Reconstruction of ESA CCI soil moisture based on DCT-PLS and in situ soil moisture

Abstract

Soil moisture (SM) is a vital variable controlling water and energy exchange between the atmosphere and land surface. Spatiotemporally continuous SM information is urgently needed for large-scale meteorological and hydrological applications. Considering the weakness of the penalized least square regression based on the discrete cosine transform (DCT-PLS) method when the missing data are not evenly distributed in the original data set, this study proposes an in situ observation-combined DCT-PLS (ODCT-PLS) to reconstruct missing values of daily surface SM from the Climate Change Initiative program of the European Space Agency (ESA CCI). The result of the reconstruction for ESA CCI SM data in the Xiliaohe River Basin from 2013 to 2020 showed that the SM reconstructed by ODCT-PLS was in better agreement with in situ soil moisture compared with that reconstructed by DCT-PLS, with the average correlation coefficient (CORR) increasing by 0.3636, the average root mean squared error (RMSE) decreasing by 0.0109 m3/m3 and the average BIAS decreasing by 0.0047 m3/m3. Compared with the original ESA CCI SM, DCT-PLS and ODCT-PLS can both restore the spatial variation of SM in the study area. The reconstruction method proposed in our study provides a valuable alternative to reconstruct the three-dimensional geophysical dataset with spatially or temporally continuous data gap. HIGHLIGHTS This paper proposed a new reconstruction method based on the measured observation data and the DCT-PLS method.; This paper utilized the measured observation data by using the CDF matching method.; The new method this paper proposed can be applied to reconstruct three-dimensional geophysical datasets whose data gaps are spatial-temporally continuous.;

Keywords

• bias correction
• dct-pls
• esa cci
• odct-pls
• reconstruction
• soil moisture