Digital Airborne Imaging Spectrometer Research Articles

Prospectivity Mapping for Epithermal Deposits of Western Milos Using a Fuzzy Multi Criteria Evaluation Approach Parameterized by Airborne Hyperspectral Remote Sensing Data

A Mineral prospectivity mapping (MPM) approach using a GIS-based weighted linear combination implementation of a Multi-Criteria Evaluation approach utilising a fuzzy Analytical Hierarchy Process to elucidate expert knowledge has been implemented to analyse the spatial distribution of epithermal deposits on the Island of Milos, Greece and model their association with exploration evidence data with the aim of providing insights into the controls on ore deposition. An integrated field and Digital Airborne Imaging Spectrometer (DAIS) hyperspectral and thermal multispectral airborne remote sensing dataset supported by field mapping and laboratory analyses, has been utilised to resolve hydrothermal alteration and parameterise the MPM. This study has highlighted the intimate spatial relationship between topographic highs and locations with high grade silicified alteration at a number of locations. The ability of high spatial resolution multispectral Thermal InfraRed (TIR) remote sensing imagery, integrated with topographic data, to resolve these silicified topographic highs provides an additional tool in the exploration of epithermal deposits. The spatial relationships between silicified lithocaps, high-grade altered rocks, faulting and topographic highs were utilised in the development of the MPM model. A close association between the modelled results and the hydrothermal alteration mapped in the field supports the accuracy of this MPM approach.

Hybrid genetic algorithm for feature selection with hyperspectral data

This letter evaluates the performance of a wrapper–filter genetic algorithm (GA) for feature selection using Digital Airborne Imaging Spectrometer hyperspectral data set. Classification accuracy by k-nearest neighbour (k-NN) and support vector machine (SVM) were used as fitness function with four filter algorithms. Results of wrapper–filter GA-based feature selection approach were compared with a steady-state GA-based approach in terms of number of selected features and the accuracy with selected feature. Result indicates the unsuitability of SVM as wrapper due to large computational cost in comparison to k-NN. Choice of filter algorithm seems to be having no significant effect on classification accuracy obtained using selected features. Comparison in terms of number of selected features and the accuracy with selected features suggests comparable performance by both wrapper–filter and steady-state GAs for the used data set.

Spectral Mixture Analysis of Hyperspectral Scenes Using Intelligently Selected Training Samples

In this letter, we address the use of artificial neural networks for spectral mixture analysis of hyperspectral scenes. We specifically focus on the issue of how to effectively train neural network architectures in the context of spectral mixture analysis applications. To address this issue, a multilayer perceptron neural architecture is combined with techniques for intelligent selection and labeling of training samples directly obtained from the input data, thus maximizing the information that can be obtained from those samples while reducing the need for <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a priori</i> information about the scene. The proposed approach is compared to unconstrained and fully constrained linear mixture models using hyperspectral data sets acquired (in the laboratory) from artificial forest scenes, using the compact airborne spectrographic imaging system. The Spreading of Photons for Radiation INTerception (SPRINT) canopy model, which assumes detailed knowledge about object geometry, was employed to evaluate the results obtained by the different methods. Our results show that the proposed approach, when trained with both pure and mixed training samples (generated automatically without <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">prior</i> information) can provide similar results to those provided by SPRINT, using very few labeled training samples. An application to real airborne data using a set of hyperspectral images collected at different altitudes by the digital airborne imaging spectrometer 7915 and the reflective optics system imaging spectrometer, operating simultaneously at multiple spatial resolutions, is also presented and discussed.

