Full Polarimetric SAR Research Articles

Gini Coefficient-Based Feature Learning for Unsupervised Cross-Domain Classification with Compact Polarimetric SAR Data

Remote sensing image classification usually needs many labeled samples so that the target nature can be fully described. For synthetic aperture radar (SAR) images, variations of the target scattering always happen to some extent due to the imaging geometry, weather conditions, and system parameters. Therefore, labeled samples in one image could not be suitable to represent the same target in other images. The domain distribution shift of different images reduces the reusability of the labeled samples. Thus, exploring cross-domain interpretation methods is of great potential for SAR images to improve the reuse rate of existing labels from historical images. In this study, an unsupervised cross-domain classification method is proposed that utilizes the Gini coefficient to rank the robust and stable polarimetric features in both the source and target domains (GRFST) such that an unsupervised domain adaptation (UDA) can be achieved. This method selects the optimal features from both the source and target domains to alleviate the domain distribution shift. Both fully polarimetric (FP) and compact polarimetric (CP) SAR features are explored for crop-domain terrain type classification. Specifically, the CP mode refers to the hybrid dual-pol mode with an arbitrary transmitting ellipse wave. This is the first attempt in the open literature to investigate the representing abilities of different CP modes for cross-domain terrain classification. Experiments are conducted from four aspects to demonstrate the performance of CP modes for cross-data, cross-scene, and cross-crop type classification. Results show that the GRFST-UDA method yields a classification accuracy of 2% to 12% higher than the traditional UDA methods. The degree of scene similarity has a certain impact on the accuracy of cross-domain crop classification. It was also found that when both the FP and circular CP SAR data are used, stable, promising results can be achieved.

Mapping high spatial resolution ionospheric total electron content by integrating Time Series InSAR with International Reference Ionosphere model

Total electron content (TEC) is a key parameter for characterizing the ionosphere. Conventional TEC measurement methods have low spatial resolution, hindering accurate representation of ionospheric spatial characteristics. Synthetic Aperture Radar (SAR) and Interferometric SAR (InSAR) have shown their potential for high-spatial-resolution TEC estimation. However, SAR-based absolute TEC estimation typically relies on full-polarimetric SAR images, while the InSAR-based method can only extract differential TEC. This study proposes a novel TEC mapping method integrating Time Series InSAR (TS-InSAR) with the International Reference Ionospheric (IRI) model and does not leverage full-polarimetric SAR data. The Advanced Land Observing Satellite-1 (ALOS-1) SAR images covering the Chile and Greenland regions were collected to test the proposed method. The TECs derived from the IRI model and International GNSS Service (IGS) and the electron density observed by the Incoherent Scattering Radar (ISR) site of SONDRESTROM were acquired to evaluate the performance and validate the accuracy of the proposed method, respectively. The results show that the proposed method can achieve TEC mapping with resolutions of tens of meters, much higher than the tens or hundreds of kilometers of the IRI and IGS TECs. The spatial features of our method-derived TECs show overall good consistency with those of the IRI and IGS TECs, but the described spatial information is more detailed. Also, compared to the original IRI model, the improvement rates of root-mean-square errors (RMSEs) between the estimated electron density and ISR observations exceeded 21.67% after the update using the TEC obtained from the proposed method. These experiments demonstrate the proposed method’s feasibility and reliability for ionospheric TEC mapping.

Morphological characterization of Maize (Zea mays.) utilising the stage-wise structural and architectural perspective from temporal fully-polarimetric SAR

The morphological shape and structure of the crop vary with phenological stages. Model and eigen based decomposition model parameters extracted from the Radarsat-2 data and the trend with respect to ground truth crop phenology were analysed. Sensitive parameters were devised through stepwise approach under 7 combinations of polarimetric variables of increasing complexity were assessed. Compared under the three machine learning algorithms (ANN, RF and SVM) where ANN rendered the maximum correlation with 0.92 with a MAE of 4 days which was implemented on a large parcel of maize mask in the study area. SVM performed poorly with highly overlapping parameters such as backscatter but performed well (r = 0.85). For assessing the crop biophysical parameters, the three algorithms were evaluated and sensitivity analysis for statistically significant polarimetric variables for biophysical parameters was performed. The assessment was performed on Multi-Layer Perception (MLP) neural network. The networks were trained with algorithms and hidden layer nodes until the MAE achieved permissible limits. Plant height could be estimated more profoundly with an r = 0.8 with a considerably good MAE of 24.9 cm but other parameters (WB, DB and LAI) were estimated in moderate correlation of 0.6–0.65 where the MAE of WB, DB and LAI were found to be 1317gm−2, 553 gm−2 and 0.78 respectively. This is the first step towards understanding the complex scattering mechanisms in Indian maize scenario assessing the growth parameters from polarimetric data. Thus, the analytical findings brought out possess the potential to serve as the reference for the future research initiatives.

