Active Microwave Research Articles

Urban Impervious Surface Extraction Based on Deep Convolutional Networks Using Intensity, Polarimetric Scattering and Interferometric Coherence Information from Sentinel-1 SAR Images

Urban impervious surface area is a key indicator for measuring the degree of urban development and the quality of an urban ecological environment. However, optical satellites struggle to effectively play a monitoring role in the tropical and subtropical regions, where there are many clouds and rain all year round. As an active microwave sensor, synthetic aperture radar (SAR) has a long wavelength and can penetrate clouds and fog to varying degrees, making it very suitable for monitoring the impervious surface in such areas. With the development of SAR remote sensing technology, a more advanced and more complex SAR imaging model, namely, polarimetric SAR, has been developed, which can provide more scattering information of ground objects and is conducive to improving the extraction accuracy of impervious surface. However, the current research on impervious surface extraction using SAR data mainly focuses on the use of SAR image intensity or amplitude information, and rarely on the use of phase and polarization information. To bridge this gap, based on Sentinel-1 dual-polarized data, we selected UNet, HRNet, and Deeplabv3+ as impervious surface extraction models; and we input the intensity, coherence, and polarization features of SAR images into the respective impervious surface extraction models to discuss their specific performances in urban impervious surface extraction. The experimental results show that among the intensity, coherence, and polarization features, intensity is the most useful feature in the extraction of urban impervious surface based on SAR images. We also analyzed the limitations of extracting an urban impervious surface based on SAR images, and give a simple and effective solution. This study can provide an effective solution for the spatial-temporal seamless monitoring of an impervious surface in cloudy and rainy areas.

Retrieving Soil Physical Properties by Assimilating SMAP Brightness Temperature Observations into the Community Land Model.

This paper coupled a unified passive and active microwave observation operator-namely, an enhanced, physically-based, discrete emission-scattering model-with the community land model (CLM) in a data assimilation (DA) system. By implementing the system default local ensemble transform Kalman filter (LETKF) algorithm, the Soil Moisture Active and Passive (SMAP) brightness temperature TBp (p = Horizontal or Vertical polarization) assimilations for only soil property retrieval and both soil properties and soil moisture estimates were investigated with the aid of in situ observations at the Maqu site. The results indicate improved estimates of soil properties of the topmost layer in comparison to measurements, as well as of the profile. Specifically, both assimilations of TBH lead to over a 48% reduction in root mean square errors (RMSEs) for the retrieved clay fraction from the background compared to the top layer measurements. Both assimilations of TBV reduce RMSEs by 36% for the sand fraction and by 28% for the clay fraction. However, the DA estimated soil moisture and land surface fluxes still exhibit discrepancies when compared to the measurements. The retrieved accurate soil properties alone are inadequate to improve those estimates. The discussed uncertainties (e.g., fixed PTF structures) in the CLM model structures should be mitigated.

Dual-frequency spectral radar retrieval of snowfall microphysics: a physics-driven deep-learning approach

Abstract. The use of meteorological radars to study snowfall microphysical properties and processes is well established, in particular via a few distinct techniques: the use of radar polarimetry, of multi-frequency radar measurements, and of the radar Doppler spectra. We propose a novel approach to retrieve snowfall properties by combining the latter two techniques, while relaxing some assumptions on, e.g., beam alignment and non-turbulent atmosphere. The method relies on a two-step deep-learning framework inspired from data compression techniques: an encoder model maps a high-dimensional signal to a low-dimensional latent space, while the decoder reconstructs the original signal from this latent space. Here, Doppler spectrograms at two frequencies constitute the high-dimensional input, while the latent features are constrained to represent the snowfall properties of interest. The decoder network is first trained to emulate Doppler spectra from a set of microphysical variables, using simulations from the Passive and Active Microwave radiative TRAnsfer model (PAMTRA) as training data. In a second step, the encoder network learns the inverse mapping, from real measured dual-frequency spectrograms to the microphysical latent space; in doing so, it leverages with a convolutional structure the spatial consistency of the measurements to mitigate the ill-posedness of the problem. The method was implemented on X- and W-band data from the ICE GENESIS campaign that took place in the Swiss Jura Mountains in January 2021. An in-depth assessment of the retrieval accuracy was performed through comparisons with colocated aircraft in situ measurements collected during three precipitation events. The agreement is overall good and opens up possibilities for acute characterization of snowfall microphysics on larger datasets. A discussion of the sensitivity and limitations of the method is also conducted. The main contribution of this work is, on the one hand, the theoretical framework itself, which can be applied to other remote-sensing retrieval applications and is thus possibly of interest to a broad audience across atmospheric sciences. On the other hand, the seven retrieved microphysical descriptors provide relevant insights into snowfall processes.

