Crop classification plays an important role in effective and controllable agricultural management. In this paper, nine scenes of Landsat8_OLI data in 2013 were collected for cotton planting area extraction in Shawan country of Xinjiang Uygur Autonomous Region, China. Normalized Difference Vegetation Index (NDVI) time series were generated to characterize the phenological pattern of each crop type. The optimal temporal reflectance image was chosen by analyzing the difference of NDVI profile between cotton and other crop types. The hierarchical classification strategy was performed on the three features of NDVI time series, NDVI statistics and reflectance. Firstly a simple decision tree was built on NDVI statistics and reflectance to extract vegetation cover; then various types of crops were distinguished by support vector machine (SVM) and maximum likelihood supervised classifier (MLC), thereby cotton plating area was extracted. A comprehensive evaluation for the cotton extraction map was performed using human-computer interaction visual interpretation and ground truth data. Results showed that MLC achieved the accuracy of 97.56% for cotton extraction. The cotton extraction map was very consistent with the ground truth data. This multi-temporal classification method is promising for crop extraction even for land cover classification.

The light-use efficiency (LUE) is one of critical parameters in the terrestrial ecosystem production studies. However, it is still a challenge how to up-scale LUE from canopy to the landscape/regional scales. One potential solution is to use automated multi-angle tower-based remote sensing platforms, which observe canopy reflectance with high spatial, temporal, spectral and angle resolution. Although some published paper on the LUE in boreal and temperate forests had used continuous multi-angle measurements of the surface reflectance, lack of study in literature investigated the vegetation physiological parameters of cropland using the surface reflectance with high spatio-temporal and high spectral data with multiple angles. To improve our understanding of physiological status of cropland, the maize within the footprint of the Daman Superstation flux tower site of Heihe Watershed Allied Telemetry Experiment Research (HiWATER) was employed in this study. Based on the observed reflectance and flux data, a Bidirectional Reflectance Distribution Function (BRDF) of vegetation index (Photochemical Reflectance Index, PRI and Vegetation Index using the Universal Pattern Decomposition method, VIUPD) at continuous time series was established by integrating of a semi-empirical kernel-driven BRDF model (RossThick-LiSparse), a footprint model (the Simple Analytical Footprint model on Eulerian coordinates for scalar Flux, SAFE-f) and a LUE model. Besides, based on the sky-condition (direct/diffused radiation) data, the relationships between the vegetation index (PRI and VIUPD) and sunlit/shaded LUE under corresponding sky conditions were established. Taking maize field as an example, the measurements were obtained during June to August, 2012. The relationships between PRI and LUE for sunlit and shaded leaves were: PRIsu=0.06339log(LUEsu) + 0.04882, PRIsh= 0.02675log(LUEsh) + 0.01619, where, the subscript su, sh represent sunlit and shaded leaves respectively; p< 0.0001, R2 (Coefficient of determination) equal 0.6443 and 0.6081 for sunlit and shaded, respectively. Then the LUE was up-scaled to landscape/regional scales based on these relationships and sky conditions, and it can be used for the estimation of gross primary productivity (GPP) of cropland using a LUE-based model with high accuracy.

Due to the budget and technical limitations, remote sensing sensors trade spatial resolution, swath width and spectral resolution. Consequently, no sensor can provide high spatial resolution, high temporal resolution and high spectral resolution simultaneously. However, the ability of earth observation at fine resolution is urgently needed for global change science. One possible solution is to “blend” the reflectance from high spatial resolution with less frequency coverage (e.g. Landsat), daily, global data (e.g. MODIS, Moderate Resolution Imaging Spectroradiometer), and high spectral resolution with low re-visit cycle (e.g. Hyperion). However, the previous algorithms for blending multi-source remotely-sensed data have some shortcomings, especially for less consideration of hyperspectral information. To overcome this shortcoming, this study has developed a SPAtial-Temporal-Spectral blending model (SPATS) which can simulate surface reflectance with high spatial-temporal-spectral resolution. SPATS is based on existing spatial-temporal image fusion model and spatialspectral image fusion model. Taking Landsat, Hyperion and MODIS data as example, the performance of SPATS was tested with both simulated and observed satellite data, especially at heterogeneous landscapes. The results show that the high spatial-temporal-spectral resolution reflectance data can be applied to a new investigation into how global landscapes are changing at different temporal scales.

