Effect of surface roughness,wavelength, illumination,and viewing zenith angles on soil surface BRDF using an imaging BRDF approach

Compared to non-imaging instruments, imaging spectrometers (ISs) can provide detailed information to investigate the influence of scene components on the bidirectional reflectance distribution function (BRDF) of a mixed target. The research reported in this article investigated soil surface reflectance changes as a function of scene components (i.e. illuminated pixels and shaded pixels), illumination and viewing zenith angles, and wavelength. Image-based BRDF data of both rough and smooth soil surfaces were acquired in a laboratory setting at three different illumination zenith angles and at four different viewing zenith angles over the full 360° azimuth range, at an interval of 20°, using a Specim V10E IS (Specim, Spectral Imaging Ltd., Oulu, Finland) mounted on the University of Lethbridge Goniometer System version 2.5 (ULGS-2.5). The BRDF of the smooth soil surface was dominated by illuminated pixels, whereas the shaded pixels were a larger component of the BRDF of the rough soil surface. As the illumination zenith angle was changed from 60° to 45° and then to 30°, the shadowing effect decreased, regardless of the soil surface. Soil surface reflectance was generally higher at the backscattering view zenith angles and decreased continuously to forward scattering view zenith angles in the light principal plane, regardless of the wavelength, due to the Specim V10E IS seeing more illuminated pixels in the backscattering angles than in the forward scattering angles. Higher soil surface reflectance was observed at higher illumination and viewing zenith angle combinations. For both soil surface roughness categories, the BRDF exhibited a greater range of values in the near-infrared than at the visible wavelengths. This research enhances our understanding of soil BRDF for various soil roughness and illumination conditions.     

Impact of surface heterogeneity on surface soil moisture retrievals from passive microwave data at the regional scale: The Upper Danube case

Water and energy fluxes at the interface between the land surface and atmosphere are strongly depending on the surface soil moisture content which is highly variable in space and time. The sensitivity of active and passive microwave remote sensing data to surface soil moisture content has been investigated in numerous studies. Recent satellite borne mission concepts, as e.g. the SMOS mission, are dedicated to provide global soil moisture information with a temporal frequency of 1-3 days to capture it's high temporal dynamics. Passive satellite microwave sensors have spatial resolutions in the order of tens of kilometres. The retrieved soil moisture fields from that sensors therefore represent surface information which is integrated over large areas. It has been shown that the heterogeneity within an image pixel might have considerable impact on the accuracy of soil moisture retrievals from passive microwave data.The paper investigates the impact of land surface heterogeneity on soil moisture retrievals from L-band passive microwave data at different spatial scales between 1 km and 40 km. The impact of sensor noise and quality of ancillary information is explicitly considered. A synthetic study is conducted where brightness temperature observations are generated using simulated land surface conditions. Soil moisture information is retrieved from these simulated observations using an iterative approach based on multiangular observations of brightness temperature. The soil moisture retrieval uncertainties resulting from the heterogeneity within the image pixels as well as the uncertainties in the a priori knowledge of surface temperature data and due to sensor noise, is investigated at different spatial scales. The investigations are made for a heterogeneous hydrological catchment in Southern Germany (Upper Danube) which is dedicated to serve as a calibration and validation site for the SMOS mission.     

Characterizing land surface anisotropy from AVHRR data at a global scale using high performance computing

We used the multi-temporal ten-day composite data from the Advanced Very High Resolution Radiometer (AVHRR) for the years 1983 to 1986 to retrieve the Bidirectional Reflectance Distribution Function (BRDF) using high performance computing techniques. Three different models are used: a simple linear model, a semi-empirical iterative model and a temporal model. The objectives of this study were to compare the performance of different BRDF models at a global scale, assess the computational requirements and optimize the algorithm implementation using high performance computational techniques, and to determine if there is any coherent spatial structure in the coefficients of different BRDF models corresponding to different land cover types. The standard error between model computed reflectances and the input data was used to quantify the performance of the models. Even though the iterative model is computationally more expensive (158 minutes) than either the simple linear model (15 minutes) or the temporal model (16 minutes), the results from all the three models were very similar when the BRDF was estimated at discrete time periods. If the BRDF models were applied without dividing the input data into discrete time intervals, then the temporal model gave better results than the other two. All the models were run on an IBM SP2 parallel machine with 16 CPUs. Most of the mountainous and snow covered areas in high latitudes had null values since the cloud screening algorithm used in the Pathfinder processing performed poorly in distinguishing between snow and clouds. The BRDF coefficients of the iterative model and the Fourier coefficients of the temporal model showed a strong spatial structure corresponding to known variations in land cover.     

