Semi-empirical approach for estimating broadband albedo of snow

Snow is a medium that exhibits highly anisotropic reflectance throughout the solar spectrum. The anisotropic nature of snow shows more variability in snow metamorphic processes for wavelengths beyond 1.0 μm than in the visible spectrum. This behavior poses challenges for the development of a model that can retrieve broadband albedo from reflectance measurements throughout the snow season. In this paper, a semi-empirical model is presented to estimate near infrared (0.8-2.5 μm) albedo of snow. This model estimates spectral albedo at a wavelength of 1.240 μm using only three variables: solar zenith angle, scattering angle and measured reflectance, which is used to retrieve near infrared albedo. To form a base for such a model, quantification of reflectance patterns and variability in varying snow condition, i.e. snow grain size, and sun-sensor geometry are prerequisite. In this study the DIScrete Ordinate Radiative Transfer (DISORT) model is used to simulate bi-directional reflectance. The performance of the developed model is evaluated by using DISORT simulated spectral albedo for various snow grain sizes and solar zenith angles, as well as the Moderate Resolution Imaging Spectroradiometer (MODIS) and in-situ measurements. The developed model is shown to be capable of estimating spectral albedo at 1.240 μm with acceptable accuracy. The mean error (ME), mean absolute error (MAE), and root mean squared error (RMSE) in the estimates are found to be 0.053, 0.055 and 0.064, respectively, for a wide range of sun-sensor geometries and snow grain sizes. The model shows better accuracy for spectral albedo estimates than for those computed using the Lambertian reflectance assumption for snow, reducing the error in the range and standard deviation by 75% and 65%, respectively. Applying the model to MODIS, the retrieved albedo is found to be in good quantitative agreement (r = 0.82) with in-situ measurements. These improvements in albedo estimation should allow more accurate use of remote sensing measurements in climate and hydrological models.     

Distribution of hydrothermally altered rocks in the Reko Diq, Pakistan mineralized area based on spectral analysis of ASTER data

The Reko Diq, Pakistan mineralized study area, approximately 10 km in diameter, is underlain by a central zone of hydrothermally altered rocks associated with Cu-Au mineralization. The surrounding country rocks are a variable mixture of unaltered volcanic rocks, fluvial deposits, and eolian quartz sand. Analysis of 15-band Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data of the study area, aided by laboratory spectral reflectance and spectral emittance measurements of field samples, shows that phyllically altered rocks are laterally extensive, and contain localized areas of argillically altered rocks.In the visible through shortwave-infrared (VNIR + SWIR) phyllically altered rocks are characterized by Al-OH absorption in ASTER band 6 because of molecular vibrations in muscovite, whereas argillically altered rocks have an absorption feature in band 5 resulting from alunite. Propylitically altered rocks form a peripheral zone and are present in scattered exposures within the main altered area. Chlorite and muscovite cause distinctive absorption features at 2.33 and 2.20 μm, respectively, although less intense 2.33 μm absorption is also present in image spectra of country rocks.Important complementary lithologic information was derived by analysis of the spectral emittance data in the 5 thermal-infrared (TIR) bands. Silicified rocks were not distinguished in the 9 VNIR + SWIR bands because of the lack of diagnostic spectral absorption features in quartz in this wavelength region. Quartz-bearing surficial deposits, as well as hydrothermally silicified rocks, were mapped in the TIR bands by using a band 13/band 12 ratio image, which is sensitive to the intensity of the quartz reststrahlen feature. Improved distinction between the quartzose surficial deposits and silicified bedrock was achieved by using matched-filter processing with TIR image spectra for reference.     

