Physical Mechanisms in Hyperspectral BRDF Data of Grass and Watercress

Sandmeier, Stefan; Müller, Ch.; Hosgood, B.; Andreoli, G.

doi:10.1016/s0034-4257(98)00060-1

Cited by 160 publications

(136 citation statements)

References 14 publications

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“…The anisotropy patterns of barley and winter wheat were dominated by backward scattering over the full measured spectral domain, with emphasis on the visible wavelength region, combined with a strong bell-shaped anisotropy in the NIR. Similar patterns for barley and wheat were also found in several other studies e.g., [51][52][53] and are typical for vertically-oriented vegetation canopies, such as grasses e.g., [14,15].…”

Section: Discussionsupporting

confidence: 86%

“…The ANIF is a ratio that describes the reflectance factor relative to the nadir reflectance factor [15]:…”

Section: Discussionmentioning

confidence: 99%

“…This effect occurs because, in the backward scattering direction, the sensor was viewing the well-illuminated side of the canopies, which resulted in a higher reflectance, and in the forward scattering direction the shadowed side of the canopies, which resulted in a lower reflectance. In addition to this effect, view zenith angles (VZAs) close to nadir allow for a view of the lower shadowed layers of the canopy and with increasing off-nadir VZAs the relative proportion of higher, well-illuminated canopy layers that are observed by the sensor increases, resulting in an increase of reflectance [15,49].…”

Section: Barley and Winter Wheatmentioning

confidence: 99%

“…Reflectance anisotropy is commonly studied by performing multi-angular reflectance measurements using goniometers in laboratories under controlled conditions e.g., [12][13][14][15] or in the field under natural conditions e.g., [16][17][18][19][20][21]. Both laboratory and field goniometer measurements have their advantages and disadvantages [22].…”

Section: Introductionmentioning

confidence: 99%

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Hyperspectral Reflectance Anisotropy Measurements Using a Pushbroom Spectrometer on an Unmanned Aerial Vehicle—Results for Barley, Winter Wheat, and Potato

et al. 2016

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Abstract:Reflectance anisotropy is a signal that contains information on the optical and structural properties of a surface and can be studied by performing multi-angular reflectance measurements that are often done using cumbersome goniometric measurements. In this paper we describe an innovative and fast method where we use a hyperspectral pushbroom spectrometer mounted on a multirotor unmanned aerial vehicle (UAV) to perform such multi-angular measurements. By hovering the UAV above a surface while rotating it around its vertical axis, we were able to sample the reflectance anisotropy within the field of view of the spectrometer, covering all view azimuth directions up to a 30 • view zenith angle. We used this method to study the reflectance anisotropy of barley, potato, and winter wheat at different growth stages. The reflectance anisotropy patterns of the crops were interpreted by analysis of the parameters obtained by fitting of the Rahman-Pinty-Verstraete (RPV) model at a 5-nm interval in the 450-915 nm range. To demonstrate the results of our method, we firstly present measurements of barley and winter wheat at two different growth stages. On the first measuring day, barley and winter wheat had structurally comparable canopies and displayed similar anisotropic reflectance patterns. On the second measuring day the anisotropy of crops differed significantly due to the crop-specific development of grain heads in the top layer of their canopies. Secondly, we show how the anisotropy is reduced for a potato canopy when it grows from an open row structure to a closed canopy. In this case, especially the backward scattering intensity was strongly diminished due to the decrease in shadowing effects that were caused by the potato rows that were still present on the first measuring day. The results of this study indicate that the presented method is capable of retrieving anisotropic reflectance characteristics of vegetation canopies and that it is a feasible alternative for field goniometer measurements.

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Section: Discussionsupporting

confidence: 86%

“…The ANIF is a ratio that describes the reflectance factor relative to the nadir reflectance factor [15]:…”

Section: Discussionmentioning

confidence: 99%

Section: Barley and Winter Wheatmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Hyperspectral Reflectance Anisotropy Measurements Using a Pushbroom Spectrometer on an Unmanned Aerial Vehicle—Results for Barley, Winter Wheat, and Potato

et al. 2016

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“…Third, the anisotropic information contained in multi-angle spectral reflectance should be more related to forest structure (Sandmeier et al, 1998;Schull et al, 2007). Although a shadow-affected spectral signal is related to tree height, spectral data still cannot provide explicit information on canopy three-dimensional structure (Chopping et al, 2008, Chopping, 2011.…”

