The Soil Moisture Active/Passive Mission (SMAP)

Entekhabi, Dara; Njoku, E. G.; O’Neill, Peggy; Spencer, M.; Jackson, Thomas J.; Entin, Jared; Im, E.; Kellogg, K.

doi:10.1109/igarss.2008.4779267

Cited by 607 publications

(756 citation statements)

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“…We compare the upscaling estimates over China cropland with the Soil Moisture Active Passive (SMAP) [30] active radar and Global Land data assimilation system (GLDAS) [31] soil moisture products to demonstrate the advantages of our model. SMAP is an orbiting observatory that measures the amount of water in the top 5 cm (2 inches) of soil everywhere on Earth's surface, which is designed to measure soil moisture every 2-3 days over a three-year period.…”

Section: (4) Smap and Gldas Datamentioning

confidence: 99%

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Upscaling of Surface Soil Moisture Using a Deep Learning Model with VIIRS RDR

Zhang

Huang

et al. 2017

IJGI

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Abstract:In current upscaling of in situ surface soil moisture practices, commonly used novel statistical or machine learning-based regression models combined with remote sensing data show some advantages in accurately capturing the satellite footprint scale of specific local or regional surface soil moisture. However, the performance of most models is largely determined by the size of the training data and the limited generalization ability to accomplish correlation extraction in regression models, which are unsuitable for larger scale practices. In this paper, a deep learning model was proposed to estimate soil moisture on a national scale. The deep learning model has the advantage of representing nonlinearities and modeling complex relationships from large-scale data. To illustrate the deep learning model for soil moisture estimation, the croplands of China were selected as the study area, and four years of Visible Infrared Imaging Radiometer Suite (VIIRS) raw data records (RDR) were used as input parameters, then the models were trained and soil moisture estimates were obtained. Results demonstrate that the estimated models captured the complex relationship between the remote sensing variables and in situ surface soil moisture with an adjusted coefficient of determination of R 2 = 0.9875 and a root mean square error (RMSE) of 0.0084 in China. These results were more accurate than the Soil Moisture Active Passive (SMAP) active radar soil moisture products and the Global Land data assimilation system (GLDAS) 0-10 cm depth soil moisture data. Our study suggests that deep learning model have potential for operational applications of upscaling in situ surface soil moisture data at the national scale.

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Section: (4) Smap and Gldas Datamentioning

confidence: 99%

“…SMAP's radar started transmitting data on January 2015, and it stopped on 7 July 2015 when its radar sensor broke. Therefore, only three months (May to July 2015) of SMAP radar daily soil moisture level 3 product (∼3 km resolution) corresponding to in situ soil moisture were obtained [30].…”

Section: (4) Smap and Gldas Datamentioning

confidence: 99%

Upscaling of Surface Soil Moisture Using a Deep Learning Model with VIIRS RDR

Zhang

Huang

et al. 2017

IJGI

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“…In 2009, the Soil Moisture and Ocean Salinity (SMOS) satellite operated by ESA was launched [2,18], and in January 2015, the Soil Moisture Active Passive (SMAP) satellite operated by NASA was launched [19]. Both missions operate in the microwave L-band, which is most suitable for SM observations [12,20].…”

Section: Introductionmentioning

confidence: 99%

Soil Moisture Retrieval Based on GPS Signal Strength Attenuation

Koch

Schlenz

Prasch

et al. 2016

Water

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Soil moisture (SM) is a highly relevant variable for agriculture, the emergence of floods and a key variable in the global energy and water cycle. In the last years, several satellite missions have been launched especially to derive large-scale products of the SM dynamics on the Earth. However, in situ validation data are often scarce. We developed a new method to retrieve SM of bare soil from measurements of low-cost GPS (Global Positioning System) sensors that receive the freely available GPS L1-band signals. The experimental setup of three GPS sensors was installed at a bare soil field at the German Weather Service (DWD) in Munich for almost 1.5 years. Two GPS antennas were installed within the soil column at a depth of 10 cm and one above the soil. SM was successfully retrieved based on GPS signal strength losses through the integral soil volume. The results show high agreement with measured and modelled SM validation data. Due to its non-destructive, cheap and low power setup, GPS sensor networks could also be used for potential applications in remote areas, aiming to serve as satellite validation data and to support the fields of agriculture, water supply, flood forecasting and climate change.

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“…The satellite uses a Microwave Imaging Radiometer with Aperture Synthesis (MIRAS) working in the L band (1.4 GHz) [61,62]. In the USA, NASA is preparing the SMAP (Soil Moisture Active Passive) mission, with a satellite launch for 2014, carrying a radiometer and synthetic aperture radar (SAR) in the L band as well (1.20-1.41 GHz), with the same accuracy and revisit time as SMOS but an improved resolution of 10 km [63]. In recent times, also measurements of spatiotemporal variability in the gravity field by the GRACE satellite mission have been used to detect changes in mass storage, from which soil moisture or snow signals could be extracted through data assimilation in combination with other measurements.…”

Section: Satellite Productsmentioning

confidence: 99%

Soil Moisture & Snow Properties Determination with GNSS in Alpine Environments: Challenges, Status, and Perspectives

et al. 2013

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Moisture content in the soil and snow in the alpine environment is an important factor, not only for environmentally oriented research, but also for decision making in agriculture and hazard management. Current observation techniques quantifying soil moisture or characterizing a snow pack often require dedicated instrumentation that measures either at point scale or at very large (satellite pixel) scale. Given the heterogeneity of both snow cover and soil moisture in alpine terrain, observations of the spatial distribution of moisture and snow-cover are lacking at spatial scales relevant for alpine hydrometeorology. This paper provides an overview of the challenges and status of the determination of soil moisture and snow properties in alpine environments. Current measurement techniques and newly proposed ones, based on the reception of reflected Global Navigation Satellite Signals (i.e., GNSS Reflectometry or GNSS-R), or the use of laser scanning are reviewed, and the perspectives offered by these new techniques to fill the current gap in the instrumentation level are discussed. Some key enabling technologies OPEN ACCESSRemote Sens. 2013, 5 3517 including the availability of modernized GNSS signals and GNSS array beamforming techniques are also considered and discussed.

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The Soil Moisture Active/Passive Mission (SMAP)

Cited by 607 publications

References 1 publication

Upscaling of Surface Soil Moisture Using a Deep Learning Model with VIIRS RDR

Upscaling of Surface Soil Moisture Using a Deep Learning Model with VIIRS RDR

Soil Moisture Retrieval Based on GPS Signal Strength Attenuation

Soil Moisture & Snow Properties Determination with GNSS in Alpine Environments: Challenges, Status, and Perspectives

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