Accurately measuring surface deformation velocity using Interferometric Synthetic Aperture Radar (InSAR) is crucial for understanding geophysical processes. However, traditional methods often face challenges in capturing subtle deformations over long distances, as errors introduced during unwrapping can accumulate over extended spatial extents. This study introduces a Multi-arc adjustment (MAA) method aimed at mitigating these errors, especially in high-precision monitoring scenarios where velocities are sensitive to the location of the reference point. Simulation results demonstrate that the MAA method significantly outperforms the traditional method, achieving substantial reductions in RMS under noisy conditions and complex phase unwrapping scenarios. Furthermore, integrating the MAA method into fault slip inversion improves the accuracy of slip distribution estimations. Applications to real datasets from the southern Tibet region and the San Andreas Fault further validate the MAA method's effectiveness. These findings underscore the MAA method's potential to enhance deformation velocity measurements in challenging environments, establishing it as a valuable tool for geodetic and tectonic studies.

Topography-Dependent Atmospheric Delay (TDAD) plays a crucial role in limiting the accuracy of deformation monitoring in Synthetic Aperture Radar Interferometry (InSAR) measurements. Although several correction methods have been proposed to achieve satisfactory results in Differential InSAR (DInSAR), the impact and analysis of TDAD in time-series InSAR have often been overlooked. This study focused on the landslide monitoring near the Baihetan hydropower station located in the rugged and steep terrain of southwestern China. Utilizing C-band Sentinel-1A satellite data, we systematically examine and analze the influence of TDAD on time-series InSAR. We introduce a Deep Neural Network (DNN) correction model to remove the TDAD, resulting in a substantial improvement in the outcomes of time-series InSAR. The validation proves the effectiveness of this correction, providing valuable insights and technical support for precise TDAD correction in future time-series InSAR applications.

Land subsidence caused by mining activity is one of the most serious anthropogenic geohazards. The rapid detection and continuous monitoring of mining subsidence facilitate the swift detection of geohazards. Traditional methods of monitoring mining subsidence have shortcomings, such as offering only a limited coverage and being time consuming. Interferometric Synthetic Aperture Radar (InSAR) has been proven to be a powerful tool to identify mining subsidence hazards from unwrapped interferograms but this method can be complex and inefficient, particularly for wide areas. In this paper, a Mask R-CNN model is presented to automatically detect mining subsidence and monitor the surface activity over wide areas using original SAR interferograms to avoid the time-consuming and error-prone phase unwrapping procedure. Using Sentinel-1 wrapped interferograms as the real dataset and simulated wrapped interferograms generated with the Gaussian surface function combined with Generic Atmospheric Correction Online Service for InSAR (GACOS) as the simulated dataset, the Mask R-CNN deep neural network was used to train the mining subsidence detection model. It turned out that the accuracy of the detection model was 91.48%, while the precision was 96.44%, the recall rate was 93.88% and the F1 index was 0.949. The detection model was then utilized to detect mining subsidence in south-western Shanxi Province, China between 2016 and 2022. A total of 152 land subsidence points were detected and long-term monitoring results were also obtained. An analysis of the state of land subsidence in the mining areas was carried out to obtain land subsidence activity during the monitoring period.

