This PDF file contains the front matter associated with SPIE Proceedings Volume 7857, including the Title Page, Copyright information, Table of Contents, Introduction, and the Conference Committee listing.

The Atmospheric Infrared Sounder (AIRS) on the EOS Aqua Spacecraft was launched in May of 2002. The AIRS Sounding Suit, AIRS along with AMSU-A and HSB, were designed to measure the atmospheric temperature and water vapor profiles, the surface and the cloud parameters for climate research and for improvement in weather forecast. Over the last 8+ years AIRS has been operating extremely stable, far surpassing original design life of 4 - 5 years. Many exciting research papers on climate have been published with AIRS data. The AIRS data are assimilated by most NWP centers and have shown considerable improvement in forecast skill. We will describe the current status of the instruments as well as the new activity on the data processing software.

Two solutions to the radiative transfer equation are described for profiling the atmosphere using ultraspectral infrared radiance measurements. The sounding retrieval algorithms are fast non-linear physical-statistical algorithms. The first solution described, applied to ground-based ultraspectral radiance measurements, is a statistical matrix inverse solution of the radiative transfer equation where the optimal matrix inverse stability factor is chosen by trial and error as that value which minimizes the RMS difference between the retrieval calculated radiance spectrum and the observed radiance spectrum. The second solution, applied to satellite and aircraft ultraspectral radiance observation, is a fast non-linear "Physical Dual-Regression " method trained to produce accurate retrievals for both clear and cloudy sky conditions. The second method relies on using Eigenvector Regression (EOF) "Clear-trained" and "Cloud-trained" retrievals of: surface skin temperature, surface emissivity PC-scores, CO2 concentration, cloud top altitude, effective cloud optical depth, and atmospheric temperature, moisture, and ozone profiles above the cloud and below thin or scattered cloud (i.e., cloud effective optical depth < 1.5 and a cloud induced temperature profile attenuation < 15 K. The "Clear-trained" regression is a relation relating a "clear sky equivalent" perturbed profile from a clouded radiance spectrum (e.g., an isothermal profile below an opague cloud cover) to the observed radiance spectrum. The "Cloud-trained" regression relates the true atmospheric profile, both above and below cloud level, to the observed radiance spectrum. Results from the application of both of these algorithms are presented in this paper.

Infrared sounders, such as the Atmospheric Infrared Sounder (AIRS), the Infrared Atmospheric Sounding Interferometer (IASI), and the Cross-track Infrared sounder (CrIS), have a cloud-impenetrable disadvantage in observing the atmosphere and surface under opaque cloudy conditions. However, recent studies indicate that hyperspectral, infrared sounders have the ability to detect cloud effective-optical and microphysical properties and to penetrate optically thin clouds in observing the atmosphere and surface to a certain degree. We have developed a retrieval scheme dealing with atmospheric conditions with cloud presence. This scheme can be used to analyze the retrieval accuracy of atmospheric and surface parameters under clear and cloudy conditions. In this paper, we present the surface emissivity results derived from IASI global measurements under both clear and cloudy conditions. The accuracy of surface emissivity derived under cloudy conditions is statistically estimated in comparison with those derived under clear sky conditions. The retrieval error caused by the clouds is shown as a function of cloud optical depth, which helps us to understand how well infrared sounders can observe the atmosphere and surface through clouds.

As a part of the Joint Polar Satellite System (JPSS, formerly the NPOESS afternoon orbit), the instruments Cross-track Infrared Sounder (CrIS) and Advanced Technology Microwave Sounder (ATMS) make up the Cross-track Infrared and Microwave Sounder Suite (CrIMSS). CrIMSS will primarily provide global temperature, moisture, and pressure profiles and calibrated radiances [1]. In preparation for the NPP launch in 2011, we have ported and tested the operational CrIMSS Environmental Data Record (EDR) algorithms using both synthetic and proxy data generated from the IASI, AMSU, MHS data from Metop-A satellite.

Advanced satellite sensors are tasked with improving global measurements of the Earth's atmosphere, clouds, and surface to enable enhancements in weather prediction, climate monitoring capability, and environmental change detection. Achieving such measurement improvements requires instrument system advancements and/or optimization of geophysical information content extraction. This manuscript focuses on the impact of spectral resolution and coverage changes on remote sensing system information content, with a specific emphasis on thermodynamic state and trace species variables obtainable from advanced atmospheric sounders such as the Atmospheric InfraRed Sounder (AIRS) on the NASA EOS Aqua satellite, the Infrared Atmospheric Sounding Interferometer (IASI) on MetOP, and the Cross-track Infrared Sounder (CrIS) system to fly on the NPP and JPSS series of satellites.

The thermal infrared band of the main sensor of the greenhouse gas observing satellite (GOSAT), the TANSO-FTS, must be calibrated with accuracy higher than 0.3 K in the brightness temperature Tbb for retrieving CO2 concentration with accuracy of 1% in the upper atmosphere. However, that accuracy has not been achieved because of some error sources. One is the systematic bias in the radiance spectrum resulting from effects of radiation emitted from internal optics and multiple scattering of target signals. Another is the polarization effect of the pointing mirror. Both effects can be merged into two parameters, gain and offset, in the two point calibration procedure. They can be tuned by comparing the spectrum with well-calibrated spectra such as those from the AIRS sensor. Based on the corrected radiance spectra, global CO2 concentrations were processed. However, they show peculiar latitudinal distribution implying the existence of temporally variant parameters that can affect the calibration. This bias can be reduced by referring to housekeeping data of the satellite in the calibration procedure. The stratospheric ozone distribution is also analyzed. The sensor demonstrated the difference in the ozone hole feature between spring 2009 and 2010 over the South Pole.