An empirical anisotropy correction model for estimating land surface albedo for radiation budget studies

Land surface albedo is one of the key parameters in the radiation budget, the hydrological cycle and climate modeling studies. It is now widely understood that large errors may occur in the estimation of surface albedo without taking into consideration the anisotropy reflectance effect, which is a general feature of the earth surface. Two major anisotropic correction methods exist for the retrieval of land surface albedo under clear sky conditions. One method involves linearly converting from top of the atmosphere (TOA) albedo to surface albedo, and another is based on the inversion of the Bidirectional Reflectance Distribution Function (BRDF) model of the surface. In the present study, a new approach that utilizes an empirical model for estimating surface albedo has been proposed for snow free land surfaces under clear sky conditions. We analyzed the bidirectional reflectance data set with numerous samples representing various land cover types, which derived from POLDER/ADEOS-1 multi-angle imagery data and distributed by MEDIAS-France. Through the analysis, an empirical relation between bidirectional reflectance and albedo was established and has been discussed in detail. The proposed model can be used for direct estimation of surface albedo from a single BRF observation when the sun-target-sensor geometry is known. No BRDF model inversion scheme is necessary. The present model has no or weak dependence on the existing land surface classifications, and is insensitive to wavelength. The theoretical absolute accuracy of the estimated albedo is approximately 0.010 for visible (0.4-0.7 μm), 0.023 for near infrared (0.7-3.0 μm) and 0.016 for shortwave (0.2-3.0 μm), respectively. Albedo consistency with viewing geometry has been verified, resulting in good agreement for albedo estimated from various viewing directions. Validation of the satellite estimated albedo derived by the proposed method, using field observations were also presented and results show it can give reasonably accurate estimation. The proposed method is expected to be a suitable candidate for practical applications of albedo estimation for sensors that do not perform multi-angle observations.     

Mapping the degree of serpentinization within ultramafic rock bodies using imaging spectrometer data

In 0·4-2·5 μm reflectance spectra of serpentinized peridotites and synthetic olivine-serpentine-magnetite mineral mixtures, serpentinization is responsible for a decrease in contrast of olivine-pyroxene iron absorption features and an appearance and increase in OH^? absorption features near 1·4 μm and 2·3 μm. It is demonstrated that the degree of serpentinization is correlated positively with the depth of the 2·3 μm absorption feature, although small amounts of magnetite may obscure the spectral contrast and decrease the overall brightness of weakly serpentinized samples. This linear relationship is applied to map the degree of serpentinization from GER 63-channel imaging spectrometer data using the following methodology: (1) vegetation masking, (2) calculating the absorption band-depth of the 2·3 μm absorption feature, (3) translating this value into percentage serpentine-group minerals using an empirical linear model, and (4) estimating the degree of serpentinization at the remaining locations using conditional simulation techniques. Comparison or the results of the simulation with 49 field samples showed differences between + 33 per cent and ? 23 per cent serpentine-group minerals estimated.     

Quantitative mapping of arid alluvial fan surfaces using field spectrometer and hyperspectral remote sensing

Mapping and dating of arid and semi-arid alluvial fans are of great importance in many Quaternary studies. Yet the most common mapping method of these features is based on visual, qualitative interpretation of air-photos. In this study we examine the feasibility of mapping arid alluvial surfaces by using airborne hyperspectral reflective remote sensing methodology. This technique was tested on Late Pleistocene to Holocene alluvial fan surfaces located in the hyperarid southern Arava valley, Israel. Results of spectral field measurements showed that the surface reflectance is controlled by two main surficial processes, which are used as relative age criteria: the degree of desert pavement development (gravel coverage %) controls the absorption feature depths, while the rock coating development influences significantly the overall reflectance of the surface, but its effect on the absorption feature depths is limited. We show that as the percent of the surface covered by gravels increases, the absorption feature depth of the common gravels, in this case carbonate at 2.33 μm, increases as well; whereas the absorption features depth of the fine particle in-between the gravels, decrease (hydroxyl and ferric absorption features at 2.21 μm, and 0.87 μm, respectively), as the fines are removed from the surface. Using these correlations we were able to map the surface gravel coverage (%) on the entire alluvial fan, by calculating the gravel coverage (%) in each pixel of the hyperspectral image. The prediction of gravel coverage (%) is with accuracy of ± 15% (e.g. gravel coverage of 50% can be predicted to be 35% to 65%). Using extensive accuracy assessment data, we show that the spectral based mapping maintained high accuracy degree (R² = 0.57 to 0.83). The quantitative methodology developed in this study for mapping alluvial surfaces can be adapted for other surfaces and piedmonts throughout the arid regions of the world.     