SEBASS hyperspectral thermal infrared data: surface emissivity measurement and mineral mapping

This study focuses on mapping surface minerals using a new hyperspectral thermal infrared (TIR) sensor: the spatially enhanced broadband array spectrograph system (SEBASS). SEBASS measures radiance in 128 contiguous spectral channels in the 7.5- to 13.5-μm region with a ground spatial resolution of 2 m. In September 1999, three SEBASS flight lines were acquired over Virginia City and Steamboat Springs, Nevada. At-sensor data were corrected for atmospheric effects using an empirical method that derives the atmospheric characteristics from the scene itself, rather than relying on a predicted model. The apparent surface radiance data were reduced to surface emissivity using an emissivity normalization technique to remove the effects of temperature. Mineral maps were created with a pixel classification routine based on matching instrument- and laboratory-measured emissivity spectra, similar to methods used for other hyperspectral data sets (e.g. AVIRIS). Linear mixtures of library spectra match SEBASS spectra reasonably well, and silicate and sulfate minerals mapped remotely, agree with the dominant minerals identified with laboratory X-ray powder diffraction and spectroscopic analyses of field samples. Though improvements in instrument calibration, atmospheric correction, and information extraction would improve the ability to map more pixels, these hyperspectral TIR data nevertheless show significant advancement over multispectral thermal imaging by mapping surface materials and lithologic units with subtle spectral differences in mineralogy.     

Carbon dioxide of Pu`u`O`o volcanic plume at Kilauea retrieved by AVIRIS hyperspectral data

A remote sensing approach permits for the first time the derivation of a map of the carbon dioxide concentration in a volcanic plume. The airborne imaging remote sensing overcomes the typical difficulties associated with the ground measurements and permits rapid and large views of the volcanic processes together with the measurements of volatile components exolving from craters. Hyperspectral images in the infrared range (1900-2100 nm), where carbon dioxide absorption lines are present, have been used. These images were acquired during an airborne campaign by the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) over the Pu`u` O`o Vent situated at the Kilauea East Rift zone, Hawaii. Using a radiative transfer model to simulate the measured up-welling spectral radiance and by applying the newly developed mapping technique, the carbon dioxide concentration map of the Pu`u` O`o Vent plume were obtained. The carbon dioxide integrated flux rate were calculated and a mean value of 396 ± 138 t d^− 1 was obtained. This result is in agreement, within the measurements errors, with those of the ground measurements taken during the airborne campaign.     

Soil moisture estimation using land surface temperature and soil temperature at 5 cm depth

Soil Temperature (ST) data, obtained from either field works or satellite imagery, has frequently been studied for Soil Moisture (SM) estimation. However, a combination of ST data at different depths and soil surface temperature, i.e., Surface Radiometric Temperature (SRT) or Land Surface Temperature (LST), has not yet been well investigated for accurate SM prediction. In this study, an empirical model was first developed to estimate SM at 5 cm Depth (SM_5D) over areas with no or sparse vegetation cover using the field SRT and field ST data at 5 cm Depth (ST_5D). A Root Mean Square Error (RMSE) and a correlation coefficient (r) of 0.037 m³ m^?3 and 0.8 were obtained using this model, respectively. Then, the SRT was substituted by the LST obtained from Landsat thermal bands and ST_5D was estimated using the ST data collected at the nearest weather station to the study area by developing a regression equation. The second model demonstrated an RMSE and r of 0.035 m³ m^?3 and 0.71, respectively. Overall, it was concluded that the proposed models had high potential for SM estimation using the ST data at different depths collected in the field or acquired by optical satellites.     