Evaluating Threatened Bird Occurrence in the Tropics by Using L-Band SAR Remote Sensing Data

The biodiversity loss in Southeast Asia indicates an urgent need for long-term monitoring, which is lacking. Much attention is being directed toward bird diversity monitoring using remote sensing, based on relation to forest structure. However, few studies have utilized space-borne active microwave remote sensing, which has considerable advantages in terms of repetitive observations over tropical areas. Here, we evaluate threatened bird occurrence from L-band satellite data explaining forest structure in Sumatra, Indonesia. First, we identified L-band parameters with strong correlations with the forest layer structure, defined as forest floor, understory, and canopy layers. Then, we analyzed the correlation between threatened bird occurrence and L-band parameters identified as explaining forest structure. The results reveal that several parameters can represent the layers of forest floor, understory, and canopy. Subsequent statistical analysis elucidated that forest-dependent and threatened bird species exhibit significant positive correlations with the selected L-band parameters explaining forest floor and understory. Our results highlight the potential of applying microwave satellite remote sensing to evaluate bird diversity through forest structure estimation, although a more comprehensive study is needed to strengthen our findings.

A Modified 2-D Notch Filter Based on Image Segmentation for RFI Mitigation in Synthetic Aperture Radar

Synthetic aperture radar (SAR), as an active microwave sensor, can inevitably receive radio frequency interference (RFI) generated by various electromagnetic equipment. When the SAR system receives RFI, it will affect SAR imaging and limit the application of SAR images. As a kind of RFI mitigation method, notch filtering method is a classical method with high efficiency and robust performance. However, the notch filtering methods pay no attention to the protection of useful signals. This paper proposed a modified 2-D notch filter based on image segmentation for RFI mitigation with signal-protected capability. (1) The adaptive gamma correction (AGC) approach was utilized to enhance the SAR image with RFI in the range-frequency and azimuth-time domain. (2) The modified selective binary and Gaussian filtering regularized level set (SBGFRLS) model was utilized to further process the image after AGC to accurately extract the contour of the useful signals with interference, which is more conducive to protecting the useful signals without interference. (3) The Generalized Singular Value Thresholding (GSVT) based low-rank sparse decomposition (LRSD) model was utilized to separate the RFI signals and the useful signals. Then, the useful signals were restored to the raw data. The simulation experiments and measured data experiments show that the proposed method can effectively mitigate RFI and protect the useful signals whether there are RFI with single source or multiple sources.

Debonding detection of defected CFRP-concrete interface using active microwave thermography

This study presents an investigation of non-destructive evaluation (NDE) of carbon fiber reinforced polymer (CFRP)-concrete interfacial debonding using a novel technology referred to as active microwave thermography (AMT). AMT utilizes microwave energy as a heating excitation to the structure and captures the anomalies of temperature contrast (TC) at the structure surface using a thermal camera. In this study, AMT was used first as an inspection method of CFRP-concrete joints. Comparative tests on CFRP-concrete joints with and without an initial man-made defect showed AMT can detect initial defects at the CFRP-concrete interface in around 15 s. Then, AMT was used to monitor the time-history progressive interfacial debonding process of the CFRP-concrete joints tested in direct shear under monotonically increasing loading. The test results showed that: (i) the initial defect at the CFRP-concrete interface can be detected by AMT quickly; (ii) the presence of an initial defect at the CFRP-concrete interface can substantially decrease the load-carrying capacity of the CFRP-concrete joint; (iii) the initial defect can alter the interfacial debonding process and global load response; and (iv) significant load drops, indicative of interfacial damage formation, could be detected by a change of slope of the local CFRP surface TC determined by AMT.

