The potential of hyperspectral bidirectional reflectance distribution function data for grass canopy characterization (original) (raw)
Related papers
1999
Complex variation in terrestrial biogeochemical cycles results from climatic, edaphic, biotic and human processes operating at multiple spatial and temporal scales. This variation cannot easily be resolved at the landscape, regional or global level without an integrated effort involving field research, modeling and remote sensing. To date, the role of remote sensing in terrestrial biogeochemical research has been limited to land-cover change analysis and estimation of two functional properties of vegetation: leaf area index (LAI) and fractional photosynthetically active radiation absorption (fAPAR). Traditional land-cover remote sensing provides the minimum information on the extent of major vegetation types needed to constrain ecosystem models to actual conditions (Wessman and Asner, 1997). Some ecosystem models also utilize remotely sensed estimates of LAI and fAPAR to derive plant carbon uptake or net primary productivity (NPP), which then feeds into soil carbon, nutrient and water algorithms that calculate a wide range of biogeochemical fluxes .
Remote Sensing of Environment, 2008
A new method is described for the retrieval of fractional cover of large woody plants (shrubs) at the landscape scale using moderate resolution multi-angle remote sensing data from the Multiangle Imaging SpectroRadiometer (MISR) and a hybrid geometric-optical (GO) canopy reflectance model. Remote sensing from space is the only feasible method for regularly mapping woody shrub cover over large areas, an important application because extensive woody shrub encroachment into former grasslands has been seen in arid and semi-arid grasslands around the world during the last 150 years. The major difficulty in applying GO models in desert grasslands is the spatially dynamic nature of the combined soil and understory background reflectance: the background is important and cannot be modeled as either a Lambertian scatterer or by using a fixed bidirectional reflectance distribution function (BRDF). Candidate predictors of the background BRDF at the Sun-target-MISR angular sampling configurations included the volume scattering kernel weight from a Li-Ross BRDF model; diffuse brightness (ρ0) from the Modified Rahman-Pinty-Verstraete (MRPV) BRDF model; other Li-Ross kernel weights (isotropic, geometric); and MISR near-nadir bidirectional reflectance factors (BRFs) in the blue, green, and near infra-red bands. The best method was multiple regression on the weights of a kernel-driven model and MISR nadir camera blue, green, and near infra-red bidirectional reflectance factors. The results of forward modeling BRFs for a 5.25 km 2 area in the USDA, ARS Jornada Experimental Range using the Simple Geometric Model (SGM) with this background showed good agreement with the MISR data in both shape and magnitude, with only minor spatial discrepancies. The simulations were shown to be accurate in terms of both absolute value and reflectance anisotropy over all 9 MISR views and for a wide range of canopy configurations (r 2 = 0.78, RMSE = 0.013, N = 3969). Inversion of the SGM allowed estimation of fractional shrub cover with a root mean square error (RMSE) of 0.03 but a relatively weak correlation (r 2 = 0.19) with the reference data (shrub cover estimated from high resolution IKONOS panchromatic imagery). The map of retrieved fractional shrub cover was an approximate spatial match to the reference map. Deviations reflect the first-order approximation of the understory BRDF in the MISR viewing plane; errors in the shrub statistics; and the 12 month lag between the two data sets.
