/2 FW was made for
the purpose of improving the soil moisture inversion as follows: (a) AMSR-E
FW values for the nonfrozen period over the 2003–2010 record were averaged
for each 25 km grid cell and compared with an ancillary MODIS 250 m MOD44W
static FW map (Carroll et al., 2009); (b) the resulting AMSR-E FW summer
average values were grouped into 1000 population ranges from 0.0 to 1.0 and
0.001 intervals; (c) for each group, mean AMSR-E FW and corresponding MOD44W
values were calculated; and (d) relationships between AMSR-E and MOD44W FW
retrievals were analyzed based on their mean group values and derived for two
respective conditions, AMSR-E FW < 0.15 and FW ≥ 0.15. The 0.15 FW
threshold was selected for describing the AMSR-E and MOD44W FW relationships
over the different AMSR-E FW levels. Soil moisture was then estimated after
open water correction using the calibrated FW record (denoted as
FWcal). The resulting empirical relationships were used for
calibrating AMSR-E/2 ascending (PM) and descending (AM) FW estimates prior to
their use in VSM retrievals against the MOD44W open water maps:
FWcal_PM=4.4267FW3+1.3447FW2+0.4114FWFW<0.15FWcal_PM=-0.4683FW2+1.0182FW-0.0458FW≥0.15,FWcal_AM=-23.752FW3+7.7518FW2+0.1565FWFW<0.15FWcal_AM=-0.4014FW2+0.9837FW-0.0422FW≥0.15.
Here we note that the ancillary MOD44W map was used solely for open water
correction of the VSM estimates and is independent from the LPDR FW
retrievals. The general LPDR retrieval process is illustrated in Fig. 1.
Evaluation of the LPDR
The resulting LPDR environmental parameters for nonfrozen land surface
conditions were evaluated based on their full-year records (2003–2010 and
2013–2015) and following similar approaches used in previous studies (Jones
et al., 2010; Du et al., 2015, 2016a). The evaluations included analyzing the
global distributions of climatological means and standard deviation (SD) or
coefficient of variation (CV) in LPDR full-year records. The LPDR ascending
and descending retrievals have similar spatial distributions, so only the
ascending result maps are presented in the following analysis. To compare
with the LPDR results, similar climatological mean and CV maps (if
applicable) from alternative reference data were utilized, including MOD44W
FW, normalized difference vegetation index (NDVI) observations from the
third-generation Global Inventory Monitoring and Modeling System project
record (GIMMS3g; Tucker et al., 2005; Pinzon and Tucker, 2014), and (AIRS)
PWV (Divakarla et al., 2006).
The LPDR algorithm retrieval process.
Global seasonal cycles defined from monthly means and CV variations in the
LPDR daily observations and full-year data records were compared against
similar aggregations from the reference data, including GIMMS3g NDVI and AIRS
PWV. In particular, the vegetation seasonality indicated by VOD and NDVI was
compared for the global domain and six major plant functional types.
The LPDR-derived FW composites over the 2003–2010 (representing AMSR-E) and
2013–2015 (representing AMSR2) periods were compared against the MOD44W
static open water map. While the MOD44W record is used for surface water
correction of Tb observations for the soil moisture retrievals
(Eqs. 5 and 6), the correction is independent of the LPDR FW retrieval (Jones
et al., 2010). The LPDR-derived Tmx and Tmn estimates
were compared with independent daily air temperature measurements from 142
World Meteorological Organization (WMO) sites for the selected years 2010
(representing AMSR-E) and 2013 (representing AMSR2). The LPDR-derived PWV
results were analyzed against AIRS PWV observations from the same 142 WMO
site locations for the 2010 and 2013 periods. Finally, the LPDR-derived daily
VSM results were compared against independent surface soil moisture
measurements from four regional soil station networks. The metrics used to
evaluate agreement between the LPDR results and independent observations
included correlation coefficient (R), root mean square error (RMSE), and
bias.
For evaluating LPDR consistency, only grid cells with high-quality retrievals
were considered in the analysis, which excluded areas with higher vegetation
biomass cover (VOD > 2.3 representing over 90 % loss of underlying
soil and open water signals from vegetation attenuation) or where the
difference between V-pol and H-pol Tb retrievals at 18 or
23 GHz was less than 1.0 K (i.e., indicating microwave signal
saturation). Grid cells containing large water bodies (FW > 0.2) were
also excluded to avoid excessive contamination of the land signal by open
water (Du et al., 2015; Jones, 2016). Moreover, we divided 365 (366 for leap
year) days of a year into 122 3-day periods and for each 3-day period
selected for the consistency evaluation, we required at least one
high-quality retrieval within the period for each year of the 2003–2010 and
2013–2015 portions of the record. Based on the above data selection
criteria, the global monthly mean of the high-quality LPDR daily estimates
were calculated for each month of the AMSR-E (2003–2010) and AMSR2
(2013–2015) full-year records and analyzed using statistical metrics,
including mean, SD, and range.
Data processing and ancillary datasets
AMSR-E and AMSR2 Tb records used for land parameter retrievals
Multifrequency Tb observations from AMSR-E and AMSR2 provide the
primary inputs for LPDR processing. The AMSR-E sensor was launched on 4 May
2002 onboard the NASA EOS Aqua satellite and operated until 4 October 2011.
