Dual-polarization Brightness Temperature Research Articles

Estimating Time Series Soil Moisture by Applying Recurrent Nonlinear Autoregressive Neural Networks to Passive Microwave Data over the Heihe River Basin, China

A method using a nonlinear auto-regressive neural network with exogenous input (NARXnn) to retrieve time series soil moisture (SM) that is spatially and temporally continuous and high quality over the Heihe River Basin (HRB) in China was investigated in this study. The input training data consisted of the X-band dual polarization brightness temperature (TB) and the Ka-band V polarization TB from the Advanced Microwave Scanning Radiometer II (AMSR2), Global Land Satellite product (GLASS) Leaf Area Index (LAI), precipitation from the Tropical Rainfall Measuring Mission (TRMM) and the Global Precipitation Measurement (GPM), and a global 30 arc-second elevation (GTOPO-30). The output training data were generated from fused SM products of the Japan Aerospace Exploration Agency (JAXA) and the Land Surface Parameter Model (LPRM). The reprocessed fused SM from two years (2013 and 2014) was inputted into the NARXnn for training; subsequently, SM during a third year (2015) was estimated. Direct and indirect validations were then performed during the period 2015 by comparing with in situ measurements, SM from JAXA, LPRM and the Global Land Data Assimilation System (GLDAS), as well as precipitation data from TRMM and GPM. The results showed that the SM predictions from NARXnn performed best, as indicated by their higher correlation coefficients (R ≥ 0.85 for the whole year of 2015), lower Bias values (absolute value of Bias ≤ 0.02) and root mean square error values (RMSE ≤ 0.06), and their improved response to precipitation. This method is being used to produce the NARXnn SM product over the HRB in China.

A Method to Determine the Margins of High Sea Ice Concentration Using AMSR-E Passive Microwave Imagery

The margin of the sea ice with high sea ice concentration is a principal feature in microwave image and a hotspot in image recognition. A method for determining the margins is developed using the feature of dual-polarized brightness temperatures at 36.5 GHz and a new parameter (contrast ratio) is used in this paper. For the microwaves, the ratio of the horizontal-polarized emissivity to the vertical-polarized emissivity is approximately equal to the ratio between horizontal-polarized and vertical-polarized brightness temperatures of sea surface, which called as the dual-polarized emissivity ratio in this study. It is found that the dual-polarized emissivity ratio of sea ice with nearly 100% sea ice concentration in Arctic at 36.5 GHz band has a value ranged between 0.92 and 0.96, as shown by satellite-observed data in figure of horizontal-po-larized brightness temperature versus vertical-polarized brightness temperature. From open water to sea ice covered area, the contrast-ratio can show the changing features of the dual-polarized brightness temperature at 36.5 GHz. The contrast ratio rapidly changes at the ice margins and its gradient appears an extreme value when the ratio changes around 0.92. This extreme value is examined by the ice concentration calculated by the MODIS data. And the results indicate that the threshold ratio coincides with the contour line of 96% sea ice concentration. So the parameter of contrast ratio could be used to determine the position of margins in microwave image.

Assimilation of SMOS brightness temperatures or soil moisture retrievals into a land surface model

Abstract. Three different data products from the Soil Moisture Ocean Salinity (SMOS) mission are assimilated separately into the Goddard Earth Observing System Model, version 5 (GEOS-5) to improve estimates of surface and root-zone soil moisture. The first product consists of multi-angle, dual-polarization brightness temperature (Tb) observations at the bottom of the atmosphere extracted from Level 1 data. The second product is a derived SMOS Tb product that mimics the data at a 40° incidence angle from the Soil Moisture Active Passive (SMAP) mission. The third product is the operational SMOS Level 2 surface soil moisture (SM) retrieval product. The assimilation system uses a spatially distributed ensemble Kalman filter (EnKF) with seasonally varying climatological bias mitigation for Tb assimilation, whereas a time-invariant cumulative density function matching is used for SM retrieval assimilation. All assimilation experiments improve the soil moisture estimates compared to model-only simulations in terms of unbiased root-mean-square differences and anomaly correlations during the period from 1 July 2010 to 1 May 2015 and for 187 sites across the US. Especially in areas where the satellite data are most sensitive to surface soil moisture, large skill improvements (e.g., an increase in the anomaly correlation by 0.1) are found in the surface soil moisture. The domain-average surface and root-zone skill metrics are similar among the various assimilation experiments, but large differences in skill are found locally. The observation-minus-forecast residuals and analysis increments reveal large differences in how the observations add value in the Tb and SM retrieval assimilation systems. The distinct patterns of these diagnostics in the two systems reflect observation and model errors patterns that are not well captured in the assigned EnKF error parameters. Consequently, a localized optimization of the EnKF error parameters is needed to further improve Tb or SM retrieval assimilation.

