@article {418,
	title = {Scaling and uncertainty in the relationship between the NDVI and land surface biophysical variables: An analysis using a scene simulation model and data from FIFE},
	journal = {Remote Sensing of Environment},
	volume = {54},
	number = {3},
	year = {1995},
	note = {JOURNAL ARTICLE; RESEARCH ARTICLE},
	pages = {233-246},
	abstract = {Biophysical inversion of remotely sensed data is constrained by the complexity of the remote sensing process. Variations in sensor response associated with solar and sensor geometries, surface directional reflectance, topography, atmospheric absorption and scattering, and sensor electrical-optical engineering interact in complex manners that are difficult to deconvolve and quantify in individual images or in time series of images. We have developed a model of the remote sensing process to allow systematic examination of these factors. The model is composed of three main components, including a ground scene model, an atmospheric model, and a sensor model, and may be used to simulate imagery produced by instruments such as the Landsat Thematic Mapper and the Advanced Very High Resolution Radiometer. Using this model, we examine the effect of subpixel variance in leaf area index (LAI) on relationships among LAI, the fraction of absorbed photosynthetically active radiation (FPAR), and the normalized difference vegetation index (NDVI). To do this, we use data from the first ISLSICP Field Experiment (FIFE) to parameterize ground scene properties within the model. Our results demonstrate interactions between sensor spatial resolution and spatial autocorrelation in ground scenes that produce a variety of effects in the relationship between both LAI and FPAR and NDVI. Specifically, sensor regularization, nonlinearity in the relationship between LAI and NDVI, and scaling the NDVI all influence the range, variance, and uncertainty associated with estimates of LAI and FPAR inverted from simulated NDVI data. These results have important implications for parameterization of land surface process models using biophysical variables such as LAI and FPAR estimated from remotely sensed data.},
	keywords = {Documentation, General{\textendash}Field Apparatus) (Mathematical Biology and Statistical Methods) (Ecology Environmental Biology{\textendash}Bioclimatology and Biometeorology) (Ecology Environmental Biology{\textendash}Plant) (Biophysics{\textendash}Biocybernetics (1972- )) (Forestry and Forest Products) Pl, General{\textendash}Field Methods) (Methods, Materials and Apparatus, Nomenclature and Terminology) (General Biology{\textendash}Information, Plantae-Unspecified (General Biology{\textendash}Taxonomy, Retrieval and Computer Applications) (Methods},
	author = {Friedl, M. A. and Davis, F. W. and Michaelsen, J. and Moritz, M. A.}
}
@article {419,
	title = {Estimating grassland biomass and Leaf Area Index using ground and satellite data},
	journal = {International Journal of Remote Sensing},
	volume = {15},
	number = {7},
	year = {1994},
	note = {English Article Current Contents/Physical, Chemical \& Earth Sciences. Reprint available from: Friedl MA BOSTON UNIV CTR REMOTE SENSING 675 COMMONWEALTH AVE BOSTON, MA 02215 USA UNIV CALIF SANTA BARBARA CTR REMOTE SENSING \& ENVIRONM OPT SANTA BARBARA, CA 93106 USA LAWRENCE LIVERMORE NATL LAB LIVERMORE, CA 94550 USA NATL CTR ATMOSPHER RES BOULDER, CO 80307 USA 0004},
	pages = {1401-1420},
	abstract = {We compared estimates of regional biomass and LAI for a tallgrass prairie site derived from ground data versus estimates derived from satellite data. Linear regression models were estimated to predict LAI and biomass from Landsat-TM data for imagery acquired on three dates spanning the growing season of 1987 using co-registered TM data and ground measurements of LAI and biomass collected at 27 grassland sites. Mapped terrain variables including burning treatment, land-use, and topographic position were included as indicator variables in the models to acccount for variance in biomass and LAI not captured in the TM data. Our results show important differences in the relationships between Kauth-Thomas greenness (from TM), LAI, biomass and the various terrain variables. In general, site-wide estimates of biomass and LAI derived from ground versus satellite-based data were comparable. However, substantial differences were observed in June. In a number of cases, the regression models exhibited significantly higher explained variance due to the incorporation of terrain variables, suggesting that for areas encompassing heterogeneous land-cover the inclusion of categorical terrain data in calibration procedures is a useful technique. [References: 46] 46},
	keywords = {675 COMMONWEALTH AVE, BOSTON, CTR REMOTE SENSING, MA 02215., Remotely sensed data. Tallgrass prairie. Canopy reflectance. Noaa-avhrr. Vegetation. Photosynthesis. Transpiration. Images. Fife. Earth sciences. Reprint available from: Friedl MA. BOSTON UNIV},
	author = {Friedl, M. A. and Michaelsen, J. and Davis, F. W. and Walker, H. and Schimel, D. S.}
}
@article {722,
	title = {Regression tree analysis of satellite and terrain data to guide vegetation sampling and surveys},
	journal = {Journal of Vegetation Science},
	volume = {5},
	year = {1994},
	note = {JOURNAL ARTICLE; RESEARCH ARTICLE},
	month = {1994},
	pages = {673-686},
