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United States Department of Agriculture

Agricultural Research Service

Title: Assessing Carbon Dynamics in Agriculture Using Remote Sensing

Authors
item Daughtry, Craig
item Hunt, Earle
item Doraiswamy, Paul

Submitted to: Evaluation of Terrestrial Carbon Storage and Dynamics by In-Situ and Remote Sensing Measurements
Publication Type: Proceedings
Publication Acceptance Date: December 1, 2002
Publication Date: December 23, 2002
Citation: Daughtry, C.S., Hunt, E.R., Doraiswamy, P.C. 2002. Assessing carbon dynamics in agriculture using remote sensing. In: Proceedings of International Symposium on Evaluation of Terrestrial Carbon Storage and Dynamics by In-Situ and Remote Sensing Measurements [CD-ROM].

Interpretive Summary: Increasing atmospheric concentrations of CO2 and other greenhouse gases is a global concern. Depending on land use and management, soil can function as either a source or sink for atmospheric CO2. Computer models can predict net carbon sequestration for different soil types and land management. However, there is a lack of data to support these models across a wide range of soil management scenarios and a lack of robust approaches for extending these models from local to global scales. Recent advances in remote sensing of vegetation and soils can potentially provide some of the biophysical parameters needed by various models to predict carbon dynamics across landscapes. Remote sensing inputs to soil carbon models include land use, crop type, crop phenology, leaf area index, and yields. Tillage practices and crop residue cover also may be determined by advanced remote sensing techniques. These inputs for soil carbon models, when implemented within a geographic information system, will provide important information on the amount and dynamics of soil organic matter across landscapes.

Technical Abstract: Soil can function as either a source or sink for atmospheric CO2 depending on land use and management. Soil carbon models can predict carbon dynamics and net carbon sequestration for different soil types and land management. However, there is a lack of data to support these models across a wide range of soil management scenarios and a lack of robust approaches for extending these models from local to global scales. Recent advances in remote sensing of vegetation and soils can potentially provide some of the biophysical parameters needed by various models to predict dynamics across landscapes. Remote sensing techniques can not directly monitor soil carbon dynamics; however, remote sensing can provide a number of crucial inputs to carbon models. Remote sensing inputs to soil carbon models include land use, crop type, crop phenology, leaf area index, and yields. Tillage practices and crop residue cover also may be determined by advanced remote sensing techniques. With these remotely sensed inputs in a geographic information system, process models can provide important information on the amount and dynamics of soil organic carbon at local, regional, and global scales.

Last Modified: 9/22/2014
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