Investigating the effects of soil moisture on thermal infrared land surface temperature and emissivity using satellite retrievals and laboratory measurements

This study investigates the effects of soil moisture (SM) on thermal infrared (TIR) land surface emissivity (LSE) using field- and satellite-measurements. Laboratory measurements were used to simulate the effects of rainfall and subsequent surface evaporation on the LSE for two different sand types. The results showed that the LSE returned to the dry equilibrium state within an hour after initial wetting, and during the drying process the SM changes were uncorrelated with changes in LSE. Satellite retrievals of LSE from the Atmospheric Infrared Sounder (AIRS) and Moderate Resolution Imaging Spectroradiometer (MODIS) were examined for an anomalous rainfall event over the Namib Desert in Namibia during April, 2006. The results showed that increases in Advanced Microwave Scanning Radiometer (AMSR-E) derived soil moisture and Tropical Rainfall Measuring Mission (TRMM) rainfall estimates corresponded closely with LSE increases of between 0.08-0.3 at 8.6 µm and up to 0.03 at 11 µm for MODIS v4 and AIRS products. This dependence was lost in the more recent MODIS v5 product which artificially removed the correlation due to a stronger coupling with the split-window algorithm, and is lost in any algorithms that force the LSE to a pre-determined constant as in split-window type algorithms like those planned for use with the NPOESS Visible Infrared Imager Radiometer Suite (VIIRS). Good agreement was found between MODIS land surface temperatures (LSTs) derived from the Temperature Emissivity Separation (TES) and day/night v4 algorithm (MOD11B1 v4), while the split-window dependent products (MOD11B1 v5 and MOD11A1) had cooler mean temperatures on the order of 1-2 K over the Namib Desert for the month of April 2006.     

Land surface emissivity retrieval from combined mid-infrared and thermal infrared data of MSG-SEVIRI

This work addressed the retrieval of Land Surface Emissivity (LSE) from combined mid-infrared and thermal infrared data of Spinning Enhanced Visible and Infra-Red Imager (SEVIRI) onboard the geostationary satellite—Meteosat Second Generation (MSG). To correct for the atmospheric effects in satellite measurements, a new atmospheric correction scheme was developed for both Middle Infra-Red (MIR) and Thermal Infra-Red (TIR) channels. For the MIR channel, because it is less sensitive to the change of water vapor content, the clear-sky and time-nearest European Centre for Median-range Weather Forecast (ECMWF) atmospheric data were used for the images where no atmospheric data are available. For TIR channels, a modified model of Diurnal Temperature Cycle (DTC) used by Göttsche and Olesen [Göttsche, F. M., and Olesen, F. S. (2001). Modeling of diurnal cycles of brightness temperature extracted from METEOSAT data. Remote Sensing of Environment, 76, 337-348.] and Schädlich et al. [Schädlich, S., Göttsche, F. M., and Olesen, F. S. (2001). Influence of land surface parameters and atmosphere on METEOSAT brightness Temperatures and generation of land surface temperature maps by temporally and spatially interpolating atmospheric correction. Remote Sensing of Environment, 75, 39-46.] was adopted. The separation of Land Surface Temperature (LST) and LSE is based on the concept of the Temperature Independent Spectral Indices (TISI) [Becker, F., and Li, Z. L. (1990a). Temperature independent spectral indices in thermal infrared bands. Remote Sensing of Environment, 32, 17-33.] constructed with one channel in MIR and one channel in TIR. The results of two different combinations (combination of channels 4 and 9 and of channels 4 and 10) and two successive days at six specific locations over North Africa show that the retrievals are consistent. The range of emissivity in MSG-SEVIRI channel 4 goes from 0.5 for bare areas to 0.96 for densely vegetated areas, whereas the emissivities in MSG-SEVIRI channels 9 and 10 are usually from 0.9 to 0.95 for bare areas and from 0.95 to 1.0 for vegetated areas. For densely vegetated areas, the emissivities in MSG-SEVIRI channel 9 are larger than the ones in channel 10, whereas the opposite is observed over bare areas. The rms differences between two combinations over the whole studied region are 0.017 for emissivity in channel 4, 0.008 for emissivity in channel 9 and 0.007 for emissivity in channel 10.     