Imaging spectroscopy of jarosite cement in the Jurassic Navajo Sandstone

Imaging spectroscopy is a powerful tool for mapping surface mineralogy. Interactions of energy and surface materials on atomic and molecular levels result in specific absorption features that are diagnostic of mineralogy. Hyperspectral airborne sensors such as HyMap have sufficient spectral resolution to identify subtle features over narrow wavelength ranges. An anomalous zone of jarosite cement within the Jurassic Navajo Sandstone in southern Utah was identified with airborne hyperspectral data. To date no other locations of sulfate-rich cements such as this have been documented in the Navajo Sandstone. Here, we use multiple spectral analysis techniques over a micro to macro spatial continuum in order to focus our evaluation on the distribution and relative abundance of jarosite in this area. In this study, imaging spectroscopy is used to help determine the extent of the distribution of jarosite [KFe₃(SO₄)₂(OH)₆], at “Mollie's Nipple” (MN), a significant geomorphic landmark located within the Grand Staircase-Escalante National Monument, in the Jurassic Navajo Sandstone of south central Utah. The extent of the jarosite across the butte is identified in this study by mapping two diagnostic absorption features, referred to as the ∼ 2.26 μm and near-infrared ferric iron absorption features (∼ 0.70-1.15 μm), independently. This analysis shows that there is a single circular zone of abundant jarosite ∼ 1 km in diameter that includes both in-situ and weathered out accumulations. Jarosite formation requires acidic and sulfate-rich fluids, which are unusual for the Navajo Sandstone. Imaging and field spectroscopy provides the spectral resolution needed to map and analyze the mineralogic characteristics of this area; characteristics that may help constrain the conditions under which this atypical butte formed.     

Novel tunable laser sources with 1.5-μm and 1.57-μm cascaded DFB reflectors for in situ gas monitoring applications

Novel tunable lasers based on 1.5-μm and 1.57-μm cascaded distributed-feedback reflectors are realized for real-time monitoring of H₂O and CO gas mixtures immediately in multi-gas sensor systems. With simple fabrication procedures, the new design allows the realization of a widely tunable laser source that can cover the H₂O and CO absorption wavelength bands. With the temperature tuning of 0.1 nm/°C and current tuning of 0.014 nm/mA, the laser can be tuned to cover over 3 nm wavelength range in each wavelength band. Experiments verify that the lasers can have more than 38 dB SMSR over the tuning range. The characteristics of high power, excellent spectral purity, and simple wavelength switching control can simplify the analysis procedures of gas sensing and thus reduce the cost. By direct absorption method, the tunable laser has been successfully adopted in a diode laser sensor system for monitoring of water vapor concentration near 1.5 μm and carbon monoxide near 1.57 μm. Less than 15% error in the line strength is observed between the measured data and HITRAN database.     

A dual functional near infrared fluorescent probe based on the bodipy fluorophores for selective detection of copper and aluminum ions

A new near infrared (NIR) fluorescent 4,4-Difluoro-4-bora-3a,4a-diaza-s-indacene (BODIPY) dye with dual functionality was synthesized and characterized. The compound 1 responds to copper ion in NIR region with high selectivity through a photo-induced electron transfer process established between the substituted benzene group in the meso position and the BODIPY core when Cu²⁺ binds with the four oxygen atoms in the structure, and results in the quenching of the fluorescence. The response range to copper ions was from 10 to 50 μM, and other metal ions including Li⁺, Na⁺, K⁺, Mg²⁺, Ca²⁺, Pb²⁺, Fe³⁺, Ag⁺, Hg²⁺, Co²⁺, Zn²⁺, Mn²⁺, Cd²⁺, Ni²⁺ and Al³⁺ had no interference. When excited at 520 nm, a new emission peak at 568 nm of compound 1 was used to detect Al³⁺ selectively from 30 μM to 110 μM without any interference from other metal ions including copper ions.     

