Brightness Temperature Estimates Research Articles

First high resolution interferometric observation of a solar prominence with ALMA

ABSTRACT We present the first observation of a solar prominence at 84 − 116 GHz using the high resolution interferometric imaging of ALMA. Simultaneous observations in Hα from Białkaw Observatory and with SDO/AIA reveal similar prominence morphology to the ALMA observation. The contribution functions of 3 mm and Hα emission are shown to have significant overlap across a range of gas pressures. We estimate the maximum millimetre-continuum optical thickness to be τ3mm ≈ 2, and the brightness temperature from the observed Hα intensity. The brightness temperature measured by ALMA is ∼6000 – 7000 K in the prominence spine, which correlates well with the estimated brightness temperature for a kinetic temperature of 8000 K.

A Physics-Based Algorithm to Couple CYGNSS Surface Reflectivity and SMAP Brightness Temperature Estimates for Accurate Soil Moisture Retrieval

Remotely sensed soil moisture (SM) with high accuracy and high spatial&#x2013;temporal resolution is crucial to meteorological, agricultural, hydrological, and environmental applications. The Cyclone Global Navigation Satellite System (CYGNSS) is the first constellation that uses the L-band signal transmitted by the GNSS satellites to develop daily SM data products. In this study, a physics-based algorithm is proposed to couple CYGNSS surface reflectivity (SR) and Soil Moisture Active Passive (SMAP) brightness temperature estimates for accurate SM retrieval. The algorithm is based on the radiative transfer model and the SMAP data to derive a combined parameter of the vegetation optical depth (<inline-formula> <tex-math notation="LaTeX">$\tau$ </tex-math></inline-formula>) and the surface roughness parameter (<inline-formula> <tex-math notation="LaTeX">$h$ </tex-math></inline-formula>). The CYGNSS L1 Version 2.1 data of the years 2017&#x2013;2018 and 2019&#x2013;2020 are used for calibration and validation, respectively. The SM estimates agree and correlate well with the SMAP SM and <i>in situ</i> SM data on a global scale (<inline-formula> <tex-math notation="LaTeX">$R =0.679$ </tex-math></inline-formula>, RMSE <inline-formula> <tex-math notation="LaTeX">$=0.051\,\,\text{m}^{3}\text{m}^{-3}$ </tex-math></inline-formula>, and MAE <inline-formula> <tex-math notation="LaTeX">$=0.045\,\,\text{m}^{3}\text{m}^{-\mathrm {3}}$ </tex-math></inline-formula> against SMAP SM; <inline-formula> <tex-math notation="LaTeX">$R = 0.729$ </tex-math></inline-formula> against <i>in situ</i> SM). The proposed algorithm makes contributions from two aspects. First, the proposed algorithm provides a physics-based algorithm using SMAP brightness temperature to calibrate the attenuation due to vegetation and surface roughness on the CYGNSS-derived SR. Unlike attenuation models that have been explored previously in the context of CYGNSS, this algorithm executes the calibration without relying on observations of <inline-formula> <tex-math notation="LaTeX">$h$ </tex-math></inline-formula> or vegetation biophysical parameters as inputs but with the SMAP brightness temperature as the only observations. Second, the proposed algorithm provides a new way for the combined usage of CYGNSS and SMAP to improve the temporal and spatial coverages of global SM with temporal coverage increased by 38.2&#x0025; and spatial coverage increased by 31.6&#x0025;.

Radio, X-Ray, and Extreme-ultraviolet Observations of Weak Energy Releases in the “Quiet” Sun

Abstract We analyzed ground-based low frequency (&lt;100 MHz) radio spectral and imaging data of the solar corona obtained with the facilities in the Gauribidanur observatory during the same time as the very weak soft X-ray flares (sub-A-class, flux &lt;10−7Wm−2 in the 1–8 Å wavelength range) from the quiet Sun observed with the X-ray Solar Monitor (XSM) on board Chandrayaan-2 during the recent solar minimum. Nonthermal type I radio burst activity was noticed in close temporal association with the X-ray events. The estimated brightness temperature (T b ) of the bursts at a typical frequency like 80 MHz is ≈3 × 105 K. Extreme-ultraviolet (EUV) observations at 94 Å with the Atmospheric Imaging Assembly (AIA) on board the Solar Dynamics Observatory (SDO) revealed a brightening close to the same location and time as the type I radio bursts. As far as we know reports of simultaneous observations of X-ray and/or EUV counterpart to weak transient radio emission at low frequencies from the quiet Sun in particular are rare. Considering this and the fact that low frequency radio observations are sensitive to weak energy releases in the solar atmosphere, the results indicate that coordinated observations of similar events would be useful to understand transient activities in the quiet Sun.

