Modeling canopy reflectance and microwave backscattering coefficient

Various approaches to model canopy reflectance (CR) in the visible/infrared region and backscattering coefficient (BSC) in the microwave region are compared and contrasted. It is noted that BSC can be related to CR in the source direction (the “hot spot” direction). By assuming a frequency dependent leaf reflectance and transmittance it is shown that the observed dependence of BSC on leaf area index, leaf angle distribution, angle of incidence, soil moisture content, and frequency can be simulated by a CR model. Thus both BSC and CR can, in principle, be calculated using a single model which has essentially the same parameters as many CR models do.     

Grass species differentiation through canopy hyperspectral reflectance

This study attempts (1) to evaluate the capability of hyperspectral reflectance to differentiate C₃ and C₄ grass species, both in isolation and in mixed canopies; (2) to identify the critical spectral ranges that differentiate the two groups and individual species within them; and (3) to determine if there is temporal variation in these capabilities. During one year, hyperspectral reflectance of C₃ and C₄ grass species was measured both in single-species and in mixed canopies. Spectral bands with higher differentiating potential were identified and species classified. For single-species canopies, hyperspectral reflectance differentiated the two functional groups and most species in all seasons. In mixed canopies, it underestimated the fractional cover of the C₄ component. The green, red, and near infrared above 820 nm spectral ranges were critical both for species and functional group differentiation. In conclusion, hyperspectral information was useful to differentiate pure canopies, but the differentiation algorithms were season-specific. Additionally, we need to improve our understanding of interactive effects of species in order to accurately estimate the composition of assemblages.     

Validation of a Markov chain canopy reflectance model

The Markov chain canopy reflectance model (MCRM) by Kuusk (1995 b) has been tested versus the ray tracing model on two different computer maquettes of field crops (Barley and Beet), and on the field data collected in the frame of the Franco-English Collaborative Reflectance Experiment in 1989 and 1990 on sugar-beet plots. Separate comparisons of single and multiple scattering components of the MCRM and the ray tracing procedure demonstrated good agreement of the models. Inversion of the MCRM on field data returned good estimates of LAI in the range LAI 0.1-4 using nadir reflectance data in three SPOT and two Landsat TM channels. The estimated chlorophyll content was well correlated to the measured one, although underestimated to some extent. The use of directional data at 45 zenith angle and four azimuth angles improved the estimates of both the LAI and the chlorophyll content. It also permitted the estimation of additional parameters of the canopy structure (leaf size, LAD, the Markov parameter).     

Influence of wind on crop canopy reflectance measurements

Remote sensing techniques of measuring red and far-red crop canopy reflectance are frequently used to estimate crop canopy characteristics. The variability introduced in reflectance data from nonvegetative factors such as wind decreases the usefulness of the techniques. The objective of this study was to quantify and minimize the variability from wind on spectral reflectances. Red and far-red reflectances were acquired above wheat, barley, and alfalfa canopies throughout days of changing wind conditions. Periods of 312 s with little changes in irradiance values were used for the analysis. Wind had negligible effect on reflectances of a short canopy such as cut alfalfa, while it had a significant effect on reflectances from canopies with a higher vertical structure, particularly during gusty conditions. Within the windy and calm periods, extreme values of spectral reflectance differed by 60% and 12%, respectively, in the red, and by 40% and 8% in the far-red for the barley canopy. For the compact and dense canopy structure of alfalfa, these differences reached a maximum of 10% under windy conditions in both spectral regions. The plant canopy architecture, the wind conditions, and the spectral regions all affected the magnitude of the influence of wind on crop canopy spectral reflectances. The mean reflectance of a canopy overestimated the true reflectance by 2–4% while the use of the median reduced this overestimation. Sampling requirements for this sensor are evaluated, and the possibility of decreasing either the sampling rate or the sampling period is discussed.     

