Abstract
Building climate data records of soil moisture (SM) requires computing long time series by merging retrievals from sensors on-board different satellites, which implies to perform a bias correction or rescaling on the original time series. Due to their long time span and high temporal frequency, model data could be used as a common reference for the rescaling. However, avoiding model dependence in observational climate data records is needed for some applications. In this article, the possibility of using as reference remote sensing data from one of the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$L$</tex-math></inline-formula> -band sensors specifically designed to measure SM is discussed. Advanced Microwave Scanning Radiometer 2 SM time series were rescaled by matching their cumulative distribution functions (CDFs) to those of Soil Moisture and Ocean Salinity (SMOS), Soil Moisture Active Passive (SMAP), and Global Land Data Assimilation System (GLDAS) NOAH model time series. The CDF computation was investigated as a function of the time series length, finding significant differences from four to nine years. Replacing temporal by spatial variance does not allow us to compute better CDFs from short time series. The rescaled time series show a high correlation ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$R>0.8$</tex-math></inline-formula> ) to the original ones and a low bias with respect to the reference ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$< $</tex-math></inline-formula> 0.03 m <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{3}\cdot$</tex-math></inline-formula> m <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{-3}$</tex-math></inline-formula> ). The time series rescaled using several SMOS or SMAP datasets were also evaluated against <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in situ</i> measurements and show performances similar to or slightly better than those rescaled using the model GLDAS. The impact of random errors and gaps of the observational data into the rescaling was evaluated. These results show that it is actually possible to use <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$L$</tex-math></inline-formula> -band data as reference to rescale time series from other sensors to build long time series of SM.
Highlights
Soil moisture (SM) was identified as one of the 50R
Example, the approach used for de-biasing satellite data As an alternative to re-scaling the data using the before data assimilation into numerical weather predic- GLDAS model, it has been proposed to use L-band data tion models [21], [4], [22] or into hydrological models from one of the two instruments designed to
The purpose of this study is to evaluate the performances of the original AM 2 time series as well as those re-scaled using ESL3, ESLP, ESN RT, ESIC, N SLP, N SL2, GLDAS
Summary
Example, the approach used for de-biasing satellite data As an alternative to re-scaling the data using the before data assimilation into numerical weather predic- GLDAS model, it has been proposed to use L-band data tion models [21], [4], [22] or into hydrological models from one of the two instruments designed to [23], [10] In these cases, the model time series are measure SM, on-board ESA Soil Moisture and Ocean naturally used as the reference to individually re-scale Salinity (SMOS) and NASA Soil Moisture Active Pasthe different remote sensing datasets. To be consistent with the AM 2 advanced land surface modeling and data assimilation overpasses considered in this study, only the descending techniques They are provided in a 0.25◦ regular latitudeoverpasses of SMAP were taken into account as they longitude grid as NetCDF files. The data are provided in the 36 km EASE-Grid v2.0
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