A Novel Simplified Algorithm for Bare Surface Soil Moisture Retrieval Using L-Band Radiometer

Soil moisture plays an important role in understanding climate change and hydrology, and L-band passive microwave radiometers have been verified as effective tools for monitoring soil moisture. This paper proposes a novel, simplified algorithm for bare surface soil moisture retrieval using L-band radiometer. The algorithm consists of two sub-algorithms: a surface emission model and a soil moisture retrieval model. In analyses of the advanced integral equation model (AIEM) simulated database, the surface emission model was developed to diminish the effects of surface roughness using dual-polarization surface reflectivity. The soil moisture retrieval model, which was calibrated using the Dobson simulated database, is based on the relationship between the adjusted real refractive index N r and the volumetric soil moisture. Soil moisture can be determined via a numerical solution that uses several freely available input parameters: dual-polarization microwave brightness temperature, surface temperature, and the contents of sand and clay. The results showed good agreement with the input soil moisture values simulated by the AIEM model, with root mean square errors (RMSEs) lower than 3% at all incidence angles. The algorithm was then verified based on data from the four-year L-band experiments conducted at Beltsville Agricultural Research Center (BARC) test sites, achieving RMSEs of 4.3% and 3.4% at 40° and 50°, respectively. These results indicate that the simplified algorithm proposed in this paper shows a very good accuracy in soil moisture retrieval. Additionally, the algorithm exhibits a better performance for the large incidence angle radiometers in L-band such as those produced by the Soil Moisture Active and Passive (SMAP).

Gravimetric Vegetation Water Content Estimation for Corn Using L-Band Bi-Angular, Dual-Polarized Brightness Temperatures and Leaf Area Index

In this study, an algorithm to retrieve the gravimetric vegetation water content (GVWC, %) of corn was developed. First, the method for obtaining the optical depth from L-band (1.4 GHz) bi-angular, dual-polarized brightness temperatures (TB) for short vegetation was investigated. Then, the quantitative relationship between the corn optical depth, corn GVWC and corn leaf area index (LAI) was constructed. Finally, using the Polarimetric L-band Microwave Radiometer (PLMR) airborne data in the 2012 Heihe Watershed Allied Telemetry Experimental Research (HiWATER) project, the Global Land Surface Satellite (GLASS) LAI product, the height and areal density of the corn stalks, the corn GVWC was estimated (corn GLASS-GVWC). Both the in situ measured corn GVWC and the corn GVWC retrieved based on the in situ measured corn LAI (corn LAINET-GVWC) were used to validate the accuracy of the corn GLASS-GVWC. The results show that the GVWC retrieval method proposed in this study is feasible for monitoring the corn GVWC. However, the accuracy of the retrieval results is highly sensitive to the accuracy of the LAI input parameters.

A simplified physically-based algorithm for surface soil moisture retrieval using AMSR-E data

A simplified physically-based algorithm for surface soil moisture inversion from satellite microwave radiometer data is presented. The algorithm is based on a radiative transfer model, and the assumption that the optical depth of the vegetation is polarization independent. The algorithm combines the effects of vegetation and roughness into a single parameter. Then the microwave polarization difference index (MPDI) is used to eliminate the effects of surface temperature, and to obtain soil moisture, through a nonlinear iterative procedure. To verify the present algorithm, the 6.9 GHz dual-polarized brightness temperature data from the Advanced Microwave Scanning Radiometer (AMSR-E) were used. Then the soil moisture values retrieved by the present algorithm were validated by in-situ data from 20 sites in the Tibetan Plateau, and compared with both the NASA AMSR-E soil moisture products, and Soil Moisture and Ocean Salinity (SMOS) soil moisture products. The results show that the soil moisture retrieved by the present algorithm agrees better with ground measurements than the two satellite products. The advantage of the algorithm is that it doesn’t require field observations of soil moisture, surface roughness, or canopy biophysical data as calibration parameters, and needs only single-frequency brightness temperature observations during the whole retrieval process.