Microwaving Mimas, Enceladus, Tethys, Dione, Rhea, Iapetus and Phoebe: Insights into the regolith properties and geological history of Saturn's icy satellites

While Saturn's main airless moons are all composed largely of water ice, their respective thermal histories and near environments have led to different regolith compositions and structures. Part of this history is recorded in their subsurface which can be probed by microwaves. Using a combined thermal and radiative transfer model, we here investigate all distant observations acquired in the passive mode of the RADAR on board the Cassini spacecraft (2004–2017) at 2.2-cm wavelength. The joint analysis of the derived disk-integrated emissivities and published radar albedos provides new insights into the purity and maturity of the regolith of Saturn's icy moons. We find that satellite-to-satellite variations and large-scale regional anomalies in microwave signatures primarily reflect different degrees of contamination of the regolith by non-ice compounds. To a lesser extent, they may also point to different concentrations of scatterers in the subsurface; these scatterers must be made of ice and/or void rather than of non-ice contaminants. Enceladus appears to have the cleanest regolith likely due to the geological youth of its surface. Observations also suggest that the current heat flux emanating from this moon is not confined to the South Pole Terrain. In the inner system, the degree of purity of the satellites' regoliths decreases from Enceladus outward likely due to the decrease of the E-ring influx. In the outer system, Phoebe's ring mantles Iapetus' leading hemisphere with a decimetric layer of optically-dark and microwave-absorbent dust. Dione is surprisingly less radar-bright and more emissive than expected from both the observed general trend and the current understanding of its geological history. Another question remains outstanding: why are Saturnian moons, and to a lesser extent Jovian moons, so radar-bright at centimetric wavelengths? Current models assuming purely-random scattering in their subsurface fail to simultaneously reproduce active and passive microwave observations, especially for Saturn's inner moons. This may be due the presence of organized and especially efficient backscattering structures in their subsurface. The challenge is now to identify structures that are geologically plausible.

First results of Antarctic sea ice type retrieval from active and passive microwave remote sensing data

Abstract. Polar sea ice is one of the Earth's climate components that has been significantly affected by the recent trend of global warming. While the sea ice area in the Arctic has been decreasing at a rate of about 4 % per decade, the multi-year ice (MYI), also called perennial ice, is decreasing at a faster rate of 10 %–15 % per decade. On the other hand, the sea ice area in the Antarctic region was slowly increasing at a rate of about 1.5 % per decade until 2014, and since then it has fluctuated without a clear trend. However, no data about ice type areas are available from that region, particularly for MYI. Due to differences in the physical and crystalline structural properties of sea ice and snow between the two polar regions, it has become difficult to identify ice types in the Antarctic. Until recently,no satellite retrieval scheme was ready to monitor the distribution and temporal development of Antarctic ice types, particularly MYI, throughout the freezing season and on timescales of several years. In this study, we have adapted a method for retrieving Arctic sea ice types and partial concentrations using microwave satellite observations to fit the Antarctic sea ice conditions. The core of the retrieval method is a mathematical scheme that needs empirical distributions of the microwave brightness temperature and backscatter input parameters for the different ice types. The first circumpolar, long-term time series of Antarctic sea ice types (MYI, first-year ice, and young ice) is being established, and so far covers the years 2013–2021. Qualitative comparison with (a) synthetic aperture radar data, (b) charts of the development stage of the sea ice, and (c) the Antarctic polynya distribution data show that the retrieved ice types, in particular the MYI, are reasonable. Although there are still some shortcomings, the new retrieval allows insight into the interannual evolution and dynamics of Antarctic sea ice types for the first time. The current time series can in principle be extended backwards to start in the year 2002 and can be continued with current and future sensors.

Blind Super-Resolution for SAR Images with Speckle Noise Based on Deep Learning Probabilistic Degradation Model and SAR Priors

As an active microwave coherent imaging technology, synthetic aperture radar (SAR) images suffer from severe speckle noise and low-resolution problems due to the limitations of the imaging system, which cause difficulties in image interpretation and target detection. However, the existing SAR super-resolution (SR) methods usually reconstruct the images by a determined degradation model and hardly consider multiplicative speckle noise, meanwhile, most SR models are trained with synthetic datasets in which the low-resolution (LR) images are down-sampled from their high-resolution (HR) counterparts. These constraints cause a serious domain gap between the synthetic and real SAR images. To solve the above problems, this paper proposes an unsupervised blind SR method for SAR images by introducing SAR priors in a cycle-GAN framework. First, a learnable probabilistic degradation model combined with SAR noise priors was presented to satisfy various SAR images produced from different platforms. Then, a degradation model and a SR model in a unified cycle-GAN framework were trained simultaneously to learn the intrinsic relationship between HR–LR domains. The model was trained with real LR and HR SAR images instead of synthetic paired images to conquer the domain gap. Finally, experimental results on both synthetic and real SAR images demonstrated the high performance of the proposed method in terms of image quality and visual perception. Additionally, we found the proposed SR method demonstrates the tremendous potential for target detection tasks by reducing missed detection and false alarms significantly.

