Crystal B. Schaaf

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.

Monitoring vegetation phenology using MODIS

Abstract Accurate measurements of regional to global scale vegetation dynamics (phenology) are required to improve models and understanding of inter-annual variability in terrestrial ecosystem carbon exchange and climate–biosphere interactions. Since the mid-1980s, satellite data have been used to study these processes. In this paper, a new methodology to monitor global vegetation phenology from time series of satellite data is presented. The method uses series of piecewise logistic functions, which are fit to remotely sensed vegetation index (VI) data, to represent intra-annual vegetation dynamics. Using this approach, transition dates for vegetation activity within annual time series of VI data can be determined from satellite data. The method allows vegetation dynamics to be monitored at large scales in a fashion that it is ecologically meaningful and does not require pre-smoothing of data or the use of user-defined thresholds. Preliminary results based on an annual time series of Moderate Resolution Imaging Spectroradiometer (MODIS) data for the northeastern United States demonstrate that the method is able to monitor vegetation phenology with good success.

An algorithm for the retrieval of albedo from space using semiempirical BRDF models

Spectral albedo may be derived from atmospherically corrected, cloud-cleared multiangular reflectance observations through the inversion of a bidirectional reflectance distribution function (BRDF) model and angular integration. This paper outlines an algorithm suitable for this task that makes use of kernel-based BRDF models. Intrinsic land surface albedos are derived, which may be used to derive actual albedo by taking into account the prevailing distribution of diffuse skylight. Spectral-to-broadband conversion is achieved using band-dependent weighting factors. The validation of a suitable BRDF model, the semiempirical Ross-Li (reciprocal RossThick-LiSparse) model and its performance under conditions of sparse angular sampling and noisy reflectances are discussed, showing that the retrievals obtained are generally reliable. The solar-zenith angle dependence of albedo may be parameterized by a simple polynomial that makes it unnecessary for the user to be familiar with the underlying BRDF model. The algorithm given is that used for the production of a BRDF/albedo standard data product from NASAs EOS-MODIS sensor, for which an at-launch status is provided. Finally, the algorithm is demonstrated on combined AVHRR and GOES observations acquired over New England, from which solar zenith angle-dependent albedo maps with a nominal spatial resolution of 1 km are derived in the visible band. The algorithm presented may be employed to derive albedo from space-based multiangular measurements and also serves as a guide for the use of the MODIS BRDF/albedo product.

Validating MODIS land surface reflectance and albedo products: methods and preliminary results

Abstract This paper presents the general methods and some preliminary results of validating Moderate-Resolution Imaging Spectroradiometer (MODIS) land surface reflectance and albedo products using ground measurements and Enhanced Thematic Mapper Plus (ETM+) imagery. Since ground “point” measurements are not suitable for direct comparisons with MODIS pixels of about 1 km over heterogeneous landscapes, upscaling based on high-resolution remotely sensed imagery is critical. In this study, ground measurements at Beltsville, MD were used to calibrate land surface reflectance and albedo products derived from ETM+ imagery at 30 m, which were then aggregated to the MODIS resolution for determining the accuracy of the following land surface products: (1) bidirectional reflectance from atmospheric correction, (2) bidirectional reflectance distribution function (BRDF), (3) broadband albedos, and (4) nadir BRDF-adjusted reflectance. The initial validation results from ground measurements and two ETM+ images acquired on October 2 and November 3, 2000 showed that these products are reasonably accurate, with typically less than 5% absolute error. Final conclusions on their accuracy depend on more validation results.

Spatially complete global spectral surface albedos: value-added datasets derived from Terra MODIS land products

Recent production of land surface anisotropy, diffuse bihemispherical (white-sky) albedo, and direct-beam directional hemispherical (black-sky) albedo from observations acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS) instruments aboard the National Aeronautics and Space Administrations Terra and Aqua satellite platforms have provided researchers with unprecedented spatial, spectral, and temporal information on the land surfaces radiative characteristics. Cloud cover, which curtails retrievals, and the presence of ephemeral and seasonal snow limit the snow-free data to approximately half the global land surfaces on an annual equal-angle basis. This precludes the MOD43B3 albedo products from being used in some remote sensing and ground-based applications, climate models, and global change research projects. An ecosystem-dependent temporal interpolation technique is described that has been developed to fill missing or seasonally snow-covered data in the official MOD43B3 albedo product. The method imposes pixel-level and local regional ecosystem-dependent phenological behavior onto retrieved pixel temporal data in such a way as to maintain pixel-level spatial and spectral detail and integrity. The phenological curves are derived from statistics based on the MODIS MOD12Q1 IGBP land cover classification product geolocated with the MOD43B3 data. The resulting snow-free value-added products provide the scientific community with spatially and temporally complete global white- and black-sky surface albedo maps and statistics. These products are stored on 1-min and coarser resolution equal-angle grids and are computed for the first seven MODIS wavelengths, ranging from 0.47-2.1 /spl mu/m and for three broadband wavelengths 0.3-0.7, 0.3-5.0, and 0.7-5.0 /spl mu/m.

New Directions in Earth Observing: Scientific Applications of Multiangle Remote Sensing

The physical interpretation of simultaneous multiangle observations represents a relatively new approach to remote sensing of terrestrial geophysical and biophysical parameters. Multiangle measurements enable retrieval of physical scene characteristics, such as aerosol type, cloud morphology and height, and land cover (e.g., vegetation canopy type), providing improved albedo accuracies as well as compositional, morphological, and structural information that facilitates addressing many key climate, environmental, and ecological issues. While multiangle data from wide field-of-view scanners have traditionally been used to build up directional “signatures” of terrestrial scenes through multitemporal compositing, these approaches either treat the multiangle variation as a problem requiring correction or normalization or invoke statistical assumptions that may not apply to specific scenes. With the advent of a new generation of global imaging spectroradiometers capable of acquiring simultaneous visible/near-IR...

