Seung-Bum Kim

The Soil Moisture Active Passive Validation Experiment 2012 (SMAPVEX12): Prelaunch Calibration and Validation of the SMAP Soil Moisture Algorithms

The National Aeronautics and Space Administration (NASA) Soil Moisture Active Passive (SMAP) satellite is scheduled for launch in January 2015. In order to develop robust soil moisture retrieval algorithms that fully exploit the unique capabilities of SMAP, algorithm developers had identified a need for long-duration combined active and passive L-band microwave observations. In response to this need, a joint Canada-U.S. field experiment (SMAPVEX12) was conducted in Manitoba (Canada) over a six-week period in 2012. Several times per week, NASA flew two aircraft carrying instruments that could simulate the observations the SMAP satellite would provide. Ground crews collected soil moisture data, crop measurements, and biomass samples in support of this campaign. The objective of SMAPVEX12 was to support the development, enhancement, and testing of SMAP soil moisture retrieval algorithms. This paper details the airborne and field data collection as well as data calibration and analysis. Early results from the SMAP active radar retrieval methods are presented and demonstrate that relative and absolute soil moisture can be delivered by this approach. Passive active L-band sensor (PALS) antenna temperatures and reflectivity, as well as backscatter, closely follow dry down and wetting events observed during SMAPVEX12. The SMAPVEX12 experiment was highly successful in achieving its objectives and provides a unique and valuable data set that will advance algorithm development.

Mechanisms Controlling the Interannual Variation of Mixed Layer Temperature Averaged over the Nino-3 Region

Abstract Processes controlling the interannual variation of mixed layer temperature (MLT) averaged over the Nino-3 domain (5°N–5°S, 150°–90°W) are studied using an ocean data assimilation product that covers the period of 1993–2003. The overall balance is such that surface heat flux opposes the MLT change but horizontal advection and subsurface processes assist the change. Advective tendencies are estimated here as the temperature fluxes through the domain’s boundaries, with the boundary temperature referenced to the domain-averaged temperature to remove the dependence on temperature scale. This allows the authors to characterize external advective processes that warm or cool the water within the domain as a whole. The zonal advective tendency is caused primarily by large-scale advection of warm-pool water through the western boundary of the domain. The meridional advective tendency is contributed to mostly by Ekman current advecting large-scale temperature anomalies through the southern boundary of the d...

Canadian Experiment for Soil Moisture in 2010 (CanEx-SM10): Overview and Preliminary Results

The Canadian Experiment for Soil Moisture in 2010 (CanEx-SM10) was carried out in Saskatchewan, Canada, from 31 May to 16 June, 2010. Its main objective was to contribute to Soil Moisture and Ocean Salinity (SMOS) mission validation and the prelaunch assessment of the proposed Soil Moisture Active and Passive (SMAP) mission. During CanEx-SM10, SMOS data as well as other passive and active microwave measurements were collected by both airborne and satellite platforms. Ground-based measurements of soil (moisture, temperature, roughness, bulk density) and vegetation characteristics (leaf area index, biomass, vegetation height) were conducted close in time to the airborne and satellite acquisitions. Moreover, two ground-based in situ networks provided continuous measurements of meteorological conditions and soil moisture and soil temperature profiles. Two sites, each covering 33 km × 71 km (about two SMOS pixels) were selected in agricultural and boreal forested areas in order to provide contrasting soil and vegetation conditions. This paper describes the measurement strategy, provides an overview of the data sets, and presents preliminary results. Over the agricultural area, the airborne L-band brightness temperatures matched up well with the SMOS data (prototype 346). The radio frequency interference observed in both SMOS and the airborne L-band radiometer data exhibited spatial and temporal variability and polarization dependency. The temporal evolution of the SMOS soil moisture product (prototype 307) matched that observed with the ground data, but the absolute soil moisture estimates did not meet the accuracy requirements (0.04 m3/m3) of the SMOS mission. AMSR-E soil moisture estimates from the National Snow and Ice Data Center more closely reflected soil moisture measurements.

