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Estimating terrestrial carbon exchange from space: How often and how well? Knox, Robert*,1, Hall, Forrest2, Huemmrich, Karl2, Gervin, Jannette1, 1 NASA's Goddard Space Flight Center, Greenbelt, MD2 University of Maryland, Baltimore, MD ABSTRACT- Data from a new space mission measuring integrated light-use efficiency could provide a breakthrough in understanding global carbon, water, and energy dynamics, and greatly improve the accuracy of model predictions for terrestrial carbon cycles and climate. Over the past decade, Gamon and others have shown that changes in photo-protective pigments are sensitive indicators of declines in light-use efficiency of plants and plant canopies. The requirements for integrated diurnal measurements from space need to be defined, before a space mission can be formulated successfully using this concept. We used tower-based CO2 flux data as idealized proxies for remote measurements, examining their sampling properties. Thousands of half-hourly CO2 flux measurements are needed before their average begins to converge on an average annual net CO2 exchange. Estimates of daily integrated fluxes (i.e., diurnal curves) are more statistically efficient, especially if the spacing between measured days is quasi-regular, rather than random. Using a few measurements per day one can distinguish among days with different net CO2 exchanges. Fluxes sampled between mid-morning to mid-afternoon are more diagnostic than early morning or late afternoon measurements. Similar results (correlation > 0.935) were obtained using 2 measurements per day with high accuracy (±5%), 3 measurements per day with medium accuracy (±10%), or 5 measurements per day at lower accuracy (±20%). An observatory in a geosynchronous or near-geosynchronous orbit could provide appropriate observations, as could a multi-satellite constellation in polar orbits, but there is a potential trade-off between the required number of observations per day and quality of each observation. Key words: gas exchange, remote sensing, light-use efficiency, sampling properties |