Remote Sensing of Environment, 2000
Imaging Spectrometer data. Most immonly occur in arid ecosystems due to land use and climate portant, the relative impact of tissue, canopy, and landscape variability. Most arid land remote sensing efforts have fofactors on pixel-level reflectance shifted with plant composicused on detecting vegetation change using spectral indices, tion and phenology. We compared the ability to resolve such as the normalized vegetation index, with limited sucthese vegetation and soil factors using Airborne Visible cess. Less attention has focused on using the continuous and Infrared Imaging Spectrometer, Moderate Resolution shortwave spectrum (0.4 lm to 2.5 lm) for studying vegeta-Imaging Spectrometer, and Landsat Thematic Mapper option in arid environments. Using field measurements and tical channels and found that few factors could be aca photon transport model, we quantified the absolute and counted for unless most of the spectral range was aderelative importance of tissue, canopy, and landscape factors quately sampled. ©Elsevier Science Inc., 2000 that drive pixel-level shortwave reflectance variation along a land-cover gradient in the Chihuahuan Desert, New Mexico. Green foliage, wood, standing litter, and bare soil had INTRODUCTION distinctive spectral properties, often via specific, narrow
Ecosystem changes can have fundamental impacts on climate, therefore determining how much shortwave radiation that is absorbed and reflected from vegetation canopies can help understand and predict near-surface climate. The shortwave radiation budget is a function of the coupling between canopy structure and leaf biochemistry. In order to interpret a remotely sensed signal from vegetation canopies, scattering must be modelled at both the leaf and canopy level, complicating the retrieval of leaf biochemistry. This study empirically tests a new approach to model both canopy and leaf scattering using a Directional Area Scattering Factor (DASF) to correct reflectance (BRF) data modelled by Monte Carlo Ray Tracing simulations of different canopies for structural effects to predict total canopy scattering. It was shown that both DASF and total canopy scattering could be accurately extracted under idealised conditions of directional-hemispherical reflectance, equal leaf asymmetry and sufficiently dense canopies with black soil. Additionally it was proven that under these conditions total canopy scattering could be predicted using information from the 710-790nm region and given no prior knowledge of leaf optical properties. However, the latter highlighted important consequences of no prior knowledge of leaf optical properties; if the leaf single scattering albedo is not known, the recollision probability cannot be found, and vice versa. This is significant since recollision probability is widely used in canopy reflectance modelling. Departures from these idealised conditions; varying view geometry, bi-directional reflectance, LAI and soil effects, were tested. Canopy scattering was extracted stably under conditions of varying view geometry and bi-directional reflectance, but LAI and soil effects were proven to influence the accuracy with which canopy scattering can be modelled using this approach. Canopy architecture, described by homogeneous and heterogeneous canopies has important influences over DASF and consequently the accuracy of retrieval of total canopy scattering.
Http Dx Doi Org 10 5589 M09 040, 2014
The reflectance signal from the forest tree canopy is influenced by the optical properties of the background formed by understory vegetation. Our study shows that the herb-moss layer in the forest tends to be brighter in the visible wavelength when the canopy above is more closed because of the specific properties of plants grown in low light. When leaf-area-based chlorophyll content falls below approximately 150 mg•m-2 , reflectance in the red region of the spectrum increases compared with that of the background. The best descriptors of the herb-moss layer for deriving optical parameters are herb layer dry mass for the visible wavelength range and total aboveground water mass for near-infrared (NIR) reflectance. In addition, chlorophyll content per leaf area considerably improves the red reflectance estimate. Résumé. Le signal de réflectance de la canopée des arbres forestiers est influencé par les propriétés optiques du second plan formé par la végétation des strates inférieures. Notre étude montre que la couche d'herbe/de mousse dans la forêt a tendance à être plus lumineuse dans le spectre visible lorsque la canopée au-dessus est plus fermée. Ceci est dû aux propriétés spécifiques des plantes adaptées à la faible luminosité. Lorsque la teneur en chlorophylle par unité de surface foliaire descend en dessous d'approximativement 150 mg•m-2 , la réflectance dans le spectre rouge augmente par rapport à celle du second plan. Les meilleurs descripteurs de la couche d'herbe/de mousse pour dériver des paramètres optiques sont : la masse sèche de la couche d'herbes pour estimer le spectre visible, et la masse totale d'eau de surface pour la réflectance dans le proche infrarouge (NIR). De plus, connaître la teneur en chlorophylle par surface foliaire améliore considérablement l'estimation de la réflectance dans le spectre rouge.
