Utility of coarse and downscaled soil moisture products at L-band for hydrologic modeling at the catchment scale

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1 GEOPHYSICAL RESEARCH LETTERS, VOL. 39,, doi: /2012gl051809, 2012 Utility of coarse and downscaled soil moisture products at L-band for hydrologic modeling at the catchment scale Giuseppe Mascaro 1,2 and Enrique R. Vivoni 2 Received 26 March 2012; revised 26 April 2012; accepted 27 April 2012; published 25 May [1] Demonstrating the utility of satellite-based soil moisture (q) products for hydrologic modeling at high resolution is a critical component of mission design. In this study, we utilize aircraft and ground q data collected during the SMEX04 experiment in Sonora (Mexico) to compare two downscaling frameworks using C-band and L-band sensors. We show that the L-band framework, which mimics the disaggregation of SMAP products, has considerably better performance than the C-band framework simulating the downscaling of AMSR-E. Disaggregated L-band q fields are able to characterize with reasonable accuracy the q variability at multiple extent scales, including the SMAP footprint and the catchment scale, and along an elevation transect. We then test the utility of coarse and downscaled C- and L-band q estimates for hydrologic simulations through data assimilation experiments using a distributed hydrologic model. Results reveal that the model prognostic capability is significantly enhanced when using L-band q fields at the SMAP scale and, to a greater extent, when downscaled L-band q fields are assimilated. L-band data assimilation leads to higher model fidelity relative to ground data as well as more realistic soil moisture patterns at the catchment scale. This study indicates the potential value of satellite-based L-band sensors for hydrologic modeling when coupled with a statistical downscaling algorithm. Citation: Mascaro, G., and E. R. Vivoni (2012), Utility of coarse and downscaled soil moisture products at L-band for hydrologic modeling at the catchment scale, Geophys. Res. Lett., 39,, doi: /2012gl Introduction [2] The availability of soil moisture (q) is fundamental for enhancing predictions of climate [Ni-Meister et al., 2006], weather [Scipal et al., 2008] and hydrologic [Bindlish et al., 2009] models. Satellite-based microwave sensors have the potential to provide q globally in the top soil layer. Existing sensors, such as the Advanced Microwave Scanning Radiometer (AMSR-E) [Njoku et al., 2003] at C-band (7.32 GHz), however, have limited accuracy as vegetation increases [Schmugge, 1998]. As a result, L-band 1 Dipartimento di Ingegneria Civile, Ambientale ed Architettura, Università di Cagliari, Cagliari, Italy. 2 School of Earth and Space Exploration and School of Sustainable Engineering and the Built Environment, Arizona State University, Tempe, Arizona, USA. Corresponding author: G. Mascaro, Dipartimento di Ingegneria Civile, Ambientale ed Architettura, Università di Cagliari, Via Marengo 3, I Cagliari, Italy. (gmascaro@unica.it) Copyright 2012 by the American Geophysical Union /12/2012GL (1.4 GHz) sensors have been selected for the Soil Moisture and Ocean Salinity [Kerr et al., 2001] and the forthcoming Soil Moisture Active and Passive (SMAP) [Das et al., 2011] missions. The use of L-band sensors should produce more accurate q estimates, as observations at these wavelengths are less sensitive to vegetation, soil roughness and radio interferences [Calvet et al., 2011]. [3] Intensive field campaigns are used to test the ability of satellite sensors to observe soil moisture through comparisons with ground and aircraft q measurements [e.g., Bindlish et al., 2008]. Field campaign data have also been utilized in the development of data assimilation schemes [e.g., Houser et al., 1998] and to characterize conditions prior to flood predictions [Brocca et al., 2010]. In addition, the fine resolution of ground and aircraft q measurements have been instrumental in the development of downscaling models that characterize the spatial variability of soil moisture within a coarse satellite pixel using statistical [e.g., Kim and Barros, 2002; Mascaro et al., 2010] or deterministic methods [e.g., Merlin et al., 2010]. [4] Here, we demonstrate the value of coarse and downscaled q products from L-band sensors, relative to products derived at C-band, for hydrologic modeling at the catchment scale in a semiarid region. We utilize ground and aircraft q data collected in Sonora, Mexico, during the SMEX04 campaign in an area with complex topography and moderate vegetation cover. First, we compare the performance of a soil moisture downscaling algorithm applied to the disaggregation of coarse AMSR-E (C-band) and SMAP (L-band) q estimates. We then evaluate the utility of C- and L-band q products at coarse and fine (downscaled) resolutions within the SMEX04 region, a watershed and along a topographic transect. Finally, using a set of data assimilation experiments, we show the utility of coarse and downscaled q products for enhancing the prognostic ability and spatial representation of soil moisture in a catchment. 