The accurate and high-throughput extraction of phenotypic traits is of great significance for crop breeding and growth monitoring. The segmentation of structural components (e.g. leaves and stems) is a prerequisite for extracting phenotypic traits. In the past decade, there has been an increase in methods attempting to separate leaves and stems in point clouds. However, previous researches mainly focus on plants at the individual level due to the interlocked and overlapped nature of leaves and the bottleneck existing for field plants to extract phenotypic traits. To address this issue, a novel two-stage leaf-stem separation model encompassing the initial separation of leaves and stems and optimization is presented in this paper. The model is based on the different geometric features of leaves and stems of maize plants defined by neighborhood points, and a cylinder is used to find the neighborhood points by considering the elongated characteristic of maize stems. After that, another elongated cylinder (0.5m high and 0.02m diameter) is used to traverse the stem points to optimize the initially separated results. Maize plants with the planting density of 45,000 plants/ha in the filling stage (Exp. 2019) were used to train and test the model in the initial separation step (Experiment 1), showing that the separation accuracy could be up to 91.3%. It was concluded that a 0.11m high and 0.07m diameter cylinder was the optimal searching parameter for the initial separation, and 0.25m was the optimal threshold for optimization. We also tested the transferability of the model (Experiment 2) for maize plants with different planting densities (45,000, 67,500, 90,000, and 105,000 plants/ha), different growth stages (jointing, silking and filling), and point clouds collected using multiple platforms (Terrestrial Laser Scanning (TLS), LiDAR Backpack (LiBackpack), and Unmanned Aerial Vehicle-Light Detection and Ranging (UAV-LiDAR)), suggesting that the model performed well for all the datasets. In addition, the simulated datasets of maize with different planting densities were used to assess the model performance at the point level, showing the separation accuracy were 0.92, 0.91, 0.91, and 0.90 for maize with the planting densities of 45,000, 67,500, 90,000, and 105,000 plants/ha, respectively. The proposed model in this study is innovative, and it has promising prospects for the high-throughput extraction of the phenotypic traits in field maize plants and could facilitate genotype selection in crop breeding and three-dimensional (3D) plant modeling.

Distributions of leaf base and inclination angles are important crop phenotypic traits, influencing light interception and productivity. Light detection and ranging (LiDAR), and especially Terrestrial laser scanning (TLS), provides unprecedented detail of the three-dimensional (3-D) structure of the crop canopy. Recent research mainly focuses on the leaf base and inclination angles of maize at the individual level or at lower planting density. It is difficult to extract the distributions of leaf base and inclination angles of maize in the field due to the interlocked and overlapped nature of leaves. In this study, we have proposed a high-throughput method to extract the distributions of leaf base and inclination angles of maize in the field. Following the separation of the leaf and stem of maize, hollow cylinders with different thicknesses were used to extract the local leaf points from the separated leaf points based on each stem fitted line, and the Density-based spatial clustering of applications with noise (DBSCAN) algorithm and singular value decomposition were used to calculate the leaf base and inclination angles. The distributions of leaf base and inclination angles of maize in the field with different cultivars (Jingjiuqingchu 16 (A1), Tianci 19 (A2), Jingnuo 2008 (A3), Nongkenuo 336 (A4), and Zhengdan 958 (A5)), planting densities (3.32 plants/m2, 4.65 plants/m2, 6.64 plants/m2, and 8.63 plants/m2), and growth stages (jointing, silking, and filling stages) were extracted and analyzed, and these performed well against the validation data. In addition to TLS data, the extraction of the distributions of leaf base and inclination angles based on LiBackpack and UAV-LiDAR data was also discussed. This further validated the potential of the method proposed in this study for the extraction of the distributions of leaf base and inclination angles of maize in the field. Furthermore, the relationship between maize with different leaf base angle distributions and the daily cumulative APAR (Absorbed Photosynthetically Active Radiation) was analyzed, which demonstrated that compact maize cultivars exhibited higher light interception capabilities than scattered ones under high planting densities. The distributions of leaf base and inclination angles exert a substantial influence on the light interception capacity of maize, thereby exerting a consequential effect on maize yield. The high-throughput extraction of these distributions in maize fields holds significant importance for studying the optimal maize cultivar in conjunction with radiative transfer models.

Small Baseline Subset InSAR (SBAS InSAR) utilizes a series of synthetic aperture radar (SAR) interferograms to generate a time series that can analyze the surface displacements of coherent points. Still, atmospheric errors in interferometric SAR (InSAR) measurements can seriously affect the reliability of the surface displacement time series. In this article, a new approach incorporating the Generic Atmospheric Correction Online Service for InSAR (GACOS) and principal component analysis (PCA) is proposed to reduce atmospheric errors in SBAS InSAR. Its application to Southern California, USA suggests that the incorporation of GACOS and PCA can efficiently reduce atmospheric effects on InSAR observations and hence improve the accuracy of InSAR-derived surface displacements. The overall standard deviations of the SAR interferograms were reduced from 4.97 to 2.02 rad after applying GACOS and PCA with the root mean square error (RMSE) reducing by 61.1% from 18 to 7 mm. In addition, comparisons between different PCA processing strategies suggest that the more principal components are removed, the smaller the standard deviations of the interferograms, but the lower the accuracy of InSAR-derived surface displacements.