Effects of vegetation and soil texture on surface soil moisture retrieval using multi-temporal optical and thermal infrared observations

To retrieve surface soil moisture (SSM) content over natural surfaces quantitatively, the effects of vegetation and soil texture on a previously developed bare SSM retrieval model are evaluated using simulated data from the common land model (CoLM). The results indicate that (1) both the accuracy and the five model parameters of the previous SSM retrieval model show relatively consistent variations when the fractional vegetation cover (FVC) varies from 0 to 0.7; and (2) the SSM exhibits a generally significant and exponential relationship with the rotation angle when the clay content is lower than 30%, with the FVC ranging from 0 to 0.7. These findings make it possible to estimate SSM directly under the conditions that the underlying surface is in the presence of spatially variable FVC and soil texture. On this basis, we further confirm the feasibility of using the previous bare SSM retrieval model to estimate SSM for FVC varying from 0 to 0.7 with a clay content lower than 30%. For the simulated data on eight cloud-free days, the total root mean square error (RMSE) of the retrieved SSM and the coefficient of determination (R²) are 0.033 m³m^?3 and 0.758, respectively. Ultimately, a preliminary validation is conducted using the ground measurements at the Bondville site; an R² = 0.328 and a RMSE = 0.058 m³m^?3 are obtained for 14 cloud-free days.     

Bare surface soil moisture retrieval from the synergistic use of optical and thermal infrared data

Land surface soil moisture (SSM) is crucial to research and applications in hydrology, ecology, and meteorology. To develop a SSM retrieval model for bare soil, an elliptical relationship between diurnal cycles of land surface temperature (LST) and net surface shortwave radiation (NSSR) is described and further verified using data that were simulated with the Common Land Model (CoLM) simulation. In addition, with a stepwise linear regression, a multi-linear model is developed to retrieve daily average SSM in terms of the ellipse parameters x₀ (horizontal coordinate of the ellipse centre), y₀ (vertical coordinate of the ellipse centre), a (semi-major axis), and θ (rotation angle), which were acquired from the elliptical relationship. The retrieval model for daily average SSM proved to be independent of soil type for a given atmospheric condition. Compared with the simulated daily average SSM, the proposed model was found to be of higher accuracy. For eight cloud-free days, the root mean square error (RMSE) ranged from 0.003 to 0.031 m³ m^?3, while the coefficient of determination (R²) ranged from 0.852 to 0.999. Finally, comparison and validation were conducted using simulated and measured data, respectively. The results indicated that the proposed model showed better accuracy than a recently reported model using simulated data. A simple calibration decreased RMSE from 0.088 m³ m^?3 to 0.051 m³ m^?3 at Bondville Companion site, and from 0.126 m³ m^?3 to 0.071 m³ m^?3 at the Bondville site. Coefficients of determination R² = 0.548 and 0.445 were achieved between the estimated daily average SSM and the measured values at the two sites, respectively. This paper suggests a promising avenue for retrieving regional SSM using LST and NSSR derived from geostationary satellites in future developments.     

Effect of surface wind speed and sensor view zenith angle dependence of emissivity on SST retrieval from thermal infrared data: ATSR

The Along Track Scanning Radiometer (ATSR) data are currently being processed at various places within the European community including the Rutherford Appleton Laboratory (RAL) in the U.K. In generating an atmospherically corrected sea-surface temperature (SST) field, the emissivity of the sea surface is assumed to be independent of the sensor view zenith angle, sea state and wavelength (Ian Barton, RAL, personal communication). The sensor view zenith angle dependence of the emissivity is generally not known because of the complications introduced by the surface wind speed. This paper attempts to evaluate the uncertainties introduced in the SST due to the variation of emissivity with the sensor view zenith angle and surface roughness generated by the wind speed.

