This PDF file contains the front matter associated with SPIE Proceedings Volume 8527, including the Title Page, Copyright Information, Table of Contents, and the Conference Committee listing.

The Atmospheric Infrared Sounder (AIRS) is a hyperspectral infrared instrument on the EOS Aqua Spacecraft, launched on May 4, 2002. AIRS has 2378 infrared channels ranging from 3.7 μm to 15.4 μm and a 13.5 km footprint at nadir. The AIRS is a “facility” instrument developed by NASA as an experimental demonstration of advanced technology for remote sensing and the benefits of high resolution infrared spectra to science investigations. AIRS, in conjunction with the Advanced Microwave Sounding Unit (AMSU), produces temperature profiles with 1K/km accuracy on a global scale, as well as water vapor profiles and trace gas amounts for CO₂, CO, SO₂, O₃ and CH₄. AIRS data are used for weather forecasting, climate process studies and validating climate models. The AIRS instrument has far exceeded its required design life of 5 years, with over 10 years of operations as of September 2012. While the instrument has performed exceptionally well, with little signs of wear, the AIRS Project continues to monitor and maintain the health of AIRS, characterize its behavior and improve performance where possible. Radiometric stability has been monitored and trending shows better than 16 mK/year stability. Spectral calibration stability is better than 1 ppm/year, and a new gain table was recently uploaded to recover 100 significantly degraded or dead channels by switching to their redundant counterpart. At this time we expect the AIRS to continue to perform well for the next decade.

The current day set of advanced atmospheric sounders began with the Atmospheric InfraRed Sounder (AIRS) on the NASA EOS Aqua satellite in orbit since 2002, the Infrared Atmospheric Sounding Interferometer (IASI) aboard MetOp- A since 2006, and the Cross-track Infrared Sounder (CrIS) instrument aboard the Suomi NPP and JPSS series of satellites which began 28 October 2011. These ultra-spectral infrared satellite sensors provide global measurements for improving monitoring and predictive capability of the Earth-atmosphere system, enabling enhancements in weather prediction, climate monitoring, and environmental change detection. This presentation examines the thermodynamic state and trace species information obtainable from these satellite systems possessing different measurement and instrument characteristics.

A fast atmospheric radiative transfer model called Combined Atmospheric Radiative Transfer model (CART) has been developed to rapidly calculate atmospheric transmittance and background radiance in the wavenumber range from 1 to 25000 cm^-1 with spectral resolution of 1 cm^-1. The spectral radiative properties of cirrus clouds at various effective sizes, optical thicknesses, and altitudes from visible to infrared wavelength region are simulated using the CART. The analyses show that the properties of cirrus clouds might be retrieved from the satellite-base spectral characteristics of cirrus clouds based on these simulations.

Hyperspectral remote sensing can provide a smooth spectral curve of a target by using a set of higher spectral resolution detectors. The spatial resolution of the hyperspectral images, however, is generally much lower than that of multispectral images due to the lower energy of incident radiation. Pansharpening is an image-fusion technique that generates higher spatial resolution multispectral images by combining lower resolution multispectral images with higher resolution panchromatic images. In this study, higher resolution hyperspectral images were generated by pansharpening of simulated lower hyperspectral and higher multispectral data. Spectral and spatial qualities of pansharpened images, then, were accessed in relation to the spectral bands of multispectral images. Airborne hyperspectral data of AVIRIS was used in this study, and it was pansharpened using six methods. Quantitative evaluations of pansharpened image are achieved using two frequently used indices, ERGAS, and the Q index.

It’s essential but challenging to retrieve spectral features as detailed as possible in current satellite imagery industry. In this research, based on the physical model of sensor response function, we present a method to recover the reflective spectrum at the front end of sensor in an iterative way and to greatly enhance the spectral details of satellite imagery. Our method is able to largely increase the cost-performance ratio of current satellite multispectral imagery and also reveals great potentials of satellite imagery in various disciplines.

