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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Hydrology and Remote Sensing Laboratory » Research » Publications at this Location » Publication #351798

Research Project: Integrating Remote Sensing, Measurements and Modeling for Multi-Scale Assessment of Water Availability, Use, and Quality in Agroecosystems

Location: Hydrology and Remote Sensing Laboratory

Title: Spatial and temporal variability of root-zone soil moisture acquired from hydrologic modeling and AirMOSS P-band radar

item Crow, Wade
item MALIK, S. - Science Systems And Applications, Inc
item MOGHADDAM, M. - University Of Southern California
item TABATABAEENEJAD, A. - University Of Southern California
item JARUWATANADILOK, S. - Jet Propulsion Laboratory
item YU, X. - University Of Delaware
item SHI, Y. - University Of Pennsylvania
item RIECHLE, R. - Goddard Space Flight Center
item HAGIMOTO, Y. - Oregon State University
item CUENCA, R. - Oregon State University

Submitted to: IEEE Journal of Selected Topics in Applied Remote Sensing
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 8/15/2018
Publication Date: 12/15/2018
Citation: Crow, W.T., Malik, S., Moghaddam, M., Tabatabaeenejad, A., Jaruwatanadilok, S., Yu, X., Shi, Y., Riechle, R., Hagimoto, Y., Cuenca, R. 2018. Spatial and temporal variability of root-zone soil moisture acquired from hydrologic modeling and AirMOSS P-band radar. IEEE Journal of Selected Topics in Applied Remote Sensing. 11(12):4578-4590.

Interpretive Summary: Accurate maps of fine-scale (~100-m) root-zone soil moisture would be extremely valuable for efforts to optimize irrigation scheduling and fertilizer application within precision agriculture systems. However, developing the means to observe - and/or estimate - root-zone soil moisture at such a fine spatial resolution has proven to be an elusive goal. Currently, the two most credible approaches are based on: 1) high-resolution, three-dimensional hydrologic modelling and 2) airborne remote sensing using low-frequency (250-500 GHz) microwave channels. This paper describes the first comparison of high-resolution, root-zone soil moisture fields generated by these two techniques. While significant differences are seen in the soil moisture fields estimated by both approaches, comparisons between the two approaches yield realistic (upper and lower) bounds of the true amount of fine-scale variability present in a range of agricultural and natural landscapes. Such bounds are useful for preliminary assessment of root-zone soil moisture variability in agricultural fields and will be used by future researchers to further the development of soil moisture modelling and remote sensing tools.

Technical Abstract: The accurate estimation of grid-scale fluxes of water, energy, and carbon requires consideration of sub-grid spatial variability in root-zone soil moisture (RZSM). The NASA Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) mission represents the first systematic attempt to repeatedly map high-resolution RZSM fields using airborne remote sensing across a range of biomes. Here we compare 3-arc-sec (~100-m) spatial resolution AirMOSS RZSM retrievals from P-band radar acquisitions over 9 separate North American study sites with analogous RZSM estimates generated by the Flux-Penn State Hydrology Model (Flux-PIHM). The two products demonstrate comparable levels of accuracy when evaluated against ground-based soil moisture products and a significant level of temporal cross-correlation. However, relative to the AirMOSS RZSM retrievals, Flux-PIHM RZSM estimates generally demonstrate much lower levels of spatial and temporal variability, and the spatial patterns captured by both products are poorly correlated. Nevertheless, based on a discussion of likely error sources affecting both products, it is argued that the spatial analysis of AirMOSS and Flux-PIHM RZSM fields provide meaningful upper and lower bounds on the potential range of RZSM spatial variability encountered across a range of natural biomes.