Exoskeletons for robot operation necessitate shoulders with high range of motions and high degrees of freedom to fit the operator’s shoulder girdle. These shoulder joints need high torque for force feedback on the operator. Existing exoskeletons struggle to simultaneously meet these requirements of high DOFs, wide ROM, and high torque due to spatial constraints. This study introduces an exoskeleton with a distal branching link mechanism that addresses this issue by concentrating on each link’s absolute and relative degrees of freedom. In the proposed exoskeleton, the end-effector’s absolute DOF, the forearm’s absolute DOF, and the end-effector and forearm’s relative DOF are matched between the operator and the exoskeleton. This is achieved while reducing the overall DOF by sharing the root link system’s DOF. Furthermore, by avoiding direct attachment of the operator to the exoskeleton’s shoulder, the design can accommodate the human shoulder’s high torque and high ROM. The study demonstrates that the branching exoskeleton outperforms existing link-fixed exoskeletons in terms of tracking the operator’s arms and the torque required by the exoskeleton’s joints. Utilizing this exoskeleton, we successfully maneuvered an actual humanoid robot to perform daily activities where the forearm posture is crucial.

Cleft palate (CP) is the second most common congenital birth defect. The etiology of CP is complicated, with involvement of various genetic and environmental factors. To investigate the gene regulatory mechanisms, we designed a powerful regulatory analytical approach to identify the conserved regulatory networks in humans and mice, from which we identified critical microRNAs (miRNAs), target genes and regulatory motifs (miRNA-TF-gene) related to CP. Using our manually curated genes and miRNAs with evidence in CP in humans and mice, we constructed miRNA and transcription factor (TF) co-regulation networks for both humans and mice. A consensus regulatory loop (miR17/miR20a-FOXE1-PDGFRA) and eight miRNAs (miR-140, miR-17, miR-18a, miR-19a, miR-19b, miR-20a, miR-451a and miR-92a) were discovered in both humans and mice. The role of miR-140, which had the strongest association with CP, was investigated in both human and mouse palate cells. The overexpression of miR-140-5p, but not miR-140-3p, significantly inhibited cell proliferation. We further examined whether miR-140 overexpression could suppress the expression of its predicted target genes (BMP2, FGF9, PAX9 and PDGFRA). Our results indicated that miR-140-5p overexpression suppressed the expression of BMP2 and FGF9 in cultured human palate cells and Fgf9 and Pdgfra in cultured mouse palate cells. In summary, our conserved miRNA-TF-gene regulatory network approach is effective in detecting consensus miRNAs, motifs, and regulatory mechanisms in human and mouse CP.

Spectral vegetation index (VI) time series data have been used to monitor and characterize the Earth's vegetative cover and its dynamics in regional to global scales [1] . VI time series data used in these analyses have been those derived from moderate resolution, polar-orbiting satellite sensors, such as Advanced Very High Resolution Radiometer (AVHRR), Moderate Resolution Imaging Spectroradiometer (MODIS), and VEGETATION [2] . Significant progresses have been made on our understanding of vegetation dynamics and their interaction with global climate with these VI time series data [3] .

Estimation of soil brightness from a reflectance spectrum involves a radiative transfer model that describes a canopy-soil system of layers. An assumption of soil line in the model becomes a source of error for the estimation of soil brightness. This study investigates the influences of soil line parameters (slope and NIR intercept) on estimation of soil brightness underneath the vegetation canopy. The investigation was carried out based on a theory of inter-band relationship under the constant soil brightness, which is known as the soil `isoline' equations. Simulation datasets from EO-l/Hyperion hyperspectral data were used to compare inversion results by varying the intercept and slope of the soil line. The result indicates several trends of the brightness estimation caused by the variations of soil line parameters. Since the soil line is known to be varied sensor by sensor, the trends clarified in this study would be beneficial to calibration of multiple soil isoline equations for inter-sensor calibration of the satellite sensors in the context of mega satellite constellation.

This study investigated an optimization technique for improving the inter-band reflectance relationship known as the vegetation isoline equations. A single factor was introduced into the recently developed vegetation isolines [Miura et al. 2015] to decompose the higher-order interaction terms of photons between the canopy layers and soil surfaces. A set of numerical experiments based on the canopy radiative transfer (RT) model revealed that the error in the optimized isoline equation could be reduced by approximately 80 percent relative to the error calculated for the asymmetric-order isoline without optimization. This accuracy improvement generally held for combinations of red and near-infrared wavelengths. These results indicated that the optimized form of the vegetation isoline could be used as an emulator for the RT model in the context of inverse problems for parameter retrievals.