Nowadays, remote sensing is an essential science in French Polynesia because of its extended territory and the remoteness of its 120 islands. There is a strong need to study the vegetation cover and its evolution (biodiversity threat, invasive species, etc.). A growing satellite images database has been acquired throughout, giving access to very high resolution optical images such as Quickbird data. These data allow accessing the vegetation canopy spectral and contextual information, texture classification has proved to be an efficient tool to map the complex vegetation found in tropical regions. The main goal of this paper is to propose an optimized SVM multispectral-texture classification method for tropical vegetation mapping. One of the texture computation drawbacks is the window treatment size, which is related to the largest texture element size. In complex tropical vegetation cover, this parameter leads to very small ground truth learning database, inducing a significant degradation of the classifications accuracy. We propose to increase the thumbnail numbers using an under-sampling method, optimizing the size and the number of the thumbnails. The other drawback is the high dimensionality of the problem when dealing with multispectral textures. We thus propose to rank and select the most pertinent textures attributes in order to reduce the dimensionality without reducing the classification accuracy. We first introduce the study context, before exposing preliminary studies on tuning the SVM learning method. The adapted method is then accurately exposed and the interesting experimental results as well as a sample of applications are presented before to conclude.

Registration of 3D range data is to find the transformation that best maps one data set to the other. In this paper, Lie group parametric representation is combined with the Expectation Maximization (EM) method to provide a unified framework. First, having a transformation fixed, the EM algorithm is introduced to find the correspondence between two data sets through correspondence probability, which covers the relationship of all points, instead of using exact correspondence such as the classical Iterative Closest Point (ICP) method. With this type of ststistical correspondence, we could deal with the presence of the degradations such as outliers and incomplete point sets. Second, having the updated correspondence fixed, and introducing Lie group parametric representation, the transformation is updated by minimizing a quadratic programming. Then, an alternative iterative strategy by the above two steps is used to approximate the desired correspondence and transformation. The comparative experiment between our Lie-EM-ICP algorithm and Lie-ICP algorithm using point cloud is presented. Our algorithm is demonstrated to be accurate and robust, especially in the presence of incomplete point sets and outliers.

Arid and semi-arid areas of northeast Iran cover about 3.4 million ha are populated by two main tree species, the broadleaf Pistacia vera. L (pistachio) and the conifer Juniperus excelsa ssp. polycarpos (Persian juniper). Natural stands of pistachio in Iran are not only environmentally important but genetically essential as seed sources for pistachio production in orchards. In this study, we estimated the optimum spectral reflectance of juniper forests and natural pistachio stands using remote sensing to help in the sustainable management and production of pistachio in Iran. In this research spectral reflectance are able to specify of multispectral from Advanced Land Observing Satellite (ALOS) that provided by JAXA. These data included PRISM is a panchromatic radiometer with a 2.5 m spatial resolution at nadir, has one band with a wavelength of 0.52–0.77 μm and AVNIR-2 is a visible and near infrared radiometer for observing land and coastal zones with a 10 m spatial resolution at nadir, has four multispectral bands: blue (0.42–0.50 μm), green (0.52–0.60 μm), red (0.61–0.69 μm), and near infrared (0.76–0.89 μm). Total ratio vegetation index (TRVI) of optimum spectral reflectance of juniper and pistachio have been evaluated. The result of TRVI for Pistachio and juniper were (R²= 0.71 and 0.55). I hope this research can provide decision of managers to helping sustainable management for arid and semi-arid regions in Iran.

