Estimating 500-m Resolution Soil Moisture Using Sentinel-1 and Optical Data Synergy

Abstract

The aim of this study is to estimate surface soil moisture at a spatial resolution of 500 m and a temporal resolution of at least 6 days, by combining remote sensing data from Sentinel-1 and optical data from Sentinel-2 and MODIS (Moderate-Resolution Imaging Spectroradiometer). The proposed methodology is based on the change detection technique, applied to a series of measurements over a three-year period (2015 to 2018). The algorithm described here as “Soil Moisture Estimations from the Synergy of Sentinel-1 and optical sensors (SMES)” proposes different options, allowing information from vegetation densities and seasonal conditions to be taken into account. The output from this algorithm is a moisture index ranging between 0 and 1, with 0 corresponding to the driest soils and 1 to the wettest soils. This methodology has been tested at different test sites (South of France, Central Tunisia, Western Benin and Southwestern Niger), characterized by a wide range of different climatic conditions. The resulting surface soil moisture estimations are compared with in situ measurements and already existing satellite-derived soil moisture ASCAT (Advanced SCATterometer) products. They are found to be well correlated, for the African regions in particular (RMSE below 6 vol.%). This outcome indicates that the proposed algorithm can be used with confidence to estimate the surface soil moisture of a wide range of climatically different sites.

Statistical Exploration of SENTINEL-1 Data, Terrain Parameters, and in-situ Data for Estimating the Near-Surface Soil Moisture in a Mediterranean Agroecosystem

“…Spaceborne remote sensing (RS) provides spatially explicit information as satellites sense the same ground trace in regular time intervals, allowing for continuous monitoring (Babaeian et al, 2019). Estimates of θ are retrieved from different sensors measuring optical and thermal spectra (e.g., Rahimzadeh-Bajgiran et al, 2013;Zhang and Zhou, 2016), by passive and active microwave sensors (e.g., Schmugge and Jackson, 1997;Das and Paul, 2015), or the synergistic use of different sensor types such as using radar and optical data from Sentinel-2, Landsat, and MODIS (e.g., Attarzadeh et al, 2018;Ayehu et al, 2020;Foucras et al, 2020;Han et al, 2020;Ma et al, 2020). Synthetic Aperture Radars (SAR) are among the most effective and flexible active microwave sensor systems (e.g., Wang and Qu, 2009;Santi et al, 2016) due to their ability to penetrate the near-surface soil layer up to a depth of 5 cm (i.e., for C-band), which in turn enables to observe θ by directly relating the microwave scattering and emission to the water content of the focused object (e.g., Paloscia et al, 2013;Santi et al, 2016;Mohanty et al, 2017;Babaeian et al, 2019).…”

Near Real-Time Irrigation Detection at Plot Scale Using Sentinel-1 Data

“…Since irrigation eventually increases the soil and the vegetation water content, the sensitivity of the radar signal to soil and vegetation water could help detect these irrigation events. Through literature, it has been widely demonstrated that the SAR backscattering coefficient (σ 0 ) is directly related to the soil and vegetation water content [14][15][16][17][18][19][20]. Mainly for the irrigation task, Hajj et al [21] have reported that a three-day-old irrigation point could still be detected using X-band SAR data.…”

Precipitation Data Retrieval and Quality Assurance from Different Data Sources for the Namoi Catchment in Australia

“…It is explicitly noted that soil moisture changes due to precipitation are well captured. The authors of [21] have even derived soil moisture at a 500 m resolution. In WaterSENSE, soil moisture is derived as part of the water cycle monitoring toolkit.…”