In our previous work, we developed a method in which ground-based, spectral measurements of direct and scattered solar radiation are employed to retrieve optical properties of tropospheric aerosols, using the three component aerosol model (TCAM), as well as column amount of molecular constituents of the atmosphere such as water vapor. We observed solar spectra under clear-sky conditions between Aug, 2007 and May, 2009 at Chiba, Japan and derived the seasonal trend of aerosol characteristics around the site. In this work, we apply these aerosol optical parameters to the atmospheric correction of Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data to obtain seasonal maps of ground surface reflectance and aerosol optical depth around Chiba. The surface reflectance (at 550 nm wavelength) derived from the TCAM model is 0.05 - 0.09, exhibiting seasonal variation depending on the vegetation coverage. As compared with the results based on standard aerosol models such as the maritime or continental model, the difference among models is generally small (< 0.002) in winter and large (> 0.005) in summer. In addition, we show preliminary results of retrieving column amount of molecules (H₂O, O₃, CO₂, and CH₄) from skylight spectra observed by the schemes of multi-axis differential optical absorption spectroscopy (MAX-DOAS).

Satellite hyperspectral imaging sensors suffer from ''smile'' and ''keystone'' properties, which appear as distortions of spectrum images. The smile property is a center wavelength shift and the keystone property is a band-to-band misregistration. These distortions degrade the spectrum information and reduce classification accuracies. Furthermore, these properties may change after the launch. Therefore, in the preprocessing of satellite hyperspectral images, the onboard correction of the smile and keystone properties is an important issue as well as the radiometric and geometric correction. The main objective of this work is to propose the prototype of the preprocessing of hyperspectral image with consideration of smile and keystone properties. Image registration based on phase correlation is used for detecting the optical properties. Cubic spline interpolation is adopted to modify the spectrum because of its good trade-off between the smoothness and shape preservation. Smile and keystone detection simulation using the EO-1 Hyperion imagery taken at various times in the past nine years proved that the optical properties have been changing due to the onboard secular distortion. Therefore, onboard optical properties should be updated periodically and built into the radiometric and geometric corrections for future satellite hyperspectral sensors. The proposed method may be the prototype of the preprocessing of future satellite hyperspectral sensors.

Fast and small foot print lossless image compressors aiming at hyper-spectral sensor for the earth observation satellite have been developed. Since more than one hundred channels are required for hyper-spectral sensors on optical observation satellites, fast compression algorithm with small foot print implementation is essential for reducing encoder size and weight resulting in realizing light-weight and small-size sensor system. The image compression method should have low complexity in order to reduce size and weight of the sensor signal processing unit, power consumption and fabrication cost. Coding efficiency and compression speed enables enlargement of the capacity of signal compression channels, which resulted in reducing signal compression channels onboard by multiplexing sensor signal channels into reduced number of compression channels. The employed method is based on FELICS1, which is hierarchical predictive coding method with resolution scaling. To improve FELICS's performance of image decorrelation and entropy coding, we applied two-dimensional interpolation prediction and adaptive Golomb-Rice coding, which enables small footprint. It supports progressive decompression using resolution scaling, whilst still delivering superior performance as measured by speed and complexity. The small footprint circuitry is embedded into the hyper-spectral sensor data formatter. In consequence, lossless compression function has been added without additional size and weight.

We had explored the relationship between particulate matters of size less than 10 micron (PM10) derived from the THEOS using regression technique over Penang Island, Malaysia. The objective of this study was to evaluate the high spatial resolution satellite data for air quality mapping. The development of the algorithm was based on the optical aerosol characteristic in the atmosphere. PM10 measurements were collected simultaneously with the image acquisition using a DustTrak Aerosol Monitor 8520. The station locations of the PM10 measurements were determined using a handheld GPS. The retrieval of surface reflectance is important to obtain the atmospheric reflectance in remotely sensed data and later used for algorithm calibration. In this study, ACTOR3 was used to retrieve the surface reflectance values from remotely sensed data. The surface reflectance values for the visible wavelengths of THEOS data was determined based on the Landsat TM data that acquired same date, nearly same time with the THEOS data. The relationship between the image reflectance values and the corresponding air quality data was determined using regression analysis. Various forms of algorithms were tested and their accuracies were noted. The algorithm that produced the highest correlation coefficient (R) and lowest root-mean-square error (RMS) was used for further analysis. Results show that the digital camera imageries can be used for estimating PM10 accurately. This study shows the potential of using the THEOS data for air quality mapping.

The validity of satellite regional algorithms depends on variability of optical properties of coastal waters and especially on relationships between concentrations of chlorophyll-a (chlA) and dissolved organic matter (DOM). The subject of the current researches is the role of characteristics of clusters in chlA - DOM scatter plot while developing of the regional biooptical algorithm. Regular undersatellite shipboard measurements of chlorophyll-a and DOM concentrations by means of contemporary laser fluorometer obtained in the various regions of Peter the Great Bay from 2007 to 2010 were used for the analysis.