	abstract = {Monitoring of regional vegetation and surface biophysical properties is tightly constrained by both the quantity and quality of ground data. Stratified sampling is often used to increase sampling efficiency, but its effectiveness hinges on appropriate classification of the land surface. A good classification must he sufficiently detailed to include the important sources of spatial variability, but at the same time it should be as parsimonious as possible to conserve scarce and expensive degrees of freedom in ground data. As part of the First ISLSCP (International Satellite Land Surface Climatology Program) Field Experiment (FIFE), we used Regression Tree Analysis to derive an ecological classification of a tail grass prairie landscape. The classification is derived from digital terrain, land use, and land cover data and is based on their association with spectral vegetation indices calculated from single-date and multi-temporal satellite imagery. The regression tree analysis produced a site stratification that is similar to the a priori scheme actually used in FIFE, but is simpler and considerably more effective in reducing sample variance in surface measurements of variables such as biomass, soil moisture and Bowen Ratio. More generally, regression tree analysis is a useful technique for identifying and estimating complex hierarchical relationships in multivariate data sets.},
	keywords = {(Aerospace and Underwater Biological Effects--General, (Ecology, (General Biology--Institutions, Administration and Legislation), (Methods, Materials and Apparatus, General--Field Methods), (Methods, Materials and Apparatus, General--Photography), Angiosperms, Biophysical Properties, Ecological Classification, Environmental Biology--Bioclimatology and Biometeorology), Environmental Biology--Plant), Gramineae, International Satellite Land Surface Climatology Program, Methods), monitoring, Monocots, Plants, Research Article, Satellite Imagery, Spermatophytes, Tall Grass Prairie Landscape, Vascular plants},
	author = {Michaelsen, J. and Schimel, D. S. and Friedl, M. A. and Davis, F. W. and Dubayah, R. C.}
}
@article {417,
	title = {Sources of variation in radiometric surface temperature over a tallgrass prairie},
	journal = {Remote Sensing of Environment},
	volume = {48},
	number = {1},
	year = {1994},
	note = {JOURNAL ARTICLE; RESEARCH ARTICLE},
	pages = {1-17},
	abstract = {Numerous studies have noted a strong negative correlation between radiometric surface temperature and spectral vegetation indices such as the NDVI, and have suggested that this relationship might be exploited in strategies to model land surface energy balance from satellites. These studies have been largely empirical in nature and the relationships among remotely sensed data, land surface properties, and land surface energy balance that produce this phenomenon remain unclear. We studied the relationship between radiometric surface temperature and NDVI over a tallgrass prairie in northeastern Kansas. The study site included a mix of landcovers, with fractional vegetation cover and exposed soil backgrounds over much of the site. We observed a persistent negative correlation between radiometric surface temperature and NDVI, but found that the relationship was highly date- and time-specific. In this context, the relationship between surface temperature and NDVI was observed to depend on landcover type, and a significant proportion of the total variance in both NDVI and radiometric surface temperature was explained by stratifying the data by landcover class. More importantly, our results show the relationship between surface temperature and NDVI to have little association with surface energy balance for data sets acquired from aircraft and helicopters on several dates during the growing seasons of 1987 and 1989. Based on results from a simulation model of the soil-canopy-sensor system, we hypothesize the observed covariance between radiometric surface temperature and NDVI to be largely caused by temperature differences between the soil background and vegetation canopy and by variation in fractional vegetation cover. This hypothesis is supported by evidence showing soil moisture to be an important secondary control on radiometric surface temperature due to its effect on soil thermal inertia, rather than as a limiting control on latent heat flux, as might be expected. These findings indicate that invertible surface energy balance models must account for the effects of landcover, soil background temperatures, and soil moisture before thermal infrared imagery can be effectively used to estimate land surface fluxes.},
	keywords = {Biochemistry and Biophysics{\textendash}Temperature) (Agronomy{\textendash}Forage Crops and Fodder) (Soil Science{\textendash}Physics and Chemistry (1970- )) Plants Vascular plants Spermatophytes Angiosperms Monocots Research Article Vegetation Index Soil Energy Balance Mathematical, Effects and Regulation{\textendash}General Measurement and Methods) (Plant Physiology, Plantae-Unspecified Gramineae (Mathematical Biology and Statistical Methods) (Ecology Environmental Biology{\textendash}Plant) (Biophysics{\textendash}Biocybernetics (1972- )) (Metabolism{\textendash}Energy and Respiratory Metabolism) (Temperature: Its Measurement},
	author = {Friedl, M. A. and Davis, F. W.}
}
@mastersthesis {416,
	title = {Correspondence between remotely sensed data and land surface energy balance over a tallgrass prairie},
	year = {1993},
	type = {phdPh.D. dissertation},
	author = {Friedl, M. A.}
}
@article {385,
	title = {Covariance of biophysical data with digital topographic and land use maps over the FIFE site},
	journal = {Journal of Geophysical Research-Atmospheres},
	volume = {97},
	number = {ND17},
	year = {1992},
	pages = {19009-19021},
	author = {Davis, F. W. and Schimel, D. S. and Friedl, M. A. and Michaelsen, J. C. and Kittel, T. G. F. and Dubayah, R. and Dozier, J.}
}