AMSR-E was succeeded by AMSR2, which was launched on 18 May 2012 onboard the
JAXA GCOM-W1 satellite. Both sensors provide global measurements of
vertically (V) and horizontally (H) polarized microwave emissions at six
frequencies (6.9, 10.7, 18.7, 23.8, 36.5, 89.0 GHz) with descending
and ascending orbital equatorial crossings at 01:30 and 13:30 local time.
Though succeeding most characteristics of its predecessor, AMSR2 is different
from AMSR-E in several aspects, including (a) an additional Tb
channel at 7.3 GHz designed for mitigating radio frequency
interference (RFI), (b) a larger (2.0 m diameter) main reflector providing
enhanced spatial resolution retrievals, and (c) an improved calibration
system (Imaoka et al., 2010).
For developing a consistent global land parameter record, the AMSR-E/2
Tb retrievals were preprocessed in four steps. (1) AMSR-E orbital
swath Tb data from the Remote Sensing Systems (RSS) version 7
product were spatially resampled and re-projected to a 25 km resolution
global Equal-Area Scalable Earth (EASE) Grid version 1 format following
previously established methods (Armstrong and Brodzik, 1995; Ashcroft and
Wentz, 1999; Brodzik and Knowles, 2002). In this study, an additional
altitude correction of the Tb orbital swath retrievals was made
to improve sensor footprint geolocation accuracy prior to the gridding
process. The altitude correction to the AMSR2 L1R data considers the actual
surface of the Earth instead of an ideal Earth ellipsoid (T. Maeda et
al., 2016), which helps to ensure reliable analysis of AMSR-E/2 land surface
retrievals over high elevation areas, including the Qinghai–Tibetan Plateau;
(2) a similar gridding process was performed on the AMSR2 L1R swath data.
(3) The AMSR2 multifrequency (X- to W-band) Tb retrievals were
empirically calibrated against the same AMSR-E channels using similar
overlapping Tb observations from the Microwave Radiation Imager
(MWRI) onboard the Chinese FY3B satellite (Du et al., 2014). However, in
contrast to Du et al. (2014) in which the Tb calibration was
conducted on a per grid cell basis for each frequency, polarization, and
orbit, the approach used for this investigation involved calibrating within
5×5 grid cell windows to minimize the impact of the different sensor
footprints. Both ascending- and descending-orbit X-band Tb data
for a given polarization were calibrated together because the largest
differences and lowest correlations were found between overlapping MWRI and
AMSR-E/2 X-band observations among all sensor frequencies utilized (Du et
al., 2014). The combined orbit X-band calibration was also found to produce
better consistency between the AMSR2 ascending and descending X-band VOD
retrievals, which are particularly sensitive to Tb calibration
uncertainties, especially for higher vegetation biomass conditions.
(4) Finally, the gridded and calibrated AMSR-E/2 Tb data were
subjected to additional screening prior to implementing the retrieval
algorithms to minimize potential noise effects from RFI, active
precipitation, frozen conditions, and permanent ice and snow cover using
previously established methods (Jones et al., 2010). The Tb
screening under frozen land surface conditions was identified using an
existing global daily freeze–thaw (FT) data record derived from a refined
classification algorithm (Kim et al., 2017) and AMSR-E/2 36.5 GHz
V-polarized Tb retrievals in a consistent 25 km resolution
global EASE-Grid projection format; the FT mask is represented as a
grid-cell-wise daily binary bit flag in the LPDR dataset and was used to
identify and screen frozen land surface conditions from further LPDR
processing and retrievals (Fig. 1).
Global distribution of WMO weather station locations where
collocated AIRS observations and WMO air temperature measurements were used
for calibrating (white circles) and validating (black circles) the LPDR PWV,
Tmx, and Tmn estimates; the locations of the four
independent soil moisture networks used for validating the LPDR VSM
retrievals are also shown (white rectangles).
Ancillary data used for algorithm calibration and LPDR performance
assessment
A variety of ancillary data were used for calibrating the LPDR algorithms and
evaluating LPDR global performance. The ancillary data included atmosphere
PWV retrievals from AIRS (Divakarla et al., 2006), a static MOD44W open water
map (Carroll et al., 2009), GIMMS3g NDVI (Pinzon and Tucker, 2014), and in
situ surface soil moisture measurements from four globally distributed
measurement networks (Jackson et al., 2010; Yang et al., 2013; Smith et
al., 2012). All ancillary data were re-projected to the same 25 km EASE-Grid
version 1 format as the LPDR to facilitate algorithm calibration and product
comparisons.
The AIRS PWV products were used for LPDR PWV algorithm calibration and
product comparisons. The LPDR iterative retrieval algorithm for PWV (Jones et
al., 2010; Sect. 2.1) was empirically calibrated and quantitatively validated
using synergistic PWV observations (version 6 level 2 swath product) from
AIRS and the Advanced Microwave Sounding Unit (AMSU) instruments (Du et
al., 2015). Both AIRS and AMSU are deployed on the Aqua satellite together
with AMSR-E and have the same local overpass time as AMSR2. The AIRS version
6 product is expected to have higher accuracy than the previous AIRS version
4 water vapor record, which shows retrieval uncertainties of less than
15 % in comparison with radiosonde measurements in 2 km troposphere
layers (Divakarla et al., 2006; Diao et al., 2013).
