The SMOS Soil Moisture Retrieval Algorithm
TL;DR: A retrieval algorithm to deliver global soil moisture (SM) maps with a desired accuracy of 0.04 m3/m3 is given, discusses the caveats, and provides a glimpse of the Cal Val exercises.
Abstract: The Soil Moisture and Ocean Salinity (SMOS) mission is European Space Agency (ESA's) second Earth Explorer Opportunity mission, launched in November 2009. It is a joint program between ESA Centre National d'Etudes Spatiales (CNES) and Centro para el Desarrollo Tecnologico Industrial. SMOS carries a single payload, an L-Band 2-D interferometric radiometer in the 1400-1427 MHz protected band. This wavelength penetrates well through the atmosphere, and hence the instrument probes the earth surface emissivity. Surface emissivity can then be related to the moisture content in the first few centimeters of soil, and, after some surface roughness and temperature corrections, to the sea surface salinity over ocean. The goal of the level 2 algorithm is thus to deliver global soil moisture (SM) maps with a desired accuracy of 0.04 m3/m3. To reach this goal, a retrieval algorithm was developed and implemented in the ground segment which processes level 1 to level 2 data. Level 1 consists mainly of angular brightness temperatures (TB), while level 2 consists of geophysical products in swath mode, i.e., as acquired by the sensor during a half orbit from pole to pole. In this context, a group of institutes prepared the SMOS algorithm theoretical basis documents to be used to produce the operational algorithm. The principle of the SM retrieval algorithm is based on an iterative approach which aims at minimizing a cost function. The main component of the cost function is given by the sum of the squared weighted differences between measured and modeled TB data, for a variety of incidence angles. The algorithm finds the best set of the parameters, e.g., SM and vegetation characteristics, which drive the direct TB model and minimizes the cost function. The end user Level 2 SM product contains SM, vegetation opacity, and estimated dielectric constant of any surface, TB computed at 42.5°, flags and quality indices, and other parameters of interest. This paper gives an overview of the algorithm, discusses the caveats, and provides a glimpse of the Cal Val exercises.
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Delft University of Technology1, UNESCO-IHE Institute for Water Education2, Vienna University of Technology3, University of Saskatchewan4, University of Illinois at Urbana–Champaign5, Swedish Meteorological and Hydrological Institute6, National Center for Atmospheric Research7, Karlsruhe Institute of Technology8, University of Bristol9, Russian Academy of Sciences10, University of Arizona11, Rhodes University12, University of Bologna13, University of Aberdeen14, Agrocampus Ouest15
TL;DR: The Prediction in Ungauged Basins (PUB) initiative of the International Association of Hydrological Sciences (IAHS) launched in 2003 and concluded by the PUB Symposium 2012 held in Delft (23-25 October 2012), set out to shift the scientific culture of hydrology towards improved scientific understanding of hydrological processes, as well as associated uncertainties and the development of models with increasing realism and predictive power as discussed by the authors.
Abstract: The Prediction in Ungauged Basins (PUB) initiative of the International Association of Hydrological Sciences (IAHS), launched in 2003 and concluded by the PUB Symposium 2012 held in Delft (23–25 October 2012), set out to shift the scientific culture of hydrology towards improved scientific understanding of hydrological processes, as well as associated uncertainties and the development of models with increasing realism and predictive power. This paper reviews the work that has been done under the six science themes of the PUB Decade and outlines the challenges ahead for the hydrological sciences community.Editor D. KoutsoyiannisCitation Hrachowitz, M., Savenije, H.H.G., Bloschl, G., McDonnell, J.J., Sivapalan, M., Pomeroy, J.W., Arheimer, B., Blume, T., Clark, M.P., Ehret, U., Fenicia, F., Freer, J.E., Gelfan, A., Gupta, H.V., Hughes, D.A., Hut, R.W., Montanari, A., Pande, S., Tetzlaff, D., Troch, P.A., Uhlenbrook, S., Wagener, T., Winsemius, H.C., Woods, R.A., Zehe, E., and Cudennec, C., 2013. A d...