The Compact Reconnaissance Imaging Spectrometer for Mars (CRISM) on the Mars Reconnaissance Orbiter (MRO), which has high spectral and spatial resolution, has greatly enhanced our understanding of the composition of hydrous minerals on Mars. Researchers have successfully identified and mapped the hydrous minerals using the spectral features of water absorption based on CRISM data. But more quantitative information will yield strong constraints on the formation conditions of the hydrous minerals and even on ancient habitable environments on Mars[1]. However, there exist few studies on quantitative retrieval of hydrous minerals mainly due to the following problems: 1) the nonlinear model usually complicated, which limits its scope of application; 2) the mixture of minerals at visible-infrared wavelengths is nonlinear, and large errors will occur if the linear model is used directly; 3) The distribution characteristics (relatively low concentration, scattered distribution, and unclear or unknown background minerals) of hydrous minerals result in difficulty in determining the spectra and the number of endmembers from the image itself, which is usually needed for unmixing.

The detection of hydrous minerals on Mars is of great importance for revealing the early water environment as well as possible biotic activity. However, few studies focus on quantitatively retrieving hydrous minerals for some difficulties. In this letter, we studied the area around the Mars Science Laboratory (MSL) landing site, to identify hydrous minerals and retrieve their abundance. Firstly, the distribution of hydrous minerals was extracted using their water absorption features. Then, a sparse unmixing algorithm was applied along with the CRISM spectral library to retrieve the abundance of hydrous minerals in this area. As a result, seven hydrous minerals were quantitatively retrieved, e.g. actinolite, montmorillonite, saponite, jarosite and so forth, and the total concentration of all hydrous minerals was as high as 40 vol% near the lower reaches of Mount Sharp. Our results were consistent with results from related research and the in-situ analysis of the MSL rover Curiosity.

This study proposed a multidimensional remote sensing data storage structure related to spatial, temporal and spectral dimensions, the integrated organization of this multidimensional remote sensing data for long time series, and the multidimensional analysis from spatial, temporal, spectral dimensions. Five data formats were included within the multidimensional data storage, which were TSQ (Temporal Sequential), TSP (Temporal Sequential Pixel), TIB (Temporal Interleaved by Band), TIP (Temporal Interleaved by Pixel) and TIS (Temporal Interleaved by Spectrum). Quick extraction for spectral, temporal, band cube, temporal cube, and feature cube can be accomplished. Meanwhile, one data format can be transformed to another data format based on five multidimensional data formats. First, a set of basic transformation was proposed to achieve the relationship between part data format. Second, indirect transformation can be achieved according to the set of basic transformation. For the demand of multidimensional conversion and extraction of four-dimensional remote sensing data, the proposed four dimensional remote sensing data structure of this study can achieve the integration organization and management of multidimensional remote sensing data, which could support the application of multidimensional analysis.

Landsat satellites series provide large amounts of data for both the regional and global vegetation time series observation. As the currently operational Landsat satellites, Landsat 7 and Landsat 8 take the responsibility of multi-decadal Landsat imagery. However, the updating or changing of Landsat sensors will bring in a certain degree of bias for the long-term continuity research. To minimize this bias between ETM+ on board Landsat 7 and OLI on board Landsat 8, we intended to obtain the relationship between these two sensors for further research based on long-term Landsat data. Firstly, the wheat reflectance spectra from the field experiments was used to simulate for ETM+ and OLI satellite-based values. Then the linear regression relationship between two sensors was established via three vegetation indices which were the Normalized Difference Vegetation Index (NDVI), Soil-adjusted Vegetation Index (SAVI) and Enhanced Vegetation Index (EVI). Lastly, three study areas (A: Harvard Forest, B: Hulun Buir Grassland and C: Kahurangi National Park in New Zealand) were selected to verify the performance of the regression relationships by using images of ETM+ and OLI for the closed available time period. The results showed that: (1) the values of OLI was 1%-3% higher than ETM+ when only the instruments setting difference was been taken into consideration. (2) the continuity between the two Landsat sensors improved averaging 3% after the calibration by the regression relationship. Thus, the results based on regression relationship of this records offer the potential of effective calibration for the Landsat time series research related to ETM+ and OLI sensors.

