Generation of continuous surface soil moisture dataset using combined optical and thermal infrared images

Abstract

Surface soil moisture (SSM) is a critical variable for understanding water and energy flux between the atmosphere and the Earth's surface. An easy to apply algorithm for deriving SSM time series that primarily uses temporal parameters derived from simulated and in situ datasets has recently been reported. This algorithm must be assessed for different biophysical and atmospheric conditions by using actual geostationary satellite images. In this study, two currently available coarse‐scale SSM datasets (microwave and reanalysis product) and aggregated in situ SSM measurements were implemented to calibrate the time‐invariable coefficients of the SSM retrieval algorithm for conditions in which conventional observations are rare. These coefficients were subsequently used to obtain SSM time series directly from Meteosat Second Generation (MSG) images over the study area of a well‐organized soil moisture network named REMEDHUS in Spain. The results show a high degree of consistency between the estimated and actual SSM time series values when using the three SSM dataset‐calibrated time‐invariable coefficients to retrieve SSM, with coefficients of determination (R2) varying from 0.304 to 0.534 and root mean square errors ranging from 0.020 m3/m3 to 0.029 m3/m3. Further evaluation with different land use types results in acceptable debiased root mean square errors between 0.021 m3/m3 and 0.048 m3/m3 when comparing the estimated MSG pixel‐scale SSM with in situ measurements. These results indicate that the investigated method is practical for deriving time‐invariable coefficients when using publicly accessed coarse‐scale SSM datasets, which is beneficial for generating continuous SSM dataset at the MSG pixel scale.

Preliminary Evaluation of the SMAP Radiometer Soil Moisture Product over China Using In Situ Data

“…Due to the advantage of remote sensing observations, many efforts have been dedicated to determining the surface soil moisture for regional or even global applications [9][10][11][12][13][14]. Early field experiments demonstrated that passive microwave emissions and active microwave scatter are a function of the dielectric constant of the surface soil layer, and that the dielectric constant is related to the soil water content.…”

Detection of Tailings Dams Using High-Resolution Satellite Imagery and a Single Shot Multibox Detector in the Jing–Jin–Ji Region, China

“…Interferometric synthetic aperture radar (InSAR) and light detection and ranging (LiDAR) data can be used to monitor structural changes in and displacement of a tailings dam before failure [29,53,54]. Optical and thermal satellite data can be used to monitor environmental impacts such as water and soil pollution near tailings dams [55,56]. Based on multi-resource remote sensing data, monitoring systems and platforms can help reduce the probability of failures effectively and also mitigate the consequences.…”

Surface Soil Moisture Retrieval Using Optical/Thermal Infrared Remote Sensing Data

“…The increasing number of geostationary satellites is facilitating the development of more practical SSM retrieval methods with higher spatial and temporal resolutions [31]- [36]. Geostationary satellites have a higher frequency of observations than do polar orbiting satellites and can produce 48 to 96 images per day with a fixed observation angle for a given pixel, substantially enriching our understanding of terrestrial water and energy budgets.…”