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article i nfo Vegetation carbon uptake and respiration constitute the largest carbon cycle of the planet with an annual turn- overinthe orderof120GT.Currently,neitherecosystemcarbon uptake (throughphotosynthesis)norecosystem carbon release (through respiration) can be measured directly during the daytime. Instead, flux-tower measure- ments rely on nighttime respiration based on the assumption of zero carbon uptake which are then projected to daytime using an exponential relationship to soil temperature at shallow soil depth. As an alternative to this approach, R could possibly also be determined from combining daytime eddy covariance measurements of net ecosystem production (NEP) and spectral observations of gross primary production (GPP). In previous work, we have shown that multi-angular observations can be used to determine GPP from the absorbed photosynthet- ically active radiation (APAR) and spectrally obtained observations of light-use efficiency (e). The difference of NEPandGPPsuggeststhatdaytime respirationisgreaterandmoredynamicthanconventionalestimatesderived from nighttime flux values. Our findings also suggest that an accelerated ecosystem metabolism results in an exponential increase in respiration which eventually diminishes net ecosystem production. Respiration was also closely related to air and soil temperature. We conclude that tower-level spectral measurements provide considerable new insights into ecosystem fluxes as they allow independent yet complementary measurements of different aspects of the carbon and energy cycle.

This paper describes the atmospheric correction (AC) component of the Multi-Angle Implementation of Atmospheric Correction algorithm (MAIAC) which introduces a new way to compute parameters of the Ross-Thick Li-Sparse (RTLS) Bi-directional reflectance distribution function (BRDF), spectral surface albedo and bidirectional reflectance factors (BRF) from satellite measurements obtained by the Moderate Resolution Imaging Spectroradiometer (MODIS). MAIAC uses a time series and spatial analysis for cloud detection, aerosol retrievals and atmospheric correction. It implements a moving window of up to 16 days of MODIS data gridded to 1 km resolution in a selected projection. The RTLS parameters are computed directly by fitting the cloud-free MODIS top of atmosphere (TOA) reflectance data stored in the processing queue. The RTLS retrieval is applied when the land surface is stable or changes slowly. In case of rapid or large magnitude change (as for instance caused by disturbance), MAIAC follows the MODIS operational BRDF/albedo algorithm and uses a scaling approach where the BRDF shape is assumed stable but its magnitude is adjusted based on the latest single measurement. To assess the stability of the surface, MAIAC features a change detection algorithm which analyzes relative change of reflectance in the Red and NIR bands during the accumulation period. To adjust for the reflectance variability with the sun-observer geometry and allow comparison among different days (view geometries), the BRFs are normalized to the fixed view geometry using the RTLS model. An empirical analysis of MODIS data suggests that the RTLS inversion remains robust when the relative change of geometry-normalized reflectance stays below 15%. This first of two papers introduces the algorithm, a second, companion paper illustrates its potential by analyzing MODIS data over a tropical rainforest and assessing errors and uncertainties of MAIAC compared to conventional MODIS products.

Forest disturbance and recovery are critical ecosystem processes, but the spatial pattern of disturbance has never been mapped across North America. The LEDAPS (Landsat Ecosystem Disturbance Adaptive Processing System) project has assembled a wall-to-wall record of stand-clearing disturbance (clearcut harvest, fire) for the United States and Canada for the period 1990–2000 using the Landsat satellite archive. Landsat TM and ETM+ data were first converted to surface reflectance using the MODIS/6S atmospheric correction approach. Disturbance and early recovery were mapped using the temporal change in a Tasseled-Cap “Disturbance Index” calculated from the early (~ 1990) and later (~ 2000) images. Validation of the continental mapping has been carried out using a sample of biennial Landsat time series from 23 locations across the United States. Although a significant amount of disturbance (30–60%) cannot be mapped due to the long interval between image acquisition dates, the biennial analyses allow a first-order correction of the decadal mapping. Our results indicate disturbance rates of up to 2–3% per year are common across the US and Canada due primarily to harvest and forest fire. Rates are highest in the southeastern US, the Pacific Northwest, Maine, and Quebec. The mean disturbance rate for the conterminous United States (the “lower 48” states and District of Columbia) is calculated as 0.9 +/− 0.2% per year, corresponding to a turnover period of 110 years.