Radiometric Performance of SMOS Full Polarimetric Imaging

This work has been conducted in the framework of several projects devoted to assess the performance of the Soil Moisture and Ocean Salinity (SMOS) mission full-pol measurement mode. Since its launch in November 2009, SMOS is producing dual-polarization brightness temperature synthesized images that are yielding a high scientific return. However, these images are affected by a non-negligible spatial amplitude error, the so-called spatial bias (SB), that degrades geophysical parameter retrieval. This effect is particularly detrimental in SMOS polarimetric images where spatial bias is masking the polarimetric physical signature to a large extend. This paper presents a method to mitigate SMOS spatial bias by taking into account the co- and cross-polar antenna patterns in the image reconstruction algorithm through the, so called, full-pol G-matrix (FPG). The method is validated by producing spatial bias maps out of the comparison between SMOS full-pol images and an accurate polarimetric brightness temperature model of the ocean. This model has been provided to SMOS ESLs (Expert Support Laboratories) by LOCEAN (Laboratoire d'Oceanographie et du Climat, France) as a test bench to validate and improve SMOS Level 1 (L1) data. Finally, a radiometric performance summary table comparing spatial bias and radiometric sensitivity between this new FPG approach and SMOS current co-polar G-matrix approach (CPG) is provided. This paper presents the best quality SMOS polarimetric images, which may lead a breakthrough in the science returns of the mission.

Soil moisture estimation over an arid environment in Mongolia from passive microwave remote sensing based on a simplified parameterization method

Satellite passive microwave remote sensing provides a major basis for the analysis of temporal and spatial variation of top soil moisture at large scales. A global soil moisture data product is currently released by the National Aeronautics and Space Administration (NASA) of USA based on AMSR-E (the Advanced Microwave Scanning Radiometer for the Earth Observing System onboard the NASA EOS Aqua satellite) global dual-polarization brightness temperature observations of the Earth. But the accuracy of this soil moisture product is not satisfactory in arid Mongolian regions. Based on a simplified parameterization method for characterizing the effects of the sparse vegetation cover and surface roughness on surface microwave emission, the estimation of daytime (local noon) and nighttime (local midnight) top soil moisture during a near two-year period from October 1, 2005 to September 20, 2007 over the arid CEOP(The Coordinated Energy and Water Cycle Observations Project)-Mongolia experiment area was conducted based on a multifrequency algorithm by mainly using AMSR-E 6.9 GHz, 10.7 GHz and 18.7 GHz vertical-polarized brightness temperature data. In the inversion model, the only model parameter asking for experimental calibration, i. e., the vegetation single scattering albedo, could be achieved through optimization technique by using in-situ soil moisture measurements. The results showed that the root mean square error (RMSE) between satellite estimated soil moisture and ground-measured soil moisture was close to 0.030 cm(3)/cm(3). Without other supplementary data else, the retrieval algorithm proposed in this paper may be applied to near real-time monitoring of the dynamic variation in top soil moisture at large scales, in an all-weather working manner, over arid and or semiarid environments characterized by natural sparse vegetation conditions. Therefore, it will be promising to provide sustaining and reliable initial near-surface soil moisture information for the long term study of climate change and water cycle over arid and or semiarid regions.

Developing a Global Data Record of Daily Landscape Freeze/Thaw Status Using Satellite Passive Microwave Remote Sensing

