Mapping high-resolution incident Photosynthetically Active Radiation over land from satellite observations
Tao
Zheng, University of Maryland, College Park, tzheng@glue.umd.edu
(Presenting)
Dongdong
Wang, University of Maryland, College Park, ddwang@umd.edu
Shunlin
Liang, University of Maryland, College Park, sliang@geog.umd.edu
Steve
Running, University of Montana, swr@ntsg.umt.edu
John
Townshend, University of Maryland, College Park, jtownshe@geog.umd.edu
Si-Chee
Tsay, NASA/GSFC, tsay@climate.gsfc.nasa.gov
Incident Photosynthetically Active Radiation (PAR) is a key variable required by almost all ecosystem models some of which calculate biomass accumulation linearly proportional to incident PAR. Current PAR products that are generated either from satellite observations or GCM reanalysis have coarse spatial resolutions and inconsistent accuracy. Because the high-resolution incident PAR over land is not a standard EOS product, the MODIS team has to disaggregate the NASA DAO PAR product of 1 by 1.5 spatial resolution to produce 1km net primary productivity and net photosynthesis products. There is a critical need for mapping incident PAR at a high resolution for modeling hydrological and carbon cycles.
We have developed a series of algorithms for mapping incident PAR from MODIS, AVHRR and GOES. The basic procedure is composed of two steps, including 1) determination of the surface reflectance from the “clearest” observations during a temporal window, and 2) calculation of incident radiation from the determined surface reflectance and TOA radiance using the table look-up approach. The outputs include direct and diffuse PAR, insolation and other intermediate variables. The algorithms have been extensively validated using FLUXNET observations.
In support of North American Carbon Program, our efforts have been mainly on mapping PAR over North America. One-year (2003) PAR product (both instantaneous and daily) from MODIS at 1km resolution is ready and being distributed to the user. We are also generating the PAR products from GOES, AVHRR and SeaWiFS data. Intercomparison and integration of these products are under way.