Estimating spatial variations in soil organic carbon using satellite hyperspectral data and map algebra

This study evaluated the effectiveness of using Hyperion hyperspectral data in improving existing remote-sensing methodologies for estimating soil organic carbon (SOC) content on farmland. The study area is Big Creek Watershed in Southern Illinois, USA. Several data-mining techniques were tested to calibrate and validate models that could be used for predicting SOC content using Hyperion bands as predictors. A combined model of stepwise regression followed by a five hidden nodes artificial neural network was selected as the best model, with a calibration coefficient of determination (R ²) of 78.9% and a root mean square error (RMSE) of 3.3 tonnes per hectare (t ha^?1). The validation RMSE, however, was found to be 11.3 t ha^?1. Map algebra was implemented to extrapolate this model and produce a SOC map for the watershed. Hyperspectral data improved marginally the predictability of SOC compared to multispectral data under natural field conditions. They could not capture small annual variations in SOC, but could measure decadal variations with moderate error. Satellite-based hyperspectral data combined with map algebra can measure total SOC pools in various ecosystem or soil types to within a few per cent error.     

Improving hyperspectral data classification by fusing band and wavelet features

The discrete wavelet transform (DWT) provides a multiresolution decomposition of hyperspectral data. Wavelet features of each level are downsampled from the band features. Fine-scale and large-scale information from hyperspectral signals can be separated and this method might provide specific discriminant capability compared to using band features alone. This article proposes using a combination of band and wavelet features (BWFs) in the stacked support vector machine (SSVM), where each feature set is solved independently by level-0 support vector machines (SVMs), and level-1 SVMs are used to correct the errors of level-0 SVMs and obtain the final classification result. The effectiveness of the proposed method was examined using two benchmark hyperspectral data sets collected over forest and urban areas, respectively. For both data sets, the proposed method significantly outperformed SVMs using band features, wavelet energy features (WEFs), wavelet concatenated features (WFs concatenated), and both BWFs and the SSVM using only WFs.     

Retrieval of atmospheric and land surface parameters from satellite-based thermal infrared hyperspectral data using a neural network technique

Land surface temperature (LST), land surface emissivity (LSE), and atmospheric profiles are of great importance in many applications. Radiances observed by satellites depend not only on land surface parameters (LST and LSE) but also on atmospheric conditions, and it is difficult to retrieve these parameters simultaneously from multispectral measurements with high accuracies. This work aims to establish a neural network (NN) to retrieve atmospheric profiles, LST, and LSE simultaneously from hyperspectral thermal infrared data suitable for various air mass types and surface conditions. The distributions of surface materials, LST, and atmospheric profiles were elaborated carefully to generate the simulated data. The simulated at-sensor radiances were divided into two sub-ranges in the spectral domain: one in the atmospheric window and the other in the water absorption band. Subsequently, the radiances were transformed in the eigen-domain in each sub-range, and then the transformed coefficients were used as the inputs for the network. Similarly, the atmospheric profiles, LST, and LSE were used as outputs after the eigen-domain transformation. The validation of the NN using the simulated data indicated that the root mean square error (RMSE) of LST is approximately 1.6 K, and the RMSE of the temperature profiles is approximately 2 K in the troposphere. Meanwhile, the RMSE of total water content is approximately 0.3 g cm^?2, and that of LSE is less than 0.01 in the spectral interval where the wave number is less than 1000 cm^?1. Two experiments using actual thermal hyperspectral satellite data were carried out to further validate the proposed NN. All of these studies showed that the proposed NN is capable of retrieving atmospheric and land surface parameters with compromised accuracies. Because of its simplicity, the proposed NN can be used to yield preliminary results employed as first estimates for physics-based retrieval models.     