Coastal habitats having high productivity provide numerous ecological services such as foods, protection from strong waves through buffering effect, fixation of CO2 through photosynthesis, fostering biodiversity etc. However, increasing human impacts and climate change decrease or degrade coastal habitats. ASEAN region is developing most rapidly in the world. In the developing region, it is necessary to grasp present spatial distributions of habitats as a baseline data with standardized mapping methods. Remote sensing is one of the most effective methods for mapping. Japan Aerospace Exploration Agency (JAXA) provides non-commercial satellite images with ultra-high spatial resolution optical sensors (10 m), AVNIR2, similar to LANDSAT TM. Using ALOS AVNIR2 images it may be possible to make habitat map in the region. In Thailand, shrimp ponds cause degradation of coastal ecosystem through cutting mangroves and eutrophicated discharge from ponds. We examined capability of remote sesing with ALOS AVNIR2 to map seagrass beds in Khung Kraben Bay, Chanthaburi Province, Thailand, surrounded by shrimp ponds. We analyzed ALOS AVNIR2 taken on 25 January 2008. Ground truth survey was conducted in October 2010 using side scan sonar and scuba diving. The survey revealed that there were broad seagrass beds consisting of Enhalus acroides. We used a decision tree to detect seagrass beds in the bay with quite turbid seawater coupled with Depth-Invariant Index proposed by Lyzenga (1985) and bottom reflectances. We could succeed to detect seagrass beds. Thus it is concluded that ALOS AVNIR2 is practical to map seagrass beds in this region.

Peatland is a large CO2 reservoir which accumulates 2000Gt of CO2, which is equal to 30% of global soil carbon. However, it has been becoming a large CO2 emission source because of peat decomposition and fire due to drainage water. This is caused by social activities such as canalizing. Especially, in Indonesia, peat swamp forests cover considerable portions of Kalimantan and 37.5% of CO2 emission source is peatland (DNPI, 2010). To take measures, it is necessary to conduct appropriate assessment of CO2 emission in broad peat swamp forest. Because hyperspectral data possess higher spectral resolutions, it is expected to evaluate the detailed forest conditions. We develop a method to assess carbon emission from peat swamp forest by using hyperspectral data in Central Kalimantan, Indonesia. Specifically, we estimate 1) forestry biomass and 2) underground water level expected as an indicator of CO2 emission from peat. In this research, we use the image taken by HyMAP which is one of the airborne hyperspectral sensors. Since the research area differs in forest types and conditions due to the past forest fire and disturbance, forest types are classified with the sparse linear discriminant analysis. Then, we conduct a biomass estimation using Normalized Difference Spectral Index (NDSI). We also analyze the relationship between underground water level and Normalized Difference Water Index (NDWI), and find the possibility of underground water level estimation with hyperspectral data. We plan to establish a highly developed method to apply hyperspectral sensor to peatland monitoring system.

The next generation of infrared remote sensing satellite instrumentation, including climate benchmark missions will require better absolute measurement accuracy than now available, and will most certainly rely on the emerging capability to fly SI traceable standards that provide irrefutable absolute measurement accuracy. As an example, instrumentation designed to measure spectrally resolved infrared radiances with an absolute brightness temperature error of better than 0.1 K will require high-emissivity (<0.999) calibration blackbodies with emissivity uncertainty of better than 0.06%, and absolute temperature uncertainties of better than 0.045K (k=3). Key elements of an On-Orbit Absolute Radiance Standard (OARS) meeting these stringent requirements have been demonstrated in the laboratory at the University of Wisconsin (UW) and refined under the NASA Instrument Incubator Program (IIP). This work recently culminated with an integrated subsystem that was used in the laboratory to demonstrate end-to-end radiometric accuracy verification for the UW Absolute Radiance Interferometer. Along with an overview of the design, we present details of a key underlying technology of the OARS that provides on-orbit absolute temperature calibration using the transient melt signatures of small quantities (<1g) of reference materials (gallium, water, and mercury) imbedded in the blackbody cavity. In addition we present performance data from the laboratory testing of the OARS.

Reliable calibration of high-accuracy spaceborne infrared spectrometers requires knowledge of both blackbody temperature and emissivity on-orbit, as well as their uncertainties. The Heated Halo is a broadband thermal source that provides a robust and compact method to measure emissivity. We present the results from the Heated Halo methodology implemented with a new Absolute Radiance Interferometer (ARI), which is a prototype space-based infrared spectrometer designed for climate benchmarking. We show the evolution of the technical readiness level of this technology and we compare our findings to models and other experimental methods of emissivity determination.

