Interferometric Synthetic Aperture Radar Coherence Research Articles

Study on Optimization Method for InSAR Baseline Considering Changes in Vegetation Coverage.

Time-series Interferometric Synthetic Aperture Radar (InSAR) technology, renowned for its high-precision, wide coverage, and all-weather capabilities, has become an essential tool for Earth observation. However, the quality of the interferometric baseline network significantly influences the monitoring accuracy of InSAR technology. Therefore, optimizing the interferometric baseline is crucial for enhancing InSAR's monitoring accuracy. Surface vegetation changes can disrupt the coherence between SAR images, introducing incoherent noise into interferograms and reducing InSAR's monitoring accuracy. To address this issue, we propose and validate an optimization method for the InSAR baseline that considers changes in vegetation coverage (OM-InSAR-BCCVC) in the Yuanmou dry-hot valley. Initially, based on the imaging times of SAR image pairs, we categorize all interferometric image pairs into those captured during months of high vegetation coverage and those from months of low vegetation coverage. We then remove the image pairs with coherence coefficients below the category average. Using the Small Baseline Subset InSAR (SBAS-InSAR) technique, we retrieve surface deformation information in the Yuanmou dry-hot valley. Landslide identification is subsequently verified using optical remote sensing images. The results show that significant seasonal changes in vegetation coverage in the Yuanmou dry-hot valley lead to noticeable seasonal variations in InSAR coherence, with the lowest coherence in July, August, and September, and the highest in January, February, and December. The average coherence threshold method is limited in this context, resulting in discontinuities in the interferometric baseline network. Compared with methods without baseline optimization, the interferometric map ratio improved by 17.5% overall after applying the OM-InSAR-BCCVC method, and the overall inversion error RMSE decreased by 0.5 rad. From January 2021 to May 2023, the radar line of sight (LOS) surface deformation rate in the Yuanmou dry-hot valley, obtained after atmospheric correction by GACOS, baseline optimization, and geometric distortion region masking, ranged from -73.87 mm/year to 127.35 mm/year. We identified fifteen landslides and potential landslide sites, primarily located in the northern part of the Yuanmou dry-hot valley, with maximum subsidence exceeding 100 mm at two notable points. The OM-InSAR-BCCVC method effectively reduces incoherent noise caused by vegetation coverage changes, thereby improving the monitoring accuracy of InSAR.

Machine learning based urban land cover classification using PolInSAR data: a study with ALOS-2 and RADARSAT-2 datasets

A substantial variation in the land cover dynamics has been observed as a consequence of increasing urban expansion. Polarimetric synthetic aperture radar (PolSAR) data is widely being used for land cover studies in urban areas due to its all-weather, day-and-night imaging capabilities. However, in densely built-up areas, challenge arises with buildings having large Azimuth Orientation Angles (AOAs). These buildings are often misclassified as vegetation due to the depolarization of radar signal causing volumetric scattering response from the structures. This study addresses this issue by proposing an approach that integrates polarimetric information with interferometric SAR (InSAR) coherence to improve the differentiation between urban structures and vegetated areas, enhancing the accuracy of urban land-cover classification. Vegetated areas exhibit lower temporal coherence due to changes in the orientation of its leaves and branches caused by wind, seasonal variations, growth phenology, and other factors. In contrast, urban structures, being relatively stable targets, maintain high temporal coherence values. In present research various decomposition and scattering parameters were evaluated, along with PolInSAR coherence derived from L-band (ALOS-2) and C-band (RADARSAT-2), using two machine learning algorithms namely, Random Forest (RF) and Convolutional Neural Network (CNN). The C-band RADARSAT-2 data, particularly with six-component decomposition parameters, performed better, achieving an overall accuracy as 85.85% using RF algorithm. To further improve classification results, optical datasets from Landsat constellation were fused with SAR parameters using Gram-Schmidt fusion technique. This fusion led to significant improvements, achieving an overall accuracy of 94.50% and kappa statistics of 0.92, when CNN algorithm was applied to the fused optical and C-band RADARSAT-2 dataset. These results demonstrate the effectiveness of combining PolInSAR and optical data for more accurate urban land-cover classification, particularly in complex urban environments.