Ship-based remotely sensed hyperspectral data of sea surface reflectance obtained in the Peter the Great Bay in 2009 and 2010 during different seasons were used. Every spectrum was fitted by analytical biooptical algorithm with five unknown variables. Setup of various initial conditions was used for each spectrum fitting procedure. Optimal values of initial and boundary conditions for Peter the Great Bay were obtained by the analysis of initial and boundary conditions which have led to "true solutions". Relationships between various unknown variables were established in order to simplify the biooptical algorithm and to optimize chooses of initial conditions.

NASA's anticipated plan for a mission dedicated to Climate (CLARREO) will hinge upon the ability to fly SI traceable standards that provide irrefutable absolute measurement accuracy. As an example, instrumentation designed to measure spectrally resolved infrared radiances will require high-emissivity calibration blackbodies that have absolute temperature uncertainties of better than 0.045K (3 sigma). A novel scheme to provide absolute calibration of temperature sensors onorbit, that uses the transient melt signatures from multiple phase change materials, has been demonstrated in the laboratory at the University of Wisconsin and is now undergoing technology advancement under NASA Instrument Incubator Program funding. Using small quantities of phase change material (less than half of a percent of the mass of the cavity), melt temperature accuracies of better than 10 mK have been demonstrated for mercury, water, and gallium (providing calibration from 233K to 303K). Refinements currently underway focus on ensuring that the melt materials in their sealed confinement housings perform as expected in the thermal and microgravity environment of a multi-year spaceflight mission. Thermal soak and cycling tests are underway to demonstrate that there is no dissolution from the housings into the melt materials that could alter melt temperature, and that there is no liquid metal embrittlement of the housings from the metal melt materials. In addition, NASA funding has been recently secured to conduct a demonstration of this scheme in the microgravity environment of the International Space Station.

A summary of the development of the Absolute Radiance Interferometer (ARI) at the University of Wisconsin Space Science and Engineering Center (UW-SSEC) will be presented. At the heart of the sensor is the ABB CLARREO Interferometer Test-Bed (CITB), based directly on the ABB Generic Flight Interferometer (GFI). This effort is funded under the NASA Instrument Incubator Program (IIP).

The Climate Absolute Radiance and Refractivity Observatory is a satellite mission that will measure the Earth's outgoing spectral radiance with accuracy better than 0.1 K in radiance temperature for climate benchmarking and forecast testing. Part of the high-accuracy calibration system is the heated halo, which provides a robust and compact method to measure the spectral emissivity of a blackbody. Measurement of the combined radiance of a blackbody, the reflection from a thermal source, and knowledge of key temperatures and the viewing geometry allow the blackbody spectral emissivity to be calculated. This allows the determination of blackbody radiance, and thus calibration of the CLARREO instrument, with high accuracy.

The hyper-multi spectral mission named HISUI (Hyper-spectral Imager SUIte) is the next Japanese earth observation project that will be on board ALOS-3 satellite. This project is the follow up mission of the Advanced Spaceborne Thermal Emission and reflection Radiometer (ASTER). HISUI is composed of hyperspectral radiometer with higher spectral resolution and multi-spectral radiometer with higher spatial resolution. The functional evaluation model is under development to confirm the spectral and radiometric performance prior to the flight model manufacture phase. This model contains the VNIR and SWIR spectrograph, the VNIR and SWIR detector assemblies with a mechanical cooler for SWIR, signal processing circuit and on-board calibration source.

The Acousto-Optical Tunable Filter (AOTF) is an electronically tunable optical filter, which base on Acousto-optic effect, and has its own special compared with other dispersive parts. Imaging spectrometer based on acousto-optic tunable filter (AOTF) is a useful high-spectral technology, especially in deep space exploration applications because its characteristics of staring imaging, electronic tunable spectral selection and simple structure. In this paper, Firstly,the dispersive principle of AOTF is introduced and its application predominance in space-based spectral detection is analyzed. The relations and trade-offs are analyzed between key parameters of AOTF including the ability in spectral and spectral resolution, aperture angle, optical aperture and spectral diffracted efficiency. Based on the application specifications, imaging spectrometer based on AOTF is systematically designed to fulfill requirements, then, the feasibility of instrument's space applications is analyzed. In the end, two applications of AOTF imaging spectrometer is described, one is a payload of the airship and the other works as the inspection device for the moon.

Remote Hyperspectral and Multispectral sensors have been developed using modern CCD and CMOS fabrication techniques combined with advanced dichroic filters. The resulting sensors are more cost effective while maintaining the high performance needed in remote sensing applications. A single device can contain multiple imaging areas tailored to different multispectral bandwidths in a highly cost effective and reliable package. This paper discusses a five band visible to near IR scanning sensor. By bonding advanced dichroic filters onto the cover glass and directly in the imaging path a highly efficient multispectral sensor is achieved. Up to 12,000 linear pixel arrays are possible¹ with this advanced filter technology approach. Individual imaging areas on the device are designed to have unique pixel sizes and clocking to enable tailored imaging performance for the individual spectral bands. Individual elements are also based on high resolution Time Delay and Integration technology^2,3 (TDI) to maximize sensitivity and throughput. Additionally for hyperspectral imagers, a split frame CCD design is discussed using high sensitivity back side illuminated (BSI) processes that can achieve high quantum efficiency. As these sensors are used in remote sensing applications, device robustness and radiation tolerance was required.

