Producing High-Resolution Land Surface Incident PAR Product from Multiple Satellite Data
Funding Agency: NASA

Principal Investigator

Co-Investigator

Incident photosynthetically active radiation (PAR) is a key variable required by almost all terrestrial ecosystem models. Unfortunately, the current incident PAR products estimated from remotely sensed data at spatial and temporal resolutions are not sufficient for carbon cycle modeling and other applications. For example, the MODIS science team has to use the NASA DAO (data assimilation office) PAR product of 2° by 2.5°spatial resolution to produce 1km NPP (net primary productivity) and PSN (net photosynthesis) products using the BIOME-BGC model. The GLO-PEM (GLObal Production Efficiency Model) has to use the TOMS (Total Ozone Mapping Spectrometer) PAR product, a monthly average at a spatial resolution of 1° by 1° from 66°N to 66°S latitude, and ISCCP-PL PAR of 2.5° resolution to produce 8km global NPP product. The MODIS and SeaWiFS teams are producing the incident PAR over the ocean operationally, but no incident PAR over land is produced. Our overall objective of this project is to generate instantaneous incident PAR products over land from MODIS/SeaWiFS/AVHRR/GOES and the daily PAR products by combining MODIS with SeaWiFS/AVHRR/GOES instantaneous incident PAR products.

The estimation of MODIS instantaneous PAR is mainly based on the MODIS atmospheric and surface products. A hybrid algorithm that combines extensive radiative transfer simulations (physical) and the neural network method (statistical) is being explored. In the first phase, we will generate these PAR products over the LBA-ECO study region and the North America in support of both the NASA LBA-ECO program and the proposed North America Carbon program. In the second phase, a global product will be generated. These incident PAR products will be validated using several global ground observation networks, and compared with several related PAR products. PAR products generated from this research will be distributed to the scientific community through the University of Maryland Global Land Cover Facility (GLCF).