The Fields of View and Directional Response Functions of Two Field Spectroradiometers
27 Feb 2012-IEEE Transactions on Geoscience and Remote Sensing (IEEE)-Vol. 50, Iss: 10, pp 3892-3907
TL;DR: The area of measurement support and the spatial and spectral responsivity of an ASD Field Spec Pro FR spectroradiometer and a SVC GER 3700 spectroradometer have been determined by measuring the directional response function (DRF) of each instrument.
Abstract: Accurately determining field-of-view has rarely been considered in field spectroscopy where specifications for fore optics used are generally limited and the influence of the spectroradiometer rarely considered. The issue can be compounded with full wavelength spectroradiometric systems which include multiple spectrometers. In these systems, the size and alignment of the viewing optics and technology adopted to transfer light from the fore optic to individual spectrometers may cause significant nonuniformity of spectral response across the area of measurement support, and this area may not align with that assumed from the specification that is supplied for the fore optic. When recording spectra from heterogeneous earth surface targets, it is important to have the area of measurement support accurately defined as individual reflecting surfaces may be present in varying proportions within this area, and these proportions need to be determined to relate spectral reflectance or spectral radiance to state variables or target classifications being considered. The area of measurement support and the spatial and spectral responsivity of an ASD Field Spec Pro FR spectroradiometer and a SVC GER 3700 spectroradiometer have been determined by measuring the directional response function (DRF) of each instrument. This research highlights several areas of concern and makes recommendations for the improvement of field spectroradiometers and field spectroscopy methodologies. These results are specific to the spectroradiometer/fore optic combinations investigated and at the measurement distances specified. Although similar characteristics can be expected for other instruments/fore optics of the same design, and at other measurement distances, the DRFs will vary from those reported here.
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TL;DR: This review evaluates the state-of-the-art methods in UAV spectral remote sensing and discusses sensor technology, measurement procedures, geometric processing, and radiometric calibration based on the literature and more than a decade of experimentation.
Abstract: In the last 10 years, development in robotics, computer vision, and sensor technology has provided new spectral remote sensing tools to capture unprecedented ultra-high spatial and high spectral resolution with unmanned aerial vehicles (UAVs). This development has led to a revolution in geospatial data collection in which not only few specialist data providers collect and deliver remotely sensed data, but a whole diverse community is potentially able to gather geospatial data that fit their needs. However, the diversification of sensing systems and user applications challenges the common application of good practice procedures that ensure the quality of the data. This challenge can only be met by establishing and communicating common procedures that have had demonstrated success in scientific experiments and operational demonstrations. In this review, we evaluate the state-of-the-art methods in UAV spectral remote sensing and discuss sensor technology, measurement procedures, geometric processing, and radiometric calibration based on the literature and more than a decade of experimentation. We follow the 'journey' of the reflected energy from the particle in the environment to its representation as a pixel in a 2D or 2.5D map, or 3D spectral point cloud. Additionally, we reflect on the current revolution in remote sensing, and identify trends, potential opportunities, and limitations.
370 citations
Cites background from "The Fields of View and Directional ..."
...weighted within their FOV, and the configuration of the fiber and fore optic might influence the measured signal [101]....
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TL;DR: The UAV STS spectrometer and the multispectral camera MCA6 were found to deliver spectral data that can match the spectral measurements of an ASD at ground level when compared over all waypoints and Variability was highest in the near-infrared bands for both sensors.
Abstract: . Unmanned aerial vehicles (UAVs) equipped with lightweight spectral sensors facilitate non-destructive, near-real-time vegetation analysis. In order to guarantee robust scientific analysis, data acquisition protocols and processing methodologies need to be developed and new sensors must be compared with state-of-the-art instruments. Four different types of optical UAV-based sensors (RGB camera, converted near-infrared camera, six-band multispectral camera and high spectral resolution spectrometer) were deployed and compared in order to evaluate their applicability for vegetation monitoring with a focus on precision agricultural applications. Data were collected in New Zealand over ryegrass pastures of various conditions and compared to ground spectral measurements. The UAV STS spectrometer and the multispectral camera MCA6 (Multiple Camera Array) were found to deliver spectral data that can match the spectral measurements of an ASD at ground level when compared over all waypoints (UAV STS: R2=0.98; MCA6: R2=0.92). Variability was highest in the near-infrared bands for both sensors while the band multispectral camera also overestimated the green peak reflectance. Reflectance factors derived from the RGB (R2=0.63) and converted near-infrared (R2=0.65) cameras resulted in lower accordance with reference measurements. The UAV spectrometer system is capable of providing narrow-band information for crop and pasture management. The six-band multispectral camera has the potential to be deployed to target specific broad wavebands if shortcomings in radiometric limitations can be addressed. Large-scale imaging of pasture variability can be achieved by either using a true colour or a modified near-infrared camera. Data quality from UAV-based sensors can only be assured, if field protocols are followed and environmental conditions allow for stable platform behaviour and illumination.
149 citations
Cites methods from "The Fields of View and Directional ..."
...However the matching of the footprint of two different spectrometers can go beyond comparing circles and rectangles due their optical path as recently shown by MacArthur et al. (2012)....
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TL;DR: This study establishes the theoretical background to comprehend the properties of spectral data acquired with 2D imagers and investigates how different data processing schemes influence the data.