Synthetic aperture radar interferometry (InSAR) technology has been widely used for landslide monitoring in mountainous areas. The troposphere in steep mountainous areas is affected by the variable topography, temperature, and humidity, which differs from that in plain areas and thus exhibits large spatial heterogeneity. Traditional InSAR troposphere correction methods are limited in this area, and the accuracy of InSAR measurements will be significantly affected. In this paper, we proposed a tropospheric delay correction method based on deep learning (AtmNet) without external data considering the spatial-heterogenetiy in each individual interferogram. The tropospheric correction and landslides monitoring based on Sentinel-1 SAR data was carried out in Mao County, a high landslide-prone area in southwest Sichuan Province (China). A simulation experiment was conducted to analyze the adaptability of the model and evaluate the effectiveness of the AtmNet method. Furthermore, we demonstrated the good performance of the AtmNet method through a comparison with the linear model (LM) and GACOS method, revealing that the proposed method could effectively model the spatial heterogeneity of tropospheric delay in steep mountains. The slope displacements that cannot be seen in the interferogram were very clear after the tropospheric delay correction. This method provides important technical support for the accurate DInSAR and time-series InSAR for landslide monitoring in steep mountainous areas in the future.

Three-dimensional ionospheric electron density is an important parameter for characterizing the ionosphere. Synthetic aperture radar (SAR), an advanced earth observation technology, has shown its potential for observing two-dimensional vertical total electron content (VTEC). However, retrieval of three-dimensional electron density is limited by the SAR imaging geometry. To solve this problem, a simple method is proposed to reconstruct the regional three-dimensional electron density by ingesting the SAR-derived VTEC into an international reference ionosphere (IRI) model. The ionospheric global (IG) index is updated by minimizing the difference between the SAR-derived and IRI-derived VTECs. Subsequently, the high-spatial-resolution electron density is reconstructed by exploiting the monotonic relationship between the electron density and the IG index. For assessing the performance of the proposed method, two full-polarimetric advanced land observing satellite (ALOS) images with descending and ascending orbits were acquired to reconstruct the three-dimensional electron density over the Alaska region. Incoherent scattering radar (ISR) electron density was collected from the Poker Flat Incoherent Scatter Radar (PFISR) system to validate the reconstructed electron density. The results show that the standard deviations of the electron density decreased by approximately 30% for the ascending orbit and 19% for the descending orbit when the proposed method was used, thereby illustrating its feasibility.

Although deep learning techniques have achieved noticeable success in aircraft detection, the scale heterogeneity, position difference, complex background interference, and speckle noise keep aircraft detection in large-scale synthetic aperture radar (SAR) images challenging. To solve these problems, we propose the geospatial transformer framework and implement it as a three-step target detection neural network, namely, the image decomposition, the multiscale geospatial contextual attention network (MGCAN), and result recomposition. First, the given large-scale SAR image is decomposed into slices via sliding windows according to the image characteristics of the aircraft. Second, slices are input into the MGCAN network for feature extraction, and the cluster distance nonmaximum suppression (CD-NMS) is utilized to determine the bounding boxes of aircraft. Finally, the detection results are produced via recomposition. Two innovative geospatial attention modules are proposed within MGCAN, namely, the efficient pyramid convolution attention fusion (EPCAF) module and the parallel residual spatial attention (PRSA) module, to extract multiscale features of the aircraft and suppress background noise. In the experiment, four large-scale SAR images with 1-m resolution from the Gaofen-3 system are tested, which are not included in the dataset. The results indicate that the detection performance of our geospatial transformer is better than Faster R-CNN, SSD, Efficientdet-D0, and YOLOV5s. The geospatial transformer integrates deep learning with SAR target characteristics to fully capture the multiscale contextual information and geospatial information of aircraft, effectively reduces complex background interference, and tackles the position difference of targets. It greatly improves the detection performance of aircraft and offers an effective approach to merge SAR domain knowledge with deep learning techniques.