Mangrove vegetations are normally present in river estuaries and along the coast where the land meets the sea. Remote sensing can be used to obtain mangrove distribution information. The objective of this study was to study the current condition of mangrove forest using remote sensing over Penang Island, Malaysia. An attempt has been made based on supervised Maximum Likelihood Classification (MLC), various land use and land cover classes have been mapped and classified. A red-green-blue (RGB) colour was used to display and quantify mangrove forest distribution using Thailand Earth Observation System (THEOS) satellite imagery. Reference data was based on ground truth. High accuracy of 91.7% was obtained in mapping of mangrove cover.

Remote sensing sensors are now able to deliver greatly increased amount of information with the used of high resolution sensor. But high or very high resolution sensors lead to noise in generally homogeneous classes as the data contains increased information with more internal variability. Conventional classification methods commonly cannot handle the complex landscape environment in the image. The result of each method has often "a salt and pepper appearances" which is a main characteristic of misclassification. It seems clear that information from neighboring pixels should increase the discrimination capabilities of the pixel based measured, and thus, improve the classification accuracy and the interpretation efficiency. This information is referred to as the spatial contextual information. In this paper, we shall present a contextual classification method based on a frequency-based approach for the purpose of land cover mapping. Additionally, classification maps are produced which have significantly less speckle error. In order to evaluate the performances of the classifier, 9 different window sizes ranging from 3x3 to 19x19 with an increment of 2 is tested.

Contrast enhancement is essentially needed in mapping preprocessing due to low average brightness and low contrast of original remote sensing images. The frequently used methods improve the visual quality of remote sensing images while compress gray ranges of skin-tone. As a result, shape and texture of these oversaturated regions are missing or distorted. The proposed method named GL-Enhancing not only enhance the contrast of remote sensing image, but also keep shape and texture. It is found that GL-Enhancing performs better than Equalization, Linear-Stretch, Gauss and SquareRoot methods in QuickBird 321bands true color image experiment. GL-Enhancing is efficient in mapping preprocessing. The proposed algorithm is easy to implement.

Because of aircraft vibration, pixel discontinuity (blank pixels) occurs frequently in ortho-images when using a top-down approach and a nearest neighboring resampling method for pushbroom images. In this paper we propose a scheme to handle the pixel discontinuity. The pixel discontinuity is induced by the attitude variation in pitch and heading of an airplane. The deviation of pixel locations needs to be analyzed first to check if the proposed scheme is applicable. We use a linear CCD imager installed on a stabilizer to filter out the high frequency noise in an airplane for image acquisition. This scheme is suitable since the angular rates of the pitch and heading are both within ±0.7°/sec statistically and the deviation of the pixel locations is less than 1.0 pixel. The proposed scheme includes the following steps: (1) Derive the pixel locations of ortho-images using a top-down approach. Then allocate the pixel values to its 4 neighboring grids by an inverse bilinear interpolation based on their weighted factors in ortho-images. (2) After completing the ortho-rectification of full images, perform the dynamic range adjustment on the ortho-images according to the maximum pixel value of raw images and ortho-images. After applying the proposed scheme, we find that the pixel discontinuity is removed and the image quality is improved substantially. The difference of pixel value between raw images and ortho-images is also presented at regions with low, middle and high radiance to evaluate the proposed scheme quantitatively.

A 2D detector array is used popularly to acquire image in spatial and spectral dimension simultaneously for hyperspectral imager. The detector array will be malfunctioned gradually and partially after long-term operations. These defective CCDs will cause vertical stripes in images. But it's not cost effective to replace the detector due to a few of defects. In this article, we propose an algorithm including two parts for hyperspectral image restoration. One is the CCDs defect parts detection according to their radiance deviation, and another is the image restoration based on inter-band radiance interpolation using Lagrange polynomial. The detection process of finding CCDs defect parts for an imager must be conducted periodically to update the CCDs health status. HOPE images with simulated defective CCDs of various performance decay level are applied for validation. We found the accuracy for images with homogeneous ground feature is higher than the ones with non-homogeneous feature. And defect CCDs with performance decay of 10% still can be designated precisely. Restoration accuracy of pixel radiance is presented for various spectral bands using proposed algorithm. We also perform the image reconstruction using interpolation of spatial neighboring pixels. Radiance deviation for restored pixels is compared between both methods. Proposed algorithm can handle the images taken by hyperspectral imager with adjoining defective CCDs both in spatial and spectral. However, the method using interpolation of neighboring pixels can't. Applying the purposed algorithm on hyperspectral images, the imager can continue operating like a good one though there are a few of defects in detector.

