Literature DB >> 30364509

Global Assessment of the SMAP Level-4 Surface and Root-Zone Soil Moisture Product Using Assimilation Diagnostics.

Rolf H Reichle1, Gabrielle J M De Lannoy2, Qing Liu1,3, Randal D Koster1, John S Kimball4, Wade T Crow5, Joseph V Ardizzone1,3, Purnendu Chakraborty1,3, Douglas W Collins1,3, Austin L Conaty1,3, Manuela Girotto1,6, Lucas A Jones4, Jana Kolassa1,6, Hans Lievens1,7, Robert A Lucchesi1,3, Edmond B Smith1,3.   

Abstract

The Soil Moisture Active Passive (SMAP) mission Level-4 Soil Moisture (L4_SM) product provides 3-hourly, 9-km resolution, global estimates of surface (0-5 cm) and root-zone (0-100 cm) soil moisture and related land surface variables from 31 March 2015 to present with ~2.5day latency. The ensemble-based L4_SM algorithm assimilates SMAP brightness temperature (Tb) observations into the Catchment land surface model. This study describes the spatially distributed L4_SM analysis and assesses the observation-minus-forecast (O-F) Tb residuals and the soil moisture and temperature analysis increments. Owing to the climatological rescaling of the Tb observations prior to assimilation, the analysis is essentially unbiased, with global mean values of ~0.37 K for the O-F Tb residuals and practically zero for the soil moisture and temperature increments. There are, however, modest regional (absolute) biases in the O-F residuals (under ~3 K), the soil moisture increments (under ~0.01 m3 m-3), and the surface soil temperature increments (under ~1 K). Typical instantaneous values are ~6 K for O-F residuals, ~0.01 (~0.003) m3 m-3 for surface (root-zone) soil moisture increments, and ~0.6 K for surface soil temperature increments. The O-F diagnostics indicate that the actual errors in the system are overestimated in deserts and densely vegetated regions and underestimated in agricultural regions and transition zones between dry and wet climates. The O-F auto-correlations suggest that the SMAP observations are used efficiently in western North America, the Sahel, and Australia, but not in many forested regions and the high northern latitudes. A case study in Australia demonstrates that assimilating SMAP observations successfully corrects short-term errors in the L4_SM rainfall forcing.

Entities:  

Year:  2017        PMID: 30364509      PMCID: PMC6196324          DOI: 10.1175/JHM-D-17-0130.1

Source DB:  PubMed          Journal:  J Hydrometeorol        ISSN: 1525-7541            Impact factor:   4.349


  1 in total

1.  L-band microwave remote sensing and land data assimilation improve the representation of pre-storm soil moisture conditions for hydrologic forecasting.

Authors:  W T Crow; F Chen; R H Reichle; Q Liu
Journal:  Geophys Res Lett       Date:  2017-05-10       Impact factor: 4.720

  1 in total
  13 in total

1.  Estimating snow mass in North America through assimilation of AMSR-E brightness temperature observations using the Catchment land surface model and support vector machines.

Authors:  Yuan Xue; Barton A Forman; Rolf H Reichle
Journal:  Water Resour Res       Date:  2018-07-23       Impact factor: 5.240

2.  Estimating surface soil moisture from SMAP observations using a Neural Network technique.

Authors:  J Kolassa; R H Reichle; Q Liu; S H Alemohammad; P Gentine; K Aida; J Asanuma; S Bircher; T Caldwell; A Colliander; M Cosh; C Holifield Collins; T J Jackson; J Martínez-Fernández; H McNairn; A Pacheco; M Thibeault; J P Walker
Journal:  Remote Sens Environ       Date:  2017-11-11       Impact factor: 10.164

3.  Spatial and temporal variability of root-zone soil moisture acquired from hydrologic modeling and AirMOSS P-band radar.

Authors:  Wade T Crow; Sushil Milak; Mahta Moghaddam; Alireza Tabatabaeenejad; Sermsak Jaruwatanadilok; Xuan Yu; Yuning Shi; Rolf H Reichle; Yutaka Hagimoto; Richard H Cuenca
Journal:  IEEE J Sel Top Appl Earth Obs Remote Sens       Date:  2018-12-24       Impact factor: 3.784

4.  Characterizing permafrost active layer dynamics and sensitivity to landscape spatial heterogeneity in Alaska.

Authors:  Yonghong Yi; John S Kimball; Richard H Chen; Mahta Moghaddam; Rolf H Reichle; Umakant Mishra; Donatella Zona; Walter C Oechel
Journal:  Cryosphere       Date:  2018-01-16       Impact factor: 5.771

5.  Data Assimilation of High-Resolution Thermal and Radar Remote Sensing Retrievals for Soil Moisture Monitoring in a Drip-Irrigated Vineyard.

Authors:  Fangni Lei; Wade T Crow; William P Kustas; Jianzhi Dong; Yun Yang; Kyle R Knipper; Martha C Anderson; Feng Gao; Claudia Notarnicola; Felix Greifeneder; Lynn M McKee; Joseph G Alfieri; Christopher Hain; Nick Dokoozlian
Journal:  Remote Sens Environ       Date:  2020-03-15       Impact factor: 10.164

6.  Watershed Modeling with Remotely Sensed Big Data: MODIS Leaf Area Index Improves Hydrology and Water Quality Predictions.

Authors:  Adnan Rajib; I Luk Kim; Heather E Golden; Charles R Lane; Sujay V Kumar; Zhiqiang Yu; Saranya Jeyalakshmi
Journal:  Remote Sens (Basel)       Date:  2020-07-04       Impact factor: 4.848

7.  Assimilation of SMOS Retrievals in the Land Information System.

Authors:  Clay B Blankenship; Jonathan L Case; Bradley T Zavodsky; William L Crosson
Journal:  IEEE Trans Geosci Remote Sens       Date:  2016-08-10       Impact factor: 8.125

8.  Toward High-Resolution Soil Moisture Monitoring by Combining Active-Passive Microwave and Optical Vegetation Remote Sensing Products with Land Surface Model.

Authors:  Kinya Toride; Yohei Sawada; Kentaro Aida; Toshio Koike
Journal:  Sensors (Basel)       Date:  2019-09-11       Impact factor: 3.576

9.  Data Assimilation to extract Soil Moisture Information from SMAP Observations.

Authors:  Jana Kolassa; Rolf H Reichle; Qing Liu; Michael Cosh; David D Bosch; Todd G Caldwell; Andreas Colliander; Chandra Holifield Collins; Thomas J Jackson; Stan J Livingston; Mahta Moghaddam; Patrick J Starks
Journal:  Remote Sens (Basel)       Date:  2017-11-17       Impact factor: 4.848

10.  PEAT-CLSM: A Specific Treatment of Peatland Hydrology in the NASA Catchment Land Surface Model.

Authors:  M Bechtold; G J M De Lannoy; R D Koster; R H Reichle; S P Mahanama; W Bleuten; M A Bourgault; C Brümmer; I Burdun; A R Desai; K Devito; T Grünwald; M Grygoruk; E R Humphreys; J Klatt; J Kurbatova; A Lohila; T M Munir; M B Nilsson; J S Price; M Röhl; A Schneider; B Tiemeyer
Journal:  J Adv Model Earth Syst       Date:  2019-05-07       Impact factor: 6.660

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