Leaf base and inclination angles are two critical 3-D structural parameters in agronomy and remote sensing for breeding and modeling. Terrestrial laser scanning (TLS) has been proven to be a promising tool to quantify leaf base and inclination angles. However, previous TLS studies often focused on leaf base and inclination angles of certain trees or plants with flat leaves, such as European beech. Few studies have worked on leaf base and inclination angles of maize plants due to their curved and elongated characteristics. In this study, a machine learning-based [support vector machine (SVM)] method and a structure-based [skeleton extraction (SE)] method were presented to extract the leaf base and inclination angles of maize plants. After separating individual leaf points from the complete point cloud and skeleton points of maize plants and then extracting geometric features, the machine learning- and structure-based methods were used to calculate leaf base and inclination angles. Our results show that the leaf base and inclination angles extracted using these two methods agreed well with ground truth, and the estimation accuracy of the machine learning-based method was obviously higher than that of the structure-based method. The mean absolute error (MAE), root-mean-squared error (RMSE), and relative RMSE (rRMSE) of the leaf base and inclination angles using the machine learning-based method were 4.56°, 6.17°, and 19.04% and 7.95°, 10.00°, and 20.24%, respectively; and those from the structure-based method were 6.22°, 7.47°, and 23.30% and 8.99°, 12.57°, and 25.85%, respectively. The machine learning-based method was also applied to a field with dense mature maize, and their MAE, RMSE, and rRMSE were 6.04°, 8.12°, and 25.90% and 11.30°, 13.52°, and 26.5%, respectively. It is demonstrated that both the machine learning- and structure-based methods are effective to estimate the leaf base and inclination angles of maize plants, although the machine learning-based method appears to outperform the structure-based method.

This letter introduces a multiple-scatterer detection method in synthetic aperture radar tomography (TomoSAR), named iterative sequential generalized likelihood ratio test (iterative-SGLRT). In this technique, the number of scatterers is sequentially decided by the generalized likelihood ratio test (GLRT) pixel by pixel, after iteratively estimating the parameters. It is a good tradeoff of the aforeproposed methods of sup-GLRT and fast-sup-GLRT on accuracy and efficiency. Simulated comparisons showed that iterative-SGLRT outperformed fast-sup-GLRT on the performances of detection probability and accuracy without substantial computation time increase, and compared with sup-GLRT, its performance loss could be negligible with computational burden greatly reduced. In addition, both iterative-SGLRT and sup-GLRT have been applied to the TerraSAR-X data set over Shenzhen city. The 3-D reconstruction of the test site and the separation of the overlaid scatterers have been achieved. In addition, verification using light detection and radar (LiDAR) indicated a root-mean-square error (RMSE) of $ {0.1\rho _{s}}$ for both methods of the height estimated. Accordingly, iterative-SGLRT is very suitable for large urban area processing for its super-resolution, high efficiency, and robustness.

Variations in water levels of seasonally ice-covered subarctic lakes are indicators of environmental and climatic change. Satellite altimetry enables remote sensing of these lakes, but the lake phenology is problematic as radar reflection surfaces include water, snow, and ice. Reflection from multiple surfaces gives rise to two-peak waveforms across ice-covered lakes. Misinterpretation of the altimetric height has caused extracted water levels to be low compared with gauge data. In this study, a modified retracker is used to determine heights from the first altimetric subwaveform. Usingin situsnow depth and ice thickness, the first reflection surface is shown to correspond closely to the snow/ice interface when the lake is frozen. The modified retracker is applied to the Great Bear Lake (GBL), Great Slave Lake (GSL), and Lake Athabasca (ATL) of the Mackenzie River Basin for the period 1992–2020. Standard deviations (Std) of differences between lake levels from Jason-2 waveforms andin situdata across GBL and GSL are 0.06 m with the new methodology compared with 0.11 and 0.08 m, respectively, using the standard Ice retracker. With an Std of 0.11 m between altimetric and gauge lake levels, TOPEX/Poseidon is less accurate than the combined Jason missions (Std: 0.07 m).

