Vahid Naeimi

Wade T Crow

Wolfgang Wagner

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Inferring the impact of radar incidence angle on soil
moisture retrieval skill using data assimilation
Wade T. Crow, USDA Hydrology and Remote Sensing Laboratory
Wolfgang Wagner and Vahid Naeimi, Institute for Photogrammertry and Remote Sensing, TU-Wien
I. INTRODUCTION
The sensitivity of radar backscatter signals to vegetation and surface properties is expected to vary significantly
as a function of radar incidence angle (θ). Consequently, the impact of θ on surface soil moisture (Θs) retrieval
skill is an key design consideration for satellite-based radars tasked with the remote estimation of Θs. Work by
Ulaby and colleagues demonstrated that low incidence angles (10-20◦) are generally preferred [8], [17], yet larger
θ values are typically required in order to achieve good spatio-temporal ground coverage. Side-looking radars such
as the scatterometer on board European Remote Sensing (ERS) satellites cover the θ range between 20 to 60◦
(approximately), while the conical scanning Soil Moisture Active Passive (SMAP) mission will acquire backscatter
measurements at a fixed mid-range incidence angle of 40◦ [13]. Unfortunately, the impact of θ on retrieval skill is
difficult to quantify because of significant uncertainties in existing backscatter (σ◦) models [2]. Even over bare soil
surfaces, σ◦ models exhibit markedly different sensitivities to θ because of difficulties describing the roughness of
natural surfaces [20]. This uncertainty is compounded over vegetated surfaces where variations in Θs uncertainty
with θ depend on the assumed strength of so-called “canopy interaction” and/or “double-bounce” backscatter terms
[21].
Theoretical models exist for capturing such terms [19], however they cannot be properly inverted due to their
complexity. Therefore, simpler model functions trained by either theoretical models and/or derived from empirical
observations are required for operational Θs retrieval. One possibility are so-called “vegetation water cloud” models
which explicitly ignore canopy interaction terms [1]. In general, backscatter models lacking such terms attribute
changes in far-range backscatter almost exclusively to vegetation [14] and predict little or no sensitivity to Θs at large
θ. Conversely, the Water Retrieval Package 5 (WARP5) backscatter model developed by TU-Wien for retrieving
Θs from ERS scatterometer and METOP Advanced Scatterometer (ASCAT) observations implicitly assumes the
presence of a large interaction term [15] and predicts the sensitivity term δσ◦[dB]/δΘs is constant across all θ.
Since the noise of radar measurements is given in dB [18], this assumption implies that the signal to noise ratio
of the Θs retrievals, and therefore their skill, does not decrease with increasing θ, even at far-range (>50
◦) and in
the presence of dense vegetation.
Attempts to resolve this discrepancy over realistic landscapes are typically hampered by a lack of sites where
ground-based Θs observations are sufficiently dense for direct comparisons with coarse-scale (>10 km) satellite
retrievals. For example, a validation study of several remotely-sensed Θs products over Western Africa using sparse
ground-based Θs measurements yielded very similar results for scatterometer soil moisture products retrieved with
WARP5 and a second backscatter model developed by [24], even though the two models treat the vegetation com-
ponent quite differently [11]. However, a recently-developed evaluation technique provides a method of evaluating
large-scale soil moisture products in the absence of ground-based Θs observations [5]–[7]. Here, we apply this
technique in an attempt to clarify the impact of θ on radar-based Θs retrieval skill.
II. BACKSCATTER MODELING
The ERS WARP5 backscatter model is similar in functionality to the cloud model, with the important exception
that it exhibits an increased sensitivity to Θs at far-range by assuming a linear relationship between Θs and σ
◦
(now in dB units) across the entire θ range. At a reference angle of 40◦, backscatter is given by
σ◦(40◦) = Θs(wetref − dryref) + dryref (1)
and can be related to backscatter at any θ through
σ◦(θ) = σ◦(40◦) + σ
′
(θ)(θ − 40◦) +
1
2
σ
′′
(θ)(θ − 40◦)2 (2)
Backscatter bounding parameters wetref and dryref in (4) are calculated from extremely high and low backscatter
values within a sufficiently long time series of σ◦ observations at a single point. In addition, wetref , dryref , σ
′
, and σ
′′
all vary seasonally due to patterns of vegetation growth and decay. Full WARP5 details and exact parameterizations
are given in [15]. Note that, starting with (4), all references to σ◦ assume dB units and a vertically transmitting
and receiving (VV) backscatter polarization.
