Comparison of gap-filling techniques applied to the CCI soil moisture database in Southern Europe

• Published in: Remote sensing of environment Vol. 258; p. 112377
• Main Authors: Almendra-Martín, Laura, Martínez-Fernández, José, Piles, María, González-Zamora, Ángel
• Format: Journal Article
• Language: English
• Published: New York Elsevier Inc 01.06.2021; Elsevier BV
• Subjects: CCI; Climate change; Contamination; Correlation coefficient; Correlation coefficients; Domains; environment; Environmental impact; Evaporation; Evaporation rate; France; Gap-filling; Geographical coordinates; Germany; ice; Interpolation; Interpolation methods; Italy; Land surface temperature; Methods; Normalized difference vegetative index; Potential evaporation; principal component analysis; Principal components analysis; Radio frequency interference; radio waves; satellites; Soil contamination; Soil moisture; Soil properties; Soil temperature; Soil texture; soil water; Southern European region; Spain; Spatial analysis; Support vector machines; Surface temperature; Texture; time series analysis; Variables; Vegetation index; Southern Europe; Italy; France; Germany; Southern European region; Spain; Support vector machines; CCI; Gap-filling; Soil moisture
• ISSN: 0034-4257; 1879-0704
• DOI: 10.1016/j.rse.2021.112377

Soil moisture (SM) is a key variable that plays an important role in land-atmosphere interactions. Monitoring SM is crucial for many applications and can help to determine the impact of climate change. Therefore, it is essential to have continuous and long-term databases for this variable. Satellite missions have contributed to this; however, the continuity of the series is compromised due to the data gaps derived by different factors, including revisit time, presence of seasonal ice or Radio Frequency Interference (RFI) contamination. In this work, the applicability of different gap-filling techniques is evaluated on the ESA Climate Change Initiative (CCI) SM combined product, which is the longest available satellite-based SM data record. The methods used were linear, cubic and autoregressive interpolation and support vector machines (SVMs). This study focused on Southern Europe and spanned the years 2003–2015. The different methods were applied in the temporal and spatial domains and evaluated using the holdout cross-validation technique. A set of variables was introduced in the SVM model to estimate SM, namely, land surface temperature, precipitation, normalized difference vegetation index (NDVI), potential evaporation, soil texture and geographical coordinates. For the SVMs, several combinations of these variables were considered, including a principal component analysis (PCA) containing all of them. Although the different methods show a generally good performance, the SVM method outperforms the rest. Using the SM of the precedent day (SMt-1) is key to obtain good estimates. The median value of the correlation coefficient (R) obtained with the SVM and the SMt-1 series in the temporal analysis was 0.83, and the RMSE was 0.025 m3m−3. Similar results were obtained in the spatial analysis, with the best performance (R = 0.88; RMSE = 0.024 m3m−3) obtained by the SVM using the SMt-1 series and the static variables. The application of PCA to input variables was not beneficial, and the interpolation methods failed when dealing with large spatial or temporal gaps. A validation of the CCI SM series with in situ SM data from four networks located in Spain, France, Germany and Italy was also performed and no substantial differences were observed between results obtained with the original and with the reconstructed series. In addition, best inputs obtained with SVM were used to evaluate the random forest (RF) method in the temporal and spatial domain. This method showed a good ability to estimate soil moisture values in the temporal domain but to a lesser extent than SVM while for the spatial domain it did not seem to be as accurate. Our results confirm that we can efficiently deal with spatio-temporal gaps on observational SM databases using the SVM method and the past time series and soil texture as supporting information. •SVM is a robust technique for filling in the gaps of the CCI Soil Moisture product.•Interpolation methods fail when dealing with large spatial or temporal gaps.•Including the SMt-1 series in the SVM models is key to obtaining good estimations.•Temperature proved to be a crucial variable for the SVM in the temporal domain.•Only SMt-1, spatial coordinates and sand are relevant for SVM in the spatial domain.