L-band Radiometer Data Research Articles

An improved algorithm for retrieving thin sea ice thickness in the Arctic Ocean from SMOS and SMAP L-band radiometer data

An improved algorithm for retrieving thin sea ice thickness in the Arctic Ocean from SMOS and SMAP L-band radiometer data

L-Band Radiometry of Alpine Seasonal Snow Cover: 4 Years at the Davos-Laret Remote Sensing Field Laboratory

This study reports on four consecutive winter campaigns (2016 &#x2013; 2020) at the &#x201C;Davos-Laret Remote Sensing Field Laboratory&#x201D; in the Swiss Alps to gain insight into the L-band microwave emission of ground covered with seasonal snow. Close-range L-band Brightness temperatures <inline-formula><tex-math notation="LaTeX">$T_\mathrm{{B}}^{p,\phi }(\theta)$</tex-math></inline-formula> were measured over the site scanning different observation nadir angles <inline-formula><tex-math notation="LaTeX">$\theta$</tex-math></inline-formula> and azimuth angles <inline-formula><tex-math notation="LaTeX">$\phi$</tex-math></inline-formula> at Horizontal and Vertical polarizations <i>p</i> = {H,V}. State parameters of the snowpack (e.g., height, density, SWE) and the subnivean soil (permittivity, temperature) were measured quasi-simultaneously using in-situ sensors and sampling, as well as meteorological data. In each campaign <inline-formula><tex-math notation="LaTeX">$T_\mathrm{{B}}^{p,\phi }(\theta)$</tex-math></inline-formula> were measured over a &#x201C;natural area&#x201D; and a &#x201C;reflector area&#x201D; with a metal mesh reflector laid on the ground before snow accumulation. The radiometer measurements over &#x201C;reflector area&#x201D; allowed to retrieve the time-series of Snow liquid Water-Content <inline-formula><tex-math notation="LaTeX">$W_\mathrm{{S}}$</tex-math></inline-formula> and Snow liquid Water-Column <inline-formula><tex-math notation="LaTeX">$WC_\mathrm{{S}}$</tex-math></inline-formula> which are employed as &#x201C;derived measurements&#x201D; to support interpretation of <inline-formula><tex-math notation="LaTeX">$T_\mathrm{{B}}^{p,\phi }(\theta)$</tex-math></inline-formula> measured over &#x201C;natural areas&#x201D; during different Winter phases. The detailed approach for the estimation of <inline-formula><tex-math notation="LaTeX">$W_\mathrm{{S}}$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">$WC_\mathrm{{S}}$</tex-math></inline-formula> using L-band radiometer data is presented. The data and analyses in this paper address the following major points: i) Determination of the characteristic features of measured <inline-formula><tex-math notation="LaTeX">$T_\mathrm{{B}}^{p,\phi }(\theta)$</tex-math></inline-formula> during different periods in each of the four winter campaigns; ii) effects of dry- and wet snow precipitation on L-band radiometer data compared to corresponding simulations; iii) effect of removal and compression of the snowpack on <inline-formula><tex-math notation="LaTeX">$T_\mathrm{{B}}^{p,\phi }(\theta)$</tex-math></inline-formula>; and iv) effects of spatial heterogeneity on brightness temperatures. Finally, the study is concluded with recommendations relevant for future close-range remote sensing campaigns.

Soil Moisture Retrieval Using SMAP L-Band Radiometer and RISAT-1 C-Band SAR Data in the Paddy Dominated Tropical Region of India

National Aeronautics and Space Administration's soil moisture active–passive (SMAP) mission potential to produce high-resolution soil moisture suffered adversely due to its L-band synthetic-aperture radar (SAR) failure. Other satellite-based L-/C-band SAR observations can be used within the SMAP active–passive algorithm. In this article, we evaluated the capability of ingesting ISRO's Radar Imaging Satellite-1 (RISAT-1) C-band SAR observations in the SMAP active–passive algorithm to obtain soil moisture at 1, 3, and 9 km over the agricultural region dominant by paddy that experiences seasonal flooding. We also improved the SMAP mission active–passive algorithm with a dynamic surface water bodies (ponding conditions) masking approach using the native RISAT-1 observations. The article shows that the use of surface water masks helps in mitigating the negative impact of surface water bodies in the active–passive disaggregation process. The SMAP–RISAT soil moisture retrievals at 1 and 3 km resolutions are found to have high unbiased root-mean-square error (ubRMSE) greater than 0.06 m3/m3 during very wet and high vegetative conditions. However, at low and moderate soil moisture states, the ubRMSE is below 0.06 m3/m3. Comparison of soil moisture retrievals at 9 km resolution with upscaled ground-based soil moisture measurements shows ubRMSE less than 0.04 m3/m3. This article is a precursor for estimating soil moisture for the upcoming RISAT-1A dataset over India. The findings will further help in the implementation of a microwave active–passive algorithm to retrieve soil moisture for future satellite missions involving radiometer-SAR instruments, and challenging geophysical conditions (i.e., dynamic surface water bodies).