The landscape freeze-thaw (F/T) state parameter derived from satellite microwave remote sensing is closely linked to the surface energy budget, hydrological activity, vegetation growing season dynamics, terrestrial carbon budgets, and land-atmosphere trace gas exchange. Satellite microwave remote sensing is well suited for global F/T monitoring due to its insensitivity to atmospheric contamination and solar illumination effects, and its strong sensitivity to the relationship between landscape dielectric properties and predominantly frozen and thawed conditions. We investigated the utility of multifrequency and dual polarization brightness temperature (Tb) measurements from the Special Sensor Microwave Imager (SSM/I) to map global patterns and daily variations in terrestrial F/T cycles. We defined a global F/T classification domain by examining biophysical cold temperature constraints to vegetation growing seasons. We applied a temporal change classification algorithm based on a seasonal thresholding scheme to classify daily F/T states from time series Tb measurements. The SSM/I F/T classification accuracy was assessed using in situ air temperature measurements from the global WMO weather station network. A single-channel classification of 37 GHz, V-polarization Tb time series provided generally improved performance over other SSM/I frequencies, polarizations and channel combinations. Mean annual F/T classification accuracies were 92.2 ±0.8 [SD] % and 85.0 ±0.7 [SD] % for respective SSM/I time series of P.M. and A.M. orbital nodes over the global domain and a 20-year (1988-2007) satellite record. The resulting database provides a continuous and relatively long-term record of daily F/T dynamics for the global biosphere with well-defined accuracy.

L-Band Reflectivity of a Wire Grid Above a Dielectric Surface

We investigated the influence of a wire grid above a paved ground on thermal brightness at 1.4 GHz. Reflectivities were derived from dual-polarized brightness temperature measurements for different grid orientations and wire spacings between 2.5 and 20 cm. For spacings larger than a quarter of a wavelength (≈5 cm), the grid had no impact on the observed reflectivities. A physical model was used to analyze the experimental results.

Use of Soil Moisture Variability in Artificial Neural Network Retrieval of Soil Moisture

Passive microwave remote sensing is one of the most promising techniques for soil moisture retrieval. However, the inversion of soil moisture from brightness temperature observations is not straightforward, as it is influenced by numerous factors such as surface roughness, vegetation cover, and soil texture. Moreover, the relationship between brightness temperature, soil moisture and the factors mentioned above is highly non-linear and ill-posed. Consequently, Artificial Neural Networks (ANNs) have been used to retrieve soil moisture from microwave data, but with limited success when dealing with data different to that from the training period. In this study, an ANN is tested for its ability to predict soil moisture at 1 km resolution on different dates following training at the same site for a specific date. A novel approach that utilizes information on the variability of soil moisture, in terms of its mean and standard deviation for a (sub) region of spatial dimension up to 40 km, is used to improve the current retrieval accuracy of the ANN method. A comparison between the ANN with and without the use of the variability information showed that this enhancement enables the ANN to achieve an average Root Mean Square Error (RMSE) of around 5.1% v/v when using the variability information, as compared to around 7.5% v/v without it. The accuracy of the soil moisture retrieval was further improved by the division of the target site into smaller regions down to 4 km in size, with the spatial variability of soil moisture calculated from within the smaller region used in the ANN. With the combination of an ANN architecture of a single hidden layer of 20 neurons and the dual-polarized brightness temperatures as input, the proposed use of variability and sub-region methodology achieves an average retrieval accuracy of 3.7% v/v. Although this accuracy is not the lowest as comparing to the research in this field, the main contribution is the ability of ANN in solving the problem of predicting “out-of-range” soil moisture values. However, the applicability of this method is highly dependent on the accuracy of the mean and standard deviation values within the sub-region, potentially limiting its routine application.

Passive L-band microwave soil moisture retrieval error arising from topography in otherwise uniform scenes

Abstract Soil moisture is an important component of the water cycle and will be measured for the first time on a global scale by a dedicated passive L-band microwave radiometer that is planned for launch in 2008. Here, the contribution of topography to the error budget is examined for a vegetated scene with uniform microwave emission. Dual-polarization brightness temperature curves were generated over a range of look angles for 1-D scenes with simple geometrical features, and the soil moisture was retrieved assuming a flat surface. The errors were small for the scenarios considered. Theoretical errors were tested for realistic topography with a DEM transect of a mountainous region, and were found to be comparable. Knowledge of the mean slope from high-resolution DEM data can be used to improve the accuracy of the retrieval.