In order to examine the effect of Asian dusts and apply to retrieval of dust properties, radiances measured by AIRS were simulated using the RTTOV-9 model. The model has been implemented with new optical properties for Asian dusts; refractive indices of mineral dust in the OPAC library and size distribution of Asian dusts retrieved from 10 years of skyradiometer measurements at Dunhuang, China. The simulations were performed using the implemented model, but with specification of AOT and height of dust layers obtained from CALIOP measurements. In the simulations, surface and atmospheric temperatures are from AIRS level 2 products while surface emissivity is specified with UW/CIMSS monthly mean global infrared surface emissivity data. Results show that effect of Asian dusts on AIRS spectra is substantial over infrared window regions (i.e.: 3.7 - 4.1 μm, 8.8 - 9.3 μm, 10 - 13 μm) for moderate and strong dust cases (AOT ≥ 0.5), while surface effect is dominant for weak dust cases (AOT < 0.5). Over 10 - 13 μm and 3.6 - 4.1 μm ranges, the simulation performances are improved when the dust effect is added. However, on the spectral range of 8.8 - 9.3 μm, the simulation overestimates radiances in comparison with AIRS measurements, probably because the mineral dust composition of OPAC does not coincide with the Asian dust. The comparison of simulated radiances with AIRS measurements shows a comparable quality for both clear and dusty conditions on the 10 - 13 μm and 3.6 - 4.1 μm ranges, suggesting that results can be incorporated for developing dust retrieval algorithm from hyperspectral images such as AIRS and IASI.

Remote sensing image classification is an important and complex problem. Conventional remote sensing image classification methods are mostly based on Bayesian subjective probability theory, but there are many defects for its uncertainty. This paper firstly introduces evidence theory and decision tree method. Then it emphatically introduces the function of support degree that evidence theory is used on pattern recognition. Combining the D-S evidence theory with the decision tree algorithm, a D-S evidence theory decision tree method is proposed, where the support degree function is the tie. The method is used to classify the classes, such as water, urban land and green land with the exclusive spectral feature parameters as input values, and produce three classification images of support degree. Then proper threshold value is chosen and according image is handled with the method of binarization. Then overlay handling is done with these images according to the type of classifications, finally the initial result is obtained. Then further accuracy assessment will be done. If initial classification accuracy is unfit for the requirement, reclassification for images with support degree of less than threshold is conducted until final classification meets the accuracy requirements. Compared to Bayesian classification, main advantages of this method are that it can perform reclassification and reach a very high accuracy. This method is finally used to classify the land use of Yantai Economic and Technological Development Zone to four classes such as urban land, green land and water, and effectively support the classification.

Monitoring of light use efficient (LUE) over space and time is a critical component of climate change research as it is a major determinant of the amount of carbon accumulated by terrestrial ecosystems. PRI (Photochemical reflectance index) has provide a fast and reliable method for estimating photosynthetic light use efficiency across species. The aim of this study was to evaluate the use of ground-based canopy reflectance measurements to detect changes in physiology of wetland vegetation in response to experimental nitrogen (N) treatment. In this paper, Bulrush with different nitrogen fertilization were selected to research the influence of varied fertilization levels on the relationship between PRI and LUE. The results proved that leaf chlorophyll contents as well as canopy PRI increased with the increase in nitrogen fertilization. For different nitrogen fertilization of Bulrush, the regression coefficients R2 varied respectively. Therefore, PRI not only can be a reliable indicator of LUE but also can reflect the growing situation of Bulrush with different precisions of LUE assessment.

It was a trend that image was classified through combining multi-source remote sensing data with non-remote sensing data by GIS technology. In this paper, technological framework of land use information extraction was established using multi-sources remote sensing data (TM and CBERS-02B), DEM, slope data, land use map and other geographic auxiliary data. The result showed :( 1) It was possible to combine TM and CBERS-02B as land use sources data because of their similar spatial resolution and spectral resolution. In this research the method of multi-level supervised classification was adopted. (2) Interpretation accuracy was improved by establishing background database through GIS technology. First, non-remote sensing information, such as topographic map, soil map, land-use map, transportation map, etc, was integrated as background database. Then land use classifications were overlapped with above database. The results showed that uncertainty could reduce by 23.2%. (3) In the study area dry land spectrums in plain area, hilly area and the Yellow River flood plain were very different and spectrums of habitation in plain and the Yellow River land wash were the same. As for above phenomenon of "same object with different spectrums" and "different objects with same spectrum", expert knowledge database was established based upon relationship between remote sensing image and geographical environment. As a result average classification accuracy was improved by 12.1%.

A 3-dimensional reconstruction model and pose parameter extracting method of parallel multi-ocular image are proposed. The multi-spectral camera is arranged as a rectangle with four channels of R, G, B and NIR. The corn canopy images in field are captured by the camera, and the distance from the camera to the corn canopy is about 0.5m. A novel matching method of feature point is proposed. Channel NIR is taken as the source, and the others are taken as destination. And then the edge of the corn leaf in source image is taken as the source feature points. Feature vector of each point is composed of its 18 directional derivatives. After that, the destination feature point is searched in destination image. First, the local area is estimated where the feature points may lie on. Then, if the intersection angle of two edges, formed by local points and source points, is smaller than a threshold, and the Euclidean distance between feature vectors of local points and source points is minimum among all of them, the local points are thought to match destination feature points, and the feature points pair set is constructed. The edge direction and distance are used as the principle to divide the different area of the image, so that the different leaf regions of canopy image are segmented. The 3-dimensional coordinate of each point in the region can be calculated. From four channel images, at least three 3-dimensional coordinates of each point can attained, and the center of gravity is the more accurately 3-dimensional coordinate. The interpolation is used to reconstruct corn leaf in space, and the pose parameters such as inclination of leaf and so on are estimated.

