Canopy Reflectance Model Research Articles

Impact of understory vegetation on forest canopy reflectance and remotely sensed LAI estimates

Forest leaf area index (LAI), is an important variable in carbon balance models. However, understory vegetation is a recognized problem that limits the accuracy of satellite-estimated forest LAI. A canopy reflectance model was used to investigate the impact of the understory vegetation on LAI estimated from reflectance values estimated from satellite sensor data. Reflectance spectra were produced by the model using detailed field data as input, i.e. forest LAI, tree structural parameters, and the composition, distribution and reflectance of the forest floor. Common deciduous and coniferous forest types in southern Sweden were investigated. A negative linear relationship ( r 2 = 0.6) was observed between field estimated LAI and the degree of understory vegetation, and the results indicated better agreement when coniferous and deciduous stands were analysed separately. The simulated spectra verified that the impact of the understory on the reflected signal from the top of the canopy is important; the reflectance values varying by up to ± 18% in the red and up to ± 10% in the near infra-red region of the spectra due to the understory. In order to predict the variation in LAI due to the understory vegetation, model inversions were performed where the input spectra were changed between the minimum, average and maximum reflectance values obtained from the forward runs. The resulting variation in LAI was found to be 1.6 units on average. The LAI of the understory could be predicted indirectly from simple stand data on forest characteristics, i.e. from allometric estimates, as an initial step in the process of estimating LAI. It is suggested here that compensation for the effect of the understory would improve the accuracy in the estimates of canopy LAI considerably.

Comparative analysis of IKONOS, SPOT, and ETM+ data for leaf area index estimation in temperate coniferous and deciduous forest stands

The increasing number of sensor types for terrestrial remote sensing has necessitated supplementary efforts to evaluate and standardize data from the different available sensors. In this study, we assess the potential use of IKONOS, ETM+, and SPOT HRVIR sensors for leaf area index (LAI) estimation in forest stands. In situ measurements of LAI in 28 coniferous and deciduous stands are compared to reflectance in the visible, near-infrared, and shortwave bands, and also to five spectral vegetation indices (SVIs): Normalised Difference Vegetation Index (NDVI), Simple Ratio (SR), Soil Adjusted Vegetation Index (SAVI), Enhanced Vegetation Index (EVI), and Atmospherically Resistant Vegetation Index (ARVI). The three sensor types show the same predictive ability for stand LAI, with an uncertainty of about 1.0 m 2/m 2 for LAI between 0.5 and 6.9 m 2/m 2. For each sensor type, the strength of the empirical relationship between LAI and NDVI remains the same, regardless of the image processing level considered [digital counts, radiances using calibration coefficients for each sensor, top of atmosphere (TOA), and top of canopy (TOC) reflectances]. On the other hand, NDVIs based on radiance, TOA reflectance, and TOC reflectance, determined from IKONOS radiometric data, are systematically lower than from SPOT and ETM+ data. The offset is approximately 0.11 NDVI units for radiance and TOA reflectance-based NDVI, and approximately 0.20 NDVI units after atmospheric corrections. The same conclusions were observed using the other indices. SVIs using IKONOS data are always lower than those computed using ETM+ and SPOT data. Factors that may explain this behavior were investigated. Based on simulations using the SAIL bidirectional canopy reflectance model coupled with the PROSPECT leaf optical properties model (i.e., PROSAIL), we show that the spectral response in radiance of IKONOS sensor in the red band is the main factor explaining the differences in SVIs between IKONOS and the other two sensors. Finally, we conclude that, for bare soils or very sparse vegetation, radiometric data acquired by IKONOS, SPOT, and ETM+ are similar and may be used without any correction. For surfaces covered with dense vegetation, a negative offset of 10% of IKONOS NDVI should be considered.