Rapid determination of soil organic matter content based on soil colour obtained by a digital camera

ABSTRACT

Soil organic matter (SOM) is an important component of soil and a significant criterion in determining the dynamics of soil quality. A rapid, low-cost method to measure SOM content is needed to support the development of precision agriculture. This article studied the quantitative relationship between SOM and soil colour using a digital camera, which is relatively inexpensive and easy to operate, as a portable tool for obtaining colour information of the soil surface. The results show that mixed samples with different soil particle sizes reduce the noise of the image and are more suitable than uniform soil samples for predicting the SOM. Among the three bands of red, green, and blue (RGB), the red band had the best correlation with SOM, and its reciprocal correlation coefficient (r) reached 0.75. The reciprocal regression model of the RGB colour model provided good prediction results for mixed soil samples, with a coefficient of determination (R²) of 0.76 and a root mean square error (RMSE) of 0.55, and the validation result had an excellent predictive ability (R²_val = 0.85 and RMSE_val = 0.53). The single-variation predictive model of CIELa*b* colour space model through transformation of the RGB colour space model performed well. The model built by colour intensity values had a strong stability and forecasting capacity. Thus, a digital camera can be used as an alternative tool to rapidly measure SOM.     

Statistical analysis of soil colour and spectroradiometric data for hyperspectral remote sensing of soil properties (example in a southern Italy Mediterranean ecosystem)

Munsell hue, value and chroma of 69 surface soil samples were both visually estimated by four observers under diffuse daylight and computed from laboratory reflectance spectra by applying the CIE 1931 standard method. Significant relationships were found between 'observed' and 'computed' colour components, and between the latter and some soil properties. Using a correspondence analysis, soil colour was shown to be important in differentiating between soil types. From the original spectra, the visible bands of the MIVIS hyperspectral sensor were simulated and related to the colour components through single and multiple regression analyses. The R² values for hue, value and chroma were 0.58, 0.81 and 0.87 respectively. The results were compared with those obtained using simulated visible Thematic Mapper (TM) bands. For each sample, a curve was fitted to both the MIVIS and TM bands. From these curves, values of colour components were computed and compared with those obtained from the original spectra. Results showed a clear improvement in colour determination. Nevertheless, the complexity and variability of the best fitting curves makes this approach difficult to apply to the images. Remote sensing of soil colour is expected to improve with future launches of higher resolution hyperspectral sensors.     

Geological classification of Volcano Teide by hyperspectral and multispectral satellite data

Tenerife is the central island of the Canary Archipelago (Spain), which consists of seven islands that represent different stages of geological evolution. The Teide-Pico Viejo (28° 16′ 30′′ N, 16° 38′ 42′′ W) stratocones formed during the last eruptive phase of the isle of Tenerife. It is an active, though currently quiescent, shield volcano that last erupted in 1909 and is located on the Tenerife Island. In the framework of the European Project FP6 Prevention, Information and Early Warning (PREVIEW)-EURORISK (http://www.preview-risk.com/), a field campaign was performed on Tenerife Island on September 2007. This campaign focused on the acquisition of in situ reflectance and emissivity spectra relative to Pico de Teide and Las Cañadas Caldera. The collected spectra represented the ‘ground truth’ and have been used for the supervised classification on multispectral (Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER)) and hyperspectral (Earth Observing 1 (EO1)-Hyperion) data. The first ever classification mapping carried out on the Teide Volcano by utilizing the remote-sensing method is reported in this article. The methods used to process and to classify the data are discussed, and a comparison with the existing geological maps is presented.     

Coupled soil-leaf-canopy and atmosphere radiative transfer modeling to simulate hyperspectral multi-angular surface reflectance and TOA radiance data

Coupling radiative transfer models for the soil background and vegetation canopy layers is facilitated by means of the four-stream flux interaction concept and use of the adding method. Also the coupling to a state-of-the-art atmospheric radiative transfer model like MODTRAN4 can be established in this way, thus enabling the realistic simulation of top-of-atmosphere radiances detected by space-borne remote sensing instruments. Possible applications of coupled modeling vary from mission design to parameter retrieval and data assimilation. This paper introduces a modified Hapke soil BRDF model, a robust version of the PROSPECT leaf model, and a modernized canopy radiative transfer model called 4SAIL2. The latter is a hybrid two-layer version of SAIL accommodating horizontal and vertical heterogeneities, featuring improved modeling of the hot spot effect and output of canopy absorptances. The integrated model is simply called SLC (soil-leaf-canopy) and has been implemented as a speed-optimized Windows DLL which allows efficient use of computer resources even when simulating massive amounts of hyperspectral multi-angular observations. In this paper various examples of possible model output are shown, including simulated satellite image products. First validation results have been obtained from atmospherically corrected hyperspectral multi-angular CHRIS-PROBA data of the Upper Rhine Valley in Germany.     