Data fusions from SAR and TM, SPOT and TM, ASTER and TM, MODIS and ETM, etc are the common methods. But that from TM and CBERS-02B is rare. With HR camera working in September 19th 2007, Chinese-Brazil Earth Resources Satellite 02B (CBERS-02B) became the first civilian high-resolution satellite in China. It could provide 2.36m panchromatic image which is better to Landsat TM. Meanwhile the spectral resolution of TM is better than CBERS-02B. So it's a good idea to take advantage of benefits from CBERS-02B HR and TM through data fusion. In this study, images of TM and CBERS-02B HR in 2007 were used as data sources. After image registration and noiseremoval process, data fusion methods of IHS and PCA were adopted. Then unsupervised classification and supervised classification were used for land use classification. Finally, classification accuracy between original image and fusion image was compared and evaluated. The result shows: (1) Compared with original TM or CBERS-02B HR image, the fusion image not only retains abundance spectrum but also enhances the object details. Residential texture, lake morphological, the relative position between roads, industrial and mining sites, etc, was identified easily. (2) Results from IHS and PCA are different. IHS image had higher spatial resolution but more spectral distortion. Spectral differences between some objects became smaller and classification accuracy was lower. Supervised classification accuracy assessment shows that overall Kappa index and overall land use classification accuracy decreased by 0.237 and 11% respectively. Meanwhile PCA image not only had high spatial resolution, but also smaller spectral distortion. Different land use / cover types can be better distinguished. (3) Disadvantages of low spatial resolution in TM and single color in CBERS-02B HR image are overcome in PCA fusion image to a certain extent. In this research under supervised classification in PCA image Kappa index of farm land, forest land and bare land increased by 0.097, 0.176 and 0.242 respectively. Overall Kappa index and overall land use classification accuracy were improved by 0.092 and 7.24% respectively.

NIR spectroscopy can be used in analysis of plant chlorophyll content on a large scale area. This offers the opportunity to use spectral reflectance as a non-destructive method for analyzing photosynthetic pigment status in plant. This research studied the variation of the chlorophyll content and spectral response at different growth stages of greenhouse tomato. Leaf spectral measurements from each treatment (4 N-levels: 0%, 33.3%, 66.6%, 100%) were taken in the greenhouse using an ASD FieldSpec HH spectrophotometer. Chlorophyll content of tomato leaves were measured by alcoholic-acetone extraction in lab. It was found that chlorophyll content of tomato leaf was increasing continuously to the maximum 50 days after the transplantation, while red edge moved to the NIR bands (long wave), and green peak position moved to the Blue bands (short wave) and green peak amplitude decreased. The chlorophyll content would decrease after fruiting stage, while red edge, green peak position and amplitude moved to the opposite direction. Regarding quantitative analysis the relationship between chlorophyll content and spectral response, red edge parameters (S_red(area of red edge), D_red (amplitude of red edge) and P_red (position of red edge ) ) in the first derivative of reflectance curve were obtained at bands of 680-760 nm. Similarly, blue edge, green peak and red valley parameters were defined to reflect spectral character. Vegetation indices were used extensively to estimate the vegetation growth status. Thus, the following wavelengths were used for developing RVI, NDVI and ARVI indices: λ440nm, λ500nm, λ550nm, λ680nm, λ770nm, P_blue (position of blue edge), P_yellow (position of yellow edge), P_red (position of red edge), P_greenpeak (position of green peak), P_redvalley (position of red valley). Seven optimal spectral parameters were chosen with the method of Karhunen-Loeve from the above-mentioned 68 self-defined property parameters. Stepwise multiple regression (SMLR), principal component regression (PCR), ridge regression (RR) and partial least squares regression (PLSR) were used to develop the prediction models of the chlorophyll content of tomato leaf. The best model was obtained by RR. Root MSE was 0.406 and R2 was 0.839.

The spectra of healthy leaves and leaves damaged by the rice brown planthopper (BPH, Nilaparvata lugens) were measured using a Spectroradiometer with spectral range of 350-1050 nm and resolution of 3 nm. The data was analyzed using the method of red edge methods. In the range of 430-530 nm and 560-730cnm, the band depth and slope were calculated. The damage degrees of rice plants caused by the BPH nymphae with different numbers were measured well by the spectral reflectance. The spectral characteristics of damaged rice under brown Planthopper, Nilaparvata lugenswere analyzed, and the reflectance was significantly negatively correlated with the number of BPHs. The red edge slope and edge area of the reflectance also significance correlated with the number of nymphae. The estimation models were constructed to estimate the BPHs using the spectral reflectance at the wavelengths of 550 nm and 760 nm and the red edge index. The results showed that accuracy of the estimation models were 66-81% and the spectral reflectance at R755 was efficient for estimating the number of BPHs.