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020
Semi-empirical, kernel-driven linear bidirectional reflectance distribution function (BRDF) models are widely used to characterize vegetation reflectance anisotropy and provide land surface bidirectional reflectance factor (BRF) products at the regional and global scales. However, these models usually imply an assumption of spherical leaf inclination. The effects of such an ideal assumption on simulating surface BRF remain few quantified. In this paper, we first evaluated the effects of leaf inclination on the most commonly-used kernel-driven RossThick-LiSparse-Reciprocal (RTLSR) model by using the reflectance benchmark simulated by the mature PROSAIL (PROSAIL+SAIL) radiative transfer model. Subsequently, we improved the RTLSR model into a four-parameter version (RTLSRV4p) with a new volumetric scattering kernel derived from the assumption of vertical leaf inclination. Finally, the proposed RTLSRV4p model was validated by PROSAIL canopy BRF simulations, in situ canopy BRF measurements and Wide-angle Infrared Dual-mode line/area Array Scanner (WIDAS) airborne observations. Validation results demonstrate that RTLSRV4p improves vegetation reflectance characterization for large leaf inclinations compared to the original RTLSR model, especially for the nearinfrared (NIR) spectral domain. When validated against the simulated canopy BRFs, the mean root-mean-square error (RMSE), mean absolute percentage error (MAPE), bias, and coefficient of determination (R 2) were improved from 0.0810, 31.63%, 0.0651, 0.6578 to 0.0453, 13.38%, 0.0326, 0.8734. Using the in situ BRF measurement, the fitted RMSE, MAPE, bias, and R 2 were improved from 0.0917, 14.31%, 0.0728, and 0.5776 to 0.0226, 3.35%, 0.0166, and 0.9744. These validation metrics were improved from 0.0423, 10.85%, 0.0347, and 0.6598 to 0.0258, 5.85%, 0.0181, and 0.8732 when compared against the WIDAS observations. The RTLSRV4p model also shown good performance in calculating the reflectance of vegetation (NIRv). These extensive validations suggest that RTLSRV4p is promising for capturing vegetation reflectance under large leaf inclinations.
BRDF normalization of hyperspectral image data
2002
Monitoring vegetative areas with airborne hyperspectral sensors is being more frequently used to relate at-canopy spectral reflectance to canopy condition. Increased application of these techniques is expected with the advent of space borne hyperspectral systems (such as EO-1 Hyperion and CHRIS-PROBA). These studies are often limited by the non-Lambe rtian nature of vegetation reflectance, the well known bidirectional reflectance distribution function (BRDF), where varying solar and viewing geometry can result in significant variations in the observed remotely sensed signal due to canopy architectural properties. This is often noted as an increased brightening of the observed signal as the scattering angle between the sun and sensor decreases. This is also true when attempting to compare images from different sensors, or from the same sensor taken at different times. Various studies have examined the sensitivity of broadband and hyperspectral vegetation indices (VI) to BRDF. These studies often conclude that the choice of VI should be based on the solar/viewing geometry and vegetation specific to the image acquisition.
Remote Sensing of Environment, 1998
Canopy radiative transfer models simulate the bidirec-model inversions by decreasing the number of observations required to retrieve canopy structural and biophysi-tional reflectance distribution function (BRDF) of vegetation covers with differing leaf and soil spectral and can-cal information from multiangle remotely sensed data. ©Elsevier Science Inc., 1998 opy structural characteristics. Numerical inversion of these models has provided estimates of vegetation structural and biophysical characteristics from multiangle, remotely * Center for the Study of Earth from Space/CIRES and Departvanced from simple light extinction algorithms to those ment of Environmental, Population and Organismic Biology, University based explicitly on scattering theory using turbid medium of Colorado, Boulder and turbid-medium/geometric-optical methods (e.g.
Evaluation of spectrodirectional alfalfa canopy data acquired during DAISEX'99
IEEE Transactions on Geoscience and Remote Sensing, 2003
Field goniometer measurements are a tool to generate a priori bidirectional reflectance distribution function (BRDF) knowledge for correction and validation of directional reflectance data acquired by air-and spaceborne sensors. This study analyzes the diurnal hemispherical-directional reflectance factor data of an Alfalfa canopy measured during the Digital Airborne Imaging Spectrometer Experiment 1999 (DAISEX'99). We analyze the variation of measured and modeled spectrodirectional vegetation data, revealing that measurement noise is negligible compared to the variation due to the canopy's anisotropy. The deviations of the spectral albedo (bihemispherical reflectance) and of field spectrometer nadir measurements throughout a day prove to be larger than modeled deviations. Calculated anisotropy factors quantify the spectral-dependent effects of the vegetation reflectance anisotropy. This paper is a contribution toward the generation of a reliable BRDF database by suggesting methods to preprocess and analyze observed directional vegetation reflectance data, with special emphasis on the spectral dimension.