Seamless downscaling of the ESA CCI soil moisture data at the daily scale with MODIS land products

Abstract

Spatial downscaling has recently become a crucial process in the regional application of coarse-resolution passive microwave surface soil moisture (SSM) products. Extensive gaps in auxiliary optical/thermal infrared observation data (mainly caused by cloud cover) and gaps in coarse-resolution passive microwave SSM data lead to spatiotemporal discontinuity in downscaled SSM maps, thereby limiting their applications. An improved downscaling method for the 25-km European Space Agency (ESA) Climate Change Initiative (CCI) SSM product was proposed to obtain daily seamless downscaled SSM series at a 1-km scale. The Moderate Resolution Imaging Spectroradiometer (MODIS) Terra daily land surface temperature (LST) and normalized difference vegetation index (NDVI) products were used as auxiliary data for the downscaling process. Prior to the spatial downscaling, an annual temperature cycle model was applied to the 1-km daily daytime LST data to fill the data gaps caused by cloud cover and to derive the spatial-seamless LST (gap-filled). Subsequently, the gap-filled ESA CCI SSM was generated at the original resolution based on the relationships among the SSM, LST, and NDVI. Finally, these were utilized to obtain a seamless downscaled series SSM at 1-km spatial resolution with a value-consistent downscaling method. The proposed method was applied to data obtained for the Iberian Peninsula from January 1, 2016 to December 31, 2018. Based on the comparison with the precipitation dataset, the downscaled SSM exhibited strong temporal correlation with rainfall events. Evaluation using the in situ SSM from the REMEDHUS network highlighted the good performance of the downscaled SSM at network level with a correlation coefficient (R) of 0.820. The root-mean-square-error, unbiased root-mean-square error (ubRMSE), and bias were 0.091, 0.033, and 0.085 m3/m3, respectively. A comparison with an alternative downscaled SSM product produced by the Barcelona Expert Center, one of the soil moisture and ocean salinity mission (SMOS)–downscaled SSM datasets, also indicated that the downscaled SSM has better spatiotemporal coverage and performance in terms of R and ubRMSE with reference to the REMEDHUS network. These results confirmed that the proposed method is an efficient and convenient downscaling process that can be used to generate high-resolution SSM data without spatiotemporal gaps. The downscaled SSM data improves the accuracy of the original passive microwave SSM product in describing regional SSM variations and shows good potential for related applications at regional scale.