Modeling directional-hemispherical reflectance and transmittance of fresh and dry leaves from 0.4 μm to 5.7 μm with the PROSPECT-VISIR model

Vegetation water content retrieval using passive remote sensing techniques in the 0.4-2.5 μm region (reflection of solar radiation) and the 8-14 μm region (emission of thermal radiation) has given rise to an abundant literature. The wavelength range in between, where the main water absorption bands are located, has surprisingly received very little attention because of the complexity of the radiometric signal that mixes both reflected and emitted fluxes. Nevertheless, it is now covered by the latest generation of passive optical sensors (e.g. SEBASS, AHS). This work aims at modeling leaf spectral reflectance and transmittance in the infrared, particularly between 3 μm and 5 μm, to improve the retrieval of vegetation water content using hyperspectral data. Two unique datasets containing 32 leaf samples each were acquired in 2008 at the USGS National Center, Reston (VA, USA) and the ONERA Research Center, Toulouse (France). Reflectance and transmittance were recorded using laboratory spectrometers in the spectral region from 0.4 μm to 14 μm, and the leaf water and dry matter contents were determined. It turns out that these spectra are strongly linked to water content up to 5.7 μm. This dependence is much weaker further into the infrared, where spectral features seem to be mainly associated with the biochemical composition of the leaf surface. The measurements show that leaves transmit light in this wavelength domain and that the transmittance of dry samples can reach 0.35 of incoming light around 5 μm, and 0.05 around 11 μm. This work extends the PROSPECT leaf optical properties model by taking into account the high absorption levels of leaf constituents (by the insertion of the complex Fresnel coefficients) and surface phenomena (by the addition of a top layer). The new model, PROSPECT-VISIR (VISible to InfraRed), simulates leaf reflectance and transmittance between 0.4 μm and 5.7 μm (at 1 nm spectral resolution) with a root mean square error (RMSE) of 0.017 and 0.018, respectively. Model inversion also allows the prediction of water (RMSE = 0.0011 g/cm²) and dry matter (RMSE = 0.0013 g/cm²) contents.     

The effects of surficial vegetation cover on mineral absorption feature parameters

The effects of surficial vegetation cover on the efficacy of automated absorption feature extraction and parameterisation of short-wave infra-red (SWIR) mineral absorption features is investigated. Synthetic spectral mixtures were generated from laboratory spectra of five SWIR absorbing minerals and live/dead vegetation. After convolution to the bandpasses of the Airborne Visible/Infra-Red Imaging Spectrometer the deepest absorption feature in each spectral mixture was extracted and parameterised in terms of wavelength position, depth, full width at half maximum depth, area and asymmetry. Live vegetation was found not to influence the wavelength position of mineral absorption features, conversely, dead vegetation induced abrupt and large shifts in wavelength position. Both live and dead vegetation reduce the depth of mineral absorption features, however, dead vegetation has the greatest impact on absorption features located at or near 2200nm. Dead vegetation was found to have a significant impact on the width, area and asymmetry of mineral absorption features.     

Carbon felt-based bioelectrocatalytic flow-through detectors: Highly sensitive amperometric determination of H2O2 based on a direct electrochemistry of covalently modified horseradish peroxidase using cyanuric chloride as a linking agent

Horseradish peroxidase (HRP) was chemically modified using cyanuric chloride (CC) as a linking agent onto a carbon felt (CF), which is a microelectrode ensemble of micro carbon fiber (>7 μm, diameter) with a random three-dimensional structure. The resulting HRP-modified CF (HRP-ccCF) exhibited well-defined redox waves based on the HRP heme Fe^III/Fe^II redox couple at −0.23 V vs. Ag/AgCl (at pH 7.0), while the HRP-adsorbed CF (HRP-CF) showed no apparent redox couple in the same potential range, indicating that the chemical modification of HRP via CC facilitated the direct electron transfer (DET) between HRP and CF. The apparent heterogeneous electron transfer rate constant k_s was estimated to be 35 s⁻¹. Cyclic voltammetry and electrochemical impedance spectroscopy revealed that the interfacial properties (i.e., structure, morphology of enzyme-layer) of covalently modified HRP (HRP-ccCF) and physically adsorbed HRP (HRP-CF) are different, resulting in the difference in the electron transfer properties. The HRP-ccCF was successfully used as a working electrode unit in bioelectrocatalytic flow-through detector for highly sensitive amperometric determination of H₂O₂. Under the optimized conditions (i.e., applied potential, 0 V vs. Ag/AgCl; carrier flow rate, 3.25 ml/min; and carrier pH 7.0), the cathodic peak current of H₂O₂ linearly increased up to 3 μM (sensitivity, 1.94 μA/μM; the detection limit, 0.08 μM [S/N = 3]) with sample through-put of ca. 90 samples/h.     