A Comparison of Sentinel-1 Biased and Unbiased Coherence for Crop Monitoring and Classification

Abstract. Synthetic Aperture Radar (SAR) holds significant potential for applications in crop monitoring and classification. Interferometric SAR (InSAR) coherence proves effective in monitoring crop growth. Currently, the coherence based on the maximum likelihood estimator is biased towards low coherence values. Therefore, the main aim of this work is to access the performance of Sentinel-1 time-series biased coherence and unbiased coherence in crop monitoring and classification. This study was conducted during the 2018 growing season (April-October) in Komoka, an agricultural region in southwestern Ontario, Canada, primarily cultivating three crops: soybean, corn, and winter wheat. To verify the ability of coherence to monitor crops, a linear correlation coefficient between temporal coherence and dual polarimetric radar vegetation index (DpRVI) was fitted. The results revealed a stable correlation between temporal coherence and DpRVI time-series, with the highest correlation observed for soybean (0.7 < R < 0.8), followed by wheat and corn. Notably, unbiased coherence of the VV channel exhibited the highest correlation (R > 0.75). In addition, we applied unbiased coherence to crop classification. The results show that unbiased coherence exhibits very promising classification performance, with the overall accuracy (84.83%) and kappa coefficient (0.76) of VV improved by 8.35% and 0.12, respectively, over biased coherence, and the overall accuracy (73.25%) and kappa coefficient (0.57) of VH improved by 7.56% and 0.14, respectively, over biased coherence, and all crop classification accuracies were also effectively improved. This study demonstrates the feasibility of coherence monitoring of crops and provides new insights in enhancing the higher separability of crops.

Snow Penetration Depth Inversion in Tibetan Plateau Based on Coherence Scattering Model with X-band Data

Abstract. The snow penetration depth is a necessary input parameter to accurately evaluate the thickness and vertical structure of snow layer, which is conducive to understanding the freeze-thaw process of ice and snow and predicting the change of ice and snow mass balance. Interferometric Synthetic Aperture Radar (InSAR) exhibits great potential in estimating snow penetration depth due to the emission of microwave band and the ability of operating under all-day and all-weather conditions. In previous studies, the penetration depth of snow was obtained based on InSAR coherence, but the external factors such as temperature, humidity, topography, dielectric constant and so on also affect the penetration dept. In order to extract the penetration depth in detail, this paper this paper adopts a uniform volume model as the basis, introduces two parameters describing the physical properties of snow: the volume ambiguity height and the volumetric decorrelation. Then, the magnitude of volumetric decorrelation is used to establish the internal relationship between the magnitude and the volume ambiguity height to estimate the snow penetration depth. In order to verify the effectiveness and feasibility of this method, a TSX/TDX InSAR pair covering southeast Tibetan Plateau in 2012 is applied to estimate the snow penetration depth. And results indicate that X-band penetration depths range mainly between −2.753 meters and −4.589 meters with an average value of −3.671 meters m and standard deviation of 0.918 meters in snowy regions on the plateau.

A hierarchical, multi‐sensor framework for peatland sub‐class and vegetation mapping throughout the Canadian boreal forest

AbstractPeatlands in the Canadian boreal forest are being negatively impacted by anthropogenic climate change, the effects of which are expected to worsen. Peatland types and sub‐classes vary in their ecohydrological characteristics and are expected to have different responses to climate change. Large‐scale modelling frameworks such as the Canadian Model for Peatlands, the Canadian Fire Behaviour Prediction System and the Canadian Land Data Assimilation System require peatland maps including information on sub‐types and vegetation as critical inputs. Additionally, peatland class and vegetation height are critical variables for wildlife habitat management and are related to the carbon cycle and wildfire fuel loading. This research aimed to create a map of peatland sub‐classes (bog, poor fen, rich fen permafrost peat complex) for the Canadian boreal forest and create an inventory of peatland vegetation height characteristics using ICESat‐2. A three‐stage hierarchical classification framework was developed to map peatland sub‐classes within the Canadian boreal forest circa 2020. Training and validation data consisted of peatland locations derived from various sources (field data, aerial photo interpretation, measurements documented in literature). A combination of multispectral data, L‐band SAR backscatter and C‐Band interferometric SAR coherence, forest structure and ancillary variables was used as model predictors. Ancillary data were used to mask agricultural areas and urban regions and account for regions that may exhibit permafrost. In the first stage of the classification, wetlands, uplands and water were classified with 86.5% accuracy. In the second stage, within the wetland areas only, peatland and mineral wetlands were differentiated with 93.3% accuracy. In the third stage, constrained to only the peatland areas, bogs, rich fens, poor fens and permafrost peat complexes were classified with 71.5% accuracy. Then, ICESat‐2 ATL08 spaceborne lidar data were used to describe regional variations in peatland vegetation height characteristics and regional and class‐wise variations based on a boreal forest wide sample. This research introduced a comprehensive large‐scale peatland sub‐class mapping framework for the Canadian boreal forest, presenting the first moderate resolution map of its kind.