2. Observations and Modeling [5] The study region is a semiarid area in Sonora (Figure 1a) characterized by complex terrain and vegetation that greens during the North American monsoon [Vivoni et al., 2007]. Land cover includes (from low to high elevation) desert scrub, subtropical scrubland and oak savanna, with a few agricultural sectors and rural towns. SMEX04 datasets consist of ground data collected at 30 sites during 3 14 August, 2004, across a transect spanning a range of soil and vegetation types [Vivoni et al., 2007, 2008]. The transect is almost entirely located (23 out of 30 sites) in the Sierra Los Locos (SLL) basin (92.5 km 2 ), where ground, aircraft and numerical modeling estimates of q have been used by Vivoni et al. [2010] to quantify the spatiotemporal 1of7

2 Figure 1. (a) Soil moisture field in the km 2 Soil Moisture Experiment 2004 (SMEX04) area on 25 August from 2-D Synthetic Aperture Radiometer (2D-STAR, L-AIR-q). The boundary of the Sierra Los Locos (SLL) basin is shown. (b) Polarimetric Scanning Radiometer (PSR) q field (C-AIR-q) on 25 August over the coarse-scale domain L L (with L = 25.6 km) used in the C-band framework to mimic the AMSR-E footprint (dashed black square). (c) L-AIR-q on 25 August over two coarse-scale domains L L (L = 12.8 km) used in the L-band framework to mimic SMAP footprint (gray squares). The SLL boundary, river network and ground sites are shown. evolution of soil moisture and the controls exerted by landscape conditions. [6] During SMEX04, two aircraft sensors collected brightness temperature over a 75 by 50 km 2 area (Figure 1a), which was converted into q maps at 800-m resolution. The sensors include: (a) the C-band Polarimetric Scanning Radiometer (PSR), which acquired images during 10 days from 5 to 26 August [Bindlish et al., 2008], and (b) the L- band 2-D Synthetic Aperture Radiometer (2D-STAR), with data observed on 5 days within the same period [Ryu et al., 2010]. Figure 1a shows an example of a 2D-STAR field on 25 August. Ryu et al. [2010] demonstrated that 2D-STAR q data captured ground observations with higher accuracy than the PSR q estimates. As an example, the q estimates in an area over the SLL basin retrieved on 25 August from PSR and 2D-STAR sensors are compared in Figures 1b and 1c. Aircraft data were used to calibrate a downscaling algorithm [Mascaro et al., 2010, 2011] able to reproduce the multifractal properties of soil moisture fields across a range of climate settings. Here, the algorithm was calibrated using two approaches: (a) the C-band framework that mimics the disaggregation of coarse AMSR-E (25 km) products using PSR data at 800-m resolution, and (b) the L-band framework that simulates downscaling of coarse SMAP (10 km) products based on 2D-STAR data. [7] The two frameworks are used to carry out soil moisture data assimilation experiments with the Triangulated Irregular Network (TIN)-based Real-time Integrated Basin Simulator (tribs) model [Ivanov et al., 2004]. tribs is a fully-distributed model where infiltration is simulated using a modified Green-Ampt approach in a sloped, heterogeneous soil column. A range of possible soil moisture states is obtained due to the interaction of the infiltration front with antecedent conditions and the water table position, and by Table 1. Summary of Soil Moisture Datasets and Their Properties Source Data Availability Spatial Coverage Usage Resolution Name PSR 5, 8 10, 12 14, August km 2 Original aircraft 800 m C-AIR-q Aggregated at AMSR-E 25.6 km C-SAT-q Downscaled from AMSR-E 800 m C-DIS-q 2D-STAR 7, 8, August km 2 Original aircraft 800 m L-AIR-q Aggregated at SMAP 12.8 km L-SAT-q Downscaled from SMAP 800 m L-DIS-q Ground 3 13, August Transect with 23 sites Average of 5 measures in a 1 m 1 m plot at each location tribs 23 July to 30 September SLL basin No assimilation Assimilation of C-SAT-q Variable, 7 m to 230 m Assimilation of C-DIS-q Assimilation of L-SAT-q Assimilation of L-DIS-q Point GR-q SIM-q SIM-C-SAT-q SIM-C-DIS-q SIM-L-SAT-q SIM-L-DIS-q 2of7