Assessing crop residue cover using shortwave infrared reflectance

Management of crop residues is an important consideration for reducing soil erosion and increasing soil organic carbon. Current methods of measuring residue cover are inadequate for characterizing the spatial variability of residue cover over large fields. The objectives of this research were to determine the spectral reflectance of crop residues and soils and to assess the limits of discrimination that can be expected in mixed scenes. Spectral reflectances of dry and wet crop residues plus three diverse soils were measured over the 400-2400 nm wavelength region. Reflectance values for scenes with varying proportions of crop residues and soils were simulated. Additional spectra of scenes with mixtures of crop residues, green vegetation, and soil were also acquired in corn, soybean, and wheat fields with different tillage treatments. The spectra of dry crop residues displayed a broad absorption feature near 2100 nm, associated with cellulose-lignin, that was absent in spectra of soils. Crop residue cover was linearly related (r²=0.89) to the Cellulose Absorption Index (CAI), which was defined as the relative depth of this absorption feature. Green vegetation cover in the scene attenuated CAI, but was linearly related to the Normalized Difference Vegetation Index (NDVI, r²=0.93). A novel method is proposed to assess soil tillage intensity classes using CAI and NDVI. Regional surveys of soil conservation practices that affect soil carbon dynamics may be feasible using advanced multispectral or hyperspectral imaging systems.     

Spectral analysis and classification of metamorphic rocks from laboratory reflectance spectra in the 0.4-2.5 μ m interval: A tool for hyperspectral data interpretation

Laboratory reflectance (0.4-2.5 w m ) spectra of 41 samples of metamorphic rocks from the Precambrian basement of Madagascar were analysed on the basis of absorption band position and shape, and classified on the basis of recurrent associations of absorption bands. Petrographic analyses allowed us to interpret the absorption features in compositional terms. Spectral and petrographic classes coincided when the principal mineralogy was also spectrally dominant (e.g. in carbonate rocks). When the principal mineralogy did not produce diagnostic spectral features (e.g. in siliceous rocks in the visible-short wave infrared region), the classification was based on spectrally dominant secondary phases. The reflectance spectra were measured on both freshly cut and exposed surfaces of the samples. Apart from a few cases of spectral features obliteration due to kaolinization, or overall albedo change related to texture variation, the two sets of spectra did not significantly differ. The responses of airborne MIVIS and AVIRIS hyperspectral sensors were simulated from spectra representative of the spectral classes, showing that significant identification and classification of well exposed metamorphic rocks are potentially possible using remote instruments providing high quality spectra. Although at present there are no plans for a spaceborne instrument of this quality, TM simulations and band composite images showed that a preliminary gross discrimination of the rocks belonging to the different classes was however possible.     

Identification of mineral components in tropical soils using reflectance spectroscopy and advanced spaceborne thermal emission and reflection radiometer (ASTER) data

Soil characteristics provide important support for understanding transformations that occur in environmental systems. Physical characteristics and chemical compositions of soils controlled by pedogenetic processes, climatic changes and land use imply different types of environmental transformations. Reflectance spectroscopy is an alternative soil mapping technique that uses spectral absorption features between visible (VIS) and short-wave infrared (SWIR) wavelengths (0.3-2.5 μm) for determining soil mineralogy. Soil analysis by means of reflectance spectroscopy and orbital optical sensors have provided favorable results in mapping transformation processes in environmental systems, particularly in arid and semiarid climates in extra-tropical terrains. In the case of inter-tropical environments, these methods cannot be readily applied due to local factors such as lack of exposed regolith, high amounts of soil moisture and the presence of dense vegetation. This study uses Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and reflectance spectroscopy data to map mineral components of soils covering a part of the state of São Paulo, Brazil, which could be linked to key aspects of environmental transformations in this tropical area (e.g., climate change, shifts in agriculture fronts, ph, and soil characteristics). We collected forty-two (42) soil samples at a depth of 0-20 cm, considering that this superficial layer corresponds to the highest correlation with soil properties detected by the ASTER sensor. These samples were measured using a FieldSpec FR spectrometer, and the derived spectra were interpreted for mineral composition. Interpretation was supported by X-ray diffraction analysis on the same samples. The spectral signatures were re-sampled to ASTER VNIR (AST1-4: 0.52-0.86 μm) and SWIR (AST5-9: 1.60-2.43 μm) spectral bandwidths and validated by comparing reflectance spectra of field samples with those extracted from atmospherically corrected and calibrated ASTER pixels. The agreement between spectral signatures measured from soil samples and those derived from ASTER imagery pixels proved plausible, with R² correlation values ranging from 0.6493 to 0.7886. This signifies that diagnostic spectral features of key minerals in tropical soils can be mapped at the spectral resolution of 9-band ASTER VNIR through SWIR reflectance. We used these spectral signatures as end-members in hyperspectral routine classifications adapted for use with ASTER data. Results proved possible the identification and remote mapping of minerals such as kaolinite, montmorillonite and gibbsite, as well as the distinction between iron-rich and iron-poor soils.     