Synthetic Aperture Radar (SAR) Meets Deep Learning

Synthetic aperture radar (SAR) is an important active microwave imaging sensor [...]

Joint Active and Passive Microwave Thermometry of Ice Sheets

Measurement of ice-sheet thermal state via microwave remote sensing techniques has the potential to provide critically needed observations on englacial temperature at the local and continental scale. Better constraints of the vertical englacial temperature structure are needed to improve understanding of thermomechanical processes, ice rheology, and basal sliding, and to reduce uncertainty in interpretations of basal conditions such as material, roughness, and thermal state. We investigate the potential to combine active and passive microwave remote sensing techniques, namely ice-penetrating radar sounding and microwave radiometry, to enable more precise, accurate, and robust measurement of ice-sheet thermal state across widely varying thermal regimes. We simulate the effects of englacial temperature profiles on the attenuation and brightness temperature and explore the performance tradeoffs for joint radar-radiometer system architectures. Our analysis shows that active and passive microwave measurements have complementary sensitivities to englacial temperature as a function of depth, and that a ground-based joint radar-radiometer system can reduce the requirements and complexity demanded of each single instrument.

Time-Efficient Adaptive Procedure for Identification of Multitone X-Parameters

A novel measurement procedure for the time-efficient identification of multitone <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$X$ </tex-math></inline-formula> -parameters of active microwave devices and circuits is introduced. Over a given bandwidth, the procedure allows for an adaptive selection of key spectral components necessary for determining the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$X$ </tex-math></inline-formula> -parameters characteristics with a minimum measurement effort. The sequential selection is based on the divide-and-conquer algorithm. It results in a huge reduction of the measurement time in comparison to the situation when model coefficients at all spectral components are identified. To validate the procedure, an off-the-shelf broadband power amplifier (PA) was characterized under wideband large-signal excitation conditions. Moreover, small-signal terms at all relevant frequencies were collected as a reference and compared with the results provided by a time-efficient adaptive procedure (TEAP). The proposed method resulted in twenty times reduction in the number of samples required to characterize the PA. As an application example, the small-signal coefficients identified with the TEAP are used to correct characterization results for the measurement instrument’s output mismatch. The mismatch-corrected output signal of the PA under test was represented accurately with the error-vector-magnitude of only 0.132%.

Bomb Cyclones over the North Pacific: Atmospheric Structure and Parameters According to Passive and Active Microwave Measurements from Space

Bomb Cyclones over the North Pacific: Atmospheric Structure and Parameters According to Passive and Active Microwave Measurements from Space

Remote Sensing of Surface Melt on Antarctica: Opportunities and Challenges

Surface melt is an important driver of ice shelf disintegration and its consequent mass loss over the Antarctic Ice Sheet. Monitoring surface melt using satellite remote sensing can enhance our understanding of ice shelf stability. However, the sensors do not measure the actual physical process of surface melt, but rather observe the presence of liquid water. Moreover, the sensor observations are influenced by the sensor characteristics and surface properties. Therefore, large inconsistencies can exist in the derived melt estimates from different sensors. In this study, we apply state-of-the-art melt detection algorithms to four frequently used remote sensing sensors: two active microwave sensors, ASCAT (Advanced Scatterometer) and Sentinel-1, a passive microwave sensor SSMIS (Special Sensor Microwave Imager/Sounder), and an optical sensor MODIS (Moderate Resolution Imaging Spectroradiometer). We intercompare the melt detection results over the entire Antarctic Ice Sheet and four selected study regions for the melt seasons 2015-2020. Our results show large spatiotemporal differences in detected melt between the sensors, with particular disagreement in blue ice areas, in aquifer regions, and during wintertime surface melt. We discuss that discrepancies between sensors are mainly due to (1) cloud obstruction and polar darkness, (2) frequency-dependent penetration of satellite signals, (3) temporal resolution, and (4) spatial resolution, as well as (5) the applied melt detection methods. Nevertheless, we argue that different sensors can complement each other, enabling improved detection of surface melt over the Antarctic Ice Sheet.