Damage Proxy Mapping with SAR interferometric coherence change

Synthetic aperture radar (SAR) can be combined interferometrically to estimate the coherence of the SAR phase. The coherence of the phase over time depends on the stability of the major objects and surface that are reflecting the SAR signal, at spatial scales near the radar wavelength and larger. When there are major changes in the radar reflection, it causes a loss of interferometric coherence, so we use methods of detecting coherence change as a proxy for damage or other sudden change to buildings, other structures, or the land surface, which we call a damage proxy map (DPM). The most common method for estimating the interferometric coherence is with spatial phase correlation measures over small areas. The simplest damage proxy map method is to calculate the coherence of an interferometric SAR (InSAR) pair before the event and another pair that spans the event and take the difference of the coherence to measure the change likely due to the event. This method can be effective for areas where the InSAR coherence is normally very high at the radar wavelength used. SAR at L-band (24 cm) wavelength has high coherence in a wider variety of environments than the shorter wavelengths. A more advanced, but requiring more computational resources, damage proxy map method involves calculating the coherence of all possible InSAR pairs for an interval of some months or years before the event to develop statistics about the coherence of each ground location as a function of InSAR pair interval and other parameters such as interferometric spatial baseline. Then InSAR pairs that span the event are compared to the pre-event statistics to estimate the likelihood that the co-event coherence has been reduced by damage or other surface changes. We have found that damage proxy maps are useful for detecting likely damage from many different types of events, including earthquakes, hurricanes, tornados, tsunami inundation, large landslides, or other causes.

Detection of areas with severely eroded soils using Sentinel-1 interferometric SAR coherence (Study area: Khuzestan province)

Detection of areas with severely eroded soils using Sentinel-1 interferometric SAR coherence (Study area: Khuzestan province)

InDeandCoE: A framework based on multi-scale feature fusion and residual learning for interferometric SAR remote sensing image denoising and coherence estimation

Synthetic aperture radar (SAR) interferometry is a high-resolution microwave remote sensing imaging method. Over the past two decades, many researchers working on remote sensing have applied this technology in various disciplines, including environmental monitoring, disaster monitoring, and elevation mapping. However, due to the existence of many influencing factors in the acquisition stage, such as atmospheric humidity and temperature, the reflected wave signals from the ground will be disturbed when received by remote sensing satellites. The presence of noise in interferograms is inevitable. Therefore, the accuracy of interferometric SAR phase denoising and coherence estimation has a decisive impact on the validity of subsequent processing results. In this paper, we pioneer the use of a nested U-net as a feature extractor for interferometric SAR phase and coherence. In addition, we build a phase filter and a coherence estimator by using the residual learning module. With the aim of determining the unique non-local similarity of InSAR images, we use non-local convolution and channel attention mechanisms to extract features in different dimensions of the interferogram. Through quantitative and qualitative experiments, the proposed method performs better in phase denoising and coherence estimation than state-of-the-art methods.

SAR Coherence in Detecting Fluvial Sediment Transport Events in Arid Environments

Coherence change detection (CCD) is a remote sensing technique used to map phenomena that, under certain conditions, can be directly related to changes in Interferometric SAR (InSAR) coherence. Mapping the areas affected by sediment transport events in arid environments is one of the most common applications of CCD. However, the reliability of these maps remains an unsolved issue. This paper focuses on verifying that InSAR coherence is indeed able to detect all the fluvial sediment transport events that have actually mobilised sediments in arid environments by building a classification model and validating its results. The proposed methodology is tested in three study areas in Salar de Atacama, Chile, using three years of Sentinel data plus a fourth year for validation, and meteorological records of rainfall, the relative humidity of the air and snow cover. The results prove that InSAR coherence can be used to remotely detect sediment transport events related to flash floods in arid environments, that it might have a greater detection ability than meteorological records and that the perpendicular baseline does have a relevant effect on the InSAR coherence that needs to be considered. All these findings will increase the reliability of maps based on InSAR coherence. In addition, the proposed method will allow focusing the mapping tasks only on the relevant dates and, once calibrated, the classification model will enable the automatised remote detection of new events.

