Showing papers on "VNIR published in 2011"

Mapping alteration minerals using sub-pixel unmixing of ASTER data in the Sarduiyeh area, SE Kerman, Iran

Abstract: This paper is an attempt to introduce the role of earth observation technology and a type of digital earth processing in mineral resources exploration and assessment. The sub-pixel distribution and quantity of alteration minerals were mapped using linear spectral unmixing (LSU) and mixture tuned matched filtering (MTMF) algorithms in the Sarduiyeh area, SE Kerman, Iran, using the visible-near infrared (VNIR) and short wave infrared (SWIR) bands of the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) instrument and the results were compared to evaluate the efficiency of methods. Three groups of alteration minerals were identified: (1) pyrophylite-alunite (2) sericite-kaolinite, and (3) chlorite-calcite-epidote. Results showed that high abundances within pixels were successfully corresponded to the alteration zones. In addition, a number of unreported altered areas were identified. Field observations and X-ray diffraction (XRD) analysis of field samples confirmed the dominant mineral p...

Spectral transformation of ASTER data and the discrimination of hydrothermal alteration minerals in a semi-arid region, SE Iran.

Abstract: We investigated the spectral transformation of Advanced Space-borne Thermal Emission and Reflection Radiometer (ASTER) remote-sensing data of a semi-desert realm. Hydroxyl-bearing mineral zones were identified and discriminated based on distinctive shortwave infrared radiation (SWIR) properties of the ASTER data. Principal component analysis (PCA), minimum noise fraction (MNF), and band ratioing methods were applied, compared and combined in order to accurately locate hydrothermal alteration zones in two major mining districts of SE Iran. The strategy in this study consisted of using PC images and associated statistics factors yielded by PCA transformation for the identification of vegetation and iron oxide minerals in the visible and near infrared radiation (VNIR), clay minerals in the shortwave infrared radiation (SWIR), and silicate rocks in the thermal infrared radiation (TIR) of the ASTER data, respectively. The PCA results were verified by comparison with obtained results by minimum noise fraction and band ratio methods and also prior knowledge about the study area. In conclusion, the discrimination of alteration zones with high potential for copper and gold mineralization by spectral transformation of the ASTER data is a reliable, fast and relatively low cost with great ability to assist exploration geologists in the reconnaissance stages of mineral exploration.

Combined use of hyperspectral VNIR reflectance spectroscopy and kriging to predict soil variables spatially

Abstract: Hyperspectral visible near infrared reflectance spectroscopy (VNIRRS) and geostatistical methods are considered for precision soil mapping. This study evaluated whether VNIR or geostatistics, or their combined use, could provide efficient approaches for assessing the soil spatially and associated reductions in sample size using soil samples from a 32 ha area (800 × 400 m) in northern Turkey. Soil variables considered were CaCO3, organic matter, clay, sand and silt contents, pH, electrical conductivity, cation exchange capacity (CEC) and exchangeable cations (Ca, Mg, Na and K). Cross-validation was used to compare the two approaches using all grid data (n = 512), systematic selections of 13, 25 and 50% of the data and random selections of 13 and 25% for calibration; the remaining data were used for validation. Partial least squares regression (PLSR) analysis was used for calibrating soil properties from first derivative VNIR reflectance spectra (VNIRRS), whereas ordinary-, co- and regression-kriging were used for spatial prediction. The VNIRRS-PLSR method provided better prediction results than ordinary kriging for soil organic matter, clay and sand contents, (R 2 values of 0.56–0.73, 0.79–0.85, 0.65–0.79, respectively) and smaller root mean squared errors of prediction (values of 2.7–4.1, 37.4–43, 46.9–61, respectively). The EC, pH, Na, K and silt content were predicted poorly by both approaches because either the variables showed little variation or the data were not spatially correlated. Overall, the prediction accuracy of VNIRRS-PLSR was not affected by sample size as much as it was for ordinary kriging. Cokriging (COK) and regression kriging (RK) were applied to a combination of values predicted by VNIR reflectance spectroscopy and measured in the laboratory to improve the accuracy of prediction of the soil properties. The results showed that both COK and RK with VNIRRS estimates improved the predictions of soil variables compared to VNIRRS and OK. The combined use of VNIRRS and multivariate geostatistics results in better spatial prediction of soil properties and enables a reduction in sampling and laboratory analyses.