Testing the method for increasing the spatial resolution of satellite radiometric images in the L-band

The results of testing a method for increasing the spatial resolution of satellite radiometric data in the L-band are presented. Data obtained by satellite-borne radiometer is characterized by low spatial resolution. The use of satellite radar data can increase the spatial resolution of radiometric images in the L-band to hundreds of meters. At the moment, the L-band radiometric data processing is possible with the use of radar data C-band. For this reason the method gives satisfactory results in the absence of cloudiness on the radar image.

Snow wetness and density retrieved from L-band satellite radiometer observations over a site in the West Greenland ablation zone

We demonstrate a novel method to retrieve snow liquid water content and density over a site in the ablation zone of the Western Greenland Ice Sheet from L-band radiometer data measured by the Soil Moisture and Ocean Salinity (SMOS) satellite. Previous demonstrations using ground-based close-range radiometry separately retrieved snow density and snow wetness over frozen and thawed ground. We apply similar techniques over the ice sheet to simultaneously retrieve snow density and wetness at the location of “Swiss Camp” from June 2010 through August 2018 at nearly daily temporal resolution. Achieved results are compared to in-situ air temperature data and to a well-known 19 GHz and 37 GHz passive-microwave melt characterization technique known as the cross-polarized gradient ratio (XPGR). The L-band based snow wetness retrievals often detect the onset of seasonal melt earlier than the XPGR algorithm without the need for empirically tuned thresholds. We also demonstrate the performance of the SMOS based snow wetness retrievals based on error statistics compared with an air temperature melt proxy. By applying temporal averaging to the SMOS based snow density retrievals, we achieve reasonable agreement with in-situ observations from May 2014 and May 2018. The demonstrated retrieval algorithm shows potential as a future SMOS data product for ice-covered regions of the cryosphere.

Estimation of active-passive microwave covariation using SMAP and Sentinel-1 data

Active and passive microwave signals over land co-vary depending on their shared scattering and emission characteristics by soil and vegetation media. Estimates of this covariation can be used beneficially to downscale coarse-resolution brightness temperatures with high-resolution backscatter for enhanced-resolution Earth observations. In this study, a forward model of covariation for vegetated soil is derived by combining two well-established models of active and passive microwave interactions. The covariation model is inverted to obtain a single-pass observation-driven estimation of active-passive microwave covariation (β) based on multi-channel radiometer and Synthetic Aperture Radar (SAR) scenes. A key feature of the estimation approach is that it is applicable to co-located spatial data scenes and does not rely on temporal information. We present applications of the estimation with combinations of SMAP (L-band) radiometry and both SMAP (L-band) and multi-angular Sentinel-1 (C-band) backscatter data. We first show that for the period of available SMAP L-band radiometer and radar data, the estimation approach for β, introduced in this study, yields similar results as the statistical time-series approach originally designed for SMAP. The new single-pass approach also allows estimation of active-passive covariation where the statistical approach cannot be applied because dynamic range of brightness temperature and backscatter are too limited to allow regression. We then apply the developed estimation method to SMAP L-band radiometer and multi-angular Sentinel-1 C-band SAR data. Here, the study quantifies the effects of microwave frequency and look-angles on the covariation by applying the estimation globally and analyzing the results as a function of vegetation cover – a key determinant of the strength of the covariation.

Faraday Rotation Retrieval Using SMOS Radiometric Data

Faraday rotation is dynamically computed from L-band radiometric data acquired by the Soil Moisture and Ocean Salinity satellite. Improved full polarimetric image reconstruction and smart spatiotemporal filtering are used to obtain good estimations of Faraday rotation, which compare very well with the values theoretically predicted from available ionosphere and geomagnetic field data.