Understanding the Spatial Variability of the Relationship between InSAR-Derived Deformation and Groundwater Level Using Machine Learning

The interferometric synthetic aperture radar (InSAR) technique was used in this study to derive the temporal and spatial information of ground deformation and explore its temporal correlation with groundwater dynamics. The random forest (RF) machine learning method was used to model the spatial variability of the temporal correlation and understand its influential contributors. The results showed that groundwater dynamics appeared to be an important factor in InSAR deformation at some bores where strong and positive correlations were observed. The RF model could explain up to 72% of spatial variances between InSAR deformation and groundwater dynamics. The spatial and temporal InSAR coherence (a proxy for the noise in InSAR results that is strongly related to vegetation) and soil moisture (difference, trend, and amplitude) were the most important factors explaining the spatial pattern of the temporal correlation between InSAR displacements and groundwater levels. This result confirms that noise sources (including deformation model fitting errors and radar signal decorrelation) and perturbation of the InSAR signal related to vegetation and surficial soils (clay content, moisture changes) should be accounted for when interpreting InSAR to support groundwater-related risk assessments and in groundwater resource management activities.

Activity of Okgye Limestone Mine in South Korea Observed by InSAR Coherence and PSInSAR Techniques

The Okgye limestone mine, which is the largest open-pit limestone mine located in a mountainous area in Korea, suffered a collapse in 2012 that claimed four casualties. Restoration work on the rocky mined-out slopes, as well as mining and dumping activities, are still in progress. Monitoring slope stability is important to prevent the sudden collapse of slopes, which can be efficiently performed by satellite-based interferometric synthetic aperture radar (InSAR) techniques. Firstly, we obtained elevation changes using InSAR-generated Copernicus 30 m DEM in 2014 and an SRTM 1Sec DEM in 2000, through which the area was roughly classified into the mining area, tailings storage area, and the mined-out area. A time series of 12-day coherence images produced by Sentinel-1B SAR were averaged annually to produce an RGB-composite image to observe the change in mining activities during 2018, 2019, and 2020. We found many persistent scatterers (PS) when observing the ground displacement, both in the ascending and descending orbits, from which we decomposed this into the vertical and east components. The largest displacement of 63.6 mm/year was observed during 2019 and 2020 in the tailings storage area in the direction of the dumping slope. For the rocky outcrops and the transmission tower, we found a seasonal oscillation, which can be interpreted as the thermal expansion of limestone and iron. This paper demonstrated that the surface stability and deformation of open-pit mines could be effectively monitored by combining InSAR DEM, coherence, and PSInSAR techniques.

Wetland Hydroperiod Analysis in Alberta Using InSAR Coherence Data

Wetlands are dynamic environments, the water and vegetation of which can change considerably over time. Thus, it is important to investigate the hydroperiod status of wetlands using advanced techniques such as remote sensing technology. Wetland hydroperiod analysis has already been investigated using optical satellite and synthetic aperture radar (SAR) backscattering data. However, interferometric SAR (InSAR) coherence products have rarely been used for wetland hydroperiod mapping. Thus, this study utilized Sentinel-1 coherence maps produced between 2017 and 2020 (48 products) to map the wetland hydroperiod over the entire province of Alberta, Canada. It was observed that a coherence value of 0.45 was an optimum threshold value to discriminate flooded from non-flooded wetlands. Moreover, the results showed that most wetlands were inundated less than 50% of the time over these four years. Furthermore, most wetlands (~40%) were seasonally inundated, and there was a small percentage of wetlands (~5%) that were never flooded. Overall, the results of this study demonstrated the high capability of InSAR coherence products for wetland hydroperiod analysis. Several suggestions are provided to improve the results in future works.

Wheat Water Deficit Monitoring Using Synthetic Aperture Radar Backscattering Coefficient and Interferometric Coherence

Due to the climate change situation, water deficit stress is becoming one of the main factors that threatens the agricultural sector in semi-arid zones. Thus, it is extremely important to provide efficient tools of water deficit monitoring and early detection. To do so, a set of Synthetic Aperture Radar (SAR) backscattering and interferometric SAR (InSAR) Sentinel-1 data, covering the period from January to June 2016, are considered over a durum wheat field in Tunisia. We first studied the temporal variation of the InSAR coherence data and the SAR backscattering coefficient as a function of the phenological stage of the wheat. Subsequently, the parameters of the SAR and InSAR coherence images were analyzed with regard to the water stress coefficient and the wheat height variations. The main findings of this study highlight the high correlation (r = 0.88) that exists between the InSAR coherence and the water stress coefficient, on the one hand, and between the backscattering coefficient, the interferometric coherence, and the water deficit coefficient (R2 = 0.95 and RMSE = 14%), on the other hand. When a water deficit occurs, the water stress coefficient increases, the crop growth decreases, and the height variation becomes low, and this leads to the increase of the InSAR coherence value. In summary, the reliability of Sentinel-1 SAR and InSAR coherence data to monitor the biophysical parameters of the durum wheat was validated in the context of water deficits in semi-arid regions.