Correction of cirrus effects in Sentinel-2 type of imagery

Abstract: Optical satellite images are often contaminated with cirrus clouds. Thin cirrus can be detected with a channel at 1.38 μm, and an established cirrus removal method exists for visible/near-infrared (VNIR) channels in atmospheric window regions, which was demonstrated with Moderate Resolution Imaging Spectrometer (MODIS) data. This contribution addresses open issues of cirrus correction for Sentinel-2 type of instruments, that is, future spaceborne sensors such as Sentinel-2 or similar instruments. These issues are (i) an extension of the existing technique to account for cirrus during the water vapour retrieval (channel at 0.94 μm) and surface reflectance calculation to avoid reflectance artefacts at 0.94 μm, (ii) a discussion of options concerning cirrus removal in the short-wave infrared (SWIR, channels at 1.6 and 2.2 μm) region and (iii) an analysis of channel parallax (view angle) requirements to achieve a high-quality cirrus removal.

Reduction of Radiometric Miscalibration—Applications to Pushbroom Sensors

Abstract: The analysis of hyperspectral images is an important task in Remote Sensing. Foregoing radiometric calibration results in the assignment of incident electromagnetic radiation to digital numbers and reduces the striping caused by slightly different responses of the pixel detectors. However, due to uncertainties in the calibration some striping remains. This publication presents a new reduction framework that efficiently reduces linear and nonlinear miscalibrations by an image-driven, radiometric recalibration and rescaling. The proposed framework—Reduction Of Miscalibration Effects (ROME)—considering spectral and spatial probability distributions, is constrained by specific minimisation and maximisation principles and incorporates image processing techniques such as Minkowski metrics and convolution. To objectively evaluate the performance of the new approach, the technique was applied to a variety of commonly used image examples and to one simulated and miscalibrated EnMAP (Environmental Mapping and Analysis Program) scene. Other examples consist of miscalibrated AISA/Eagle VNIR (Visible and Near Infrared) and Hawk SWIR (Short Wave Infrared) scenes of rural areas of the region Fichtwald in Germany and Hyperion scenes of the Jalal-Abad district in Southern Kyrgyzstan. Recovery rates of approximately 97% for linear and approximately 94% for nonlinear miscalibrated data were achieved, clearly demonstrating the benefits of the new approach and its potential for broad applicability to miscalibrated pushbroom sensor data.

The application of visible/near-infrared reflectance (VNIR) spectroscopy to chert: A case study from the Dover Quarry sites, Tennessee

Abstract: Prehistoric cultural material is commonly composed of chert, due in large part to its physical properties that are conducive to tool manufacture. Despite its ubiquity, archaeologists are faced with an arduous task when attempting to source chert artifacts to known quarries/deposits. The application of visible/near-infrared reflectance (VNIR) spectroscopy to chert sourcing attains a cost-efficient, fast, non-destructive, and accurate means of identifying material type and geologic/geographic origin. This study examines the merits of VNIR spectroscopy within chert sourcing studies by highlighting a material-based case study from the Dover Quarry sites, Tennessee. Results demonstrate the ability of VNIR spectroscopy to differentiate chert types from different geologic formations with 98% accuracy. However, accuracy significantly decreased when attempting to distinguish particular outcrops within the same formation. Although the application of VNIR spectroscopy to chert sourcing is in its experimental phase, the preliminary results compare favorably with other provenance techniques whose aim is to quantify inter-outcrop variation. © 2011 Wiley Periodicals, Inc.