Hyperspectral imaging can provide both continuously spatial and spectral information of the Earth\'s surface that allows us mapping of the regional geological and mineral resources information. One of the most successful applications of hyperspectral imaging remote sensing identified was regional geological and mineral resource surveys. A light-weight airborne hyperspectral imaging system (LAHIS) has been developed in China. The hardware of the compact LAHIS include hyperspectral VNIR imaging sensors, a hyperspectral SWIR imaging sensors, high resolution optical remote sensor and a POS (IMU and DGPS). The weight of the system is less than 23kg. The VNIR hyperspectral imaging sensors measures incoming radiation in 250 contiguous spectral channels in the 400–1000 nm wavelength range with spectral resolution of better than 2-3 nm and creates images of 334 pixels for a line of targets with a nominal instantaneous field of view (IFOV) of ~1 mrad. The SWIR hyperspectral imaging sensors measures incoming radiation in 256 contiguous spectral channels in the 1000–2500 nm wavelength range with spectral resolution of better than 6 nm and creates images of 320 pixels for a line of targets with a nominal instantaneous field of view (IFOV) of ~2 mrad. The 400 to 2500nm spectral range provides abundant information about the regional geological and mineral resources information. Two ground mineral scan experiment and an UAV carried flying experiment has been done. The experiment results show the LAHIS have achieved relative high performance levels in terms of signal to noise ratio and image quality. The potential applications for light-weight airborne hyperspectral imaging system in the regional geological and mineral resources survey are tremendous.

Lots of researches have been increasingly focusing on time series analysis of remote sensing datasets, deriving phenology time and trajectory parameters by carve fitting and detecting changes due to natural or artificial factors. For these applications extraction of various characteristic parameters is an indispensable and fundamental procedure. However, there is a lack of an integrated method currently to manage long time-series remote sensing imagery, meanwhile as a direct access to extracting time series of various characteristic parameters. In this paper we propose a user-friendly program for managing time series of remote sensing datasets, what’s more, extracting time series data for areas like point, rectangle and general polygon, according to user-defined formula automatically computing and constructing time series of various characteristic parameters. In addition, spectral data for one day, after a point or scope is selected, is able to be extracted and processed using general spectral analysis methods. This program tries to manage four dimensional (including time, spatial and spectral dimensions) remote sensing datasets, and be applicable to outputting time series of characteristic parameters and spectral data, providing an innovatively fast and flexible tool for time-series studies.

The telluric O2-B and O2-A have been proved to be capable of solar-induced vegetation fluorescence (SIF) retrieval based on Fraunhofer line depth (FLD) principle. However, most FLD based algorithms mainly aim for SIF detection in O2-A, not suitable in O2-B. One of the critical reasons is that it is very difficult to model the sudden varying reflectance around O2-B band located in the red-edge spectral region (about 680-800 nm). In order to resolve this issue, this study proposes a new method based on the established inverted Gaussian reflectance model (IGM) and FLD principle using hyperspectral radiative transfer simulations with 1 nm bandwidth in 400-1000 nm range. Results show that the proposed method can better capture the spectrally non-linear characterization of the reflectance in 680-800 nm and thereby enables retrieval in O2-B, yielding much more accurate SIFs than typical FLD methods, including sFLD, 3FLD and iFLD.

Remotely sensed solar-induced chlorophyll fluorescence (SIF) has been considered an ideal probe in monitoring global vegetation photosynthesis. However, challenges in accurate estimate of faint SIF (less than 5% of the total reflected radiation in near infrared bands) from the observed apparent reflected radiation greatly limit its wide applications. Currently, the telluric O2-B (~688nm) and O2-A (~761nm) have been proved to be capable of SIF retrieval based on Fraunhofer line depth (FLD) principle. They may still work well even using conventional ground-based commercial spectrometers with typical spectral resolutions of 2~5 nm and high enough signal-to-noise ratio (e.g., the ASD spectrometer). Nevertheless, almost all current FLD based algorithms were mainly developed for O2-A, a few concentrating on the other SIF emission peak in O2-B. One of the critical reasons is that it is very difficult to model the sudden varying reflectance around O2-B band located in the red-edge spectral region (about 680-800 nm). Diurnal