Differences among the normalized difference vegetation indices (NDVIs) simulated from multiple sensors were analyzed using soil isoline equations (SIEs). The upscaled NDVI image was obtained by generating images of red and near-infrared (NIR) bands using an upscaling factor through (1) convolution of a spectral response function (SRF) and hyperspectral data from the EO-1 HYPERION, and (2) adaptation of an area-averaging window. Per-pixel classification was conducted as a solution to the inverse problem of the SIEs, which emulated the spectral behaviors in the red and NIR reflectance subspaces. We compared the NDVIs obtained using three combinations of SRFs that described the existing sensors and identified different trends in the NDVI biases within each class for all sensor combinations, regardless of the scaling factor. These results suggested the potential utility of the SIE-based map for separating clusters of NDVI differences using to the sensor characteristics of the SRF and the spatial resolution.

Intercalibration among remotely-sensed data products such as spectral vegetation indices (VIs) from sensors onboard different satellites has been investigated for integrational use of multiple datasets. To facilitate intercalibration technique of VIs, the relationships among the VI values from different sensors need to be fully understood. This study evaluated the accuracies of NDVI translation technique based on soil iso-line equations. The evaluation was performed by comparing the errors in intersensor relationships of NDVI simulated by radiative transfer model and propagted error in NDVI from the sensor specific signal-to-noise ratio (SNR) of each band. The numerical results indicated that the translation equation showed enough accuracy relative to the propagated error originated from SNR, if the soil brightness underneath the canopy layer can be estimated prior to the translation.

This paper demonstrates that singular configuration of a two-link robot arm is advantageous in doing the task of pulling or lifting up a heavy object with small joint torques. As a result of numerical optimization, the initial configuration that minimizes the joint torques necessary for the task is close to the singular configuration where the arm is stretched out. The singular configuration has the dynamic feature that the joint angle vector can be accelerated in a certain direction almost independently of the mass of the object. Then, the joint torques can generate large kinetic energy efficiently at the singular configuration, and the energy can be utilized to pull or lift up the object. The experimental results show that the dynamic feature is practically useful, even when a feedback control is applied in order to make the object follow a planned trajectory.

Preservation of the spectral characteristics in multispectral images is important in the development of pansharpening methods because it affects the accuracy of subsequent applications, such as visual interpretation, land cover classification, and change detection. The combinations of the spectral properties (observation wavelength and width of spectral bands) of multispectral and panchromatic images affect both the spatial and spectral quality of pansharpened images. Therefore, the clarification of the relations between spectral bands and quality of pansharpened image is important for improving our understanding of pansharpening methods, and for developing better schemes for image fusion. The objective of this study is to investigate the effect of bandwidth of panchromatic image on the spectral quality of pansharpened multispectral image by the simulation using hyperspectral data. Fifty-four panchromatic images with shifting bandwidths were generated using airborne hyperspectral data by averaging of different band combinations. Four bands of multispectral images were generated similarly from hyperspectral data, and spatially degraded by a factor of 6 in order to simulate low-resolution images. The pansharpened multispectral images were produced from these multispectral and panchromatic data by means of 5 pansharpening methods. The results were evaluated by the comparison with the non-degraded multispectral bands both qualitatively by visual assessment, and quantitatively by statistical index ERGAS. Quality of near infrared band was worse than visible bands, mainly due to the spectral position of panchromatic band in visible wave region. It was, however, gradually improved as longer bandwidth of panchromatic band that was expanded into near infrared region. Gram-Schmidt Spectral Sharpening generated somewhat better quality among the method from the view point of ERGAS. It was concluded that wider spectral bandwidth resulted in balanced quality of images in all bands, though the appearances of images are rather dissimilar to the original.

For retrievals of soil optical properties, relationships between the two reflectances of different wavelengths become a key information. This relationship is known as soil isolines from the fact that the reflectances of two different wavelengths form a contour line (isoline) in a reflectance subspace when the soil properties are set to be constant. In our previous work, a relationship between two reflectances of different wavelengths has been derived under a constant soil optical properties. Although accuracy of the derived relationship has been demonstrated numerically, its validity had not been fully investigated from fundamental and physical points of view. The objective of this study is to provide theoretical validity of the derived soil isoline equation and numerically demonstrate its validity by a radiative transfer model of a canopy-soil system of layers. Specifically, the polynomial fittings employed during the derivation will be validated by conducting an analysis based on a theory of radiative transfer. As a result, a similar form of the previously derived soil isoline equation has been derived successfully based on the analytical form of TOC reflectance.