Estimating equivalent water thickness in a conifer forest using Landsat TM and ASTER data: a comparison study

Remotely sensed data from the Landsat thematic mapper (TM) and the advanced spaceborne thermal emission and reflection radiometer (ASTER) can be used to make physically based estimates of equivalent water thickness (EWT) in a Pinus ponderosa forest ecosystem. EWT is an important parameter for studying ecosystem dynamics and fire potential and is likely to be of interest in monitoring biological responses to climate change. Near infrared (NIR) and shortwave infrared (SWIR) reflectances were simulated using the LIBERTY and GeoSAIL leaf and canopy reflectance models; the results were used to calculate a NIR/SWIR ratio and a normalized NIR/SWIR index. Index–EWT relationships were modeled and inverted for EWT derivation. Landsat TM and ASTER images were able to produce EWT estimates with average errors of ±17.3% and 19.4%, respectively. TM band 5 and ASTER band 4 contained the most valuable information among the SWIR bands for EWT estimation. Exclusion of plots with dense understory vegetation significantly reduced point scatter, especially with Landsat (r2 = 0.85, ±13% average error), indicating that this method can provide robust EWT quantification where conifer trees predominate.

Detection of Flowering Leafy Spurge With Satellite Multispectral Imagery

The distribution and abundance of flowering leafy spurge (Euphorbia esula L.) can be determined with hyperspectral remote sensing, but the availability of hyperspectral sensors is limited. Hence, the Landsat 7 Enhanced Thematic Mapper Plus (ETM+) and System Pour d’Observation de la Terre (SPOT) 4 imagery were acquired to test the ability of these sensors to detect leafy spurge. The green:red band ratio was the vegetation index with the highest correlations to flowering leafy spurge cover, but the correlations were weak and not useful for predictions. With Airborne Visible Infrared Imaging Spectrometer (AVIRIS) data, the green:red band ratio was also weakly correlated to flowering leafy spurge cover, although the output from a hyperspectral unmixing algorithm was highly correlated with cover using the same data, indicating simple indices have limited power for detecting leafy spurge. Canopy reflectance modeling using the Scattering by Arbitrarily Inclined Leaves (SAIL) model suggests the weak correlations were caused by variations in leaf area index. It is important to develop spectral libraries in order to use canopy reflectance simulation models that can reduce the time and effort of remote sensing analysis for detecting leafy spurge and other invasive weeds.

Radiative transfer, interception and scattering in coniferous forests: models and applications for production ecology and remote sensing

This work develops methods to account for shoot structure in models of coniferous canopy radiative transfer. Shoot structure, as it varies along the light gradient inside canopy, affects the efficiency of light interception per unit needle area, foliage biomass, or foliage nitrogen. The clumping of needles in the shoot volume also causes a notable amount of multiple scattering of light within coniferous shoots. The effect of shoot structure on light interception is treated in the context of canopy level photosynthesis and resource use models, and the phenomenon of within-shoot multiple scattering in the context of physical canopy reflectance models for remote sensing purposes. Light interception. A method for estimating the amount of PAR (Photosynthetically Active Radiation) intercepted by a conifer shoot is presented. The method combines modelling of the directional distribution of radiation above canopy, fish-eye photographs taken at shoot locations to measure canopy gap fraction, and geometrical measurements of shoot orientation and structure. Data on light availability, shoot and needle structure and nitrogen content has been collected from canopies of Pacific silver fir (Abies amabilis (Dougl.) Forbes) and Norway spruce (Picea abies (L.) Karst.). Shoot structure acclimated to light gradient inside canopy so that more shaded shoots have better light interception efficiency. Light interception efficiency of shoots varied about two-fold per needle area, about four-fold per needle dry mass, and about five-fold per nitrogen content. Comparison of fertilized and control stands of Norway spruce indicated that light interception efficiency is not greatly affected by fertilization. Light scattering. Structure of coniferous shoots gives rise to multiple scattering of light between the needles of the shoot. Using geometric models of shoots, multiple scattering was studied by photon tracing simulations. Based on simulation results, the dependence of the scattering coefficient of shoot from the scattering coefficient of needles is shown to follow a simple one-parameter model. The single parameter, termed the recollision probability, describes the level of clumping of the needles in the shoot, is wavelength independent, and can be connected to previously used clumping indices. By using the recollision probability to correct for the within-shoot multiple scattering, canopy radiative transfer models which have used leaves as basic elements can use shoots as basic elements, and thus be applied for coniferous forests. Preliminary testing of this approach seems to explain, at least partially, why coniferous forests appear darker than broadleaved forests in satellite data.

