Potential of bias correction for downscaling passive microwave and soil moisture data

Kornelsen, Kurt C.; Cosh, Michael H.; Coulibaly, Paulin

doi:10.1002/2015jd023550

Cited by 12 publications

(4 citation statements)

References 65 publications

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“…Several algorithms are available for soil moisture retrievals using passive microwave instruments, including the single‐channel algorithm [ Jackson , ; Jackson et al ., , ], the multifrequency‐polarization iterative algorithm [ Njoku and Li , ; Njoku et al ., ; Koike et al ., ], the look‐up table algorithm [ Fujii et al ., ], the polarization index algorithm [ Paloscia et al ., ], and the Dual‐Channel Algorithm based on the Qp model (QDCA) [ Shi et al ., , ]. Recently, with these algorithms considerable satellite soil moisture validation work has been conducted in the United States [ Collow et al ., ; Leroux et al ., ; Ford et al ., ; Kornelsen et al ., ; Kim et al ., ; Chan et al ., ; Zhang et al ., ], Europe [ Mittelbach et al ., ; Pierdicca et al ., ; Kim et al ., ; Chan et al ., ; Gruber et al ., ], Australia [ Panciera et al ., ; Draper et al ., ; Su et al ., ; Kim et al ., ; O'Neill et al ., ; Yee et al ., ; Cho et al ., ], and the Tibetan Plateau, China[ Su et al ., , ; Liu et al ., ; Chen et al ., ; Bi et al ., ]. However, limited comparative work has been performed in northeast China.…”

Section: Introductionmentioning

confidence: 99%

Evaluation and analysis of AMSR‐2, SMOS, and SMAP soil moisture products in the Genhe area of China

Cui

Jiang

et al. 2017

JGR Atmospheres

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High‐precision soil moisture products play an important role in estimating forest carbon storage and carbon emissions in Genhe, China. In this paper, we evaluated the Soil Moisture and Ocean Salinity (SMOS) L3 product, the Soil Moisture Active Passive (SMAP) L3 product, and four soil moisture products derived from the Advanced Microwave Scanning Radiometer 2 (AMSR‐2), i.e., the Dual Channel Algorithm based on the Qp model (QDCA) product, the Japan Aerospace Exploration Agency (JAXA) L3 product, and the Land Parameter Retrieval Model (LPRM) C band and X band products in the Genhe area of China. The results indicated that the root‐mean‐square error (RMSE) and bias of the QDCA product were lower than those of the other AMSR‐2 products, although the QDCA still fell outside of the acceptable range with a volumetric error of no greater than 6%. The JAXA product underestimated the soil moisture and had a constant bias of 0.089–0.099 m3 m−3. The LPRM C‐band and X‐band products had a constant variable season bias of 0.261–0.576 m3 m−3. The quality of the SMOS was better than that of the AMSR‐2 products; however, the results were noisy and unstable. The SMAP was closest to the ground measurements and presented a low RMSE (0.039–0.063 m3 m−3) and bias (0.022–0.050 m3 m−3). Finally, an assessment was performed on the parameters in these soil moisture algorithms.

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Section: Introductionmentioning

confidence: 99%

Evaluation and analysis of AMSR‐2, SMOS, and SMAP soil moisture products in the Genhe area of China

Cui

Jiang

et al. 2017

JGR Atmospheres

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“…The LWW network has been widely used for monitoring hydrological and meteorological measurements since 1961. It has been extensively used for assessing satellite soil moisture products, due to the large dynamic range of soil moisture and flat terrain in this region [20,21,23,31]. (2) REMEDHUS Network: The REMEDHUS network is a dense network in Spain, which is located in the central semiarid sector of the Duero Basin, with an area of 1300 km 2 .…”

Section: Study Area and In-situ Soil Moisture Datamentioning

confidence: 99%

Soil Moisture Mapping from Satellites: An Intercomparison of SMAP, SMOS, FY3B, AMSR2, and ESA CCI over Two Dense Network Regions at Different Spatial Scales