Correlation between soil apparent electroconductivity and plant hyperspectral reflectance in a managed wetland

The apparent electrical conductivity (σ_a) of soil is influenced by a complex combination of soil physical and chemical properties. For this reason, σ_a is proposed as an indicator of plant stress and potential community structure changes in an alkaline wetland setting. However, assessing soil σ_a is relatively laborious and difficult to accomplish over large wetland areas. This work examines the feasibility of using the hyperspectral reflectance of the vegetation canopy to characterize the σ_a of the underlying substrate in a study conducted in a Central California managed wetland. σ_a determined by electromagnetic (EM) inductance was tested for correlation with in-situ hyperspectral reflectance measurements, focusing on a key waterfowl forage species, swamp timothy (Crypsis schoenoides). Three typical hyperspectral indices, individual narrow-band reflectance, first-derivative reflectance and a narrow-band normalized difference spectral index (NDSI), were developed and related to soil σ_a using univariate regression models. The coefficient of determination (R ²) was used to determine optimal models for predicting σ_a, with the highest value of R ² at 2206 nm for the individual narrow bands (R ²?=?0.56), 462 nm for the first-derivative reflectance (R ²?=?0.59), and 1549 and 2205 nm for the narrow-band NDSI (R ²?=?0.57). The root mean squared error (RMSE) and relative root mean squared error (RRMSE) were computed using leave-one-out cross-validation (LOOCV) for accuracy assessment. The results demonstrate that the three indices tested are valid for estimating σ_a, with the first-derivative reflectance performing better (RMSE?=?30.3 mS m^?1, RRMSE?=?16.1%) than the individual narrow-band reflectance (RMSE?=?32.3 mS m^?1, RRMSE?=?17.1%) and the narrow-band NDSI (RMSE?=?31.5 mS m^?1, RRMSE?=?16.7%). The results presented in this paper demonstrate the feasibility of linking plant–soil σ_a interactions using hyperspectral indices based on in-situ spectral measurements.     

Enhanced age estimation by considering the areas of non-skin and the non-uniform illumination of visible light camera sensor

Most previous research on human age estimation based on the detection of multiple feature points using the active appearance model (AAM) method. However, it is difficult to use the AAM-based methods in actual applications, because their performance is strongly affected by image backgrounds, head movements, and non-uniform facial region illumination. Furthermore, they require significant processing time. Other age estimation methods based on a detected face box area may be considered as an alternative; however, noise areas that include hair, backgrounds, and non-uniform illumination of visible light camera sensor may be inadvertently included in the face box, which reduces age estimation accuracy. Therefore, we propose a new age estimation method that is robust to these noise areas. Our proposed method is novel in following four ways. First, we propose an age estimation method using a weighted multi-level local binary pattern (wMLBP) based on a fuzzy-logic system. Second, two input values (the difference between the mean gray levels of the sub-block and the central area of the face, and the distance from the sub-block to the center of the facial region) are determined considering the noise areas of hair, background, and non-uniform illumination of visible light camera sensor. Then, the optimal weights are determined using a fuzzy-logic system with these two input values, which does not require a time-consuming training process. Third, by assigning an optimal weight to the histogram features extracted by the MLBP method in each sub-block, age estimation accuracy is enhanced. Finally, the age is estimated using a SVR method based on a combination of weighted MLBP features and Gabor wavelet features. Experimental results obtained using the public PAL and MORPH age databases demonstrate that the accuracy of our method is superior to other previous methods.     