848 citations
Vienna University of Technology1, Centre national de la recherche scientifique2, European Centre for Medium-Range Weather Forecasts3, National Research Council4, ETH Zurich5, Finnish Meteorological Institute6, Nanjing University of Information Science and Technology7, VU University Amsterdam8, Ghent University9, University College Cork10, Starlab11
TL;DR: The European Space Agency (ESA) released the first multi-decadal, global satellite-observed soil moisture (SM) dataset as part of its Climate Change Initiative (CCI) program.
756 citations
California Institute of Technology1, United States Department of Agriculture2, Goddard Space Flight Center3, Massachusetts Institute of Technology4, University of Texas at Austin5, Monash University, Clayton campus6, University of Guelph7, Agriculture and Agri-Food Canada8, Comisión Nacional de Actividades Espaciales9, University of Salamanca10, National Oceanic and Atmospheric Administration11, University of Colorado Boulder12, University of Arizona13, Centre national de la recherche scientifique14, Centre National D'Etudes Spatiales15
TL;DR: The Level 2 Passive Soil Moisture Product (L2_SM_P) as discussed by the authors was developed by the National Aeronautics and Space Administration (NASA) soil moisture active passive (SMAP) satellite mission and is available from the Distributed Active Archive Center at the National Snow and Ice Data Center.
Abstract: The National Aeronautics and Space Administration (NASA) Soil Moisture Active Passive (SMAP) satellite mission was launched on January 31, 2015. The observatory was developed to provide global mapping of high-resolution soil moisture and freeze-thaw state every two to three days using an L-band (active) radar and an L-band (passive) radiometer. After an irrecoverable hardware failure of the radar on July 7, 2015, the radiometer-only soil moisture product became the only operational soil moisture product for SMAP. The product provides soil moisture estimates posted on a 36 km Earth-fixed grid produced using brightness temperature observations from descending passes. Within months after the commissioning of the SMAP radiometer, the product was assessed to have attained preliminary (beta) science quality, and data were released to the public for evaluation in September 2015. The product is available from the NASA Distributed Active Archive Center at the National Snow and Ice Data Center. This paper provides a summary of the Level 2 Passive Soil Moisture Product (L2_SM_P) and its validation against in situ ground measurements collected from different data sources. Initial in situ comparisons conducted between March 31, 2015 and October 26, 2015, at a limited number of core validation sites (CVSs) and several hundred sparse network points, indicate that the V-pol Single Channel Algorithm (SCA-V) currently delivers the best performance among algorithms considered for L2_SM_P, based on several metrics. The accuracy of the soil moisture retrievals averaged over the CVSs was 0.038 m3/m3 unbiased root-mean-square difference (ubRMSD), which approaches the SMAP mission requirement of 0.040 m3/m3.
426 citations
TL;DR: In this article, an experimental monitoring and forecast system for sub-Saharan Africa is described that is based on satellite data and seasonal climate model predictions, which can help address many of the problems inherent to developing regions.
Abstract: Drought is one of the leading impediments to development in Africa. Much of the continent is dependent on rain-fed agriculture, which makes it particularly susceptible to climate variability. Monitoring drought and providing timely seasonal forecasts are essential for integrated drought risk reduction. Current approaches in developing regions have generally been limited, however, in part because of unreliable monitoring networks. Operational seasonal climate forecasts are also deficient and often reliant on statistical regressions, which are unable to provide detailed information relevant for drought assessment. However, the wealth of data from satellites and recent advancements in large-scale hydrological modeling and seasonal climate model predictions have enabled the development of state-of-the-art monitoring and prediction systems that can help address many of the problems inherent to developing regions. An experimental drought monitoring and forecast system for sub-Saharan Africa is described that is...
383 citations
TL;DR: In this paper, the state of the art in large-scale soil moisture monitoring and identifying some critical needs for research to optimize the use of increasingly available soil moisture data are discussed.