Inversion of a forest reflectance model to estimate structural canopy variables from hyperspectral remote sensing data

The potential of canopy reflectance modelling to retrieve simultaneously several structural variables in managed Norway spruce stands was investigated using the “Invertible Forest Reflectance Model”, INFORM. INFORM is an innovative extension of the FLIM model, with crown transparency, infinite crown reflectance and understory reflectance simulated using physically based sub-models (SAILH, LIBERTY and PROSPECT). The INFORM model was inverted with hyperspectral airborne HyMap data using a neural network approach. INFORM based estimates of forest structural variables were produced using site-specific ranges of stand structural variables. A relatively simple three layer feed-forward backpropagation neural network with two input neurons, one neuron in the hidden layer and three output neurons was employed to map leaf area index (LAI), crown coverage and stem density. To identify the optimum 2-band spectral subset to be used in the inversion process, all 2-band combinations of the HyMap dataset were systematically evaluated for model inversion. Field measurements of structural variables from 39 forest stands were used to validate the maps produced from HyMap imagery. Using two HyMap wavebands at 837 nm and 1148 nm the obtained accuracy of the LAI map amounts to an rmse of 0.58 (relative rmse = 18% of mean, R 2 = 0.73). With HyMap data resampled to Landsat TM spectral bands and using two “optimum” bands at 840 nm and 1650 nm, rmse was 0.66 and relative rmse 21%. In contrast to approaches based on empirical relations between spectral vegetation indices and structural variables, the main advantage of the inversion approach is that it does not require previous calibration.

Time‐series validation of MODIS land biophysical products in a Kalahari woodland, Africa

Monthly measurements of leaf area index (LAI) and the fraction of absorbed photosynthetically active radiation (f APAR) taken at approximately monthly intervals were collected along three 750 m transects in a Kalahari woodland near Mongu in western Zambia. These data were compared with MODIS NDVI (MOD13, Collection 3) and MODIS LAI and f APAR products (MOD15, Collection 3) over a 2 year period (2000–2002). MODIS and ground‐measured LAI values corresponded well, while there was a significant bias between MODIS and ground‐measured f APAR even though both MODIS variables are produced from the same algorithm. Solar zenith angle effects, differences between intercepted and absorbed photosynthetically active radiation, and differences in measurement of f APAR (photon counts versus energy) were examined and rejected as explanations for the discrepancies between MODIS and ground‐measured f APAR. Canopy reflectance model simulations produced different values of f APAR with the same LAI when canopy cover was varied, indicating that errors in the estimation of canopy cover in the MODIS algorithm due to the land cover classification used are a possible cause of the f APAR discrepancy. This is one of the first studies of MODIS land product performance in a time‐series context. Despite a bias in f APAR, our results demonstrate that the woodland canopy phonology is captured in the MODIS product.

Retrieval of wheat leaf area index using price approach based on inversion of canopy reflectance model

This paper reports estimation of the Leaf Area Index (LAI) of wheat crop from IRS-LISS-III data using Price (1993) approach. Empirical approach for LAI estimation with different NDVI estimation procedures viz. radiance, apparent reflectance and dark object subtraction (DOS) based atmospheric correction were also evaluated. Validation of LAI retrieval and NDVI normalizations were carried out using field level measurements of crop LAI and spectral property using canopy analyzer and spectro-radiometer, respectively over selected fields in Bhopal District, Madhya Pradesh. It was observed that empirical relations are sensitive to the NDVI estimation approach and DOS method performed better as compared to other two approaches. It was also observed that LAI estimation from Price algorithm is sensitive to the crop attenuation coefficients. Crop specific attenuation coefficients reported in literature for Indian cultivars gave higher accuracy. The root mean square (RMS) error of 0.77 for LAI estimation was achieved using above described approach.

Cotton pixel identification with CBERS-02 CCD data based on spectral knowledge

In this paper thein-situ experiment data are collected from the Spectral Library to estimate the possible values of the primary structural parameters in each cotton growing season by statistics. Based on these values the spectra of the cotton major growing seasons were simulated and analyzed using the canopy reflectance model, SAILH. In this way we also simulated the cotton pixel spectra corresponding to CBERS-02 CCD and took them as the reference spectra for spectra fitting. Then No. 143 Regiment of No. 8 Agricultural Division of Xinjiang Production and Construction Corps (XPCC) was chosen as the study area, and two spectra fitting methods, Mahalanobis distance and spectra angle, were used to identify cotton pixel from CBERS-02 CCD image of the study area. At last we analyzed the effect of the calibration coefficients error on cotton identifying. The results showed that within 4% error the spectral angle classifier can still have a good performance.

