The quality of SAR interferometric phase is significantly influenced by decorrelation noise, especially in densely vegetated regions. In this study, we developed a predictive model that incorporates normalized difference vegetation index (NDVI) and the spatiotemporal baselines of SAR image pairs, allowing for the a priori estimation of interferometric coherence. This predicted SAR coherence is then used to facilitate the selection of optimal interferograms for displacement time series analysis. We applied our method to a densely vegetated mining area in Yuncheng County, Shandong Province, China. The results indicate that our approach achieves high accuracy in predicting SAR coherence. Furthermore, the mean coherence of the selected interferograms is 15% higher than that obtained using the traditional method based on small spatiotemporal baseline criteria, resulting in approximately a 24% improvement in ground displacement measurement accuracy. This proposed method provides an effective strategy for selecting optimal interferometric pairs from extensive SAR datasets, thereby enhancing the practicality and efficiency of InSAR in mapping ground surface deformation.

Synthetic Aperture Radar (SAR) data, characterized by high spatial resolution and all-weather observation capabilities, holds tremendous potential for application in soil moisture monitoring and crop growth assessment. Currently, the Water Cloud Model (WCM) is widely applied in SAR-based soil moisture inversion. However, the traditional WCM’s reliance on optical vegetation indices, which cannot be synchronized with SAR data, and the incomplete polarization scattering information contained in existing SAR vegetation indices, adversely effect the accuracy of SAR soil moisture retravel. Thus, this paper proposed a novel dual-polarization SAR vegetation index to soil moisture retravel in croplands. Firstly, the method establishes a Polarization Scattering Contrast Parameter (mcp) by combining SAR data covariance elements with backscattering information. Subsequently, based mcp, the Polarization Scattering Correlation Contrast parameter (Rcp) is defined, incorporating the degree of polarization, to integrate both scattering contrast and polarization state characteristics. Then, we proposed a novel dual-polarization SAR vegetation index (DRVIs) based Rcp. Finally, the surface soil moisture retravel in cropland area was realized using DRVIs and multiparameter WCM. This study conducted experimental in the winter wheat and summer corn covered areas of the Eastern Henan Plain, China. The experimental results indicate that during the wheat growing season, the correlation coefficient between the surface soil moisture products and estimated results reached 0.72. And during the corn growing season, the correlation coefficient was approximately 0.64. The research will contribute to expending the application of SAR data in vegetation growth monitoring and soil moisture retrieval.

Surface soil moisture (SSM) is an essential component of surface ecosystems. However, the existing microwave SSM retrieval methods are beleaguered with issues such as insufficient decoupling of terrestrial scattering characteristics and excessive reliance on empirical parameters within the models. This paper proposes a novel generalized SAR backscattering model (GSBM) for time-series retrieval of SSM, considering different land-cover influences. Initially, the GSBM is introduced, categorizing land cover into built-up areas, water bodies, vegetation-covered areas, and soils. Subsequently, we establish a unique SAR Water Cloud Model (SWCM) with the dual-polarization SAR vegetation index (DRVIs). A high-quality soil backscatter coefficient is obtained by employing the SWCM to eliminate vegetation's influence. Ultimately, the dry and wet reference values of soil backscatter are calculated to retrieve the relative SSM time series. Based on Sentinel-1 data, we select the Golmud area for a three-year spatiotemporal monitoring of SSM. Experimental results show that our method improves the correlation coefficients (r) between SAR backscatter and in situ soil moisture data from 0.63 to 0.73, improving about 16%. The r between SSM retrieval results and in situ data was above 0.66 at 500 m, 1 km and 3 km spatial resolutions. Consequently, the proposed method underscores the advantages of simplicity in parameters, high estimation precision, and robust adaptability, thereby augmenting the potential for large-scale global monitoring applications.

The optical flow (OF) algorithm has been employed in the extraction of glacier flow velocity fields due to its efficient data processing capabilities. However, rapid changes on the glacier surface result in decreased similarity in remote sensing image series, leading to an increase in outliers within OF-driven offset stacks. This limits the accuracy of flow velocity field extraction. In this letter, we propose a novel adaptive Stacking-OF method designed to accurately compute the glacier flow velocity field. Our approach introduces a crucial temporal baseline criterion to define an effective subset of OF-driven offsets that are used as input for subsequent operations. We systematically filter outliers from offset stacks obtained by iterative regression analysis. Finally, a Stacking operation is employed to calculate the glacier flow velocity field based on the refined multitemporal offset series. We conducted tests on Gongba Glacier, using Landsat 8 and Sentinel-2 satellite images from 2018 to 2020. The experimental results demonstrate the excellent advantages of our proposed method in refining the stacked offset data. Consequently, the retrieved glacier flow velocity field is rendered more precise and reliable.