For calibrating LPDR-derived PWV, Tmx, and Tmn
retrievals over different land cover types, in situ daily Tmx and
Tmn measurements were obtained along with coincident AIRS PWV
retrievals for year 2010 from 250 globally distributed WMO weather station
locations (Fig. 2). The spatial distribution of the WMO stations selected was
designed to be representative of major global land cover classes (Justice et
al., 2002; Friedl et al., 2010). The WMO air temperature record was obtained
from the National Climate Data Center (NCDC) Global Summary of the Day (GSOD
version 7) using previously established criteria (Jones et al., 2010). The
calibration was made for the year 2010 and the derived relationships were
applied to the entire AMSR-E/2 record. Independent daily air temperature
measurements and collocated AIRS PWV retrievals from 142 other globally
distributed WMO weather stations (Fig. 2) operating from 2010 to 2013 were
selected for the evaluation of LPDR-derived Tmx, Tmn,
and PWV accuracy; relative consistency in performance between the AMSR-E
(represented by the year 2010) and AMSR2 (represented by the year 2013)
portions of the LPDR record was also assessed.
The LPDR-derived FW record was evaluated against the higher-resolution
(250 m), global-scale MOD44W static open water product (Carroll et
al., 2009). The MOD44W product is derived from a compilation of the SRTM
(Shuttle Radar Topography Mission) water body dataset and the MODIS MOD44C
Collection 5 (2000–2008) open water classification (Haran et al., 2005;
Carroll et al., 2009). The MOD44W map was re-projected and aggregated to the
same 25 km EASE-Grid format as the LPDR prior to the comparisons.
The LPDR-derived VOD record was evaluated over the global domain using
synergistic satellite optical–IR observations of vegetation greenness defined
from NDVI. The GIMMS3g (version 1) global NDVI record derived from calibrated
NOAA Advanced Very High Resolution Radiometer (AVHRR) sensor observations
(Pinzon and Tucker, 2014) has been widely used in evaluating global
vegetation status and changes (Zhu et al., 2016); the bimonthly NDVI data
were re-projected from their native 1/12∘ spatial resolution and
geographic projection format to the same 25 km resolution global EASE-Grid
format as the LPDR for the 2003 to 2015 record. The NDVI is sensitive to
changes in vegetation greenness and differs from LPDR-derived 10.65 GHz VOD
sensitivity to canopy biomass and water content variations, including both
photosynthetic (e.g., foliar) and non-photosynthetic (e.g., stem and branch)
elements (Jones et al., 2013). Both satellite NDVI and VOD records have been
shown to provide similar synergistic canopy phenology metrics distinguishing
both seasonal and spatial differences among different plant functional types
(Jones et al., 2011).
The LPDR VSM retrieval accuracy was evaluated using a similar approach as Du
et al. (2016a) by comparing the satellite X-band (10.65 GHz) daily
soil moisture retrievals against collocated in situ surface soil moisture
measurements from four globally distributed soil moisture measurement
networks (Fig. 2). The four soil moisture regional networks represent the
approximate spatial heterogeneity and sensing depth as the AMSR-E/2
Tb footprint retrievals and were designed for validating
satellite regional soil moisture retrievals as detailed in Jackson et
al. (2010), Smith et al. (2012), and Yang et al. (2013). The Little River
network (LR; centroid 83.61∘ W, 31.65∘ N) has a humid
climate representing forest, cropland, and pasture vegetation (Jackson et
al., 2010). The Little Washita network (LW; centroid 98.1∘ W,
34.95∘ N) has a subhumid climate dominated by rangeland and pasture
vegetation (Jackson et al., 2010). A 3-year (2003–2005) LR and LW daily soil
moisture record representing surface (0–5 cm of depth) soil layer
conditions was used for this study. The Nagqu (NQ; centroid
91.875∘ E, 31.625∘ N) soil moisture network was located on
the Tibetan Plateau in western China. Surface soil moisture measurements
extending from August 2010 to September 2011 from the NQ network were used
for evaluating LPDR performance in an environment characterized as high
elevation with large surface soil moisture variability and sparse vegetation
(Chen et al., 2013; Yang et al., 2013). The Yanco (YC; centroid
146.0915∘ E, 34.842∘ S) network is part of the larger
Murrumbidgee Soil Moisture Monitoring Network (MSMMN) in Australia (Smith et
al., 2012; Panciera et al., 2014) and represents a Southern Hemisphere
semiarid agricultural and grazing landscape; a 2-year (2009–2010) YC surface
soil moisture record was also used for this study.
LPDR fractional water mean (a) and 2 times the coefficient
of variation (b) over the years 2003–2010 and 2013–2015.
Results
Fractional open water
The LPDR FW composites (Fig. 3a) for nonfrozen periods capture
characteristic global inundation patterns consistent with the ancillary
MOD44W static water map (Fig. S1 in the Supplement), including extensive
wetland complexes in the pan-Arctic region, Bangladesh, and Argentina and
major river systems such as the Amazon, Mississippi, Yangtze, and Yenisei.