Land surface modeling over the Dry Chaco: the impact of model structures, and soil, vegetation and land cover parameters

Abstract. In this study, we tested the impact of a revised set of soil, vegetation and land cover parameters on the performance of three different state-of-the-art land surface models (LSMs) within the NASA Land Information System (LIS). The impact of this revision was tested over the South American Dry Chaco, an ecoregion characterized by deforestation and forest degradation since the 1980s. Most large-scale LSMs may lack the ability to correctly represent the ongoing deforestation processes in this region, because most LSMs use climatological vegetation indices and static land cover information. The default LIS parameters were revised with (i) improved soil parameters, (ii) satellite-based interannually varying vegetation indices (leaf area index and green vegetation fraction) instead of climatological vegetation indices, and (iii) yearly land cover information instead of static land cover. A relative comparison in terms of water budget components and “efficiency space” for various baseline and revised experiments showed that large regional and long-term differences in the simulated water budget partitioning relate to different LSM structures, whereas smaller local differences resulted from updated soil, vegetation and land cover parameters. Furthermore, the different LSM structures redistributed water differently in response to these parameter updates. A time-series comparison of the simulations to independent satellite-based estimates of evapotranspiration and brightness temperature (Tb) showed that no LSM setup significantly outperformed another for the entire region and that not all LSM simulations improved with updated parameter values. However, the revised soil parameters generally reduced the bias between simulated surface soil moisture and pixel-scale in situ observations and the bias between simulated Tb and regional Soil Moisture Ocean Salinity (SMOS) observations. Our results suggest that the different hydrological responses of various LSMs to vegetation changes may need further attention to gain benefits from vegetation data assimilation.

Comprehensive Vector Radiative Transfer Model for Estimating Sea Surface Salinity From L-Band Microwave Radiometry

Sea surface salinity (SSS) retrieval from satellite-based microwave radiometer is hampered by uncertainties due to atmospheric and surface scattering contributions. This study presents a comprehensive vector radiative transfer model (VRTM) for estimation of brightness temperature (TB) (for SSS retrieval) from L-band radiometry based on a matrix-operator method. It includes an efficient two-scale model (TSM) that combines a geometrical optics (GO) model and a small-perturbation model (SPM) for computing both the small- and large-scale scattering components of the sea surface. Moreover, it considers the influence of rain effects on TB by including the radiation extinction term (scattering and attenuation). The simulation results using the VRTM were validated with those obtained from the RT4 model for flat sea surface conditions and the RTTOV model for rough sea surface conditions. The relative difference in the estimated TB among the models was small (<; 1%) for low wind speeds (<; 1 m/s) and increased up to 3% for high wind speeds and observation angles. Simulations on the influence of wind speed on TB with various parameterizations were further examined. Compared with SMOS-MIRAS and Aquarius measurements, the VRTM simulations agreed well with satellite measurements for both vertically and horizontally polarized TBs with biases of less than 2.2 K for observation angles from 20° to 65°. The binned TBs showed even better results, with a standard deviation of less than 1.45 K and an absolute mean error of less than 1.2 K.

Improved groundwater table and L-band brightness temperature estimates for Northern Hemisphere peatlands using new model physics and SMOS observations in a global data assimilation framework

There is an urgent need to include northern peatland hydrology in global Earth system models to better understand land-atmosphere interactions and sensitivities of peatland functions to climate change, and, ultimately, to improve climate change predictions. In this study, we introduced for the first time peatland-specific model physics into an assimilation scheme for L-band brightness temperature (Tb) data from the Soil Moisture Ocean Salinity (SMOS) mission to improve groundwater table estimates. We conducted two sets of model-only and data assimilation experiments using the Catchment Land Surface Model (CLSM), applying (over peatlands only) in one of them a peatland-specific adaptation (PEATCLSM). The evaluation against in-situ measurements of peatland groundwater table depth indicates the superiority of PEATCLSM model physics and additionally improved performance after assimilating SMOS Tb observations. The better performance of PEATCLSM over nearly all Northern Hemisphere peatlands is further supported by the better agreement between SMOS Tb observations and Tb estimates from the model-only and data assimilation runs. Within the data assimilation scheme, PEATCLSM reduces Tb observation-minus-forecast residuals and leads to reduced data assimilation updates of water storage components and, thus, reduced water budget imbalances in the assimilation system.