Using the Cox and Munk formulation, Takashima and Takayama have simulated the sea water emissivities as a function of wind speed of up to 15ms^-1 and a range of the sensor view zenith angles. Their emissivity data for 11 and 12μm channels corresponding to the viewing geometry of the ATSR have been used in this work. It is shown that, depending on the value of the SST, there can be significant errors due to the sensor view zenith angle and sea surface roughness dependence of the emissivity. For example, if the SST is 10°C and the wind speed is 0ms^-1, then the errors due to the sensor view zenith angle dependence of the emissivity are shown to be 0·77°C and 0·55°C in 11 and 12μm channels, respectively, and at 15 ms^-l the respective errors reach about 1·ldeg K and 0·86 deg K. The errors due to the deviations of the emissivities from unity for nadir view in calm conditions are about 2·1°C and 3·5°C, respectively, in the 11 and 12μm channels. All of these errors are additive, indicating the importance of the calibration and validation.     

Preliminary validation of two temporal parameter-based soil moisture retrieval models using a satellite product and in situ soil moisture measurements over the REMEDHUS network

This study aims to preliminarily validate two newly developed temporal parameter-based surface soil moisture (SSM) retrieval models, namely the mid-morning model and daytime model, using both microwave satellite soil moisture product and in situ SSM measurements over a well-organized soil moisture network named REd de MEDición de la HUmedad del Suelo (REMEDHUS) in Spain. Ground SSM measurements and geostationary satellite observations were primarily implemented to obtain the model coefficients for the two SSM retrieval models for each cloud-free day. These model coefficients were subsequently used to estimate SSM using the Meteosat Second Generation products over the study area. Preliminary verification using both a satellite product and in situ SSM measurements demonstrated that SSM variation can be well detected by both SSM retrieval models. Specifically, a generally similar accuracy (coefficient of determination R²: 0.419–0.379, root mean square error: 0.046–0.051 m³ m^?3, Bias: ?0.020 to ?0.025 m³ m^?3) was found for the mid-morning model and the daytime model with the microwave missions based climate change initiative SSM product, respectively. Moreover, except for the comparable R² (0.614–0.675), a better accuracy (Bias: 0.032–0.044 m³ m^?3, RMSE: 0.043–0.050 m³ m^?3) are achieved for the daytime model and the mid-morning model with network SSM measurements, respectively. These results indicate that the daytime model exhibited generally comparable or better accuracy than that of the mid-morning model over the study area. This study has strengthened the feasibility of using multi-temporal information derived from the geostationary satellites to estimate SSM in future research.     

An assessment of radiance in Landsat TM middle and thermal infrared wavebands for the detection of tropical forest regeneration

It has been postulated that tropical forests regenerating after deforestation constitute an unmeasured terrestrial sink of atmospheric carbon, and that the strength of this sink is a function of regeneration stage. Such regeneration stages can be characterized by biophysical properties, such as leaf and wood biomass, which influence the radiance emitted and/or reflected from the forest canopy. Remotely sensed data can therefore be used to estimate these biophysical properties and thereby determine the forest regenerative stage. Studies conducted on temperate forests have related biophysical properties successfully with red and near-infrared radiance, particularly within the Normalized Difference Vegetation Index (NDVI). However, only weak correlations have generally been observed for tropical forests and it is suggested here that the relationship between forest biophysical properties and middle and thermal infrared radiance may be stronger than that between those properties and visible and near-infrared radiance.

An assessment of Landsat Thematic Mapper (TM) data revealed that radiance acquired in middle and thermal infrared wavebands contained significant information for the detection of regeneration stages in Amazonian tropical forests. It was demonstrated that tropical forest regeneration stages were most separable using middle infrared and thermal infrared wavebands and that the correlation with regeneration stage was stronger with middle infrared, thermal infrared or combinations of these wavebands than they were with visible, near infrared or combinations of these wavebands. For example, correlation coefficients increased from — 0·26 (insignificant at 95 per cent confidence level) when using the NDVI, to up to 0·93 (significant at 99 per cent confidence level) for a vegetation index containing data acquired in the middle and thermal infrared wavebands. These results point to the value of using data acquired in middle and thermal infrared wavebands for the study of tropical forests.     

