Multitemporal Soil Moisture Retrieval over Bare Agricultural Areas by Means of Alpha Model with Multisensor SAR Data

Abstract

e objective of this research is to optimize the Alpha approximation model for soil moisture retrieval using multitemporal SAR data. e Alpha model requires prior knowledge of soil moisture range to constrain soil moisture estimation. e solution of the Alpha model is an undetermined problem due to the fact that the number of observation equations is less than the number of unknown parameters. is research primarily focused on the optimization of Alpha model by employing multisensor and multitemporal SAR data. e disadvantage of the Alpha model can be eliminated by the combination of multisensor SAR data. e optimized Alpha model was evaluated on the basis of a comprehensive campaign for soil moisture retrieval, which acquired multisensor time series SAR data and coincident field measurements. e agreement between the estimated and measured soil moisture was within a root mean square error of 0.08 cm 3 /cm 3 for both methods. e optimized Alpha model shows an obvious improvement for soil moisture retrieval. e results demonstrated that multisensor and multitemporal SAR data are favorable for time series soil moisture retrieval over bare agricultural areas.

Soil Moisture Monitoring Using Remote Sensing Data and a Stepwise-Cluster Prediction Model: The Case of Upper Blue Nile Basin, Ethiopia

“…Gao et al [27] proposed a capable soil moisture prediction model in the domain of change detection method through combining Sentinel-1 SAR and Sentinel-2 optical data, their validation lead to a RMSE equal to0.059 m 3 /m 3 . Alternatively, Zhang et al [28] introduced soil moisture estimation techniques using the Alpha approximation model and multi-temporal SAR data obtained from RADARSAT-2 and Senteinel-1 sensors, characterized by an RMSE of 0.08 cm 3 /cm 3 . While Hosseini et al [29] presented an integrated statistical soil moisture prediction model based on RADARSAT-2 data.…”

The significance of soil properties to the estimation of soil moisture from C-band synthetic aperture radar

“…The determination of these parameters by remote sensing is of interest, because surface roughness is one of the main sources of m v retrieval errors from satellite SAR sensors (Martinez-Agirre et al, 2017;Lievens et al, 2009). A number of strategies to eliminate surface roughness effects in the context of soil moisture retrieval from SAR were reviewed by McNairn and Brisco (2004), who discovered that to separate the effects of soil moisture and soil surface roughness requires diversity in measurement (Gorrab et al, 2016) comprising, multi-frequency (such as combining different SAR sensors (Zhang et al, 2018)), multi-angle (which might be achieved with different orbits of the same sensor (Wang et al, 2016)), or multi-polarisation. Mattia et al (1997) found that a co-polarised correlation coefficient based on circular polarisation is strongly correlated with surface roughness, but not soil moisture.…”

Land consumption monitoring: an innovative method integrating SAR and optical data

“…For example, JERS-1 SAR series images were used to analyze land use changes (Angelis et al 2002), and COSMO-Skymed data were used for land cover classification (Satalino et al 2011). Other works include an unsupervised change detection (Yousif and Ban 2015), a soil moisture retrieval (Zhang et al 2018), or the analysis of natural hazards (Poursanidis et al Poursanidis and Chrysoulakis 2017).…”