soil moisture from satellite

soil moisture from satellite
October 28, 2020

This metric is applied to determine the CSM. (2014) who find similar surface and root zone soil moisture in mean climatological conditions, or Qiu et al. Figure 3 shows the time-average surface soil moisture from January 1979 to August 1987 for SMMR retrievals and the Catchment Land Surface Model. This is because the values of the ESA CCI soil moisture are derived by scaling the satellite‐observed temporal dynamics against modeled data (Dorigo et al., 2017). These information products highlight where conditions are wetter or drier than normal. The Plain Language Summary has been added, and this version may be considered the authoritative version of record. It is possible to retrieve surface soil moisture from low-frequency active and passive microwave data collected by satellite with varying degrees of accuracy. Because of the paucity of calibration and validation data, any calibration to a regional dataset would in effect invalidate the global applicability of the algorithm. Similar results are reported for example by Hirschi et al. Time series correlation between surface soil moisture from SMMR and the Catchment model (Jan 1979–Aug 1987): (left) correlation coefficient (cc) and (right) anomaly correlation coefficient (acc). Hydrologic data assimilation with the ensemble Kalman filter. Here, we focus on the U.S. Great Plains and parts of Eurasia just north of the Black and Caspian Seas. Correlations (equation 1) are calculated per grid cell, and per season. There are large regions (including Australia, India, and central Eurasia) with fewer than two rain gauges per 2.5° grid cell that show reasonable agreement between SMMR and model soil moisture, despite the fact that in these regions the precipitation data are primarily based on satellite-derived estimates. Spatially averaged time series cc and acc between surface soil moisture from SMMR, GSMDB, and CLSM. As for the soil types, the CSM for clay is wetter than for all soil textures combined, which is expected because clay has a more negative matric potential than coarser soil textures with dominant sand and silt fractions, and is in line with earlier findings (Akbar et al., 2018; Feldman et al., 2019). Furthermore, SMMR and the ground data correlate very well in Illinois and Iowa, where the Catchment model correlates reasonably with the ground data and poorly with SMMR. In summary, we obtain three key results: 1) The surface soil moisture climatologies of SMMR retrievals, model integrations of observed antecedent meteorological forcing data, and ground measurements are markedly different. The satellite called SMAP (Soil Moisture Active Passive) will measure land surface soil moisture content and whether the ground is frozen or thawed. There are notable differences in the correlation coefficients between the various regions (Fig. Again, the patterns of relatively strong and weak seasonal cycles largely agree between SMMR and the model, with strong seasonal cycles in India, the central United States, and central Eurasia (the two maps correlate with R2 = 0.30).

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