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Record W2582059183 · doi:10.1016/j.rse.2017.01.021

Validation of SMAP surface soil moisture products with core validation sites

2017· article· en· W2582059183 on OpenAlexafffund
Andreas Colliander, Thomas J. Jackson, Rajat Bindlish, S. Chan, Narendra N. Das, S.B. Kim, Michael H. Cosh, R. S. Dunbar, L. Dang, L. Pashaian, Jun Asanuma, K. Aida, Aaron Berg, Tracy Rowlandson, David D. Bosch, Todd G. Caldwell, K. K. Caylor, David C. Goodrich, Hala Al Jassar, Ernesto López-Baeza, José Martínez‐Fernández, Ángel González‐Zamora, Stanley Livingston, Heather McNairn, A. Pacheco, Mahta Moghaddam, Carsten Montzka, Claudia Notarnicola, Georg Niedrist, Thierry Pellarin, John H. Prueger, Jouni Pulliainen, Kimmo Rautiainen, Judith Ramos, M. S. Seyfried, Patrick J. Starks, Zhongbo Su, Yijian Zeng, R. van der Velde, M. Thibeault, Wouter Dorigo, Mariëtte Vreugdenhil, Jeffrey P. Walker, Xiaoling Wu, A. Monerris, Peggy O’Neill, Dara Entekhabi, E. G. Njoku, Simon Yueh

Bibliographic record

VenueRemote Sensing of Environment · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicSoil Moisture and Remote Sensing
Canadian institutionsAgriculture and Agri-Food CanadaUniversity of Guelph
FundersJet Propulsion LaboratoryCanadian Space AgencyAustralian Research CouncilKuwait Foundation for the Advancement of SciencesEnvironment CanadaCalifornia Institute of TechnologyNational Aeronautics and Space Administration
KeywordsEnvironmental scienceRemote sensingRadiometerRadarWater contentCalibrationPixelMean squared errorMoistureScale (ratio)Image resolutionMeteorologyComputer scienceGeologyMathematicsStatistics

Abstract

fetched live from OpenAlex

The NASA Soil Moisture Active Passive (SMAP) mission has utilized a set of core validation sites as the primary methodology in assessing the soil moisture retrieval algorithm performance. Those sites provide well-calibrated in situ soil moisture measurements within SMAP product grid pixels for diverse conditions and locations. The estimation of the average soil moisture within the SMAP product grid pixels based on in situ measurements is more reliable when location specific calibration of the sensors has been performed and there is adequate replication over the spatial domain, with an up-scaling function based on analysis using independent estimates of the soil moisture distribution. SMAP fulfilled these requirements through a collaborative Cal/Val Partner program. This paper presents the results from 34 candidate core validation sites for the first eleven months of the SMAP mission. As a result of the screening of the sites prior to the availability of SMAP data, out of the 34 candidate sites 18 sites fulfilled all the requirements at one of the resolution scales (at least). The rest of the sites are used as secondary information in algorithm evaluation. The results indicate that the SMAP radiometer-based soil moisture data product meets its expected performance of 0.04 m 3 /m 3 volumetric soil moisture (unbiased root mean square error); the combined radar-radiometer product is close to its expected performance of 0.04 m 3 /m 3 , and the radar-based product meets its target accuracy of 0.06 m 3 /m 3 (the lengths of the combined and radar-based products are truncated to about 10 weeks because of the SMAP radar failure). Upon completing the intensive Cal/Val phase of the mission the SMAP project will continue to enhance the products in the primary and extended geographic domains, in co-operation with the Cal/Val Partners, by continuing the comparisons over the existing core validation sites and inclusion of candidate sites that can address shortcomings.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.022
GPT teacher head0.231
Teacher spread0.209 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations739
Published2017
Admission routes2
Has abstractyes

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