Estimation of solar-induced fluorescence using the canopy reflectance index

This article presents a methodology to quantitatively extract the solar-induced fluorescence (SIF) using the canopy reflectance index. The sensitivity analysis was conducted with a spectral vegetation Fluorescence Model (FluorMOD), and the results demonstrate that Sun zenith angle (θ), fluorescence quantum efficiency (Fi), leaf inclination distribution function (LIDF), leaf temperature (T), leaf area index, and leaf chlorophyll a + b content (chl-a+b) had large effects on the fluorescence radiance at 761 nm (LF,761). Based on the results of the sensitivity analysis, the input parameters θ, Fi, LIDF, T, and chl-a+b varied within a certain range during the generation of the simulated data. Based on the simulated data, R₇₄₀/R₆₃₀, R₆₈₅/R₈₅₀, and R₇₅₀/R₇₁₀ were thought to be the best candidates to extract the fluorescence radiation. The quantitative relationships between the fluorescence retrieved by R₇₄₀/R₆₃₀, R₆₈₅/R₈₅₀, and R₇₅₀/R₇₁₀ and LF,761 were analysed and expressed as functions of θ, Fi, T, and reflectance index. The correlation coefficients (r) between the fluorescence retrieved using R₆₈₅/R₈₅₀, R₇₄₀/R₆₃₀, and R₇₅₀/R₇₁₀ and LF,761 are 0.94, 0.95, and 0.95, respectively, and the root mean square errors (RMSEs) were 0.32, 0.29, and 0.30 W m^?2 μm^?1 sr^?1, respectively. Through comparison with FLD and 3FLD, the method presented in this article yielded better results, and could be used to estimate the fluorescence. This methodology provides new insights into the quantitative retrieval of SIF from the reflectance spectrum.     

Comparative study of suits and sail canopy reflectance models

In order to fully exploit the potential of remote sensing from aircraft and spacecraft altitude to map vegetation and estimate key agronomic parameters such as leaf area index (LAI) and biomass, the relationships between the canopy reflectance and properties of canopy elements must be well understood. A number of canopy reflectance models exist in the literature. Much of the model(s) verifications have been done primarily with observations from nadir direction. This was partly due to near nadir view observations of the Landsat series of satellites and partly due to the convenience of such measurements. The most significant development in these models, their ability to predict angular reflectance properties could not, however, be evaluated. The Laboratory for Applications of Remote Sensing, Purdue University, with strong support from modeling community, collected two excellent data sets, one on corn and one on soybeans, that contain the full hemisphere of off-nadir reflectance measurements. These data sets have been used to verify the multilayer one-component (leaves) Suits and SAIL canopy reflectance models. Software to evaluate multilayer multicomponent canopies was developed, but could not be evaluated because of the lack of information on stems. This evaluation suggests that these and similar other models have generic deficiency. The SAIL model, because of a more realistic inclusion of the leaf angle distribution, is in better agreement with observations than the Suits model. Further improvement and additional evaluation of these models is needed.     

Application of photon recollision probability in coniferous canopy reflectance simulations

A new semi-physical forest reflectance model, PARAS, is presented in the paper. PARAS is a simple parameterization model for taking into account the effect of within-shoot scattering on coniferous canopy reflectance. Multiple scattering at the small scale represented by a shoot is a conifer-specific characteristic which causes the spectral signature of coniferous forests to differ from that of broadleaved forests. This has for long led to problems in remote sensing of canopy structural variables in coniferous dominated regions. The PARAS model uses a relationship between photon recollision probability and leaf area index (LAI) for simulating forest reflectance. The recollision probability is a measurable, wavelength independent variable which is defined as the probability with which a photon scattered in the canopy interacts with a phytoelement again. In this study, we present application results using PARAS in simulating reflectance of coniferous forests for approximately 800 Scots pine and Norway spruce dominated stands. The results of this study clearly indicate that a major improvement in simulating canopy reflectance in near-infrared (NIR) is achieved by simply accounting for the within-shoot scattering. In other words, the low NIR reflectance observed in coniferous areas is mainly due to within-shoot scattering. In the red wavelength the effect of within-shoot scattering was not pronounced due to the high level of needle absorption in the red range. To conclude the paper, further application possibilities of the presented parameterization model are discussed.     

Towards estimation of canopy foliar biomass with spectral reflectance measurements

Canopy foliar biomass, defined as the product of leaf dry matter content and leaf area index, is an important measurement for global biogeochemical cycles. This study explores the potential for retrieving foliar biomass in green canopies using a spectral index, the Normalized Dry Matter Index (NDMI). This narrow-band index is based on absorption at the C-H bond stretch overtone and is correlated with leaf dry matter content in fresh green leaves. PROSPECT and SAIL model simulations suggest that the NDMI at the canopy scale is able to minimize the effects of leaf thickness and leaf water content and to maximize sensitivity to variation in canopy foliar biomass. The simulation outputs were analyzed with an ANOVA, and 87% of the variation in the NDMI is explained by leaf dry matter content. The NDMI was linearly related to foliar biomass (g cm^− 2) from model simulations (R² = 0.97). The NDMI calculated from spectral reflectances for one to four stacked leaves was also correlated with total leaf biomass (R² = 0.59). These results suggest that it may be possible to determine foliar biomass from airborne and satellite-borne imaging spectrometers, such as NASA's HyspIRI mission.     