Large FW seasonal variations (Fig. 3b) associated with seasonal precipitation
and/or snowmelt events occur over the Mississippi basin, the Paraná River basin,
northern Canada and Eurasia, the Indian subcontinent, southern Tibet, and
eastern China. The LPDR FW record also distinguishes dynamic flooding events
not represented by the ancillary static water map, including extensive water
inundation (Fig. 3a) and large seasonal FW variations (Fig. 3b) in Bangladesh
where the summer monsoon brings large precipitation-driven flooding (Brouwer
et al., 2007).
Quantitative comparisons of LPDR FW annual means in relation to MOD44W were
made for respective AMSR-E (2003–2010) and AMSR2 (2013–2015) full-year
records (Table 1). Both AMSR2 and AMSR-E FW annual means show favorable
spatial correspondence with the MOD44W results (R≥0.75,
RMSE ≤ 0.06). The LPDR inundated area percentage also shows a mean
1.50 % wet bias relative to the MOD44W product, which may partially
result from better LPDR microwave sensitivity to surface water dynamics,
including water beneath vegetation (Du et al., 2016b). Higher LPDR FW levels
along coastlines are due to larger water cover of coastal grid cells within
the land mask. The LPDR results also show generally larger coastal FW levels
than MOD44W, indicating ocean contamination of adjacent land grid cells
within the coarser AMSR-E/2 Tb footprint.
Comparisons of FW global averages over AMSR-E (2003–2010) and AMSR2
(2013–2015) periods in relation to the MOD44W static open water map. All
products were projected into a consistent 25.0 km resolution EASE-Grid
format; positive and negative bias indicates FW overestimation and underestimation,
respectively, relative to the static water map.
AMSR-E/2 FW vs. MOD44W
R
RMSD
Bias
Asc
Dsc
Asc
Dsc
Asc
Dsc
AMSR-E
0.767
0.750
0.057
0.057
0.016
0.012
AMSR2
0.795
0.775
0.054
0.054
0.017
0.013
R denotes Pearson correlation coefficient; RMSD denotes root
mean square difference; Asc and Dsc denote respective ascending and
descending orbits.
LPDR daily Tmn, Tmx, and ascending- or
descending-orbit-based PWV accuracy in relation to respective in situ air temperature
measurements and AIRS PWV observations for 142 global WMO site locations for
the
selected years 2010 (AMSR-E) and 2013 (AMSR2).
Tmx (∘C)
Tmn (∘C)
R
RMSE
Bias*
R
RMSE
Bias
AMSR-E
0.928
3.428
0.637
0.899
3.307
0.061
AMSR2
0.917
3.484
0.260
0.899
3.150
0.265
PWV (mm) from
PWV (mm) from
ascending orbits
descending orbits
R
RMSE
Bias
R
RMSE
Bias
AMSR-E
0.926
4.241
0.266
0.923
4.788
0.197
AMSR2
0.914
4.473
-0.369
0.911
4.941
-0.050
* Bias is calculated from retrievals minus observations.
LPDR PWV climatology mean (a) and 2 times the coefficient
of variation (b) from the combined 2003–2010 and 2013–2015
record.
LPDR and AIRS PWV monthly means and seasonal variability (2 times
the standard deviation or 2× SD) over the globe and combined for the
2003–2010 and 2013–2015 period.
Atmosphere precipitable water vapor
The spatial distributions of LPDR PWV climatology mean (Fig. 4a) and CV
(Fig. 4b) results derived from ascending-orbit Tb retrievals and
full-year observations were compared with benchmark satellite PWV retrievals
from AIRS (Fig. S2). Both LPDR and AIRS PWV retrievals show similar global
patterns and latitudinal distributions, with generally higher water vapor
levels at lower latitudes and warmer climate zones, which is consistent with the
near-exponential relationship between atmospheric temperature and moisture-holding capacity except for dry desert regions distinguished by lower
characteristic PWV levels. Especially large PWV levels are observed over the
Bay of Bengal and adjacent regions (Fig. 4a) where a large amount of water
vapor is transported by the summer monsoon (Uma et al., 2014). Large PWV
seasonal variations (CV) are apparent in regions with distinct dry and wet
seasons, including the Indian subcontinent, eastern China, and the African
Sahel (Fig. 4b); these spatial and temporal patterns are consistent between
the LPDR and AIRS products. The LPDR shows larger PWV seasonal variability in
tropical rainforest regions (Fig. 4b) than the AIRS observations, which is
attributed to ill-conditioned LPDR retrievals associated with microwave
signal saturation over dense vegetation cover. Relatively large CV values in
regions with average dry-air conditions (e.g., the Tibetan Plateau) reflect the strong
sensitivity of the CV metric to small mean humidity values in the denominator
(% CV = 100⋅ SD/mean). Overall, the LPDR and AIRS ascending- and
descending-orbit-derived PWV monthly means are highly correlated (R=0.99)
(Fig. 5) with a major peak in the Northern Hemisphere summer months (July and
August) and a secondary peak in the Southern Hemisphere summer months
(January and February).
The LPDR PWV retrievals were quantitatively validated against the AIRS
observations at 142 global WMO weather station locations for the years 2010 and
2013 (Table 2). The AMSR-E/2 retrievals show strong agreement with the AIRS
PWV product (R≥0.91; RMSE ≤ 4.94 mm), though a slight PWV
overestimation and underestimation are indicated for the respective AMSR-E
(bias ≤ 0.27 mm) and AMSR2 (bias ≥ -0.37 mm)
portions of the record (Table 2).