Required sampling density of ground-based soil moisture and brightness temperature observations for calibration and validation of L-band satellite observations based on a virtual reality

Abstract. Microwave remote sensing is the most promising tool for monitoring near-surface soil moisture distributions globally. With the Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) missions in orbit, considerable efforts are being made to evaluate derived soil moisture products via ground observations, microwave transfer simulation, and independent remote sensing retrievals. Due to the large footprint of the satellite radiometers of about 40 km in diameter and the spatial heterogeneity of soil moisture, minimum sampling densities for soil moisture are required to challenge the targeted precision. Here we use 400 m resolution simulations with the regional Terrestrial System Modeling Platform (TerrSysMP) and its coupling with the Community Microwave Emission Modelling platform (CMEM) to quantify the maximum sampling distance allowed for soil moisture and brightness temperature validation. Our analysis suggests that an overall sampling distance of finer than 6 km is required to validate the targeted accuracy of 0.04 cm3 cm−3 with a 70 % confidence level in SMOS and SMAP estimates over typical mid-latitude European regions. The maximum allowed sampling distance depends on the land-surface heterogeneity and the meteorological situation, which influences the soil moisture patterns, and ranges from about 6 to 17 km for a 70 % confidence level for a typical year. At the maximum allowed sampling distance on a 70 % confidence level, the accuracy of footprint-averaged soil moisture is equal to or better than brightness temperature estimates over the same area. Estimates strongly deteriorate with larger sampling distances. For the evaluation of the smaller footprints of the active and active–passive products of SMAP the required sampling densities increase; e.g., when a grid resolution of 3 km diameter is sampled by three sites of footprints of 9 km sampled by five sites required, only 50 %–60 % of the pixels have a sampling error below the nominal values. The required minimum sampling densities for ground-based radiometer networks to estimate footprint-averaged brightness temperature are higher than for soil moisture due to the non-linearities of radiative transfer, and only weakly correlated in space and time. This study provides a basis for a better understanding of the sometimes strong mismatches between derived satellite soil moisture products and ground-based measurements.

О КАЛИБРОВКЕ ИЗОБРАЖЕНИЙ СИБИРСКОГО РАДИОГЕЛИОГРАФА

We discuss the method for automatic calibration of solar radio images derived from the first stage of the multiwave Siberian Radioheliograph (SRH-48). SRH is a 48-element T-shaped radiointerferometer in the 4–8 GHz frequency range. Since the end of 2017, the number of frequencies has been increased to 32. The method is implemented in Python programming language. We give examples of calibration for SRH instrument values in brightness temperature of the quiet and active Sun at several frequencies for four eruptive events: June 19, 2017, July 25, 2016, April 24, 2017, and April 19, 2017. In conclusion, we present spectra for three events, as well as brightness temperature estimates by flux, angular size of filaments, and emission measure.

CALIBRATION OF SIBERIAN RADIOHELIOGRAPH IMAGES

We discuss the method for automatic calibration of solar radio images derived from the first stage of the multiwave Siberian Radioheliograph (SRH-48). SRH is a 48-element T-shaped radiointerferometer in the 4–8 GHz frequency range. Since the end of 2017, the number of frequencies has been increased to 32. The method is implemented in Python programming language. We give examples of calibration for SRH instrument values in brightness temperature of the quiet and active Sun at several frequencies for four eruptive events: June 19, 2017, July 25, 2016, April 24, 2017, and April 19, 2017. In conclusion, we present spectra for three events, as well as brightness temperature estimates by flux, angular size of filaments, and emission measure.