Relationships between two data records such as spectral vegetation index (VI) from different sensors have been paid much attention for accuracy improvement in long-term data set. Such relationships vary with differences in spectral response function, spatial resolution, and viewing and illumination angles. This study focuses on the influences of observation geometries on VI relationship between two sensors. Specifically, an analytical relationship of VI between two sensors has been analyzed from both numerical and theoretical points of view. A set of numerical simulations have been conducted 1) to compare the slope of the vegetation isoline equation with the angular-based VIs, and 2) to compare the NIR-intercept with the distance-based VIs. The results clearly indicated that the slope of vegetation isoline is invariant to the variations of relative azimuthal angle with much less dependence on the view-zenith angle comparing to the BRDF effects on VIs. The theoretical justifications of those behaviors have also been shown based on a formulation of the vegetation isoline. It is concluded that those two variables (the isoline slope and intercept) can be a good candidate as the key variables for the purpose of the angular-standardization of higher-order data products.

Integration of multiple optical sensors enhances spatial, spectral, and temporal resolutions of NDVI products; it enables to monitor dynamics in global vegetation over several decades with high data qualities. However, systematic errors appear in NDVI among sensors owing to discrepancies in, for example, spatial resolution called scaling effects. The mechanisms underlying scaling effects in NDVI had been investigated under two-endmember (vegetation and non-vegetation) linear mixture model in previous studies. Since those studies have been limited to the theoretical analysis, validations with actual satellite data is needed. Objective of this study is to validate the theory of scaling effects in NDVI for practical use in the context of inter-sensor calibration by using ALOS-AVNIR2 data. Results of numerical simulation by AVNIR2 well agreed with theory of scaling effects, indicating that the theory can be applied into inter-sensor calibration of multiple resolutions.

Area-averaged values of fraction of vegetation cover (FVC) derived from spectral data of different spatial resolution are perturbed by systematic biases, called scaling effect. The scaling effect degrades the compatibilities in FVC from multiple sensors. Objectives of this study is to compare scaling effects in FVC by two algorithms based on linear mixture model with vegetation index (VI); Isoline-based LMM and VI-based LMM. Previous study indicated that Isoline-based LMM has degree of freedom, which is the endmember spectra in the model that enables to mitigate the scaling effect under the two-endmember LMM. Results of numerical simulations using satellite data indicate that Isoline-based LMM with an optimum set of endmember spectra outperformed VI-based LMM with regard to the scaling effect, as expected from the scaling theory. It implies that the degree of the scaling effect may be controlled in some extent by choosing an appropriate set of endmember spectra.

This study introduces an analytical approach to derive a relationship between values of Normalized Difference Vegetation Index (NDVI) obtained from datasets of two sensors with different band passes in the context of inter-sensor cross-calibration of such vegetation index (VI) products. Derivation of the relationship has been performed based on a concept of soil isolines. Starting from the soil isoline equations, an inter-sensor NDVI relationship was derived and represented by a system of equation with a single common parameter. In the derived form of NDVI relationship, all the coefficients were written by the soil reflectances (independent of canopy layer.) The functional form of the relationship becomes rational function of the first-order polynomials, when the soil isoline equations are approximated by the form of first-order polynomials. Those results indicate a functional form suitable to model a relationship of NDVI from two sensors.

In estimation of fractional area covered by vegetation (fraction of vegetation cover, FVC) often involves choices of endmember spectra. The determination/estimation of those spectra is performed with uncertainties, hence becomes a source of error in FVC. This study aims at investigating those propagated errors in FVC targeting a retrieval algorithm with a constraint by spectral vegetation index. In this algorithm, two types of endmember species, vegetation and non-vegetation, are assumed. A band correlated noise was also assumed in both spectra. It was found that when the bias errors in the endmember spectra satisfy certain conditions, the propagated errors in FVC are cancelled out. This condition was derived analytically in terms of the bias-free (true) endmember spectra and a spectra of a target pixel. On the contrary the biases of the endmember spectra will be empathized when those biases satisfy a condition that is also derived analytically in this study.