Integrating imaging spectroscopy and neural networks to map grass quality in the Kruger National Park, South Africa

A new integrated approach, involving continuum-removed absorption features, the red edge position and neural networks, is developed and applied to map grass nitrogen concentration in an African savanna rangeland. Nitrogen, which largely determines the nutritional quality of grasslands, is commonly the most limiting nutrient for grazers. Therefore, the remote sensing of foliar nitrogen concentration in savanna rangelands is important for an improved understanding of the distribution and feeding patterns of wildlife. Continuum removal was applied on two absorption features located in the visible (R_550-757) and the SWIR (R_2015-2199) from an atmospherically corrected HYMAP MKI image. A feature selection algorithm was used to select wavelength variables from the absorption features. Selected band depths from the absorption features as well as the red edge position (REP) were input into a backpropagation neural network. The best-trained neural network was used to map nitrogen concentration over the whole study area. Results indicate that the new integrated approach could explain 60% of the variation in savanna grass nitrogen concentration on an independent test data set, with a root mean square error (rmse) of 0.13 (±8.30% of the mean observed nitrogen concentration). This result is better compared to the result obtained using multiple linear regression, which yielded an R² of 38%, with a RMSE of 0.16 (±10.30% of the mean observed nitrogen concentration) on an independent test data set. The study demonstrates the potential of airborne hyperspectral data and neural networks to estimate and ultimately to map nitrogen concentration in the mixed species environments of Southern Africa.     

Spectroscopic determination of leaf water content using continuous wavelet analysis

The gravimetric water content (GWC, %), a commonly used measure of leaf water content, describes the ratio of water to dry matter for each individual leaf. To date, the relationship between spectral reflectance and GWC in leaves is poorly understood due to the confounding effects of unpredictably varying water and dry matter ratios on spectral response. Few studies have attempted to estimate GWC from leaf reflectance spectra, particularly for a variety of species. This paper investigates the spectroscopic estimation of leaf GWC using continuous wavelet analysis applied to the reflectance spectra (350-2500 nm) of 265 leaf samples from 47 species observed in tropical forests of Panama. A continuous wavelet transform was performed on each of the reflectance spectra to generate a wavelet power scalogram compiled as a function of wavelength and scale. Linear relationships were built between wavelet power and GWC expressed as a function of dry mass (LWC_D) and fresh mass (LWC_F) in order to identify wavelet features (coefficients) that are most sensitive to changes in GWC. The derived wavelet features were then compared to three established spectral indices used to estimate GWC across a wide range of species.Eight wavelet features observed between 1300 and 2500 nm provided strong correlations with LWC_D, though correlations between spectral indices and leaf GWC were poor. In particular, two features captured amplitude variations in the broad shape of the reflectance spectra and three features captured variations in the shape and depth of dry matter (e.g., protein, lignin, cellulose) absorptions centered near 1730 and 2100 nm. The eight wavelet features used to predict LWC_D and LWC_F were not significantly different; however, predictive models used to determine LWC_D and LWC_F differed. The most accurate estimates of LWC_D and LWC_F obtained from a single wavelet feature showed root mean square errors (RMSEs) of 28.34% (R² = 0.62) and 4.86% (R² = 0.69), respectively. Models using a combination of features resulted in a noticeable improvement predicting LWC_D and LWC_F with RMSEs of 26.04% (R² = 0.71) and 4.34% (R² = 0.75), respectively. These results provide new insights into the role of dry matter absorption features in the shortwave infrared (SWIR) spectral region for the accurate spectral estimation of LWC_D and LWC_F. This emerging spectral analytical approach can be applied to other complex datasets including a broad range of species, and may be adapted to estimate basic leaf biochemical elements such as nitrogen, chlorophyll, cellulose, and lignin.     

