Development and assessment of the SMAP enhanced passive soil moisture product

Chan, S.; Bindlish, Rajat; O'Neill, Peggy E.; Jackson, Thomas J.; Njoku, E. G.; Dunbar, Scott; Chaubell, Julián; Piepmeier, J.R.; Yueh, Simon; Entekhabi, Dara; Colliander, Andreas; Chen, F.; Cosh, Michael H.; Caldwell, Todd; Walker, Jeffrey P.; Berg, Aaron; McNairn, Heather; Thibeault, M.; Martínez-Fernández, José; Uldall, F.; Seyfried, M. S.; Bosch, David D.; Starks, Patrick J.; Collins, Chandra Holifield; Prueger, John H.; Velde, R. van der; Asanuma, Jun; Palecki, Michael A.; Small, E. E.; Zréda, Marek; Calvet, J.-C.; Crow, Wade T.; Kerr, Yann H.

doi:10.1016/j.rse.2017.08.025

Cited by 357 publications

(309 citation statements)

References 19 publications

Supporting

Mentioning

298

Contrasting

Order By: Relevance

“…The SMAP radiometer has a native spatial resolution of 36 km, but this product utilizes the Backus-Gilbert optimal interpolation algorithm to post soil moisture retrievals onto the 9 km Equal-Area Scalable Earth grid version 2 (EASE-2) . The enhanced resolution version reveals spatial features not apparent in the 36 km standard product and similarly meets the mission goal of 0.040 cm 3 cm −3 unbiased root mean squared error (Chan et al, 2018). We use only morning overpasses because the SMAP algorithm assigns one temperature to both the soil and its overlying canopy, a condition that is best met in the morning hours (Entekhabi et al, 2014;Jackson et al, 2012).…”

Section: Smap Retrievalsmentioning

confidence: 87%

Controls on surface soil drying rates observed by SMAP and simulated by the Noah land surface model

Shellito

Small

Livneh

2018

Hydrol. Earth Syst. Sci.

View full text Add to dashboard Cite

Abstract. Drydown periods that follow precipitation events provide an opportunity to assess controls on soil evaporation on a continental scale. We use SMAP (Soil Moisture Active Passive) observations and Noah simulations from drydown periods to quantify the role of soil moisture, potential evaporation, vegetation cover, and soil texture on soil drying rates. Rates are determined using finite differences over intervals of 1 to 3 days. In the Noah model, the drying rates are a good approximation of direct soil evaporation rates, and our work suggests that SMAP-observed drying is also predominantly affected by direct soil evaporation. Data cover the domain of the North American Land Data Assimilation System Phase 2 and span the first 1.8 years of SMAP's operation.Drying of surface soil moisture observed by SMAP is faster than that simulated by Noah. SMAP drying is fastest when surface soil moisture levels are high, potential evaporation is high, and when vegetation cover is low. Soil texture plays a minor role in SMAP drying rates. Noah simulations show similar responses to soil moisture and potential evaporation, but vegetation has a minimal effect and soil texture has a much larger effect compared to SMAP. When drying rates are normalized by potential evaporation, SMAP observations and Noah simulations both show that increases in vegetation cover lead to decreases in evaporative efficiency from the surface soil. However, the magnitude of this effect simulated by Noah is much weaker than that determined from SMAP observations.

show abstract

Section: Smap Retrievalsmentioning

confidence: 87%

Controls on surface soil drying rates observed by SMAP and simulated by the Noah land surface model

Shellito

Small

Livneh

2018

Hydrol. Earth Syst. Sci.

View full text Add to dashboard Cite

show abstract

“…Notable exceptions are the Little River (LR) and South Fork (SF) watersheds, both of which have previously been identified as sites with large discrepancies between the SMAP retrievals and the in situ measurements [10,49]. At LR, DA-NN consistently degrades the model skill in both soil layers and across all metrics, whereas DA-NN-lCDF yields small or no skill changes.…”

Section: Evaluation Against In Situ Measurementsmentioning

confidence: 99%

Data Assimilation to Extract Soil Moisture Information from SMAP Observations

et al. 2017

View full text Add to dashboard Cite

Abstract: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 construction, the NN retrievals are consistent with the global climatology of the Catchment model soil moisture. Assimilating the NN retrievals without further bias correction improved the surface and root zone correlations against in situ measurements from 14 SMAP core validation sites (CVS) by 0.12 and 0.16, respectively, over the model-only skill, and reduced the surface and root zone unbiased root-mean-square error (ubRMSE) by 0.005 m 3 m −3 and 0.001 m 3 m −3 , respectively. The assimilation reduced the average absolute surface bias against the CVS measurements by 0.009 m 3 m −3 , but increased the root zone bias by 0.014 m 3 m −3 . Assimilating the NN retrievals after a localized bias correction yielded slightly lower surface correlation and ubRMSE improvements, but generally the skill differences were small. The assimilation of the physically-based SMAP Level-2 passive soil moisture retrievals using a global bias correction yielded similar skill improvements, as did the direct assimilation of locally bias-corrected SMAP brightness temperatures within the SMAP Level-4 soil moisture algorithm. The results show that global bias correction methods may be able to extract more independent information from SMAP observations compared to local bias correction methods, but without accurate quality control and observation error characterization they are also more vulnerable to adverse effects from retrieval errors related to uncertainties in the retrieval inputs and algorithm. Furthermore, the results show that using global bias correction approaches without a simultaneous re-calibration of the land model processes can lead to skill degradation in other land surface variables.

show abstract

“…To date, the presence of random errors in remotely sensed soil moisture products has limited their application in GCM SMTC coupling studies. However, the recently launched Soil Moisture Passive/Active (SMAP) mission provides soil moisture retrievals with improved accuracy (Chan et al, ; Chen et al, ) and has already proven valuable for diagnosing soil moisture coupling bias in off‐line land surface models (Crow et al, ).…”

Section: Introductionmentioning

confidence: 99%

Use of Satellite Soil Moisture to Diagnose Climate Model Representations of European Soil Moisture‐Air Temperature Coupling Strength

Dong¹,

Crow²

2018

Geophysical Research Letters

View full text Add to dashboard Cite

Soil moisture‐air temperature coupling (SMTC) strength affects European summer air temperature variability. However, due to a lack of spatially extensive ground‐based soil moisture observations, General Circulation Model predictions of European‐mean SMTC strength have not been adequately verified and contain substantial uncertainties. Here we utilize remotely sensed soil moisture to evaluate estimates of SMTC strength provided by seven Coupled Model Intercomparison Project Phase 5 (CMIP5) General Circulation Models (GCMs). Soil moisture retrievals provided by the Soil Moisture Active/Passive (SMAP) mission are shown to be uniquely suited for this purpose. Comparisons to SMAP‐based estimates suggest that CMIP5 GCMs generally underestimate SMTC strengths—particularly in central Europe. This result is consistent with previous modeling work and highlights the value of L‐band soil moisture remote sensing for land surface‐atmosphere coupling analyses.

show abstract

Development and assessment of the SMAP enhanced passive soil moisture product

Cited by 357 publications

References 19 publications

Controls on surface soil drying rates observed by SMAP and simulated by the Noah land surface model

Controls on surface soil drying rates observed by SMAP and simulated by the Noah land surface model

Data Assimilation to Extract Soil Moisture Information from SMAP Observations

Use of Satellite Soil Moisture to Diagnose Climate Model Representations of European Soil Moisture‐Air Temperature Coupling Strength

Contact Info

Product

Resources

About