Mapping of semi-arid iron bearing red sands on emerged areas around lake marshes (Tablas de Daimiel, Spain) using hyperspectral DAIS 7915 spectrometer data

Wetlands are particularly sensitive environments receiving attention from the natural sciences community due to their wealth of both flora and fauna, and often considered as natural parks. In the Tablas de Daimiel (La Mancha, Central Spain), Digital Airborne Imaging Spectrometer data (DAIS 7915) have been analyzed to map geological processes on areas around the receding wetland which have never been flooded by water in the past. Sediments permanently exposed to the atmosphere dehydrate and oxide, developing different mineralogical associations arranged on planation surfaces. Such planation surfaces are key in the geological knowledge of recent climate change and landscape evolution. Progressive iron oxide/hydroxide rate and decarbonation can be spectrally followed on the Holocene sands framing the current marshy area. Such mineralogical changes are geologically registered on flat surfaces at different heights over the receding shore of the paleolake. Interacting erosion and sedimentation processes are responsible for the development of the flat morphological surfaces with increasing dryness. Maps are built for four different morphological units consisting of planation surfaces following chronologically the receding marsh during the last 2000 years before the present. Interactive spectral responses of mineralogical associations are described on the imagery, field and laboratory spectra.

Does single broadband or multispectral thermal data add information for classification of visible, near‐ and shortwave infrared imagery of urban areas?

The potential value of combining broadband and multispectral thermal infrared (TIR) data with multispectral and hyperspectral visible, near‐infrared (VNIR) and shortwave infrared (SWIR) data was investigated within the context of urban land‐cover classification. Using a case study of airborne Digital Airborne Imaging Spectrometer (DAIS) imagery of Strasbourg, France, the relative contribution of TIR wavelengths to classification accuracy was investigated for hyperspectral and simulated multispectral IKONOS, SPOT and Landsat Thematic Mapper (TM) bands. A support vector machines (SVM) classifier was used because this method was found to be very effective at handling the complex distributions of the heterogeneous land cover classes. The overall classification accuracy varied greatly with different band combinations. The inclusion of a single broad thermal band increased classification accuracy by as much as 20% for simulated IKONOS bands, but only 4% for hyperspectral VNIR and SWIR data. Adding multispectral TIR data raised the average accuracy approximately a further 10% for each band combination studied. Thermal wavelengths were found to be particularly useful for reducing the confusion between road and roof surfaces.

On the use of small training sets for neural network-based characterization of mixed pixels in remotely sensed hyperspectral images

In this work, neural network-based models involved in hyperspectral image spectra separation are considered. Focus is on how to select the most highly informative samples for effectively training the neural architecture. This issue is addressed here by several new algorithms for intelligent selection of training samples: (1) a border-training algorithm (BTA) which selects training samples located in the vicinity of the hyperplanes that can optimally separate the classes; (2) a mixed-signature algorithm (MSA) which selects the most spectrally mixed pixels in the hyperspectral data as training samples; and (3) a morphological-erosion algorithm (MEA) which incorporates spatial information (via mathematical morphology concepts) to select spectrally mixed training samples located in spatially homogeneous regions. These algorithms, along with other standard techniques based on orthogonal projections and a simple Maximin-distance algorithm, are used to train a multi-layer perceptron (MLP), selected in this work as a representative neural architecture for spectral mixture analysis. Experimental results are provided using both a database of nonlinear mixed spectra with absolute ground truth and a set of real hyperspectral images, collected at different altitudes by the digital airborne imaging spectrometer (DAIS 7915) and reflective optics system imaging spectrometer (ROSIS) operating simultaneously at multiple spatial resolutions.