The objective of this paper is the description of the development and the validation, using airborne hyper-spectral imagery data, of a non-conventional technique for the vegetation information extraction. The proposed approach namely the universal pattern decomposition method (UPDM) is tailored for hyper-spectral imagery analysis, which can be explained using two analysis methods: spectral mixing analysis and multivariate analysis. For the former, the UPDM expresses the spectrum of each pixel as the linear sum of three fixed, standard spectral patterns (i.e., the patterns of water, vegetation, and soil); each coefficient represents the ratio of spectral patterns of three components. If we think of the UPDM as multivariate analysis, standard patterns are interpreted as an oblique coordinate system, and coefficients are thought of as the coordinates of a pixel';s reflectance. The later explanation is much more comprehensible than the former for the reason of additional supplementary pattern presence when necessary. The vegetation index based on the UPDM (VIUPD) is expressed as a linear sum of the pattern decomposition coefficients. Here, the VIUPD was used to examine vegetation amounts and degree of terrestrial vegetation vigor; VIUPD results were compared with results by the normalized difference vegetation index (NDVI), and an enhanced vegetation index (EVI). This paper described the calculation of VIUPD, using AVIRIS airborne remotely sensed data. The results showed that the VIUPD reflects vegetation and vegetation activity more sensitively than the NDVI and EVI.

Comparing with the satellite and the common aerial vehicle, the UAVRSS (unmanned aerial vehicle remote sensing system) is a new type of aviation remote sensing for earth observation. It can work in an ultrahigh (10-30km) or ultra-low space (150-400m) for its long time, instantaneous transmission, low-cost, loading ability, etc. In this paper, through the characteristic analyses of the UAVRSS and the images/data from two aspects, some new applications are developed for the UAVRSS images.

The objective of this paper is the description of the development and the validation, using airborne hyper-spectral imagery data, of a non-conventional technique for the vegetation information extraction. The proposed approach namely the universal pattern decomposition method (UPDM) is tailored for hyper-spectral imagery analysis, which can be explained using two analysis methods: spectral mixing analysis and multivariate analysis. For the former, the UPDM expresses the spectrum of each pixel as the linear sum of three fixed, standard spectral patterns (i.e., the patterns of water, vegetation, and soil); each coefficient represents the ratio of spectral patterns of three components. If we think of the UPDM as multivariate analysis, standard patterns are interpreted as an oblique coordinate system, and coefficients are thought of as the coordinates of a pixel';s reflectance. The later explanation is much more comprehensible than the former for the reason of additional supplementary pattern presence when necessary. This paper validates the UPDM using AVIRIS airborne imagery, and the results provide an expected assumption.

In this paper, the potential of using hyperspectral data to identify, the materials covered by different paints is discussed. The study was carried out basing on spectra measured on the ground, and the spectral characteristics of building materials covered by different paint are analyzed. Result shows that changes of the spectral characteristics are highly relevant to the paint color and its thickness for different materials. It also indicates that the spectral characteristics in short-wave infrared range is least affected by the paint and can be used to identify the materials under the paint.

A Field Imaging Spectrometer System (FISS) based on a cooling area CCD was developed. This paper describes the principle, structural design, main technology parameters and data processing flow, as well as some application experiments using the FISS instrument. The FISS instrument shows huge potential applications in geology, food, agriculture, forestry and other respective field. ? 2011 IEEE.

This paper introduces the noise term to the radiative transfer equation. The noise appears to be amplified when the atmospheric correction. After researching the algorithms of Aster-TES and ISSTES based on TASI dataset, the results show that: the accuracy of temperature and emissivity retrieved from improved algorithm is higher than the other two; the images by Aster-tes have better definition on space dimension. These results have important practical value on the temperature and emissivity separation from low emissivity surfaces, such as mental and some man-made surfaces.

Based on our previous work, this paper further investigated radiometric properties of a novel field imaging spectrometer system (FISS), the first imaging sensor aimed specifically at field imaging spectrometry in China. Using a well-calibrated integrating sphere and a diffuse reflectance standard, two independent laboratory and field experiments were carefully designed to jointly calibrate the FISS radiometrically. Among all the radiometric properties of FISS, four critical ones were chosen and thoroughly analyzed, including radiometric temporal stability, pixel uniformity, response linearity and system noise. Calibration results show that (i) the 2-dimensional cooled array CCD in FISS undergoes an average pixel non-uniformity of less than 7% and temporal stability of ?2.5% both evaluated by the coefficient of variation; (ii) FISS has a good radiometric linearity between input radiance and output DN values with the calibration accuracy of R2 ≥ 0.99 and NRMSE (normalized root-mean-square-error) ?0.01 for each spectral channel; and (iii) the FISS system performs in a high signal to noise ratio level due to the employment of a semiconductor refrigeration technology in the array CCD camera, which produces a low dark offset of ?5DN if the cooled temperature lower than 20 °C. Additionally, to meet the requirement of precision quantitative applications, FISS was relative radiometrically corrected by histogram equalization and the spectral smile problem of FISS was discussed as well. In contrast with our earlier work, this study not only further demonstrates that FISS has achieved high performance, but provides useful guidelines to make good use of it, meanwhile promotes better understanding of FISS-type sensors.