Retrieval of forest structural parameters using a ground-based lidar instrument (Echidna ® )

A prototype upward-scanning, under-canopy, near-infrared light detection and ranging (lidar) system, the Echidna® validation instrument (EVI), built by CSIRO Australia, retrieves forest stand structural parameters, including mean diameter at breast height (DBH), stand height, distance to tree, stem count density (stems/area), leaf-area index (LAI), and stand foliage profile (LAI with height) with very good accuracy in early trials. We validated retrievals with ground-truth data collected from two sites near Tumbarumba, New South Wales, Australia. In a ponderosa pine plantation, LAI values of 1.84 and 2.18 retrieved by two different methods using a single EVI scan bracketed a value of 1.98 estimated by allometric equations. In a natural, but managed, Eucalypus stand, eight scans provided mean LAI values of 2.28–2.47, depending on the method, which compare favorably with a value of 2.4 from hemispherical photography. The retrieved foliage profile clearly showed two canopy layers. A “find-trunks” algorithm processed the EVI scans at both sites to identify stems, determine their diameters, and measure their distances from the scan center. Distances were retrieved very accurately (r2 = 0.99). The accuracy of EVI diameter retrieval decreased somewhat with distance as a function of angular resolution of the instrument but remained unbiased. We estimated stand basal area, mean diameter, and stem count density using the Relaskop method of variable radius plot sampling and found agreement with manual Relaskop values within about 2% after correcting for the obscuring of far trunks by near trunks (occlusion). These early trials prove the potential of under-canopy, upward-scanning lidar to retrieve forest structural parameters quickly and accurately.

The footprint of urban climates on vegetation phenology

[1] Human activity, through changing land use and other activities, is the most fundamental source of environmental change on the Earth. Urbanization and the resultant ‘‘urban heat islands’’ provide a means for evaluating the effect of climate warming on vegetation phenology. Using data from the Moderate Resolution Imaging Spectroradiometer, we analyzed urban-rural differences in vegetation phenological transition dates and land surface temperatures for urban areas larger than 10 km 2 in eastern North America. The results show that the effect of urban climates on vegetation phenology decays exponentially with distance from urban areas with substantial influence up to 10 km beyond the edge of urban land cover, and that the ecological ‘‘footprint’’ of urban climates is about 2.4 times that of urban land use in eastern North America. The net effect is an increase in the growing season by about 15 days in urban areas relative to adjacent unaffected rural areas. INDEX TERMS: 0315 Atmospheric Composition and Structure: Biosphere/atmosphere interactions; 0330 Atmospheric Composition and Structure: Geochemical cycles; 1615 Global Change: Biogeochemical processes (4805); 1620 Global Change: Climate dynamics (3309); 1640 Global Change: Remote sensing. Citation: Zhang, X., M. A. Friedl, C. B. Schaaf, A. H. Strahler, and A. Schneider (2004), The footprint of urban climates on vegetation phenology, Geophys. Res. Lett., 31, L12209, doi:10.1029/2004GL020137.

Detecting vegetation structure using a kernel-based BRDF model

The magnitude of the anisotropy of vegetation is mainly determined by its spectral and structural features. It can be described by the bidirectional reflectance distribution function (BRDF). The parameters of physical BRDF models are related to the biophysical structural information. However, for a semiempirical kernel-based BRDF model, the relationship between BRDF parameters and vegetation structure is no longer as clear as with a physical BRDF model. To reveal this relationship, a structural scattering index (SSI) and a relative structural scattering index (RSSI) are derived based on the BRDF parameters in this paper. The investigation of SSI and RSSI show that they have both theoretical and practical meaning and can be used to distinguish different land cover types or to detect structural changes.

Sensitivity of vegetation phenology detection to the temporal resolution of satellite data

Vegetation phenology derived from satellite data has increasingly received attention for applications in environmental monitoring and modelling. The accuracy of phenological estimates, however, is unknown at the regional and global level because field validation data are insufficient. To assess the accuracy of satellite‐derived phenology, this study investigates the sensitivity of phenology detection to both the temporal resolution of sampling and the number of consecutive missing values (usually representing cloud cover) in the time series of satellite data. To do this, time series of daily vegetation index data for various ecosystems are modelled and simulated using data from Moderate‐Resolution Imaging Spectroradiometer (MODIS) data. The annual temporal data are then fitted using piecewise logistic functions, which are employed to calculate curvature change rate for detecting phenological transition dates. The results show that vegetation phenology can be estimated with a high precision from time series with temporal resolutions of 6–16 days even if daily data contains some uncertainties. If the temporal resolution is no coarser than 16 days for time series sampled using an average composite, the absolute errors are less than 3 days. On the other hand, the phase shift of temporal sampling is shown to have limited impacts on phenology detection. However, the accuracy of phenology detection may be reduced greatly if missing values in the time series of 16‐day MODIS data occur around the onsets of phenological transition dates. Even so, the probability that the absolute error in phenological estimates is greater than 5 days is less than 4% when only one period is missing in the time series of 16‐day data during vegetation growing seasons; this probability increases to 20% if there are two consecutive missing values.