MIRAS end-to-end calibration: application to SMOS L1 processor

End-to-end calibration of the Microwave Imaging Radiometer by Aperture Synthesis (MIRAS) radiometer refers to processing the measured raw data up to dual-polarization brightness temperature maps over the earth's surface, which is the level 1 product of the Soil Moisture and Ocean Salinity (SMOS) mission. The process starts with a self-correction of comparators offset and quadrature error and is followed by the calibration procedure itself. This one is based on periodically injecting correlated and uncorrelated noise to all receivers in order to measure their relevant parameters, which are then used to correct the raw data. This can deal with most of the errors associated with the receivers but does not correct for antenna errors, which must be included in the image reconstruction algorithm. Relative S-parameters of the noise injection network and of the input switch are needed as additional data, whereas the whole process is independent of the exact value of the noise source power and of the distribution network physical temperature. On the other hand, the approach relies on having at least one very well-calibrated reference receiver, which is implemented as a noise injection radiometer. The result is the calibrated visibility function, which is inverted by the image reconstruction algorithm to get the brightness temperature as a function of the director cosines at the antenna reference plane. The final step is a coordinate rotation to obtain the horizontal and vertical brightness temperature maps over the earth. The procedures presented are validated using a complete SMOS simulator previously developed by the authors.

Design and test of the ground-based L-band Radiometer for Estimating Water In Soils (LEWIS)

In the framework of the preparation of the Soil Moisture and Ocean Salinity (SMOS) mission, several field experiments are required so as to address specific modeling issues. The goal is to improve current models and to test retrieval algorithms. However, adequate ground instrumentation is scarce and not readily available "off the shelf". In this context, a high-accuracy L-band radiometer was required for a specific long-term campaign for the preparation of the SMOS mission. For this purpose, a dual-polarized radiometer was designed and built to check algorithms for surface soil moisture retrieval from multiangular dual-polarized brightness temperatures. This radiometer has been tested in the field for 20 months and is operational since end of January 2003. The aim of this paper is to give details of the system architecture, calibration procedures, together with the performances obtained and some preliminary results.

Soil moisture inversion at L-band using a dual-polarization technique: A model-based sensitivity analysis

The inverse problem of estimating soil moisture from vegetated and non-vegetated terrain is investigated using a passive microwave technique. The radiometer response at L-band is modelled by the Peake's approach that involves the integration of bistatic scattering coefficients to calculate surface emissivity. For vegetated terrain, a discrete scatter model employing the distorted Born approximation is used to obtain bistatic scattering coefficients. For non-vegetated rough surface, the Kirchhoff 's rough surface approximation is used to calculate bistatic scattering coefficients. A series of vegetated and non-vegetated surfaces are simulated for this study by changing vegetation densities and ground roughness conditions. The forward model is simplified so that it can be inverted with a minimum of auxiliary information about the vegetation and the ground. An inversion procedure is set up such that the ratio of horizontal to vertical Fresnel reflectivity is expressed in terms of dual-polarized microwave brightness temperatures (available from the forward model or a radiometer). The reflectivity ratio expression is then solved to obtain soil moisture. In the case of weak vegetation such as soybean canopy, no information regarding vegetation is necessary in the inversion procedure because the difference between horizontal and vertical optical depth is assumed to be negligibly small at low to medium viewing angles. The consequence of this assumption on the soil moisture estimation from canopies of higher Leaf Area Index (LAI) is analysed. Similarly, in the case of non-vegetated rough terrain, inversion is carried out without information about rough surface because the rms surface roughness height cancels out in ratioing of the Fresnel reflectivity. To account for instrument and other errors in experimental data, a random noise is added to model data and the rms error in soil moisture inversion is calculated for vegetated and non-vegetated terrain. A comparison of the dual polarization with single (horizontal) polarization technique reveals that the dual polarization is a better technique for soil moisture estimation. The sensitivities and implications of a priori information are discussed in the context of developing a soil moisture estimation algorithm from satellites to be launched in the forthcoming space missions.

Determination of microwave vegetation optical depth and single scattering albedo from large scale soil moisture and Nimbus/SMMR satellite observations

The single scattering albedo and optical depth of typical savanna vegetation in Botswana (Africa) have been determined by inverse modelling using satellite observed microwave signatures and surface soil moisture. Soil emissivity was modelled using a multi-layer radiative transfer model. The study is based on large scale surface moisture data and Nimbus/SMMR 6-6 GHz and 37 GHz dual polarized brightness temperatures over a 3-year period. As compared to the optical depths, the derived single scattering albedos displayed only minor seasonal variations, whereas the values fit well within the range reported in the literature from laboratory and field experiments. Both 6-6 and 37GHz optical depths were found to be significantly related to NDVI-values derived from NOAA/AVHRR.