Fraction of vegetation cover (FVC) retrieved by NDVI-isoline based linear mixture model (LMM) suffers from systematic biases due to difference in spatial resolution among sensors, known as 'scaling effect'. Scaling effect of the FVC has been investigated analytically in a previous work under the two-endmember assumption. It implies that the scaling effect (systematic bias) will be minimized by using a certain combinations of endmember spectra. The objective of this study is to examine influence of endmember spectra on the scaling effect of FVC with LANDSAT7/ETM+ data. The results demonstrated that there exists endmember spectrum of vegetation and non-vegetation which mitigate the scaling effect. Findings in this study should contribute to cross-sensor calibra tion of FVC estimation necessary to improve consistency and accuracy in monitoring of vegetation cover.

An underlying assumption in retrievals of biophysical and geological parameters from remotely sensed satellite data is an existence of inter-band relationship. Such relationships are often visualized as isolines in cross plots of reflectance spectra. One type of isolines observed under constant soil properties is the soil isoline, which would provide useful in- formation in retrievals of soil parameters. The purpose of this study is to derive such a relationship observed in a red-NIR reflectance subspace under a fixed condition of soil surface (constant reflectance spectrum of the soil surface). The de- rived expression is then used to relate two NDVI values observed by different pairs of wavelengths in the context of inter-sensor cross calibrations of NDVI. The derived expressions are demonstrated numerically with radiative transfer models of leaf and canopy. The results indicate validity of the derived expression and their potential to be used for determining an appropriate functional form in modeling a relationship between two NDVIs from different sensors.

This study introduces a series of vegetation isoline equations derived by accounting higher-order interaction terms between canopy layer and soil surface. Our focus is to investigate accuracies of the derived isoline equations in the context of applications for hyperspectral data analysis. The objective is to derive vegetation isoline equations that relate reflectances between two wavelengths with higher order interaction terms under an assumption of partial vegetation cover (fraction of vegetation cover is less than unity). Accuracies of the derived isoline equations were evaluated numerically based on a radiative transfer model. The results show that the accuracy in the isoline equations are improved from the original form (the first-order approximation case) by including the higher-order interaction terms. However, a special caution is needed for the choice of the wavelengths and the form of the equations (which depends on the order of interaction terms considered during the derivation), since the accuracies of the isoline equations depends largely on those choices.

This study introduces a parallel implementation of unmixing algorithm for a variable-endmember linear mixture model (VELMM). The model had been developed to retrieve leaf area index (LAI) and fraction of vegetation cover (FVC) from remotely sensed surface reflectance. Since the model consists of a linear sum of two nonlinear functions, the unmixing algorithm involves a constrained optimization process which requires a major computation time. To examine its practicality, an implementation of the algorithm was conducted in a distributed memory multi-core system with STAR-P/MATLAB development environment. The results indicate that approximately two orders of speed up can be achieved using 256 cores to process a LANDSAT7-ETM+ full scene comparing to single core case. It was concluded that the algorithm can gain much practicality on a distributed parallel environment.

This paper introduces derivations of vegetation isoline equation mainly in red and near-infrared reflectance space to improve its accuracy for the purpose of parameter retrieval such as leaf area index and fraction of vegetation cover within a target pixel. The former isoline equations were all derived by truncating the terms of photons which went through the interactions between canopy and soil layers more than once. The improvement is achieved by accounting the terms of higher order interactions which were neglected in the former derivations. As a result, the final form of the isoline equation becomes quadratic due to the inclusions of the second order interaction term. The numerical results indicates clearly that the accuracy, defined as difference of the derived isolines from the true isolines which includes all the interaction terms, was improved significantly from the former isoline equations by adding only one more interaction term (the second order interaction) only in the near-infrared band. It is concluded that higher accuracy can be achieved by adding one more term during the derivation while retaining the final form relatively simple.

Multi-sensor continuity/compatibility of spectral vegetation indices (VIs) is a complicated issue due to differences in both sensor characteristics and product generation algorithms. In this study, we focused on the spectral issue (spectral bandpass differences) and examined various functional forms to approximate the "isoline-based" translation equation of Yoshioka et al. for the normalized difference vegetation index (NDVI). This Yoshioka translation equation was derived based on the physics of atmosphere-vegetation-photon interactions and, thus, is advantageous in accurately inter-relating and translating the NDVI across multiple sensors. The results indicated that a polynomial approximation to the Yoshioka translation equation was an appropriate form, in which the soil-adjusted vegetation index and aerosol optical thickness (AOT) were used as predictor variables. Radiometric variables that would be correlated well with atmospheric contaminations (e.g., AOT) need to be sought for the practical applications of the Yoshioka translation equation.