Separating a color signal into illumination and surface reflectancecomponents: theory and applications

A separation algorithm for achieving color constancy and theorems concerning its accuracy are presented. The algorithm requires extra information, over and above the usual three values mapping human cone responses, from the optical system. However, with this additional information-specifically, a sampling across the visible range of the reflected, color-signal spectrum impinging on the optical sensor-the authors are able to separate the illumination spectrum from the surface reflectance spectrum contained in the color-signal spectrum which is, of course, the product of these two spectra. At the heart of the separation algorithm is a general statistical method for finding the best illumination and reflectance spectra, within a space represented by finite-dimensional linear models of statistically typical spectra, whose product closely corresponds to the spectrum of the actual color signal. Using this method, the authors are able to increase the dimensionality of the finite-dimensional linear model for surfaces to a realistic value. One method of generating the spectral samples required for the separation algorithm is to use the chromatic aberration effects of a lens. An example of this is given. The accuracy achieved in a large range of tests is detailed, and it is shown that agreement with actual surface reflectance is excellent     

Testing LiSK BRDF models over a semi-arid grassland region with visible and near-infrared ATSR-2 and AVHRR data

This paper assesses the capability of the Roujean and LiSparse-MODIS-RossThin linear semi-empirical kernel-driven (LiSK) bidirectional reflectance distribution function (BRDF) models to predict bidirectional reflectance at geometries other than those of the observations used to invert the model, when the models are inverted against a sparse set of angular samples from 21 orbits (3-19 August 1996) of the operational Advanced Very High Resolution Radiometers (AVHRRs) on NOAA TIROS series AM (morning) and PM (evening) satellites. Red ('visible') and near-infrared (NIR) spectral reflectance estimates acquired at 4:40 GMT on 14 August 1996 by the Along-Track Scanning Radiometer-2 (ATSR-2) sensor flown on the European Space Agency's ERS-2 satellite are used as reference data. The test area is a semi-arid grassland region in Inner Mongolia, P.R. China, bounded by 42.84°-44.71° N and 112.40°-116.05° E. The results show that in spite of the difficulties posed by such a task, LiSK models can be inverted against multiangular AVHRR observations to predict bidirectional reflectance at the acquisition geometry of the ATSR-2 with reasonable accuracy: the rms. error of the reflectance predictions made by both models is less than 4% for the nadir views and less than or equal to 6% in the forward views. These error values are less than one-half those provided by a 13 August 1996 AM AVHRR scene in the 0.65 w m channel and about one-seventh of those for the AVHRR scene in the 0.87 w m (NIR) channel, in both nadir and forward views.     

Forecast of NDVI in coniferous areas using temporal ARIMA analysis and climatic data at a regional scale

Important issues such as the prediction of drought, fire risk and forest disease are based on analysis of forest vegetation response. A method of forecasting the short-term response of forest vegetation on the basis of an autoregressive integrated moving average (ARIMA) analysis was designed in this study. We used 10-day maximum value composite (MVC) bands of the Normalized Difference Vegetation Index (NDVI) obtained from National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) data from 1993 to 1997. Using the theory of stochastic processes (Box–Jenkins), the MVC-NDVI series was analysed and a seasonal ARIMA (SARIMA) model was developed for forecasting NDVI in the following 10-day periods. The SARIMA model identified a moving-average regular term with a 10-day lag and an autoregressive 37 10-day period seasonal term with a one-season (1-year) component. The study also demonstrated a slight relationship between the NDVI and the precipitation level in some species of conifers by using climatic time series and the analysis of dynamic models and allowed us to elaborate an image of the immediate future NDVI for the study area (Castile and Leon, Spain).     

Mapping surface roughness and soil moisture using multi-angle radar imagery without ancillary data

The Integral Equation Model (IEM) is the most widely-used, physically based radar backscatter model for sparsely vegetated landscapes. In general, IEM quantifies the magnitude of backscattering as a function of moisture content and surface roughness, which are unknown, and the known radar configurations. Estimating surface roughness or soil moisture by solving the IEM with two unknowns is a classic example of under-determination and is at the core of the problems associated with the use of radar imagery coupled with IEM-like models. This study offers a solution strategy to this problem by the use of multi-angle radar images, and thus provides estimates of roughness and soil moisture without the use of ancillary field data. Results showed that radar images can provide estimates of surface soil moisture at the watershed scale with good accuracy. Results at the field scale were less accurate, likely due to the influence of image speckle. Results also showed that subsurface roughness caused by rock fragments in the study sites caused error in conventional applications of IEM based on field measurements, but was minimized by using the multi-angle approach.