Remote sensing of terrestrial primary production and carbon cycle

18Citations
Citations of this article
97Readers
Mendeley users who have this article in their library.
Get full text

Abstract

The objective of this chapter is to review the historical development of and the recent advances in the application of satellite remote sensing data for estimating terrestrial gross and net primary production (GPP and NPP), while also monitoring carbon cycle related ecosystem dynamics and changes. We achieve this objective by separating the topic into five sections: 1. A review of the history of using satellite data to study the carbon cycle, concentrating on using the Normalized Difference Vegetation Index (NDVI) and its derived Fraction of Photosynthetically Active Radiation (FPAR) and Leaf Area Index (LAI) for biomass and NPP estimations 2. A description of recent advances in the application of Moderate Resolution Imaging Spectroradiometer (MODIS) data to estimates of GPP and NPP, along with related findings using MODIS Land Surface Temperature (LST) and the Enhanced Vegetation Index (EVI) 3. A discussion of the application of long-term satellite data to the study of terrestrial ecosystems, including phenology monitoring, changes in regional carbon storage resulting from land use change, carbon flux changes induced by climate change, disturbance detection, and validation of ecosystem models 4. A proposed general scheme for applying satellite data to terrestrial ecosystem studies, highlighting the role of modeling 5. A summary that emphasizes the continuity of vegetation monitoring with satellites The use of remote sensing information for studying terrestrial primary production and the global carbon cycle is significant both for an increased understanding of the earth system and improved management of land and natural resources. © Springer Science + Business Media B.V., 2008.

Cite

CITATION STYLE

APA

Zhao, M., & Running, S. W. (2008). Remote sensing of terrestrial primary production and carbon cycle. In Advances in Land Remote Sensing: System, Modeling, Inversion and Application (pp. 423–444). Springer Verlag. https://doi.org/10.1007/978-1-4020-6450-0_16

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free