Daily maximum and minimum surface air temperature
The LPDR-derived global mean and CV variability maps for Tmx are
presented in Fig. 6, while the Tmn results show similar global
and seasonal patterns. The LPDR results show characteristic global
temperature patterns following major climate zones and latitudinal gradients
and are similar to the PWV results (Fig. 4) but with generally greater surface
spatial complexity influenced by proximity to coastal areas, vegetation and
land cover conditions, and elevation-driven temperature lapse rates (Du et
al., 2015). The LPDR results show expected smaller seasonal temperature
variability (CV) near the Equator and larger variability at higher latitudes,
especially in the interior of large landmasses such as North America and
Asia. The resulting temperature maps (Fig. 6) only represent nonfrozen land
surface conditions rather than complete annual cycles (i.e., Sects. 2.3, 3.1).
We also note that the LPDR surface air temperatures are derived from
ascending- and descending-orbit Tb retrievals empirically adjusted to
represent daily Tmx and Tmn conditions using in situ
temperature measurements from sparse global weather stations. Thus the LPDR
results may deviate from actual daily maximum and minimum temperature
conditions for some areas and periods; these and other uncertainties impact
LPDR accuracy and performance, which are evaluated in the following
temperature assessment.
LPDR Tmx mean (a) and 2 times the coefficient
of variation (b) for the years 2003–2010 and 2013–2015.
The LPDR-derived Tmx and Tmn retrievals were
validated against independent in situ daily air temperature measurements from
142 global WMO weather stations for the years 2010 and 2013 (Table 2).
Overall, the LPDR temperatures corresponded favorably with the WMO
temperature measurements (R≥0.90; RMSE ≤ 3.48 ∘C). The
AMSR-E (2010) and AMSR2 (2013) retrievals show similar Tmx and
Tmn retrieval accuracy, with associated RMSE differences within
0.16 K in relation to the WMO daily temperature measurements. These
results indicate improved LPDR temperature accuracy relative to previously
reported AMSR2-derived accuracies for Tmx
(RMSE = 3.64 ∘C) and Tmn
(RMSE = 3.54 ∘C) from a prior study (Du et al., 2014); the
higher LPDR temperature accuracy (RMSE ≤ 3.48 ∘C) suggests an
improvement in sensor inter-calibration and algorithm refinements
(Sect. 3.1). However, the calibrated AMSR2 Tb is not identical to
that of AMSR-E as reflected by a maximum 0.38 ∘C difference in their
Tmx and Tmn retrieval biases against WMO measurements
(Table 2). To evaluate the impact of the fractional water corrections on the
LPDR v2 air temperature retrievals, Eqs. (1)–(4) were re-derived using the
same procedure (Sect. 2.2) but assuming zero fractional water cover. The
results indicated approximately 13 % improved RMSE performance in the
Tmx and Tmn retrievals using the FW correction
relative to air temperature retrievals derived without accounting for
fractional water influence.
Pearson correlations (R) between LPDR VOD and GIMMS3g NDVI
climatology monthly means for the aggregate 2003–2010 and 2013–2015
observation record. The comparisons were made for all global vegetation and
selected land cover areas, including ENF, EBF, DNF, DBF, grassland, and
cropland. Both products were projected into a consistent 25.0 km resolution
EASE-Grid format. VOD results are delineated for LPDR ascending- and
descending-orbit records.
Pearson correlation
Global
ENF
EBF
DNF
DBF
Grassland
Cropland
coefficient
Ascending
0.878
0.715
0.218
0.893
0.201
0.903
0.665
Descending
0.937
0.898
-0.116
0.944
0.871
0.951
0.845
Vegetation optical depth
The previous UMT LPDR v1 AMSR-E VOD record was assessed globally (Jones et
al., 2011) and has been used for a range of regional ecosystem studies,
including vegetation phenology and disturbance recovery assessments (Liu et
al., 2013; Jones et al., 2013, 2014). The VOD record can also be used as a
data quality mask for the VSM retrievals because soil moisture retrieval
accuracy is generally degraded under higher vegetation biomass levels (Du et
al., 2016a). In this study, the LPDR-derived VOD was compared with the
GIMMS3g NDVI record based on an assumption of proportionality between
vegetation canopy biomass and greenness variations (Jones et al., 2011). The
evaluation results of the previous and current studies are consistent,
including generally favorable correlations between VOD and optical vegetation
indices and reduced correspondence at higher biomass levels.
