The Soil Moisture Active Passive (SMAP) Mission
Massachusetts Institute of Technology1, California Institute of Technology2, Goddard Space Flight Center3, Agricultural Research Service4, Ohio State University5, University of Montana6, University of Michigan7, United States Department of Agriculture8, University of California, Santa Barbara9, University of Washington10
06 May 2010-Vol. 98, Iss: 5, pp 704-716
TL;DR: 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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California Institute of Technology1, Agricultural Research Service2, University of Tsukuba3, University of Guelph4, University of Texas at Austin5, Princeton University6, Kuwait University7, University of Valencia8, University of Salamanca9, Agriculture and Agri-Food Canada10, University of Southern California11, European Academy of Bozen12, University of Grenoble13, Finnish Meteorological Institute14, National Autonomous University of Mexico15, University of Twente16, Comisión Nacional de Actividades Espaciales17, Vienna University of Technology18, Monash University19, Goddard Space Flight Center20, Massachusetts Institute of Technology21
TL;DR: The NASA Soil Moisture Active Passive (SMAP) mission has utilized a set of core validation sites as the primary methodology in assessing the soil moisture retrieval algorithm performance as mentioned in this paper.
487 citations
TL;DR: The Advanced Scatterometer (ASCAT) is a C-band active microwave remote sensing instrument flown on board of the Meteorological Operational (METOP) satellite series as discussed by the authors.
Abstract: Many physical, chemical and biological processes taking place at the land surface are strongly influenced by the amount of water stored within the upper soil layers. Therefore, many scientific disciplines require soil moisture observations for developing, evaluating and improving their models. One of these disciplines is meteorology where soil moisture is important due to its control on the exchange of heat and water between the soil and the lower atmosphere. Soil moisture observations may thus help to improve the forecasts of air temperature, air humidity and precipitation. However, until recently, soil moisture observations had only been available over a limited number of regional soil moisture networks. This has hampered scientific progress as regards the characterisation of land surface processes not just in meteorology but many other scientific disciplines as well. Fortunately, in recent years, satellite soil moisture data have increasingly become available. One of the freely available global soil moisture data sets is derived from the backscatter measurements acquired by the Advanced Scatterometer (ASCAT) that is a C-band active microwave remote sensing instrument flown on board of the Meteorological Operational (METOP) satellite series. ASCAT was designed to observe wind speed and direction over the oceans and was initially not foreseen for monitoring soil moisture over land. Yet, as argued in this review paper, the characteristics of the ASCAT instrument, most importantly its wavelength (5.7 cm), its high radiometric accuracy, and its multiple-viewing capabilities make it an attractive sensor for measuring soil moisture. Moreover, given the operational status of ASCAT, and its promising long-term prospects, many geoscientific applications might benefit from using ASCAT soil moisture data. Nonetheless, the ASCAT soil moisture product is relatively complex, requiring a good understanding of its properties before it can be successfully used in applications. To provide a comprehensive overview of themajor characteristics and caveats of the ASCATsoil moisture product, this paper describes the ASCAT instrument and the soil moisture processor and near-real-time distribution service implemented by the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT).A review of themost recent validation studies shows that the quality of ASCAT soil moisture product is – with the exception of arid environments –comparable to, and over some regions (e.g. Europe) even better than currently available soil moisture data derived from passive microwave sensors. Further, a review of applications studies shows that the use of the ASCAT soil moisture product is particularly advanced in the fields of numerical weather prediction and hydrologic modelling. But also in other application areas such as yield monitoring, epidemiologic modelling, or societal risks assessment some first progress can be noted. Considering the generally positive evaluation results, it is expected that the ASCAT soil moisture product will increasingly be used by a growing number of rather diverse land applications.
484 citations
TL;DR: In this paper, the authors evaluated the skill of a new, merged soil moisture product (ECV_SM) that has been developed in the framework of the European Space Agency's Water Cycle Multi-mission Observation Strategy and Climate Change Initiative projects.