Monitoring changes in glacial lake (GL) area is of great significance for revealing climate change and analyzing the risk of GL outburst floods (GLOFs). However, in the case of Southeastern Tibet Plateau (SETP), the availability of optical images in high mountain areas is low, and Synthetic Aperture Radar (SAR) amplitude images have significant geometric distortions. Most GL extraction studies are mainly focused on autumn and winter seasons. In order to obtain accurate GL boundaries at any time and to capture seasonal changes of GLs in a wide area, this letter proposes a high mountain GL extraction framework based on the coupling of Google Earth Engine (GEE) and historical GL catalog data, which focuses on local GL extraction problems. GL extraction and validation were performed using various clustering and adaptive threshold segmentation methods, all of which showed strong stability and reliability of the proposed scheme. Finally, we employed a superpixel clustering algorithm to estimate the area of GLs as of August 1, 2019, and then compared the results with two widely used spatially referenced datasets. The results indicate that our method achieves a comprehensive Intersection over Union (IoU) of up to 95%. The proposed method can effectively support the extraction of wide-area summer GLs and the monitoring of seasonal changes in GL area, thus enabling the dynamic updating of GL information at a high temporal frequency.

Synthetic aperture radar (SAR) is a crucial technique in Earth observation, providing vast amounts of data for monitoring the Earth’s surface. However, SAR’s side-looking imaging characteristics often result in significant geometric distortions in complex terrains such as mountainous gorges. Current methods struggle to accurately compute both active and passive geometric distortions when orbital state vector information is not available. This study aims to address this challenge by concentrating on the Southeastern Tibetan Plateau (SETP) and introducing a DEM-based radar incidence angle-tracking method (Angle-Tracking) based on ray tracing principles. The fundamental aspects of this method include constructing the discrete range direction vector (DRDV) to establish calculation directions, refining grid distribution via cubic-patch cells, and identifying potential topographic occluder points (PTOPs) to minimize redundancy in iterative computations. Through the utilization of this approach, we have acquired and disclosed the distribution of geometric distortion in ascending and descending Sentinel-1 data over the SETP region. Cross validation with results computed from precise orbital data showcases the efficacy of the angle-tracking method in identifying geometric distortions in the absence of satellite state vector information. Furthermore, the angle-tracking method demonstrates effectiveness when implemented on cloud computing platforms such as Google Earth Engine (GEE), thereby enhancing the feasibility of SAR-based research in mountainous areas.

Leaf area index (LAI) is an important biophysical variable widely used in ecosystem models. Recent advances in remote sensing technology have led to the development of different high-resolution LAI products but their consistency and accuracy remains unknown. This study compared high-resolution LAI remote sensing products derived from three different types of instruments, including NEON Airborne Observatory Platform Imaging Spectrometer (NIS), Sentinel-2 (S2), and Global Ecosystem Dynamics Investigation (GEDI) lidar products. These products were also compared with in-situ data derived from Digital Hemispheric Photos (DHPs) over 19 NEON forest sites in the US. Results at plot level (i.e. ~20m) suggested there were only moderate agreements between different remote sensing products (r2ranging from 0.23 to 0.35) and multiple factors could affect comparison results such as geolocation error and slope. Their agreements were much better at site level (r2ranging from 0.59 to 0.76). In comparison to DHPs estimate at site level, GEDI achieved a better agreement (r2=0.53) than that of NIS and S2 (r2= 0.33 and 0.30 respectively). These results suggest, despite large uncertainties at plot level, there is a great potential of improving LAI estimates at regional landscape levels by fusing different remote sensing products.

As a supplement to time-series interferometric synthetic aperture radar (TS-InSAR), time-series multiple-aperture InSAR (TS-MAI) can measure the spatiotemporal changes in SAR along-track surface deformation. TS-MAI is often applied with low-frequency SAR data (e.g., L-band data) due to its ability to retain high interferometric coherence. However, the low-frequency SAR signal is vulnerable to ionospheric delays, which can significantly degrade the measurement accuracy of TS-MAI. This letter presents an approach to correct the ionospheric errors in TS-MAI. A polynomial cubic model is employed to constrain the ground deformation, which is then incorporated into the observation model for effectively separating the deformation signal and the ionospheric delays. The proposed method is tested using the L-band ALOS-1 PALSAR-1 datasets covering the Tocopilla area in Chile between November 2007 and March 2011. The correction performance and accuracy of the proposed method are demonstrated by comparing the range split-spectrum interferometry (RSSI)-based method and the local GPS data, respectively. The root mean square error (RMSE) improvement rates between TS-MAI and GPS are 72.17% for the SRGD site and 84.51% for the VLZL site, and their correlation coefficients increase from 0.23 and 0.50 to 0.52 and 0.61 after the correction.