Utilizing a single-temporal full polarimetric Gaofen-3 SAR image to map coseismic landslide inventory following the 2017 Mw 7.0 Jiuzhaigou earthquake (China)

On August 8, 2017, a magnitude 7.0 earthquake struck Jiuzhaigou County in Sichuan Province, triggering numerous coseismic landslides. The prompt identification of these landslides is imperative for emergency rescue efforts and post-earthquake hazard assessments. Optical satellite and unmanned aerial vehicle (UAV) images are often obstructed by cloud cover and fog following earthquakes. In contrast, polarimetric synthetic aperture radar (PolSAR), unaffected by adverse weather conditions, emerges as an indispensable tool. However, the utilization of spaceborne single-temporal SAR for mapping the inventory of coseismic landslides is infrequent and encounters constraints due to several limitations. In this study, we analyzed the amplitude feature and polarimetric decomposition of multiple ground categories in a full PolSAR image, and proposed an automated method to accurately identify coseismic landslides using a single-temporal full PolSAR image. The coseismic landslide inventory following the Jiuzhaigou Earthquake was mapped and validated using high-resolution UAV images. Detailed analysis was conducted to identify error sources leading to omissions and false positives. Additionally, we evaluated various machine learning models to compare their performance with our proposed method. Finally, we conducted a comprehensive discussion on the strengths and weaknesses of different data types (PolSAR, optical satellite, and UAV) for coseismic landslide identification. Our results indicate that the proposed PolSAR-based method achieves high accuracy in coseismic landslide inventory mapping, offering an effective solution for timely post-earthquake emergency responses in complex environments and all weather conditions in the future.

Potential of a Multistate Quantum Model for Full Polarimetric SAR Change Detection

Potential of a Multistate Quantum Model for Full Polarimetric SAR Change Detection

Performance evaluation of backscattering coefficients and polarimetric decomposition parameters for marsh vegetation mapping using multi-sensor and multi-frequency SAR images

Wetland vegetation is the basis for wetland ecosystems to regulate climate change, carbon sequestration and maintain biodiversity. Therefore, high-precision mapping and dynamic monitoring of wetland vegetation are essential for the effective management, restoration and sustainable development of wetland ecosystems. This study addressed to explore classification performance of backscattering coefficients and polarimetric decomposition parameters of multi-frequency SAR images, including single-polarimetric X-band TerraSAR (TS), full-polarimetric C-band Radarsat-2 (RS) and L-band ALOS PALSAR-2 (PS) for marsh vegetation mapping in Honghe National Nature Reserve, Northeast China. We proposed two transfer-learning strategies, and examined the feasibility of transfer-learning vegetation classifications between optical and SAR sensors, different frequencies SAR (X-, C- and L-band) and its derivative images, respectively. This paper further compared transfer-learning classification performance of marsh vegetation from polarimetric decomposition parameter images to backscattering coefficient images under the same SAR sensors. The results indicated that: (1) The three SAR images performed good classification ability in identifying marsh vegetation with the overall accuracies (OA) ranging from 0.74 to 0.88. For the same frequency SAR images, polarimetric decomposition parameters outperformed backscattering coefficients with an OA improvement ranging from 0.24 % to 2.41 %; (2) The longer wavelength SAR images produced better classification results, and L-band PS images realized the highest classification accuracy (OA = 0.871); (3) Full-polarimetric SAR images obtained the better transfer-learning classifications (OA > 0.8), and the transfer-learning classification ability of the same frequency SAR images outperformed the different frequencies; (4) The transfer learning classification results between SAR sensors outperformed that of between optical and SAR images. The results of this study provide a scientific basis for wetland change monitoring conservation and sustainable development.