The synthetic aperture radar (SAR) interferometry (InSAR) has been developed for more than 20 years for historical surface deformation reconstruction. In particular, the onboard Sentinel-1/A/B satellite, newly planned NASA-ISRO SAR (NISAR), and Germany Tandem-L will continue to provide unprecedented SAR data with an increased number of acquisitions. However, processing of real-time SAR data has been experiencing challenges regarding the InSAR deformation parameter estimation over a long time with the small baseline subsets (SBAS) InSAR technology. We use sequential adjustment for the estimation of the deformation parameters, which uses Bayesian estimation theory under the least square criteria to inverse long time-series deformation dynamically. Finally, both simulated and real Sentinel-1A SAR data verify the performance of the sequential estimation. It can be regarded as an effective data processing tool in the coming era of SAR big data.

The lack of high-resolution digital elevation model (DEM) data presents one major limitation for deformation mapping using synthetic aperture radar interferometry (InSAR) techniques with high-spatial-resolution radar imagery (e.g., TerraSAR-X). This article presents a baseline linear combination (BLC) approach to generate interferograms with nearly zero baselines so as to minimize the effects of the uncertainties in the DEM used. It incorporates the baseline combination (BC) method with adjacent gradient networking to successfully unwrap the interferograms even in abruptly discontinuous areas, which in turn can be used to estimate a high-resolution DEM. The BLC approach does not require any deformation model; instead, it utilizes nearly zero-baseline interferograms to assist with 3-D phase unwrapping. Application of the BLC approach to the TerraSAR-X data set in Shenzhen, China, shows that the BLC-derived DEM agrees with the digital surface model (DSM) obtained from light detection and ranging (LiDAR) with a correlation coefficient of 0.998 and a root-mean-square error (RMSE) of 2.05 m, demonstrating the effectiveness of the BLC approach. Note that the BLC approach is not only able to be employed in urban areas with high buildings but also in mountain areas with steep slopes.

Plant biomass is an important parameter for crop management and yield estimation. The potential of compact polarimetric (CP) synthetic aperture radar (SAR) data in estimating biomass of oilseed rape crop (Brassica napus L.) is investigated in this study. Five CP SAR imagery was simulated using five fully polarimetric Radarsat-2 data, and the dynamic evolution of polarimetric features, relying on different polarimetric decomposition methods (m-χ, m-δ, and Freeman-Durden), with the crop growth, was compared. It was found that the Dbl indicator, by the m-χ decomposition method, can reflect well the dynamic growth of canola. Therefore, a method of monitoring fresh and dry biomass of canola was put forward. The result showed that the root mean square error (RMSE) was 56.5g/m2, 448.2g/m2, and the relative error (RE) was 23.9%, 25.0% for fresh and dry biomass, respectively. In addition, the precision of the model will be affected when the crop becomes mature since its vegetation water content declines. The results were also compared with those of the fully polarization SAR. It revealed that the performance of CP SAR on rapeseed monitoring can achieve the level of fully polarization SAR, considering the advantages of CP SAR, such as wider coverage and less data volume etc. It revealed that the polarization information was necessary in quantitatively monitoring of broad leaf crops, such as rapeseed, and CP SAR has a great potential in crop monitoring.