Ionospheric phase delay is a critical error source in Time Series Interferometric Synthetic Aperture Radar (TS-InSAR) for the purpose of monitoring ground surface deformation with SAR data obtained from low-frequency radar systems. Recently, the Range Split-Spectrum Interferometry (RSSI) method has been employed to estimate and rectify ionospheric errors in TS-InSAR. However, the performance of the RSSI method is largely restricted by the significant linear scale factors resulting from the current small SAR bandwidth. In this study, we propose a Reformulating RSSI (Re-RSSI)-based method for correcting the ionospheric error in TS-InSAR by optimizing the linear scale factors, with the aim of improving the accuracy of TS-InSAR measurements. We evaluate the Re-RSSI method using 121 ALOS-1 PALSAR images that cover two distinct regions: the low-latitude Lazufre volcano region and the high-latitude Anaktuvuk River tundra fire region. Our results demonstrate that the Re-RSSI method can effectively remove time series ionospheric errors at both test sites, where we detected ionospheric delays of approximately 2.5 cm/yr and 2.0 cm/yr, respectively. Using Global Navigation Satellite System (GNSS) measurements as ground truth, we achieved an 86.59% improvement rate in root mean square error (RMSE) with the Re-RSSI method, which is significantly higher than the 66.40% improvement rate achieved with the traditional RSSI method.

The pixel offset-tracking (PO) technique developed from correlation matching for use on synthetic aperture radar (SAR) images has been widely employed to monitor glacier dynamics. However, the decorrelation caused by rapid changes in a glacier surface reduces the integrity of flow velocity extraction. In this letter, we propose a novel method, termed the optical flow (OF) small baseline subset(SBAS), developed from the optical flow algorithm, which is defined as the apparent motion of individual pixels on the image plane. The OF algorithm can compute dense flows at the individual pixel level with a low computational cost and may serve as an alternative to PO for estimating the glacier velocity field. During processing, We selected the image pairs having short spatiotemporal baselines and calculated their offset series according to the OF algorithm. We then used an interval estimation strategy to eliminate outliers to refine stacked offsets. Finally, the least-squares method was used to calculate the displacement series for each pixel. We tested the proposed method utilizing eight ALOS-2/PALSAR-2 images on a temperate debris-covered glacier, the Hailuogou Glacier on the southeastern Tibetan Plateau. Compared with PO-SBAS, our method effectively improves the coverage of glacier flow velocity monitoring from 73.5% to 99.6%.

The global positioning system interferometric reflectometry (GPS-IR) method has the advantage of acquiring observations continuously in all weather conditions, which has great application potential in vegetation remote sensing. However, L-band electromagnetic wave signals are susceptible to various environmental factors, resulting in deviations in GPS-IR observation data at certain times. The accuracy and reliability of the vegetation index retrieving results are challenging to achieve by using single-frequency GPS data. This letter proposed a novel method to combine dual-frequency data for retrieving normalized difference vegetation index (NDVI). We integrated the multipath observations based on the theory of information entropy. Subsequently, a unary linear regression model was established to retrieve the NDVI from the calculated normalized microwave reflection index (NMRI). For validation purposes, the comparative analysis was conducted between the proposed model and the previous single-frequency NDVI retrieving model in terms of retrieval accuracy, based on the continuous observation data acquired by four GPS reference stations in the past five years. The experimental results indicated that the proposed model is available for retrieving the NDVI, with the correlation coefficient of 0.749–0.815 and the root mean square error (RMSE) of 0.056–0.081. Compared with the results acquired by the previous single-frequency NDVI retrieving model, the correlation coefficient of the retrieved NDVI was increased by an average of 18.5%, and the RMSE was reduced by 30.3%. The proposed method in this letter helps further improve the accuracy and continuity of NDVI observation data in some local areas, which contributes to grasping the growth status of vegetation comprehensively.

