Spatial representativeness of soil moisture using in situ, remote sensing, and land reanalysis data

Abstract

This study investigates the spatial representativeness of the temporal dynamics of absolute soil moisture and its temporal anomalies over North America based on a range of data sets. We use three main data sources: in situ observations, the remote-sensing-based data set of the European Space Agency Climate Change Initiative on the Essential Climate Variable soil moisture (ECV-SM), and land surface model estimates from European Centre for Medium-Range Weather Forecasts's ERA-Land. The intercomparisons of the three soil moisture data sources are performed at the in situ locations as well as for the full-gridded products. The applied method allows us to quantify the spatial footprint of soil moisture. At the in situ locations it is shown that for absolute soil moisture the ECV-SM and ERA-Land products perform similarly, while for the temporal anomalies the ECV-SM product shows more similarity in spatial representativeness with the in situ data. When taking into account all grid cells of the ECV-SM and ERA-Land products to calculate spatial representativeness, we find the largest differences in spatial representativeness for the absolute values. The differences in spatial representativeness between the single products can be related to some of their intrinsic characteristics, i.e., for ECV-SM low similarities are found in topographically complex terrain and areas with dense vegetation, while for ERA-Land the smoothed model topography and surface properties affect soil moisture and its spatial representativeness. Additionally, we show that the applied method is robust and can be used to analyze existing networks to provide insight into the locations in which higher station density would be of most benefit.

Consistency and Discrepancy of Global Surface Soil Moisture Changes From Multiple Model‐Based Data Sets Against Satellite Observations

“…Previous studies have demonstrated the good performance of ECV soil moisture against in situ measurements (e.g., Chakravorty et al, ; Dorigo et al, ; Zeng et al, ). Regional studies, such as those in China, India, and North America, indicated that ECV outperforms other soil moisture data sets (An et al, ; Chakravorty et al, ; Jia et al, ; Nicolai‐Shaw et al, ; Qiu et al, ; Zeng et al, ). In recent studies, ECV soil moisture has been widely accepted and used as reliable soil moisture observations for estimating long‐term changes of hydrological cycle in different regions (e.g., Chen et al, ; Feng, ; Feng & Zhang, ; Zohaib et al, ).…”

“…Compared to model simulations, satellite‐based data sets provide more direct estimates of global surface soil moisture through monitoring temporal dynamics of the soil moisture field. Regional studies have demonstrated that ECV products show more similarity in spatial representativeness with the in situ measurements than reanalysis data sets (e.g., Nicolai‐Shaw et al, ). Previous comparisons of ECV soil moisture against in situ measurements in China showed that ECV has moderately high accuracy with in situ measurements and is better in identifying soil moisture trends than GLDAS and ERA‐Interim reanalysis data sets (An et al, ; Jia et al, ; Qiu et al, ).…”

Attribution of Global Soil Moisture Drying to Human Activities: A Quantitative Viewpoint

“…Additionally, GLDAS has been proven to be able to reflect global trend patterns (e.g., Dorigo et al, 2012) and regional trend patterns of SM, such as in China (e.g., Cheng et al, 2017;Jia et al, 2018). Due to the good performance of GLDAS, it has been widely used to analyze global and regional SM changes (e.g., Cheng et al, 2015;Cheng & Huang, 2016;Zawadzki & Kedzior, 2014), assess land-atmosphere coupling (e.g., Liu et al, 2017;Zhang et al, 2008), and validate SM retrieved from satellites (e.g., SM from European Space Agency Climate Change Initiative program, a merged remotely sensed data, was rescaled by GLDAS NOAH; Nicolai-Shaw et al, 2015). In this study, we also evaluated the GLDAS SM accuracy through a comparison of three commonly used global reanalysis data sets and one ground-based observation data set in China (see Texts S1 and S2 and Figures S1-S6 in the supporting information; Bi et al, 2016;Chen et al, 2016;Cheng et al, 2006Cheng et al, , 2015Cheng et al, , 2017Cheng & Huang, 2016;Dorigo et al, 2012;Jia et al, 2018;Kim et al, 2018;Liu et al, 2009Liu et al, , 2017Nicolai-Shaw et al, 2015;Seneviratne et al, 2010;Spennemann et al, 2015;Zawadzki & Kedzior, 2014;Zhang et al, 2008).…”

Nonparametric Data Assimilation Scheme for Land Hydrological Applications

“…The results can be evaluated using representative soil moisture sites within the basin. Here we use an analysis suggested by De Lannoy, Pauwels, et al () to acquire the representative soil moisture in situ measurements (see other methods in, e.g., Famiglietti et al, ; Orlowsky & Seneviratne, ; Nicolai‐Shaw et al, ). The method is based on relative differences d m , n for site m and time step n , which can be calculated as (De Lannoy, Pauwels, et al, ) where S M m , n is the soil moisture measurement at m and n , and represents the spatially averaged soil moisture.…”