Extraterrestrial Solar Spectral Irradiance (ESSI) is an important parameter for calculating Mean Solar Exoatmospheric Irradiance (MSEI) of each band for a given satellite. In order to find optimal ESSI dataset for calculation of MSEI, 5 ESSI datasets from MODTRAN4.0 software and ESSI dataset simulated by Santa Barbara DISORT Atmospheric Radiative Transfer (SBDART) tool were selected to compute MSEI of each band for HJ-1A CCD1, CEBERS02 CCD, Landsat TM5(band1?band4) and ASTER(band1?band9). Comparisons between the calculated MSEI and the published MSEI were made. It is found that ESSI dataset simulated by SBDART tool and MODTRAN oldkur.dat dataset are best suitable for calculation of MSEI and the MSEI results are consistent with published MSEI. There are big errors using MODTRAN thkur.dat and newkur.dat datasets to compute MSEI for these satellites, so that they are not recommended to calculate MSEI for given satellites.

Multi-sensor NDVI(Normal Different Vegetation Index) continuity is very important in long-term earth observation and climate research. Correlation coefficient is chosen to analyse the continuity of NDVI between medium resolution satellites: Beijing-1, TM(LandSat-5), SPOT-4. After the selection of sample pixels(remove pixels that contain water and great topographic area), results shows that Beijing-1 NDVI has very good linear correlativity with TM and SPOT(correlation coefficients higher than 0.97). And further statistical comparison between Beijing-1 NDVI and TM, SPOT NDVI data shows that Beijing-1 NDVI are more similar to TM';s than SPOT';s. The present results suggest that Beijing-1 NDVI has very good continuity with TM and SPOT NDVI in flat area. This shows that Beijing-1 has very good detectation ability and will extend Beijing-1';s application field. ? 2010 IEEE.

We all know that several methods were proposed to retrieve temperature and emissivity, among which the TES and ISSTES are better than the other methods. We use 40 man-made materials spectra from ASTER spectral library, and assess the stability and accuracy of these methods to MMD, MMR, epsilon (max) input, and band number, respectively. The results show that the accuracy of TES method is connected with epsilon (min) input, while ISSTES has nothing to do with epsilon (min); Using piecewise function to simulate the MMR or MMD relationship can improve the accuracy; Band number is an important factor for RMSE on temperature and emissivity, and 16-bands may be enough for ISSTES algorithm.

Because field imaging spectrometer has high spatial resolution, soil particles as noise can make many unreasonable edges for its vegetation images. In this paper, in order to gain satisfied edges, we present an edge detection method based on b-spline wavelet transform and 8-connected components. The results show that this method is better than Canny operator with 8-connected components. ?2010 IEEE.

Solar induced Chlorophyll Fluorescence (CF) is an effective indicator for plant photosynthetic activity status and is directly related to O2-A absorption feature. In this study the CF intensity is estimated from spaceborne sensor, EO-1 Hyperion. The surface reflectance image is derived by atmospheric correction using MODTRAN4.0 with concurrent atmospheric parameters provided by the AErosol RObotic NETwork (AERONET) Level 2.0 data. Then, the CF signal is decupled from reflected radiation at canopy level by Fraunhofer Line Discriminator (FLD) principle and corresponding surface reflectance. In turn, some vegetation indices, like Normalized Difference Vegetation Index (NDVI), Modified Soil Adjusted Vegetation Index (MSAVI), Photochemical Reflectance Index (PRI) and Normalized Difference at O2-A absorption Index (NDOI), are derived the from surface reflectance image. Some degree of correlation between these vegetation indices (except for NDVI) and estimated CF signal is expected for vegetated areas and this correlation is much more obvious for grass areas.

The traditional look up table (LUT) scheme may have some problems when applied in some cases. To avoid these problems, a general LUT with 1 nm spectral resolution is presented in this work. At first, the atmospheric optical parameters were derived by running MODTRAN4 twice. Then, these datasets were loaded into SQLite for accessing rapidly and then the 6-D linear interpolation was used for extracting desired datasets. Finally, some experiments were carried out to test the performance of the proposed technique. Results show that it is very fast to access data from SQLite database, and the proposed technique has high performance and is convenient in most of the cases.