Seasonal variations in vegetation water content retrieved from microwave remote sensing over Amazon intact forests

Vegetation optical depth (VOD) is seasonally sensitive to plant water content and aboveground biomass. This index has a strong penetrability within the vegetation canopy and is less impacted by atmosphere aerosol contamination effects, clouds and sun illumination than optical vegetation indices. VOD is thus increasingly applied in ecological applications, e.g., carbon stock, phenology and vegetation monitoring. However, VOD retrieval over dense forests is subject to uncertainties caused by the thick canopy and complex multiple scattering effects. Thus, a comprehensive evaluation of VOD products over dense forests is needed for effective and accurate applications. This study evaluated the seasonal variations of eight recently developed/reprocessed VOD products at different frequencies (e.g., Ku-, X-, C- and L-band) over Amazon intact forests, supported by the ORCHIDEE-CAN-NHA model-simulated vegetation water content. Furthermore, we also explored the potential causes of VOD retrieval issues, in terms of retrieval algorithm uncertainties.We first confirmed that soil water availability dominated seasonal dynamics of vegetation water content over Amazon intact forests. This was verified by model-simulated vegetation water content and by C-band radar backscatter observations. Generally, evening or midday vegetation water content shows higher correlations with soil moisture than morning or midnight vegetation water content. In terms of ability of morning or midnight VOD products to follow the seasonality of soil moisture, active microwave ASCAT-IB C-VOD (median seasonal correlation with soil moisture (R) ∼ 0.50) outperforms the passive microwave VOD products, followed by passive microwave AMSR2 X-VOD (R ∼ 0.26) and VODCA X-VOD (R ∼ 0.16). However, SMOS-IC L-VOD (R ∼ −0.15) and AMSR2 C1-VOD (R ∼ −0.20) show obviously negative seasonal correlations with soil moisture across most pixels. This implausible behavior is likely to be caused by the inappropriate setting of time-invariant scattering effects in the passive microwave VOD retrieval algorithms, which could lead to an overestimation of the VOD amplitude during dry seasons. Thus, we recommend that the seasonal scattering effects be considered in the passive microwave VOD retrieval algorithms. These findings can contribute to the improvement of VOD retrieval algorithms and help with the development of their ecological applications over Amazon dense forests.

A Multi-Scale Feature Pyramid Network for Detection and Instance Segmentation of Marine Ships in SAR Images

In the remote sensing field, synthetic aperture radar (SAR) is a type of active microwave imaging sensor working in all-weather and all-day conditions, providing high-resolution SAR images of objects such as marine ships. Detection and instance segmentation of marine ships in SAR images has become an important question in remote sensing, but current deep learning models cannot accurately quantify marine ships because of the multi-scale property of marine ships in SAR images. In this paper, we propose a multi-scale feature pyramid network (MS-FPN) to achieve the simultaneous detection and instance segmentation of marine ships in SAR images. The proposed MS-FPN model uses a pyramid structure, and it is mainly composed of two proposed modules, namely the atrous convolutional pyramid (ACP) module and the multi-scale attention mechanism (MSAM) module. The ACP module is designed to extract both the shallow and deep feature maps, and these multi-scale feature maps are crucial for the description of multi-scale marine ships, especially the small ones. The MSAM module is designed to adaptively learn and select important feature maps obtained from different scales, leading to improved detection and segmentation accuracy. Quantitative comparison of the proposed MS-FPN model with several classical and recently developed deep learning models, using the high-resolution SAR images dataset (HRSID) that contains multi-scale marine ship SAR images, demonstrated the superior performance of MS-FPN over other models.

Analysis of Conditional Stability and Unconditional Stability and Instability for Microwave 3-Ports

A full and rigorous treatment of conditional and unconditional stability and instability for an active 3-port microwave circuit, represented at a given frequency by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$S$ </tex-math></inline-formula> -parameters, is carried out. By terminating a given port with a variable termination one may study at the input and the output of the resulting 2-port circuit the occurrence of unconditional stability and instability, as well as of special cases of conditional stability, defined as reactive unconditional stability or instability. Such conditions are especially relevant in microwave oscillator design and were exhaustively identified for the first time and treated in previous analysis by the authors. Drawing from its conclusions, one obtains in the plane of the variable termination and with a simple algorithm, a single parametric representation, providing at once, in a closed and direct form, the entire collection of boundaries of the regions, wherein the listed conditions occur at ports of the resulting 2-port circuit. Guidelines for the design of microwave amplifiers and oscillators, both in series and shunt configuration, are sketched. Numerical examples, validated by simulations, are also presented.

