The Soil Moisture Active Passive (SMAP) Mission

Entekhabi, Dara; Njoku, E. G.; O'Neill, Peggy E.; Kellogg, K.; Crow, Wade T.; Edelstein, W.; Entin, Jared; Goodman, Seymour E.; Jackson, Thomas J.; Johnson, Joel T.; Kimball, J. S.; Piepmeier, Jeffrey R.; Koster, Randal D.; Martin, Michael A.; McDonald, K. C.; Moghaddam, Mahta; Moran, Susan; Reichle, Rolf H.; Shi, Jiancheng; Spencer, M.; Thurman, Samuel W; Tsang, Leung; Zyl, Jakob van

doi:10.1109/jproc.2010.2043918

Cited by 2,808 publications

(1,268 citation statements)

References 34 publications

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“…Only recently has satellite remote sensing of soil moisture advanced sufficiently to make it possible to monitor drying rates at large scales (Entekhabi et al, 2010;Kerr, 2006), thus allowing scientists to evaluate the physical controls on soil drying across a wider range of 15 conditions. McColl et al (2017a) studied global soil drying dynamics by fitting SMAP surface soil moisture observations to an exponential decay function.…”

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confidence: 99%

Controls on surface soil drying rates observed by SMAP and simulated by the Noah land surface model

Shellito¹,

Small²

2017

Preprint

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Abstract. Drydown periods that follow precipitation events provide an opportunity to assess the mechanisms by which soil moisture dissipates from the land surface. We use SMAP (Soil Moisture Active Passive) observations and Noah simulations from drydown periods to quantify the role of soil moisture, potential evaporation, vegetation cover, and soil texture on soil drying rates. Rates are determined using finite differences over intervals of 1 to 3 days. In the Noah model, the drying rates are a good approximation of direct soil evaporation rates. Data cover the domain of the North American Land Data 10 Assimilation System phase 2 and span the first 1.8 years of SMAP's operation.Drying of surface soil moisture observed by SMAP is faster than that simulated by Noah. SMAP drying is fastest when surface soil moisture levels are high, potential evaporation is high, and when vegetation cover is low. Soil texture plays a minor role in SMAP drying rates. Noah simulations show similar responses to soil moisture and potential evaporation, but vegetation has a minimal effect and soil texture has a much larger effect compared to SMAP. When drying rates are 15 normalized by potential evaporation, SMAP observations and Noah simulations both show that increases in vegetation cover lead to decreases in evaporative efficiency from the surface soil. However, the magnitude of this effect simulated by Noah is much weaker than that determined from SMAP observations.

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confidence: 99%

Controls on surface soil drying rates observed by SMAP and simulated by the Noah land surface model

Shellito¹,

Small²

2017

Preprint

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“…Thus far, three innovative space missions with passive L-band (1-2 GHz corresponding to vacuum wavelengths of 30-15 cm) microwave instruments aboard have been launched, with the objective to provide frequent-revisit global mapping of surface soil moisture. The European Space Agency (ESA) Soil Moisture and Ocean Salinity (SMOS) satellite launched in 2009 was the first L-band radiometer mission [6], followed by the U.S. National Aeronautics and Space Administration (NASA) and Argentinean Space Agency (CONAE) Aquarius/Satélite de Aplicaciones Científicas (SAC)-D mission in 2011 (and stopped in 2015) [7], and the NASA Soil Moisture Active Passive (SMAP) satellite launched in 2015 [8].…”

Section: Introductionmentioning

confidence: 99%

Passive L-Band Microwave Remote Sensing of Organic Soil Surface Layers: A Tower-Based Experiment

et al. 2018

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Organic soils play a key role in global warming because they store large amount of soil carbon which might be degraded with changing soil temperatures or soil water contents. There is thus a strong need to monitor these soils and, in particular, their hydrological characteristics using, for instance, space-borne L-band brightness temperature observations. However, there are still open issues with respect to soil moisture retrieval techniques over organic soils. In view of this, organic soil blocks with their vegetation cover were collected from a heathland in the Skjern River catchment in western Denmark and then transported to a remote sensing field laboratory in Germany where their structure was reconstituted. The controlled conditions at this field laboratory made it possible to perform tower-based L-band radiometer measurements of the soils over a period of two months. Brightness temperature data were inverted using a radiative transfer (RT) model for estimating the time variations in the soil dielectric permittivity and the vegetation optical depth. In addition, the effective vegetation scattering albedo parameter of the RT model was retrieved based on a two-step inversion approach. The remote estimations of the dielectric permittivity were compared to in situ measurements. The results indicated that the radiometer-derived dielectric permittivities were significantly correlated with the in situ measurements, but their values were systematically lower compared to the in situ ones. This could be explained by the difference between the operating frequency of the L-band radiometer (1.4 GHz) and that of the in situ sensors (70 MHz). The effective vegetation scattering albedo parameter was found to be polarization dependent. While the scattering effect within the vegetation could be neglected at horizontal polarization, it was found to be important at vertical polarization. The vegetation optical depth estimated values over time oscillated between 0.10 and 0.19 with a mean value of 0.13. This study provides further insights into the characterization of the L-band brightness temperature signatures of organic soil surface layers and, in particular, into the parametrization of the RT model for these specific soils. Therefore, the results of this study are expected to improve the performance of space-borne remote sensing soil moisture products over areas dominated by organic soils.

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“…In mountainous terrains remote sensing approaches (e.g., Kerr et al, 2001Kerr et al, , 2010Entekhabi et al, 2010) are most often not applicable. Due to their coarse resolution and the complex retrieval algorithms for the signals in steep topographic terrains, they are unable to capture the true soil moisture in regions with pronounced topography that changes over small spatial scale.…”

Section: Introductionmentioning

confidence: 99%

Soil Moisture Data for the Validation of Permafrost Models Using Direct and Indirect Measurement Approaches at Three Alpine Sites

et al. 2016

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In regions affected by seasonal and permanently frozen conditions soil moisture influences the thermal regime of the ground as well as its ice content, which is one of the main factors controlling the sensitivity of mountain permafrost to climate changes. In this study, several well established soil moisture monitoring techniques were combined with data from geophysical measurements to assess the spatial distribution and temporal evolution of soil moisture at three high elevation sites with different ground properties and thermal regimes. The observed temporal evolution of measured soil moisture is characteristic for sites with seasonal freeze/thaw cycles and consistent with the respective site-specific properties, demonstrating the general applicability of continuous monitoring of soil moisture at high elevation areas. The obtained soil moisture data were then used for the calibration and validation of two different model approaches used in permafrost research in order to characterize the lateral and vertical distribution of ice content in the ground. Calibration of the geophysically based four-phase model (4PM) with spatially distributed soil moisture data yielded satisfactory two dimensional distributions of water-, ice-, and air content. Similarly, soil moisture time series significantly improved the calibration of the one-dimensional heat and mass transfer model COUP, yielding physically consistent soil moisture and temperature data matching observations at different depths.

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The Soil Moisture Active Passive (SMAP) Mission

Abstract: This paper describes an instrument designed to distinguish frozen from thawed land surfaces from an Earth satellite by bouncing signals back to Earth from deployable mesh antennas.

Cited by 2,808 publications

References 34 publications

Controls on surface soil drying rates observed by SMAP and simulated by the Noah land surface model

Controls on surface soil drying rates observed by SMAP and simulated by the Noah land surface model

Passive L-Band Microwave Remote Sensing of Organic Soil Surface Layers: A Tower-Based Experiment

Soil Moisture Data for the Validation of Permafrost Models Using Direct and Indirect Measurement Approaches at Three Alpine Sites

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