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Spectral indices (SI) derived from crop reflectance data are sensitive to chlorophyll a and b content (Chl). However, the SI--Chl relationship might be confounded by variation in leaf area index (LAI) and soil background reflectance, especially in semiarid environments where water determines crop growth. This study evaluated the sensitivity of SI to variation in soil reflectance and how this may affect overall SI performance for ground-based sensing of Chl in dryland wheat (Triticum aestivum L.). Selected SI were computed from spectra simulated by the PROSPECT-SAIL radiative-transfer model for 5 LAI values, 7 Chl values, and 121 dry soil surface reflectance spectra. These spectra represented soils across major wheat growing areas in the United States. Soil properties and reflectance varied widely among the soils indicated by the high SI variation for LAI values < 1.5. Overall, soil background variation contributed less to the observed SI variability ( Abbreviations: Chl, chlorophyll a and b content; GESAVI, Generalized soil adjusted vegetation index; LAI, leaf area index; MCARI, modified chlorophyll absorption in reflective index; NDRE, normalized difference red edge index; NDVI, normalized difference vegetation index; OSAVI, optimized soil adjusted vegetation index; SAVI, Soil Adjusted Vegetation Index; SI spectral index; MTV2, Second Modified Triangular Vegetation Index; TCARI, transformed chlorophyll absorption in reflectance index; TSAVI, Transformed Soil Adjusted Vegetation Index.

Models represent our primary method for integration of small-scale, process-level phenomena into a comprehensive description of forest-stand or ecosystem function. They also represent a key method for testing hypotheses about the response of forest ecosystems to multiple changing environmental conditions. This paper describes the evaluation of 13 stand-level models varying in their spatial, mechanistic, and temporal complexity for their ability to capture intra- and interannual components of the water and carbon cycle for an upland, oak-dominated forest of eastern Tennessee. Comparisons between model simulations and observations were conducted for hourly, daily, and annual time steps. Data for the comparisons were obtained from a wide range of methods including: eddy covariance, sapflow, chamber-based soil respiration, biometric estimates of stand-level net primary production and growth, and soil water content by time or frequency domain reflectometry. Response surfaces of carbon and water flux as a function of environmental drivers, and a variety of goodness-of-fit statistics (bias, absolute bias, and model efficiency) were used to judge model performance. A single model did not consistently perform the best at all time steps or for all variables considered. Intermodel comparisons showed good agreement for water cycle fluxes, but considerable disagreement among models for predicted carbon fluxes. The mean of all model outputs, however, was nearly always the best fit to the observations. Not surprisingly, models missing key forest components or processes, such as roots or modeled soil water content, were unable to provide accurate predictions of ecosystem responses to short-term drought phenomenon. Nevertheless, an inability to correctly capture short-term physiological processes under drought was not necessarily an indicator of poor annual water and carbon budget simulations. This is possible because droughts in the subject ecosystem were of short duration and therefore had a small cumulative impact. Models using hourly time steps and detailed mechanistic processes, and having a realistic spatial representation of the forest ecosystem provided the best predictions of observed data. Predictive ability of all models deteriorated under drought conditions, suggesting that further work is needed to evaluate and improve ecosystem model performance under unusual conditions, such as drought, that are a common focus of environmental change discussions. Key words: autotrophic respiration; carbon budget; computer models; evaporation; evapotranspiration; NEE; NPP; transpiration; water budget.