Object-based retrieval of biophysical canopy variables using artificial neural nets and radiative transfer models

The retrieval of biophysical variables using canopy reflectance models is hampered by the fact that the inverse problem is ill-posed. This leads to unstable and often inaccurate inversion results. In order to regularize the model inversion, a novel approach has been developed and tested on synthetic Landsat TM reflectance data. The method takes into account the neighbouring radiometric information of the pixel of interest, named object signature. The neighbourhood data can either be extracted from gliding windows, already segmented images, or using digitized field boundaries. The extracted radiometric data of the neighbourhood pixels are used to calculate 42 descriptive statistical properties that comprehensively characterize the spectral (co)variance of the image object (e.g. mean and standard deviation of the distributions, intercorrelations between spectral bands, etc.). Together with the habitual spectral signature of the pixel being inverted (6 variables), this object signature (42 variables) is used as input in an artificial neural net to estimate simultaneously three important biophysical variables (i.e. leaf area index, leaf chlorophyll, and leaf water content). The use of neural nets for the model inversion avoids time-consuming iterative optimizations and provides a computational effective way to consider simultaneously pixel and object signatures. In order to “learn” the relation between spectral signatures and biophysical variables, the neural nets were previously trained on large synthetic data sets. The data sets consist of pixel signatures and the corresponding signatures of image objects representing various agricultural fields. The signatures were simulated with the SAILH+PROSPECT canopy reflectance model, assuming largely varying intra- and interfield distributions of the model input parameters. To demonstrate the benefits of the object-based inversion, neural nets were also trained on the pixel signatures alone. For this purpose, the object signatures were simply replaced by randomly generated white noise—all other conditions being the same. The intercomparison based on 30,000 independent validation patterns showed that the proposed method significantly enhances the estimation accuracies: for example, the leaf area index (LAI) is estimated with a percental root mean square error (PRMSE) of 18.3% (object-based) compared to 25.1% (pixel based); the corresponding numbers for the leaf chlorophyll content are 12.6% compared to 15.9%; for the equivalent leaf water thickness, 10.6% and 13.9%, respectively. The benefit of the object signature was strongest for the LAI. Concerning this important biophysical variable, the novel concept accounted for almost one-half of the remaining unexplained variance of the traditional pixel-based approach. Increased accuracies were attributed to the fact that intrafield variations of biophysical canopy variables lead to object signatures that are modulated by the actual average leaf angle (ALA) of the canopy. Since ALA can be considered constant insight given an agricultural field, the concurrent use of pixel and object signatures significantly reduces confounding effects between LAI and ALA typical for traditional inversion approaches. Compared to competing approaches, the algorithm can also be applied to monotemporal imagery and does not require a priori information or the identification of crop type.

Inversion of a radiative transfer model with hyperspectral observations for LAI mapping in poplar plantations

The potential of radiative transfer modelling and inversion techniques for operational uses is investigated in order to retrieve leaf area index in a poplar plantation. The 1-D bidirectional canopy reflectance model SAIL, coupled with the leaf optical properties model PROSPECT, was inverted with hyperspectral airborne DAIS data by means of an iterative method. The root mean square error in LAI estimation was determined against in situ measurements in order to evaluate the impact of different inversion strategies on the LAI retrieval accuracy. These included the selection of an optimal spectral sampling set, the exploitation of prior knowledge in the inversion process and the use of multiview angle data. We claim that the best configuration is achieved by exploiting multiview DAIS data and prior knowledge information about the model variables (RMSE of 0.39 m 2 m −2). It is also shown that the use of prior knowledge and the selection of a limited number of bands forming the optimal spectral sampling are instrumental in increasing the accuracy of the inversion process. Our analysis confirms the operational potential of model inversion for biophysical parameter retrieval.