Margin-based feature selection for hyperspectral data

A margin-based feature selection approach is explored for hyperspectral data. This approach is based on measuring the confidence of a classifier when making predictions on a test data. Greedy feature flip and iterative search algorithms, which attempts to maximise the margin-based evaluation functions, were used in the present study. Evaluation functions use linear, zero–one and sigmoid utility functions where a utility function controls the contribution of each margin term to the overall score. The results obtained by margin-based feature selection technique were compared to a support vector machine-based recurring feature elimination approach. Two different hyperspectral data sets, one consisting of 65 bands (DAIS data) and other with 185 bands (AVIRIS data) were used. With digital airborne imaging spectrometer (DAIS) data, the classification accuracy by greedy feature flip algorithm and sigmoid utility function was 93.02% using a total of 24 selected features in comparison to an accuracy of 91.76% with full set of 65 features. The results suggest a significant increase in classification accuracy with 24 selected features. The classification accuracy (93.4%) achieved by the iterative search margin-based algorithm with 20 selected features using sigmoid utility function is also significantly more accurate than that achieved with 65 features. To judge the usefulness of margin-based feature selection approaches, another hyperspectral data set consisting of 185 features was used. A total of 65 selected features were used to evaluate the performance of margin-based feature selection approach. The results suggest a significantly improved performance by greedy feature flip-based feature selection technique with this data set also. This study also suggest that margin-based feature selection algorithms provide a comparable performance to support vector machine-based recurring feature elimination approach.

Multi-Channel Morphological Profiles for Classification of Hyperspectral Images Using Support Vector Machines

Hyperspectral imaging is a new remote sensing technique that generates hundreds of images, corresponding to different wavelength channels, for the same area on the surface of the Earth. Supervised classification of hyperspectral image data sets is a challenging problem due to the limited availability of training samples (which are very difficult and costly to obtain in practice) and the extremely high dimensionality of the data. In this paper, we explore the use of multi-channel morphological profiles for feature extraction prior to classification of remotely sensed hyperspectral data sets using support vector machines (SVMs). In order to introduce multi-channel morphological transformations, which rely on ordering of pixel vectors in multidimensional space, several vector ordering strategies are investigated. A reduced implementation which builds the multi-channel morphological profile based on the first components resulting from a dimensional reduction transformation applied to the input data is also proposed. Our experimental results, conducted using three representative hyperspectral data sets collected by NASA's Airborne Visible-Infrared Imaging Spectrometer (AVIRIS) sensor and the German Digital Airborne Imaging Spectrometer (DAIS 7915), reveal that multi-channel morphological profiles can improve single-channel morphological profiles in the task of extracting relevant features for classification of hyperspectral data using small training sets.

Applicability of hyperspectral technology for in situ phytoremediation

The characterization of heavy metal polluted abandoned mining sites is a complicated assignment due to the variable spatial distribution of the pollutants, therefore complex integrated method is required in order to assess precisely the amount and the distribution of the contaminants. The examined area is flotation sludge reservoir of abandoned Pb-Zn mining site with serious heavy metal contamination. located in Gyöngyösoroszi, Northern Hungary.The hyperspectral image of the flotation sludge is obtained by using a Digital Airborne Imaging Spectrometer DAIS 7915, in the frame of DLR HySens first Hungarian hyperspectral flight campaign (21/08/2002). Parallel to the flight campaign heavy metal content of soil samples were examined from the area of the flotation sludge. The analysis of hyperspectral data was verified by the examination of mine tailing samples by FPXRF (Field Portable X-ray Fluorescence spectrometry) (NITON XL-703).Determinations of heavy metal containing minerals are based on the spectral profiles of the pixels of the area with using USGIS standard spectral profiles of the examined materials (galena, pyrite, sphalerite, goethite and jarosit).&#x0D; Applying the Spectral Angle Mapper with BandMax classification the distribution of minerals (galena, pyrite, sphalerite, goethite, jarosit) in the area was defined. The mineral formation occurs especially at the levees and the barren places of the Szárazvölgyi flotation sludge reservoir. Based on the statistic results of the samples, principal component analysis and correlation coefficient between the different metal content of the samples were calculated. The highest correlations were found between Pb-Zn, Fe-Zn and between Fe-Pb. This prove the results of the principal component analysis, where usually Pb, Zn, Fe introduce the main component.&#x0D; Canopy analysis was also carried out with the hyperspectal image in order to classify the differences between vegetation types at the Szárazvölgy flotation sludge reservoir and analyse the applicability of it. Supervised classification methods were used to distinguish 8 vegetation types based on the spectral properties of the area. The results of the classifications were compared to a ground truth image, based on ortophoto, topographic map, and GPS based field data collection. According to results of the comparison, the paralellpiped classification method is proved to be appropriate method based on the overall accuracy of canopy classification, which was 54% due to heterogeneity of the vegetation. &#x0D; The results of hyperspectral data and FPXRF analysis suggest that Pb, Zn and Fe containing minerals have similar spatial distribution in the examined and barren area.&#x0D; Based on this study hyperspectral remote sensing is likely to be an effective tool for the characterization and modeling the distribution of Pb, Zn and Fe containing minerals at the examined heavy metal polluted sites. Further more, based on the vegetation analysis plant species for phytoremediation can be defined.

