Paper
10 September 2008 Soil Moisture Active/Passive (SMAP) Mission concept
D. Entekhabi, T. J. Jackson, E. Njoku, P. O'Neill, J. Entin
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Abstract
Soil Moisture Active/Passive (SMAP) Mission is one of the first satellites being developed by NASA in response to the National Research Council's Decadal Survey. SMAP will make global measurements of the moisture present at 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 better estimates of water and energy transfers between Earth's surface and atmosphere, which are primary driving factors for weather and climate. Soil moisture measurements are also of great importance in assessing flooding potential and as input to flood prediction models. Conversely, observations of widespread low soil moisture levels can provide early warning of drought conditions, reduced water supply and crop loss. SMAP observations can help mitigate these natural hazards, resulting in potentially great economic and social benefits. SMAP freeze/thaw timing observations will also reduce a major uncertainty in quantifying the global carbon balance and will help resolve the problem of the missing carbon sink. The SMAP mission concept would utilize L-band radar and radiometry. These instruments will share a rotating 6-meter mesh antenna to provide high-resolution and high-accuracy global maps of soil moisture and freeze/thaw state every two to three days.
© (2008) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
D. Entekhabi, T. J. Jackson, E. Njoku, P. O'Neill, and J. Entin "Soil Moisture Active/Passive (SMAP) Mission concept", Proc. SPIE 7085, Atmospheric and Environmental Remote Sensing Data Processing and Utilization IV: Readiness for GEOSS II, 70850H (10 September 2008); https://doi.org/10.1117/12.795910
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Cited by 27 scholarly publications.
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KEYWORDS
Soil science

Algorithm development

Radar

Spatial resolution

Carbon

Climatology

Data modeling

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