Land cover change on Mt. Pinatubo, the Philippines, monitored using ASTER VNIR

Abstract: Ashfall and pyroclastic flows from the large eruption of June 1991 destroyed much of the vegetation on the flanks of Mt. Pinatubo. Subsequent vegetation recovery has helped stabilize slopes and reduce debris flow hazard. In this project, visible and near-infrared (VNIR) satellite imagery from the advanced spaceborne thermal emission and reflection radiometer (ASTER) captured at a similar time of year in 2001, 2004 and 2008 were used to quantify vegetation recovery within 22 upland watersheds on the mountain, 10–16 years after the eruption took place. Differences in the normalized difference vegetation index (NDVI) derived from these images were used to measure the areal extent of losses and gains in ground cover and derive average net rates of change in ground cover. The success of this approach was dependent on post-processing ASTER imagery to correct for the effects of variation in satellite-sun geometry and vegetation reflectance and to calibrate and adjust the derived NDVI images for the influence of ...

Visible to SWIR hyperspectral imaging for produce safety and quality evaluation

Abstract: Hyperspectral imaging techniques, combining the advantages of spectroscopy and imaging, have found wider use in food quality and safety evaluation applications during the past decade. In light of the prevalent use of hyperspectral imaging techniques in the visible to near-infrared (VNIR: 400–1,000 nm) for agro-food evaluations, seldom reported are the instrument artifacts that may affect the quality of image data. Furthermore, hyperspectral-based research has focused on the development of image processing and detection aspects with minimal attention given to illustrating the underlying value of imaging with sufficient spatial resolution in the regions spanning from the visible to short-wavelength infrared (SWIR: 1,000–1,700). We have developed multiple generations of line-scan based hyperspectral imaging systems and expanded the imaging capabilities in the SWIR. With the use of our most recently developed VNIR and SWIR hyperspectral imaging systems, spectral and spatial attributes of apples with defects from 400 to 1,700 nm are presented. In addition, we characterize the second-order effect in the 800–1,000 nm range that emanates from the use of a diffraction grating in the VNIR hyperspectral imaging system. We have devised methods to perform SWIR spectral calibration and to remove the bad pixels inherent to the SWIR InGaAs focal plane array used in the imaging system. We envision that hyperspectral imaging techniques will continue to play a significant role in the agro-food sector as critical research tools, and in further applications for rapid inspection of produce safety and quality.

Evaluation of cross-track illumination in EO-1 Hyperion imagery for lithological mapping

Abstract: Hyperspectral remote sensing data is a powerful tool for discriminating lithological units and for the preparation of mineral maps for alteration studies. The spaceborne hyperspectral Hyperion sensor, despite its narrow swath width ∼7.5 km, possesses great potential with its 196 channels within the wavelength range 426.82–2395.50 nm. Although it has many advantages such as low cost and on-demand coverage, much uncertainty exists in the utility of its applications. For example, poor signal-to-noise ratio, the presence of sensor-specific defects and thicker atmospheric column due to its spaceborne platform makes certain environmental and geological applications difficult or impossible. In this article we demonstrate these calibration-related uncertainties, which are manifest from the preprocessing stage to the classification stage. In addition, the intimate mixing of minerals within specific targets, for example within individual outcropping lithological units or endmembers, adds uncertainty to our spectral discrimination results. The aim of this study was to develop and evaluate an approach for geological mapping of outcrops with Earth Observing-1 EO-1 Hyperion data. Atmospheric corrections and correction for cross-track illumination CTI variations smile were determined at different wavelength regions: the visible–near-infrared VNIR; 420–1000 nm and shortwave infrared SWIR; 1000–2400 nm regions. Our methodology was tested in a selected site at Central Anatolia, Turkey containing minimal vegetation cover. The results obtained from the image analyses were then compared and assessed with field observations and spectral measurements.

Visible and Near-IR Reflectance Spectra of Mars Analogue Materials Under Arid Conditions for Interpretation of Martian Surface Mineralogy

Abstract: Visible and near-IR (VNIR) spectra from the hyper-spectral imagers MRO-CRISM and Mars Express OMEGA in martian orbit have signatures from Fe-bearing phases (e.g., olivine, pyroxene, and jarosite), H2O/OH-bearing phases (e.g., smectites and other phyllosilicates, sulfates, and high-SiO2 phases), and carbonate [e.g., 1-5]. Mineralogical assignments of martian spectral features are made on the basis of VNIR spectra acquired in the laboratory under appropriate environmental conditions on samples whose mineralogical composition is known. We report here additional results for our ongoing project [6] to acquire VNIR spectra under arid conditions.