Biophysical structural information such as biomass, LAI, and NPP are important inputs to regional scale models of ecosystem processes and photosynthetic activity within boreal forests. However, traditional methods such as NDVI for deriving these variables from remotely sensed data have been inconsistent and unsatisfactory due to factors such as the confounding influence of background reflectance and canopy geometry on the overall pixel signal. To address this problem, we present new results which use spectral mixture analysis to determine areal fractions of sunlit canopy, sunlit background, and shadow at subpixel scales for predicting these biophysical variables. Geometric-optical reflectance models are used to estimate sunlit canopy component reflectance for input to the analysis together with field measures of background and shadow reflectance. In this article, we compare cylinder, cone, and spheroid models of canopy geometry and evaluate the importance of solar zenith angle variations in reflectance estimates for mixture fractions. These are computed from helicopter MMR radiometer data for 31 stands of black spruce along a gradient of stand densities near the southern fringe of the North American boreal forest. Component fractions are evaluated against ground data derived from dense-grid point analyses of coincident high resolution color photography and also for predicting biophysical variables. In general, the Li–Strahler spheroid model was better than the cone and cylinder models and the importance of correcting for solar zenith angle (SZA) was illustrated, with significant improvements noted for higher SZA as a result of corrections for canopy mutual shadowing. The best overall results were obtained from the shadow fraction using a spheroid model of canopy geometry at SZA 45°. Linear regression analyses showed biomass could be estimated with r2 values of 0.83 and a standard error (S.E.) of 1.7 kg/m2; LAI: r2=0.82, S.E.=0.46; and NPP: r2=0.86, S.E.=0.05 kg/m2/yr. These results were significantly higher than with NDVI for estimating biomass (r2=0.44), LAI (r2=0.60), and NPP (r2=0.56). Current and future areas of research are outlined towards improving our understanding of carbon cycling in large forested ecosystems as a function of variability in the physical climate system and environmental change.

The ISLSCP Workshop was held in Columbia, Maryland, 23–26 June 1992 with over 240 scientists and science managers attending. The goal was to assess the progress of the last decade in the areas of modeling, satellite data algorithm development, and field experiments. This article includes: 1. 1. A review of the state and direction of biosphere-atmosphere model development and an assessment of the data needs of the models. Models covering a large range of timescales were considered: energy-water-carbon (seconds to seasons); carbon cycles and biogeochemistry (days to years); and ecological structure and function (years to millennia). 2. 2. A reference to current satellite data algorithms and other global data sources. The areas covered in the workshop were: near-surface meteorology, surface radiation budget, precipitation, runoff, snow and ice, soils and soil moisture, and land cover type and land cover attributes. These are discussed in detail in other articles in this issue. 3. 3. A review of completed and planned major field experiments. The major experiments of the last decade are summarized and the lessons noted. The participating scientists agreed on the need to rapidly assemble and circulate global data sets of variables and parameters required to initialize, force, and validate the global biosphere-atmosphere models. A prioritized list of data sets required to meet this need is set out and discussed. Lastly, initiatives taken by ISLSCP to satisfy these requirements are reviewed: Initiative I. Immediate Generation of High Priority Global Data Sets Some essential global data sets are to be put together within 2 years and released to the community by mid-1994. These data sets will be mapped to a common spatial resolution (1° × 1°) and will cover the period 1987–1988. 1. 1. Vegetation: Global sets of vegetation-related parameters are to be generated with a monthly time resolution. Available AVHRR data sets are to be used as the basis for this effort, and algorithms will be applied to calculate fields of cover type, phenology, FPAR, and leaf area index. These fields can then in turn be used to infer other surface parameters such as roughness, albedo, biomass, etc. 2. 2. Hydrometeorology: Global meteorological fields retrieved from numerical reanalysis will be manipulated to provide near-surface forcing data sets for temperature, humidity, windspeed, etc. Observations of precipitation, runoff, and snow cover are to be worked up into easily accessible forms. 3. 3. Radiation Budget: A closer interface with the International Satellite Cloud Climatology Project (ISCCP) and related projects will be pursued to obtain surface radiation budget components at higher spatial resolutions, as well as fields of incident PAR. 4. 4. Soils: A small project was set in place with the goal of providing a useful soil type and properties data set for use by global modelers. Initiative II: Improved, Follow-on Data Sets The data sets specified in Initiative I are to be generated within a 2-year period. However, it is clear that great improvements could be made over this first data release which must rely on preexisting data sets and robust, available algorithms. Work was started to scope out what needs to be done to produce improved versions of all the Initiative I data sets within 5 years, by mid-1997. Initiative III: Improved Communications within the Land Science Community Future discussions within ISLSCP and between ISLSCP and other bodies will focus on the need to coordinate different research thrusts within the land science community, particularly the modeling, algorithm and field experiment work.