Spectrally resolved infrared (IR) and far infrared (FIR) radiances measured from orbit with extremely high absolute accuracy (< 0.1 K, k = 3, brightness temperature at scene temperature) constitute a critical observation for future climate benchmark missions. The challenge in the IR/FIR Fourier Transform Spectrometer (FTS) sensor development for a climate benchmark measurement mission is to achieve the required ultra-high accuracy with a design that can be flight qualified, has long design life, and is reasonably small, simple, and affordable. In this area, our approach is to make use of components with strong spaceflight heritage (direct analogs with high TRL) combined into a functional package for detailed performance testing. The required simplicity is achievable due to the large differences in the sampling and noise requirements for the benchmark climate measurement from those of the typical remote sensing infrared sounders for weather research or operations. A summary of the instrument design and development, and the radiometric performance of the Absolute Radiance Interferometer (ARI) at the University of Wisconsin Space Science and Engineering Center (UW-SSEC) will be presented.

The Superconducting Submillimeter-Wave Limb-Emission Sounder (SMILES) is a space-station-borne limb sounder for the stratospheric and mesospheric observations using frequency bands around 625 and 650 GHz. SMILES was developed cooperatively by National Institute of Information and Communications Technology (NICT) and Japan Aerospace Exploration Agency (JAXA). SMILES operated from October 2009 to April 2010 on the International Space Station (ISS). The calibration process of the observed submillimeter spectra is continuously improved also in the cooperation of NICT and JAXA. This paper gives an overview of the SMILES calibration, including intensity and spectral calibrations and the field-of-view calibration. The largest error source in the calibration of the spectrum is the uncertainties in the linearity correction of the receiver gain and the spectral response function of spectrometer channels. The efforts of our calibration improvement are focused on these calibrations. The linearity correction is based on the results of the gain nonlinearity measurement in prelaunch tests. The correction is modified so as to be consistent with in-orbit measurements. The spectral response functions of the spectrometers are estimated also from the in-orbit experiments. The tangent-height precision was another calibration issue that needed improvement in the preliminary version of a product of calibrated spectra. The improvement of the tangent-height precision will contribute the accuracy improvement in the volume-mixing-ratio product through a reduction in error by misplacement of the tangent height for each limb measurement.

Development and application of airborne and aerospace hyperspectral imager press for high precision geometry and spectral calibration of pixels of image cube. The research of geometry and spectral calibration of pushbroom hyperspectral imager, its target is giving the coordinate of angle field of view and center wavelength of each detect unit in focal plane detector of hyperspectral imager, and achieves the high precision, full field of view, full channel geometry and spectral calibration. It is importance for imaging quantitative and deep application of hyperspectal imager. The paper takes the geometry and spectral calibration of pushbroom dispersive hyperspectral imager as case study, and research on the constitution and analysis of imaging mathematical model. Aimed especially at grating-dispersive hyperspectral imaging, the specialty of the imaging mode and dispersive method has been concretely analyzed. Based on the analysis, the theory and feasible method of geometry and spectral calibration of dispersive hyperspectral imager is set up. The key technique has been solved is As follows: 1). the imaging mathematical model and feasible method of geometry and spectral calibration for full pixels of image cube has been set up, the feasibility of the calibration method has been analyzed. 2). the engineering model and method of the geometry and spectral calibration of pushbroom dispersive hyperspectral imager has been set up and the calibration equipment has been constructed, and the calibration precision has been analyzed.