Accurate estimation of photosynthetic phenology is of great importance for understanding carbon cycles. Most vegetation indices (VIs) calculated from remotely sensed reflectances represent the canopy structure and have high uncertainty in detecting the photosynthetic phenology. We compared the start/end of the photosynthetically active season (SOS/EOS) extracted from the normalized difference vegetation index (NDVI), the enhanced vegetation index (EVI), the near-infrared reflectance of vegetation (NIRv) and the product of NIRv and solar incident radiation (NIRvP) over northern deciduous broadleaf forests, and we used the metrics generated from solar-induced chlorophyll fluorescence (SIF), a proxy for photosynthesis, as reference. We found that the growing season extracted from the structural VIs was generally longer than the duration of photosynthetic activity retrieved from SIF: SOS derived from NDVI < NIRvP < EVI ≈ NIRv ≈ SIF and EOS from NDVI > NIRv ≈ EVI > NIRvP ≈ SIF. We investigated the mechanism underlying these phenological discrepancies using the paradigm of light-use efficiency. Our results show that the divergent performances of VIs were related to main factors limiting photosynthesis, which vary across different growth stages. The fraction of absorbed photosynthetically active radiation absorbed by chlorophyll (FAPARchl) that is well characterized by both EVI and NIRv, was the dominant factor of spring photosynthetic phenology, whilst NIRvP that is a proxy of the total amount of photosynthetically active radiation absorbed by chlorophyll (APARchl) was the dominant factor in autumn when radiation determines photosynthetic phenology. As such, we suggest that these factors be accounted for when selecting VIs for the extraction of photosynthetic phenology, i.e., EVI and NIRv are more suitable for accurate retrieval of SOS, and NIRvP is more suitable for accurate retrieval of EOS.

The near-infrared reflectance of vegetation (NIRv) has been increasingly used as a proxy of gross primary production (GPP) across various temporal scales, ecosystems, and climate conditions. However, topography significantly distorts NIRv and GPP estimations over mountainous areas. We evaluated the topographic effects on NIRv and applied a path length correction (PLC) for improving its performance over mountainous areas. The proposed topographically corrected NIRv (referred to TCNIRv) was evaluated by multiple Landsat-8 operational land imager (OLI) images with concurrent${ in}~{ situ}$GPP measurements over the Lägeren mountainous forest area. TCNIRv reduced topographic effects in the original NIRv and it was comparable to the normalized difference vegetation index (NDVI) and the green normalized difference vegetation index (GNDVI), which are often deemed to be independent of topographic effects. In addition, TCNIRv better agreed with GPP than the other vegetation indices (VIs): coefficient of determination$R^{2} $= 0.90 and root mean square error RMSE = 1.40$\text{g}\cdot $Cm$^{-2} \cdot \text{d}$−1for TCNIRv compared to$R^{2} $= 0.71 and RMSE = 2.47$\text{g}\cdot $Cm−2$\cdot \text{d}$−1for NIRv. The evaluation shows that TCNIRv is a reliable proxy of GPP, and because of its simplicity and physical soundness, it will facilitate vegetation monitoring over complex topography mountainous areas.

Multiple aperture interferometric synthetic aperture radar (MAI) can measure ground displacements along SAR track. However, MAI measurements may suffer from severe ionospheric error, especially for long-wavelength SAR sensors. This study presents a new approach, azimuth split-spectrum interferometry (AziSSI), to correct ionospheric errors in MAI measurements. The proposed method can straightforwardly resolve ionospheric phase delay by exploiting two subband MAI interferograms with different centroid frequencies. We utilized two groups of ALOS-1 PALSAR images, which cover the 2008 Wenchuan earthquake containing strong coseismic ground deformation and a stable coast region in Chile, respectively, to test the proposed method. Experimental results show that the AziSSI method can successfully recover the coseismic displacements induced by the Wenchuan earthquake and circumvent the azimuth stripes for the Chile case’s MAI measurements. The maximum ionosphere-induced azimuth shifts are about 2.2 m for the Wenchuan case and 2.5 m for the Chile case. We validated the correction performance of the AziSSI method with the Wenchuan case by comparing the compensated azimuth displacements with the simulated deformation from a forward fault slip model and coseismic global positioning system (GPS) measurements. The validations show that the deformation patterns after the ionospheric correction are consistent with the simulations of the sinistral slip of the causative fault. The root-mean-square error between the GPS and MAI measurements is reduced from 0.77 to 0.28 m before and after the ionospheric correction, indicating that AziSSI can significantly compensate ionospheric errors for MAI.