Detection and classification of oil spills in MODIS satellite imagery

Abstract: Using satellite imagery to achieve an early and accurate identification of oil spills will contribute towards the reduction of their impact on the marine ecosystem. Satellite imagery provided by the synthetic aperture radar (SAR) sensors are widely used for this task over the multi-temporal and multi-band visible near infra-red (VNIR) sensors. This is due to the SAR imaging capabilities through clouds, dust storms, soot and at night times, which limit the capability of VNIR sensors. However, gaps in knowledge exist regarding whether satellite ocean-colour sensors are capable of identifying unreported oil spills as true positives and whether they are able to discriminate them from lookalikes with the least uncertainty, particularly in arid land regions characterised with nearly cloud-free conditions. It was therefore, the goal of this research to develop reliable and robust methodology for data processing and interpretation of oil spills observed by VNIR sensors. The Moderate Resolution Imaging Spectroradiometer (MODIS) is a VNIR-type sensor that was selected for this project for a number of reasons: it is characterised with adequate multi-spectral features (36 spectral bands 0.405-14.385 ?m) spread over three spatial resolutions (250, 500 and 1000 m); and its data is freely distributed in near-realtime. MODIS bio-geophysical products processed in this study such as sea surface temperature (SST4 and SST) and chlorophyll-a (Chlor-a) have also proven their usefulness in providing complementary data. As a result of this investigation, two methods were proposed: The spectral contrast shift (SCS) and the surface algal bloom index (SABI). The SCS identifies oil spills and classifies their thickness by using MODIS extreme (maximum and minimum) top-of-atmosphere radiance (TOA) values in the 250 m/pixel resolution bands: the red (?1=645 nm) and the NIR (?2 =859 nm) measured over a relatively small area selected to encompass part of an unknown class and part of the surrounding pure sea water. The method has produced consistent and highly sensitive results independent of sun-glint illuminations. Oil spills have SCS values lying within the range 0.02-0.04±0.002 varying by 0.01 corresponding to different thicknesses of oil. The SCS succeeded also in classifying surface floating blooms having SCS values greater than or equal to 0.20. The SABI is a four-band relationship, which according to MODIS 500 m/pixel resolution, is made up of the difference between the TOA radiance responses in the NIR and the red bands (aggregated from the 250 m resolution group) to the sum of the TOA radiance responses in the blue (?3=469 nm) and green (?4=555 nm) bands. The SABI aims to discriminate biological floating species that may appear as an oil spill look-alike without the need to perform complex corrections for atmosphere and sun-glint effects. The SABI succeeded in classifying 95% of surface blooms that had values greater than or equal to a baseline value of -0.10. Oil spills, however, always appear at values lower than the surface bloom baseline value.

The enhanced MODIS airborne simulator hyperspectral imager

Abstract: The EMAS-HS or Enhanced MODIS Airborne Simulator is an upgrade to the solar reflected and thermal infrared channels of NASA's MODIS Airborne Simulator (MAS). In the solar reflected bands, the MAS scanner functionality will be augmented with the addition of this separate pushbroom hyperspectral instrument. As well as increasing the spectral resolution of MAS beyond 10 nm, this spectrometer is designed to maintain a stable calibration that can be transferred to the existing MAS sensor. The design emphasizes environmental control and on-board radiometric stability monitoring. The system is designed for high-altitude missions on the ER-2 and the Global Hawk platforms. System trades optimize performance in MODIS spectral bands that support land, cloud, aerosol, and atmospheric water studies. The primary science mission driving the development is high altitude cloud imaging, with secondary missions possible for ocean color. The sensor uses two Offner spectrometers to cover the 380-2400 nm spectral range. It features an all-reflective telescope with a 50° full field-of-view. A dichroic cold mirror will split the image from the telescope, with longer radiation transmitted to the SWIR spectrometer. The VNIR spectrometer uses a TE-cooled Si CCD detector that samples the spectrum at 2.5 nm intervals, while the SWIR spectrometer uses a Stirling-cooled hybrid HgCdTe detector to sample the spectrum at 10 nm per band. Both spectrometers will feature 1.05 mRad instantaneous fields-of-view registered to the MAS scanner IFOV's.