Geomorphologic mapping from hyperspectral data, using Gaussian mixtures and lower confidence bounds

Probabilistic classification under the Gaussian mixture model is normally based on posterior probability (p.p.) estimates of class membership. The question, how accurate they are for a given pixel, is traditionally left without attention, which may lead to unreasonable optimism about the classification results obtained. Addressing the issue, Koltunov and Ben‐Dor have proposed an unsupervised, lower confidence bound (l.c.b.)‐based method for thematic interpretation of remote sensing data. This method predicts the sampling properties of the p.p. estimators of a given pixel, to assess reliability of the estimates. The present paper describes a modified version of the method. In particular, instead of defining the l.c. bounds in terms of two first moments of the sampling distribution, as has been suggested previously, we use percentiles. Combining this with a probabilistic model of supervised identification of the mixture components yields the post‐classification uncertainty value for a given pixel and the confidence level, at which this value is proven to be maximal. In the application to an arid landscape in the Southern Negev desert, Israel, the compressed raw hyperspectral data acquired by the Digital Airborne Imaging Spectrometer (DIAS‐7915) was clustered once, whereas two thematic tasks were solved corresponding to different map legends, identification procedures, and the associated requirements to the level of detail and reliability of the thematic maps. The reference data collected in the field have provided evidence for accurate algorithmically estimated confidence bounds of the classification quality. The classification has revealed new information about the geomorphological subunits forming the study area.

Integrating hyperspectral imagery at different scales to estimate component surface temperatures

Different methods for classifying land cover and extracting temperatures of surface components from hyperspectral images at different scales were compared using airborne imagery (Reflective Optics System Imaging Spectrometer (ROSIS) at 1.2 m spatial resolution and Digital Airborne Imaging Spectrometer (DAIS 7915) at 3.3 m spatial resolution) for a ‘montado/dehesa’ landscape in the Alentejo, Portugal. For calibration purposes, surface temperatures and stomatal conductance of component vegetation types were also measured at ground level. Manual classification was compared with a range of automated classification methods to determine the most accurate method for the study area. The ‘scale’ for each cover type was characterized by analysing the frequency distribution of contiguous pixels of each cover type at 1.2 m. Temperatures of different surface components were estimated using different combinations of 1.2 m and 3.3 m data (using spectral angle mapper classification) as well as linear spectral unmixing and disaggregation approaches for extracting thermal information at sub‐pixel resolution. The relative advantages of the different methods are discussed and a recommended strategy for integrating hyperspectral imagery at different scales to extract component surface temperatures in montado/dehesa‐type systems is proposed.

Empirical versus Model‐based Atmospheric Correction of Digital Airborne Imaging Spectrometer Hyperspectral Data

This paper reports the results of a quantitative comparison of empirical and model based atmospheric correction techniques for the radiometric calibration of a Digital Airborne Imaging Spectrometer (DAIS) 3715 hyperspectral image dataset. Empirical line calibration (ELC) and the radiative transfer based model Atmospheric CORrection Now (ACORN) were applied to transform the hyperspectral dataset from values of radiance to scaled percent reflectance. An additional spectral polishing technique called single spectrum enhancement (SSE) was implemented a posteriori to refine the transformation results. To evaluate the accuracy of the radiometric calibration techniques, spectra extracted from the processed images were analytically compared to spectral measurements collected in situ with a handheld spectroradiometer at 46 sample point locations. Average RMSE errors were as follows: ELC without SSE = 0.1415, ACORN without SSE = 0.0645, ELC with SSE = 0.0345, and ACORN with SSE = 0.0314. Based on the results of this analysis, spectral polishing through the use of SSE appears to introduce the greatest improvement in the removal of deleterious atmospheric effects when compared to in situ data, regardless of the choice of the model (i.e. ACORN or ELC).