Soil Moisture Retrieval Using Time-Series Radar Observations Over Bare Surfaces

A time-series algorithm is proposed to retrieve bare surface soil moisture and rms height using two copolarized (HH and VV) L-band backscattering coefficients (σ0). The retrieval approach inverts a forward model for radar scattering from an isotropic bare surface. Because real-time inversion of a complex forward model is often computationally impractical, the inversion is implemented using a precomputed lookup table representation of σ0 obtained from numerical Maxwell model in 3-D simulations. The retrieval process assumes that surface roughness properties are constant during the time-series interval, so that only a single rms height estimate is produced for the entire time series. The use of this rms height estimate as a constraint simplifies the associated soil moisture retrievals at each time step. A Monte-Carlo simulation of this algorithm with 0.7 dB radar measurement error (1-sigma) shows that retrievals using six time steps outperform a “snapshot” method (which retrieves rms height and soil moisture at each time step) by a factor of about two in rms soil moisture error. A second study using measured data having 6 to 11 time steps shows an rms error of 0.044 cm3/cm3 for soil moisture with a correlation coefficient of 0.89 between retrieved and in situ data. Surface rms height estimates are also found accurate to 10 to 30% of in situ measurements. It is also shown that retrieval performance is not sensitive to errors in knowledge of the surface roughness correlation length for most of the bare surface conditions examined.

The Closure of the Ocean Mixed Layer Temperature Budget Using Level-Coordinate Model Fields

Abstract Entrainment is an important element of the mixed layer mass, heat, and temperature budgets. Conventional procedures to estimate entrainment heat advection often do not permit the closure of heat and temperature budgets because of inaccuracies in its formulation. In this study a rigorous approach to evaluate the effect of entrainment using the output of a general circulation model (GCM) that does not have an explicit prognostic mixed layer model is described. The integral elements of the evaluation are 1) the rigorous estimates of the temperature difference between mixed layer water and entrained water at each horizontal grid point, 2) the formulation of the temperature difference such that the budget closes over a volume greater than one horizontal grid point, and 3) the apparent warming of the mixed layer during the mixed layer shoaling to account for the weak vertical temperature gradient within the mixed layer. This evaluation of entrainment heat advection is compared with the estimates by oth...

Soil Moisture Retrieval Using L-Band Radar Observations

An algorithm for surface soil moisture estimation using L-band radar observations is introduced. The formulation envelops a wide range of land surface conditions based on three limiting cases defined in terms of end-members: smooth bare soil, rough bare soil, and a maximum vegetation covered soil. Parameterizations for these end-members are obtained using forward electromagnetic scattering models. Modulation due to soil surface roughness and overlying vegetation scattering effects between end-members are accounted using the radar vegetation index and the newly introduced radar roughness index. Hence, the retrieval algorithm developed here does not depend on ancillary vegetation or roughness information. The algorithm is tested with ground-based truck-mounted bare soil observations and observations from several airborne field campaigns that represent a wide range of surface conditions.

Surface Soil Moisture Retrieval Using the L-Band Synthetic Aperture Radar Onboard the Soil Moisture Active–Passive Satellite and Evaluation at Core Validation Sites

This paper evaluates the retrieval of soil moisture in the top 5-cm layer at 3-km spatial resolution using L-band dual-copolarized Soil Moisture Active-Passive (SMAP) synthetic aperture radar (SAR) data that mapped the globe every three days from mid-April to early July, 2015. Surface soil moisture retrievals using radar observations have been challenging in the past due to complicating factors of surface roughness and vegetation scattering. Here, physically based forward models of radar scattering for individual vegetation types are inverted using a time-series approach to retrieve soil moisture while correcting for the effects of static roughness and dynamic vegetation. Compared with the past studies in homogeneous field scales, this paper performs a stringent test with the satellite data in the presence of terrain slope, subpixel heterogeneity, and vegetation growth. The retrieval process also addresses any deficiencies in the forward model by removing any time-averaged bias between model and observations and by adjusting the strength of vegetation contributions. The retrievals are assessed at 14 core validation sites representing a wide range of global soil and vegetation conditions over grass, pasture, shrub, woody savanna, corn, wheat, and soybean fields. The predictions of the forward models used agree with SMAP measurements to within 0.5 dB unbiased-root-mean-square error (ubRMSE) and −0.05 dB (bias) for both copolarizations. Soil moisture retrievals have an accuracy of 0.052 m3/m3 ubRMSE, −0.015 m3/m3 bias, and a correlation of 0.50, compared to in situ measurements, thus meeting the accuracy target of 0.06 m3/m3 ubRMSE. The successful retrieval demonstrates the feasibility of a physically based time series retrieval with L-band SAR data for characterizing soil moisture over diverse conditions of soil moisture, surface roughness, and vegetation.

