Soil Carbon Monitoring from Rangelands

At Woodwell Climate Research Center, I worked as a postdoc to develop a rangeland carbon tracking and monitoring (RCTM) system which combines remote sensing imagery with both empirical and process-based modeling for the estimation of rangeland productivity and soil organic carbon (SOC). Several components were involved in developing RCTM: (1) downscaling soil moisture estimates using a data fusion approach; (2) designing a target field sampling plan that is implemented within the high-performance computing (HPC) platform; (3) conducting Bayesian calibration using flux tower datasets and field measurements; (4) building a Google Earth Engine (GEE)-based decision support tool by visualizing modeled results. The decision support tool is currently designed for the ranch level but will be expanded to the regional scale in the future. In addition to building the RCTM system, my research also features developing a grassland C monitoring plan for the Northern Great Plains and the implementation of high-resolution, HPC-enabled mapping of baseline SOC using a digital soil mapping approach.

Related publications:

1. Xia, Y., Watts, J. Machmuller, M., and Sanderman, J. Machine Learning Based Estimation of Field-Scale Daily, High Resolution, Multi-Depth Soil Moisture for the Western and Midwestern United States. PeerJ. 10: e14275. doi: 10.7717/peerj.14275.

2. Test version of the decision support tool in GEE: https://rangelands.users.earthengine.app/view/app2023

3. Preprint of the RCTM system: https://essopenarchive.org/users/762265/articles/739040-coupling-remote-sensing-with-a-process-model-for-the-simulation-of-rangeland-carbon-dynamics

4. Xia, Y., M. Wander, and K. McSweeney. 2022. Digital Mapping of Agricultural Soil Organic Carbon Using Soil Forming Factors: A Review of Current Efforts at the Regional and National Scales. Frontiers in Soil Science. 7: e890437. doi: 10.3389/fsoil.2022.890437.