Numerical modelling of radiative transfer and multi-frequency measurement of thermal emission from crop canopies

Abstract Multi-frequency microwave radiometers (3-09, 5-4, 9-6, 36GHz) designed by the Changchun Institute of Geography have been used to measure dual-polarized brightness temperature of different crop canopies in the People's Republic of China. A numerical modelling of radiative transfer of multi-layer random media is developed. All parameters calculated by the empirical formulations arc chosen in a consistent way. Numerical results arc compared with the experimental measurements. The functional dependence of brightness tempera-lure on frequency, polarization, observation angle and crop parameters such as moisture content, crop height, etc. arc numerically discussed.

Surface‐based passive microwave studies of multiyear sea ice

Multifrequency passive microwave observations are reported from surface based field experiments carried out over multiyear sea ice in the eastern Arctic Basin, the Beaufort Sea, the Canadian archipelago and the northern Greenland Sea. Dual polarized brightness temperatures at 6.7, 10, 18.7, 37, and 90 GHz were obtained at selected multiyear ice sites covering a wide range of surface properties. Considerable variation was found in the observed spectra over short distances on individual floes under winter conditions where values of [ev(37 GHz) − ev(18 GHz)]/[ev(37 GHz) + ev(18 GHz)] ranged from nearly zero to approximately −0.08. Concurrent surface characterization observations consisting of temperature, density, salinity, and grain size profiles in the snow and the upper meter of the ice were carried out frequently in conjunction with Cold Regions Research and Engineering Laboratory personnel. The present analysis shows that the magnitude of the spectral gradient of emissivity is directly related to the existence and thickness of a decomposed surface ice layer with very high porosity. Spectra for melt ponds with a frozen surface layer closely resembled those of lake ice and show a positive spectral gradient. As soon as the ponds were completely frozen, their spectra became nearly the same as those for first‐year ice. Fine‐grained winter snow produced a mild decrease in the emissivities at 90 GHz with increasing snow thickness from 0 to 0.2 m at which point the snow appears to be optically thick. During the melt season the ice emissivities eν depend most strongly on whether the surface layers are experiencing temporary freezing or not. Under melting conditions, eν depends on the liquid water content only over a very small range (e.g., 0–5% by mass at 90 GHz). If the liquid water in the surface layers is freezing, which can occur at intervals throughout the melt season, the spectral gradient is negative with a slope which depends on the thickness of the refrozen layer. The variance among emissivity spectra for multiyear ice appears to be influenced primarily by the distribution of melt ponds and by the presence of significant amounts of scattering inhomogeneities greater than 1 mm in diameter in the snow and in the upper 20–30 cm of the ice. We expect the signatures of multiyear ice to be regionally dependent, perhaps yielding environmental information about the Arctic ice pack.

A Satellite Passive 37-GHz Scattering-based Method for Measuring Oceanic Rain Rates

A combination of theory and measurement is used to develop a scattering-based method for quantitatively measuring rainfall over the ocean from Nimbus-7 Scanning Multichannel Microwave Radiometer (SMMR) 37-GHz observations. This technique takes the observed scattering effects of precipitation on 37-GHz brightness temperatures and applies it to the oceanic environment. It requires an estimate of the effective radiating temperature of the cloudy portion of the atmosphere, and a brightness temperature measurement of the cloud-free ocean surface. These two measurements bound all possible combinations of clear and cloudy conditions within a footprint in terms of bipolarized brightness temperatures. Any satellite-observed brightness temperature T(B) lower than these values is assumed to reflect scattering, which at 37 GHz is only due to precipitation-size hydrometeors. Because the technique involves linear transformation between dual polarized brightness temperature and rain rate, there are no nonlinear 'footprint filling' effects and a unique footprint-averaged rain rate results. It is shown that these SMMR-derived rain rates for five cases of convection over the Gulf of Mexico are closely related to simultaneously derived radar rain rates, having a correlation of 0.90. This technique is then applied to a massive squall line over the Gulf of Mexico, and the resulting rain rate distribution reflects features found in cloud top heights and texture inferred from GOES infrared and visible imagery.