The Acousto-Optic Tunable Filter (AOTF) is an electronically tunable optical filter based 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. Because the diffraction of light in AOTF filters is dependent on both wavelength and angle of incidence, the Spectral and geometrical calibration must therefore be performed over the entire spectral range of AOTF hyper-spectral imaging systems. In this paper, the dispersive principle of AOTF is introduced firstly and its application predominance in space-based spectral detection is analyzed. Then, a method for calibration of acousto-optic tunable filter (AOTF) hyper-spectral imaging systems is proposed and evaluated. This paper introduces the calibration of a VIS-NIR Imaging Spectrometer (VNIS) by the method. The VNIS is a payload instrument for lunar detection and provides programmable spectral selection from 0.45 to 0.95μm. The results indicate that the method is accurate and efficient. Therefore, the proposed method is suitable for spectral and geometrical calibration of imaging spectrometers based on AOTF.

Optical parameters such as radius of curvature (RoC), direction of the optical axis, offset of the apex relative to the outer diameter, et al. of the primary mirror of a Cassegrain telescope by a coordinate measuring machine (CMM) is presented. These parameters are measured by a novel technique developed by the authors. RoC, tilt, and wedge of a lens can also be measured by the technique. Geometric parameters, such as diameters, central obscuration diameter, and perpendicularity of mirror edge, the mirror, et al. can be measured taking the advantage of the geometric measurement function. The optical and geometric parameters are measured by this method on a set of primary and secondary mirrors, and four corrector lenses of a Cassegrain telescope.

The Hyperspectral Imager Suite (HISUI) is the Japanese next-generation Earth observation project, which will be onboard ALOS-3 platform. HISUI sensor will be composed of hyperspectral imager (185 spectral bands in VNIR-SWIR region with 30 m spatial resolution) and multispectral imager (4 spectral bands in VNIR region with 5 [m] spatial resolution), and is being developed by Japanese Ministry of Economy, Trade, and Industry (METI) as its third spaceborne optical imager mission after JERS OPS and Terra ASTER. HISUI will provide the earth observation data for global energy and resource issues as well as for other applications such as environmental monitoring and forestry. This paper shows the radiometric calibration plan for HISUI long-term observation.

Fast and small-footprint lossless compressors for multi and hyper-spectral sensors have been developed. The compressors are employed for HISUI (Hyper-spectral Imager SUIte: the next Japanese earth observation project that will be on board ALOS-3). By using spectral correlations, the compressor achieved the throughput of 30Mpel/sec for hyper-spectral images and 34Mpel/sec for multi-spectral images, which covers the data acquisition throughput of HISUI, on a radiation tolerant FPGA (field-programmable-gated-array). We also implemented the compressor on the evaluation model device of HISUI, and confirmed its feasibility and compression performance of actual hyper-spectral sensor data.

Climate change and air quality are the most pressing environmental issues of the 21st century. Despite decades of research, the sources and sinks of key greenhouse gases remain highly uncertain [IPCC, 2007] making atmospheric composition predictions difficult. The Geostationary Remote Infrared Pollution Sounder (GRIPS) will measure carbon dioxide (CO2), carbon monoxide (CO), methane (CH4), and nitrous oxide (N2O) with unprecedented precision to reduce substantially this uncertainty. The GRIPS instrument uses gas filter correlation radiometry (GFCR) to detect reflected and thermal IR radiation from geostationary orbit. GRIPS is designed to haves sensitivity down to the Earth’s surface at ~8 km nadir resolution. GRIPS can also resolve CO2, CO, and CH4 anomalies in the planetary boundary layer and the free troposphere to quantify lofting, diurnal variations and long-range transport. With repeated measurements throughout the day GRIPS can maximize the number of cloud free measurements determining biogenic and anthropogenic sources, sinks, and fluxes. Finally, the GFCR technique is, to first order, insensitive to aerosols interference. GRIPS is highly complementary to the Orbiting Carbon Observatory, OCO-2, and other existing and planned missions.

Hyperspectral Imager Suite (HISUI) is a Japanese future spaceborne hyperspectral instrument being developed by Ministry of Economy, Trade, and Industry (METI) and will be launched in 2015 or later. HISUI’s operation strategic study is described in this paper. In HISUI project, Operation Mission Planning (OMP) team will make long- and short-term observation strategy of the sensor. OMP is important for HISUI especially for hyperspectral sensor, and relationship between the limitations of sensor operation and the planned observation scenarios have to be studied. Major factors of the limitations are the combinations of downlink rate, observation time (15 minutes per orbit) and the swath of the sensor (30 km). The achievements of global mapping or regional monitoring need to be simulated precisely before launch. We have prepared daily global high resolution (30 second in latitude and longitude) climate data for the simulation.