3 Figure 2. (a) ANC calibration relation in the C-band framework and c estimates from observed q fields for domain A (squares). (b) REG calibration relation in the L-band framework and c estimates from observed q fields of domains B and C (circles and asterisks). (c) ECDFs of C-AIR-q on 25 August, compared against the 90% confidence intervals derived by 50 C-DIS-q fields (dashed gray lines). Inset is a scatterplot between the STD of C-AIR-q versus the ensemble mean of the STDs of 50 C-DIS-q fields for all domains. (d) Same as Figure 2c, but for L-AIR-q and L-DIS-q in domain B with similar inset. lateral water movement in the saturated and unsaturated zones. Vivoni et al. [2010] applied tribs at an eddy covariance tower and in the SLL basin, revealing good agreement with the tower water and energy fluxes and ground q observations in the basin. Here, we utilize the simulation conditions of Vivoni et al. [2010] from 23 July to 30 September to conduct a set of data assimilation experiments with coarse and downscaled soil moisture products. Table 1 summarizes the characteristics of the q data and experiments used in the study, including the name conventions adopted. 3. Soil Moisture Downscaling [8] The downscaling model reproduces observed multifractal properties by means of a log-poisson generator based on two parameters, c and b, which are estimated through scale-invariance and multifractal analysis between a fine (l) and a coarse (L) scale [Deidda, 2000]. The model is applied by determining calibration relations that link the parameters to a few predictors computed in the coarse domain (L L). Hence, starting from coarse-scale q data, an ensemble of fields reproducing the probability distribution of the observed small-scale q field is generated by stochastic multifractal cascades. Mascaro et al. [2010, 2011] applied the model from L = 25.6 km to l = 0.8 km by selecting coarse domains in multiple campaign datasets (SGP97, SGP99, SMEX02, SMEX04). b was found to be a constant, regionspecific value, and c was linked with the spatial mean soil moisture q as follows for SMEX04: c ¼ c þ ae gq hi ; with parameters c, a and g. In a calibration approach named ancillary (ANC), the parameters were linked to landscapes factors (terrain, soil, vegetation) affecting soil moisture dynamics for different q. Additional details can be found in Mascaro et al. [2010, 2011]. [9] Here, in the C-band framework, we used the ANC calibration relation estimated by Mascaro et al. [2011] in SMEX04 using C-AIR-q (Table 1). As an example, the boundaries of a domain with L = 25.6 km are shown in Figure 1b (domain A), while the corresponding calibration line (1) is shown in Figure 2a with the c values estimated on the C-AIR-q fields in the domain. A constant b = 0.71 was found to be appropriate for this framework. We also tested the use of operational AMSR-E products for q. However, comparison between satellite estimates and aggregated PSR values revealed poor agreement (not shown), likely due to ð1þ 3of7

4 Figure 3. (a) Relation between the spatial standard deviation s q and mean soil moisture m q in the SLL basin for each day of observation of available products (Table 1). The dashed line is the linear regression s q = m m q + q line for all points, with expected value and STD (parentheses) of m and q of (0.017) and (0.163), respectively. Two bootstrapping techniques provide similar values: (i) (0.013) and (0.082) with a total of 6 samples, each obtained by eliminating the (m q, s q ) points of one soil moisture product; and (iii) (0.013) and (0.097) with a total of 1000 samples, each created by randomly sampling with replacement of 70% of the (m q, s q ) points. (b) (c) Comparison of soil moisture products as a function of elevation for (b) 8 and (c) 24 August. GR-q are plotted as error bars with average and standard deviation of 5 measurements per plot. C-AIR-q and L-AIR-q are values in colocated pixels with ground data. SIM-q are shown as average of q in colocated and neighbor polygons (continuous line) with STD (dashed lines). Horizontal lines are the mean STD of 50 values provided by C-DIS-q and L-DIS-q in the colocated pixels. the degraded retrieval performance in this region with moderate vegetation [Jackson et al., 2009]. As a result and for consistency with the L-band framework, the coarse mean soil moisture q (C-SAT-q) was obtained by aggregating the 800-m C-AIR-q up to 25.6 km. [10] In the L-band framework, we calibrated the model using L-AIR-q in the range from L = 12.8 km to l = 0.8 km. For this aim, we selected 65 coarse domains with a criterion similar to Mascaro et al. [2011] and estimated c and b in each domain. As an example, two domains, B and C, are shown in Figure 1c. As in the first framework, we found b to assume a constant value of 0.71 and a relation between c and q described by (1). However, we found similar values of c, a and g across all domains, suggesting that a single, regional parameter set can be assumed. A number of reasons are possible for the low variability