463 citations
Cites background from "The Soil Moisture Active Passive (S..."
...…dedicated soil moisture mission SMOS (Mecklenburg et al., 2012) while within the next few years the soil moisture data record is expected to be complemented with novel dedicated soil moisture missions like SMAP (Entekhabi et al., 2010) and SAOCOM (http://www.conae.gov.ar/eng/satelites/saocom.html)....
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...In 2009, the European Space Agency (ESA) launched the first dedicated soil moisture mission SMOS (Mecklenburg et al., 2012) while within the next few years the soil moisture data record is expected to be complemented with novel dedicated soil moisture missions like SMAP (Entekhabi et al., 2010) and SAOCOM (http://www.conae.gov.ar/eng/satelites/saocom.html)....
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..., 2012) while within the next few years the soil moisture data record is expected to be complemented with novel dedicated soil moisture missions like SMAP (Entekhabi et al., 2010) and SAOCOM (http://www....
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TL;DR: In this article, a review of existing methods for downscaling satellite remotely sensed soil moisture is presented and compared in terms of their advantages and limitations, and the accuracy level of these methods based on published validation studies.
Abstract: Satellite remote sensing technology has been widely used to estimate surface soil moisture. Numerous efforts have been devoted to develop global soil moisture products. However, these global soil moisture products, normally retrieved from microwave remote sensing data, are typically not suitable for regional hydrological and agricultural applications such as irrigation management and flood predictions, due to their coarse spatial resolution. Therefore, various downscaling methods have been proposed to improve the coarse resolution soil moisture products. The purpose of this paper is to review existing methods for downscaling satellite remotely sensed soil moisture. These methods are assessed and compared in terms of their advantages and limitations. This review also provides the accuracy level of these methods based on published validation studies. In the final part, problems and future trends associated with these methods are analyzed.
409 citations
Goddard Space Flight Center1, University of Maryland, College Park2, National Center for Atmospheric Research3, Forschungszentrum Jülich4, Science Applications International Corporation5, Portland State University6, University of Texas at Arlington7, University of Duisburg-Essen8, Lancaster University9, Australian National University10, Delft University of Technology11, Silver Spring Networks12, University Corporation for Atmospheric Research13, Kyoto University14, Wageningen University and Research Centre15, North Central College16
TL;DR: It is recommended that cost-effective transition of hydrologic DA from research to operations should be helped by developing community-based, generic modeling and DA tools or frameworks, and through fostering collaborative efforts among hydrologics modellers, DA developers, and operational forecasters.
Abstract: Data assimilation (DA) holds considerable potential for improving hydrologic predictions as demonstrated in numerous research studies. However, advances in hydrologic DA research have not been adequately or timely implemented in operational forecast systems to improve the skill of forecasts for better informed real-world decision making. This is due in part to a lack of mechanisms to properly quantify the uncertainty in observations and forecast models in real-time forecasting situations and to conduct the merging of data and models in a way that is adequately efficient and transparent to operational forecasters. The need for effective DA of useful hydrologic data into the forecast process has become increasingly recognized in recent years. This motivated a hydrologic DA workshop in Delft, the Netherlands in November 2010, which focused on advancing DA in operational hydrologic forecasting and water resources management. As an outcome of the workshop, this paper reviews, in relevant detail, the current status of DA applications in both hydrologic research and operational practices, and discusses the existing or potential hurdles and challenges in transitioning hydrologic DA research into cost-effective operational forecasting tools, as well as the potential pathways and newly emerging opportunities for overcoming these challenges. Several related aspects are discussed, including (1) theoretical or mathematical aspects in DA algorithms, (2) the estimation of different types of uncertainty, (3) new observations and their objective use in hydrologic DA, (4) the use of DA for real-time control of water resources systems, and (5) the development of community-based, generic DA tools for hydrologic applications. It is recommended that cost-effective transition of hydrologic DA from research to operations should be helped by developing community-based, generic modeling and DA tools or frameworks, and through fostering collaborative efforts among hydrologic modellers, DA developers, and operational forecasters.
