Data Assimilation to Extract Soil Moisture Information from SMAP Observations
نویسندگان
چکیده
منابع مشابه
Data Assimilation to Extract Soil Moisture Information from SMAP Observations
This study compares different methods to extract soil moisture information through the assimilation of Soil Moisture Active Passive (SMAP) observations. Neural network (NN) and physically-based SMAP soil moisture retrievals were assimilated into the National Aeronautics and Space Administration (NASA) Catchment model over the contiguous United States for April 2015 to March 2017. By constructio...
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Global Soil Moisture from Satellite Observations, Land Surface Models, and Ground Data: Implications for Data Assimilation
Three independent surface soil moisture datasets for the period 1979–87 are compared: 1) global retrievals from the Scanning Multichannel Microwave Radiometer (SMMR), 2) global soil moisture derived from observed meteorological forcing using the NASA Catchment Land Surface Model, and 3) ground-based measurements in Eurasia and North America from the Global Soil Moisture Data Bank. Time-average ...
متن کاملRecent Advances on Soil Moisture Data Assimilation
This study reviews recent progress on soil moisture data assimilation. Data assimilation is a process of merging observations with a system dynamic model to provide an improved estimate of the states of the environment. The application of data assimilation in hydrology is relatively new, however, rapid progress has been made in the last decade or so with the available remotely sensed soil moist...
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21 NASA's Soil Moisture Active Passive (SMAP) mission will carry the first combined spaceborne L-22 band radiometer and Synthetic Aperture Radar (SAR) system with the objective of mapping near-23 surface soil moisture and freeze/thaw state globally every 2-3 days. SMAP will provide three soil 24 moisture products; (i) high-resolution from radar (~3km), (ii) low-resolution from radiometer 25 (~3...
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ژورنال
عنوان ژورنال: Remote Sensing
سال: 2017
ISSN: 2072-4292
DOI: 10.3390/rs9111179