Terrestrial biological activity is fundamental to the production of food, ¤ber, and fuel and is o¬en considered the most important measure of global change (Running et al., 2000, 2004). Biological activity on Earth depends ultimately on solar radiation and its conversion into biochemical energy through photosynthesis. Ÿe fundamental paradigm measuring photosynthesis in terrestrial vegetation was ¤rst proposed by Monteith (1972) who showed us that stress-free annual crop productivity was linearly related to vegetation-absorbed photosynthetically active radiation (APAR) (e.g., Figure 26.1). Chapter 1 by Dr. Alfredo Huete et al. traces the development of various methods and approaches that have been applied in measuring, modeling, and mapping photosynthesis, accurately and routinely, using remote sensing data. In Chapter 1, they review the integration of remote sensing

with traditional in situ methods and the more recent eddy covariance tower approach for estimating gross primary productivity (GPP) and net primary productivity (NPP) at global scales. Light Detection and Ranging (LiDAR) integration with ¤eld inventory plots now provides calibrated estimates of aboveground carbon stocks, which can be scaled up using satellite data of vegetation cover, topography, and rainfall to model carbon stocks. A series of six productivity models are presented and discussed, based on the light use e¶ciency (LUE) concept and primarily dependent on satellite data inputs. Ÿese include the following: (1) NPP derived from the integral of growing season normalized di¦erence vegetation index (NDVI), as surrogate of vegetation APAR radiation and, more recently, integral of enhanced vegetation index (EVI); (2) biome-biogeochemical cycles (BGC) model that calculates daily GPP as a function of incoming solar radiation, light use conversion coe¶cients, and environmental stresses; (3) vegetation index-tower GPP relationships where spectral

vegetation indices (VIs) are directly related to eddy covariance tower carbon ¨ux measurements; (4) temperature and greenness (T-G) model that combines land surface temperature and EVI products from Moderate Resolution Imaging Spectrometer (MODIS); (5) greenness and radiation (G-R) model where chlorophyll-related spectral indices are coupled with measures of light energy, photosynthetically active radiation (PAR), to provide robust estimates of GPP; and (6) satellite-based vegetation photosynthesis model (VPM) that estimates GPP using satellite inputs of EVI and the land surface water index (LSWI), along with phenology and temperature scalars. Many of the limitations in productivity assessments concern the di¶culty in deriving independent estimates of LUE, and the hyperspectral-based photochemical re¨ectance index (PRI), a scaled LUE measure based on light absorption processes by carotenoids, is discussed as a way to advance the accuracies of remote sensing retrievals of productivity. Signi¤cant and promising advances in direct estimates of GPP, even under stress conditions, have been demonstrated with new spaceborne measures of solar-induced chlorophyll ¨ uorescence (SIF) based on near-infrared light reemitted from illuminated plants, as a by-product of photosynthesis, and thereby strongly correlated with GPP (Guanter et al., 2010, Frankenberg et al., 2011).