among domains in this framework, including: (a) a shorter range of scale invariance (0.8 to 12.8 km); (b) different features of 2D-STAR, and (c) a lower number of available images. The calibration relation (labeled regional, REG) is shown in Figure 2c with the c estimates in domains B and C. [11] The performance of C- and L-band frameworks is illustrated in Figures 2c and 2d, where the Empirical Cumulative Density Function (ECDF) of the 800-m q field observed on 25 August is compared against the 90% confidence intervals from an ensemble of 50 downscaled fields. Inspection of the ECDFs in all domains revealed that the downscaling algorithm in the L-band framework captured better the q distributions within the satellite footprint, especially in the left tail. In fact, in the retrieval algorithm of PSR, q tends to be artificially set to a lower bound of 2% in many pixels (arrow in Figure 2c), thus affecting the multifractal analysis and, in turn, degrading the downscaling performance [Mascaro and Vivoni, 2012]. This effect is not present in 2D-STAR (Figure 2d), likely due to the higher sensitivity of the L-band sensor to soil moisture variations, even in dry conditions. Despite this difference, both frameworks capture well the small-scale standard deviation (STD), as shown in Figures 2c and 2d (insets), with root mean square errors (RMSE) of 0.67% and 0.39% for the C- and L-band frameworks, respectively. 4. Hydrologic Evaluation and Data Assimilation 4.1. Variability Within the Watershed and Along a Topographic Transect [12] Vivoni et al. [2008, 2010] compared the variability of GR-q, C-AIR-q and SIM-q in the SLL basin. Here, we extend this analysis with L-AIR-q, C-DIS-q and L-DIS-q. Figure 3a reports the relation between the spatial standard deviation (s q )ofq and the mean soil water content (m q )in the SLL basin for all products for each day of observation. The downscaled results are averages of an ensemble of 50 synthetic fields. s q increases with m q in a fairly linear fashion for all products, consistent with studies in other semiarid areas [e.g., Martinez-Fernandez and Ceballos, 2003]. The robustness of the parameters of the regression line estimated with all products (dashed line) was assessed through two sets of bootstrap tests, summarized in the caption of Figure 3. Note that the downscaled data are able to capture well the pattern of the corresponding aircraft products, except C-DISq that slightly overestimates s q of C-AIR-q. This comparison shows the capability of the downscaling model to capture the 4of7

5 q variability over a wide range of extent scales, including the size of a medium catchment (100 km 2 ). If the products are compared directly in concomitant days (not shown), the variability of m q and s q computed for C-AIR-q and C-DIS-q is always much wider than that of ground and simulated data, since C-band retrievals overestimate soil moisture in wetter days [Vivoni et al., 2008, 2010]. In contrast, L-AIR-q and L-DIS-q exhibit patterns very close to those of GR-q and SIM-q, implying that L-band q products, observed from aircraft or disaggregated from the SMAP footprint, are able to capture the soil moisture mean and variability at the catchment scale with much higher accuracy than estimates derived from C-band products. [13] To extend the spatial analysis, the products are compared in Figures 3b and 3c as a function of transect elevation and wetness condition (wet day of 8 August and dry day of 24 August). In the case of the disaggregated q, a deterministic comparison is meaningless, as the multifractal model produces spatially homogeneous fields, so that the ensemble statistics do not vary with location and elevation within a coarse pixel. Hence, the comparison was made by plotting, for each downscaled product, two horizontal lines representing the mean STD of 50 synthetic fields. In the wet day, C-AIR-q slightly decreases from low to high elevation, with large discrepancies with other products. Patterns of L-AIR-q, SIM-q and GR-q are consistent with each other. The lines of downscaled q data envelop the corresponding aircraft data, with mean STD equal to % for C-DIS-q, and to % and % for L-DIS-q at elevations lower and higher than 1100 m, respectively (due to the two coarse pixels covering the SLL basin for 2D-STAR). Note that L-DIS-q are in reasonable agreement with ground and model data. In the dry period, all products lack significant variations with elevation, except for the model that has slightly higher q at lower sites. The C-AIR-q are always close to the lower bound of 2%, while L-AIR-q show some variability between low and high elevation. For the dry day, the ensemble mean of the downscaled q fields is 3.3% in both frameworks, with