Section: Objectives Of This Studymentioning

confidence: 99%

A combined GLAS and MODIS estimation of the global distribution of mean forest canopy height

Wang

Ding

et al. 2016

Remote Sensing of Environment

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Mapping the global distribution of forest canopy height is important for estimating forest biomass and terrestrial carbon flux. In this study, we present a global map of mean forest canopy height at 500 m spatial resolution obtained by combining Geoscience Laser Altimeter System (GLAS) data acquired from 2005 to 2006 and 13 ancillary variables, including seven climatic variables and six remote sensing variables (nadir BRDF-adjusted reflectance at red and NIR bands, tree cover, anisotropic factor, accumulated Enhanced Vegetation Index, and elevation). The original contributions of this study include the following: (1) The wavelet method was applied to complement the GLA14 product to identify the ground peak and the top-canopy peak. We found that it was useful for dealing with waveforms with low reconstruction accuracy. (2) GLAS data from the leafless season were not used for nonevergreen forest because the height retrieval results exhibited underestimation and strong variations. (3) The anisotropic factor (ANIF), an indicator related to surface structure, was included as an ancillary variable for the first time and was determined to be important for height modeling in the Asian and North American regions. (4) The balanced random forest (BRF) algorithm was applied to register GLAS mean forest canopy height to a 500 m grid considering the small proportion of extreme height classes (tall and short trees), and it achieved good performance in terms of modeling accuracy (RMSE = 2.75 to 4.45 m) and preserving data variation. An inter-comparison among three global forest height maps [the present study, Lefsky (2010), andSimard et al. (2011)] was implemented in a pixel-by-pixel manner. High agreement (R 2 = 0.73, RMSE = 4.49 m) was determined between the present study and Simard et al., whereas the result from Lefsky was notably different from the other two results (R 2 = 0.14, RMSE = 8.92 m, compared with the present study; R 2 = 0.11, RMSE = 11.19 m, compared with Simard et al.). Large disparities were generally associated with evergreen broadleaf forests in South America, deciduous needleleaf forests in Europe and Russian North Asia, and evergreen needleleaf forests on the West Coast of North America. Differences in the height metric were a main factor affecting the disparities among the three results. Validation against field survey data acquired from the Distributed Active Archive Center indicated the accuracy of our mean forest canopy height map (R 2 = 0.63, RMSE = 4.68 m, n = 59).

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Polarized reflectance factors of vegetation covers from laboratory and field: A comparison with modeled results

Sun

et al. 2017

JGR Atmospheres

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Light scattered from surfaces is partly polarized, which has implications for remote sensing of vegetation. To study the contribution of polarized light to multiangular reflectance over vegetation covers, we produced a hyperspectral bidirectional polarized reflectance data set for four types of vegetation cover acquired over a wide‐viewing range in the laboratory and field. The data sets were analyzed with respect to basic physical polarized reflectance mechanisms. Moreover, we compared the modeled polarization from five existing polarimetric models (1‐D) with our measured polarization of vegetation covers at selected wavelengths. The analyses indicated that our measured polarized reflectance factors of a special vegetation cover, in which the leaves of vegetation were horizontally oriented, were much greater than previous results. All of the models produced good fits to the measurements, except for the special vegetation cover, which leaded to markedly different distribution patterns for the five models. We also found that the maximum relative difference between modeled and measured polarization was in the backward scattering direction near the principal plane. These results suggested that we can model the polarization accurately by using the existing polarimetric models in visible and near‐infrared bands; more measurements should be performed in the laboratory and the field to illustrate the inherent polarization property of vegetation covers and to avoid underestimating the contribution of vegetation covers on the polarization of atmosphere in the polarized remote sensing study. Our study not only deepens our understanding of the scattering properties of vegetation covers but also provides the polarization properties of vegetated surfaces for the study of atmosphere polarization.

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Physical Mechanisms in Hyperspectral BRDF Data of Grass and Watercress

Cited by 160 publications

References 14 publications

Hyperspectral Reflectance Anisotropy Measurements Using a Pushbroom Spectrometer on an Unmanned Aerial Vehicle—Results for Barley, Winter Wheat, and Potato

Hyperspectral Reflectance Anisotropy Measurements Using a Pushbroom Spectrometer on an Unmanned Aerial Vehicle—Results for Barley, Winter Wheat, and Potato

A combined GLAS and MODIS estimation of the global distribution of mean forest canopy height

Polarized reflectance factors of vegetation covers from laboratory and field: A comparison with modeled results

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