Global millimeter VLBI array survey of ultracompact extragalactic radio sources at 86 GHz

Context. Very long baseline interferometry (VLBI) observations at 86 GHz (wavelength, λ = 3 mm) reach a resolution of about 50 μas, probing the collimation and acceleration regions of relativistic outflows in active galactic nuclei (AGN). The physical conditions in these regions can be studied by performing 86 GHz VLBI surveys of representative samples of compact extragalactic radio sources. Aims. To extend the statistical studies of compact extragalactic jets, a large global 86 GHz VLBI survey of 162 compact radio sources was conducted in 2010–2011 using the Global Millimeter VLBI Array (GMVA). Methods. The survey observations were made in a snapshot mode, with up to five scans per target spread over a range of hour angles in order to optimize the visibility coverage. The survey data attained a typical baseline sensitivity of 0.1 Jy and a typical image sensitivity of 5 mJy beam−1, providing successful detections and images for all of the survey targets. For 138 objects, the survey provides the first ever VLBI images made at 86 GHz. Gaussian model fitting of the visibility data was applied to represent the structure of the observed sources and to estimate the flux densities and sizes of distinct emitting regions (components) in their jets. These estimates were used for calculating the brightness temperature (Tb) at the jet base (core) and in one or more moving regions (jet components) downstream from the core. These model-fit-based estimates of Tb were compared to the estimates of brightness temperature limits made directly from the visibility data, demonstrating a good agreement between the two methods. Results. The apparent brightness temperature estimates for the jet cores in our sample range from 2.5 × 109 K to 1.3 × 1012 K, with the mean value of 1.8 × 1011 K. The apparent brightness temperature estimates for the inner jet components in our sample range from 7.0 × 107 K to 4.0 × 1011 K. A simple population model with a single intrinsic value of brightness temperature, T0, is applied to reproduce the observed distribution. It yields T0 = (3.77−0.14+0.10) × 1011 K for the jet cores, implying that the inverse Compton losses dominate the emission. In the nearest jet components, T0 = (1.42−0.19+0.16) × 1011 K is found, which is slightly higher than the equipartition limit of ∼5 × 1010 K expected for these jet regions. For objects with sufficient structural detail detected, the adiabatic energy losses are shown to dominate the observed changes of brightness temperature along the jet.

Evaluation of SMAP downscaled brightness temperature using SMAPEx-4/5 airborne observations

The Soil Moisture Active and Passive (SMAP) mission, launched by the National Aeronautics and Space Administration (NASA) on 31st January 2015, was designed to provide global soil moisture every 2 to 3 days at 9 km resolution by downscaling SMAP passive microwave observations obtained at 36 km resolution using active microwave observations at 3 km resolution, and then retrieving soil moisture from the resulting 9 km brightness temperature product. This study evaluated the SMAP Active/Passive (AP) downscaling algorithm together with other resolution enhancement techniques. Airborne passive microwave observations acquired at 1 km resolution over the Murrumbidgee River catchment in south-eastern Australia during the fourth and fifth Soil Moisture Active Passive Experiments (SMAPEx-4/5) were used as reference data. The SMAPEx-4/5 data were collected in May and September 2015, respectively, and aggregated to 9 km for direct comparison with a number of available resolution-enhanced brightness temperature estimates. The results show that the SMAP AP downscaled brightness temperature had a correlation coefficient (R) of 0.84 and Root-Mean-Squared Error (RMSE) of ~10 K, while SMAP Enhanced, Nearest Neighbour, Weighted Average, and the Smoothing Filter-based Modulation (SFIM) brightness temperature estimates had somewhat better performance (RMSEs of ~7 K and an R exceeding 0.9). Although the SFIM had the lowest unbiased RMSE of ~6 K, the effect of cloud cover on Ka-band observations limits data availability.

Imaging spectroscopy of solar radio burst fine structures

Solar radio observations provide a unique diagnostic of the outer solar atmosphere. However, the inhomogeneous turbulent corona strongly affects the propagation of the emitted radio waves, so decoupling the intrinsic properties of the emitting source from the effects of radio wave propagation has long been a major challenge in solar physics. Here we report quantitative spatial and frequency characterization of solar radio burst fine structures observed with the Low Frequency Array, an instrument with high-time resolution that also permits imaging at scales much shorter than those corresponding to radio wave propagation in the corona. The observations demonstrate that radio wave propagation effects, and not the properties of the intrinsic emission source, dominate the observed spatial characteristics of radio burst images. These results permit more accurate estimates of source brightness temperatures, and open opportunities for quantitative study of the mechanisms that create the turbulent coronal medium through which the emitted radiation propagates.

Brightness temperatures of galactic masers observed in the RadioAstron project

AbstractWe present estimates of brightness temperature for 5 galactic masers in star-forming regions detected at space baselines. Very compact features with angular sizes of ~23-60 μas were detected in these regions with corresponding linear sizes of ~4-10×106 km. Brightness temperatures range from 1014 up to 1016 K.