392 citations
Cites methods from "The Soil Moisture Active Passive (S..."
...New sensors, such as the ESA Sentinel-1 mission and the NASA Soil Moisture Active Passive mission (SMAP, Entekhabi et al., 2010), will be launched in the next couple of years....
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References
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12 Apr 2010
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.
Abstract: It is now well understood that data on soil moisture and sea surface salinity (SSS) are required to improve meteorological and climate predictions. These two quantities are not yet available globally or with adequate temporal or spatial sampling. It is recognized that a spaceborne L-band radiometer with a suitable antenna is the most promising way of fulfilling this gap. With these scientific objectives and technical solution at the heart of a proposed mission concept the European Space Agency (ESA) selected the Soil Moisture and Ocean Salinity (SMOS) mission as its second Earth Explorer Opportunity Mission. The development of the SMOS mission was led by ESA in collaboration with the Centre National d'Etudes Spatiales (CNES) in France and the Centro para el Desarrollo Tecnologico Industrial (CDTI) in Spain. SMOS carries a single payload, an L-Band 2-D interferometric radiometer operating in the 1400-1427-MHz protected band . The instrument receives the radiation emitted from Earth's surface, which can then be related to the moisture content in the first few centimeters of soil over land, and to salinity in the surface waters of the oceans. SMOS 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. SMOS has a revisit time of less than 3 days so as to retrieve soil moisture and ocean salinity data, meeting the mission's science objectives. The caveat in relation to its sampling requirements is that SMOS will have a somewhat reduced sensitivity when compared to conventional radiometers. The SMOS satellite was launched successfully on November 2, 2009.
1,553 citations
27 Dec 1999
TL;DR: The sections in this article are==================�€�€€ Æ£££€££ ££€ ££ £€ £ £ £€£ £ ££ €££ € £££ $££
Abstract: The sections in this article are
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Radiometers
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Radar Scattering
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Radar Scatterometers
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Radar Altimeters
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Ground-Penetrating Radars
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Imaging Radars
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Real-Aperture Radars
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Synthetic-Aperture Radars
1,093 citations
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.
Abstract: 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. The algorithm is optimized for bare surfaces and requires two copolarized channels at a frequency between 1.5 and 11 GHz. It gives best results for kh/spl les/2.5, /spl mu//sub /spl upsi///spl les/35%, and /spl theta//spl ges/30/spl deg/. Omitting the usually weaker hv-polarized returns makes the algorithm less sensitive to system cross-talk and system noise, simplifies the calibration process and adds robustness to the algorithm in the presence of vegetation. However, inversion results indicate that significant amounts of vegetation (NDVI>0.4) cause the algorithm to underestimate soil moisture and overestimate RMS height. A simple criteria based on the /spl sigma//sub hv//sup 0///spl sigma//sub vv//sup 0/ ratio is developed to select the areas where the inversion is not impaired by the vegetation. The inversion accuracy is assessed on the original scatterometer data sets but also on several SAR data sets by comparing the derived soil moisture values with in-situ measurements collected over a variety of scenes between 1991 and 1994. Both spaceborne (SIR-C) and airborne (AIRSAR) data are used in the test. Over this large sample of conditions, the RMS error in the soil moisture estimate is found to be less than 4.2% soil moisture. >
1,054 citations
"The Soil Moisture Active Passive (S..." refers background in this paper
...Various algorithms for retrieval of soil moisture from radar backscatter have been developed, but they perform adequately only in low-vegetation water content conditions [27]....
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Book•
01 Jan 1986
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.
Abstract: Monumental as a compilation of the present engineering state of the art of microwave remote sensing. -- International Journal of Remote Sensing
959 citations
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.
902 citations
Additional excerpts
...The studies summarized in [17] established a...
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