P. Lewis

First operational BRDF, albedo nadir reflectance products from MODIS

With the launch of NASA’s Terra satellite and the MODerate Resolution Imaging Spectroradiometer (MODIS), operational Bidirectional Reflectance Distribution Function (BRDF) and albedo products are now being made available to the scientific community. The MODIS BRDF/Albedo algorithm makes use of a semiempirical kernel-driven bidirectional reflectance model and multidate, multispectral data to provide global 1-km gridded and tiled products of the land surface every 16 days. These products include directional hemispherical albedo (black-sky albedo), bihemispherical albedo (white-sky albedo), Nadir BRDF-Adjusted surface Reflectances (NBAR), model parameters describing the BRDF, and extensive quality assurance information. The algorithm has been consistently producing albedo and NBAR for the public since July 2000. Initial evaluations indicate a stable BRDF/Albedo Product, where, for example, the spatial and temporal progression of phenological characteristics is easily detected in the NBAR and albedo results. These early beta and provisional products auger well for the routine production of stable MODIS-derived BRDF parameters, nadir reflectances, and albedos for use by the global observation and modeling communities.

The Moderate Resolution Imaging Spectroradiometer (MODIS): land remote sensing for global change research

The first Moderate Resolution Imaging Spectroradiometer (MODIS) instrument is planned for launch by NASA in 1998. This instrument will provide a new and improved capability for terrestrial satellite remote sensing aimed at meeting the needs of global change research. The MODIS standard products will provide new and improved tools for moderate resolution land surface monitoring. These higher order data products have been designed to remove the burden of certain common types of data processing from the user community and meet the more general needs of global-to-regional monitoring, modeling, and assessment. The near-daily coverage of moderate resolution data from MODIS, coupled with the planned increase in high-resolution sampling from Landsat 7, will provide a powerful combination of observations. The full potential of MODIS will be realized once a stable and well-calibrated time-series of multispectral data has been established. In this paper the proposed MODIS standard products for land applications are described along with the current plans for data quality assessment and product validation.

Burned area mapping using multi-temporal moderate spatial resolution data—a bi-directional reflectance model-based expectation approach

Abstract While remote sensing offers the capability for monitoring land surface changes, extracting the change information from satellite data requires effective and automated change detection techniques. The majority of change detection techniques rely on empirically derived thresholds to differentiate changes from background variations, which are often considered noise. Over large areas, reliable threshold definition is problematic due to variations in both the surface state and those imposed by the sensing system. A new approach to change detection, applicable to high-temporal frequency satellite data, that maps the location and approximate day of change occurrence is described. The algorithm may be applied to a range of change detection applications by using appropriate wavelengths. The approach is applied here to the problem of mapping burned areas using moderate spatial resolution satellite data. A bi-directional reflectance model is inverted against multi-temporal land surface reflectance observations, providing an expectation and uncertainty of subsequent observations through time. The algorithm deals with angular variations observed in multi-temporal satellite data and enables the use of a statistical measure to detect change from a previously observed state. The algorithm is applied independently to geolocated pixels over a long time series of reflectance observations. Large discrepancies between predicted and measured values are attributed to change. A temporal consistency threshold is used to differentiate between temporary changes considered as noise and persistent changes of interest. The algorithm is adaptive to the number, viewing and illumination geometry of the observations, and to the amount of noise in the data. The approach is demonstrated using 56 days of Moderate Resolution Imaging Spectroradiometer (MODIS) land surface reflectance data generated for Southern Africa during the 2000 burning season. Qualitatively, the results show high correspondence with contemporaneous MODIS active fire detection results and reveal a coherent spatio-temporal progression of burning. Validation of these results is in progress and recommendations for future research, pending the availability of independent validation data sets, are made. This approach is now being considered for the MODIS burned area algorithm.