A distributed land cover classification of FP and CP SAR observation using MapReduce-based multi-layer perceptron algorithm over the Mumbai mangrove region of India

ABSTRACT Globally, the rapid loss of mangrove forests often creates long-lasting environmental damage alongside coastlines, mudflats, and river banks. All-weather, physical monitoring of such areas is almost impossible because of inaccessibility to swampy areas and a hostile substrate. Subsequently, conventional field surveys are relatively unavailable to monitor human encroachment in coastal areas like the Mumbai mangrove region of India. In this context, the polarimetric synthetic aperture radar (PolSAR) remote sensing tool becomes a potential candidate for mangrove conservation and management. Since the Mumbai mangrove region of India exists over the extensive land cover, the large-scale classification can articulate the continuous encroachment because of the location of human settlements or activities across this metropolitan coastal city. For this, the traditional algorithms need the essential improvisation to apply for large-scale data analysis, aiming simultaneously at getting the highest decisive and time efficiency. Here, we introduce a shallow learning model of MapReduce-based Multi-Layer Perception (MLP) algorithm to classify the hybrid compact polarimetric (CP) and fully polarimetric (FP) feature space. Even though a shallow learning model of the automated method is easily scalable, it requires a distinctive shallow learnable feature set for better land cover classification. In this effort, this paper investigates the efficacy of derived feature space compared to direct polarimetric measurements of sensors and shows that the shallow learnable feature set is more effective with both CP & FP observations. Simultaneously, the relevancy of the proposed distributed model of MLP is also justified compared to distributed extreme learning machine (DELM) algorithm and provides a practically implementable scaled-MLP algorithm of shallow learning model. Ultimately, this paper comes up with a better polarimetric signature of land types for both the CP and FP datasets, which can be used in an alternative manner as per data availability for a multi-sensor data analysis.

FULL POLARIMETRIC TIME SERIES IMAGE ANALYSIS FOR CROP TYPE MAPPING

Abstract. Crop information and quality are not only fundamental for experts using spatial decision support systems but also have many applications in irrigation management, economic analysis for import or export, food safety, and achieving sustainable agriculture. Remote sensing is a cheap and fast way of reaching this goal. Full polarimetric SAR unlike optical sensors is an all-weather system providing geometrical and physical properties of the earth’s surface events. Due to the dynamic changes in crop properties through their phenological stages, crop type mapping has been challenging. As a result, accurate, reliable, and cost-effective crop type mapping using minimum data and processing has been the goal of the remote sensing and precision agriculture community. In this study, a new method based on time series analysis of full polarimetric SAR data combined with radar indices, polarimetric decompositions followed by the three αs extracted from H/A/α decomposition, and unsupervised H/α/Wishart classification bands as features generated from only 5 dates of RADARSAT CONSTELLATION MISSION 2 data were used for classification of crops. Applying random forest and cat boost algorithm as classifiers an accuracy of 87.4% and 75% was respectively achieved. indicating that both algorithms have promising results. Although the random forest algorithm had better results, the cat boost algorithm had less noise in each field and more homogenous farms were detected.

Retrieval performances of different crop growth descriptors from full- and compact-polarimetric SAR decompositions

This study investigates the different performances between Compact-Polarimetric (CP) and Full-Polarimetric (FP) SAR to retrieve multiple crop growth parameters including Vegetation Water Content (VWC), Leaf Area Index (LAI), height, and dry biomass. The objective is to study at which conditions the CP SAR can obtain comparable retrieval accuracy as FP SAR for specific crop types and growth descriptors. Polarimetric decompositions were used to extract CP and FP explanatory variables to quantify crop growth status. Then, the sensitivities of CP and FP variables to different crop descriptors were analyzed, revealing higher sensitivity to height than LAI, VWC, and dry biomass. In order to reduce the information redundancy of sets of CP and FP SAR variables, Partial Least Square (PLS) approach was used to develop new orthogonal SAR parameters, while Step-Wise Regression (SWR) was used to determine the optimal SAR parameters, followed by retrievals of multiple crop growth descriptors using the developed estimators. Validated by the ground measurements, the retrieval performances are found to be highly dependent on polarimetry dimension (CP or FP), crop types, and targeted crop descriptors. With crop growth and enhanced depolarization effects, the CP SAR can capture the increasing volume power and decreasing surface power but at a lower rate than FP SAR. The m-δ and m-χ decompositions provide similar scattering components which differ from the Random Volume Over Ground (RVOG) decomposition. The third Stokes parameter g3 of the CP SAR data that indicates the strength of the circular polarization component is a common important parameter to infer the multiple crop parameters of interest. In agreement with the sensitivity analysis, better retrieval accuracies were obtained for height and dry biomass than VWC and LAI. For short vegetation such as soybean, full polarimetry is required to obtain the estimates of all four crop growth descriptors. On the contrary, for tall crop growth parameters, such as canola (height and dry biomass), corn (height, VWC, dry biomass), and wheat (LAI, VWC, dry biomass), the CP SAR features can provide comparable robust retrieval accuracy as the FP SAR features. This study deepens our insights into the CP SAR of the RADARSAT Constellation Mission (RCM) to timely monitor crop growth and health status.

