Editorial for the Special Issue “Soil Moisture Retrieval using Radar Remote Sensing Sensors”

Abstract

Soil moisture is a key parameter when it comes to understanding the processes related to the water cycle on continental surfaces (infiltration, evapotranspiration, runoff, etc [...]

Soil Moisture Estimation Using Citizen Observatory Data, Microwave Satellite Imagery, and Environmental Covariates

“…Many SAR analysis and modelling studies have relied on the coefficient of determination (i.e., R 2 ) statistics, and in some cases, considering R 2 = 0.30 as the threshold criteria for accepting a given model for reliable estimation and prediction [20]. The corresponding literature of the use of SAR data for SM estimation reported R 2 values of 0.24 [29], 0.4 to 0.58 [23], and up to 0.71 [24].…”

“…To date, a large proportion of satellite imagery-based SM estimations focus on global and national scales [18,19]. Freely accessible satellite radar data, like Sentinel-1, have great potential in SM estimation [20], but reliable products require field data for calibration, which is one of the most limiting factors. Therefore, systems able to measure dynamic soil properties like SM are very rare, and are limited to a few observation sites and are mainly lacking the spatial interpolation procedures [21,22].…”

“…Zribi et al [20], reviewed emerging SM estimation studies. Gharechelou et al [25] ran several interpolation methods (inverse distance weighting, kriging, and co-kriging) to derive and evaluate SM maps for an arid area.…”

Estimation of surface soil moisture by integrating environmental data and remote-sensing satellites

“…A significant proportion of SM forecasts based on satellite imagery are focused on global and national scales to date [18,19]. Freely available satellite radar data, such as Sentinel-1, have tremendous potential for SM estimation [20], but authentic products need field calibration data; one of the most restricting variables. Systems capable of calculating diverse soil properties, such as SM, are rare and are limited to a few measurement sites and spatial interpolation techniques are mostly absent [21,22].…”

“…Zribi et al [20], reviewed emerging research of SM estimation. To extract and test SM maps for an arid region, Gharechelou et al [25] ran multiple interpolation methods (inverse distance weighting, kriging, and co-kriging).…”