Topographic controls on active microwave behavior of mountain snowpacks

An uncalibrated distributed multiphysics snow model driven by downscaled weather forecasts (30-m, 15-min) was implemented as a Radar Observing System Simulator (ROSS) in Senator Beck Basin (SBB), Colorado to elucidate topographic controls on C-, X- and Ku-bands active microwave sensing of mountain snowpacks. Phase-space maps of time-evolving grid-scale ROSS volume backscatter show the accumulation branch of the backscatter-snow water equivalent (σ-SWE) hysteresis seasonal loop that is the physical basis for radar retrieval (direct inference) of SWE and snowpack physical properties. There is good agreement in the accumulation season (R2 ∼ 0.7) between Sentinel-1 and ROSS predictions corrected using average Sentinel-1 measurements under snow free conditions to estimate snow-ground backscatter, capturing well spatial patterns tied to elevation, slope, and aspect. Root Mean Square Deviations (RMSDs) do not exceed ±3.2 dB for ripening snowpacks in early spring and ±2.4 dB for dry snowpacks in the accumulation season when the mean absolute bias is <1 dB for all land-cover types with topographic slopes ≤ 30°. Grid-point RMSDs are attributed to the underestimation of snowfall on upwind slopes compounded with forecast errors for the weather near the ground. Like Sentinel-1, ROSS backscatter fields exhibit frequency-independent single-scaling behavior in the 60–150 m scale range for dry snowpacks in the accumulation season, while frequency-dependent scaling behavior emerges in the ablation season. This study demonstrates skillful physical modeling capabilities to emulate Sentinel-1 observations in complex terrain. Conversely, it suggests high readiness to retrieve snow mass and snowpack properties in mountainous regions from radar measurements at high-spatial resolutions enabled by SAR technology.

Noninvasive measurement of subcutaneous temperature using active antenna and high gain low noise microwave radiometer

ABSTRACT This paper presents a radiometric thermometer for non-invasive measurement of the internal body temperature. The proposed radiometric thermometer designed in 1.575 GHz band consists of a near-field probe and a high gain Dick radiometer. A phantom based on Agar material was developed to emulate the real tissue of skin and muscle for experiments. The measured results show that the active probe antenna provides additional 12.5 dB gain with noise figure less than 1dB, and the radiometer has a system gain of 50 dB and total noise figure of 1.738 dB at 1.575 GHz. The output voltage measured by the proposed 1.575 GHz microwave radiometer is 2.994 V when the water temperature is set to 25 °C, while it is 3.129 V when the water temperature is set to 80 °C. The study demonstrates that using the active probe connected to the radiometer can identify temperature difference of the body.

Snow Depth Inversion in Forest Areas from Sentinel-1 Data Based on Phase Deviation Correction

At present, snow depth inversion based on active microwave remote sensing is concerned essentially with areas having a relatively simple underlying surface. The existence of forests reduces the sensitivity of microwaves to snow, which often makes the snow depth inversion results uncertain. This paper presents a snow depth estimation algorithm for forest areas by introducing a forest phase to characterize the effect of forests on backscattering electromagnetic wave. Firstly, the interferogram is generated with the differential interference of two-pass master-slave Synthetic Aperture Radar (SAR) images, and the real phase under snow cover condition is obtained by phase unwrapping. Secondly, the phase models for forest and non-forest areas are constructed. The effects of forest cover are modeled as forest phase in the forest phase model, which is estimated under the assumption of snow depth consistency on both sides of the boundaries between forest and non-forest areas. Finally, snow depth is estimated by the snow phase-depth model. The correctness of the proposed forest snow depth inversion algorithm was verified by taking the Jiagedaqi area of Greater Xing’an Mountains as the study area and sentinel-1 dual polarization images as the data source. Finally, the snow depth distribution of the study area was obtained with a spatial resolution of 30 m on 7 December 2020. The experimental results show that the snow depth values estimated in Jiagedaqi area are mainly between 40–120 cm, and the average snow depth value is 80.27 cm. Taking the snow depth value of 84.69 cm reckoned from hourly accumulated snowfall in Jiagedaqi as the reference snow depth, the results of the estimated snow depth are relatively consistent and well-founded. With the introduction of the forest phase, the average snow depth values estimated in the forest area increase by 5.98 cm, which reduces the underestimation of the snow depth in forest areas.