A comparison of thermal infrared emissivity spectra measured in situ,in the laboratory,and derived from thermal infrared multispectral scanner (TIMS) data in Cuprite,Nevada, U.S.A.

In order to obtain ground truth data for multispectral thermal infrared sensors such as TIMS and ASTER, in situ spectral emissivity measurements were made during field surveys. These spectral emissivity measurements and laboratory spectral reflectance measurements of field samples were compared to emissivity spectra extracted from TIMS data at the surveyed points. The results indicate that emissivity spectra derived from the TIMS data agree well in shape with the spectra measured in situ or in the laboratory.     

A new method for estimating of evapotranspiration and surface soil moisture from optical and thermal infrared measurements: the simplified triangle

Earth Observation (EO) provides a promising approach towards deriving accurate spatiotemporal estimates of key parameters characterizing land surface interactions, such as latent (LE) and sensible (H) heat fluxes as well as soil moisture content. This paper proposes a very simple method to implement, yet reliable to calculate evapotranspiration fraction (EF) and surface moisture availability (M_o) from remotely sensed imagery of Normalized Difference Vegetation Index (NDVI) and surface radiometric temperature (T_ir). The method is unique in that it derives all of its information solely from these two images. As such, it does not depend on knowing ancillary surface or atmospheric parameters, nor does it require the use of a land surface model. The procedure for computing spatiotemporal estimates of these important land surface parameters is outlined herein stepwise for practical application by the user. Moreover, as the newly developed scheme is not tied to any particular sensor, it can also be implemented with technologically advanced EO sensors launched recently or planned to be launched such as Landsat 8 and Sentinel 3. The latter offers a number of key advantages in terms of future implementation of the method and wider use for research and practical applications alike.     

The effect of soil moisture and wind speed on aerosol optical thickness retrieval in a desert environment using SEVIRI thermal channels

Dust emission and deposition are associated with several factors such as surface roughness, land cover, soil properties, soil moisture (SM), and wind speed (WS). A combination of land surface and remote-sensing models has recently been investigated for dust detection and monitoring. The thermal bands of the Meteosat Second Generation Spinning Enhanced Visible and Infrared Imager (MSG/SEVIRI) satellite are widely used for qualitative detection of dust over desert because of their high spectral and temporal resolutions. In this work, the contribution of ground-measured WS data and satellite-measured SM data on aerosol optical thickness (AOT) retrieval was investigated using an artificial neural network (ANN) model. ANNs have been applied in similar applications and have shown a higher performance than simple multiple-regression models. This performance is mainly due to the ANN's ability to capture complex and non-linear relationships between inputs and outputs. A combination of MSG/SEVIRI brightness temperature (BT)/brightness temperature differences (BTDs), BTD_3.9–10.8, BTD_8.7–10.8, BTD_10.8–12, and BT_3.9, was used as input to the base ANN model while Aerosol Robotic Network (AERONET) AOT (level 2) data at 0.5 μm were used as output. These input/output sets were obtained from two stations (Hamim and Mezaira) lying in the inland desert of the United Arab Emirates (UAE). About 3800 observations were collected, of which two-thirds were used to train the ANN model and the remaining third was kept as an independent set to assess the accuracy of the trained model. Later, Advanced Microwave Scanning Radiometer Earth Observing System (AMSR-E) SM data and ground-measured WS data were used as additional inputs to the base model to investigate their contribution to the AOT retrieval. SM data consist of daytime AMSR-E-derived daily and collected from a National Snow and Ice Data Centre (NSIDC)-archived database. Hourly average WS data were also collected at 10 m height in the same AERONET sites from two stations managed by the UAE National Centre of Meteorology and Seismology. All ground and satellite measurements were extracted for the closest time to AERONET measurements. The use of these additional inputs has been shown to have a positive impact on the accuracy of simulated AOT. The addition of these inputs to the base ANN increased R ² from 0.68 to 0.76 and reduced root mean square error from 0.113 to 0.09.     