Influences of the MJO on the space‐time organization of tropical convection

AbstractThe fact that the Madden‐Julian Oscillation (MJO) is characterized by large‐scale patterns of enhanced tropical rainfall has been widely recognized for decades. However, the precise nature of any two‐way feedback between the MJO and the properties of smaller‐scale organization that makes up its convective envelope is not well understood. Satellite estimates of brightness temperature are used here as a proxy for tropical rainfall, and a variety of diagnostics are applied to determine the degree to which tropical convection is affected either locally or globally by the MJO. To address the multiscale nature of tropical convective organization, the approach ranges from space‐time spectral analysis to an object‐tracking algorithm. In addition to the intensity and distribution of global tropical rainfall, the relationship between the MJO and other tropical processes such as convectively coupled equatorial waves, mesoscale convective systems, and the diurnal cycle of tropical convection is also analyzed. The main findings of this paper are that, aside from the well‐known increase in rainfall activity across scales within the MJO convective envelope, the MJO does not favor any particular scale or type of organization, and there is no clear signature of the MJO in terms of the globally integrated distribution of brightness temperature or rainfall.

Enhanced contextual forest fire detection with prediction interval analysis of surface temperature using vegetation amount

ABSTRACTEarly detection of small forest fires is important for forest management because it can prevent fires from spreading and causing severe environmental and economic damage. This study proposes a new method that uses a negative relationship between vegetation amount and land surface temperature to determine a temperature threshold for detecting small forest fires. The proposed method analyses the differences between brightness temperature in remote-sensing data and that estimated from a regression model of brightness temperature and vegetation amount measured by the normalized difference vegetation index. The upper prediction interval of estimated brightness temperature based on a statistical test of the differences was used for the temperature threshold. This method was compared with the Moderate Resolution Imaging Spectroradiometer contextual algorithm using two accuracy measures: precision and recall. The results showed that the proposed method improved the recall accuracy, and its precision accuracy was similar to that of the contextual algorithm. This indicates that the proposed method detected more small forest fires with a similar false detection rate as that of traditional methods.

THE ALMA SPECTROSCOPIC SURVEY IN THE HUBBLE ULTRA DEEP FIELD: IMPLICATIONS FOR SPECTRAL LINE INTENSITY MAPPING AT MILLIMETER WAVELENGTHS AND CMB SPECTRAL DISTORTIONS

ABSTRACT We present direct estimates of the mean sky brightness temperature in observing bands around 99 and 242 GHz due to line emission from distant galaxies. These values are calculated from the summed line emission observed in a blind, deep survey for spectral line emission from high redshift galaxies using ALMA (the ALMA spectral deep field observations “ASPECS” survey). In the 99 GHz band, the mean brightness will be dominated by rotational transitions of CO from intermediate and high redshift galaxies. In the 242 GHz band, the emission could be a combination of higher order CO lines, and possibly [C ii] 158 μm line emission from very high redshift galaxies (z ∼ 6–7). The mean line surface brightness is a quantity that is relevant to measurements of spectral distortions of the cosmic microwave background, and as a potential tool for studying large-scale structures in the early universe using intensity mapping. While the cosmic volume and the number of detections are admittedly small, this pilot survey provides a direct measure of the mean line surface brightness, independent of conversion factors, excitation, or other galaxy formation model assumptions. The mean surface brightness in the 99 GHZ band is: T B = 0.94 ± 0.09 μK. In the 242 GHz band, the mean brightness is: T B = 0.55 ± 0.033 μK. These should be interpreted as lower limits on the average sky signal, since we only include lines detected individually in the blind survey, while in a low resolution intensity mapping experiment, there will also be the summed contribution from lower luminosity galaxies that cannot be detected individually in the current blind survey.