Ecosystem process, biosphere-atmosphere transfer, and carbon exchange models all require parameterization of the land surface, including land vegetation cover and soil moisture. Although not yet a demonstrated global capability, the most feasible method for obtaining these parameters and updating them periodically, is satellite remote sensing. In this paper we will summarize our understanding of the desired land surface parameters, including soil moisture, and provide an assessment of the state of the art of surface state remote sensing algorithms to infer those parameters on a global basis. First, we will consider a) modeling requirements for land cover parameters, including vegetation community composition and biophysical parameters, for example, leaf area index (LAI), biomass density, fraction of photosynthetically active radiation (Fear) absorbed by the vegetated land surface, and b) modeling requirements for soil moisture. We will then review the status of remote sensing algorithms for obtaining these parameters and examine a number of issues involved in the global implementation and testing of these algorithms. Finally, we will look at future needs to make global mapping of land cover parameters a reality.

Field measurements of carbon dioxide and water vapor fluxes were analyzed in conjunction with reflectances obtained from a helicopter-mounted Modular Multiband Radiometer at a grassland study site during the First International Satellite Land Surface Climatology Project Field Experiment. These measurements are representative of the canopy scale and were made over a range of meteorological and soil moisture conditions during different stages in the annual life cycle of the prairie vegetation, and thus provide a good basis for investigating hpotheses/relationships potentially useful in remote sensing applications. We tested the hypothesis (Sellers, 1987) that the simple ratio vegetation index should be near-linearly related to the derivatives of the unstressed canopy stomatal conductance and the unstressed canopy photosynthesis with respect to photosynthetically active radiation. Even though there is some scatter in our data, the results seem to support this hypothesis.

The theoretical analyses of Sellers (1985, 1987), which linked canopy spectral reflectance properties to (unstressed) photosynthetic rates and conductances, are critically reviewed and significant shortcomings are identified. These are addressed in this article principally through the incorporation of a more sophisticated and realistic treatment of leaf physiological processes within a new canopy integration scheme. The results indicate that area-averaged spectral vegetation indices, as obtained from coarse resolution satellite sensors, may give good estimates of the area-integrals of photosynthesis and conductance even for spatially heterogenous (though physiologically uniform) vegetation covers.

In the application of remote sensing at optical wavelengths to vegetated land surfaces from satellite-borne high-resolution instruments, such as those scheduled for the Earth Observing System, an understanding of the various physical mechanisms that contribute to the measured signal is important. In this context, numerical radiative transfer in three-dimensional coupled medium of atmosphere and vegetation has several applications, as in the development of correction routines for atmospheric perturbations, target information retrieval techniques, study of terrain elevation and adjacency effects, etc. A numerical method for solving the radiative transfer equation in three spatial dimensions was recently developed and analyzed for its numerical behavior. In this article, continued development of the method is reported, including an efficient acceleration algorithm. The reliability of coding and accuracy of the algorithm are evaluated by benchmarking. Parameterization of the method and results of a simulation are presented to document the utility of the code for applications in optical remote sensing studies of vegetated land surfaces. A simple model of the hot spot effect and sample calculations are presented. Finally, the radiative transfer method is tested with experimental data of vegetation canopy reflectance factors at two wavelengths.

Results obtained from the FIFE experiments conducted in 1987 and 1989 are summarized. Data analyses indicate that the hypotheses linking energy balance components to surface biology and remote sensing are reasonable at a point level, and that satellite remote sensing can potentially provide useful estimates of the surface energy budget. An investigation of atmospheric scattering and absorption effects on satellite remote sensing of surface radiance shows that the magnitude of atmospheric opacity variations within the FIFE site and with season can have a large effect on satellite measured values of surface radiances. Comparisons of atmospherically corrected TM radiances with surface measured radiances agreed to within about two percent at the visible and near-infrared wavelengths and to 6 percent in the midinfrared.

A method is developed for relating scene digital counts among several images of the same scene by identifying radiometric control sets with mean reflectances that are basically constant. The average digital-count values of the control sets are utilized to compute linear transforms that relate digital count values between images. Two Landsat TM images are studied by means of the technique using simulations of a wide range of atmospheric conditions. In the visible and near-IR bands the algorithm effectively adjusts the surface reflectance for the effects of relative atmospheric differences to within 1 percent. The proposed method is found to be an effective relative correction procedure that can be used when atmospheric optical-depth data and calibration coefficients are not available.