The effect of clumping on canopy scattering and its directional properties: a model simulation using spectral invariants

The spectral invariants theory predicts that the bidirectional reflectance factor (BRF) of a vegetation canopy can be expressed in terms of the canopy interceptance (i₀), the recollision probability (p), and the directional escape probability (ρ). These spectral invariant parameters together form a novel canopy structural parameter – the directional area scattering factor (DASF). The DASF can be retrieved from remotely sensed hyperspectral imagery and has been found to be useful, e.g. for the separation of tree species. The spectral invariants theory, however, does not provide an interpretation of which specific canopy structural properties are captured by the DASF. In this study, we examined the possible link between the DASF and the canopy clumping index (β). A simple model was designed to simulate the effect of β on canopy first order scattering, which was assumed to govern the directional behaviour of the DASF. The model is based on a modified spectral invariants approach, where the assumption of constant p is relaxed so that the first order recollision probability (p₁) and single scattering are calculated separately, and canopy BRF is expressed as the sum of the first and multiple order components. Simulations were performed on model canopies, where radiation penetration is described using a traditional statistical approach but allowing non-random foliage distributions caused by clumping. The results indicated a strong dependency between the modelled DASF and the canopy clumping index.     

Comparison of low brightness temperatures derived from the AVHRR thermal channels with in situ measurements in Antarctica

Data from the National Oceanic and Atmospheric Administration (NOAA) satellites' Advanced Very High Resolution Radiometers (AVHRRs) represent the longest record (more than 25 years) of continuously available satellite‐based thermal measurements, and have well‐chosen spatial and spectral resolutions. As a consequence, these data are used extensively to develop cloud climatologies. However, for such applications, accurate calibration and intercalibration of both solar and thermal channels of the AVHRRs is necessary so as to homogenize the data obtained from the different AVHRR sensors. AVHRR thermal channels 4 and 5 are routinely used in threshold‐based hierarchical decision‐tree cloud detection and classification algorithms, and therefore an evaluation of the stability of these channels at low temperatures is important. In this letter, the AVHRR channel 4 and 5 brightness temperatures (BTs) are compared at five stations in Antarctica. The data for the period of June, July and August (the coldest months of every year and with minimal atmospheric influence) from 1982 to 2006 were used for the evaluations. The calibration and intercalibration of the thermal channels are found to be very robust. The root mean square errors (RMSEs) range from 2.2 to 3.4 K and the correlation coefficients from 0.84 to 0.95. No apparent artefacts or artificial jumps in the BTs are visible in the data series after changes of sensors. The BTs from the thermal channels of the AVHRRs can be used for preparing cloud climatologies, as their intercalibration is found to be consistent across different afternoon satellites.     

Improving the disaggregation of MODIS land surface temperatures in an urban environment: a statistical downscaling approach using high-resolution emissivity

Spatially and temporally dense land surface temperature (LST) data are necessary to capture the high variability of the urban thermal environment. Sensors on board satellites with high revisit time cannot provide adequately detailed spatial information; thus, the downscaling of LST is recognized as being an important and inevitable intermediate process. In this paper, improvement in the downscaled LST accuracy is investigated, employing the statistical downscaling methodology in an urban setting. A new approach is proposed, where thermal radiances are disaggregated using multiple regression analysis and are then combined with emissivity values derived from a high-resolution image classification. Predictors include reflectance values, built-up and vegetation indices, and topographic data. Surface classification is performed utilizing machine learning techniques and fusing Sentinel-2 imagery with ancillary data. Thermal data from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor are downscaled from their original resolution to 100 m in the city of Athens, Greece. Validation of sharpened temperatures is performed using the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) surface temperature product and in-situ measurements. It is demonstrated that the proposed downscaling framework using ridge regression has the potential to produce reliable, high temporal LST estimates with an average error of fewer than 2 K, while consistently having a better accuracy than the reference, single-predictor downscaling of the MODIS LST product.     