The LPDR VOD pattern and seasonal variability (CV) are generally consistent
with the global pattern in vegetation cover indicated from the NDVI record
(Fig. S3).The LPDR-derived mean annual VOD results (Fig. 7a) show
characteristic global patterns in vegetation biomass, including higher VOD in
tropical rainforests (e.g., the Amazon Basin, the Congo Basin, and Southeast
Asia) and much lower VOD in arid and sparsely vegetated areas, including the
Sahara and Sonoran deserts and Central Australia. Moderate VOD levels occur
in grassland, shrubland, and cropland areas, including the central USA,
sub-Saharan Africa, central China, and India. Larger VOD relative seasonal
variability (Fig. 7b) occurs over predominantly deciduous and lower biomass
areas, including grassland, shrubland, and cropland. Large VOD seasonal
variations also occur in semiarid climate zones with distinctive wet and dry
cycles, including the African Sahel where plant growth depends on seasonal
rainfall (Proud and Rasmussen, 2011). A few VOD change hotspots occur in
wetland areas (e.g., the Iberá Wetlands in Argentina and the Bangweulu
Wetlands in Zambia), which may reflect emergent vegetation overlying a
standing water background during the wet season. Lower VOD seasonality occurs
in the tropics, which is consistent with a smaller seasonal climate cycle
near the equatorial zone. Arid areas show the generally low VOD levels and
seasonality consistent with sparse vegetation cover except for some areas,
including portions of the Arabian Peninsula, where relatively large VOD
seasonality may be a result of irrigation activities (Siebert et al., 2005).
Annual mean (a) and 2 times the coefficient of
variation (b) of LPDR 25 km global X-band VOD daily estimates from
AMSR-E/2 ascending observations encompassing the years 2003–2010 and
2013–2015.
Monthly means and variations (2× SD) of LPDR X-band
vegetation optical depth (VOD) and GIMMS3g NDVI for all global
vegetation (a) and selected land cover types, including
ENF (b), EBF (c), DNF (d), DBF (e),
grassland (f), and cropland (g) areas over the aggregate
2003–2010 and 2013–2015 observation period.
LPDR 25 km X-band volumetric soil moisture (VSM) mean (a)
and 2 times the coefficient of variation in percentage of mean
values (b) derived from the aggregate 2003–2010 and 2013–2015
observation record.
Both VOD and NDVI display similar seasonal cycles represented by their mean
monthly time series (R≥0.88) but with temporal phase offsets in VOD and
NDVI cycles for different land cover types (Fig. 8). Here, the mean seasonal
cycle in VOD and NDVI is depicted for major IGBP global land cover types,
including evergreen needleleaf forest (ENF), evergreen broadleaf forest
(EBF), deciduous needleleaf forest (DNF), deciduous broadleaf forest (DBF),
grassland, and cropland. The LPDR VOD and GIMMS3g NDVI comparison results are
summarized in Table 3 and show strong correspondence for both ascending-orbit
(0.67≤R≤0.90) and descending-orbit (0.84≤R≤0.95) retrievals
for ENF, DNF, grassland, and cropland areas with relatively well-defined
seasonal cycles. A VOD temporal phase shift relative to NDVI is evident for
croplands and detectable for other land cover types, reflecting different
vegetation biophysical properties that the microwave and optical–infrared
instruments are sensitive to (Jones et al., 2011, 2012). Weaker and even
negative VOD and NDVI correlations in EBF regions coincide with lower
characteristic canopy seasonality in the tropics, but may reflect degraded
signal-to-noise ratios due to persistent cloud and atmospheric aerosol
effects limiting effective NDVI retrievals and VOD and NDVI saturation over
dense canopies (Jones et al., 2011). For dense canopies, NDVI seasonality can
be strongly driven by the onset of new leaves flushing (E. E. Maeda et
al., 2016), while the asynchrony between leaf flush and vegetation growth may
also affect the VOD and NDVI correlations (Jones et al., 2014). The VOD
estimates derived from the descending-orbit Tb retrievals also
show overall stronger correspondence with NDVI than the ascending retrievals,
especially for DBF regions (descending orbit R=0.87; ascending orbit R=0.20). Differences in NDVI correspondence between the ascending- and
descending-orbit VOD records may reflect regional VOD retrieval uncertainties
contributed by deficiencies in the underlying LPDR algorithm assumptions and
parameterizations, which are discussed below (Sect. 5).
Summary of satellite LPDR soil moisture retrieval accuracy in
relation to in situ surface soil moisture measurements from four globally
distributed validation watersheds.
Statistics
Little River
Little Washita
Nagqu
Yanco
All sites
(USA; 2003–2005)
(USA; 2003–2005)
(China; 2010–2011)
(Australia; 2009–2010)
Ascending orbits
R
0.627
0.762
0.790
0.755
0.815
RMSE
0.035
0.036
0.051
0.059
0.045
Bias
0.041
0.053
-0.102
-0.042
0.012
Descending orbits
R
0.696
0.733
0.831
0.787
0.835
RMSE
0.032
0.036
0.042
0.055
0.042
Bias
0.071
0.086
-0.063
-0.031
0.038
R is correlation coefficient; RMSE (root mean square error) and
bias are in cm3cm-3. RMSE and all site statistics except bias
are calculated with watershed bias corrected.
Soil moisture
The global soil moisture pattern depicted by the LPDR X-band VSM record
(Fig. 9) is generally consistent with the known global climatology, including
characteristically wet surface soil moisture conditions in northern
high-latitude areas and drier soil moisture extremes in deserts and semiarid
regions such as the African Sahara, the southwestern USA, and Central
Australia. Wetter VSM conditions along coastal boundaries may reflect
remaining ocean Tb contamination of adjacent land grid cells
within the coarser sensor footprint despite explicit FW correction of the VSM
retrievals. Relatively large seasonal soil moisture variations are associated
with areas having distinctive wet and dry seasons, including the African
Sahel, Central USA, the Indian subcontinent, and southern Tibet. For arid
regions such as Central Australia, high relative (%) seasonal CV
variability is due to low average VSM conditions. Lower VSM variability
occurs over higher vegetation biomass (VOD) areas, including forests, where
AMSR-E/2 soil moisture sensitivity and VSM retrieval performance are expected
to be lower due to loss of soil sensitivity; the global range of effective
VSM retrievals and other LPDR observations is represented by the data quality
metrics described below (Sect. 5.2).