Leaf area index within vegetated surface and its fractional area to a target pixel (fraction of vegetation cover) are two important factors for estimation of cooling potential by green vegetation in the context of mitigating heat island effects. This paper explains a technique to retrieve these two parameters from satellite images utilizing three bands of visible, near-infrared, and shortwave infrared for better estimation of mitigation potential. The technique assumes linear mixture model with variable endmember approach in which endmember spectra of vegetation and non-vegetation targets are varied continuously by representing these spectra as a function of leaf area index and soil brightness, respectively. A numerical algorithm is examplained to unmix the variable endmember LMM. The proposed technique was applied to the data acquired by LANDSAT7-ETM+ to discuss its validity on retrievals of the two biophysical parameters.

Surface reflectance products obtained by satellite optical sensors are very important for understanding of earth surface properties. Land researchers often use the multiple satellite sensors' products for validation and scale-up studies. There are various surface reflectance products in each project of satellite sensors. Advanced Space-borne Thermal Emission and Reflection Radiometer (ASTER) and Moderate Resolution Imaging Spectroradiometer (MODIS) are onboard the same platform, Terra satellite. One MODIS scene includes several ASTER scenes, and it is very useful for cross-calibration, validation, and scale-up studies. On the other hand, we have developed the system on GEO Grid, which can apply the same atmospheric correction algorithm in VNIR for ASTER and MODIS data. This system can generate rayleigh/ozone/water vapor corrected reflectance quickly by using other satellite atmospheric products. This paper shows the evaluation of our in-house products and ASTER/MODIS surface reflectance products (AST07 and MOD09GHK) obtained Railroad Valley in US.

Choice of area averaging strategy to produce data of normalized difference vegetation index (NDVI) is one of the sources of uncertainty, which is eventually propagated into the simulation results of global climate changes. The purpose of this paper is to investigate this scaling effects of NDVI, especially, on its monotonicity as a function of spatial resolution defined as a number of sampling points within a target area. A simple two-endmember linear mixture model (LMM) is used to analyze the differences among the results of area averaged NDVI with different resolutions. It is proofed analytically that the area averaged NDVI changes monotonically as the spatial resolution becomes higher (more number of sampling points within a fixed area) under the two-endmember LMM, only if a certain condition is satisfied. On the other hand, when the condition is not satisfied, the monotonic behavior of the averaged NDVI value is no longer guaranteed even within an assumption of two-endmember LMM.

Nippon Telegraph and Telephone Corporation (NTT) developed the wireless IP access system (WIPAS), a point-to-multipoint fixed wireless access (FWA) system utilizing the 26-GHz frequency band for home and small office-home office (SOHO) users to provide broadband Internet access service. WIPAS provides a broadband access service by combining wireless technology with optical fiber. We describe this structure as "fiber + radio." WIPAS provides a best-effort-type IP service with the transmission rate of 80 Mbit/s. WIPAS is compatible with FTTH in terms of cost, transmission speed and user interface, and is a part of NTT's broadband access infrastructure. The access point (AP, hub station) was downsized so that it can be mounted on a telephone pole. The wireless terminal (WT, user equipment) was downsized to a volume of approximately two liters. To expand service areas for WIPAS services, we employed an adaptive modulation scheme. We simulated line of sight (LOS) and considered transmission distance of WIPAS. This paper describes the system concepts, specifications, technologies, and estimation of transmission distance of WIPAS

Vegetation over land plays an important role to the carbon cycle, which influences global warming. It is necessary to measure vegetation amount accurately on global scale to understand the carbon cycle mechanism. The Japan Aerospace Exploration Agency (JAXA; former NASDA) has successfully launched a new Advanced Earth Orbiting Satellite (ADEOS-II) aboard an H-2A booster on December 14, 2002. The ADEOS-II satellite is focused on monitoring of global climate change on the Earth. Unfortunately, the operation of ADEOS-II satellite has stopped on October 24 of 2003, but very important VNIR/SWIR/MTIR data have been obtained in northern hemisphere for vegetation dynamics by GLI sensor. These data have enough capability to monitor the density and vigor of green vegetation. GLI data has high potential for vegetation monitoring, and it will contribute to the future satellite sensor. 23 channels are dedicated for land observations in the two spatial resolutions ( 1km/250m ). MODIS sensor has also 1km/250m resolution and various land products, which include VIs. This paper shows the preliminary evaluation of GLI land products for vegetation monitoring using MODIS VIs product.

