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Andreas Colliander
,
Thomas J. Jackson
,
Aaron Berg
,
D. D. Bosch
,
Todd Caldwell
,
Steven Chan
,
Michael H. Cosh
,
C. Holifield Collins
,
Jose Martínez-Fernández
,
Heather McNairn
,
J. H. Prueger
,
P. J. Starks
,
Jeffrey P. Walker
, and
Simon H. Yueh

Abstract

Soil moisture retrieval is particularly challenging during and immediately after precipitation events because of the transient movement of water in the shallow subsurface. Conventional L-band microwave radiometer–based soil moisture products use algorithms that assume a static state and a constant vertical soil moisture distribution. This study assessed the retrieval performance of a SMAP radiometer-based soil moisture product during and immediately after rain events. The removal of the rain event samples systematically improved the unbiased root-mean-square error (ubRMSE) from 0.037 (all measurements) to 0.028 m3 m−3 (transitory measurements screened out), while the magnitude of the bias became larger (from −0.005 to −0.014 m3 m−3); RMSE improved from 0.047 to 0.042 m3 m−3, and the Pearson correlation saw a minor positive change from 0.813 to 0.824. The results indicate that removing samples during the transitional period causes the comparison to improve, but also suggests that the true bias may be larger than the one estimated using all the samples. Furthermore, the results revealed that the effect was stronger for areas with high clay content. An assessment of the performance of the product during the rain events (overpass within 3 h from the start of the rain) showed that the ubRMSE degraded from the benchmarked 0.036 m3 m−3 (during no rain events at all) to 0.043 m3 m−3 (during rain). The results also showed that the bias became wetter, which is expected because SMAP sensed the water on the surface before propagating to the in situ sensors. SMAP maintains its soil moisture sensitivity even during rain events and screening of rain events may not be necessary to ensure sufficient soil moisture retrieval quality.

Free access
Fan Chen
,
Wade T. Crow
,
Michael H. Cosh
,
Andreas Colliander
,
Jun Asanuma
,
Aaron Berg
,
David D. Bosch
,
Todd G. Caldwell
,
Chandra Holifield Collins
,
Karsten Høgh Jensen
,
Jose Martínez-Fernández
,
Heather McNairn
,
Patrick J. Starks
,
Zhongbo Su
, and
Jeffrey P. Walker

Abstract

Despite extensive efforts to maximize ground coverage and improve upscaling functions within core validation sites (CVS) of the NASA Soil Moisture Active Passive (SMAP) mission, spatial averages of point-scale soil moisture observations often fail to accurately capture the true average of the reference pixels. Therefore, some level of pixel-scale sampling error from in situ observations must be considered during the validation of SMAP soil moisture retrievals. Here, uncertainties in the SMAP core site average soil moisture (CSASM) due to spatial sampling errors are examined and their impact on CSASM-based SMAP calibration and validation metrics is discussed. The estimated uncertainty (due to spatial sampling limitations) of mean CSASM over time is found to be large, translating into relatively large sampling uncertainty levels for SMAP retrieval bias when calculated against CSASM. As a result, CSASM-based SMAP bias estimates are statistically insignificant at nearly all SMAP CVS. In addition, observations from temporary networks suggest that these (already large) bias uncertainties may be underestimated due to undersampled spatial variability. The unbiased root-mean-square error (ubRMSE) of CSASM is estimated via two approaches: classical sampling theory and triple collocation, both of which suggest that CSASM ubRMSE is generally within the range of 0.01–0.02 m3 m−3. Although limitations in both methods likely lead to underestimation of ubRMSE, the results suggest that CSASM captures the temporal dynamics of the footprint-scale soil moisture relatively well and is thus a reliable reference for SMAP ubRMSE calculations. Therefore, spatial sampling errors are revealed to have very different impacts on efforts to estimate SMAP bias and ubRMSE metrics using CVS data.