Interferometric synthetic aperture radar coherence constraints in heavily vegetated tropics

Success of Interferometry Synthetic Aperture Radar (InSAR) technique for deriving digital elevation model (DEM) in the heavily vegetated humid tropic using Sentinel-1 SAR has been controversial, owing to the difficulty in achieving good coherence. Yet achievable coherence with Sentinel-1 SAR over the heavily vegetated humid tropic has rarely been reported. Consequently, this paper evaluates coherence achievable with Sentinel-1 SAR over the heavily vegetated humid tropic. Twelve Sentinel-1 SAR over part of Johor selected based on perpendicular and temporal baselines considerations, were used as pairs of six reference and secondary images respectively and processed using Sentinel Application Platform (SNAP) to derive coherence maps and analysed accordingly. The result shows that regardless of image pairs baseline characteristics, coherence of above 0.6 over the heavily vegetated humid tropic with Sentinel-1 SAR can only be achieved in barely 10% of the study area. This result shall serve as eye-opener to geoscience community, especially SAR enthusiast looking forward to leveraging on SAR clouds penetrating ability; and Sentinel-1 SAR open access and short revisit circle to apply Sentinel-1 SAR in deriving DEM over heavily vegetated humid tropical environment. However, future studies shall focus on assessing consistency of good coherence patches and their usability in acquiring elevation.

INTEGRATING INSAR COHERENCE AND BACKSCATTERING FOR IDENTIFICATION OF TEMPORARY SURFACE WATER, CASE STUDY: SOUTH KALIMANTAN FLOODING, INDONESIA

Abstract. Accurate and reliable information about the spatiotemporal level of surface water can be provided by Temporary Surface Water (TSW) monitoring. TSW is defined as a waterbody experiencing frequent drying phases which also correspond to surfaces frequently affected by flooding. In Indonesia, flooding is a frequent disaster, which is about 30% of the total number of disasters that occurred. In mid-January 2021, South Kalimantan Province, Indonesia was hit by a flood. The loss impact of this disaster is estimated at Rp. 1.349 trillion. Synthetic Aperture Radar (SAR), one type of remote sensing, can be used to map the spatial distribution of flood inundation. SAR has the advantage of not being constrained by day or night, weather conditions, and cloud cover or fog. Nowadays, the availability of Sentinel-1 SAR images can increase the use of SAR imagery for flood mapping. Specular reflections that occur on the smooth water surface produce a darker color in the SAR backscattering data, which makes floodwater distinguishable on dry land surfaces. However, inundation between buildings can cause changes in the backscattering value. Several studies have shown that the Interferometric SAR coherence method is a good method for mapping floods in urban areas. The main goal of this study is thus to map Temporary Surface Water (flood) of the South Kalimantan flood event in Indonesia using Sentinel-1 SAR imagery. With backscattering data, flood in a non-urban area can be mapped, but it is difficult to map flood in an urban area. Therefore, we will try to integrate InSAR coherence and SAR backscattering data in order to map flood in the whole study area. The result shows that using backscattering data, bare soil or non-urban flood inundation in the whole study area can be mapped with an overall accuracy of about 76.4%. Yet, flood in an urban area cannot be mapped only with backscattering data. The result from coherence imagery can map flood inundation in an urban area. Thus, integration from both of them can map flood inundation in the whole study area, either urban or non-urban.

Mapping Floods in Urban Areas From Dual-Polarization InSAR Coherence Data

Previous studies have shown that the decrease of temporal interferometric synthetic aperture radar (InSAR) coherence could be exploited to detect the appearance of floodwater in urban areas. However, as of today, approaches based on this principle only make use of single co-polarization images for identifying the presence of floodwater in the double-bounce feature. In this study, we take advantage of both co- and cross-polarization images to detect significant decreases of the multitemporal InSAR coherence in order to enhance the mapping of floodwater in urban areas. We consider that not only double-bounce scattering, but also multiple-bounce may occur in urban areas depending on how the building facades are oriented with respect to the synthetic aperture radar (SAR) sensor’s line of sight. The Sentinel-1 (S-1) mission is particularly well suited for applying and testing this kind of approach due to the systematic availability of dual-polarization data. Using as a test case, the widespread flooding in the city of Houston, USA, caused by Hurricane Harvey in 2017, we demonstrate that the proposed methodology leads to an increase of the accuracy of the urban flood maps from 75.2% when only using the VV polarization, to 82.9% when using the dual polarization information.