Imaging spectrometers provide the unique combination of both spatially contiguous spectra and spectrally contiguous images of the Earth's surface that allows spatial mapping of these minerals. One of the successful applications of imaging spectrometers remote sensing identified was geological mapping and mineral exploration. A Light weight Airborne Imaging Spectrometer System (LAISS) has been developed in China. The hardware of the compact LAISS include a VNIR imaging spectrometer, a SWIR imaging spectrometer, a high resolution camera and a position and attitude device. The weight of the system is less than 20kg. The VNIR imaging spectrometer measures incoming radiation in 344 contiguous spectral channels in the 400-1000 nm wavelength range with spectral resolution of better than 5 nm and creates images of 464 pixels for a line of targets with a nominal instantaneous field of view (IFOV) of ?1 mrad. The SWIR imaging spectrometer measures incoming radiation in the 1000-2500 nm wavelength range with spectral resolution of better than 10 nm with a nominal instantaneous field of view (IFOV) of ?2 mrad. The 400 to 2500nm spectral range provides abundant information about many important Earth-surface minerals. A ground mineral scan experiment and an UAV carried flying experiment has been done. The experiment results show the LAISS have achieved relative high performance levels in terms of signal to noise ratio and image quality. The potential applications for light weight airborne imaging spectrometer system in mineral exploration are tremendous.

Methane is an important greenhouse gas contributing to global climate change, and its warming effect is just second to carbon dioxide. Satellite remote sensing technique can obtain large scale distribution of trace gases, and it has been an important tool in the field of atmospheric observation. This paper presents the annual variations of methane in China based on the vertical columns of methane measured by the SCIAMACHY sensor on board ENVISAT. The variability of yearly averaged CH4 concentration in China and the whole world during 2003-2009 shows that the rapid growth of CH4 in China during 2005-2006 widened the difference between China';s CH4 level and the whole world';s level. China';s methane level has close relations with global climate change.

Gas hydrates are ice-like crystalline solids composed of water and gas, which widespread in permafrost regions and beneath the sea in sediments of outer continental margins. It is a new kind of potential and clean energy resource, and the dissociation of hydrate also play a great role in climate change due to their strong greenhouse effect. In this research, monthly methane concentration of Muli area from 2003 to 2008 is firstly analyzed, where natural gas hydrate sample was detected in 2008. It is found that monthly methane concentration of this area in December is obviously higher than that of surrounding area. And before 2006, the monthly methane concentration of August in this area is higher than that of other months, which is the same with the distribution of the whole country, however, the rule changes after that. The monthly methane concentration of winter in Muli area becomes the same high with that of summer. Compared with the timely earthquake data of this area, it is known that monthly methane concentration of March, 2007 abnormal increased for a little earthquake of magnitude 4.2 happened February 23rd, 2007. Based on the analysis results of Muli area, monthly methane concentration in permafrost area of China from 2003 to 2008 is analyzed to monitor the possible methane seepages of potential gas hydrate

Hyperspectral data offers a powerful tool for predicting soil heavy metal contamination due to its high spectral resolution and many continuous bands. Band selection, however, is the prerequisite for heavy metal inversion by hyperspectral data. In this study, soil reflectance spectra and their Cu contents were measured for 181 soil samples in the laboratory. Based on these dataset, band selection was conducted to inverse Cu contents using stepwise regression approach, and prediction accuracies of Cu based on partial least-squares regression (PLSR) model with different selected bands were analyzed. In addition, the influences of spectral resolution on prediction results of Cu were discussed by a Gaussian re-sampling function. It demonstrated that the optimal band number was 10 for Cu inversion and the corresponding model had prediction accuracy of R2 = 0.7523 and RMSE = 0.4699; the optimal spectral resolution was 32nm and the model on this basis had an accuracy of R2 =0.7028 and RMSE =0.5147. Results of this study may provide scientific verification for designing low-cost and practical hyperspectral spaceborne sensors, and theoretical bases for simulating spaceborne sensors to predict soil heavy metals contents in the future.