At present, most part of coast zone in China belong to Case II waters with a large volume of shallow waters. Through theories and experiences of ocean water color remote sensing has a prominent improvement, there still exist many problems mainly as follows: (a) there is not a special sensor for heat pollution of coast water remote sensing up to now; (b) though many scholars have developed many water quality parameter retrieval models in the open ocean, there still exists a large gap from practical applications in turbid coastal waters. It is much more difficult due to the presence of high concentrations of suspended sediments and dissolved organic material, which overwhelm the spectral signal of sea water. Hyperspectral remote sensing allows a sensor on a moving platform to gather emitted radiation from the Earth's surface, which opens a way to reach a better analysis and understanding of coast water. Operative Modular Imaging Spectrometer (OMIS) is a type of representative imaging spectrometer developed by the Chinese Academy of Sciences. OMIS collects reflective and radiation light from ground by RC telescope with the scanning mirror cross track and flight of plane along track. In this paper, we explore the use of OMIS as the airborne sensor for the heat pollution monitoring in coast water, on the basis of an analysis on the mixed-spectrum arising from the image correcting process for geometric distortion. An airborne experiment was conducted in the winter of 2009 on the coast of the East Sea in China.

Variable rate fertilization can meet the needs of crop growth with low pollution of the environment resulted in by excess fertilization, and has therefore become an important part of precision agriculture. Variable rate fertilization requires a precise access to growing crops and spatial distribution. It is the key to precision agriculture technology in accessing the crop information based on spectroscopy and remote sensing technologies. This paper outlines our efforts to find a way to combine the information of growth with the spatial location information in a common way. Ground-based Remote Sensing Instrument GreenSeeker is used to analyze the biological characteristics of winter wheat in the spatial variability. The experiments are conducted during the period of rviving, early jointing, and late jointing. The measurement result is calculated according to GreenSeeker canopy NDVI data and the canopy chlorophyll content is obtained by using laboratory analysis. The analysis of NDVI data of canopy leaves and chlorophyll content and spatial distribution trends shows that the NDVI data of canopy are influenced by environmental factors such as the surface coverage during the period of reviving. The data of chlorophyll are at a low level and quite different at region distribution. As the wheat growth stage changes, the spatial variability and the chlorophyll content are going to decrease, and in more evenly distributed. It is proved that the analysis of spatial distribution can accurately grasp the biological characteristics and distribution information of the winter wheat in experimental area, and provide the basis for variable management.

Space-borne LWIR FPA imaging system is an earth observation camera with waveband being 8.0~12.5μm, which adopts the technical scheme of two lines of 256×1 LWIR FPA detectors, 60K deep hypothermic Stirling cryocooler and on-axis two-reflection RC primary optical system. The imaging system has obtained good temperature sensitivity and acquired LWIR image with good image quality by configuring suitable detector cold shield and using -30°C cryogenic optical system and high performance information processing circuit.

Atmospheric components (aerosol and molecules) scatter and absorb solar radiation. This study investigated the used of a handheld spectroradiometer for the retrieval of atmospheric optical thickness (AOT) values over Penang Island derives this period. The objective of this study is to introduce a new technique for retrieval of aerosol optical thickness (AOT) for air quality determination. Measured spectroradiometer data was used to calculate the aerosol optical thickness (AOT) values at the earth surface. The transmittance values were measured using a handheld spectroradiometer over Penang Island. Particulate matters of size less than 2.5 micron (PM2.5) were collected simultaneously with the acquisition of the transmittance measurements. The results of the calculated AOT were used to retrieve the air quality at Penang, Malaysia. The retrieved AOT data were linearly correlated with the particulate matter of less than 2.5 micro meter (PM2.5). An AOT map and PM10 map were generated using interpolation technique. The relationship between AOT and PM10 was investigated and we obtained a linear relationship between these two parameters. Finally, an interpolating technique was used to generate a PM2.5 map over Penang Island.

Temporally and Spatially Modulated Fourier Transform Imaging Spectrometer (TSMFTIS) is a new imaging spectrometer without moving mirror and slit. Through scanning, it can acquire sequential images superposed with interference fringes. The interferogram can be acquired by orderly arranging the extracted interference information of the same spatial point from the sequential images, and the spectrum can be recovered by using FFT. Therefore, the attitude of the bearing platform will affect the images so as to reduce the accuracy of the recovered spectrums. Since current attitude measurement accuracy can not meet the needs of error correction, in this paper, the image registration method is applied to acquire the accurate translations for the future correction between two sequential images. The single-step DFT registration method is applied to register the selected window areas away from the null optical path difference position in sequential images. That is full utilizing of common information meanwhile reducing impact of interference fringes and improving registration accuracy and efficiency. In the simulation experiment, a common large remote sensing image is used as ground object. The Fourier shift principle is applied to acquire simulation scanning images with sub-pixel displacement. Artificial spectral data cube produced with the RGB values of each image is utilized as the input data of the TSMFTIS, and sequential images superposed with interference fringes are acquired. Registration according to the method mentioned above is performed and the results are compared with the accurate values. It shows that the method is feasible and can achieve sub-pixel level accuracy.

Remote sensing data have been widely used for land cover mapping using supervised and unsupervised methods. The produced land cover maps are useful for various applications. This paper presents a technique for land use/cover mapping using THEOS data of the Penang Island, Malaysia. The objective is to assess the capability of a THEOS image to provide useful remotely sensed images for land cover mapping. The land cover information was extracted from the visible digital spectral bands using PCI Geomatica 10.3 software package. A frequency based contextual classifier was applied to the imagery to extract the spectral information from the acquired scene. Contextual classification is employed when neighbouring pixels are taken into account. The accuracy of each classification map was assessed using the reference data set consisted of a large number of samples collected per category. The study revealed that the frequency based contextual classifier produced superior result and achieved a high degree of accuracy. The preliminary result indicates that THEOS image can be provided useful data for remote sensing to retrieve land cover information at local scale.

