## 1 Introduction  
Vegetation indices (VIs) to monitor terrestrial landscapes by satellite sensors were first developed in the 1970s and have been highly successful in assessing vegetation condition, foliage, cover, phenology, and processes such as evapotranspiration (ET) and primary productivity, related to the fraction of photosynthetically active radiation absorbed by a canopy (fPAR) [1-3]. VIs are robust satellite data products computed the same way across all pixels in time and space, regardless of surface conditions. As ratios, they can be easily cross-calibrated across sensor systems, ensuring continuity of data sets for long-term monitoring of the land surface and climate-related processes. For example, there is a global record of Normalized Difference Vegetation Index (NDVI) data since 1981 from the NOAA Advanced Very High Resolution Radiometer (AVHRR) that has contributed to global climate, ecosystem, and agricultural studies. A new generation of VI data from the Moderate Resolution Imaging Spectrometer (MODIS) on the Terra satellite has been inter-calibrated with AVHRR NDVI, and provides near daily coverage of the earth at 250 m pixel resolution [3]. [PERSON] and [PERSON] [1] and [PERSON] _et al_. [2] have recently reviewed the numerous applications of satellite VIs in ecological studies. VIs are now indispensable tools in land cover classification, climate- and land-use-change detection, drought monitoring, and habitat loss, to name just a few applications. In this paper we briefly review the theoretical basis for VIs and give some examples of their use and misuse in two particular applications, estimating ecosystem carbon and moisture fluxes, important topics in global change studies. Numerous studies have shown that satellite-derived VIs are optical measures of canopy \"greenness\", a composite property of leaf chlorophyll content, leaf area, canopy cover and structure. However, VIs have also been employed as proxies for individual, and often land-cover-dependent, vegetation parameters such as fractional vegetation cover (f\({}_{\text{c}}\)), leaf area index (LAI), roughness lengths for turbulent transfer, albedo, emissivity and other biophysical properties of the landscape. These parameters are often required in Soil-Vegetation-Atmosphere Transfer (SVAT), Surface Energy Balance (SEB), and Global Climate Models (GCM) that attempt to predict surface fluxes based on physical models. Unfortunately, these canopy attributes are often only moderately correlated with VIs or their derivatives, and remote sensing models that use VIs to estimate these parameters are subject to error and uncertainty. On the other hand, networks of flux towers now provide real-time, plot-level estimates of carbon assimilation and moisture and energy fluxes in major biome types around the world. We give recent examples in which VIs have been combined with data from flux towers and meteorological stations to accurately scale carbon and moisture fluxes over large landscape units, circumventing in some cases the need to solve complex SVAT or SEB equations with limited input data. It seems surprising that VIs are so closely related to carbon and moisture fluxes, because fluxes are controlled in part by stomatal resistance, which can vary considerably over short time periods. However, over longer time periods (weeks or months), plants tend to adjust their foliage density to match the capacity of the environment to support photosynthesis (the Resource Optimization Theory) [4]. Leaves are expensive to produce and maintain, and when plants are nutrient-limited, water-stressed, or exposed to other unfavorable conditions, they reduce their leaf area to use resources efficiently, although full optimization is never achieved [5]. Hence, foliage density measured by time-series VIs can be a powerful tool in measuring the physiological status of vegetation. Our goal is to point the way towards using VIs directly in scaling carbon and moisture fluxes rather than as proxies for canopy state attributes that are difficult to measure [6].