Accurate radiometric calibration (RC) of a scientific sensor is of crucial importance in ensuring the credibility of its products or comparing results derived from different sensors or times. It';s found that variations of optional system imaging settings may affect the sensor';s radiometric calibration results. However, previous researches seldom emphasized their influences on current RC models. From this point, this paper conducted sensitivity analyses of the RC model to three key optional system settings referring to the integration time ( t ), aperture ( F ) and detector temperature (T ) using our newly developed Field Imaging Spectrometer System (FISS). Quantitative determinations of their influences on sensor radiometric coefficients were respectively implemented. Results indicated that the three system settings play important roles in the detector';s radiometric response and change RC coefficients in different ways. Finally, an operational RC model taking into account the sensor';s imaging settings is initially discussed.

The TASI instrument has 32 spectral band covering 8μm ?11.5μm. The main goal of this paper is to analyze the SNR of each band and the temperature accuracy based on wavebands selection. SNR is calculated by RLSD algorithm. The TES algorithm selects three empirical relationships (MMD, MMR, and VAR). The band of least SNR was removed one by one from residual bands, and the temperature errors were computed. The results show that temperature accuracy of TES using 17-20 wavebands is highest for MMR and VAR algorithm, but it is different from above case. So it is important to evaluate the quality of thermal images, and remove the high-noises wavebands for MMR and VAR relationship before TES. ? 2011 IEEE.

Karst rocky desertification is a typical type of land desertification. It is associated with human disturbance and fragile eco-geological setting with high complexity and heterogeneity in karst regions. For the particularity of karst ecosystems and complexity of rocky desertification, Fractional cover of photosynthetic vegetation (PV), non-photosynthetic vegetation (NPV), bare soil (Soil), and exposed bedrock (Rock) were selected as the main symptoms of the ground desertification and key ecological evaluation indicators. In this study, field spectral reflectance measurements were used to develop several karst spectral indices (KSI) based on unique spectral features of non-vegetation land-cover types. The relationship of the mixed spectra of main land cover types and their responding fraction cover is analyzed preliminarily. The study indicates that the proposed karst spectral indices (KSI) have potential ability to estimate fractions of non-photosynthetic vegetation (NPV) and bare soil (Soil), with higher coefficient of determination (R2 of 0.73 and 0.70, respectively), while the karst spectral indices(KSI) can provide some information for fractions of exposed bedrock (Rock), with the maximum R 2 of 0.58.

As one of the most important payloads on Chang'e-1 satellite, the Interference Imaging Spectrometer (IIM) got large amounts of hyperspectral data for retrieving elements and mineral information of the lunar surface. The signal to noise ratio (SNR) of the IIM hyperspectral data has been assessed for the further applications of the IIM data. As the assessment results show, the SNR of the IIM data shows good value on the whole and the middle bands are much higher than the bands at the both ends. As the reflectance peak of the plagioclase and the pyroxene spectrum, the bands centered around 750nm exhibit high SNR, which is of great importance for retrieving elements and minerals on the lunar surface. However, the bands centered around 930nm, as the absorption valley, exhibit poor SNR. As the SNRs are not high enough on the whole, the noises of the first nine bands should be reduced carefully before used, although they have an obvious trend of rising. Moreover, the first band and the last one have too low SNRs to use.

With the deterioration of ecological environment, rare plants on the earth are decreasing rapidly, so there is an urgent need for the study on sophisticated vegetation classification. Hyperspectral data have great potential in sophisticated classification. FISS(Field Imaging Spectrometer System) is a newly developed system, and pixels of FISS images could be considered as pure pixels with high spatial and spectral resolution, which makes FISS a perfect option on the study of classification methodology. This study aims to evaluate different methods based on FISS data and find out the best one of sophisticated vegetation classification. The methods are as follows: Maximum Likelihood (ML), Spectral Angle Mapping (SAM), Artificial Neural Net (ANN), Support Vector Machine (SVM) and Composite Kernel Support Vector Machine (C-SVM). Firstly, segmented principal components transformation is adopted for spectral dimensionality reduction, and all bands are divided into 2 subsets according to the correlation matrix. Secondly, 16 principal components are saved. After that, 5 methods mentioned above are tested. The Overall Accuracy and Kappa coefficient of C-SVM, SVM and ANN are higher than 90%, and C-SVM obtains the highest accuracy, which is consistent with visual interpretation. The result shows that C-SVM, SVM and ANN are more suitable for sophisticated vegetation classification of hyperspectral data, and C-SVM is the best option. ? 2011 SPIE.

