Spatial Gap-Filling of ESA CCI Satellite-Derived Soil Moisture Based on Geostatistical Techniques and Multiple Regression

Abstract

Soil moisture plays a key role in the Earth’s water and carbon cycles, but acquisition of continuous (i.e., gap-free) soil moisture measurements across large regions is a challenging task due to limitations of currently available point measurements. Satellites offer critical information for soil moisture over large areas on a regular basis (e.g., European Space Agency Climate Change Initiative (ESA CCI), National Aeronautics and Space Administration Soil Moisture Active Passive (NASA SMAP)); however, there are regions where satellite-derived soil moisture cannot be estimated because of certain conditions such as high canopy density, frozen soil, or extremely dry soil. We compared and tested three approaches, ordinary kriging (OK), regression kriging (RK), and generalized linear models (GLMs), to model soil moisture and fill spatial data gaps from the ESA CCI product version 4.5 from January 2000 to September 2012, over a region of 465,777 km2 across the Midwest of the USA. We tested our proposed methods to fill gaps in the original ESA CCI product and two data subsets, removing 25% and 50% of the initially available valid pixels. We found a significant correlation (r = 0.558, RMSE = 0.069 m3m−3) between the original satellite-derived soil moisture product with ground-truth data from the North American Soil Moisture Database (NASMD). Predicted soil moisture using OK also had significant correlation with NASMD data when using 100% (r = 0.579, RMSE = 0.067 m3m−3), 75% (r = 0.575, RMSE = 0.067 m3m−3), and 50% (r = 0.569, RMSE = 0.067 m3m−3) of available valid pixels for each month of the study period. RK showed comparable values to OK when using different percentages of available valid pixels, 100% (r = 0.582, RMSE = 0.067 m3m−3), 75% (r = 0.582, RMSE = 0.067 m3m−3), and 50% (r = 0.571, RMSE = 0.067 m3m−3). GLM had slightly lower correlation with NASMD data (average r = 0.475, RMSE = 0.070 m3m−3) when using the same subsets of available data (i.e., 100%, 75%, 50%). Our results provide support for using geostatistical approaches (OK and RK) as alternative techniques to gap-fill missing spatial values of satellite-derived soil moisture.

Generating seamless global daily AMSR2 soil moisture (SGD-SM) long-term products for the years 2013–2019

“…Surface soil moisture is a crucial Earth land characteristic in describing the hydrologic cycle system (Wigneron et al, 2003;Lievens et al, 2015). It can be applied for monitoring droughts and floods in agriculture (Samaniego et al, 2018) and geologic hazards (Long et al, 2014). To obtain the global and high-frequency soil moisture products, many active or passive satellite sensors have been launched, such as the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E), Advanced Microwave Scanning Radiometer 2 (AMSR2), and Soil Moisture Active and Passive (SMAP), Soil Moisture and Ocean Salinity (SMOS) among others (McColl et al, 2017;Ma et al, 2019).…”

Gap-free global annual soil moisture: 15 km grids for 1991–2018

“…Soil moisture data are essential for scientific inquiry in a variety of research areas. These data enable scientists to characterize hydrological patterns (Greve and Seneviratne, 2015), quantify the influence of soil moisture on terrestrial carbon dynamics (van der Molen et al, 2011), identify trends in global climate variability (Seneviratne et al, 2013), analyze the response of ecosystems to moisture decline (Zhou et al, 2014), or detect the impact of moisture on models of landatmosphere interactions (May et al, 2016). The integrity of current soil moisture data is fundamental for a comprehensive understanding of the global water cycle (Al-Yaari et al, 2019).…”

The Impact of Drought on Soil Moisture Trends across Brazilian Biomes

“…For the variogram analysis we computed the relationships between the distance of randomly distributed soil moisture observations and the accumulated variance of their respective values. We used the aforementioned relationships to predict the satellite soil moisture trend in areas where no data is available and also provided a spatial explicit measure of error following a geostatistical framework (Hiemstra et al 2009, Llamas et al, 2020.…”