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- Author or Editor: Wade T. Crow x
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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.
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.
Abstract
The Soil Moisture Active Passive (SMAP) mission Level-4 Soil Moisture (L4_SM) product provides 3-hourly, 9-km resolution, global estimates of surface (0–5 cm) and root-zone (0–100 cm) soil moisture and related land surface variables from 31 March 2015 to present with ~2.5-day latency. The ensemble-based L4_SM algorithm assimilates SMAP brightness temperature (Tb) observations into the Catchment land surface model. This study describes the spatially distributed L4_SM analysis and assesses the observation-minus-forecast (O − F) Tb residuals and the soil moisture and temperature analysis increments. Owing to the climatological rescaling of the Tb observations prior to assimilation, the analysis is essentially unbiased, with global mean values of ~0.37 K for the O − F Tb residuals and practically zero for the soil moisture and temperature increments. There are, however, modest regional (absolute) biases in the O − F residuals (under ~3 K), the soil moisture increments (under ~0.01 m3 m−3), and the surface soil temperature increments (under ~1 K). Typical instantaneous values are ~6 K for O − F residuals, ~0.01 (~0.003) m3 m−3 for surface (root zone) soil moisture increments, and ~0.6 K for surface soil temperature increments. The O − F diagnostics indicate that the actual errors in the system are overestimated in deserts and densely vegetated regions and underestimated in agricultural regions and transition zones between dry and wet climates. The O − F autocorrelations suggest that the SMAP observations are used efficiently in western North America, the Sahel, and Australia, but not in many forested regions and the high northern latitudes. A case study in Australia demonstrates that assimilating SMAP observations successfully corrects short-term errors in the L4_SM rainfall forcing.
Abstract
The Soil Moisture Active Passive (SMAP) mission Level-4 Soil Moisture (L4_SM) product provides 3-hourly, 9-km resolution, global estimates of surface (0–5 cm) and root-zone (0–100 cm) soil moisture and related land surface variables from 31 March 2015 to present with ~2.5-day latency. The ensemble-based L4_SM algorithm assimilates SMAP brightness temperature (Tb) observations into the Catchment land surface model. This study describes the spatially distributed L4_SM analysis and assesses the observation-minus-forecast (O − F) Tb residuals and the soil moisture and temperature analysis increments. Owing to the climatological rescaling of the Tb observations prior to assimilation, the analysis is essentially unbiased, with global mean values of ~0.37 K for the O − F Tb residuals and practically zero for the soil moisture and temperature increments. There are, however, modest regional (absolute) biases in the O − F residuals (under ~3 K), the soil moisture increments (under ~0.01 m3 m−3), and the surface soil temperature increments (under ~1 K). Typical instantaneous values are ~6 K for O − F residuals, ~0.01 (~0.003) m3 m−3 for surface (root zone) soil moisture increments, and ~0.6 K for surface soil temperature increments. The O − F diagnostics indicate that the actual errors in the system are overestimated in deserts and densely vegetated regions and underestimated in agricultural regions and transition zones between dry and wet climates. The O − F autocorrelations suggest that the SMAP observations are used efficiently in western North America, the Sahel, and Australia, but not in many forested regions and the high northern latitudes. A case study in Australia demonstrates that assimilating SMAP observations successfully corrects short-term errors in the L4_SM rainfall forcing.