Assessment of GF3 Full-Polarimetric SAR Data for Dryland Crop Classification with Different Polarimetric Decomposition Methods

Crop classification is one of the most important agricultural applications of remote sensing. Many studies have investigated crop classification using SAR data, while few studies have focused on the classification of dryland crops by the new Gaofen-3 (GF3) SAR data. In this paper, taking Hengshui city as the study area, the performance of the Freeman–Durden, Sato4, Singh4 and multi-component decomposition methods for dryland crop type classification applications are evaluated, and the potential of full-polarimetric GF3 data in dryland crop type classification are also investigated. The results show that the multi-component decomposition method produces the most accurate overall classifications (88.37%). Compared with the typical polarization decomposition techniques, the accuracy of the classification results using the new decomposition method is improved. In addition, the Freeman method generally yields the third-most accurate results, and the Sato4 (87.40%) and Singh4 (87.34%) methods yield secondary results. The overall classification accuracy of the GF3 data is very positive. These results demonstrate the great promising potential of GF3 SAR data for dryland crop monitoring applications.

Non-Local Means Filters for Full Polarimetric Synthetic Aperture Radar Images with Stochastic Distances

In this paper, we present a Non-Local Means filtering method for PolSAR (Polarimetric Synthetic Aperture Radar) imagery. We adopted three stochastic distances to measure the similarity of samples. We obtain p-values using the asymptotic distribution of test statistics based on these distances. Then we use this similarity evidence as input for a smooth activating function that yields the weights of local convolution matrices. Non-local filters are computationally demanding, so we provide an efficient implementation that allows us to experiment with different settings and find optimal parameters.

Edge-Preserving classification of polarimetric SAR images using Wishart distribution and conditional random field

ABSTRACT Classification of polarimetric SAR images into different ground covers has important applications in fields such as land mapping, agriculture monitoring, and assessment. The Wishart supervised classifier is one of the most widely used and general purpose classifier for polarimetric SAR data. However, it is a pixel-based classifier, so the performance is greatly affected by inherent speckle noise. The impact of speckle noise can be reduced by considering the spatial information from neighbouring pixels for classification tasks. In this paper, we aim to improve classification results by incorporating spatial-contextual information along with preservation of significant details such as edges and micro-regions. For this purpose, a conditional random field (CRF) based model is proposed for polarimetric SAR data along with Wishart and Wishart mixture model (WMM) classifiers, namely Wishart-CRF and WMM-CRF, to perform the classification. The model is compared with the Markov random field (MRF) based model as well as neural network-based models. The results are analysed in terms of accuracy and preservation of details such as edges and micro-regions. The model is assessed using three full polarimetric SAR benchmark data sets. The CRF model exhibits better classification results by significantly reducing the noise and preserving the finer details of edges and small regions.

Investigating the radar scattering response of different land-features in the Greater Cairo area of Egypt, using the full-polarimetric SAR data

Investigating the radar scattering response of different land-features in the Greater Cairo area of Egypt, using the full-polarimetric SAR data

Polarimetric Analysis of ALOS PALSAR data (POL-SAR) over Test Areas in North-West Bulgaria – Polarimetric Descriptors, Decompositions and Classifications