Hydrocarbon Index – an algorithm for hyperspectral detection of hydrocarbons

Previous studies have shown that spectral signatures of hydrocarbon-bearing materials are characterized by prominent absorption features at 1.73 and 2.31?µm. Many other materials also show absorption features at wavelengths in the interval from 2.0 to 2.5?µm, yielding a mixed response in spectral signatures. In contrast to this wavelength range, most materials show similar spectral characteristics in the 1.73?µm range. Mainly hydrocarbon-bearing materials produce an absorption feature which is unique and prominent at 1.73?µm. A Hydrocarbon Index (HI) was developed and tested for the direct detection of hydrocarbons. The HI transforms multispectral data into a single image band that shows the distribution of hydrocarbons on the ground surface. The HI takes advantage of reflection differences around the 1.73?µm feature in hydrocarbon spectra. The HI indicates the presence of the 1.73?µm hydrocarbon absorption feature in a pixel spectrum. HI values can be easily calculated from radiance and reflectance data recorded by high signal-to-noise ratio hyperspectral scanners.     

Utilization of Hyperion data over Dongargarh,India, for mapping altered/weathered and clay minerals along with field spectral measurements

Hyperion data acquired over Dongargarh area, Chattisgarh (India), in December 2006 have been analysed to identify dominant mineral types present in the area, with special emphasis on mapping the altered/weathered and clay minerals present in the rocks and soils. Various advanced spectral processes such as reflectance calibration of the Hyperion data, minimum noise fraction transformation, spectral feature fitting (SFF) and spectral angle mapper (SAM) have been used for comparison/mapping in conjunction with spectra of rocks and soils that have been collected in the field using Analytical Spectral Devices's FieldSpec instrument. In this study, 40 shortwave infrared channels ranging from 2.0 to 2.4 μm were analysed mainly to identify and map the major altered/weathered and clay minerals by studying the absorption bands around the 2.2 and 2.3 μm wavelength regions. The absorption characteristics were the results of O–H stretching in the lattices of various hydrous minerals, in particular, clay minerals, constituting altered/weathered rocks and soils. SAM and SFF techniques implemented in Spectral Analyst were applied to identify the minerals present in the scene. A score of 0–1 was generated for both SAM and SFF, where a value of 1 indicated a perfect match showing the exact mineral type. Endmember spectra were matched with those of the minerals as available in the United States Geological Survey Spectral Library. Four minerals, oligoclase, rectorite, kaolinite and desert varnish, have been identified in the studied area. The SAM classifier was then applied to produce a mineral map over a subset of the Hyperion scene. The dominant lithology of the area included Dongargarh granite, Bijli rhyolite and Pitepani volcanics of Palaeo-Proterozoic age. Feldspar is one of the most dominant mineral constituents of all the above-mentioned rocks, which is highly susceptible to chemical weathering and produces various types of clay minerals. Oligoclase (a feldspar) was found in these areas where mostly rock outcrops were encountered. Kaolinite was also found mainly near exposed rocks, as it was formed due to the weathering of feldspar. Rectorite is the other clay mineral type that is observed mostly in the southern part of the studied area, where Bijli rhyolite dominates the lithology. However, the most predominant mineral type coating observed in this study is desert varnish, which is nothing but an assemblage of very fine clay minerals and forms a thin veneer on rock/soil surfaces, rendering a dark appearance to the latter. Thus, from this study, it could be inferred that Hyperion data can be well utilized to identify and map altered/weathered and clay minerals based on the study of the shape, size and position of spectral absorption features, which were otherwise absent in the signatures of the broadband sensors.     