Abstract: Soil moisture is an essential climate variable influencing land-atmosphere interactions, an essential hydrologic variable impacting rainfall-runoff processes, an essential ecological variable regulating net ecosystem exchange, and an essential agricultural variable constraining food security. Large-scale soil moisture monitoring has advanced in recent years, creating opportunities to transform scientific understanding of soil moisture and related processes. These advances are being driven by researchers from a broad range of disciplines, but this complicates collaboration and communication; and, for some applications, the science required to utilize large-scale soil moisture data is poorly developed. In this review, we describe the state of the art in large-scale soil moisture monitoring and identify some critical needs for research to optimize the use of increasingly available soil moisture data. We review representative examples of (i) emerging in situ and proximal sensing techniques, (ii) dedicated soil moisture remote sensing missions, (iii) soil moisture monitoring networks, and (iv) applications of large-scale soil moisture measurements. Significant near-term progress seems possible in the use of large-scale soil moisture data for drought monitoring. Assimilation of soil moisture data for meteorological or hydrologic forecasting also shows promise, but significant challenges related to spatial variability and model structures remain. Little progress has been made in the use of large-scale soil moisture observations within the context of ecological or agricultural modeling. Opportunities abound to advance the science and practice of large-scale soil moisture monitoring for the sake of improved Earth system monitoring, modeling, and forecasting.
355 citations
Cites methods from "The SMOS Soil Moisture Retrieval Al..."
...A retrieval algorithm incorporating an L-band microwave emission forward model is applied to the TB data to estimate soil moisture (Kerr et al., 2012)....
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References
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Book•
01 Aug 1982
TL;DR: In this article, the authors present a model of a MICROWAVE REMOTE SENSING FUNDAMENTALS and RADIOMETRY, which is based on the idea of surface scattering.
Abstract: EN BIBLIOTECA: V.1: MICROWAVE REMOTE SENSING FUNDAMENTALS AND RADIOMETRY. V.2: RADAR REMOTE SENSING AND SURFACE SCATTERING AND EMISSION THEORY
3,501 citations
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
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
2,474 citations
"The SMOS Soil Moisture Retrieval Al..." refers background in this paper
...L-Band seemed the best way to go, and as soon as tractable solutions became possible for the antenna, the SMOS [27], [28], Aquarius [29], and SMAP [30], [31] concepts emerged....
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TL;DR: In this paper, the authors evaluated the microwave dielectric behavior of soil-water mixtures as a function of water content and soil textural composition for the 1.4-to 18-GHz region.
Abstract: This paper is the second in a series evaluating the microwave dielectric behavior of soil-water mixtures as a function of water content and soil textural composition. Part II draws upon the data presented in Part 1 [13] to develop appropriate empirical and theoretical dielectric mixing models for the 1.4-to 18-GHz region. A semiempirical mixing model based upon the index of refraction is presented, requiring only easily ascertained soil physical parameters such as volumetric moisture and soil textural composition as inputs. In addition, a theoretical model accounting explicitly for the presence of a hydration layer of bound water adjacent to hydrophilic soil particle surfaces is presented. A four-component dielectric mixing model treats the soil-water system as a host medium of dry soil solids containing randomly distributed and randomly oriented disc-shaped inclusions of bound water, bulk water, and air. The bulk water component is considered to be dependent upon frequency, temperature, and salinity. The soil solution is differentiated by means of a soil physical model into 1) a bound component and 2) a bulk soil solution. The performance of each model is evaluated as a function of soil moisture, soil texture, and frequency, using the dielectric measurements of five soils ranging from sandy loam to silty clay (as presented in Part I [13]) at frequencies between 1.4 and 18 GHz. The semiempirical mixing model yields an excellent fit to the measured data at frequencies above 4 GHz. At 1.
1,805 citations
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
"The SMOS Soil Moisture Retrieval Al..." refers background in this paper
...SMOS characteristics are given in [32]–[34] and are summarized in this section....
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..., the set of TBs corresponding to multiangular views of a single target [32]....
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