75 citations
TL;DR: These integrated models will be used in an up-coming study to extrapolate AWB over 60 × 60 m transects to evaluate spaceborne multispectral broad bands and hyperspectral narrowbands.
Abstract: Ground-based estimates of aboveground wet (fresh) biomass (AWB) are an important input for crop growth models. In this study, we developed empirical equations of AWB for rice, maize, cotton, and alfalfa, by combining several in situ non-spectral and spectral predictors. The non-spectral predictors included: crop height (H), fraction of absorbed photosynthetically active radiation (FAPAR), leaf area index (LAI), and fraction of vegetation cover (FVC). The spectral predictors included 196 hyperspectral narrowbands (HNBs) from 350 to 2500 nm. The models for rice, maize, cotton, and alfalfa included H and HNBs in the near infrared (NIR); H, FAPAR, and HNBs in the NIR; H and HNBs in the visible and NIR; and FVC and HNBs in the visible; respectively. In each case, the non-spectral predictors were the most important, while the HNBs explained additional and statistically significant predictors, but with lower variance. The final models selected for validation yielded an R2 of 0.84, 0.59, 0.91, and 0.86 for rice, maize, cotton, and alfalfa, which when compared to models using HNBs alone from a previous study using the same spectral data, explained an additional 12%, 29%, 14%, and 6% in AWB variance. These integrated models will be used in an up-coming study to extrapolate AWB over 60 × 60 m transects to evaluate spaceborne multispectral broad bands and hyperspectral narrowbands.
69 citations
Cites background from "The Fields of View and Directional ..."
...To improve spatial and spectral uniformity across quadrats (see [54]), five replicate spectra were taken at random positions over each quadrat....
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TL;DR: In this paper, the impact of biological impurities on the spectral signatures and broadband darkening of terrestrial snow, sea ice, glaciers and ice sheets has been investigated using radiative transfer theory.
Abstract: . The darkening effects of biological impurities on ice and snow have been recognised as a control on the surface energy balance of terrestrial snow, sea ice, glaciers and ice sheets. With a heightened interest in understanding the impacts of a changing climate on snow and ice processes, quantifying the impact of biological impurities on ice and snow albedo ( bioalbedo ) and its evolution through time is a rapidly growing field of research. However, rigorous quantification of bioalbedo has remained elusive because of difficulties in isolating the biological contribution to ice albedo from that of inorganic impurities and the variable optical properties of the ice itself. For this reason, isolation of the biological signature in reflectance data obtained from aerial/orbital platforms has not been achieved, even when ground-based biological measurements have been available. This paper provides the cell-specific optical properties that are required to model the spectral signatures and broadband darkening of ice. Applying radiative transfer theory, these properties provide the physical basis needed to link biological and glaciological ground measurements with remotely sensed reflectance data. Using these new capabilities we confirm that biological impurities can influence ice albedo, then we identify 10 challenges to the measurement of bioalbedo in the field with the aim of improving future experimental designs to better quantify bioalbedo feedbacks. These challenges are (1) ambiguity in terminology, (2) characterising snow or ice optical properties, (3) characterising solar irradiance, (4) determining optical properties of cells, (5) measuring biomass, (6) characterising vertical distribution of cells, (7) characterising abiotic impurities, (8) surface anisotropy, (9) measuring indirect albedo feedbacks, and (10) measurement and instrument configurations. This paper aims to provide a broad audience of glaciologists and biologists with an overview of radiative transfer and albedo that could support future experimental design.
66 citations
Cites background from "The Fields of View and Directional ..."
...Since there are uncertainties related to the true IFOV of a spectral radiometer, even when collimated with a fore-optic (MacArthur et al., 2012), the sample surface should be (a) approximately homogeneous and (b) significantly larger than the area observed by the spectral radiometer....
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References
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TL;DR: In this paper, an Introduction to Applied Geostatistics is presented, with a focus on the application of applied geometrics in the area of geostatistic applications.
Abstract: (1991). An Introduction to Applied Geostatistics. Technometrics: Vol. 33, No. 4, pp. 483-485.
4,911 citations
"The Fields of View and Directional ..." refers background in this paper
...The darkroom walls were black and spectrally flat with a reflectance of less than 5% and black spectrally flat optical cloth, also with a reflectance of less than 5%, covered the optical table and the vertical linear stage to reduce spurious reflections from the light source....
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Book•
01 Jan 1989TL;DR: In this paper, Krigeage and continuite spatiale were used for interpolation of a variogramme with anisotropic interpolation reference record created on 2005-06-20, modified on 2011-09-01.
Abstract: Keywords: Krigeage ; continuite spatiale ; variogramme ; incertitudes ; anisotropie ; interpolation Reference Record created on 2005-06-20, modified on 2011-09-01
4,371 citations
Book•
18 Feb 1988
TL;DR: Computer processing of remote-sensed images, Computer processing of remotely-sensing images, and so on.
Abstract: Computer processing of remotely-sensed images , Computer processing of remotely-sensed images , مرکز فناوری اطلاعات و اطلاع رسانی کشاورزی
828 citations
TL;DR: New methods to identify and quantify individual pigments in the presence of overlapping absorption features would provide a major advance in understanding their biological functions, quantifying net carbon exchange, and identifying plant stresses.
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01 Jan 2002
319 citations