A simple parameterization of canopy reflectance using photon recollision probability

Information on the fractions of incident radiation reflected, transmitted and absorbed by a plant canopy is crucial in remote sensing of vegetation and modeling of canopy microclimate. Photon recollision probability p allows to calculate easily the spectral behavior of canopy scattering, i.e. the sum of canopy reflectance and transmittance. However, to divide the scattered radiation into reflected and transmitted fluxes, additional models are needed. In this paper, we present a simple formula to estimate the fraction of radiation scattered upwards by a canopy. The new method is semi-empirical, makes use of the concept of photon recollision probability, and is derived from an analysis of modeling results. Although a physical interpretation is given for the single additional parameter needed in the formula, the scattering asymmetry parameter q, the method is not strictly based on the radiative transfer equation. Our results indicate that the method is accurate for low to moderate leaf area index (LAI) values, and provides a reasonable approximation even at LAI = 8. In addition, we present a method to compute p using numerical radiative transfer models.     

Panicle contribution to bidirectional reflectance factors of a wheat canopy

Bidirectional reflectance factors (BRFs) of crop stands are strongly influenced by canopy architecture. In wheat, as well as in many other crops, canopy architecture changes dramatically with the phenological development of the plant community.

A ground-based experiment was performed to examine the effect of panicles of winter wheat (Triticum aestivum L.) at the flowering stage on canopy BRFs. Reflectance factors were measured in the field with a portable radiometer in the red (0-63-0-69 μm) and near-infrared (0-76-0-90 μm) wavelength intervals. Observations were made at three viewing angles and 14 solar zenith angles during two consecutive days on a control target and on a target where panicles had been removed.

Panicles did not contribute significantly to the red nor to the near-infrared (NIR) reflectance factors computed from nadir observations. Off-nadir NIR reflectance was also not altered by the presence of panicles, but was moderately sensitive to illumination angle. Off-nadir red reflectance in the backscattcring direction was higher in the canopy with panicles than in the canopy without panicles: at a solar zenith angle of about 50° the difference in the reflectance of the two targets reached a maximum of about 39 per cent.

These findings imply a potential to identify crops and their phenological development by more fully exploiting reflectance at several different viewing and solar angles.     

Inversion of vegetation canopy reflectance models for estimating agronomic variables. III. Estimation using only canopy reflectance data as illustrated by the suits model

The technique for estimating agronomic and spectral parameters for a vegetation canopy from the canopy reflectance (CR) data, described earlier (Goel et al., 1984), has been improved. These improvements are twofold: first, one can now, in principle, estimate various parameters using only CR data, in the infrared band, for a set of solar/view angles; second, the method is now computationally much more efficient. These improvements are illustrated via Suits' model. An analysis of the sensitivity of the calculated agronomic and spectral parameters to changes in the CR is also carried out. This analysis suggests that, in general, for expected levels of errors in the measurement of CRs and the accuracy with which the Suits model is likely to represent CR, one is unlikely to be able to estimate agronomic parameters like leaf area index (LAI) and average leaf angle (ALA) using only measured CR data. Such a determination will likely require ancillary data on the reflectance and transmittance of vegetation elements and on the soil reflectance.     

Extension of a uniform canopy reflectance model to include row effects

The reflectance model for a uniform crop canopy is extended to include the effects of crops planted in rows. The effect of row structure is assumed to be caused by the variation in density of vegetation across rows rather than to a profile in canopy height. The calculation of crop reflectance using vegetation density modulation across rows follows a parallel procedure to that for a uniform canopy. Predictions using the row model for wheat show that the effect of changes in sun to row azimuth are greatest in Landsat Band 5 (red band) and can result in underestimation of crop biomass.     

Seasonal canopy reflectance patterns of wheat,sorghum, and soybean

Reflectance characteristics of agronomic crops are of major importance in the energy exchanges of a surface. In addition, unique reflectance patterns may be an aid in crop identification by means of remote sensing. Our study suggests that the ratio of the reflectances of the 545-nm to the 655-nm wavebands provides information about the viewed surface, regardless of the crop. The reflectance ratio is less than unity early and late in the growing season. For all crops studied, the ratio closely followed crop growth and development and appeared to be more desirable than the near-infrared reflectance as an index of growth.     