Radiative transfer modeling within a heterogeneous canopy for estimation of forest fire fuel properties

Imaging spectrometer data were acquired over conifer stands to retrieve spatially distributed information on canopy structure and foliage water content, which may be used to assess fire risk and to manage the impact of forest fires. The study relied on a comprehensive field campaign using stratified systematic unaligned sampling ranging from full spectroradiometric characterization of the canopy to conventional measurements of biochemical and biophysical variables. Airborne imaging spectrometer data (DAIS7915 and ROSIS) were acquired parallel to the ground measurements, describing the canopy reflectance of the observed forest. Coniferous canopies are highly heterogeneous and thus the transfer of incident radiation within the canopy is dominated by its structure. We demonstrated the viability of radiative transfer representation and compared the performance of two hybrid canopy reflectance models, GeoSAIL and FLIGHT, within this heterogeneous medium. Despite the different nature and canopy representation of these models, they yielded similar results. Subsequently, the inversion of a hyperspectral GeoSAIL version demonstrated the feasibility of estimating structure and foliage water content of a coniferous canopy based on radiative transfer modeling. Estimates of the canopy variables showed reasonably accurate results and were validated through ground measurements.

Estimating live fuel moisture content from remotely sensed reflectance

Fuel moisture content (FMC) is used in forest fire danger models to characterise the moisture status of the foliage. FMC expresses the amount of water in a leaf relative to the amount of dry matter and differs from measures of leaf water content which express the amount of water in a leaf relative to its area. FMC is related to both leaf water content and leaf dry matter content, and the relationships between FMC and remotely sensed reflectance will therefore be affected by variation in both leaf biophysical properties. This paper uses spectral reflectance data from the Leaf Optical Properties EXperiment (LOPEX) and modelled data from the Prospect leaf reflectance model to examine the relationships between FMC, leaf equivalent water thickness (EWT) and a range of spectral vegetation indices (VI) designed to estimate leaf and canopy water content. Significant correlations were found between FMC and all of the selected vegetation indices for both modelled and measured data, but statistically stronger relationships were found with leaf EWT; overall, the water index (WI) was found to be most strongly correlated with FMC. The accuracy of FMC estimation was very low when the global range of FMC was examined, but for a restricted range of 0–100%, FMC was estimated with a root-mean-square error (RMSE) of 15% in the model simulations and 51% with the measured data. The paper shows that the estimation of live FMC from remotely sensed vegetation indices is likely to be problematic when there is variability in both leaf water content and leaf dry matter content in the target leaves. Estimating FMC from remotely sensed data at the canopy level is likely to be further complicated by spatial and temporal variations in leaf area index (LAI). Further research is required to assess the potential of canopy reflectance model inversion to estimate live fuel moisture content where a priori information on vegetation properties may be used to constrain the inversion process.

Needle chlorophyll content estimation through model inversion using hyperspectral data from boreal conifer forest canopies

Leaf chlorophyll content in coniferous forest canopies, a measure of stand condition, is the target of studies and models linking leaf reflectance and transmittance and canopy hyperspectral reflectance imagery. The viability of estimation of needle chlorophyll content from airborne hyperspectral optical data through inversion of linked leaf level and canopy level radiative transfer models is discussed in this paper. This study is focused on five sites of Jack Pine ( Pinus banksiana Lamb.) in the Algoma Region (Canada), where field, laboratory and airborne data were collected in 1998 and 1999 campaigns. Airborne hyperspectral CASI data of 72 bands in the visible and near-infrared region and 2 m spatial resolution were collected from 20×20 m study sites of Jack Pine in 2 consecutive years. It was found that needle chlorophyll content could be estimated at the leaf level ( r 2=0.4) by inversion of the PROSPECT leaf model from needle reflectance and transmittance spectra collected with a special needle carrier apparatus coupled to the Li-Cor 1800 integrating sphere. The Jack Pine forest stands used for this study with LAI>2, and the high spatial resolution hyperspectral reflectance collected, allowed the use of the SPRINT canopy reflectance model coupled to PROSPECT for needle chlorophyll content estimation by model inversion. The optical index R750/R710 was used as the merit function in the numerical inversion to minimize the effect of shadows and LAI variation in the mean canopy reflectance from the 20×20 m plots. Estimates of needle pigment content from airborne hyperspectral reflectance using this linked leaf-canopy model inversion methodology showed an r 2=0.4 and RMSE=8.1 μg/cm 2 when targeting sunlit crown pixels in Jack Pine sites with pigment content ranging between 26.8 and 56.8 μg/cm 2 (1570–3320 μg/g).