Relating two spectral vegetation indices (VI) is a fundamental issue for decades in the satellite remote sensing community. Recently, this issue has attracted much attention from varieties of environmental science communities in the context of data continuity by successive satellite sensors which may affect the accuracy of global circulation models. This work introduces a derivation of relationships between two VI's. The derived expression was numerically demonstrated by applying to the case of the normalized difference vegetation index (NDVI) observed at different pairs of wavelengths. The result indicates a direction toward NDVI translations among past, current and future sensors.

The relationship between the reflectances at two different wavelengths over a three-layer system comprising atmosphere, canopy, and soil layers is derived. This work is an extension of the isoline equation previously derived in a red and near-infrared (NIR) reflectance space for a canopy-soil system. As a result of retaining only the zeroth and first-order interaction terms between the layers, the relationship has a linear form in which the slope and offset are functions of the optical properties of each layer. Numerical examples of isolines based on the derived expressions are obtained under various conditions and are shown to demonstrate some of the known behaviors of isolines. Since the derived expression relates a pair of reflectances, it is expected to be useful for the analysis of satellite data products involving algebraic manipulations of spectral reflectances, such as spectral vegetation indexes.

The availability of similar satellite data products from multiple sensors has focused much attention on the issue of continuity across satellite data products from past, current, and future sensors. Hyperspectral datasets acquired over a variety of land cover types are extremely useful in attempting to resolve spectral differences in the global datasets from different sensors. The datasets from the Earth Observing 1 (EO-1) Hyperion sensor are very suitable for this purpose, as is airborne hyperspectral data. In this paper, we examine the possibility of translating vegetation index (VI) data between two sensors by using imagery from the Hyperion sensor and utilizing the vegetation isoline concept. The objectives of this paper are to introduce and test a VI translation technique, focused on the spectral differences associated with sensor spectral bandpass filters. The translation of global VI datasets from one sensor to another requires a methodology applicable over various land cover types and throughout the wide ranges in VI values. To meet these requirements, a technique is proposed that utilizes adjustable translation coefficients, based on an estimation of the leaf area index value relative to a numerical canopy model. The theoretical basis of the proposed translation algorithm is explained in terms of the vegetation isoline concept. Its performance was tested through a numerical experiment with a Hyperion image, focusing on the normalized difference vegetation index (NDVI) as a representative vegetation index. The results indicate the potential of the isoline-based translation technique for stable translation throughout wide ranges of NDVI values.

In order to use satellite data from multiple sensors for monitoring of the Earth's vegetation, satellite data product continuity and compatibility need to be investigated. In this study, we assessed inter-sensor compatibility of the normalized difference vegetation index (NDVI) by simulating bandpasses of the NOAA-14 AVHRR, Terra-MODIS, and Landsat-7 ETM+ from airborne hyperspectral and EO-1 Hyperion data acquired over a savanna-forest transitional zone in Brazil. Our results showed that the simulated reflectances among the sensors examined here were near-linearly related, but with relationships that were land cover-dependent, e.g., reflectances over green vegetation targets formed separate relationships. The deviations were larger for greener targets. On the other hand, the NDVI crossplots among the sensors formed single, land-cover independent relationships. They were, however, curve-linearly related with the degree of curve-linearity increasing with atmosphere contamination. Also, land cover dependencies appeared in the NDVI cross-plots when contaminated by atmosphere. These results suggest that inter-sensor calibration and continuity of the NDVI are achievable, but require a careful examination of surface and atmospheric conditions and the development of a theoretical basis.

