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Open AccessJournal ArticleDOI

The Soil Moisture Active Passive (SMAP) Mission

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TLDR
The Soil Moisture Active Passive mission is one of the first Earth observation satellites being developed by NASA in response to the National Research Council's Decadal Survey to make global measurements of the soil moisture present at the Earth's land surface.
Abstract
The Soil Moisture Active Passive (SMAP) mission is one of the first Earth observation satellites being developed by NASA in response to the National Research Council's Decadal Survey SMAP will make global measurements of the soil moisture present at the Earth's land surface and will distinguish frozen from thawed land surfaces Direct observations of soil moisture and freeze/thaw state from space will allow significantly improved estimates of water, energy, and carbon transfers between the land and the atmosphere The accuracy of numerical models of the atmosphere used in weather prediction and climate projections are critically dependent on the correct characterization of these transfers Soil moisture measurements are also directly applicable to flood assessment and drought monitoring SMAP observations can help monitor these natural hazards, resulting in potentially great economic and social benefits SMAP observations of soil moisture and freeze/thaw timing will also reduce a major uncertainty in quantifying the global carbon balance by helping to resolve an apparent missing carbon sink on land over the boreal latitudes The SMAP mission concept will utilize L-band radar and radiometer instruments sharing a rotating 6-m mesh reflector antenna to provide high-resolution and high-accuracy global maps of soil moisture and freeze/thaw state every two to three days In addition, the SMAP project will use these observations with advanced modeling and data assimilation to provide deeper root-zone soil moisture and net ecosystem exchange of carbon SMAP is scheduled for launch in the 2014-2015 time frame

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Citations
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Journal ArticleDOI

L-Band Relative Permittivity of Organic Soil Surface Layers—A New Dataset of Resonant Cavity Measurements and Model Evaluation

TL;DR: This study improves still insufficient understanding of L-band emission of organic substrates in prospect of enhancing soil moisture estimations in the high latitudes undergoing most rapid climatic changes by fitting a simple empirical model to the data obtained from all collected organic samples.
Journal ArticleDOI

Validation of SMAP soil moisture for the SMAPVEX15 field campaign using a hyper-resolution model

TL;DR: In this article, a hyper-resolution land surface model is used to upscale in situ soil moisture measurements for the SMAPVEX15 (SMAP Validation Experiment 2015) field campaign during 2-18 August 2015.
Journal ArticleDOI

Comparing the Normalized Difference Infrared Index (NDII) with root zone storage in a lumped conceptual model

TL;DR: In this article, the authors used the NDII as a proxy for moisture deficit and hence for the amount of moisture stored in the root zone of vegetation, which is a crucial component of hydrological models.
Journal ArticleDOI

Multivariate data assimilation of GRACE, SMOS, SMAP measurements for improved regional soil moisture and groundwater storage estimates

TL;DR: In this article, the authors evaluated the benefit of assimilating remote sensing data into the Community Atmosphere and Biosphere Land Exchange (CABLE) land surface model and found that the added value of multivariate data assimilation for simultaneously improving different model states, thus leading to a more robust DA system.
References
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Journal ArticleDOI

The SMOS Mission: New Tool for Monitoring Key Elements ofthe Global Water Cycle

TL;DR: The SMOS satellite was launched successfully on November 2, 2009, and will achieve an unprecedented maximum spatial resolution of 50 km at L-band over land (43 km on average over the field of view), providing multiangular dual polarized (or fully polarized) brightness temperatures over the globe.
Reference EntryDOI

Microwave Remote Sensing

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Journal ArticleDOI

Measuring soil moisture with imaging radars

TL;DR: An empirical algorithm for the retrieval of soil moisture content and surface root mean square (RMS) height from remotely sensed radar data was developed using scatterometer data and inversion results indicate that significant amounts of vegetation cause the algorithm to underestimate soil moisture and overestimate RMS height.
Book

Radar remote sensing and surface scattering and emission theory

TL;DR: Monumental as discussed by the authors is a compilation of the present engineering state of the art of microwave remote sensing, presented as a survey of the state-of-the-art in the field.
Journal ArticleDOI

Vegetation effects on the microwave emission of soils

TL;DR: In this article, the authors evaluated published data to determine the functional dependence of a vegetation parameter on vegetation characteristics, and they proposed a model that attempted to meet these requirements by estimating the vegetation parameter b that characterizes the canopy.
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