Remotely measuring changes in vegetation moisture is important for agricultural and environmental applications. While radiometry is sensitive to moisture, the resolution is low; optical and infrared measurements can be very fine-resolution but cannot directly measure moisture, instead relying on proxies such as brightness at wavelengths sensitive to chlorophyll. InSAR (interferometric synthetic aperture radar) produces images at fine resolution, and its measurement frequencies are sensitive to water. InSAR closure phase, a measurement of the residual phase from a circular combination of three multilooked interferograms, has been shown to relate to changing moisture content within a radar image. Here, we show that InSAR closure phase tracks several metrics of vegetation moisture from in situ measurements. In a forest in central Massachusetts, cumulative InSAR closure phase at C-band, while not correlated with soil moisture or L-band vegetation optical depth, is anticorrelated with the canopy wetness and well- correlated with the xylem dielectric constant.

We describe a novel algorithm to accurately characterize burned area and generate a time series of active burned areal extent during an actively burning wildfire based upon changes in the second-order statistics of interferometric synthetic aperture radar (InSAR) phase measurements. We present this algorithm and demonstrate its use with Sentinel-1 InSAR data collected during the 2020 Cameron Peak Fire, which burned along the Front Range in Colorado, USA. We show that this algorithm can successfully discriminate recently burned and actively burning areas within a fire zone from unburned areas at high spatial resolution (~10s of m). We further introduce a method for estimating a time series of burned areal extent from interferometric observations of burned area-change via a singular value decomposition (SVD) inversion. We compare the results of our algorithm with fire progression maps from the National Interagency Fire Center (NIFC) and find good agreement on total burned area (IoU=0.65) and excellent agreement on burned area extent (mIoU=0.91).

Soil moisture can vary spatially at the scale of agricultural fields (~ 10 − 100 m), which is generally too fine to resolve using passive radiometric methods. Active radar provides an opportunity for finer resolution measurements; in particular, the interferometric synthetic aperture radar (InSAR) closure phase parameter is sensitive to changing soil moisture. We have developed a model showing that systematic non-zero closure phase can result from scattering from objects at different depths in a medium of time-varying dielectric, such as from changes in soil moisture. The model predicts that interference between surface and subsurface reflections is needed for closure phase to be non-zero. We find that, under certain circumstances, we can estimate soil moisture from closure phase using a data reduction approach that includes a cumulative sum of closure phase over time and removal of a trend. The correlation between cumulative closure phase and soil moisture suggests that the closure phase is related to the change in soil moisture. We examine a large test region in Oklahoma, where the detrended cumulative closure phase from Sentinel-1 data demonstrates some agreement within situsoil moisture measurements. In other areas, the match is weaker, implying a terrain dependence for the quality of fit. Cumulative InSAR closure phase promises to provide a valuable new method to remotely estimate soil moisture.

Meltwater percolation and refreezing in porous firn is difficult to observe, but plays a key role in controlling the rate of melt-water runoff from the Greenland Ice Sheet. Coherent change detection (CCD) with repeated airborne ice-penetrating radar acquisitions is a promising method for resolving these processes. However, existing surveys were not designed for interferometric processing, and CCD has not yet been applied to near-surface ice-penetrating radar data. Here, we develop a workflow for estimating the coherence between repeat acquisitions by the Operation IceBridge Accumulation Radar. We demonstrate that stratigraphic structures such as ice slabs and ice layers maintain good coherence even over a temporal baseline of one year. However, porous firn with heterogeneous ice lensing has poor coherence, suggesting that tighter baseline control and improved co-registration and motion compensation methods would be required to reliably observe subsurface change in the percolation zone.

Rising air temperatures in the Arctic threaten the stability of permafrost and will result in an increase in the severity and frequency of tundra wildfires. Wildfires can dramatically alter local hydrology, vegetation, topography, and permafrost physical properties and processes, including emission of greenhouse gases. Despite this, permafrost-wildfire interactions remain a poorly understood component of the global carbon cycle. Here, we use interferometric synthetic aperture radar (InSAR) observations to quantify the annual subsidence and deformation rates of seasonally thawing/freezing permafrost across a study region characterized by unburned tundra and tundra recently burned from a series of wildfires in 2015. We propose a method to estimate and remove the component of the interferometric phase measurement due to time-varying soil moisture. We resolve elevated seasonal deformation rates over recently burned tundra in comparison to unburned tundra. Although burned deformation rates tend to revert to unburned values within a decade post-fire, surface soil moisture values within fire scars remain elevated. Lastly, we comment on the unique opportunities that the upcoming NISAR mission will enable to better study permafrost-wildfire interactions.

InSAR (interferometric synthetic aperture radar) phase closure, the net phase from linking three multilooked interferograms formed from three acquisitions, has been linked to soil moisture [1]. Here, we show one possible way to predict soil moisture from InSAR phase closure. Our data reduction approach includes an integration of the phase closure over time and subtraction of a random walk component to relate the differential phase values to soil moisture level. We find that for a large test region of Oklahoma, the integrated phase closure using Sentinel-1 data tracks the soil moisture observed in the field. In other cases, the match is less than good. If we can determine under what circumstances these InSAR measurements provide a good match to soil moisture, we have a valuable utility to remotely estimate soil moisture at scales useful for agricultural assessments, as conventional radiometric measurements cover hundreds of adjacent fields in each resolution cell.