Monitoring surface soil moisture and freeze-thaw state with the high-resolution radar of the Soil Moisture Active/Passive (SMAP) mission

An approach is described for retrieving surface soil moisture and freeze/thaw state using 3-km resolution L-band radar data of the planned Soil Moisture Active and Passive (SMAP) mission. SMAP radar backscatter coefficients are simulated using radar scattering models and land surface hydrology model output generated over the contiguous United States (CONUS). A Monte-Carlo simulation is performed to assess the error budget of the soil moisture retrievals in the presence of radar measurement error and error in surface roughness. The estimated soil moisture retrieval accuracy is better than 0.06 cm3/cm3 for vegetation water content less than 1.2 kg/m2 and soil moisture in the range of 0 to 0.3 cm3/cm3. The retrieval performance improves if radar speckle is reduced by additional observations (e.g., including both fore- and aft-scan data). It is currently assumed that the surface roughness is known with 10% error, but a time-series method is under development to estimate the roughness. The surface freeze/thaw state retrieval is simulated using a surface hydrology process model forced with climatology. The simulation illustrates a SMAP daily composite freeze/thaw product derived using a time-series algorithm applied to the SMAP high-resolution radar data.

Detection of Inland Open Water Surfaces Using Dual Polarization L-Band Radar for the Soil Moisture Active Passive Mission

A dual-copolarization algorithm to classify inland open water bodies free of flooded vegetation using an L-band radar is presented and evaluated, with a view to applying the method to the Soil Moisture Active Passive (SMAP) mission for hydrological science and soil moisture retrieval applications. Past radar-based water body detection algorithms have applied a threshold to a single-polarization measurement, with water body detection declared if the observed cross section is less than the specified threshold. However, such methods are subject to ambiguities associated with scene variability and terrain slopes, making a universal threshold value difficult to derive and complicating the global application of such methods. Because SMAP will provide measurements in both HH and VV polarizations, the copolarization ratio is also available for water body detection. A threshold of -3 dB applied to the HH/VV polarization ratio is found effective in detecting water bodies at 40° incidence angle based on analysis of theoretical model predictions and measurements from airborne synthetic aperture radar and the spaceborne Aquarius scatterometer. When the water surface is calm and its radar response is very small (i.e., at the radar thermal noise level), the HH/VV ratio method fails. However, a combination of an HH/VV threshold (at -3 dB) and an HH threshold (at -25 dB) is shown to allow water body classification even in this situation. This proposed “combined” algorithm is assessed in four different geophysical scenarios. The resulting water body detection error is shown to be less than 10% for these cases, which satisfies SMAP requirements to allow accurate soil moisture retrieval, and the corresponding false alarm rate is smaller than 2%. The robustness of the proposed approach to subpixel heterogeneity has been also investigated. The performance of the algorithm remains sensitive to the noise level of the radar observations: for SMAP, a radar noise-equivalent sigma 0 of -28.5 dB or less is required in order to facilitate acceptable performance.

Sea surface salinity variability in the East China Sea observed by the Aquarius instrument

This study demonstrates that the spaceborne Aquarius instrument is able to monitor the sea surface salinity (SSS) variations in the East China Sea (ECS) with the spatial resolution of about 150 km at 7 day interval, where routine observations are difficult. The two geophysical contaminants enter the sidelobes of the Aquarius antenna and bias the coastal SSS low: the emission from the land surface and the radiofrequency interference (RFI). Away from about one Aquarius pixel (150 km) from the coastline, the Aquarius SSS is fairly insensitive (less than about 0.2 psu) to the radiometric details of the method to correct for the land emission. The ascending orbits appear to be affected by unfiltered RFI much less than the descending tracks. The Aquarius SSS along the ascending tracks is low over the ECS by 0.40–0.93 psu (with respect to the in situ data during the two separate 7 day periods) and is biased low by 0.41–1.07 psu (accuracy, or the time-mean of difference from the regional model along three Aquarius tracks over a 18 month period). The presence of the ascending and descending differences in the Aquarius SSS, and the spatially widespread bias suggest that the bias is attributed to the unfiltered RFI originating from strong point sources (rather than to the land contamination from weak distributed sources, or to other seasonally varying geophysical contaminants). Despite the bias, the Aquarius data describe well the temporal and spatial variability of the ECS SSS. The temporal trend and magnitude of salinity changes agree remarkably between Aquarius and a regional numerical model, during both the freshwater discharge season from the Yangtze river and the rest of the year. The precision of the Aquarius observation in the ECS is comparable with the Aquarius mission requirement (0.2 psu one-sigma for a monthly average over the open ocean). The river discharge rate correlates with the Aquarius SSS with the coefficient of 0.71 on a seasonal scale with the discharge leading the SSS changes. The Aquarius SSS increases away from the coast, in response to the river outflow. The interannual changes in the Aquarius SSS capture the effect of the regional drought in summer 2013.