It is a feasible method to detect soil total nitrogen (TN) content with remote sensing based on NIR spectroscopy. However, the accuracy of soil TN model was affected seriously by soil particle size. The spectral scanning results showed that at the same soil TN content level, with the decrease of the soil particle size, the reflectance of soil samples was reduced and the trend was not linear relationship. At the short wavelength (760-1100 nm) wave bands, there were a little of differences; while at the long wavelength (1100-2500 nm) wave bands, there were great differences. Two methods were adopted to eliminate the effect of soil particle size. The first method was to establish TN model by the first order differential preconditioning method of the spectral data. The second method was to establish TN model with mixed calibration set of different particle size soil samples after data preprocessing. Through the combination of the two methods, The RC, RV, RMSEC, RMSEP and RPD of the model improved from 0.85, 0.31, 0.046, 0.132, 0.866 to 0.92, 0.86, 0.018, 0.091, 2.700 respectively. The results showed that the effect of soil particle size on prediction of soil TN can be eliminated effectively.

In order to estimate the nutrient status of maize, the multi-spectral image was used to monitor the chlorophyll content in the field. The experiments were conducted under three different fertilizer treatments (High, Normal and Low). A multispectral CCD camera was used to collect ground-based images of maize canopy in green (G, 520~600nm), red (R, 630~690nm) and near-infrared (NIR, 760~900nm) band. Leaves of maize were randomly sampled to detect the chlorophyll content by UV-Vis spectrophotometer. The images were processed following image preprocessing, canopy segmentation and parameter calculation: Firstly, the median filtering was used to improve the visual contrast of image. Secondly, the leaves of maize canopy were segmented in NIR image. Thirdly, the average gray value (G_IA, R_IA and NIR_IA) and the vegetation indices (DVI, RVI, NDVI, et al.) widely used in remote sensing were calculated. A new vegetation index, combination of normalized difference vegetation index (CNDVI), was developed. After the correlation analysis between image parameter and chlorophyll content, six parameters (G_IA, R_IA, NIR_IA, GRVI, GNDVI and CNDVI) were selected to estimate chlorophyll content at shooting and trumpet stages respectively. The results of MLR predicting models showed that the R² was 0.88 and the adjust R² was 0.64 at shooting stage; the R² was 0.77 and the adjust R² was 0.31 at trumpet stage. It was indicated that vegetation indices derived from multispectral image could be used to monitor the chlorophyll content. It provided a feasible method for the chlorophyll content detection.

Water stress is one of the most common limitations of fruit growth. Water is the most limiting resource for crop growth. In grapevines, as well as in other fruit crops, fruit quality benefits from a certain level of water deficit which facilitates to balance vegetative and reproductive growth and the flow of carbohydrates to reproductive structures. A multi-modal sensor system was designed to measure the reflectance signature of grape plant surfaces and identify different water stress levels in this paper. The multi-modal sensor system was equipped with one 3CCD camera (three channels in R, G, and IR). The multi-modal sensor can capture and analyze grape canopy from its reflectance features, and identify the different water stress levels. This research aims at solving the aforementioned problems. The core technology of this multi-modal sensor system could further be used as a decision support system that combines multi-modal sensory data to improve plant stress detection and identify the causes of stress. The images were taken by multi-modal sensor which could output images in spectral bands of near-infrared, green and red channel. Based on the analysis of the acquired images, color features based on color space and reflectance features based on image process method were calculated. The results showed that these parameters had the potential as water stress indicators. More experiments and analysis are needed to validate the conclusion.