et al. 2017

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Abstract:A good knowledge of the quality of the satellite soil moisture products is of great importance for their application and improvement. This paper examines the performance of eight satellite-based soil moisture products, including the Soil Moisture Active Passive (SMAP) passive Level 3 (L3), the Soil Moisture and Ocean Salinity (SMOS) Centre Aval de Traitement des Données SMOS (CATDS) L3, the Japan Aerospace Exploration Agency (JAXA) Advanced Microwave Scanning Radiometer 2 (AMSR2) L3, the Land Parameter Retrieval Model (LPRM) AMSR2 L3, the European Space Agency (ESA) Climate Change Initiative (CCI) L3, the Chinese Fengyun-3B (FY3B) L2 soil moisture products at a coarse resolution of~0.25 • , and the newly released SMAP enhanced passive L3 and JAXA AMSR2 L3 soil moisture products at a medium resolution of~0.1 • . The ground soil moisture used for validation were collected from two well-calibrated and dense networks, including the Little Washita Watershed (LWW) network in the United States and the REMEDHUS network in Spain, each with different land cover. The results show that the SMAP passive soil moisture product outperformed the other products in the LWW network region, with an unbiased root mean square (ubRMSE) of 0.027 m 3 m −3 , whereas the FY3B soil moisture performed the best in the REMEDHUS network region, with an ubRMSE of 0.025 m 3 m −3 . The JAXA product performed much better at 0.25 • than at 0.1 • , but at both resolutions it underestimated soil moisture most of the time (bias < −0.05 m 3 m −3 ). The SMAP-enhanced passive soil moisture product captured the temporal variation of ground measurements well, with a correlation coefficient larger than 0.8, and was generally superior to the JAXA product. The LPRM showed much larger amplitude and temporal variation than the ground soil moisture, with a wet bias larger than 0.09 m 3 m −3 . The underestimation of surface temperature may have contributed to the general dry bias found in the SMAP (−0.018 m 3 m −3 for LWW and 0.016 m 3 m −3 for REMEDHUS) and SMOS (−0.004 m 3 m −3 for LWW and −0.012 m 3 m −3 for REMEDHUS) soil moisture products. The ESA CCI product showed satisfactory performance with acceptable error metrics (ubRMSE < 0.045 m 3 m −3 ), revealing the effectiveness of merging active and passive soil moisture products. The good performance of SMAP and FY3B demonstrates the

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“…This may be caused by the limitations of the model (e.g., imperfect structure, simplifications, suboptimal parameters) (De Lannoy et al 2007) or nonuniform representation of land surface variables of the remote sensing system [e.g., shallower (,5 cm) observed depth of soil moisture] (Sahoo et al 2013), among others. The difference in climatology can be even more dramatic for TB observations (Kornelsen et al 2015). A common practice is to correct for the bias prior to data assimilation.…”

Section: Bias Correctionmentioning

confidence: 99%

Improving Soil Moisture and Surface Turbulent Heat Flux Estimates by Assimilation of SMAP Brightness Temperatures or Soil Moisture Retrievals and GOES Land Surface Temperature Retrievals

Steele‐Dunne

Lannoy

2020

Journal of Hydrometeorology

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Surface heat fluxes are vital to hydrological and environmental studies, but mapping them accurately over a large area remains a problem. In this study, brightness temperature (TB) observations or soil moisture retrievals from the NASA Soil Moisture Active Passive (SMAP) mission and land surface temperature (LST) product from the Geostationary Operational Environmental Satellite (GOES) are assimilated together into a coupled water and heat transfer model to improve surface heat flux estimates. A particle filter is used to assimilate SMAP data, while a particle smoothing method is adopted to assimilate GOES LST time series, correcting for both systematic biases via parameter updating and for short-term error via state updating. One experiment assimilates SMAP TB at horizontal polarization and GOES LST, a second experiment assimilates SMAP TB at vertical polarization and GOES LST, and a third experiment assimilates SMAP soil moisture retrievals along with GOES LST. The aim is to examine if the assimilation of physically consistent TB and LST observations could yield improved surface heat flux estimates. It is demonstrated that all three assimilation experiments improved flux estimates compared to a no-assimilation case. Assimilating TB data tends to produce smaller bias in soil moisture estimates compared to assimilating soil moisture retrievals, but the estimates are influenced by the respective bias correction approaches. Despite the differences in soil moisture estimates, the flux estimates from different assimilation experiments are in general very similar.

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Potential of bias correction for downscaling passive microwave and soil moisture data

Cited by 12 publications

References 65 publications

Evaluation and analysis of AMSR‐2, SMOS, and SMAP soil moisture products in the Genhe area of China

Evaluation and analysis of AMSR‐2, SMOS, and SMAP soil moisture products in the Genhe area of China

Soil Moisture Mapping from Satellites: An Intercomparison of SMAP, SMOS, FY3B, AMSR2, and ESA CCI over Two Dense Network Regions at Different Spatial Scales

Improving Soil Moisture and Surface Turbulent Heat Flux Estimates by Assimilation of SMAP Brightness Temperatures or Soil Moisture Retrievals and GOES Land Surface Temperature Retrievals

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