Estimating Soil Carbon Sequestration Potential: Regional Differences and Remote Sensing

Transcription

1 Estimating Soil Carbon Sequestration Potential: Regional Differences and Remote Sensing Tris West Environmental Sciences Division Technical Working Group on Agricultural Greenhouse Gases (T-AGG): Experts Meeting Chicago, Illinois April 22 & 23, 2010

2 Regional Differences: Causes Climate Annual weather Soils (texture and water holding capacity) Management (crop rotation; tillage & residue management; manure & grazing management) Management can be manipulated, and is currently done through conservation programs and education 2 Managed by UT-Battelle

3 Regional Differences: Some Results 3 Managed by UT-Battelle West and Six Climatic Change 80:

4 Regional Differences: Sequestration Dynamics Soil carbon (a) Soil carbon (b) Saturation level Steady State 2 Sd 3 C 3 R 3 Sd C 1 2 R 2 Steady State 1 Sd 2 C 1 R 1 D2 D 1 Time I 1 I 2 I 3 D 3 Carbon input 4 Managed by UT-Battelle West and Six Climatic Change 80:

5 Sequestration potential can be defined as: (1) Sequestration rate (soil carbon accumulation per unit area and per soil depth) X (2) Potential land area available for carbon sequestration activities = (3) Total carbon sequestration potential 5 Managed by UT-Battelle

6 Use of Remote sensing data and products for modeling agricultural systems and soil carbon sequestration Identify crops and fields [EVI, NDVI] Identify underlying soil attributes Estimate management practices [CAI] Estimate NPP [LAI] All of the above can be developed in conjunction with existing inventory data. 6 Managed by UT-Battelle

7 Integration of field data, inventory data, and remote sensing for soil carbon accounting Land cover County boundaries Carbon dynamics based on analyses of field data Spatial data: Composite data set of land cover classes, county boundaries, and spatial soil units Final product: Spatiallydelineated changes in soil carbon updated annually Soil carbon Data: Harvested crop area per county 7 Data: Tillage intensity per crop per county Managed by UT-Battelle West et al Soil Science Society of America Journal 72:

8 Soil carbon change, Results commensurate with 30m-resolution Landsat-based Land Cover Data 8 Managed by UT-Battelle West et al Soil Science Society of America Journal 72:

9 Geospatial estimates of net carbon flux from croplands Results commensurate with 1km-resolution MODIS-based Land Cover Data On-site net carbon flux from US croplands in 2004 associated with land management = -C uptake +decomposition soil C accumulation +fossil CO2 emissions +CO2 from aglime. Net negative flows FROM the atmosphere, net positive flows TO the atmosphere. 9 Managed by UT-Battelle Method and more recent results in West et al. Ecological Applications (in press)

10 Moving from MODIS to Cropland Data Layer, including use of flux tower measurements (a) (b) Bondville, Illinois flux site as represented by the Cropland Data Layer CDL 2001 CDL 2002 (c) (d) CDL 2003 CDL Managed by UT-Battelle

11 Annually aggregated NEE from Bondville flux tower compared to our C accounting approach, using different land cover data sets 0 Estimated net ec cosystem exchange for corn/soyb bean rotation in Bondville, Il llinois (g C yr -1 ) Flux Tower CDL-based estimate MODIS-based estimate County-based estimate Time (yr) NEE = estimated -NPP + harvested carbon + decomposed biomass + soil carbon change + CO2 from lime application + on-farm fossil fuel emissions 11 Managed by UT-Battelle

12 Shift in crop phenology does not always change annual yield, but does change temporal signature of carbon uptake and release 1 Average MODIS NDV VI for Iowa Crops Corn (2007) Corn (2008) Soybean (2007) Soybean (2008) Day of the Year (DOY) 12 Managed by UT-Battelle NDVI processed by Prasad Bandaru, ORNL

13 Ideal sensor for agricultural monitoring Important bands: 480 nm (blue) 550 nm (green) 670 nm (red) 710 nm (red-edge) 850 nm (NIR) 1650 nm (SWIR) 2030 nm (SWIR) 2100 nm (SWIR) 2210 nm (SWIR) 11 & 12 µm (Thermal IR) aerosols chlorophyll vegetation cover chlorophyll vegetation cover vegetation water content cellulose cellulose cellulose vegetation stress, ET 13 Managed by UT-Battelle Compiled by Guy Serbin (USDA Foreign Agriculture Service)

14 Conclusions Integration of ACTUAL cropland cover, annually, nationally can be done now, further development of standardized approach could be considered Integration of crop phenology (inter-annual carbon uptake and residue contribution) per crop species can be done in near future (1-3 years). Crop residue management needs long-term effort (5+ years). National database on soils and on land management, with focus on soil carbon change, could be better coordinated and possibly revised (i.e., SSURGO, NRI, USDA NASS, USDA ERS) 14 Managed by UT-Battelle

15 15 Managed by UT-Battelle

16 Estimating Future Land Management and Carbon Budgets Predicting land-use change Improved estimates of available land for bioenergy crops 16 Managed by UT-Battelle