Geostatistical classification for remote sensing: an introduction

Traditional spectral classification of remotely sensed images applied on a pixel-by-pixel basis ignores the potentially useful spatial information between the values of proximate pixels. For some 30 years the spatial information inherent in remotely sensed images has been employed, albeit by a limited number of researchers, to enhance spectral classification. This has been achieved primarily by filtering the original imagery to (i) derive texture ‘wavebands’ for subsequent use in classification or (ii) smooth the imagery prior to (or after) classification. Recently, the variogram has been used to represent formally the spatial dependence in remotely sensed images and used in texture classification in place of simple variance filters. However, the variogram has also been employed in soil survey as a smoothing function for unsupervised classification. In this review paper, various methods of incorporating spatial information into the classification of remotely sensed images are considered. The focus of the paper is on the variogram in classification both as a measure of texture and as a guide to choice of smoothing function. In the latter case, the paper focuses on the technique developed for soil survey and considers the modification that would be necessary for the remote sensing case.

Third Radiation Transfer Model Intercomparison (RAMI) exercise: Documenting progress in canopy reflectance models

The Radiation Transfer Model Intercomparison ( RAMI) initiative benchmarks canopy reflectance models under well-controlled experimental conditions. Launched for the first time in 1999, this triennial community exercise encourages the systematic evaluation of canopy reflectance models on a voluntary basis. The first phase of RAMI focused on documenting the spread among radiative transfer (RT) simulations over a small set of primarily 1-D canopies. The second phase expanded the scope to include structurally complex 3-D plant architectures with and without background topography. Here sometimes significant discrepancies were noted which effectively prevented the definition of a reliable surrogate truth, over heterogeneous vegetation canopies, against which other RT models could then be compared. The present paper documents the outcome of the third phase of RAMI, highlighting both the significant progress that has been made in terms of model agreement since RAMI-2 and the capability of/need for RT models to accurately reproduce local estimates of radiative quantities under conditions that are reminiscent of in situ measurements. Our assessment of the self-consistency and the relative and absolute performance of 3-D Monte Carlo models in RAMI-3 supports their usage in the generation of a surrogate truth for all RAMI test cases. This development then leads ( 1) to the presentation of the RAMI Online Model Checker (ROMC), an open-access web-based interface to evaluate RT models automatically, and ( 2) to a reassessment of the role, scope, and opportunities of the RAMI project in the future.

Hyperspectral remote sensing of foliar nitrogen content

A strong positive correlation between vegetation canopy bidirectional reflectance factor (BRF) in the near infrared (NIR) spectral region and foliar mass-based nitrogen concentration (%N) has been reported in some temperate and boreal forests. This relationship, if true, would indicate an additional role for nitrogen in the climate system via its influence on surface albedo and may offer a simple approach for monitoring foliar nitrogen using satellite data. We report, however, that the previously reported correlation is an artifact—it is a consequence of variations in canopy structure, rather than of %N. The data underlying this relationship were collected at sites with varying proportions of foliar nitrogen-poor needleleaf and nitrogen-rich broadleaf species, whose canopy structure differs considerably. When the BRF data are corrected for canopy-structure effects, the residual reflectance variations are negatively related to %N at all wavelengths in the interval 423–855 nm. This suggests that the observed positive correlation between BRF and %N conveys no information about %N. We find that to infer leaf biochemical constituents, e.g., N content, from remotely sensed data, BRF spectra in the interval 710–790 nm provide critical information for correction of structural influences. Our analysis also suggests that surface characteristics of leaves impact remote sensing of its internal constituents. This further decreases the ability to remotely sense canopy foliar nitrogen. Finally, the analysis presented here is generic to the problem of remote sensing of leaf-tissue constituents and is therefore not a specific critique of articles espousing remote sensing of foliar %N.

Radiation Transfer Model Intercomparison (RAMI) exercise: Results from the second phase

[1]xa0The Radiation Transfer Model Intercomparison (RAMI) initiative is a community-driven exercise to benchmark the models of radiation transfer (RT) used to represent the reflectance of terrestrial surfaces. Systematic model intercomparisons started in 1999 as a self-organized, open-access, voluntary activity of the RT modeling community. The results of the first phase were published by Pinty et al. [2001]. The present paper describes the benchmarking protocol and the results achieved during the second phase, which took place during 2002. This second phase included two major components: The first one included a rerun of all direct-mode tests proposed during the first phase, to accommodate the evaluation of models that have been upgraded since, and the participation of new models into the entire exercise. The second component was designed to probe the performance of three-dimensional models in complex heterogeneous environments, which closely mimic the observations of actual space instruments operating at various spatial resolutions over forest canopy systems. Phases 1 and 2 of RAMI both confirm not only that a majority of the radiation transfer models participating in RAMI are in good agreement between themselves for relatively simple radiation transfer problems but also that these models exhibit significant discrepancies when considering more complex but nevertheless realistic geophysical scenarios. Specific recommendations are provided to guide the future of this benchmarking program (Phase 3 and beyond).