Improving Soil Moisture Data Retrieval From Airborne L-Band Radiometer Data by Considering Spatially Varying Roughness

This study presents the retrieval of near-surface soil moisture data below crop canopies (winter rye and winter barley) from airborne L-band radiometer observations using a radiative transfer model at very dry soil moisture conditions (<15 Vol.%). Using physically based models, the roughness parameterization plays a crucial role for the description of the surface emissivity. A two-step optimization procedure was performed for choosing an optimal roughness value to minimize the uncertainty of soil moisture estimates. A crop-type specific roughness parameterization within the model did not show satisfactory soil moisture results. Instead, a “pixel”-based (spatially varying) roughness parameter optimization provided significantly improved results, also indicating a strong relationship between the optimal roughness parameter value and the Normalized Difference Vegetation Index (NDVI) derived from imaging spectrometer data. Our results demonstrate the importance of treating surface roughness as spatially variable when retrieving soil moisture information from high spatial resolution L-band brightness temperature data. Furthermore, the results strongly indicate that a combination of passive microwave observations and optical remote sensing data of the vegetation improve the mapping and monitoring of surface soil moisture.

L-Band Passive and Active Microwave Geophysical Model Functions of Ocean Surface Winds and Applications to Aquarius Retrieval

The L-band passive and active microwave geophysical model functions (GMFs) of ocean surface winds from the Aquarius data are derived. The matchups of Aquarius data with the Special Sensor Microwave Imager (SSM/I) and National Centers for Environmental Prediction (NCEP) winds were performed and were binned as a function of wind speed and direction. The radar HH GMF is in good agreement with the PALSAR GMF. For wind speeds above 10 m·s-1, the L-band ocean backscatter shows positive upwind-crosswind (UC) asymmetry; however, the UC asymmetry becomes negative between about 3 and 8 m·s-1. The negative UC (NUC) asymmetry has not been observed in higher frequency (above C-band) GMFs for ASCAT or QuikSCAT. Unexpectedly, the NUC symmetry also appears in the L-band radiometer data. We find direction dependence in the Aquarius TBV, TBH, and third Stokes data with peak-to-peak modulations increasing from about a few tenths to 2 K in the range of 10-25- m·s-1 wind speed. The validity of the GMFs is tested through application to wind and salinity retrieval from Aquarius data using the combined active-passive algorithm. Error assessment using the triple collocation analyses of SSM/I, NCEP, and Aquarius winds indicates that the retrieved Aquarius wind speed accuracy is excellent, with a random error of about 0.75 m·s-1. The wind direction retrievals also appear reasonable and accurate above 10 m·s-1. The results of the error analysis indicate that the uncertainty of the GMFs for the wind speed correction of vertically polarized brightness temperatures is about 0.14 K for wind speed up to 10 m·s-1.

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.

Optimization of L-Band Sea Surface Emissivity Models Deduced From SMOS Data

The Soil Moisture and Ocean Salinity (SMOS) satellite, launched in November 2009, carries the first interferometric radiometer at L-band (1.4 GHz) in orbit. Over the open ocean and for moderate wind speeds (WSs), the SMOS brightness temperatures (TB) are at first order consistent with simulated TB of theoretical prelaunch models implemented in the European Space Agency Level 2 Ocean Salinity processor. However, we found large discrepancies between measurements and model simulations when WS is above 12 ms-1. A new set of parameters for a sea wave spectrum and a foam coverage model that can be used for simulating L-band radiometer data over a large range of WS is proposed based on the deduced wind-induced components from the SMOS data. The quality of the SMOS retrieved sea surface salinity (SSS) with the new emissivity model is estimated by comparing it with the World Ocean Atlas 2005 climatological SSS and the Array for Real-Time Geostrophic Oceanography (ARGO) SSS.