a STD of 1.7% (shown as single set of horizontal lines in Figure 3c). Overall, this confirms the higher accuracy of the L-band framework, as disaggregated q estimates compare better than C-band against ground data and simulations from a distributed model along a transect spanning a wide range of elevation, vegetation and soil conditions Utility of Coarse and Downscaled Soil Moisture for Hydrologic Modeling [14] Assimilation of remotely-sensed soil moisture data into distributed hydrologic models can improve their capability to simulate land surface fluxes and runoff generation [e.g., Crow and Ryu, 2009]. We carried out experiments by ingesting coarse and downscaled products from each framework on both the wet day (8 August) and dry day (24 August). A total of 103 simulations were conducted with identical model parameterizations to Vivoni et al. [2010]: no assimilation (SIM-q), coarse assimilation (SIM-C-SAT-q, SIM-L-SAT-q) and downscaled assimilation (50 downscaled fields for each framework as SIM-C-DIS-q and SIM-L- DIS-q). For the coarse products, direct insertion was used in the model to assimilate soil moisture in the top 0-5 cm layer [Houser et al., 1998]. This approach assumes that the ingested observation is correct, thus leading to a modification of the simulated spatial pattern. For the downscaled products, a statistical correction assimilation method was applied such that simulated q values in each model element are varied, according to the criteria proposed by Houser et al. [1998], to match the m q and s q provided by the downscaled field. This scheme assumes the statistics of the observation to be correct and preserves the simulated spatial pattern. The modification of the 0 5 cm q required in both schemes was accomplished in tribs by updating the states of the modified Green-Ampt infiltration model [Ivanov et al., 2004], including the position of top and wetting fronts, and the water table, while preserving the overall water balance in each model element. Future work will be devoted to refine the assimilation schemes, including the development of a method where q in each element is modified to match the entire statistical distribution of the disaggregated field. [15] Time series of the basin-averaged (m q ) soil moisture within the SLL are presented in Figures 4a and 4b from the assimilation of coarse and downscaled products at C- and L-band, respectively. These are compared against basinaveraged SIM-q and GR-q, whose RMSE is 1.2%. Note that m q for SIM-C-SAT-q considerably overestimates GR-q and SIM-q during the wet day, while it sharply decreases to 2% by the dry day (Figure 4a). For coarse C-band, the RMSE with GR-q rises to 2.3%. Performance improves when assimilating downscaled C-band data, with skill increasing in time, as shown by the approach of SIM-C-DIS-q to SIM-q and a decrease in ensemble spread. Results indicate that when C-band q products are ingested on the wet day, an artificially high water content is introduced, leading to unrealistic fluxes during the dry down. In contrast, the assimilation of coarse L-band data enhances the model prognostic capability as compared to the original model (SIM-q). The m q for SIM-L-SAT-q captures better GR-q after the wet day, and no sharp change is introduced on the dry day, due to the higher sensitivity of the L-band sensor in dry conditions. As a result, the RMSE with GR-q is lowered to 1.0%. While the ensemble spread of SIM-L-DIS-q is much narrower than SIM-C-DIS-q, enhancements in capturing m q are more limited when ingesting downscaled L- band data as compared to C-band. Overall, assimilating accurate coarse and downscaled L-band q products leads to a more reliable simulation of the states and fluxes in the basin. [16] The ingestion of downscaled fields is preferable to coarse products since the model is updated while accounting for the spatial variability of soil moisture within the basin. This is shown in Figures 4c 4f through the spatial distribution of surface (0 to 5 cm) q obtained 24 hours after the first assimilation date, selected to allow soil moisture redistribution processes to take place. The assimilation of coarse C- and L-band products leads to low spatial variability since q is mainly distributed around one (SIM-C-SAT-q, Figure 4c) or two (SIM-L-SAT-q, Figure 4e) values (note the frequency distributions for each map). In contrast, the spatial variability of q is much higher when assimilating downscaled data from C-band (SIM-C-DIS-q, Figure 4d) and L-band (SIM-L-DIS-q, Figure 4f). Note that spatial patterns in SIM-L-DIS-q show finer details related to terrain, soil, vegetation and rainfall fields [Vivoni et al., 2010]. Thus, accounting for the spatial heterogeneity of q through assimilation of a downscaled L-band product leads to more realistic soil moisture patterns at the catchment scale. Ultimately, this impact the generation of surface fluxes, such as 5of7

