NASA Terrestrial Hydrology: Documenting Soil Moisture and Frozen Season Constraints to Vegetation Productivity
This project aims to clarify the role of root zone soil moisture and landscape freeze-thaw status as environmental constraints to terrestrial water mobility and the supply of plant-available moisture underpinning vegetation activity and productivity. Vegetation gross primary production (GPP) is the principal mechanism for terrestrial ecosystem uptake and storage of atmospheric CO2, and is a major factor determining biosphere-atmosphere carbon interactions and climate feedbacks. Our objectives include: 1) improving the representation and understanding of plant available moisture constraints to vegetation activity and GPP using a satellite data driven carbon model framework; 2) applying the enhanced model framework to quantify the integrated effects of earlier and longer non-frozen seasons and associated changes in land surface water mobility and root zone soil moisture on GPP; 3) documenting the regional productivity response and recovery to intensifying drought disturbance. Satellite observations enlisted in our analysis include GOME-2 and OCO-2 derived solar-induced canopy fluorescence (SIF) used as a surrogate for GPP; MODIS vegetation greenness and photochemical reflectance (PRI) indices; model enhanced GPP observations derived from MODIS MOD17 and SMAP L4C operational products; other land parameter retrievals from AMSR and SMAP microwave sensors. Our results are improving understanding of linkages between terrestrial hydrology and carbon cycle processes, including the roles and relationships between summer water supply and frozen season constraints on vegetation growing seasons, and the net effect of recent environmental changes to GPP and potential carbon sink strength over North America. We are also contributing to model sensitivity and performance assessments, and validation activities that inform science research applications and product calibration, validation and refinement for the NASA SMAP mission. This study is also contributing to better understanding of the role of water in land-atmosphere interactions, and root zone soil moisture variability and frozen season constraints on plant-available water supply, vegetation activity and productivity.
A., G., I. Velicogna, J.S. Kimball, J. Du, Y. Kim, A. Colliander, and E. Njoku, 2017. Characterizing drought impact on vegetation-moisture sensitivity using satellite observations of vegetation growth and water storage changes: A case study in Texas and surrounding semiarid areas. Environmental Research Letters, 12, 054006.
Du, J., J.S. Kimball, J. Galantowicz, S.-B. Kim, S.K. Chan, R. Reichle, L.A. Jones, and J.D. Watts. 2018. Assessing global surface water inundation dynamics using combined satellite information from SMAP, AMSR2 and Landsat. Remote Sensing of Environment, 213, doi:10.1016/j.rse.2018.04.054.
Du, J., J.S. Kimball, L.A. Jones, Y. Kim, J. Glassy, and J.D. Watts, 2017. A global satellite environmental data record derived from AMSR-E and AMSR2 microwave earth observations, 2017. Earth System Science Data, 9, 791-808.
He, M., J.S. Kimball, S. Running, A. Ballantyne, K. Guan, and F. Huemmrich, 2016. Satellite detection of soil moisture related water stress impacts on ecosystem productivity using the MODIS-based photochemical reflectance index. Remote Sensing of Environment, 186, 173-183.
Kim, Y., J.S. Kimball, K. Didan, and G.M. Henebry, 2014. Response of vegetation growth and productivity to spring climate indicators in the conterminous United States derived from satellite remote sensing data fusion. Agricultural and Forest Meteorology, 194, 132-143.
Kim, Y., J.S. Kimball, K. Zhang, K. Didan, I. Velicogna, and K.C. McDonald, 2014. Attribution of divergent northern vegetation growth responses to lengthening non-frozen seasons using satellite optical-NIR and microwave remote sensing. International Journal of Remote Sensing 35, 10, 3700-3721.
Madani, N., J.S. Kimball, A.P. Ballantyne, D.L.R. Affleck, P.M. van Bodegom, P.B. Reich, J. Kattge, A. Sala, M. Nazeri, M.O. Jones, M. Zhao, and S.W. Running, 2018. Future ecosystem productivity will be affected by altered plant community distribution. Scientific Reports, 8, 2870, DOI:10.1038/s41598-018-21172-9.
Madani, N., J.S. Kimball, and S.W. Running, 2017. Improving global gross primary productivity estimates by computing optimum light use efficiencies using flux tower data. JGR-Biogeosciences, 122, 11, 2939-2951, DOI:10.1002/2017JG004142.
Madani, N., J.S. Kimball, D.L.R. Affleck, J. Kattge, J. Graham, P.M. van Bodegom, P.B. Reich, and S.W. Running, 2014. Improving ecosystem productivity modeling through spatially explicit estimation of optimal light use efficiency. JGR. Biogeosci., 119, 9, 1755-1769.
John Kimball (PI), Mingzhu He, Nima Madani, Youngwook Kim