Mapping microphytobenthos biomass by non-linear inversion of visible-infrared hyperspectral images

This study presents an innovative approach to map microphytobenthos biomass and fractional cover in Bourgneuf Bay (French Atlantic coast) using Digital Airborne Imaging Spectrometer (DAIS) hyperspectral data. Microphytobenthos is a microalgae forming a biofilm on the mudflat. Its spatial distribution is heterogeneous so it varies on a finer scale than that of airborne instrument spatial resolution, leading to a “mixed pixel” problem. Moreover, some microphytobenthic species form, at low tide, a biofilm deposited at the surface of the sediment substrate. The resulting signal is a highly non-linear combination of spectral endmembers due to microscale intimate mixtures. This prevents the use of classical linear unmixing models to retrieve biomass from reflectance spectra. A Modified Gaussian Model (MGM) is therefore used to remove the effects of surface roughness, shadowing and any other unknown processes that contribute to the overall shape (continuum) of the reflectance spectra. Then, relationships between microphytobenthos biomass and spectral shapes are derived from a spectral database compiled from laboratory reflectance spectra of microalgal monospecific cultures with different biomasses. Finally, microphytobenthos biomass and fractional cover are retrieved from the DAIS image by comparing the reflectance spectra of each pixel to a library of synthetic spectra corresponding to combinations of various biomasses and substrate percent cover. This new approach, when compared to more classically used ones such as indices, linear unmixing or spectral distance analysis, is proven to enable a much more reliable determination of biomass despite the large variety of substrates found in Bourgneuf Bay.

Assessing vineyard condition with hyperspectral indices: Leaf and canopy reflectance simulation in a row-structured discontinuous canopy

Methods for chlorosis detection and physiological condition monitoring in Vitis vinifera L. through accurate chlorophyll a and b content ( C ab) estimation at leaf and canopy levels are presented in this manuscript. A total of 24 vineyards were identified for field and airborne data collection with the Compact Airborne Spectrographic Imager (CASI), the Reflective Optics System Imaging Spectrometer (ROSIS) and the Digital Airborne Imaging Spectrometer (DAIS-7915) hyperspectral sensors in 2002 and 2003 in northern Spain, comprising 103 study areas of 10 × 10 m in size, with a total of 1467 leaves collected for determination of pigment concentration. A subsample of 605 leaves was used for measuring the optical properties of reflectance and transmittance with a Li-Cor 1800-12 Integrating Sphere coupled by a 200 μm diameter single mode fiber to an Ocean Optics model USB2000 spectrometer. Several narrow-band vegetation indices were calculated from leaf reflectance spectra, and the PROSPECT leaf optical model was used for inversion using the extensive database of leaf optical properties. Results showed that the best indicators for chlorophyll content estimation in V. vinifera L. leaves were narrow-band hyperspectral indices calculated in the 700–750 nm spectral region ( r 2 ranging between 0.8 and 0.9), with poor performance of traditional indices such as the Normalized Difference Vegetation Index (NDVI). Results for other biochemicals indicated that the Structure Insensitive Pigment Index (SIPI) and the Photochemical Reflectance Index (PRI) were more sensitive to carotenoids C x+ c and chlorophyll–carotenoid ratios C ab / C x+ c than to chlorophyll content C ab. Chlorophyll a and b estimation by inversion of the PROSPECT leaf model on V. vinifera L. spectra was successful, yielding a determination coefficient of r 2 = 0.95, with an RMSE = 5.3 μg/cm 2. The validity of leaf-level indices for chlorophyll content estimation at the canopy level in V. vinifera L. was studied using the scaling-up approach that links PROSPECT and rowMCRM canopy reflectance simulation to account for the effects of vineyard structure, vine dimensions, row orientation and soil and shadow effects on the canopy reflectance. The index calculated as a combination of the Transformed Chlorophyll Absorption in Reflectance Index (TCARI), and the Optimized Soil-Adjusted Vegetation Index (OSAVI) in the form TCARI/OSAVI was the most consistent index for estimating C ab on aggregated and pure vine pixels extracted from 1 m CASI and ROSIS hyperspectral imagery. Predictive relationships were developed with PROSPECT–rowMCRM model between C ab and TCARI/OSAVI as function of LAI, using field-measured vine dimensions and image-extracted soil background, row-orientation and viewing geometry values. Prediction relationships for C ab content with TCARI/OSAVI were successfully applied to the 103 study sites imaged on 24 fields by ROSIS and CASI airborne sensors, yielding r 2 = 0.67 and RMSE = 11.5 μg/cm 2.