Near-surface soil moisture estimation by combining airborne L-band brightness temperature observations and imaging hyperspectral data at the field scale

The observation of spatially distributed soil moisture fields is an essential component for a large range of hydrological, climate, and agricultural applications. While direct measurements are expensive and limited to small spatial domains, the inversion of airborne and satellite L-band radiometer data has shown the potential to provide spatial estimates of near surface soil moisture from the local up to the global scale. When using L-band radiometer observations for soil moisture retrieval, a major limitation is the attenuation of the microwave signal by the vegetation, hampering the signal inversion and thereby making spatially distributed plant information necessary. Usually vegetation types are considered with a vegetation type specific global parameterization, e.g., for leaf area index (LAI). Within this study we evaluate and address the effect of spatially varying LAI on high spatial resolution (pixel size 50 m) airborne L-band brightness temperature of crop canopies that are usually regarded homogeneous. To account for within field variations of LAI we used airborne imaging spectrometer data (pixel size 1.5 m) to empirically create maps of LAI using spectral greenness vegetation indices. We found clear (R2<0.90) functional relationships between spatially varying L-band brightness temperature and LAI variations within crop canopies that in literature are usually assumed homogeneous. Very good (R2 = 0.93) near surface soil moisture estimates were achieved using multi-variate regression and adding plant specific spectral information to the independent variable set for final soil moisture retrieval. The study shows that a multi-sensor campaign using airborne L-band radiometer and imaging spectrometers provide a powerful data set for monitoring patterns of near surface soil moisture and vegetation canopy at the field scale with high accuracy.

Experimental Study on Radiometric Performance of Synthetic Aperture Radiometer HUT-2D—Measurements of Natural Targets

This paper describes the analysis of L-band radiometric measurement data gathered with the synthetic aperture radiometer HUT-2D during several ground-based and airborne measurement campaigns. The radiometric data are analyzed from the instrument's performance point of view, aiming to verify the theoretical performance of an instrument of this kind and to assess the performance of the HUT-2D radiometer system in particular. The data sets considered for the study consist of measurements of well-known natural targets, such as cosmic background radiation, and measurements of pure water scenes, the brightness temperature of which is possible to model based on in situ measurements. We define four figures of merit, which are applicable for synthetic aperture radiometers. These are radiometric resolution, image bias, pixel-to-pixel random error, and temporal stability. Then, we use the selected data sets to assess these in the case of HUT-2D. The experimental results are discussed and compared to the theoretical values, where applicable. Also, we discuss possibilities to improve the presented performance. The main results of this paper are the consolidated performance parameters of the HUT-2D instrument. We study and discuss the properties of the error components related to the technology in a general level, and study the scalability of the errors as a function of the measured targets. In particular, the stability of the direction-dependent error component is pointed out, and a mitigation guideline is proposed.

Soil Moisture Retrieval Using a Two-Dimensional L-Band Synthetic Aperture Radiometer in a Semiarid Environment

Surface soil moisture was retrieved from the L-band radiometer data collected in semiarid regions during the Soil Moisture Experiment in 2004. The 2-D synthetic aperture radiometer (2D-STAR) was flown over regional-scale study sites located in AZ, USA, and Sonora, Mexico (SO). The study sites are characterized by a range of topographic relief with a land cover that varies from bare soil to grass and scrubland and includes areas with high rock fraction near the soil surface. The 2D-STAR retrieval of soil moisture was in good agreement with the ground-based estimates of surface soil moisture in both AZ (raise = 0.012 m3 m-3) and SO (rmse = 0.011 m3 m-3). The 2D-STAR also showed a good performance in the Walnut Gulch Experimental Watershed (rmse = 0.014 m3 m-3) where the surface soil featured high rock fraction was as high as 60%. Comparison of the results with the Polarimetric Scanning Radiometer at the Cand X-band data indicates the superior soil moisture retrieval performance of the L-band data over the regions with high rock fraction and moderate vegetation density.

Calibration of a soil moisture model over grassland using L-band microwave radiometry

Land surface models are widely used to simulate water and energy fluxes at the land surface. To perform realistic simulations, an appropriate model parameterization and calibration is required. The present paper investigates the general potential of using L-band microwave radiometer data for the calibration of a simple soil wetness model. A ground based L-band radiometer was used to measure the dual polarized microwave emission from a grass covered area throughout the vegetation period in 2004. It was found that L-band microwave data provided a robust proxy for surface soil moisture conditions for the used data set which is consistent with previous findings. The microwave data is used for the calibration of a simple soil moisture model. Using the L-band data resulted in improved simulation skills of surface soil water dynamics as well as a better representation of deeper soil water storage. The correlation r between the soil moisture model estimates and the actual soil moisture was improved from to for the surface and (root zone) soil water content, respectively. The results of the study reveal the general potential of using L-band microwave radiometry for an improved parameterization of land surface models which might be an interesting application for proposed and recent L-band microwave satellite missions such as the NASA Soil Moisture Active/Passive (SMAP) and the European Soil Moisture and Ocean Salinity Mission (SMOS).