The Permafrost Dynamics Observatory (PDO) combines L-band interferometric synthetic aperture radar (InSAR) and P-band polarimetric synthetic aperture radar (PolSAR) to simultaneously estimate the seasonal thaw depth and soil moisture profile of the active layer in permafrost regions. L-band InSAR can measure seasonal subsidence due to thawing of the active layer and P-band PolSAR backscatter is sensitive to subsurface soil moisture. A joint retrieval scheme is developed as both subsidence and soil moisture are essential to accurate active layer thickness (ALT) estimation. The PDO joint retrieval has been applied to airborne L- and P-band SAR data acquired over Arctic-boreal region during the 2017 Arctic-Boreal Vulnerability Experiment (ABoVE) airborne campaign. In this paper, we describe the forward models and joint inversion used in the PDO retrievals and compare the results with in-situ ALT and soil moisture data estimated from ground-penetrating radar (GPR).

Frequency-domain beamforming has become increasingly popular for fast processing of large synthetic aperture data in medical ultrasound. Here, we modify the Range Doppler Algorithm (RDA) to focus ultrasound signals from multistatic acquisitions. RDA, which was first proposed for fast beamforming of monostatic data in radar remote sensing, is suitable for fast processing of large datasets because all operations are done in one dimension at a time, allowing for an efficient and intuitive implementation. We demonstrate through simulation that multistatic RDA achieves similar image quality as traditionally used, multistatic delay-and-sum (DAS), while increasing the reconstruction speed by approximately a factor of three. We also show that the RDA and DAS images from the multistatic acquisition show reduced sidelobe levels compared to their counterparts from the monostatic acquisition. Demonstrated version of the multistatic RDA might be applicable beyond ultrasound medical imaging, such as for processing of synthetic aperture radar (SAR) data from satellite constellations.

The accuracy of geophysical parameter estimation made with interferometric synthetic aperture radar (InSAR) time-series techniques can be improved with rapidly increasing available data volumes and with the development of noise covariance matrices applicable to joint analysis of networks of interferograms. In this article, we present a new decorrelation phase covariance model and discuss its role in noise reduction in unwrapped interferometric phase stacks. We demonstrate with an example in which we average unwrapped interferogram phase stacks that span over a transient event how a noise covariance model can aid in noise reduction. Our model suggests that, for rapidly decorrelating surfaces (i.e., surfaces with much shorter correlation time than SAR acquisition intervals), it is preferable to incorporate all available interferograms from long observation windows. For slowly decorrelating surfaces (i.e., surfaces with longer correlation time than SAR acquisition intervals), our model suggests that a small subset of interferometric pairs is sufficient. We validate our model and three existing models of decorrelation phase covariance matrices in both Cascadia, a region with heavy vegetation cover, and Death Valley, a desert region with C-band Sentinel-1 A observations. Our proposed model matches observations with the smallest average discrepancy between theory and observations.

Seasonal subsidence measured by repeat-pass interferometric synthetic aperture radar (InSAR) can be used to infer the active layer thickness (ALT) in permafrost regions. The differential volume of soil water undergoing the phase change over the thaw season is one of the factors impacting the seasonal subsidence and is a function of both soil moisture profile and thaw depth. Without the information about soil moisture, this InSAR approach can have large biases in the ALT estimates when soil moisture profile is below saturation. Soil moisture and ALT can also be estimated from polarimetric synthetic aperture radar (PolSAR) backscatter observations but the sensing depth of the PolSAR approach is limited when deep ALT is present. In this paper, we integrated these two approaches and applied a joint retrieval method to estimate the soil moisture profiles and ALT from the L-band InSAR and P-band PolSAR data acquired over the Arctic-boreal region during the 2017 Arctic-Boreal Vulnerability Experiment (ABoVE) airborne campaign.

Phase inconsistencies, or closure phase, in interferometric synthetic aperture radar (InSAR) images are associated with the lack of phase closure for any given triplet of SAR scenes. While nonzero phase closure is fundamentally due to signal decorrelation between SAR scenes within a triplet, there has been particular interest in relating closure phase to temporal variations in surface scattering properties, namely surface and near-subsurface soil moisture state. In this manuscript, we will provide a brief overview of closure phase, and propose a methodology for retrieving soil moisture information from closure phase observations. We will briefly discuss implementation of this methodology, and then discuss the degree to which the assumed interferometric soil moisture model, and the assumed statistics of suface scatterers impacts the sensitivity of closure phase observations to surface soil moisture.

Here we present a physics-based decorrelation phase covariance model and discuss its role in effective decorrelation noise reduction in interferogram stacks. We test our model in both Cascadia - a rapidly decorrelating region, and Death Valley - a slowly decorrelating region, with observations collected by Sentinel-1. We find that in Cascadia, including redundant interferograms in the stack reduces phase variance from 0.28 rad2to 0.04 rad2, while in Death Valley, both redundant and independent interferogram stacking yield phase variances of 0.10 rad2. Both observations are consistent with predictions from our model. Comparing with three existing decorrelation phase covariance models, our proposed model matches observations with the smallest average discrepancy between theory and observations - 0.017 rad2in Cascadia and 0.066 rad2in Death Valley.

We propose a novel method for quantifying and correcting phase errors in interferometric synthetic aperture radar (InSAR) data associated with signal decorrelation. This proposed method relates the observed phase nonclosure (referred to as the closure phase) of triplet combinations of any three individual SAR scenes to the decorrelative phase signal in individual interferograms (pairs of SAR scenes). A singular value decomposition (SVD) method is applied to solve the minimum-norm least-squares best fitting estimate of the decorrelation phase for any arbitrary collection of SAR images. This decorrelative phase is then removed from individual interferograms; these corrected interferograms can then be used with existing InSAR time-series analysis algorithms. We demonstrate this method on the Advanced Land Observation Satellite Phased Array type L-band Synthetic Aperture Radar (ALOS PALSAR) scenes of a groundwater pumping subsidence feature in the Central Valley of California and briefly discuss potential future applications of this algorithm to study a variety of environmental and surface physical processes that contribute to InSAR signal decorrelation.