In order to rapidly and accurately acquire winter wheat growing information and nitrogen content, a non-destructive testing method was developed combined with multi-spectral imaging technique and remote sensing technology to research wheat growing and nutrition status. Firstly, a 2-CCD multi-spectral image collecting platform was developed to acquire visible image and NIR image synchronously, meanwhile, the canopy spectral reflectance and the nitrogen content of wheat leaves were measured and analyzed to research the characteristics of the canopy spectral reflectance. Secondly, using calibration panels the experiential linear calibration model was established between image gray value and spectral reflectance. Thirdly, NIR image was processed to segment wheat canopy from soil and then gray value of wheat leaves was achieved by image processing of Red, Green, and Blue channels. Finally, the gray value of wheat leaves was transformed into spectral reflectance by aforementioned experiential linear model, and the vegetation index were calculated and analyzed to research the winter wheat growing and nitrogen content status. Experiment results showed that it was reasonable to diagnose nitrogen content of winter wheat based on multi-spectral imaging system and experiential linear model. There existed remarkable correlation between vegetation index (NDVI, GNDVI) and nitrogen content of winter wheat, and the correlation coefficients (R2 ) were 0.633 and 0.6.

The purpose of this study was to evaluate the original PALSAR radar, and radar texture, for land cover classification. The primary methodology was standard image processing, including spectral signature extraction and the application of a statistical decision rule to classify the surface features .Seven land covers were identified and the probability of correct classification of classes was assessed by using the Transformed Divergence (TD) separability measures. TD values were obtained for all original and texture derived bands along with various multiple band combinations. The radar texture bands greatly improved upon the TD values in comparison to the original radar values. Combination of original radar and radar texture bands consistently showed excellent Transformed Divergence (TD) separability. The use of texture was able to improve separability between different land cover/use classes.

One of the advantages of acousto-optic tunable filter (AOTF) is its spectral selectivity, not only central wavelength, but also bandwidth and passband form, can be controlled by RF signals, which provides us the opportunity to develop a more flexible hyper-spectral imager. Traditional hyper-spectral imagers collect hyper-spectral images with certain spectral resolutions and spectral sampling intervals, which means a huge amount of data and low data efficiency. So, a more flexible device to meet different applications with less but more effective data is an attractive idea. The idea is brought out this year in SITP, CAS. A custom-made AOTF which can endure higher power is adopted to realize such a hyper-spectral imager. A RF generating system is designed to produce and control 12 channels of RF signals independently, and a PC is used to control the system and record the digital images obtained by a CCD camera. If the controlling RF frequencies are close enough to each other, the consecutive output passbands will combine into one wider band, and the programmable spectral resolution and passband form are available. If the RF signals are discrete, the image of several discrete spectral bands combination which can simply be treated as a fused image is available. In this paper, the theory and structure of the system is set out, some important details of design principle are introduced, some of the original test results and a few of experimental images are showed.

The University of Wisconsin-Madison Space Science and Engineering Center (UW-SSEC) is certifying the calibration performance of a new generation of instruments for the measurement of the downwelling atmospheric infrared spectrum at the surface. The E-AERI instrument series is the commercial follow-on to the successful Atmospheric Emitted Radiance Interferometer (AERI) which was developed at UW-SSEC in the early 1990s with support from the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) program. This paper describes the E-AERI instrument specification, the UW-SSEC certification methodology, and examples of preliminary of results obtained to date. The E-AERI instrument is a commercially available product of ABB/Bomem of Quebec, Canada using technology licensed by the UW-SSEC. The E-AERI meets the same specification as the original AERI instrument in a fully automated system for use in both research and operational profiling networks.

It is a non-destructive and real-time method to detect the soil nutrient content by using spectroscopy analysis technology. In order to isolate the effective spectral for TN content from the soil spectra effectively, the NIR model predicting TN was developed based on wavelet packet analysis. 100 soil samples were collected for calibration and validation from the field. First, using the high-precision NIR detecting instrument to scan the target and obtaining the continuous spectra of soil samples in the laboratory. Secondly, with three different orthogonal wavelets (bior4.4, db4, sym4) as the generating functions, the original signal of each soil sample was decomposed and reconstructed based on the respective wavelet packet. Then the multiple linear regression (MLR) models for TN were established based on each drawn characteristic spectrum. Finally, three models were compared and analyzed, and the model with the highest forecasting accuracy was obtained based on db4, which determined R² reached 0.904. The research concluded that wavelet packet analysis could eliminate or substantially reduce the factors outside the parameters to the spectrum directly or indirectly, and the obstacles in establishing linear models for soil parameters were removed. It is feasible and potential to the real-time prediction of TN content.

Karst landform is widely distributed in the Southwest of China where karst rocky desertification (KRD) is evolving from bad to worse. This paper establishes hyperspectral image intepretation symbols for KRD identification spectroscopically, in other word, KRD spectral library, provides a new operable method of image intepretation and conducts KRD classification with spectral angle mapper method. The study results can provide support for the status quo ecological survey and the eco-restoration of KRD area.