This study is focused on utilizing full-polarimetric L-band radar data from ALOS PALSAR (JAXA) by means of Polarimetry (POL-SAR), over mountainous test sites in Bulgaria. General aim is to show feasibility of the Polarimetry to describe natural targets, which exhibits various scattering mechanisms in respect to their bio-physical and geometrical properties. Firstly, the importance of Covariance and Coherent matrices is shown, which is followed by calculation of the polarimetric coherences with their particular significance. The mathematical and physical model based decompositions are applied to describe backscattering media from scattering mechanisms. Radar indices resulted from H/A/α-decomposition showed radnomization of scattering mechanisms over forest areas, whilst two major scattering mechanisms are observed mainly in crop lands. Α comparison is made between polarimetric descriptors from acquisitions in different seasonality over mountainous forest and agricultural lands. Polarimetric segmentations and classifications are applied, with 8 (H/A) and 16 (H/A/α) components. Finally, a forest mask is proposed based on relevant polarimetric descriptors. Study showed good utilization and importance of the full-polarimetric L-band SAR data, derived from ALOS PALSAR, in natural targets and forest areas. This report resulted from a course GEO414 -“Polarimetrie”, held at the University of Jena, Lehrstuhl für Fernerkundung, in the framework of ERASMUS+, with the kind support of – Dr. T. Jagdhuber (DLR) and Prof. C. Schmullius.

Hybrid Three-Component Scattering Power Characterization From Polarimetric SAR Data Isolating Dominant Scattering Mechanisms

Rapid advancements have been made in model-based decomposition techniques for polarimetric Synthetic Aperture Radar (PolSAR) data. Improvements have been primarily driven by including additional scattering models to the three-component model-based method first introduced by Freeman and Durden. Nevertheless, the three-component method is still extensively used due to its simplicity and ease of interpretability. Recently, the paradigm of the decomposition strategy has been changed to non-model types with notable success. Thus utilizing this new approach, we propose a hybrid (i.e., combining non-model and model-based) three-component methodology in this work. The proposed method primarily involves three steps: (i) the generalized eigendecomposition technique is first used to determine the optimum volume scattering power, (ii) the residual rank-2 coherency matrix (i.e., volume scattering model deducted) is appropriately transformed using two unitary transformations to decorrelate the odd and even bounce scattering components, and (iii) compute the odd and even bounce scattering power contributions using the newly developed scattering-type parameter obtained from the rank-2 matrix. Each step carries relevant physical significance that is appropriately addressed in this work. The proposed methodology is first demonstrated using some specific coherency matrices from canonical targets and a few matrices extracted from different landcover types from full-polarimetric SAR images. We then apply the proposed method over diverse landcover types using two full-polarimetric SAR images. We compare the results with the state-of-the-art three-component model-based decomposition techniques to validate the effectiveness of the proposed method that deals with the existing challenges of model-based decomposition methods.

A Novel Weighted Amplitude Modulation (WAM) System for Ambiguity Suppression of Spaceborne Hybrid Quad-Pol SAR

Quadrature-polarimetric synthetic aperture radar (quad-pol SAR) has extensive applications, including climate zones classification, extraction of surface roughness, soil moisture mapping, moving target indication, and rice mapping. Hybrid quad-pol SAR ameliorates the range ambiguity performance of conventional quad-pol SAR. However, the azimuth ambiguity of its cross-polarized (cross-pol) echo signals is serious, limiting the swath width of SAR. Therefore, this paper proposes a spaceborne weighted amplitude modulation (WAM) full-polarimetric (full-pol) SAR system, and it can suppress the azimuth ambiguity of hybrid quad-pol SAR. The performance boost of the azimuth ambiguity by the two imaging modes of the proposed SAR system is detailed and evaluated with the L-band system parameters. Moreover, the chirp scaling algorithm (CSA) is adopted to execute scene simulations for the two imaging modes. The results indicate that the proposed SAR system can effectively suppress the azimuth ambiguity of hybrid quad-pol SAR and verify the theoretical analysis.

Brightness Compensation for GF-3 SAR Images Based on Cycle-Consistent Adversarial Networks

GF-3 is the first C-Band full-polarimetric synthetic aperture radar (SAR) satellite with a space resolution up to 1m in China. The uneven brightness of SAR images is a problem when using GF-3 images, which makes it difficult to use and produce SAR images. In this paper, a brightness compensation method is proposed for GF-3 SAR images with unbalanced brightness in some areas based on a deep learning model named Cycle-Consistent Adversarial Networks (CycleGAN). The proposed method makes the image brightness relatively consistent, and it is compared with the MASK dodging algorithm, Wallis dodging algorithm and histogram equalization in terms of the profiles, brightness mean, standard deviation, and average gradient. Results of brightness compensation show that, the proposed method makes the inner brightness differences smaller, and the image quality is obviously improved, which provides even brightness image for subsequent applications, and has great practical significance.

