scholarly journals Web Geoprocessing Services for Disseminating and Analyzing SMAP Derived Soil Moisture Data Products

2020 ◽  
Author(s):  
Chen Zhang ◽  
Zhengwei Yang ◽  
Liping Di ◽  
Eugene Yu ◽  
Li Lin ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Soil Moisture Data ◽  
Data Products ◽  
Geoprocessing Services

scholarly journals A Preliminary Assessment of the Impact of Assimilating Satellite Soil Moisture Data Products on NCEP Global Forecast System

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Weizhong Zheng ◽  
Xiwu Zhan ◽  
Jicheng Liu ◽  
Michael Ek
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Prediction Models ◽  
Precipitation Forecast ◽  
Global Forecast System ◽  
Forecast System ◽  
Numerical Weather ◽  
Soil Moisture Data ◽  
Data Products ◽  
Ncep Global Forecast System

It is well documented that soil moisture has a strong impact on precipitation forecasts of numerical weather prediction models. Several microwave satellite soil moisture retrieval data products have also been available for applications. However, these observational data products have not been employed in any operational numerical weather or climate prediction models. In this study, a preliminary test of assimilating satellite soil moisture data products from the NOAA-NESDIS Soil Moisture Operational Product System (SMOPS) into the NOAA-NCEP Global Forecast System (GFS) is conducted. Using the ensemble Kalman filter (EnKF) introduced in recent year publications and implemented in the GFS, the multiple satellite blended daily global soil moisture data from SMOPS for the month of April 2012 are assimilated into the GFS. The forecasts of surface variables, anomaly correlations of isobar heights, and precipitation forecast skills of the GFS with and without the soil moisture data assimilation are assessed. The surface and deep layer soil moisture estimates of the GFS after the satellite soil moisture assimilation are found to have slightly better agreement with the ground soil moisture measurements at dozens of sites across the continental United States (CONUS). Forecasts of surface humidity and air temperature, 500 hPa height anomaly correlations, and the precipitation forecast skill demonstrated certain level of improvements after the soil moisture assimilation against those without the soil moisture assimilation. However, the methodology for the soil moisture data assimilation into operational GFS runs still requires further development efforts and tests.

scholarly journals Validation of Soil Moisture Data Products from the NASA SMAP Mission

2021 ◽  
pp. 1-1
Author(s):  
Andreas Colliander ◽  
Rolf Reichle ◽  
Wade Crow ◽  
Michael H. Cosh ◽  
Fan Chen ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Soil Moisture Data ◽  
Data Products

scholarly journals HYDROLOGIC AND CRYOSPHERIC PROCESSES OBSERVED FROM SPACE

2015 ◽  
Vol XL-7/W3 ◽  
pp. 1101-1110 ◽  
Author(s):  
M. Menenti ◽  
X. Li ◽  
J. Wang ◽  
H. Vereecken ◽  
J. Li ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Flow Velocity ◽  
Incidence Angle ◽  
Tibet Plateau ◽  
Ancillary Data ◽  
Soil Moisture Data ◽  
Global Comparison ◽  
Data Products ◽  
Glacier Flow ◽  
Qinghai Tibet Plateau

Ten Dragon 3 projects deal with hydrologic and cryosphere processes, with a focus on the Himalayas and Qinghai – Tibet Plateau, but not limited to that. At the 1st Dragon 3 Progress Symposium in 2013 a significant potential for a better and deeper integration appeared very clearly and we worked out an overview of the ten projects identifying specific issues and objectives shared by at least two projects. At the Mid Term Symposium in 2014 a joint session was held over two days. As regards cryospheric processes science highlights covered: Glacier flow velocity by optical and SAR features tracking and InSAR; Patterns in space and time of glacier flow velocity; Mass change estimated with DTM-s and altimetry; Reflectance and LST used to classify glacier surface and understand surface processes, Inventory and changes in the number and area of lakes in the Qinghai – Tibet Plateau 1970, 1990, 2000 and 2010; Deformation of permafrost along the Qinghai – Tibet railway. <br><br> Highlights on hydrologic processes included: Global comparison of SMOS, ASCAT and ERA soil moisture data products; Relative deviations evaluated by climate zone; Soil moisture data products improved with ancillary data; Assimilation of FY - , TRMM and GPM precipitation data products in WRF; Improved algorithm and data products on fractional snow cover; Improvement of MODIS ET with assimilation of LST; TRMM data products evaluated in the Yangtze; Calibration of river basin models using LST; System to calibrate, correct and normalize (spatial, spectral) data collected by imaging spectral radiometers; Integration of data acquired by different sensors, e.g. ET Monitor with optical and microwave (SMOS, FY – 3) data; Hydrological data products used both for forcing and evaluation of Qinghai – Tibet Plateau hydrological model; Wetlands vulnerability assessed through changes in land cover 1987 – 2013; Multi incidence angle and multi – temporal SAR to monitor water extent. In the general session a proposal for a Dragon Water Cycle Initiative was presented.

scholarly journals Global soil moisture data derived through machine learning trained with in-situ measurements

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Sungmin O. ◽  
Rene Orth
Keyword(s):  
Machine Learning ◽  
Soil Moisture ◽  
Large Scale ◽  
Short Term Memory ◽  
Temporal Dynamics ◽  
Soil Moisture Data ◽  
Wide Range ◽  
Global Soil

AbstractWhile soil moisture information is essential for a wide range of hydrologic and climate applications, spatially-continuous soil moisture data is only available from satellite observations or model simulations. Here we present a global, long-term dataset of soil moisture derived through machine learning trained with in-situ measurements, SoMo.ml. We train a Long Short-Term Memory (LSTM) model to extrapolate daily soil moisture dynamics in space and in time, based on in-situ data collected from more than 1,000 stations across the globe. SoMo.ml provides multi-layer soil moisture data (0–10 cm, 10–30 cm, and 30–50 cm) at 0.25° spatial and daily temporal resolution over the period 2000–2019. The performance of the resulting dataset is evaluated through cross validation and inter-comparison with existing soil moisture datasets. SoMo.ml performs especially well in terms of temporal dynamics, making it particularly useful for applications requiring time-varying soil moisture, such as anomaly detection and memory analyses. SoMo.ml complements the existing suite of modelled and satellite-based datasets given its distinct derivation, to support large-scale hydrological, meteorological, and ecological analyses.

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