Spectral database plays a very important role in hyperspectral remote sensing research, the development of hyperspectral remote sensing make new demands on spectral database. Multi-dimensional database technology is the latest stage in the evolution of database technology.It has a wide range of applications in access and analysis large volumes of data. Hyperspectral image data have similarity with the cube which is the form of multi-dimensional database organizing data. The feasibility and necessity was discussed for the applications of multi-dimensional database technology in hyperspectral data management and analysis in the first part. In the following part, according to the theory of multi-dimensional database, related concept of hyperspectral remote sensing and the goal of rock and mineral';s hyperspectral multi-dimensional database, the basic concepts were defined for hyperspectral multi-dimensional database for rock and mineral. The database was designed based on the concepts.The functional requirements of database were analysed. A multi-dimensional data structure which is nested was proposed for rock and mineral, the fact table and dimension hierarchy was designed for rock and mineral';s hyperspectral multi-dimensional database, the snowflake schema was used to constructe multidimensional data model for rock and mineral. All these laid the foundation for the implemention of hyperspectral multi-dimensional database for rock and mineral. ? 2011 IEEE.

Chinese licorice, the root of Glycyrrhiza uralensis Fisch, is one of the more widely used herbal drugs in China. Glycyrrhizic acid (GA), the one of the main active ingredients in G. uralensis, is generally used as an indicator to assess the quality of G. uralensis. At present, the methods of determining GA content are laborious and time consuming and cannot be applied to remotely sensed technology. In this study, we developed methods using canopy spectral data in visible-shortwave infrared region to qualitatively classify G. uralensis samples and to quantitatively predict the GA content by a partial least square (PLS) model. The results showed that our methods provided acceptable results and implied the ability of determining GA content from remote sensing data. ? 2011 IEEE.

The objective of this paper is to develop a method for estimating the cold storage time of pork using hyperspectra image cubes data accessed by a self-developed Filed Imaging Spectrometer System (FISS). Data were preprocessed by Minimum Noise Fraction (MNF) transform and first derivative to eliminate high-frequency random noise and baseline offset and to improve the multicollinearity. Wilks'; lambda stepwise method was performed to select proper wavelengths and partial least square regression (PLSR) was executed to build the model and get the regression equation. Fisher Linear Discriminant Analysis (LDA) was applied to discriminate the cold storage time of pork. The total of 600 samples was chosen, half of them as training samples and half as validation ones. The results showed that overall distinguishing rate reached 99% with seven selected bands of fresh or frozen-thawed pork samples. For the cold storage time, classification accuracy was higher than 83% with eleven selected bands. It indicates that FISS might be used as a screening method to identify the quality of frozen meat. ? 2011 IEEE.

Satellite measurements of the distribution of the global atmospheric CO2 would get its continuous change. The atmospheric infrared sounder (AIRS) enables us to monitor the global distribution and transport of middle troposphere CO2. Mount merapi is an active strato-volcano located on the border between central java and yogyakarta, indonesia. The AIRS data were acquired from 15th october to 15th november in 2008, 2009 and 2010 to monitor the temporal-spatial distribution of erupted CO2 from the volcano. Mid-tropospheric CO2 concentration would increase gradually and reach its peak in one day from eruption. The dispersal range of erupted CO2 was -7.540831+/-7.5 degrees, 110.444817+/-5 degrees in the graticules centering around gunung merapi. Having a high correlation with the eruptions, the mid-troposphere CO2 concentration of 2010 showed different trend comparing with 2008 and 2009 trend. The 4-day CO2 concentration data of 2010 over the volcano tended to increase by 2.9 ppmv and 4.1 ppmv comparing with that of 2009 and 2008 respectively. These observations provide the evidence that extensive release of CO2 occurs during the volcano eruption time and using the AIRS CO2 products to monitor the temporal-spatial distribution of erupted CO2 from volcanoes is possible.

Hyperion data are always contaminated by serious vertical striping artefacts, which obscure the true radiation information in the images and reduce the accuracy of Hyperspectral images, therefore, the stripped columns must be corrected before performing further processing and quantitative analyses. This paper introduces the principle of moment matching method, emphasizes the analysis of the formative reason for "edge effect", and then proposes an improved approach to destripe Hyperion data. Two other methods for destriping have been introduced as well in this paper to compare with the proposed method from both visual effect and quantitative assessment. It turns out that the improved method based on moment matching could achieve the greatest effect for destriping Hyperion data.