6 Figure 4. Time series of simulated basin-averaged soil moisture (0-5 cm) mq with assimilation of coarse and downscaled (a) C-band and (b) L-band q products at two times (8 and 24 August indicated by arrows) and comparison to hypsometric averages of GR-q in basin from Vivoni et al. [2008], along with the mean areal precipitation (MAP). Spatial distributions of soil moisture (0 5 cm) for (c) the C-band coarse (SIM-C-SAT-q) and (d) downscaled (SIM-C-DIS-q) and (e) the L-band coarse (SIM-L-SAT-q) and (f) downscaled (SIM-L-DIS-q) products on 9 August. The downscaled fields correspond to averages of an ensemble of 50 simulations. The insets are the frequency distributions of q within the basin. The marked zones in soil moisture shown in Figure 4d represent Thiessen polygon areas from the rain gauge forcing. evapotranspiration and runoff, in a nonlinear fashion [Crow and Wood, 2002; Vivoni et al., 2010]. Furthermore, assimilation of an ensemble of downscaled products provides the opportunity to issue probabilistic hydrologic predictions. 5. Conclusions [17] In this study, we compared the utility of coarse and downscaled q products derived from C- and L-band sensors for hydrologic applications at the catchment scale. We found an improved performance of a downscaling algorithm with L-band data derived from a coarse scale similar to the forthcoming SMAP for the semiarid region of moderate vegetation and complex terrain. Disaggregation of L-band q data allowed reproducing with higher accuracy: (a) the subfootprint q distribution across the entire region, (b) the q variability in a watershed located within a set of coarse pixels, and (c) the variation of soil moisture along a topographic transect in the basin during wet and dry conditions. The comparison between C- and L-band products was then extended to include a set of data assimilation experiments with coarse and downscaled data ingested in a distributed hydrologic model. Results reveal that more accurate L-band q products enhanced the model prognostic capability as compared to no assimilation, while the assimilation of coarse C-band data degraded performance. The downscaled L-band assimilation also improved the spatial representation of soil moisture within the basin. This holds promise for the utility of L-band soil moisture products from current and forthcoming satellite missions in hydrologic modeling applications at high resolution. [18] Acknowledgments. The Editor and the authors thank the two anonymous reviewers for assisting with the evaluation of this paper. References Bindlish, R., et al. (2008), Aircraft based soil moisture retrievals under mixed vegetation and topographic conditions, Remote Sens. Environ., 112(2), , doi: /j.rse Bindlish, R., W. T. Crow, and T. J. Jackson (2009), Role of passive microwave remote sensing in improving flood forecasts, IEEE Geosci. Remote Sens. Lett., 6(1), , doi: /lgrs Brocca, L., et al. (2010), Improving runoff prediction through the assimilation of the ASCAT soil moisture product, Hydrol. Earth Syst. Sci., 14, , doi: /hess Calvet, J. C., et al. (2011), Sensitivity of passive microwave observations to soil moisture and vegetation water content: L-band to W-band, IEEE Trans. Geosci. Remote Sens., 49(4), , doi: / TGRS Crow, W. T., and D. Ryu (2009), A new data assimilation approach for improving runoff prediction using remotely-sensed soil moisture retrievals, Hydrol. Earth Syst. Sci., 13, 1 16, doi: /hess Crow, W. T., and E. F. Wood (2002), The value of coarse-scale soil moisture observations for regional surface energy balance modeling, J. Hydrometeorol., 3, , doi: / (2002)003<0467: TVOCSS>2.0.CO;2. Das, N. N., D. Entekhabi, and E. G. Njoku (2011), An algorithm for merging SMAP radiometer and radar data for high resolution soil moisture retrieval, IEEE Trans. Geosci. Remote Sens., 49(5), , doi: /tgrs Deidda, R. (2000), Rainfall downscaling in a space-time multifractal framework, Water Resour. Res., 36(7), , doi: / 2000WR Houser, P. R., et al. (1998), Integration of soil moisture remote sensing and hydrologic modeling using data assimilation, Water Resour. Res., 34(12), , doi: /1998wr Ivanov, V. Y., E. R. Vivoni, R. L. Bras, and D. Entekhabi (2004), Catchment hydrologic response with a fully distributed triangulated irregular network model, Water Resour. Res., 40, W11102, doi: / 2004WR Jackson, T. J., R. Bindlish, and M. Cosh (2009), Validation of AMSR-E soil moisture products using in situ observations, J. Remote Sens. Soc. Jpn., 29(1), of 7

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