Rahman, Abdullah (Faiz): UTRGV (Project Lead)
Sims, Daniel: Indiana University (Co-Investigator)
Project Funding:
2010 - 2013
NRA: 2009 NASA: Terrestrial Ecology
Funded by NASA
Abstract:
Our goal in this proposed research is to provide an operational approach to answering the CCSP question on how to quantify magnitude and distributions of carbon fluxes across the Eastern USA. This work will build on results from our previous funding. We were the first to show that a landscape-level PRI can be derived using two Ocean bands of MODIS (namely, mPRI), and that this mPRI is well correlated with LUE of temperate deciduous
forests. In addition, we have developed a semi-empirical remote sensing model (TG model) for ecosystem gross primary production (GPP) that does not depend on the LUE concept. Although both of these models work well within certain constraints, they do have limitations. The mPRI/LUE relationship is further complicated by the effects of sun-sensor geometry on sun/shadow fraction at the MODIS sub-pixel level.
Here we propose both a new measurement scheme to quantify these confounding effects at sub-pixel spatial scales and a change in our modeling approach to avoid some of the difficulty with region-wide variations in the mPRI/LUE relationship. Our new modeling approach aims to combine the strengths, and minimize the weaknesses, of the mPRI and TG models. mPRI is based on the physiology of photosynthesis over short time periods, whereas the TG model is based on longer term acclimation responses that result in changes in leaf area and/or chlorophyll contents. Thus our proposal is to use the TG model to estimate the longer term (weeks to months) ecosystem LUE values and then modify these LUE values with mPRI in response to short term stresses. Using this combined approach, which is analogous to MOD-17 approach but totally remote-sensing based and independent of coarse scale weather data, we will produce daily maps of GPP at MODIS 1-km pixel scale, for the forested areas of the Eastern USA.
Publications:
Brzostek, E. R., Dragoni, D., Schmid, H. P., Rahman, A. F., Sims, D., Wayson, C. A., Johnson, D. J., Phillips, R. P. 2014. Chronic water stress reduces tree growth and the carbon sink of deciduous hardwood forests. Global Change Biology. 20(8), 2531-2539. DOI: 10.1111/gcb.12528
Sims, D. A., Brzostek, E. R., Rahman, A. F., Dragoni, D., Phillips, R. P. 2014. An improved approach for remotely sensing water stress impacts on forest C uptake. Global Change Biology. 20(9), 2856-2866. DOI: 10.1111/gcb.12537
Sims, D. A., Rahman, A. F., Vermote, E. F., Jiang, Z. 2011. Seasonal and inter-annual variation in view angle effects on MODIS vegetation indices at three forest sites. Remote Sensing of Environment. 115(12), 3112-3120. DOI: 10.1016/j.rse.2011.06.018
2011 NASA Carbon Cycle & Ecosystems Joint Science Workshop Poster(s)
- What Does a MODIS Pixel Actually “See” in a Mixed Deciduous Forest?
-- (Abdullah Faiz Rahman, Daniel A Sims)
[abstract]
More details may be found in the following project profile(s):