EXTREME BRIGHTNESS TEMPERATURES AND REFRACTIVE SUBSTRUCTURE IN 3C 273 WITH RADIOASTRON

ABSTRACT Earth–space interferometry with RadioAstron provides the highest direct angular resolution ever achieved in astronomy at any wavelength. RadioAstron detections of the classic quasar 3C 273 on interferometric baselines up to 171,000 km suggest brightness temperatures exceeding expected limits from the “inverse-Compton catastrophe” by two orders of magnitude. We show that at 18 cm, these estimates most likely arise from refractive substructure introduced by scattering in the interstellar medium. We use the scattering properties to estimate an intrinsic brightness temperature of 7 &times; 10 12 K ?> , which is consistent with expected theoretical limits, but which is ∼15 times lower than estimates that neglect substructure. At 6.2 cm, the substructure influences the measured values appreciably but gives an estimated brightness temperature that is comparable to models that do not account for the substructure. At 1.35 cm ?> , the substructure does not affect the extremely high inferred brightness temperatures, in excess of 10 13 K ?> . We also demonstrate that for a source having a Gaussian surface brightness profile, a single long-baseline estimate of refractive substructure determines an absolute minimum brightness temperature, if the scattering properties along a given line of sight are known, and that this minimum accurately approximates the apparent brightness temperature over a wide range of total flux densities.

Optimum Image Formation for Spaceborne Microwave Radiometer Products.

This paper considers some of the issues of radiometer brightness image formation and reconstruction for use in the NASA-sponsored Calibrated Passive Microwave Daily Equal-Area Scalable Earth Grid 2.0 Brightness Temperature Earth System Data Record project, which generates a multisensor multidecadal time series of high-resolution radiometer products designed to support climate studies. Two primary reconstruction algorithms are considered: the Backus-Gilbert approach and the radiometer form of the scatterometer image reconstruction (SIR) algorithm. These are compared with the conventional drop-in-the-bucket (DIB) gridded image formation approach. Tradeoff study results for the various algorithm options are presented to select optimum values for the grid resolution, the number of SIR iterations, and the BG gamma parameter. We find that although both approaches are effective in improving the spatial resolution of the surface brightness temperature estimates compared to DIB, SIR requires significantly less computation. The sensitivity of the reconstruction to the accuracy of the measurement spatial response function (MRF) is explored. The partial reconstruction of the methods can tolerate errors in the description of the sensor measurement response function, which simplifies the processing of historic sensor data for which the MRF is not known as well as modern sensors. Simulation tradeoff results are confirmed using actual data.

Fusion of Images Generated by Radiometric and Active Noise SAR

Abstract The work is devoted to fusion of radar and radiometer images. Noise waveform SAR generates radar images of reflective objects of its field of view. A bistatic radiometer with synthetic aperture estimates the thermal radio emissions of the objects along their angular coordinates and even range. The estimated brightness temperatures of rough and smooth surfaces are different, as well as the radar responses from them. Identification of the parameters of objects surfaces may be done using results of joint processing of images generated by both sensors. The optimum and quasi-optimum criteria for fusion of the images were obtained. The latter was experimentally checked. It approves the opportunity to fuse the images for further estimation of some parameters of objects surfaces. The results obtained may be used in environmental and security applications.

Impact of sub-pixel heterogeneity on modelled brightness temperature for an agricultural region

Knowledge of sub-pixel heterogeneity, particularly at the passive microwave scale, can improve the brightness temperature (and ultimately the soil moisture) estimation. However, the impact of surface heterogeneity (in terms of soil moisture, soil temperature and vegetation water content) on brightness temperature in an agricultural setting is relatively unknown. The Soil Moisture Active Passive Validation Experiment 2012 (SMAPVEX12) provided an opportunity to evaluate sub-pixel heterogeneity at the scale of a Soil Moisture Ocean Salinity (SMOS) or the Soil Moisture Active Passive (SMAP) radiometer footprint using field measured data. The first objective of this study was to determine if accounting for surface heterogeneity reduced the error between estimated brightness temperature (Tb) and Tb measured by SMOS. It was found that when accounting for variation in surface soil moisture, temperature and vegetation water content within the pixel footprint, the error between the modelled Tb and the measured Tb was less than if a homogeneous pixel were modelled. The correlation between the surface parameters and the error associated with not accounting for surface heterogeneity were investigated. It was found that there was low to moderate correlation between the error and the coefficient of variance associated with the measured soil moisture, soil temperature and vegetation volumetric water content during the field campaign. However, it was found that the correlations changed depending on the stage of vegetation growth and the amount of time following a precipitation event. At the start of the field campaign (following a precipitation event), there was strong correlation between the error and all three surface parameters (r≥0.75). Following a precipitation event close to the middle of the field campaign (during which there was rapid growth in vegetation), there was strong correlation between the error and the variability in vegetation water content (r=0.89), moderate correlation with soil moisture (r=0.61) and low correlation with soil temperature (r=0.26).