Main content area

CCI soil moisture assessment with SMOS soil moisture and in situ data under different environmental conditions and spatial scales in Spain

González-Zamora, Á., Sánchez, N., Pablos, M., Martínez-Fernández, J.
Remote sensing of environment 2019 v.225 pp. 469-482
Soil Moisture and Ocean Salinity satellite, climate change, data collection, databases, environmental factors, forests, land use, pastures, remote sensing, soil water, Spain
In this research, the active, passive and combined Climate Change Initiative (CCI) Soil Moisture (SM) products were evaluated in comparison with in situ SM measurements from five networks in Spain that have different spatial and temporal scales, densities and environmental conditions. Three of these networks, namely Rinconada, Morille and the Soil Moisture Measurement Stations Network of the University of Salamanca (REMEDHUS), are small- to medium-scale networks and have high station densities, whereas the other two (Inforiego and FluxNet) are sparse and large-scale networks.The results of the comparisons with the former v02.2 version (before the inclusion of the SM retrieved by the Soil Moisture and Ocean Salinity mission, SMOS, in the CCI dataset) showed that the combined CCI performed better than the active or passive, affording correlation coefficients (R) above 0.8 and errors between 0.03 and 0.08 m3 m−3 for the area-average, with biases close to zero. Regarding the land uses and environmental conditions, the stations that were located in the agricultural areas and some forested areas showed the best results, and those that were located in pasture and certain specific agricultural locations showed the poorest results.To test the opportunity of including SMOS in CCI, both datasets were compared over the same areas and coincident periods. After the results, the combined CCI and SMOS SM products matched very well (R = 0.83 on average), although the SMOS and CCI under- and overestimate the ground soil moisture measurements, respectively.Finally, the new version of the combined CCI (v03.2, after including SMOS) showed similar correlations to the previous one, but it significantly reduced the bias, leading to slightly lower errors (RMSD and cRMSD). Hence, it was shown that including SMOS in the CCI database enhanced its performance.The results in this work may improve knowledge of the CCI SM and its potential applications.