In order to detect apple tree growth status fast and accurately, four sensitive wavebands (364nm, 652nm, 766nm, 810nm) were obtained by analyzing the correlation between the apple leaves spectra and their nitrogen contents plus adopting the segment reduced precise sampling methods. A rapid determination model of apple leaf nitrogen content suitable for portable detector was built. Then a portable spectroscopy-based device was developed. It consists of an optical unit and a control unit. The optical channel was consisted of convex lens, optical filter, photoelectric detector and airtight mechanical exine. The optical unit was used to capture, transit, transform and submit the optical signal. The controller was consisted of operation, input, display, data storage and power control unit adopting JN5139 as main control unit. Controller was the coordinator in building the wireless network. And it was also responsible for receiving the measured data from sensor, calculating vegetation index, and displaying and storing the calculated results. The experiments showed that the correlation coefficient between the measured nitrogen content and the predicted nitrogen content reached to 0.857. It illustrated that the apple tree nitrogen detector was practical and could be used to detect leaf nitrogen content in apple orchard.

In order to monitor the growth statues of apple tree non-destructively and effectively, the field experiments were conducted at five different stages of apple tree annual growth season. The spectral reflectance of apple leaves was collected and the nutrient parameters of leaf (chlorophyll content (LCC) and moisture content (LMC)) were measured in the lab. The relationship between the apple tree leaf spectral reflectance and the apple growth parameters was analyzed. In order to select optimal spectral bands, the transformation forms of spectra were calculated including first derivative, second derivative, reciprocal, logarithm, the logarithm of reciprocal and the first derivative of logarithm. The sensitive detecting wavelengths were selected based on the correlation between the apple tree leaf spectra (original spectra and its transformation forms) and the apple tree growing parameters (LCC and LMC). The result showed that the original spectrum was most correlated with LCC from 511nm to 590nm and 688nm to 718nm; the correlation coefficients of September were the highest and the maximum value was 0.6. Three apple tree growth models were built using Multiple Linear Regression Analysis (MLRA), Principal Component Analysis (PCA) and Artificial Neural Network (ANN) respectively. The result showed that the forecasting model based on PCA was the optimal model to predict the apple leaves chlorophyll, and its calibration R² was 0.851 and validation R² was 0.8289. The apple leaves moisture content forecasting model based on ANN was optimal, and its calibration R² was 0.8561 and validation R² was 0.8375.

Raman spectra signature can provide structural information based on vibrational transitions of irradiated molecules. In this work, the quantity reflecting mechanism of soil phosphorus concentration was studied based on Raman spectroscopy. 15 sand soil samples with different phosphate content were made in laboratory and the Raman signatures were measured. The relationship between sand soil Phosphorus concentration and soil Raman spectra was explored. Then the effective Raman signal was extracted from the original Raman spectra by using bior4.4 wavelet packet. The relationship between sand soil phosphorus and their extracted signals was analyzed and the PLS (Partial Least Squares) model for predicting phosphorus concentration in the soil was established and compared. The maximum accuracy model comes from the extracted effective Raman spectra after the first level decomposing. The calibration R² was close to 1 and the validation R² reached to 0.937. It showed high potential in soil phosphorus detecting by using Raman spectroscopy.

In quantitative analysis of spectral data, noises and background interference always degrades the accuracy of spectral feature extraction. Continuum-removal analysis enables the isolation of absorption features of interest, thus increasing the coefficients of determination and facilitating the identification of more sensible absorption features. The purpose of this study was to test continuum-removal methodology with Visual-NIR spectral data of tomato leaf. Through analyzing the correlation between continuum-removal spectrum and nitrogen content, 15 characteristics parameters reflected changing tendency of nitrogen content were chosen, which is at 335, 405, 500, 520, 540, 550, 560, 580, 620, 640, 683, 704, 720, 736 and 770 nm. Finally, the variance inflation analysis and stepwise regression method was used to develop the prediction model of the nitrogen content of tomato leaf. The result showed that the predicted model, which used the values of continuum-removal spectrum at 335 and 720nm as input variables, had high predictive ability, with R² of 0.755. The root mean square errors of prediction using a leave-one-out cross validation method were 0.513. These results suggest that the continuum-removal spectroscopy analysis has better potential to diagnose tomato growth in greenhouse.