Absorbing and scattering properties of boundary layer aerosols over Dibrugarh,Northeast India

The absorbing aerosols, primarily black carbon (BC), play a unique and important role in the Earth’s climate system primarily by warming the atmosphere. This warming effect contrasts with the cooling effect of aerosols such as sulphates that are mostly of the scattering type. With a view to studying the characteristics of both absorbing and scattering aerosols within the boundary layer, collocated measurements using an aethalometer and a nephelometer were carried out over Dibrugarh (27.3° N, 94.6° E, 111 m amsl), Northeast India. The diurnal variation of BC mass concentration (M_BC) shows a primary peak during late evening (2000–2200 local time (LT)) while a weak secondary peak is observed in the morning (0600–0800 LT). A seasonal shift in diurnal peak M_BC was also observed. Both diurnal and seasonal variations in the scattering coefficient (β_sca) resemble that of M_BC. It may, therefore, be inferred that the majority of both absorbing and scattering types of aerosol prevalent over the study location have common production sources. The seasonal spectral variation in absorption coefficient (β_abs) shows monotonic decrease from shorter to longer wavelength in all seasons. The wavelength dependence of absorption by aerosols, as obtained from the absorption Ångström exponent (α_abs), indicates a stronger presence of absorbing aerosols originating from biomass burning than those originating from fossil fuel burning over Dibrugarh. The high values of single-scattering albedo (SSA) obtained over Dibrugarh reveal that the scattering type of aerosol is predominant in the ambient air. SSA, together with M_BCm, is a useful parameter for estimation of radiative forcing and hence the climatic impact of aerosols.     

Analysis of a small agricultural watershed using remote sensing techniques

Salinization of land and sweet water is an increasing problem worldwide. In the Carpathian Basin, particularly in arid and semi‐arid regions, irrigation is a contributing factor to the secondary salinization problems, one of the major problems affecting soils in Hungary. Conventional broadband sensors such as SPOT, Landsat MSS, and Landsat ETM+ are not suitable for mapping soil properties, because their bandwidth of 100–200 mm cannot resolve diagnostic spectral features of terrestrial materials. Analytical techniques, developed for analysis of broadband spectral data, are incapable of taking advantage of the full range of information present in hyperspectral remote sensing imagery. In our pilot project in Tedej farm in the Great Plain Region, Hungary, the DAIS sensor was used to assess salinity risk, covering the spectral range from the visible to the thermal infrared wavelengths at 5 m spatial resolution, and other major indicators of soil salinization (NDVI, SAVI, canopy cover) were quantified with advanced remote sensing techniques using the TETRACAM ADC agricultural multispectral camera which offers red/green and NIR imaging at megapixel resolution. As a result, prominent absorption bands around 1450 nm and 1950 nm wavelength in most soil spectra are attributed to water and hydroxyl ions. Occasional weaker absorption bands caused by water also occur at 970, 1200, and 1700 nm. Absorption features near the 400 nm wavelength for all samples are also noticeable. Absorption bands at 1800 and 2300 nm are attributed to gypsum, while strong absorption features near 2350 nm are assigned to calcite (CaCo₃). Saline soils exhibited significantly higher reflectance values all throughout the 325–2500 nm wavelengths of the spectrum. Soils with a high amount of soluble salts gave a higher average reflectance than soils with a low salt content. In the project, an ADC camera‐based real‐time integrated system was developed to take advantage of more specialized spectral information and to provide even more accurate and useful data directly from the field. The results revealed that the NDVI and SAVI index and the canopy cover mapping taken with multispectral cameras can be useful as an indirect marker and help for detecting salinization. However, we did not find a strong correlation between NDVI and soil salinity. This is probably because the detection and assessment of lower levels of salinity are difficult, mainly owing to the nature of the remotely sensed images; with such images, it is not possible to obtain information on the third dimension of the 3‐D soil body. Also, the impact of salinity on electromagnetic properties needs to be explored further to understand how it can be derived indirectly from remotely sensed information. With the rapid validation of remotely sensed hyperspectral data, the decision in the future, with the best trade‐off between irrigation and sustainable land use made by agricultural specialists in this region, can be more environmentally sound and more accurate using the results from the pilot.