The application of ASTER imageries and mathematical evaluation method in detecting cyanobacteria in biological soil crust, Chadormalu area, central Iran

Abstract: Soil surfaces in arid and semi-arid lands often lack photoautotrophic life but are covered by communities of soil surface covering organisms able to tolerate dehydration, and thus adapted to aridity. One important objective of multi-spectral remote sensing instruments is the detection of the optical characteristics of the Earth’s surface using high spectral resolution bands. In this study ASTER imagery and reflected radiation in VNIR bands were used to investigate biological Soil Crusts (BSCs) in the field. By applying IARR (Internal Average Relative Reflectance), FCC (False Color Composite), MNF (Minimum Noise Fraction Transform), and MEM (Mathematical Evaluation Method) techniques, BSCs are successfully detected in the Chadormalu desert area of central Iran. This study clearly shows the capability of ASTER data (VNIR bands) to detect BSC or cyanobacteria soil crusts. The proposed MEM method, despite being approximative is suitable for detecting microorganisms in inaccessible areas such as other planet surfaces or remote areas on earth.

Combining multispectral aerial imagery and digital surface models to extract urban buildings.

Abstract: This paper presents an automated classification of buildings in Coleraine, Northern Ireland. The classification was generated using very high spatial resolution data (10 cm) from a Digital Mapping Camera (DMC) for March 2009. The visible to near infrared (VNIR) bands of the DMC enabled a supervised classification to be performed to extract buildings from vegetation. A Digital Surface Model (DSM) was also created from the image to differentiate between buildings and other land classes with similar spectral profiles, such as roads. The supervised classification had the lowest classification accuracy (50%) while the DSM had an accuracy of 81%. The combination of the DSM and the supervised classification achieved an overall classification accuracy of 95%. Two spatial metrics (percentage of the landscape and number of patches) were also used to test the level of agreement between the classification and digitised building data. The results suggest that fine resolution multispectral aerial imagery can automatically detect buildings to a very high level of accuracy. Current space borne sensors, such as IKONOS and QuickBird, lag behind airborne sensors with VNIR bands provided at a much coarser spatial resolution (4m and 2.4m respectively). Techniques must be developed from current airborne sensors that can be applied to new space borne sensors in the future. The ability to generate DSMs from high resolution aerial imagery will afford new insights into the three-dimensional aspects of urban areas which will in turn inform future urban planning.

Detection of ancient Egyptian archaeological sites using satellite remote sensing and digital image processing

Abstract: Satellite remote sensing is playing an increasingly important role in the detection and documentation of archaeological sites. Surveying an area from the ground using traditional methods often presents challenges due to the time and costs involved. In contrast, the multispectral synoptic approach afforded by the satellite sensor makes it possible to cover much larger areas in greater spectral detail and more cost effectively. This is especially the case for larger scale regional surveys, which are helping to contribute to a better understanding of ancient Egyptian settlement patterns. This study presents an overview of satellite remote sensing data products, methodologies, and image processing techniques for detecting lost or undiscovered archaeological sites with reference to Egypt and the Near East. Key regions of the electromagnetic spectrum useful for site detection are discussed, including the visible near-infrared (VNIR), shortwave infrared (SWIR), thermal infrared (TIR), and microwave (radar). The potential of using Google Earth as both a data provider and a visualization tool is also examined. Finally, a case study is presented for detecting tell sites in Egypt using Landsat ETM+, ASTER, and Google Earth imagery. The results indicated that principal components analysis (PCA) was successfully able to detect and differentiate tell sites from modern settlements in Egypt's northwestern Nile Delta region.