III. THE Rvalue METRIC
Directly inferring the impact of θ on Θs retrieval skill requires the availability of large-scale Θs measurements
derived from ground-based sampling. Since such observations are rarely available, we will explore the application
of an alternative strategy based solely on ground-based precipitation measurements. The Rvalue metric for remotely-
sensed Θs retrieval is based on sampling the Pearson’s correlation coefficient between data assimilation analysis
increments, realized upon the assimilation of a remotely-sensed Θs product into a water balance model, and known
rainfall errors [5]–[7]. The typical model implementation is using daily, satellite-based precipitation accumulation
estimates (P sat) to derive the Antecedent Precipitation Index (API)
APIi = γiAPIi−1 + P
sat
i
(3)
where γ is the unit-less API coefficient, i is a daily time index and P sat has units of mm. In the interest of
simplicity, γ is assumed equal to a constant value of 0.85.
Using a Rauch-Tung-Strebel smoother (RTS), ΘRS retrievals are assimilated into (6). At each retrieval time, the
RTS smoother either removes or adds water to (6) in response to information contained in ΘRS. The time-series
of these changes are referred to as analysis increments. Given a sufficiently long time series of data, the Pearson’s
correlation coefficient (R) between 5-day sums of analysis increments and precipitation errors can be sampled for
a particular geographic location. Following [5], the negative of this sampled coefficient is referred to as the Rvalue
coefficient for a particular soil moisture product. The magnitude of Rvalue reflects the efficiency with which the
assimilation of ΘRS can compensate (6) for stochastic error in P
sat. Higher Rvalue corresponds greater amount
of added value in ΘRS estimates. In fact, comparisons with extensive ground-based Θs observations at isolated
test-bed sites reveal a linear relationship between Rvalue and the R between anomalies in ΘRS and ground-based
Θs observations [7]. Therefore, the Rvalue metric is a robust proxy for relative variations in soil moisture retrieval
skill. While alternative Rvalue approaches could be designed with more complex water balance models, a statistical
analysis of verification results in [7] implies that more complex models are unlikely to improve its reliability as a
skill metric. In practical terms, the current Rvalue approach also has the added benefit of not requiring the availability
of ground-based Θs observations or any other ancillary information and is thus broadly applicable at continental
and global scales. Our specific purpose here is to use the Rvalue approach to provide supporting evidence regarding
the appropriate relationship between soil moisture retrieval skill and θ.
IV. METHODOLOGY
A. Soil Moisture and Precipitation Data
The ERS scatterometer ΘRS dataset is derived using the WARP5 model presented by [15] and 5.3 GHz VV-
polarization σ◦ measurements obtained from the ERS-1 and -2 satellites between August 1991 and May 2007.
P gauge is obtained from the gauge-based National Center for Environmental Prediction (NCEP) Climate Prediction
Center (CPC) retrospective CONUS rainfall product [12]. Following the convention used in CPC processing, daily
rainfall accumulations are defined as total observed precipitation between 12 and 12 UTC. Because daily satellite-
based rainfall products do not extend back for the entire length of the ERS dataset, P sat is generated through the
artificial degradation of P gauge.
Our analysis is based on 1◦ simulations run within two separate regions of the United States: a Southern Great
Plains (SGP) regional domain between 32.5-40.5◦N and 94.5-103.5◦W and a Southeastern (SE) regional domain
<26 26-35 35-43 43-50 >50
Incidence Angle Range [degrees]
0.1
0.2
0.3
0.4
0.5
R
va
lu
e [
-]
ERS WARP5 (SGP domain)
ERS WARP5 (SE domain)
Fig. 1. The observed variation of domain-averaged Rvalue with θ for the real ERS data case over the lightly-vegetated Southern Great
Plains (SGP) and moderately-vegetated Southeastern (SE) United States domains. Error bars represent the 2σ sampling uncertainty range of
domain-averaged Rvalue.
covering 30.5-38.5◦N and 79.5-88.5◦W. Landcover in the SGP domain is generally short grassland and rangeland
with low levels of vegetation biomass. In contrast, the SE domain is more heavily vegetated with a combination
of upload forested areas and valley-based cropland. Prior to the analysis, all data is processed onto a daily, 1◦
latitude/longitude grid, and the subsequent Rvalue analysis is applied separately to each 1
◦ box.
