Uncertainty of Reference Pixel Soil Moisture Averages Sampled at SMAP Core Validation Sites

• Published in: Journal of hydrometeorology 2019-08, Vol.20 (8), p.1553-1569
• Main Authors: Chen, Fan, Crow, Wade T., Cosh, Michael H., Colliander, Andreas, Asanuma, Jun, Berg, Aaron, Bosch, David D., Caldwell, Todd G., Collins, Chandra Holifield, Jensen, Karsten Høgh, Martínez-Fernández, Jose, Mcnairn, Heather, Starks, Patrick J., Su, Zhongbo, Walker, Jeffrey P.
• Format: Article
• Language: English
• Subjects: Bias; Confidence intervals; Errors; Estimates; Ground cover; Irrigation; Mathematical analysis; Pixels; Remote sensing; Sampling; Sampling error; Sensors; Soil; Soil dynamics; Soil moisture; Soils; Spatial variability; Spatial variations; Statistical analysis; Uncertainty
• ISSN: 1525-755X; 1525-7541
• DOI: 10.1175/jhm-d-19-0049.1

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 pointscale 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 mm−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.