Prediction and map-making of soil organic matter of soil profile based on imaging spectroscopy: A case in Hubei China

Abstract: In this paper, the content and distribution of soil organic matter (SOM) along a soil profile was predicted using two hyperspectral imaging cameras, a visible and near infrared (VNIR) camera (400–1000 nm) and a near infrared (NIR) camera (900–1700 nm). Three to five soil samples were taken from the different horizons of each soil profile. Each sample was divided into four subsamples of different soil particle sizes and one not-messed subsample. All samples were placed under the cameras to obtain hyperspectral data to establish different SOM partial least square regression (PLSR) prediction models. The performance of the spectral prediction models of five subsamples was acceptable. And the coefficient of determination of validation dataset (R2 val ) and the ratio of prediction to deviation (RPD) of the not-messed soil samples were 0.833 and 2.005, respectively. Then the model was used to predict the SOM content and make the distribution map of SOM along profile.

Image Processing Method of Airborne WiDAS Sensor in WATER Campaign

Abstract: WiDAS(Wide-angle Infrared Dual-mode line/area Array Scanner) is one of the major airborne sensors flew in the WATER(Watershed Allied Telemetry Experimental Research) field campaign.It acquires multi-angular information about surface reflectance and emission anisotropy through wide-angle imaging.This paper describes the algorithm and key parameters for WiDAS data processing.The detectors,image resolutions,and object spectral characteristics are much different between WiDAS VNIR bands and TIR/MIR bands.So,we adapt different data processing algorithm for VNIR bands and TIR/MIR bands.In VNIR bands,the CCD cameras are calibrated with integration sphere;images in different bands are aligned with simple warp function;atmosphere correction is performed with 6S model and measured aerosol optical thickness.In TIR/MIR bands,the IR cameras are calibrated with blackbody;image alignment have to use compound wrap function and more complex matching algorithm;and atmosphere correction is performed with MODTRAN model and measured atmosphere profile.The method to extract multiangular observations of ground object from standard WiDAS data products is also described.This paper can be informative when using WiDAS data in quantitative researches.

Retrieval of spectral response functions for the hyperspectral sensor of HISUI (Hyperspectral Imager SUIte) by means of onboard calibration sources

Abstract: HISUI (Hyper-spectral Imager SUIte), which is the next Japanese earth observation project, has been developed under the contract with Ministry of Economy, Trade and Industry(METI) and New Energy and Industrial Technology Development Organization(NEDO). HISUI is composed of hyper-spectral sensor and multi-spectral sensor. The hyperspectral sensor is an imaging spectrometer with two separate spectral channels: one for the VNIR range from 400 to 970 nm and the other for the SWIR range from 900 to 2500 nm. Ground sampling distance is 30 m with spatial swath width of 30 km. The spectral sampling will be better than 10 nm in the VNIR and 12.5 nm in the SWIR. The multi-spectral sensor has four VNIR spectral bands with spatial resolution of 5m and swath width of 90 km. HISUI will be installed in ALOS-3 that is an earth observing satellite in the project formation phase by JAXA in FY 2015. This paper is concerned with the retrieval of spectral response functions (SRF) for the hyper-spectral sensor. The center wavelength and bandwidth of spectral response functions of hyper-spectral sensor may shift and broaden due to the distortion in the spectrometer, the optics and the detector assembly. Therefore it is necessary to measure or estimate the deviation of the wavelength and the bandwidth broadening of the SRFs. In this paper, we describe the methods of retrieval of the SRF's parameters (Gaussian functions assumed) by means of onboard calibration sources and we show some simulation's results and the usefulness of this method.

Hyperspectral and multiple imaging controlled enclosure for sample examination

Abstract: The invention pertains to the creation of an enclosed hyperspectral and/or multispectral and/or ultraspectral and/or full spectrum and/or full frame and/or scanning imaging device that will use a multitude of spectral ranges, fluorescence features, polarized filtration, zoom lenses, and con-focal capabilities. The system would also have related imaging attachments, and triggered lights for multiple ranges, including UV, VNIR, SWIR and LWIR. The system collectively will be constructed so that it can hold 4 imaging devices. The system would have a high throughput and processing computer system with large volume data storage and redundancy in order to process large data loads quickly and efficiently. This system would be used to analyze explosives and/or other targets close up from large quantities to micro quantities and feature upgradeable transmission containers for imaging specific targets in their gaseous forms so that viable libraries and classification features can be constructed.