The LPDR VSM retrievals were compared against globally distributed validation
watershed measurements (Table 4). The LPDR results show overall favorable VSM
accuracy in relation to independent in situ soil moisture observations from
the globally distributed monitoring sites within the effective LPDR domain
(0.63≤R≤0.84; 0.03 ≤ bias-corrected
RMSE ≤ 0.06 cm3cm-3). The apparent retrieval biases
(-0.10 to 0.09) may partially reflect inconsistencies in horizontal and
vertical representativeness between the in situ soil moisture measurements
and AMSR-E/2 Tb retrievals (Du et al., 2016a). These results
indicate similar or better accuracy than the reported performance of other
AMSR-E soil moisture products (Jackson et al., 2010; Du et al., 2016a) and
generally better LPDR performance for descending-orbit (AM) than
ascending-orbit (PM) VSM retrievals.
Discussion
The latest (v2) LPDR incorporates recent algorithm refinements and updates
over the original baseline algorithms and data record (Jones et al., 2010)
while also including an extended global data record spanning both AMSR-E and
AMSR2 observation periods (June 2002–December 2015). The resulting data
record produces global environmental patterns and seasonal dynamics
consistent with characteristic climate and land cover variability; the LPDR
also shows favorable agreement with a diverse set of independent observation
benchmarks. The LPDR algorithms and parameter estimates are internally
consistent and include an integrated set of environmental parameters
representing atmosphere, vegetation, surface, and soil conditions derived from
simultaneous satellite multifrequency Tb observations. The
iterative algorithm and multiparameter retrieval approach enable
the decomposition of the satellite observations into atmosphere, vegetation,
standing water, and soil moisture components. In particular, the dynamic
open water (FW) correction in the LPDR algorithm benefits VSM retrievals over
areas with significant spatial and seasonal inundation variability. The
current algorithm is limited to nonfrozen land surface conditions determined
using an independent AMSR-E/2 FT product (Kim et al., 2017), while the FT
parameter is represented as a simplified daily frozen flag in the LPDR.
Potential extension of the LPDR to represent snow cover properties and frozen
conditions would enable continuous land parameter observations over a full
annual cycle while incorporating observations of other key environmental
indicators of the changing cryosphere. The complex microwave emission and
scattering signatures of snow, lake ice, frozen soil, and vegetation must
first be carefully accounted for to enable the further development and extension
of the LPDR retrieval algorithms (Tedesco et al., 2010; Du et al., 2017).
LPDR data format
The resulting LPDR is available in a 25 km resolution global EASE-Grid (v1)
projection and GeoTIFF file format. The data files are organized by ascending
and descending orbits on a daily basis. Each GeoTIFF file consists of six 2-D
(1383 columns, 586 rows) data arrays representing five float-type retrieval
data bands (FW, Tmx or Tmn, Tc, PWV, VSM)
and one byte-type QC band. A set of product QC flags are included to inform
the user about the estimated quality of retrieved parameters or missing data.
The QC binary bit flags are summarized in Table 5 and indicate the presence
or absence of the following land surface conditions: frozen ground (bit 1),
snow or ice presence (bit 2), strong precipitation (bit 3), RFI at
18.7 GHz (bit 4), RFI at 10.65 GHz (bit 5), dense vegetation
with VOD > 2.3 (bit 6), large water bodies with FW > 0.2 (bit 7), and
saturated microwave signals (difference between V-pol and H-pol brightness
temperature at 18 or 23 GHz less than 1.0 K; bit 8). The
percentages of land areas with high QC retrievals were summarized by seasons
and sensor orbits (Table 6).
LPDR data quality flag description.
Bit number
Land surface condition
Indication
1
Frozen ground
No LPDR retrieval
2
Snow or ice presence
No LPDR retrieval
3
Strong precipitation
No LPDR retrieval
4
RFI at 18.7 GHz
No LPDR retrieval
5
RFI at 10.65 GHz
No LPDR retrieval
6
Dense vegetation with VOD > 2.3
Possible large retrieval uncertainty
7
Large water bodies with FW > 0.2
Possible large retrieval uncertainty
8
Saturated microwave signals with V-pol and H-pol Tb
Possible large retrieval uncertainty
difference at 18 or 23 GHz less than 1.0 K
The percentages of land areas having high QC retrievals summarized
by seasons and sensor orbits; seasons aggregated by spring (MAM), summer
(JJA), autumn (SON), and winter (DJF) months of the Northern Hemisphere.