A method to account for shoot scale clumping in coniferous canopy reflectance models

The three-dimensional structure of a coniferous shoot gives rise to multiple scattering of light between the needles of the shoot, causing the shoot spectral reflectance to differ from that of a flat leaf. Forest reflectance models based on the radiative transfer equation handle shoot level clumping by correcting the radiation attenuation coefficient with a clumping index. The clumping index causes a reduction in the interception of radiation by the canopy at a fixed leaf area index (LAI). In this study, we show how within-shoot multiple scattering is related to shoot scale clumping and derive a similar, but wavelength dependent, correction to the scattering coefficient. The results provide a method for integrating shoot structure into current radiative transfer equation based forest reflectance models. The method was applied to explore the effect of shoot scale clumping on canopy spectral reflectance using simple model canopies with a homogeneous higher level structure. The clumping of needles into shoots caused a wavelength dependent reduction in canopy reflectance, as compared to that of a leaf canopy with similar interception. This is proposed to be one reason why coniferous and broad-leaved canopies occupy different regions in the spectral space and exhibit different dependency of spectral vegetation indices on LAI.

Water content estimation in vegetation with MODIS reflectance data and model inversion methods

Statistical and radiative-transfer physically based studies have previously demonstrated the relationship between leaf water content and leaf-level reflectance in the near-infrared spectral region. The successful scaling up of such methods to the canopy level requires modeling the effect of canopy structure and viewing geometry on reflectance bands and optical indices used for estimation of water content, such as normalized difference water index (NDWI), simple ratio water index (SRWI) and plant water index (PWI). This study conducts a radiative transfer simulation, linking leaf and canopy models, to study the effects of leaf structure, dry matter content, leaf area index (LAI), and the viewing geometry, on the estimation of leaf equivalent water thickness from canopy-level reflectance. The applicability of radiative transfer model inversion methods to MODIS is studied, investigating its spectral capability for water content estimation. A modeling study is conducted, simulating leaf and canopy MODIS-equivalent synthetic spectra with random input variables to test different inversion assumptions. A field sampling campaign to assess the investigated simulation methods was undertaken for analysis of leaf water content from leaf samples in 10 study sites of chaparral vegetation in California, USA, between March and September 2000. MODIS reflectance data were processed from the same period for equivalent water thickness estimation by model inversion linking the PROSPECT leaf model and SAILH canopy reflectance model. MODIS reflectance data, viewing geometry values, and LAI were used as inputs in the model inversion for estimation of leaf equivalent water thickness, dry matter, and leaf structure. Results showed good correlation between the time series of MODIS-estimated equivalent water thickness and ground measured leaf fuel moisture (LFM) content ( r 2=0.7), demonstrating that these inversion methods could potentially be used for global monitoring of leaf water content in vegetation.

Training a neural network with a canopy reflectance model to estimate crop leaf area index

This paper outlines the strategies available for estimating the biophysical properties of crop canopies from remotely sensed data. Spectral reflectance and biophysical data were obtained over 132 plots of sugar beet (Beta vulgaris L.) and in the first part of the paper the strength of the relationships between vegetation indices (VI) and leaf area index (LAI) are examined. In the second part, an approach is tested in which a canopy reflectance model is used to generate simulated spectra for a wide range of biophysical conditions and these data are used to train an artificial neural network (ANN). The advantage of the second approach is that a priori knowledge of the measurement conditions including soil reflectance, canopy architecture and solar position can be included explicitly in the modelling. The results show that the estimation of sugar beet LAI using a trained neural network is more reliable than the use of VI and has the potential to replace the use of VI for operational applications. The use of a priori data on the variation in soil spectral reflectance gave rise to a small increase in LAI estimation accuracy.

