This paper aims to contribute to clarifying the relationship between phase decorrelation and water content in vegetation and to study the impact of the temporal variability of vegetation water content on InSAR estimates of terrain deformation. We combine three interferograms obtained from three SAR images, of the same area acquired at different times, to derive maps of decorrelation phases. It was found that the magnitude and temporal variability of the decorrelation phase is low in bare soil and high in agricultural and forest areas. Furthermore, it was found that the temporal variation of vegetation water content is related to the phase bias of the cumulative displacement computed with short-interval interferograms.

Results of an experiment aiming to estimate soil moisture (SM) using indexes based on hyperspectral and multispectral data, and decorrelation phase from SAR interferometry are presented. SM estimates are compared with in-situ measurements of SM. The issue of spaceborne and in-situ datasets not overlapping in time is studied. Results are obtained using PRISMA, Sentinel-1 and Sentinel-2 data.

Soil moisture is an important component in investigations of land-surface climate and hydrology. Recently several methodologies and techniques have been proposed, allowing soil moisture retrieving from SAR remote sensing techniques at large scales. In this work we investigate the relation between the closure phases and the time varying soil moisture. We combine three interferograms obtained from three SAR images of the same area acquired at different times, to derive maps of bi -coherence and phase triplet. The results show that there is a linear correlation between the modelled phases derived from soil moisture measurements and the closure phases. The correlation coefficient was R2=0.76 and R2=0.86 for the descending and ascending passes, respectively. However, a scale effect of the closure phases was found when compared with the derived model phases. The scale is about 10% for both passes, meaning that the estimated closure phases underestimate the soil moisture changes.

A methodology to generate calibrated maps of soil moisture from C-band synthetic aperture radar (SAR) images processed by SAR interferometry (InSAR) technique is presented. The proposed methodology uses atmospheric phase delay (APD) maps obtained from a time series of Sentinel-1 interferograms, to disentangle the APD and soil moisture contributions to Sentinel-1 interferograms. We show how the high spatial resolution and short temporal baseline of Sentinel-1 image can help to estimate soil moisture using a daisy chain InSAR processing. The estimated soil moisture maps are compared within situdata collected by five soil moisture sensors installed in an experimental field, characterized by bare soil, located close to Lisbon, Portugal. Results show that after removing the APD effects in SAR interferogram, there is a correction of the bias in the soil moisture estimation and an improvement in the correlation coefficient with the soil moisture measurements, from 0.38 to 0.78. Soil moisture changes were measured during a sequence of rain events in the winter season. A root-mean-square (rms) error less than 0.04 m3/m3was found over a variety of meteorological conditions.

The observation of soil moisture is fundamental for several climate sciences. Remote sensing had proved that it is possible to observe soil moisture from both Synthetic Aperture Radar (SAR) and SAR interferometry (InSAR) observables. This paper shows the use of machine learning regression algorithms to estimate soil moisture change using the InSAR coherence and phase and the soil type. Random Forest Regression and Extra-Tree and Bagging Regression were used. The purpose is to evaluate the improvement gain with the inclusion of “non-conventional” data such as the soil type on the estimation of soil moisture variations in time. The results point out that the inclusion of the soil type improves the estimation with coefficient of determination - R2 up to 72%.