The May–June 2002 International H₂O Project was held in the U.S. Southern Great Plains to determine ways that moisture data could be collected and utilized in numerical forecast models most effectively. We describe the surface and boundary layer components, and indicate how the data can be acquired. These data document the eddy transport of heat and water vapor from the surface to the atmosphere (in terms of sensible heat flux H and latent heat flux LE), as well as radiative, atmospheric, soil, and vegetative factors that affect it, so that the moisture and heat supply to the atmosphere can be related to surface properties both for observational studies and tests of land surface models. The surface dataset was collected at 10 surface flux towers at locations representing the major types of land cover and extending from southeast Kansas to the Oklahoma Panhandle. At each location, the components of the surface energy budget (H, LE, net radiation, and soil heat flux) are documented each half-hour, along with the weather (wind, temperature, mixing ratio, air pressure, and precipitation), soil temperature, moisture, and matric potential down to 70–90 cm beneath the surface at 9 of the 10 sites. Observations of soil and vegetation properties and their horizontal changes were taken near all 10 towers during periodic visits. Aircraft measurements of H and LE from repeated low-level flight tracks along three tracks collocated with the surface sites extend the flux tower measurements horizontally. We illustrate the effects of vegetation and soil moisture on the H and LE and their horizontal variability.

The Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) P-band synthetic aperture radar (SAR) was flown more than 1200 h from August 2012 to September 2015, covering regions of 2500 km² spread over nine major biomes in North America. The flights, as a part of the NASA AirMOSS Earth Venture Suborbital 1 (EVS-1) mission, collected radar data used to map root-zone soil moisture (RZSM) at 3-arcsec resolution. We previously reported the baseline retrieval algorithm and demonstrated its performance for a semiarid shrubland (Walnut Gulch, AZ, USA); we represented the RZSM profile as a continuous quadratic function and solved a radar scattering nonlinear optimization problem to obtain the unknown polynomial coefficients. In this article, we expand the retrievals to other AirMOSS sites that, in addition to the semiarid shrubland, include grassland and crops (MOISST, OK, USA), woody savanna (Tonzi Ranch, CA, USA), temperate conifer forest (Metolius, OR, USA), and boreal forest (Saskatchewan, Canada). Due to a wide range of land covers, soil types, and soil moisture regimes, we parameterize the forward model and constrain the inverse algorithm for each site separately. We present the full set of retrievals for these sites, validating the results against in situ observations. Error sources and strategies to minimize their effects are discussed. The concept of sensing depth is introduced. We find that the retrieval errors are smallest for the top 25 cm of soil with a root-mean-square error (RMSE) of less than 0.05 m³/m³. The RMSE remains around 0.06 m³/m³ even for depths reaching 45 cm, which is the typical sensing depth for the sites considered. These AirMOSS RZSM products (known as Level-2/3 RZSM, or L2/3-RZSM, products) are the first of their kind in that it is the first time RZSM has been retrieved directly from remote sensing observation.

Thirty-meter resolution Landsat data were used to evaluate the effects of irrigation management in the Wood River Valley, Upper Klamath Basin, Oregon. In an effort to reduce water use and leave more of the water resource in-stream, 4,674 ha of previously flood irrigated pasture was managed as dryland pasture. Ground-based measurements over one irrigated and one unirrigated pasture site were used to monitor the difference in evapotranspiration (ET) using the Bowen ratio-energy balance method. These data sets represent point measurements of the response to irrigation, but do not allow for the spatial integration of effects of irrigated versus unirrigated land treatment. Four Landsat scenes of the Wood River Valley during the 2004 growing season were evaluated using reconstructed METRIC algorithms. Comparisons of ET algorithm output with ground-based data for all components of the energy balance, including net radiation, soil heat flux, sensible heat flux and evapotranspiration, were made for the four scenes. The excellent net radiation estimates, along with reasonable estimates of the other components, are demonstrated along with the capability to integrate results to the basin scale.

The accurate estimation of grid-scale fluxes of water, energy, and carbon requires consideration of subgrid 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 nine separate North American study sites with analogous RZSM estimates generated by the Flux-Penn State Integrated Hydrologic 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 provides meaningful upper and lower bounds on the potential range of RZSM spatial variability encountered across a range of natural biomes.

Current estimates of Net Ecosystem Exchange (NEE) at regional and continental scales contain significant uncertainty. It has been shown that root-zone soil moisture (RZSM) content has a first-order effect on NEE. The objective of the NASA Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) project is to provide measurements to estimate RZSM using a P-band airborne radar. Flight operations are conducted over representative sites of the nine major North American biomes which have been instrumented with soil water content sensors in various locations and over various depth layers which will be correlated to the P-band radar return signal. The hypothesis of the AirMOSS project is that integrating spatially and temporally resolved observations of RZSM into ecosystem dynamics models can significantly reduce the uncertainty of NEE estimates and carbon balance estimates. The calibration of the sensor systems is described and results are demonstrated for multiday up to annual time series at various sites. The success of fitting four models (e.g., power, first-, second-, and third-order polynomials) to the RZSM content with depth is tested by using in situ data collected in Tonzi Ranch, CA, USA, in 2014. Under the testing condition, all models satisfy the mission goal most of the duration. The second- and third-order models perform better during the dry season than the simpler power and first-order models. However, during periods of rapidly changing values of RZSM content, none of the models are able to meet the root-mean-squared error objective.