Data Fusion of Visible/Near-infrared Spectroscopy and Spectral Scattering for Apple Quality Assessment

Abstract: Visible/near-infrared (VNIR) spectroscopy and spectral scattering are based on different sensing principles, and they have shown different abilities for predicting apple fruit firmness and soluble solids content (SSC). Hence the two techniques could work synergistically to improve the quality prediction of apples. In this research, VNIR spectroscopic and spectral scattering data for the wavelength range of 460–1,100 nm were collected for 6,631 apples of ‘Delicious', 'Golden Delicious' and 'Jonagold' cultivars during the 2009 and 2010 harvest seasons and for three months after the refrigerated air storage. Partial least squares models were developed for each sensor and their combination to predict the fruit firmness and SSC for both single-year and cross-year data sets. The spectral scattering technique generally performed better in predicting firmness, whereas the VNIR technique was superior for prediction of SSC. Overall, the data fusion of the two sensors produced significant improvements (p<0.05) for prediction of the firmness and SSC than individual sensors. Cross-year prediction results for firmness and SSC were lower, compared with prediction results for each year. However, the cross-year prediction model for SSC was more robust and less sensitive to the harvest-year effect, compared to the firmness prediction model. Sensor fusion can provide more robust and accurate firmness and SSC assessment for apples.

Downscaling Albedo from moderate-resolution imaging spectroradiometer (MODIS) to advanced space-borne thermal emission and reflection radiometer (ASTER) over an agricultural area utilizing ASTER visible-near infrared spectral bands

Abstract: Due to the limitation in spatial and spectral resolution, a few numbers of satellite data are applicable in field scale surface Albedo modeling. ASTER was an alternative for surface energy balance modeling, but since April 2008, shortwave detector has stopped recording due to the high-abnormal-temperature problem. Beside, temporal resolution of ASTER is insufficient for field-scale monitoring of surface parameters. Thus, this study was aimed first; to examine the capability of ASTER VNIR bands in estimation of surface Albedo and second, to downscale Albedo from MODIS to ASTER using Albedo resulted from ASTER VNIR bands. Combination of these two stages is expected to be a solution for field scale monitoring of surface Albedo from MODIS and ASTER data acquired after April 2008. Results confirmed that bands 1 and 3 which is available after April 2008 on ASTER data can be modeled for estimation of surface Albedo with less than 0.024% loss of information where land cover consist of soil and vegetation. From four downscaling methods, namely FSIM, PBIM, wavelet transfer and high pass filter (HPF) examined in this study, we also found that the most precise subpixel estimate were obtained by FSIM downscaling method (R2 = 0.96, RMSE = 0.01); although, the outputs of three other methods were significant.

Spectroscopy as a tool for geochemical modeling

Abstract: This study focused on testing the feasibility of up-scaling ground-spectra-derived parameters to HyMap spectral and spatial resolution and whether they could be further used for a quantitative determination of the following geochemical parameters: As, pH and C lignite content. The study was carried on the Sokolov lignite mine as it represents a site with extreme material heterogeneity and high heavy-metal gradients. A new segmentation method based on the unique spectral properties of acid materials was developed and applied to the multi-line HyMap image data corrected for BRDF and atmospheric effects. The quantitative parameters were calculated for multiple absorption features identified within the VIS/VNIR/SWIR regions (simple band ratios, absorption band depth and quantitative spectral feature parameters calculated dynamically for each spectral measurement (centre of the absorption band (λ), depth of the absorption band (D), width of the absorption band (Width), and asymmetry of the absorption band (S)). The degree of spectral similarity between the ground and image spectra was assessed. The linear models for pH, As and the C lignite content of the whole and segmented images were cross-validated on the selected homogenous areas defined in the HS images using ground truth. For the segmented images, reliable results were achieved as follows: As: R 2 =0.84, C lignite : R 2 =0.88 and R 2 pH: R 2 = 0.57.