Deep Learning-Based Damage Mapping With InSAR Coherence Time Series

Satellite remote sensing is playing an increasing role in the rapid mapping of damage after natural disasters. In particular, synthetic aperture radar (SAR) can image the Earth's surface and map damage in all weather conditions, day and night. However, current SAR damage mapping methods struggle to separate damage from other changes in the Earth's surface. In this study, we propose a novel approach to damage mapping, combining deep learning with the full time history of SAR observations of an impacted region in order to detect anomalous variations in the Earth's surface properties due to a natural disaster. We quantify Earth surface change using time series of Interferometric SAR coherence, then use a recurrent neural network (RNN) as a probabilistic anomaly detector on these coherence time series. The RNN is first trained on pre-event coherence time series, and then forecasts a probability distribution of the coherence between pre- and post-event SAR images. The difference between the forecast and observed co-event coherence provides a measure of the confidence in the identification of damage. The method allows the user to choose a damage detection threshold that is customized for each location, based on the local behavior of coherence through time before the event. We apply this method to calculate estimates of damage for three earthquakes using multi-year time series of Sentinel-1 SAR acquisitions. Our approach shows good agreement with observed damage and quantitative improvement compared to using pre- to co-event coherence loss as a damage proxy.

Sentinel-1-derived coherence time-series for crop monitoring in Indian agriculture region

Synthetic aperture radar (SAR) remote sensing has been widely used for crop monitoring due to its high-resolution imaging and all-weather data acquisition capabilities. In this study, time-series interferometric SAR (InSAR) coherence products generated from Sentinel-1 data were employed to monitor phenological stages, determine total cropping duration and estimate sowing as well as harvest dates for the Bengal-gram crop. The Bengal-gram crop was cultivated in the study region lies in Chhattisgarh, India, during the Rabi 2018–2019 season. Upon analysis, the temporal fluctuations in InSAR coherence were observed as a function of crop phenological stages as well as sowing and harvesting events. The lowest coherence value (i.e. 0.29) represents the peak vegetation. Sowing and harvesting dates were determined by observing the sudden shifts in coherence trend i.e. from high to low and low to high, respectively. The temporal analysis performed using InSAR coherence was also cross-verified with the time-series normalized difference vegetation index (NDVI) index derived using Sentinel-2 bands. Based on detailed analysis, we found that the time-series analysis of InSAR coherence provides reliable information on the crop growth stages and this approach can be used to monitor other crops as well.

Evaluation of Sentinel-1 Interferometric SAR Coherence efficiency for Land Cover Mapping

Evaluation of Sentinel-1 Interferometric SAR Coherence efficiency for Land Cover Mapping

High‐Resolution Soil Moisture Evolution in Hyper‐Arid Regions: A Comparison of InSAR, SAR, Microwave, Optical, and Data Assimilation Systems in the Southern Arabian Peninsula

AbstractWe present an analysis of a 3‐year time series of Synthetic Aperture Radar (SAR) imagery covering the southern Arabian Peninsula, spanning two devastating typhoons that impacted the region in 2018. The limited vegetation and precipitation in the area allows for clear separation of soil moisture changes from other factors that typically also affect microwave imagery. We generate soil moisture proxies from Interferometric SAR (InSAR) coherence and compare them with a range of other soil moisture products. We estimate the decay timescale over which the surface returns to its pre‐storm state for each event and data type, and find that the radar coherence‐based timescales tend to be longer than those for the other products. We also find that the coherence‐based timescales of the two events differ from each other in a manner that is consistent with seasonal temperature fluctuations and the total rainfall that occurred each time. The higher spatial resolution of the InSAR coherence results and differing sensitivity to the vertical distribution of soil moisture complements the other more temporally dense products, particularly in regions of high heterogeneity along the edge of the storm and within regions where the surface properties change dramatically over small spatial scales. As the frequency and strength of tropical cyclones increase, and as already scarce water resources are utilized further in arid regions, InSAR coherence data constitutes another tool that researchers can use to expand our understanding of these challenges.