Summary form only given. The tropical peatland is one of the largest terrestrial carbon stores. However, deforestation, drainage, fires and conversion development have progressed all over this region. Through both legal and illegal logging, and conversion to agricultural use over the period of 1985~2006, about 12.1 M ha of peatland was deforested and drained in Southeast Asia, of which 1.5 M ha were tropical peat swamp forests in Central Kalimantan, Indonesia [1]. Those progressions result in decomposition of the surface peat and losing carbon to the atmosphere as CO2, which results in reducing their strength as a current C store and their capacity for future soil C storage. However, related quantitative estimation of CO2emission is limited. Using field-based surveying to monitoring peatland surface height changes over the large areas typical of drained in the past, is challenging such that measurements are more likely to describe a small area and be only a snapshot in time. Upscaling and understanding the rate of change in surface height trough time may be overcome using remote sensing approaches. Our objective is to investigate the peat subsidence and carbon emission by Interferometry Synthetic Aperture Radar (InSAR) time series. Here we present data on the change in peatland surface height in Central Kalimantan, Indonesia, detected using the Interferometry Synthetic Aperture Radar (InSAR) Small BAseline Set (SBAS) approach[2, 3]. Using data from December 2006 to September 2010, we have generated a map of the rate of change of mean height, and time series of change in drained peatland. To do this we used two independent ALOS L-band tracks SAR images, as there is a lack of ground data for validation, correlation in output provides confidence the results are representative. Our analysis to date shows that (Figure 1): 1) the rate of change in surface height (decrease) can vary; 2) the decrease in surface height can be up to -7.65 cm/year; 3) the largest decrease in surface height observed was 25 cm.

A major source of error for repeat-pass interferometric synthetic aperture radar is the phase delay in radio signal propagation through the atmosphere, particularly the part due to tropospheric water vapor. These effects become more significant for ScanSAR observations due to their wider coverage (e.g., 400 km$\times$400 km for ENVISAT Advanced Synthetic Aperture Radar (ASAR) wide swath (WS) mode versus 100 km$\times$100 km for ASAR image mode). In this letter, we demonstrate for the first time that a Medium Resolution Imaging Spectrometer water vapor correction model can significantly reduce atmospheric water vapor effects on ASAR WS interferograms, with the phase variation in non-deforming areas decreasing from 3.8 cm before correction to 0.4 cm after correction.

Atmospheric water-vapor effects represent a major limitation of interferometric synthetic aperture radar (InSAR) techniques, including InSAR time-series (TS) approaches (e.g., persistent or permanent scatterers and small-baseline subset). For the first time, this paper demonstrates the use of InSAR TS with precipitable water-vapor (InSAR TS$+$PWV) correction model for deformation mapping. We use MEdium Resolution Imaging Spectrometer (MERIS) near-infrafred (NIR) water-vapor data for InSAR atmospheric correction when they are available. For the dates when the NIR data are blocked by clouds, an atmospheric phase screen (APS) model has been developed to estimate atmospheric effects using partially water-vapor-corrected interferograms. Cross validation reveals that the estimated APS agreed with MERIS-derived line-of-sight path delays with a small standard deviation (0.3–0.5 cm) and a high correlation coefficient (0.84–0.98). This paper shows that a better TS of postseismic motion after the 2003 Bam (Iran) earthquake is achievable after reduction of water-vapor effects using the InSAR TS$+$PWV technique with coincident MERIS NIR water-vapor data.

A method based on random field theory and Gibbs-Markov random fields equivalency within Bayesian statistical framework is used to derive 3-D surface motion maps from sparse global positioning system (GPS) measurements and differential interferometric synthetic aperture radar (DInSAR) interferogram in the southern California region. The minimization of the Gibbs energy function is performed analytically, which is possible in the case when neighboring pixels are considered independent. The problem is well posed and the solution is unique and stable and not biased by the continuity condition. The technique produces a 3-D field containing estimates of surface motion on the spatial scale of the DInSAR image, over a given time period, complete with error estimates. Significant improvement in the accuracy of the vertical component and moderate improvement in the accuracy of the horizontal components of velocity are achieved in comparison with the GPS data alone. The method can be expanded to account for other available data sets, such as additional interferograms, lidar, or leveling data, in order to achieve even higher accuracy