Ascending
Descending
Northern
Southern
Northern
Southern
Hemisphere
Hemisphere
Hemisphere
Hemisphere
MAM
95.8 %
92.6 %
93.1 %
88.4 %
JJA
95.3 %
92.6 %
94.4 %
89.2 %
SON
95.1 %
93.5 %
93.4 %
89.2 %
DJF
76.5 %
92.2 %
73.0 %
88.3 %
Temporal frequency distribution map of estimated high-quality (QC)
retrievals, which exclude areas with dense vegetation (VOD > 2.3),
saturated microwave signals (V-pol and H-pol Tb difference at 18
or 23 GHz less than 1.0 K), and large water bodies
(FW > 0.2).
Data record consistency
The LPDR record described in this study extends from June 2002 to December
2015 and captures both short-term (diurnal, daily, annual) variability and
longer-term (annual, decadal) climate trends over the global terrestrial
environment for a diverse set of significant environmental parameters.
Potential differences in Tb characteristics and algorithm
performance between the AMSR-E and AMSR2 portions of the LPDR are expected to
introduce artifacts and degrade LPDR precision for analyzing long-term
environmental changes. LPDR data consistency was examined through statistical
comparison of best-quality (QC) retrievals between the AMSR-E and AMSR2 portions
of the record (Sect. 2.3); the global pattern and temporal frequency of the estimated
best retrievals are presented in Fig. 10. As summarized in Table S1 in the
Supplement, the land parameter retrievals have similar mean values,
variations, and ranges between the AMSR-E and AMSR2 portions of the record,
indicating general LPDR consistency and quality. However, the underlying
Tb retrieval biases between the two sensors are not completely
removed by the sensor inter-calibration process (Du et al., 2014), which may
propagate to uncertainty in the higher-order LPDR retrievals and trends. For
ascending retrievals, the AMSR2 biases relative to AMSR-E for the LPDR parameters
FW, PWV, Tmx, VOD, and VSM are about 0.00, -0.50 mm,
-0.24 ∘C, -0.03, and -0.01 cm3cm-3,
respectively. For descending retrievals, the corresponding biases are
0.00, -0.45 mm, 0.13 ∘C, 0.01, and
0.01 cm3cm-3. The AMSR2 record also tends to have smaller PWV-
and VOD-derived SD variability and ranges compared with AMSR-E (Table S1).
Similar differences between AMSR-E and AMSR2 retrievals are also evident in
the validation assessments against the independent observations, including
WMO surface air temperature measurements and AIRS PWV (Table 2).
LPDR uncertainty
While the v2 data record provides new refinements and enhancements over the
earlier LPDR baseline, several product uncertainty and consistency issues
remain unresolved. The LPDR VOD and VSM analysis (Sect. 4.4 and 4.5)
indicated generally better performance for descending- than ascending-orbit
retrievals. Better descending (∼ 01:30) performance may result from
seasonal changes in thermal gradients between surface air, canopy, and ground
layer conditions through the process of leaf development (Durre and Wallace,
2001), which is not accounted for in the VOD retrieval algorithm (Jones et
al., 2012). The AMSR-E/2 descending observations reflect relatively
isothermal early morning conditions that promote better VOD and VSM
performance relative to ascending observations under midday
(∼ 13:30) conditions characterized by larger thermal gradients.
The LPDR retrievals in more densely vegetated areas (e.g., VOD > 2.3) are
expected to have greater uncertainty and should be used with caution; these
areas are flagged in the LPDR QC data fields and distinguished from areas
with expected higher-quality retrievals (Fig. 10). In more densely vegetated
areas, the higher-frequency AMSR-E/2 Tb retrievals are more
likely to have smaller polarization differences and signal saturation,
resulting in less sensitivity to VOD and PWV and higher retrieval
uncertainties. For this reason, differences in VOD and PWV retrievals between
AMSR-E and AMSR2 may be magnified over more densely vegetated areas where
sensor inter-calibration uncertainties further lower the signal-to-noise ratio.
Denser vegetation cover also promotes stronger attenuation of underlying
soil and water microwave signals, increasing VSM retrieval uncertainty in these
areas (Du et al., 2016a). Similarly, the retrieval accuracy for standing
water with overlying vegetation cover, a different scenario from the exposed open
water with surrounding vegetation cover assumed in this study, is expected to
decrease exponentially under higher VOD levels (Du et al., 2016b). The land
parameter grid cells and retrievals along coastlines and other large water
bodies are likely to be affected by water contamination of the coarse sensor
Tb footprint, though these effects are partially accounted for by
representation of FW on the associated land parameter retrievals within a
grid cell. Regions with larger FW cover may have higher retrieval
uncertainties, which are represented as a water flag (FW > 0.2) in the
LPDR quality mask (Fig. 10).
The AMSR2 and AMSR-E Tb records used for this study were
previously calibrated (Du et al., 2014), but remaining artifacts from the
different sensor spatial resolutions and instrument calibration systems
likely contribute to differences in land parameter characteristics and
performance between the two sensor periods of the record. Though small in
quantity, the AMSR2 retrieval biases relative to AMSR-E (Tables 2 and S1)
should be considered when analyzing long-term environmental trends.
Differences in parameter accuracy and performance between AMSR2 and AMSR-E
observations and a limited (12.5 years) LPDR (v2) period of record constrain
capabilities for assessing subtle environmental trends. Future LPDR releases
are expected to benefit from continuing AMSR2 operations and calibration
refinements to the integrated AMSR-E/2 Tb record, enabling more
accurate environmental change assessments.