A multi-angle spectrometer for automatic measurement of plant canopy reflectance spectra

The paper describes the design and operation of a multi-angle spectrometer (MAS) for automatic measurement of near-field spectral reflectances of plant canopies at hourly intervals. A novel feature of the instrument is a rotating periscope connected to a spectrometer via a fiber optic cable. Canopy reflectances are calculated for multiple view azimuths, at a single zenith angle from measurements of spectrometer dark current, incoming solar irradiance and reflected radiances. Spectral measurements are made between 300 and 1150 nm wavelength at a band-to-band spacing of 3 nm, and a bandwidth (full-width, half maximum) of 10 nm. Preliminary data analysis showed that the canopy reflectance model of Kuusk [Kuusk, A. (1995). A fast, invertible canopy reflectance model. Remote Sensing of Environment 51, 342-350] reproduced the observed large differences in visible and near-infrared (NIR) reflectances, but the model was unable to predict quantitatively the observed variations in the measured reflectance spectra with azimuth, particularly in the NIR. Discrepancies between model and measurements are likely due to the inhomogeneous nature of the forest canopy in contrast to the assumption of a uniformly absorbing turbid medium in the model. Measurements using the MAS can be used to investigate directional dependences of reflectance indices and for testing BRDF models used to separate geometrical and plant physiological contributions to the reflectance signals. The MAS provides continuous sampling of reflectance indices which can be compared with canopy properties such as chlorophyll content and photosynthetic capacity.     

Atmospheric optical depth effects on angular anisotropy of plant canopy reflectance

Abstract

A field experiment was conducted to determine whether changes in atmospheric aerosol optical depth would effect changes in bi-directional reflectance distributions of vegetation canopies. Measurements were made of the directionally reflected radiance distributions of two pasture grass canopies (same species, different growth forms) and one soya bean plant canopy under different sky irradiance distributions, which resulted from a variation in aerosol optical depth. The reflected radiance data were analysed in the solar principal plane in two narrow spectral bands, one visible (662 nm) and one infrared (826 nm). The observed changes in reflectance for both wavelengths from irradiance distribution variation is interpreted to be due largely to changes in the percentage of shadowed area viewed by the sensor for the incomplete canopies (pasture grass). For the complete coverage vegetation canopy (soya bean) studied, the effects of specular reflection and the increased diffuse irradiance penetration into the canopy are concluded to be primary physical mechanisms responsible for reflectance changes. Observed reflectivities were found to be lower on a hazy day (higher optical depth with a greater diffuse fraction) than on a clear day, with solar zenith angles at about 58^° on both days, for full-coverage soya bean canopies. The reduced reflectance most likely results from a diminished specular reflection and a greater diffuse radiation penetration into the canopy, which effects an increased energy absorption at large solar zenith angles. The opposite was true for fractional coverage grass canopies at solar zenith angles of about 56^° since the shadowing was less on the hazy day and, therefore, the soil/litter background was more fully illuminated. In the near-infrared waveband the changes in reflectance are much less than in the visible and, therefore, normalized difference vegetation index values differ substantially under clear and hazy sky conditions for the same vegetation canopy conditions. Thus, the influence of atmospheric optical depth must be considered for accurate remote sensing and in situ data interpretation.     

Mapping leaf chlorophyll and leaf area index using inverse and forward canopy reflectance modeling and SPOT reflectance data

Reflectance data in the green, red and near-infrared wavelength region were acquired by the SPOT high resolution visible and geometric imaging instruments for an agricultural area in Denmark (56°N, 9°E) for the purpose of estimating leaf chlorophyll content (C_ab) and green leaf area index (LAI). SPOT reflectance observations were atmospherically corrected using aerosol data from MODIS and profiles of air temperature, humidity and ozone from the Atmospheric Infrared Sounder (AIRS), and used as input for the inversion of a canopy reflectance model. Computationally efficient inversion schemes were developed for the retrieval of soil and land cover-specific parameters which were used to build multiple species and site dependent formulations relating the two biophysical properties of interest to vegetation indices or single spectral band reflectances. Subsequently, the family of model generated relationships, each a function of soil background and canopy characteristics, was employed for a fast pixel-wise mapping of C_ab and LAI.The biophysical parameter retrieval scheme is completely automated and image-based and solves for the soil background reflectance signal, leaf mesophyll structure, specific dry matter content, Markov clumping characteristics, C_ab and LAI without utilizing calibration measurements.Despite the high vulnerability of near-infrared reflectances (ρ_nir) to variations in background properties, an efficient correction for background influences and a strong sensitivity of ρ_nir to LAI, caused LAI-ρ_nir relationships to be very useful and preferable over LAI-NDVI relationships for LAI prediction when LAI > 2. Reflectances in the green waveband (ρ_green) were chosen for producing maps of C_ab.The application of LAI-NDVI, LAI-ρ_nir and C_ab-ρ_green relationships provided reliable quantitative estimates of C_ab and LAI for agricultural crops characterized by contrasting architectures and leaf biochemical constituents with overall root mean square deviations between estimates and in-situ measurements of 0.74 for LAI and 5.0 μg cm^− 2 for C_ab.The results of this study illustrate the non-uniqueness of spectral reflectance relationships and the potential of physically-based inverse and forward canopy reflectance modeling techniques for a reasonably fast and accurate retrieval of key biophysical parameters at regional scales.     