B. Rvalue Approach
In order to examine the relative variation of Rvalue with θ, all ERS soil moisture retrievals are divided into five
separate θ bins: <26◦, 26-35◦, 35-43◦, 43-50◦ and >50◦. These particular bins are selected so each contains an
approximately equal fraction of all ERS WARP5 retrievals. Here, θ is assumed to be the average of the fore-, aft-
and mid-beam incidence angles for ERS measurements within a single 1◦ grid-box on a given day. Rvalue is then
individually estimated for ERS WARP5 Θs retrievals falling within each θ range. Relative variations in Rvalue for
this case reveal the manner in which θ changes impact Θs retrieval skill. Error bars for sampled Rvalue estimates
are based on the application of Fisher’s z-transformation to ensure normality (see [16] p. 148).
V. RESULTS
Figure 1 shows the variation of Rvalue with θ for the real ERS data case. Rvalue results are presented as spatial
averages of all 1◦ Rvalue results calculated within each domain. For the SGP domain, calculated Rvalue declines
slightly with θ. Since the Rvalue metric has a strong linear relationship with the Pearson correlation coefficient
between retrieved and ground-observed Θs anomalies [7], the ratio R̂value = Rvalue (> 50
◦)
/
Rvalue (< 26
◦)
approximates the corresponding ratio in correlation-based skill. Based on this reasoning, the highest θ range in
Figure 1 (for the SGP domain) retains 77% of the correlation-based anomaly skill found in the lowest θ range
(i.e. R̂value = 0.77). Reflecting the impact of increased vegetation biomass and thus lower retrieval skill, relatively
lower Rvalue results are noted over the SE domain. In addition, slightly more sensitivity to θ is found as the R̂value
ratio falls to 0.70.
VI. CONCLUSIONS
The impact of θ on Θs retrieval skill represents an areas of significant uncertainty for efforts to apply spaceborne
radars to operationally estimate Θs over continental-scale regions. Here, we attempt to clarify this issue by applying
a new data assimilation-based evaluation method for remotely-sensed Θs products. Despite a slight reduction in
skill with increasing θ, statistically significant skill is detectable at all θ ranges within the TU-Wien WARP5 surface
Θs data product. Specifically, θ retrievals based on far-field (θ > 50
◦) ERS observations in the SGP (SE) domain
retain 77% (70%) of the correlation-based skill present in retrievals at the lowest available ERS θ range. Additional
results presented in our presentation will examine the degree to which observed variations with θ are consistent
will assumptions underlying the WARP5 and cloud vegetation backscattering models.
REFERENCES
[1] E. Attema, and F. Ulaby,“Vegetation modeled as water cloud,” Radio Science, vol. 13, pp. 357-364, 1978.
[2] B.W. Barrett, E. Dwyer, and P. Whelan, “Soil moisture retrieval from active spaceborne microwave observations: An evaluation of current
techniques,” Remote Sensing, vol. 1, pp. 210-242, 2009.
[3] Z. Bartalis, K. Scipal, and W. Wagner, “Azimuthal anisotropy of scatterometer measurements over land,” IEEE Geosci. Rem. Sens., vol.
44, no. 8, pp. 2083-2092, Aug. 2006.
[4] Z. Bartalis, W. Wagner, V. Naeimi, S. Hasenauer, K. Scipal, H. Bonekamp, J. Figa, and C. Anderson, “Initial soil moisture retrievals
from the METOP-A Advanced Scatterometer (ASCAT),” Geophy. Res. Lett, vol. 34, L20401, 2007.
[5] W.T. Crow, “A novel method for quantifying value in spaceborne soil moisture retrievals,” J. Hydrolmeteor., vol. 8, no. 1, pp. 56-57,
2007.
[6] W.T. Crow and X. Zhan, “Continental-scale evaluation of remotely-sensed soil moisture products,” IEEE Geosci. Rem. Sens. Lett., vol.
4, no. 3, pp. 451-455, July 2007.
[7] W.T. Crow, D.G. Mirrales, and M.H. Cosh, “A quasi-global evaluation system for satellite-based surface soil moisture retrievals,” accepted,
IEEE Geosci. Rem. Sens..
[8] M.C. Dobson, and F.T Ulaby, “Active microwave soil moisture research,” IEEE Geosci. Rem. Sens., vol. 24, no. 1, pp. 23-35, Jan. 1986.