Atmospheric water vapour content retrieval from visible and thermal data in the framework of the DAISEX campaigns

In this paper three methods are presented that retrieve the atmospheric water vapour from DAIS (digital airborne imaging spectrometer) data in the framework of the DAISEX (DAIS Experiment) campaigns carried out by ESA (European Space Agency). The three methodologies analysed in the paper are: (i) the ratio technique, in which the water vapour is obtained from visible and near‐infrared bands; (ii) the split‐window technique; and (iii) the split‐window covariance‐variance ratio technique, in which the water vapour content is retrieved from thermal infrared bands. A comparison between the atmospheric water vapour content extracted from the DAIS images using these techniques and that obtained from in situ radiosoundings shows a root mean square deviation of around 0.1 g cm−2 for the first two methods and 0.4 g cm−2 for the last one. Finally, as an application, the atmospheric water vapour retrieved was used to perform the atmospheric correction for DAIS thermal bands and retrieve land surface temperatures, obtaining in this way root mean square deviations less than 2 K for the least absorbent bands.

A simple algorithm to estimate evapotranspiration from DAIS data: Application to the DAISEX campaigns

The knowledge of evapotranspiration is of great interest in many applications such as assessing irrigation water use. In this paper, a methodology is presented to estimate evapotranspiration using the surface energy balance model S-SEBI (Simplified Surface Energy Balance Index) and the evaporative fraction. The method is applicable under the assumptions of constant atmospheric conditions and sufficient wet and dry pixels over the image. The model uses remotely sensed parameters such as albedo, MSAVI (Modified Soil Adjusted Vegetation Index) and surface temperature and emissivity images. The methodology has been applied over the Barrax test site, located in the province of Albacete (Castilla La Mancha, Spain), in the framework of the DAISEX (Digital Airborne Imaging Spectrometer Experiment) campaigns carried out by ESA (European Space Agency). Net radiation, soil heat fluxes and evapotranspiration have been obtained from DAIS images for alfalfa, corn and grass plots. The results show that alfalfa plot gives the highest values, around 5 mm d −1, whereas corn plot gives the lowest, around 3.5 mm d −1. The proposed methodology leading to an accuracy for the daily evapotranspiration lower than 1 mm d −1.

Dimensionality reduction and classification of hyperspectral image data using sequences of extended morphological transformations

This work describes sequences of extended morphological transformations for filtering and classification of high-dimensional remotely sensed hyperspectral datasets. The proposed approaches are based on the generalization of concepts from mathematical morphology theory to multichannel imagery. A new vector organization scheme is described, and fundamental morphological vector operations are defined by extension. Extended morphological transformations, characterized by simultaneously considering the spatial and spectral information contained in hyperspectral datasets, are applied to agricultural and urban classification problems where efficacy in discriminating between subtly different ground covers is required. The methods are tested using real hyperspectral imagery collected by the National Aeronautics and Space Administration Jet Propulsion Laboratory Airborne Visible-Infrared Imaging Spectrometer and the German Aerospace Agency Digital Airborne Imaging Spectrometer (DAIS 7915). Experimental results reveal that, by designing morphological filtering methods that take into account the complementary nature of spatial and spectral information in a simultaneous manner, it is possible to alleviate the problems related to each of them when taken separately.