Estimation of nuclear power plants influence on the Baltic Sea thermal state by using infrared thermal satellite data

The influence of the activity of nuclear power plants on the state of the environment attracts constant attention from environmental and scientific organizations. The great amount of heated water thrown off permanently into a water basin by nuclear power plants is one of the negative factors, one which seriously disturbs the thermal balance of water basins and leads to irreversible environmental changes. The degree of algae growth in a basin depends on the amount of inflowing biogenic substances, as well as the water temperature of a reservoir. The main aim of this research was to develop the remote method of monitoring the influence of nuclear power plants on the thermal state of water basins and to compare it with different natural inputs of heat. The objects of this research were the operating nuclear power plants of the Baltic Sea rim.     

Influence of the canopy BRDF characteristics and illumination conditions on the retrieval of solar-induced chlorophyll fluorescence

The Fraunhofer line discrimination (FLD) principle is the main approach used for the retrieval of solar-induced chlorophyll fluorescence (SIF). The basic assumption of the FLD principle is that the apparent reflectance spectra without SIF in-filling are smooth in the region of the absorption bands. However, in fact, this assumption is not valid due to the so-called ‘direct radiation in-filling’ effect caused by the nonlinear contribution of direct and diffuse radiation at the oxygen absorption bands, which are widely used for ground-based SIF retrieval. In this study, we first analysed the physical mechanism of the direct radiation in-filling effect on the oxygen absorption bands and found that the bias in the SIF retrieval caused by the direct radiation in-filling effect at the oxygen-A (O₂-A) band was less than 20% based on the use of a simulated data set. Second, we established a simple correction model of the direct radiation in-filling effect. We found that the direct radiation in-filling effect at the O₂-A band was directly proportional to the difference between the reflectance of the direct and diffuse radiation, and that the coefficient of proportionality was well correlated with the diffuse-to-global radiation ratio in the form of a quadratic function, with a coefficient of determination (R²) of 0.97. Finally, the model was validated using both simulated and field data sets. The validation results show that the bias in the SIF retrieval caused by the direct radiation in-filling effect can be efficiently corrected using the model proposed in this article. This study thus provides a possible approach to estimating and correcting for the direct radiation-infilling effect using prior knowledge of the bidirectional reflectance distribution function characteristics of direct and diffuse radiation for specific targets.     

Spectral reflectance and emissivity features of broad leaf plants: Prospects for remote sensing in the thermal infrared (8.0-14.0 μm)

Field emissivity measurements were made of leaves collected from nine deciduous tree and agricultural plant species. The data show, for the first time, that it is possible to discriminate subtle spectral emissivity features of leaves from the natural background emission. Under conditions of controlled measurement geometry (leaves arranged to cover a flat surface), the field emissivity spectra agreed fairly well with emissivity values calculated from laboratory directional hemispherical reflectance measurements. Spectral features associated with a variety of leaf chemical constituents, including cellulose, cutin, xylan, silica, and oleanolic acid could be identified in the field emissivity data. Structural aspects of leaf surfaces also influenced spectral behavior, notably the abundance of trichomes, as well as wax thickness and texture.Field spectral measurements made at increasing distances from natural plant canopies showed progressive attenuation of the spectral emissivity features. This attenuation is ascribed to increased multiple scattering that superimposes an opposite-in-sign reflected component on the emittance, and to the increasing number of canopy voids within the instrument field of view. Errors associated with the removal of atmospheric features and with the non-isotropic thermal characteristics of canopies also contribute to the loss of spectral information at greater measurement distances.In contrast to visible and short-wave infrared data, thermal infrared spectra of broad leaf plants show considerable spectral diversity, suggesting that such data eventually could be utilized to map vegetation composition. However, remotely measuring the subtle emissivity features of leaves still presents major challenges. To be successful, sensors operating in the 8-14 μm atmospheric window must have high signal-to-noise and a small enough instantaneous field of view to allow measurements of only a few leaf surfaces. Methods for atmospheric compensation, temperature-emissivity separation, and spectral feature analysis also will need to be refined to allow the recognition, and perhaps, exploitation of leaf thermal infrared spectral properties.     