Assimilation of an L-Band Microwave Soil Moisture Proxy to Compensate for Uncertainties in Precipitation Data

The accuracy of hydrological model simulations is dependent on the reliability of model input data like, for example, meteorological information or land cover and soil information. Uncertainties of simulations of soil water fluxes are hereby directly related to the accuracy of available precipitation data. As precipitation is characterized by small temporal and spatial correlation lengths, the uncertainties in precipitation data increase with decreasing density of available precipitation gauges. As soil moisture directly depends on precipitation dynamics, its variation can be used as a proxy for precipitation variability. Remote sensing techniques allow for monitoring of surface soil moisture dynamics at different spatiotemporal scales. In particular, low-frequency microwave data are most sensitive to soil moisture dynamics. This paper investigates the potential of integrating L-band (1-2 GHz) microwave radiometer data into a simple model for soil wetness to compensate for uncertainties in a priori information of precipitation. The study is based on a short-term ground-based L-band radiometer data set over grassland. A high correlation between the microwave signature and surface soil moisture was found, which is consistent with previous findings. An analytical data assimilation scheme for the integration of that information into a soil wetness model, based on an antecedent precipitation index (API), was established. The results revealed that the data assimilation filter adds or removes an amount of water partially compensating for the actual precipitation error. The correlation coefficient between the filter update and the actual precipitation error was found to be 0.6 les r les 0.8, and the model simulations did show a better coincidence with in situ soil moisture records when integrating the microwave data. The results indicate high potential for use of L-band microwave data to compensate for uncertainties in precipitation data.

The use of a semi-empirical emissivity model for a rough estimation of sea surface salinity from an airborne microwave radiometer

In preparation for the European Space Agency SMOS (Soil Moisture and Ocean Salinity) satellite mission, radiometric and oceanographic measurements were performed in December 2000 and January 2001 and in November 2001 from a fixed platform in the NW Mediterranean to improve the modelling of the sea surface emissivity at L-band and new semi-empirical models were derived. Now one of these models has been used to retrieve sea surface salinity from L-band radiometric data acquired with a different instrument and different location. These data were acquired in August 2003 over the continental shelf within the influence of the Rio de la Plata, from Argentina to Brazil, southern Atlantic ocean with the STARRS airborne radiometer. Results show that the radiometer is capable of realistically detecting natural variations in surface salinity even though the model was derived in very different oceanographic conditions and from data collected by a different instrument.

Multispectral Remote Sensing of Saline Seeps

An aircraft experiment was conducted in early summer of 1981 to determine the feasibility of optical and microwave remote sensing techniques for the detection of fully developed and incipient saline seeps in South Dakota and Montana. The NASA C-130 earth resources aircraft was used to acquire L-band and C-band scatterometer data (backscattering coefficient profiles), radiometer data (brightness temperature profiles), and color-infrared photography; additional passive microwave data and thermal images were acquired by the L- band radiometer on the Beechcraft D-18 aircraft operated by South Dakota State University. Intensive soil moisture and salinity data were collected on a uniform 20-m grid spacing and at several depths for the 600 × 600 m South Dakota site. The two Montana sites were over-flown with flight lines several kilometers in length, and ground truth information was obtained by identifying known geological and geohydrological units with varying soil salinities on a regional basis. The C-130 radiometers (both L- and C-bands) were effective in detecting wet soil areas including fully developed seeps; however, incipient seeps were not accurately detected by the radiometers. The D-18 L-band radiometer data did not appear to be sensitive to soil wetness. The C-130 scatterometer data profiles, although showing some sensitivity to soil moisture, were greatly influenced by surface roughness and appear to be ineffective in accurately delineating either fully developed or incipient seeps. Thermal-IR scanner data acquired by the D-18 aircraft did not appear to provide a reliable means for identifying potential seeps.