https://www.fs.fed.us/nrs/pubs/jrnl/2010/nrs_2010_chen-j_001.pdf

Carbon budget of Ontario's managed forests and harvested wood products, 2001–2100

The availability of global products of leaf area index (LAI) using remote sensing (RS) data makes it possible to model land surface processes at regional and global scales. Due to the lack of continuous observations of in-situ LAI, studies on the seasonal variability of RS LAI products are rare in the literature. Moreover, the application of existing global LAI products is largely restricted by the underestimation of winter LAI over high-latitude evergreen coniferous forests. Low winter LAI values, due to the influence of background, the presence of ice and snow on leaves and the seasonal variation of leaf pigments, exaggerate the LAI seasonality, possibly causing errors in land surface modeling. The aim of this thesis is to gain a better understanding of the quality of existing RS LAI products, to improve these products, and to further investigate the influence of the improvements on modeling carbon, water and energy fluxes. Frequent measurements of LAI in all seasons were conducted at two white pine sites near Turkey Point, Ontario, Canada, using direct and indirect methods. The in-situ LAI measurements were then used to validate the University of Toronto RS LAI product Version 2. Results show that RS LAI captures mostly canopy structural information from the onset of needleleaf growth to the seasonal peak, but captures both leaf chlorophyll and structural information from the iii seasonal peak to the completion of needleleaf fall. This finding has significant implications for the application of current RS LAI products to terrestrial productivity modeling and prompts the need for separating leaf structure and chlorophyll information. A simple scheme for correcting the distorted LAI seasonality of evergreen conifers is proposed in my research, through which an improved LAI product over conifers in Canada has been developed. It is demonstrated that the improved product greatly increases the accuracy of the simulation of land surface carbon, water and energy fluxes in coniferous forests in Canada.

/pdf/improving-the-estimation-of-seasonal-leaf-area-index-of-4mbu8knjtx.pdf

Improving the Estimation of Seasonal Leaf Area Index of Coniferous Forests for Better Carbon and Water Flux Estimation

This paper aims to simulate China's terrestrial NPP using a carbon-water coupled process model based on remote sensing, and explore tempo-spatial patterns of China's terrestrial NPP and the mechanisms of its responses to various environmental factors. For these purposes, a national wide input database and a validation database have been set up on a 1 day-1 km tempo-spatial resolution, and the BEPS model has been improved and applied. Using these databases and BEPS model Maps of NPP for the entire China's landmass in 2001 have been produced: China's terrestrial total NPP and mean NPP were 2.235GtC and 235.2C.m(2).yr; and gross primary productivity (GPP), total GPP and mean GPP were 4.418GtC and 465gC.m(2).yr; autotrophic respiration (RA) have been estimated; total RA and mean RA were 2.227GtC and 234gC.m(2).yr; On average, NPP was 50.6% of GPP. In addition, the responses of NPP to changes in some key factors in 2001 have been analyzed.

Net primary productivity distribution in China from a process model driven by remote sensing

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Net primary productivity distribution in China from a process model driven by remote sensing