Reliability of the estimation of vegetation characteristics by inversion of three canopy reflectance models on airborne POLDER data

The Bidirectional Reflectance Distribution Function (BRDF) of several plant canopies was extensively sampled by the POLDER air- borne instrument in the Alpilles-ReSeDA campaign. The 16 flights carried out over the Alpilles test site from January to October 1997 cover all the plant growth stages. Estimation of biophysical variables was undertaken by inversion of three one-dimensional radiative transfer models, SAIL, KUUSK and IAPI, coupled with the PROSPECT leaf optical properties model, in order to fully utilize both the directional and the spectral information of the images. This study mainly focuses on the capability of model inversion to retrieve the leaf area index (LAI) of wheat, maize, sunflower and alfalfa crops, for which ground validation was available. In order to evaluate the quality of inversions and to map the LAI or the chlorophyll content Cab, the associated estimation errors are determined for these two biophysical variables.

Narrow-waveband reflectance ratios for remote estimation of nitrogen status in cotton.

Tailoring nitrogen (N) fertilizer applications to cotton (Gossypium hirsutum L.) in response to leaf N status may optimize N use efficiency and reduce off-site effects of excessive fertilizer use. This study compared leaf and canopy reflectance within the 350 to 950 nm range in order to identify reflectance ratios sensitive to leaf chlorophyll (Chl), and hence N status, in cotton. Plants were grown outdoors in large pots using half-strength Hoagland's (control) solution until some three-row plots received a restricted supply of N. Treatments comprised control, 20% of control N at first flower bud (square) onward; 0 and 20% of control N at first flower onward; and 0% of control N at fruit-filling onward. Despite leaf N values ranging from 51 to 19 g kg-1 across treatments and sampling dates, a weak correlation was obtained between Chl and N (r2 = 0.32, df = 70). In general, N stress led to increased reflectance at 695 +/- 2.5 nm (R695) and decreased reflectance at R410, and changes in leaf N were best correlated with either R695 or R755 in leaves and either R410 or R700 in canopies. The strongest associations between leaf constituent and canopy reflectance ratio were Chl vs. R415/R695 (r2 = 0.72), carotenoids vs. R415/R685 (r2 = 0.79), and N vs. R415/R710 (r2 = 0.70). The R415 measure appears to be a more stable spectral feature under N stress, as compared with more pronounced changes along the reflectance red edge (690-730 nm). Multiple regression identified a three-waveband canopy reflectance model that explained 80% of the variability in leaf N. Results indicate that remote sensing of N status in cotton is feasible using narrow-waveband reflectance ratios that involve the violet or blue region of the spectrum (400 to 450 nm) and the more commonly featured red-edge region.

The spectral/temporal manifestation of forest succession in optical imagery: The potential of multitemporal imagery

Forest succession is a fundamental ecological process, which has significant implications for sustainable natural resource management as well as ecosystem biological, biophysical, and biogeochemical processes. Remote sensing is perhaps the only viable option to monitor succession in forest ecosystems over large areas in a timely and cost efficient manner. This study integrates the geometric-optical and radiative transfer (GORT) canopy reflectance model with the ZELIG forest ecosystem dynamics model to study the manifestation of forest succession in optical imagery. The spectral/temporal trajectories associated with forest succession are highly nonlinear. The rate of change in spectral space through time is faster during the early years of succession. The succession trajectories do not proceed linearly in the spectral space, but rather curvilinearly, and are strongly influenced by initial background/understory conditions and topography. The nonlinear nature of the trajectories implies that adequate characterization of forest development with remote sensing requires multiple images through time. Validation using multiple Landsat TM images for the H.J. Andrews Experimental Forest (HJA) in the Cascade Range of Oregon found the expected successional trajectories in successfully regenerated young stands. The simulated effects of topography on successional trajectories exist in the Landsat data. The primary challenge in using multitemporal imagery to monitor forest succession is to minimize the effects of various kinds of noise, including varying atmospheric effects, phenology, changes in sun and viewing angles, and topography. The current linkage of GORT–ZELIG only captures the spectral/temporal changes for young stands. Successional changes associated with mature and old-growth stands due to changes in the materials comprising the canopy are not captured. Further research is necessary to include changes associated with mature and old-growth forests, and to develop operational algorithms to utilize information from multitemporal imagery to monitor forest succession.