Full access
C. Bruce Baker
,
Michael Cosh
,
John Bolten
,
Mark Brusberg
,
Todd Caldwell
,
Stephanie Connolly
,
Iliyana Dobreva
,
Nathan Edwards
,
Peter E. Goble
,
Tyson E. Ochsner
,
Steven M. Quiring
,
Michael Robotham
,
Marina Skumanich
,
Mark Svoboda
,
W. Alex White
, and
Molly Woloszyn

Abstract

Soil moisture is a critical land surface variable, impacting the water, energy, and carbon cycles. While in situ soil moisture monitoring networks are still developing, there is no cohesive strategy or framework to coordinate, integrate, or disseminate these diverse data sources in a synergistic way that can improve our ability to understand climate variability at the national, state, and local levels. Thus, a national strategy is needed to guide network deployment, sustainable network operation, data integration and dissemination, and user-focused product development. The National Coordinated Soil Moisture Monitoring Network (NCSMMN) is a federally led, multi-institution effort that aims to address these needs by capitalizing on existing wide-ranging soil moisture monitoring activities, increasing the utility of observational data, and supporting their strategic application to the full range of decision-making needs. The goals of the NCSMMN are to 1) establish a national “network of networks” that effectively demonstrates data integration and operational coordination of diverse in situ networks; 2) build a community of practice around soil moisture measurement, interpretation, and application—a “network of people” that links data providers, researchers, and the public; and 3) support research and development (R&D) on techniques to merge in situ soil moisture data with remotely sensed and modeled hydrologic data to create user-friendly soil moisture maps and associated tools. The overarching mission of the NCSMMN is to provide coordinated high-quality, nationwide soil moisture information for the public good by supporting applications like drought and flood monitoring, water resource management, agricultural and forestry planning, and fire danger ratings.

Free access
Rolf H. Reichle
,
Gabrielle J. M. De Lannoy
,
Qing Liu
,
Joseph V. Ardizzone
,
Andreas Colliander
,
Austin Conaty
,
Wade Crow
,
Thomas J. Jackson
,
Lucas A. Jones
,
John S. Kimball
,
Randal D. Koster
,
Sarith P. Mahanama
,
Edmond B. Smith
,
Aaron Berg
,
Simone Bircher
,
David Bosch
,
Todd G. Caldwell
,
Michael Cosh
,
Ángel González-Zamora
,
Chandra D. Holifield Collins
,
Karsten H. Jensen
,
Stan Livingston
,
Ernesto Lopez-Baeza
,
José Martínez-Fernández
,
Heather McNairn
,
Mahta Moghaddam
,
Anna Pacheco
,
Thierry Pellarin
,
John Prueger
,
Tracy Rowlandson
,
Mark Seyfried
,
Patrick Starks
,
Zhongbo Su
,
Marc Thibeault
,
Rogier van der Velde
,
Jeffrey Walker
,
Xiaoling Wu
, and
Yijian Zeng

Abstract

The Soil Moisture Active Passive (SMAP) mission Level-4 Surface and Root-Zone Soil Moisture (L4_SM) data product is generated by assimilating SMAP L-band brightness temperature observations into the NASA Catchment land surface model. The L4_SM product is available from 31 March 2015 to present (within 3 days from real time) and provides 3-hourly, global, 9-km resolution estimates of surface (0–5 cm) and root-zone (0–100 cm) soil moisture and land surface conditions. This study presents an overview of the L4_SM algorithm, validation approach, and product assessment versus in situ measurements. Core validation sites provide spatially averaged surface (root zone) soil moisture measurements for 43 (17) “reference pixels” at 9- and 36-km gridcell scales located in 17 (7) distinct watersheds. Sparse networks provide point-scale measurements of surface (root zone) soil moisture at 406 (311) locations. Core validation site results indicate that the L4_SM product meets its soil moisture accuracy requirement, specified as an unbiased RMSE (ubRMSE, or standard deviation of the error) of 0.04 m3 m−3 or better. The ubRMSE for L4_SM surface (root zone) soil moisture is 0.038 m3 m−3 (0.030 m3 m−3) at the 9-km scale and 0.035 m3 m−3 (0.026 m3 m−3) at the 36-km scale. The L4_SM estimates improve (significantly at the 5% level for surface soil moisture) over model-only estimates, which do not benefit from the assimilation of SMAP brightness temperature observations and have a 9-km surface (root zone) ubRMSE of 0.042 m3 m−3 (0.032 m3 m−3). Time series correlations exhibit similar relative performance. The sparse network results corroborate these findings over a greater variety of climate and land cover conditions.

Full access