A simple,accurate method to compute brightness temperature and/or radiance for the thermal infrared channels of the Advanced Very High Resolution Radiometer (AVHRR)

Users of thermal infrared data from the AVHRR on a NOAA polar-orbiting operational satellite convert the count value output to radiance units, and then assign an equivalent blackbody temperature to the radiance value. Assigning a blackbody temperature to the radiance value is an indirect process, which requires knowledge of the AVHRR spectral response function and a fairly complex calculation. Both difficulties can be avoided by the simple two-step process shown in this Letter. First, blackbody temperature is estimated from a square-root of the measured radiance, then the estimate is refined by values from a ‘universal’ correction curve. The RMS difference between this approximation and the complex calculation is a few hundredths deg K for temperatures in the 200-320 deg K range. The inverse computation, radiance from temperature, is accurate to within 0·01-0·02mWm^?2sr^?1 (cm^?1)^?1. Results are shown for the NOAA-7, -9, -11, and -12 spacecraft.     

Potential of Dubois model for soil moisture retrieval in prairie areas using SAR and optical data

Soil moisture retrieval is often confounded by the influence of vegetation and surface roughness on the backscattered radar signal in vegetated areas. In this study, a semi-empirical methodology is proposed to retrieve soil moisture in prairie areas. The effect of vegetation is eliminated by the ratio vegetation method and water cloud model (WCM), respectively. The conditions of vegetation are characterized by leaf area index (LAI), vegetation water content (VWC), normalized difference vegetation index (NDVI), and enhanced vegetation index (EVI), respectively. To remove the dependence on surface roughness, the dielectric constant is explicitly expressed as the function of co-polarization backscattering coefficients and sensor parameters based on the Dubois model. The ground measurements and satellite data collected from the Ruoergai and Wutumeiren prairies of China allow for validating the feasibility and effectiveness of the proposed methodology. From the perspective of soil moisture retrieval accuracy, the ratio vegetation method performs better than WCM. In the Ruoergai prairie, the best soil moisture retrieval result is obtained when EVI is used, with correlation coefficient (r) and root mean square error (RMSE) of 0.87 and 3.50 vol.%, respectively. While in the Wutumeiren prairie, the lowest retrieval error is obtained when LAI is used, with r and RMSE values of 0.79 and 5.73 vol.%, respectively. These results demonstrate that the Dubois model has a potential for enhancing soil moisture retrieval in prairie areas using synthetic aperture radar (SAR) and optical data.     

Application of high-resolution thermal infrared remote sensing and GIS to assess the urban heat island effect

Day and night airborne thermal infrared image data at 5 m spatial resolution acquired with the 15-channel (0.45mum-12.2mum) Advanced Thermal and Land Applications Sensor (ATLAS) over Alabama, Huntsville on 7 September, 1994 were used to study changes in the thermal signatures of urban land cover types between day and night. Thermal channel number 13 (9.60 mum-10.2mum) data with the best noise-equivalent temperature change (NEDeltaT) of 0.25 C after atmospheric corrections and temperature calibration were selected for use in this analysis. This research also examined the relation between land cover irradiance and vegetation amount, using the Normalized Difference Vegetation Index (NDVI), obtained by ratioing the difference and the sum of the red (channel number 3: 0.60-0.63mum) and reflected infrared (channel number 6: 0.76-0.90mum) ATLAS data. Based on the mean radiance values, standard deviations, and NDVI extracted from 351 pairs of polygons of day and night channel number 13 images for the city of Huntsville, a spatial model of warming and cooling characteristics of commercial, residential, agricultural, vegetation, and water features was developed using a GIS approach. There is a strong negative correlation between NDVI and irradiance of residential, agricultural, and vacant/transitional land cover types, indicating that the irradiance of a land cover type is greatly influenced by the amount of vegetation present. The predominance of forests, agricultural, and residential uses associated with varying degrees of tree cover showed great contrasts with commercial and services land cover types in the centre of the city, and favours the development of urban heat islands. The high-resolution thermal infrared images match the complexity of the urban environment, and are capable of characterizing accurately the urban land cover types for the spatial modeling of the urban heat island effect using a GIS approach.