Coupling forest canopy and understory reflectance in the Arctic latitudes of Finland

The Arctic region is predicted to experience considerable climatic and environmental changes as the global atmospheric CO₂ increases. Growing awareness of the role of tundra and taiga ecosystems and their transition zone in the climate change process has resulted in a recent increase in remote sensing studies focusing on the Arctic latitudes. Remote sensing of biophysical properties of the canopy layer in the forested part of the region is often, however, challenged by the dominating role of the understory in the spectral signal. In this paper, we examine the influence of understory vegetation on forest reflectance in the Arctic region of Finland during no-snow conditions. The study is based on SPOT HRVIR images, field goniospectrometry, 300 ground reference plots and a physically-based forest reflectance model (PARAS). The results indicate that lichen-dominated forest site types can be distinguished from sites dominated by dwarf shrubs. The paper also contains results from applying an analytical method for calculating photon recollision probability from canopy transmittance data for forest stands, and then using it to simulate the reflectance of the same stands.     

Mapping forest vegetation using Landsat TM imagery and a canopy reflectance model

Estimates of mean tree size and cover for each forest stand from an invertible forest canopy reflectance model are part of a new forest vegetation mapping system. Image segmentation defines stands which are sorted into general growth forms using per-pixel image classifications. Ecological models based on terrain relations predict species associations for the conifer, hardwood, and brush growth forms. The combination of the model-based estimates of tree size and cover with species associations yields general-purpose vegetation maps useful for a variety of land management needs. Results of timber inventories in the Tahoe and Stanislaus National Forests indicate the vegetation maps form a useful basis for stratification. Patterns in timber volumes for the strata reveal that the cover estimates are more reliable than the tree size estimates. A map accuracy assessment of the Stanislaus National Forest shows high overall map accuracy and also illustrates the problems in estimating tree size.     

Estimating aboveground forest biomass from canopy reflectance model inversion in mountainous terrain

The amount and spatial distribution of aboveground forest biomass (AGB) are required inputs to forest carbon budgets and ecosystem productivity models. Satellite remote sensing offers distinct advantages for large area and multi-temporal applications, however, conventional empirical methods for estimating forest canopy structure and AGB can be difficult in areas of high relief and variable terrain. This paper introduces a new method for obtaining AGB from forest structure estimates using a physically-based canopy reflectance (CR) model inversion approach. A geometric-optical CR model was run in multiple forward mode (MFM) using SPOT-5 imagery to derive forest structure and biomass at Kananaskis, Alberta in the Canadian Rocky Mountains. The approach first estimates tree crown dimensions and stem density for satellite image pixels which are then related to tree biomass and AGB using a crown spheroid surface area approach. MFM estimates of AGB were evaluated for 36 deciduous (trembling aspen) and conifer (lodgepole pine) field validation sites and compared against spectral mixture analysis (SMA) and normalised difference vegetation index (NDVI) biomass predictions from atmospherically and topographically corrected (SCS+C) imagery. MFM provided the lowest error for all validation plots of 31.7 tonnes/hectare (t/ha) versus SMA (32.6 t/ha error) and NDVI (34.7 t/ha) as well as for conifer plots (MFM: 23.0 t/ha; SMA 27.9 t/ha; NDVI 29.7 t/ha) but had higher error than SMA and NDVI for deciduous plots (by 4.5 t/ha and 2.1 t/ha, respectively). The MFM approach was considerably more stable over the full range of biomass values (67 to 243 t/ha) measured in the field. Field plots with biomass > 1 standard deviation from the field mean (over 30% of plots) had biomass estimation errors of 37.9 t/ha using MFM compared with 65.5 t/ha and 67.5 t/ha error from SMA and NDVI, respectively. In addition to providing more accurate overall results and greater stability over the range of biomass values, the MFM approach also provides a suite of other biophysical structural outputs such as density, crown dimensions, LAI, height and sub-pixel scale fractions. Its explicit physical-basis and minimal ground data requirements are also more appropriate for larger area, multi-scene, multi-date applications with variable scene geometry and in high relief terrain. MFM thus warrants consideration for applications in mountainous and other, less complex terrain for purposes such as forest inventory updates, ecological modeling and terrestrial biomass and carbon monitoring studies.