Sensitivity of Compact Polarimetric SAR Parameters to Modeled Lake Ice Growth

Synthetic aperture radar (SAR) is a valuable tool for lake ice monitoring. The recently proposed SAR configuration for Earth observation called compact polarimetric (CP) SAR could be a good compromised choice between conventional (single or dual) and fully polarimetric (FP) SAR for operational ice applications, including lake ice. Given its enhanced radar target information compared with conventional SAR systems over wider swath coverage compared with FP SAR, CP systems could play important role in the new generation of Earth observation systems. Herein, we study the evolution of CP SAR parameters from simulated CP SAR data in relation to early ice growth. Focus of the study is on four lakes located in Cornwallis Island, Canadian Central Arctic. We adopt parameters extracted from dual circular polarimetric and right circular transmit, linear (horizontal and vertical) receive configurations. In this study, we consider the ice thickness calculated from an established empirical model. Meteorological and ice climatological data were used to support the analysis. Results demonstrated a potential connection between a number of CP parameters and lake ice growth. Furthermore, we were able to highlight the relationship between the density of air bubbles in ice layer and the intensity of volume scattering mechanism, leading to the identification of lakes with increased gas production activities. Thus, differences between lakes in terms of density of air bubbles were detected and statistically evaluated.

Discrimination of multi-crop scenarios with polarimetric SAR data using Wishart mixture model

Discrimination of crop varieties spanned over heterogeneous agriculture land is a vital application of polarimetric SAR images for agriculture monitoring and assessment. The covariance matrix of polarimetric SAR images is observed to follow a complex Wishart distribution for major classification tasks. It is true for homogeneous regions, but for heterogeneous regions, the covariance matrix follows a mixture of multiple Wishart distributions. We aim to improve the classification accuracy when the terrain under observation is heterogeneous. For this purpose, Wishart mixture model is employed along with expectation-maximization (EM) algorithm for parameter estimation. Elbow method helps us to devise the number of mixtures. The convergence of the EM algorithm depends on the choice of initial points. So, to improve the robustness of the model, different initialization approaches, such as random, K-means, and global K-means, are embedded in the EM algorithm. Further, the degrees of freedom is one of the crucial parameters of Wishart distribution. Therefore, the impact of different degrees of freedom is analyzed on classification accuracy. The method that is equipped with initialization technique along with optimum degrees of freedom is assessed using three full polarimetric SAR data sets of agriculture lands. The first two are benchmark data sets of Flevoland, Netherlands, region acquired by AIRSAR sensor, and third is our study area of Mysore, India, acquired by RADARSAT-2 sensor.

RETRACTED ARTICLE: Application of land ecological environment risk assessment based on SAR image

The ecological environment of land is an important part of the ecosystem. Extracting land eco-environmental information is very important to realize environmental change monitoring and sustainable development. In the past, people used surface measurement and optical remote sensing technology to extract land ecological environment information. According to the data measured by the surface survey method, the accuracy is relatively low, and it is difficult to meet the application needs of people. However, because of its own characteristics, optical remote sensing is easy to be disturbed by the weather and cannot extract the land ecological environment information timely and accurately. The development of SAR technology provides a new way to extract land ecological environment information. This technology cannot be affected by cloud, rain, and fog and can be used for all day monitoring of land ecological environment risk assessment. In addition, the full polarimetric SAR image directly opens up a new method of land ecological information extraction. According to Wishart H/α, the classification algorithm extracts the land eco-environmental cover information from the full polarimetric SAR image. The classification algorithm is applied to all the polarization data in a certain area, and the coverage information is extracted according to the scattering mechanism of the terrestrial ecological environment. After comparing the extraction accuracy with the optical image, it is found that the land ecological environment coverage information is extracted by different methods under different visual values of multi-view processing. Using error analysis to improve the classification algorithm, improve the accuracy of land eco-environmental cover information extraction, the past accuracy of 54.0% to 81.7%, and the accuracy of 73.1% to 88.0%. Based on the impact of the application of SAR image in land ecological environment risk assessment, a new concept assessment model is established. Through this method, the past evaluation model can be improved.