Simulating Landsat ETM+ imagery using DAIS 7915 hyperspectral scanner data

Any application in remote sensing requires specific datasets that are defined in terms of the spectral and spatial characteristics of the sensor. The question as to which spatial resolution is preferable for a particular problem is a difficult one, and represents a critical decision in the planning of a project. Remote sensing data with different spatial resolutions are often required. In such cases, an alternative to the purchase of new images is simulation. Simulation is often used to generate images that will be acquired by a future sensor such as using aircraft imagery to simulate a new, as yet unavailable, orbital sensor. Simulation has usually been carried out as a spatial degradation process. However, to make a realistic simulation it is necessary to take a number of factors into consideration. Two such factors are the point spread function (PSF) and the spectral response of the sensor. In this study, Digital Airborne Imaging Spectrometer (DAIS 7915) data for a test site from the La Mancha Alta region of Spain are used to simulate a Landsat Enhanced Thematic Mapper Plus (ETM+) image. The results are promising in that comparable images are produced by considering the PSF and spectral response of the Landsat ETM+.

Usage of Different Spectral Bands in Agricultural Environmental Protection

Hyper and multispectral imaging systems are widely used in agricultural and environmental protection. Remote sensing techniques are suitable for evaluating environmental protection hazarsd, as well as for agriculture resource exploration. In our research we compared aerial hyper and multispectral images, as well as multispectral digital camera images with the background data from the test site. Hyperspectral records were obtained using a new 80-channeled aerial spectrometer (Digital Airborne Imaging Spectrometer /DAIS 7915/. We have chosen two farms where intensive crop cultivation takes place, as test sites, so soil degradation and spreading of weeds can be intensive as a result of land use and irrigation. We took additional images of air and ground with a TETRACAM ADC wide band multispectral camera, which can sense blue, green and near infrared bands. We had detailed GIS database about the test site. Weed and vegetation map of the area in the spring and the summer was made in 2002. For soil salt content analysis, we gathered detailed data frome an 80x100 m area. When analyzing the images, we evaluated image reliability, and the connection between the bands and the soil type, pH and salt content, and weed mapping. In the case of hyperspectral images, our aim was to choose and analyze the appropriate band combinations. With a TETRACAM ADC camera, we made images at different times, and we calculated canopy, NDVI and SAVI indexes. Using the background data mentioned above, the aim of our study was to develop a spectral library, which can be used to analyze the environmental effects of agricultural land use.

Single-channel and two-channel methods for land surface temperature retrieval from DAIS data and its application to the Barrax site

In this paper, a methodology using a single-channel and a two-channel method is presented to estimate the land surface temperature from the DAIS (Digital Airborne Imaging Spectrometer) thermal channels 74 (8.747 µm), 75 (9.648 µm), 76 (10.482 µm), 77 (11.266 µm), 78 (11.997 µm) and 79 (12.668 µm). The land surface temperature retrieved with both methods has been validated over the Barrax site (Albacete, Spain) in the framework of the DAISEX (Digital Airborne Imaging Spectrometer Experiment) field campaigns. Prior to the validation an analysis of the DAIS data quality has been performed in order to check the agreement between in situ data and the values extracted from the DAIS images supplied by the DLR (German Optoelectronic Institute). Suitable differences between in situ and DAIS data have been found. To solve this problem a linear re-calibration of the DAIS thermal channels has been applied using two ground calibration points (bare soil and water). For the land surface temperature retrieved, rms deviations of 0.96 K using a single-channel method and 1.46 K using a two-channel method with the DAIS thermal channels 77 and 78 have been obtained considering re-calibrated data.