Hydrology and Earth System Sciences
Hydrology and Earth System Sciences
HESS
Articles | Volume 24, issue 4
Articles | Volume 24, issue 4
https://doi.org/10.5194/hess-24-1957-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-24-1957-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 24, issue 4
Research article
 | 
17 Apr 2020
Research article |  | 17 Apr 2020

Required sampling density of ground-based soil moisture and brightness temperature observations for calibration and validation of L-band satellite observations based on a virtual reality

Shaoning Lv, Bernd Schalge, Pablo Saavedra Garfias, and Clemens Simmer

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Latest update: 26 Dec 2024
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Short summary
Passive remote sensing of soil moisture has good potential to improve weather forecasting via data assimilation in theory. We use the virtual reality data set (VR01) to infer the impact of sampling density on soil moisture ground cal/val activity. It shows how the sampling error is growing with an increasing sampling distance for a SMOS–SMAP scale footprint in about 40 km, 9 km, and 3 km. The conclusion will help in understanding the passive remote sensing soil moisture products.
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