[9] M.C. Dobson, F.T. Ulaby, M.T. Hallikainen, and M.A. EL-Rayes, “Microwave dielectric behavior of wet soil-part 2: Dielectric mixing
models,” IEEE Geosci. Rem. Sens., vol. 38, no. 1, pp. 1635-1643, Jan. 1985.
[10] A. K. Fung, Z. Li, and K. S. Chen, “Backscattering from a randomly rough dielectric surface,” IEEE Geosci. Rem. Sens., vol. 30, no.
2, pp. 356-369, March 1992.
[11] C. Gruhier, P. de Rosnay, S. Hasenauer, T. Holmes, R. de Jeu, Y. Kerr, E. Mougin, E. Njoku, F. Timouk, W. Wagner, and M. Zribi,
“Soil moisture active and passive microwave products: intercomparison and evaluation over a Sahelian site,” Hydrology and Earth System
Sciences Discussions, vol. 6, no. 4, pp. 5303-5339, 2009.
[12] R.W. Higgins, W. Shi and E. Yarosh, “Improved United States precipitation quality control system and analysis,” NCEP/Climate
Prediction Center ATLAS, vol. 7, 40 pp., 2000.
[13] Y. Kim, and J.J. Van Zyl, “A time-series approach to estimate soil moisture using polarimetric radar data,” IEEE Geosci. Rem. Sens.,
vol. 47, no. 8, pp. 2519-2527, Aug. 2009.
[14] R.D Magagi, and Y.H Kerr, “Retrieval of soil moisture and vegetation characteristics by use of ERS-1 wind scatterometer over arid
and semi-arid areas,” J. Hydrol., vol. 188-189, pp. 361-384, 1997.
[15] V. Naeimi, K. Scipal, Z. Bartalis, S. Hasenauer, and W. Wagner, “An improved soil moisture retrieval algorithm for ERS and METOP
scatterometer observations,” IEEE Geosci. Rem. Sens., vol. 47, no. 7, pp. 1999-2013, July 2009.
[16] H. von Storch, and F.W. Zwiers, Statistical Analysis in Climate Research, Cambridge University Press, 494 pp, 2002.
[17] F.T. Ulaby, and P.P. Batlivala, “Optimum radar parameters for mapping soil moisture,” IEEE Trans. Geosci. Electron., vol. 14, no. 2,
pp. 81-93, April 1976.
[18] F.T. Ulaby, R.K. Moore, and A.K. Fung, “Microwave Remote Sensing - Active and Passive, Vol. III: From Theory to Applications,”
Norwood: Artech House, 1986. 7,
[19] F.T. Ulaby, K. Sarabandi, K. McDonald, M. Whitt, and M.C. Dobson, “Michigan Microwave Canopy Scattering Model (MIMICS),”
Int. J. Rem. Sens., vol. 11, no. 7, pp. 1223-1253, July 1990.
[20] N.E. Verhoest, H. Lievens, W. Wagner, J. Alvarez-Mozos, M.S. Moran, and F. Mattia, “On the soil roughness parameterization problem
in soil moisture retrieval of bare surfaces from Synthetic Aperture Radar,” Sensors, vol. 8, pp. 4213-4248, 2008.
[21] W. Wagner, G. Blschl, P. Pampaloni, J.-C. Calvet, B. Bizzarri, J.-P. Wigneron, and Y. Kerr, “Operational readiness of microwave remote
sensing of soil moisture for hydrologic applications,” Nordic Hydrology, 38, pp. 1-20, 2007.
[22] W. Wagner, G. Lemoine, M. Borgeaud, and H. Rott, “A Study of Vegetation Cover Effects on ERS Scatterometer Data,” IEEE Geosci.
Rem. Sens., vol. 37, no. 2, pp. 938-948, March 1999a.
[23] W. Wagner, G. Lemoine, and H. Rott, “A Method for Estimating Soil Moisture from ERS Scatterometer and Soil Data,” Rem. Sens.
Envion., vol. 70, 191-207, 1999b.
[24] M. Zribi, C. Andre, and B. Dechambre, “A method for soil moisture estimation in Western Africa based on the ERS scatterometer,”
IEEE Geosci. Rem. Sens., vol. 46, no. 2, pp. 438-448, Feb. 2008.


Inferring the impact of radar incidence angle on soil moisture retrieval skill using data assimilation

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Inferring the impact of radar incidence angle on soil moisture retrieval skill using data assimilation

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