Utilization of satellite date from multiple platforms increases our chances of more frequent and accurate observations of the Earth's surface in both global and regional scale For the purpose of vegetation monitoring, this will be particularly true by combining the data from sensors of various spatial, spectral, and temporal resolutions, e.g. the combinations of data from AVHRR (broad band), MODIS (narrow band) and ETM+ (higher spatial resolution). Even though the same spectral vegetation index can be obtained from these sensors, the two main issues need to be considered, one is the systematic differences caused by the spectral response functions, and the other is the differences in spatial resolutions. This paper investigates the spectral issue and its role in the spectral calibration of NDVI among sensors. Hyperspectral data from Hyperion onboard the EO-1 platform were used to simulate outputs from various sensors by band convolution. The data were initially corrected for Rayleigh scattering and Ozone absorption to produce the top-of-the-canopy reflectance as a starting point. The technique first designs a sensor-specific vegetation index (VI) and background brightness index (BI) by accounting for the differences in band-pass filters. These VIs and BIs are then used to estimate the common parameters (sensor independent parameters) attributed to vegetation amount and background brightness. Finally, these parameters are used for the translation of VI among sensors.

A simple deterministic technique of two-band spectral inversion is introduced to estimate a leaf area index (LAI) by minimizing the influences of canopy background brightness, which is known as a main source of uncertainties in spectral vegetation indices. The technique is based on vegetation isolines that characterize the behavior of canopy reflectance spectra under various background brightness conditions. Analytical expressions of vegetation isolines were used to determine the inversion formula and their coefficients by relating the LAI to the slope and offset of vegetation isolines. The main objective of this simple approach is to produce a quick estimation of biophysical parameter to translate vegetation index products among different sensors such as Terra-MODIS, NOAAA -AVHRR and GLI onboard the ADEOS-II satellite which is planned to be launched late in 2002. This paper explains an inversion technique based on the vegetation isoline equation and demonstrates its performance using the SAIL canopy model combined with the PROSPECT leaf model. The results show that the inversion technique accurately estimates LAI under the simulations regardless the soil brightness. Although the number of parameters used in the simulations was limited for the demonstration purpose, the results indicate the usefulness of this technique by combining the statistical approaches to constrain the possible combinations of vegetation parameters. The results also indicate the potential of this technique toward inter-sensor spectral calibration of standard vegetation indices by accounting the differences in band-pass filters.

The impact of reflectance calibration uncertainties on the accuracies of several vegetation indices (VIs) was evaluated for the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor onboard the TERRA platform. A set of uncertainty propagation equations were designed to model the propagation of calibration uncertainties from top-of-atmosphere (TOA) reflectances to atmospherically-corrected VIs. The soil-adjusted vegetation index (SAVI), the atmospherically-resistant vegetation index (ARVI), and the enhanced vegetation index (EVI) were evaluated along with the normalized difference vegetation index (NDVI). The resultant VI uncertainties associated with calibration u/sub cal/(VI) varied with both surface reflectances and atmospheric conditions. Uncertainties in the NDVI and ARVI were highly dependent on pixel brightness, with the largest uncertainties occurring over dark targets with little or no vegetation. The SAVI uncertainties were nearly constant throughout a range of target brightness and vegetation abundance. The EVI uncertainties linearly increased with increasing EVI values. Atmosphere turbidities increased calibration uncertainties in all the VIs through their effect on TOA reflectances. The VI uncertainties were also found to decrease when the calibration errors were positively correlated between bands. Using field observational canopy reflectance data, the mean VI uncertainties were estimated to be /spl plusmn/0.01 VI units for the NDVI and SAVI, and /spl plusmn/0.02 VI units for the ARVI and EVI under normal atmosphere conditions (/spl ges/20 km visibility) and for a 2% reflectance calibration uncertainty.

A technique to derive vegetation isoline equations in red-NIR reflectance space for homogeneous canopies is proposed and demonstrated. A canopy radiative transfer model, known as the Cooper-Smith-Pitts model, is utilized with truncation of the higher order interaction term between the canopy and soil layers. The technique consists of two model simulations, one with a perfect absorber as canopy background and the other with an arbitrary background to estimate the canopy optical properties necessary for the determination of the isoline parameters. These cases are independent of the soil optical properties of any specific site. Hence, the results can be used for any type or series of soils to construct the vegetation isoline equation. A set of simulations was also conducted using the SAIL model to demonstrate the vegetation isoline derivation by the proposed technique. Reflectances and vegetation indices (VI) estimated from the vegetation isoline generally showed good agreement with those simulated by the SAIL model, especially for relatively darker soil. The isoline equation and derivation were found to be useful for further study of two-band VIs and their variation with canopy background.