The visible and NIR spectral reflectance were measured for apple leaves by using a spectrophotometer in fruit-bearing, fruit-falling and fruit-maturing period respectively, and the nitrogen content of each sample was measured in the lab. The analysis of correlation between nitrogen content of apple tree leaves and their hyperspectral data was conducted. Then the low frequency signal and high frequency noise reduction signal were extracted by using wavelet packet decomposition algorithm. At the same time, the original spectral reflectance was denoised taking advantage of the wavelet filtering technology. And then the principal components spectra were collected after PCA (Principal Component Analysis). It was known that the model built based on noise reduction principal components spectra reached higher accuracy than the other three ones in fruit-bearing period and physiological fruit-maturing period. Their calibration R² reached 0.9529 and 0.9501, and validation R² reached 0.7285 and 0.7303 respectively. While in the fruit-falling period the model based on low frequency principal components spectra reached the highest accuracy, and its calibration R² reached 0.9921 and validation R² reached 0.6234. The results showed that it was an effective way to improve ability of predicting apple tree nitrogen content based on hyperspectral analysis by using wavelet packet algorithm.

In order to guarantee the observed data with high spatial and wavelength resolution of hyperspectral/multispectral imagers, it is necessary to evaluate the difference of the spectral sensitivity among the detector devices arrayed two-dimensionally and correct spectral and spatial misregistrations and the effect of stray light. However, there are tens of thousands of detectors in hyperspectral imagers, so they have to be evaluated in parallel by the special technique. Therefore, a light-source system which has high radiance with the spatial uniformity and widely tunable wavelength-range is required instead of the conventional lamp system. In this presentation, we report the new setup of the supercontinuum(SC)-source-monochromator system and its fundamental performance. The SC source covers a wavelength range of 450-2400 nm, and its total output power is up to 6 W. We effectively coupled a high-power SC laser to a single monochromator and obtained spatial uniformity through an integrating sphere or a relay-optics system. The radiance three or more magnitudes higher than a tungsten halogen lamp was measured with the supercontinuum-source based system. The stability of output power and the spatial uniformity of radiance at the integrating-sphere port were also evaluated. Using the system, spectral misregistrations and responsivities of a hyperspectral imager, which is consist of a polychromator and two-dimensional array of CCD, were measured.

Plant nutritional status can be evaluated with remote sensing. In order to detect the temporal and spatial variation of spectral characteristics in apple orchard, the experiments were carried out. Firstly the flower/ leaf samples from 15 year-on trees and 5 year-off t rees were collected. The real time reflectance spectra of flowers/leaves from three parts (base, middle, top) of each main branch were measured by using the ASD spectrometer. And then the temporal and spatial variations of spectral characteristics were analyzed. The results showed that leaves from the top of the branch had higher reflectance than the other parts of the branch at the same time. The reflectance spectra of apple trees changed significantly at different stages. Furthermore, the reflectance spectra varied in different parts of the apple trees as well as in different trees. Accordingly the temporal curve and spatial figure were obtained and the growing informat ion can be analyzed from them.

Recent advances in image sensor and information technologies have enabled the development of small hyperspectral imaging systems. EBA JAPAN (Tokyo, Japan) has developed a novel grating-based, portable hyperspectral imaging camera NH-1 and NH-7 that can acquire a 2D spatial image (640 x 480 and 1280 x 1024 pixels, respectively) with a single exposure using an internal self-scanning system. The imagers cover a wavelength range of 350 - 1100 nm, with a spectral resolution of 5 nm. Because of their small weight of 750 g, the NH camera systems can easily be installed on a small UAV platform. We show the results from the analysis of data obtained by remote sensing applications including land vegetation and atmospheric monitoring from both ground- and airborne/UAV-based observations.