Theoretical noise sensitivity of BRDF and albedo retrieval from the EOS-MODIS and MISR sensors with respect to angular sampling

The sensitivity of the semiempirical RossThick-LiSparse Ambrals BRDF model to random noise in observed multiangular reflectances was investigated through a study of the impact of angular sampling. The mathematical properties of (linear, additive) kernel-driven BRDF models allow the analytical derivation of so-called weights of determination or noise amplification factors which quantify the uncertainty in retrieved parameters such as nadir-view reflectance or albedo at various solar zenith angles, or in the BRDF model parameters themselves. The study was carried out using simulated angular sampling for the MODIS and MISR instruments to be flown on NASAs Earth Observing System AM-1 platform, as a function of latitude, day of year and sampling period. A similar study was carried out for comparison using the modified RPV BRDF model, a multiplicative model. Results show that the retrieved parameters, reflectance and albedo can be expected to have noise amplification factors that are less than unity, indicating that the retrievals are stable with respect to random noise under the angular sampling schemes occurring. The BRDF model parameters themselves were found to be more susceptible to noise than many of the derived products, especially for the modified RPV model. The effect of different angular sampling regimes on the uncertainty of derived information was further explored. This study provides an indication of the reliability to be expected from the operational BRDF/albedo products from the MODIS and MISR instruments. The findings may qualitatively also apply to AVHRR, SPOT VEGETATION and similar satellite angular sampling regimes.

Monte Carlo ray tracing in optical canopy reflectance modelling

This paper reviews the use of Monte Carlo (MC) methods in optical canopy reflectance modelling. Their utility, and, more specifically, MC ray tracing for the numerical simulation of the radiation field within a vegetation canopy, are outlined. General issues pertinent to implementation and exploitation of such methods are discussed, such as the descriptions of canopy structure and radiometric properties required for their use. Strategies for the reduction of variance, which form the core of the application of MC methods to canopy reflectance modelling are presented, and examples given of the type of information which may be obtained from canopy reflectance modelling using MC ray tracing. The use of MC methods in the development of models of canopy development, driven by fundamental properties such as radiation interception are discussed.

Investigation of the Utility of Spectral Vegetation Indices for Determining Information on Coniferous Forests

Abstract This work presents an investigation of the forest information content of spectral vegetation indices including the SR, NDVI, PVI, SAVI, TSAVI, and GEMI. The results were derived from two separate reflectance models including a geometric–optical and a Monte Carlo ray-tracing model, assuming nadir viewing. The effects of the atmosphere on the indices were also examined, using the 6S atmospheric code, and a short section is devoted to an analysis of Landsat Thematic Mapper data. Overall, the indices were not linear with respect to forest cover and, in many situations, an index did not have a unique value for a given cover. The general form of the indices’ responses to changes in cover could be explained by shadowing effects, linked to the heterogeneous nature of forest. The indices were also significantly affected by perturbations including solar zenith angle, background reflectance, stand structure, and leaf-area index. The relative importance of these perturbations depended on both the cover and on the particular spectral index tested. Existing indices should be selected according to the characteristics of the site under investigation, since no single index performed well over all covers. At low covers, GEMI performed best, where a decrease in the value of GEMI corresponded to an increase in crown cover. There was some evidence that the soil-adjusted indices (PVI, SAVI, TSAVI, and GEMI) partially reduced background reflectance effects in the data, although the reductions were not always associated with increased sensitivity to crown cover. At high covers, SAVI and TSAVI were found to perform best, since these indices had large dynamic ranges and small susceptibility to atmospheric perturbations. Further work is required to develop spectral indices specifically for conifer forests.