Topographic and angular corrections on Sentinel-2 imagery are crucial for the generation of consistent surface reflectance. We propose a novel topographic-angular integrated normalization approach based on the combination of the path length correction (PLC) and C-factor approaches. The PLC-C normalization approach is a semiphysical method with limited use of auxiliary data: only a digital elevation model and a fixed set of kernel coefficients, ensuring its transferability for operational implementation. For the validation, we used two Sentinel-2A images over a mountainous area observed in backward (BS) and forward scattering (FS) directions from laterally adjacent orbit swaths. PLC-C significantly reduced both the topographic and directional anisotropy effects: the overlapping ratio between BS and FS observations was increased from 84.1% to 92.8% for the near-infrared band, and from 81.0% to 93.1% for the red band; the coefficient of variation of the reflectances across different aspects, which was used as a criterion of topographic effects, was reduced from 9.8%/12.2% to 3.6%/5.7% in BS/FS direction for the near-infrared band, and from 8.1%/9.7% to 4.5%/4.2% for the red band. PLC-C will contribute to the generation of analysis ready data from Sentinel-2 top of canopy reflectance.

The persistent scatterer interferometric synthetic aperture radar (PSI) has proven to be a powerful tool for monitoring surface deformation. However, the conventional deformation models cannot be fully adapted to analysis of the complicated deformation process. Taking into account seasonal response and tectonic movement, this letter presents an integrated deformation model for separating deformation components through PSI time series, thus obtaining the linear deformation rate, the deformation acceleration and the seasonal deformation amplitude at each PS. An iterative solution method is proposed to estimate the parameters in the integrated model. The experiments are carried out for subsidence detection over the northwestern part of Tianjin (China) by using the 40 high-resolution TerraSAR-X SAR images acquired between 2009 and 2010 and the ground truth data collected by precise leveling at seven benchmarks and six man-made corner reflectors. The testing results indicate that the integrated model has better adaptability to the subsidence process in the study area than the conventional models. The further analysis shows that the deformation results derived from the iterative solution are in better agreement with the ground truth data. These demonstrate that the proposed methodology is effective for monitoring the surface deformation with remarkable nonlinear property.

In this paper, we present an improved approach of the multitemporal interferometric synthetic aperture radar (InSAR) for detecting land subsidence in coastal areas by using the time series of high-resolution SAR images. In particular, our algorithm extends the capability of the temporarily coherent point InSAR (TCPInSAR) technique that can be used to detect subsidence even in the case of a small number of SAR images available for a study area. The proposed approach is implemented by using the interferograms with ultrashort spatial baselines (USBs) through several procedures, including the selection of USB interferometric pairs, TCP identification, TCP networking and modeling, as well as TCP solution by a least squares estimator. As the topographic effects in coastal areas are negligible in the USB interferograms, an external digital elevation model is no longer necessary for differential processing, thus simplifying both TCP modeling and parameter estimating. The USB-based TCPInSAR algorithm has been tested with the high-resolution TerraSAR-X images acquired over Tianjin (close to Bohai Bay) in China, and validated by using the ground-based leveling measurements. The testing results indicate that the density and coverage extent of TCPs can be increased dramatically by using the proposed algorithm, and the quality of subsidence measurements derived by the USB-based TCPInSAR can be raised.

Synthetic aperture radar interferometry has been applied widely in recent years to ground deformation monitoring although difficulties are often encountered when applying the technology, among which the spatial and temporal decorrelation and atmospheric artifacts are the most prominent. The permanent-scatterer interferometric synthetic aperture radar (PS-InSAR) technique has overcome some of the difficulties by focusing only on the temporally coherent radar targets in a time series of synthetic aperture radar (SAR) images. This paper presents an improved PS-InSAR technique by introducing PS-neighborhood networking and empirical mode decomposition (EMD) approaches in the PS-InSAR solution. Linear deformation rates and topographic errors are estimated based on a least squares method, while the nonlinear deformation and atmospheric signals are computed by singular value decomposition and the EMD method. An area in Phoenix, AZ, is used as a test site to determine its historical subsidence with 39 C-band SAR images acquired by European Remote Sensing 1 and 2 satellites from 1992 to 2000.

Synthetic aperture radar interferometry has been applied widely in recent years to ground deformation monitoring although difficulties are often encountered when applying the technology, among which the spatial and temporal decorrelation and atmospheric artifacts are the most prominent. The persistent-scatterer interferometric synthetic aperture radar (PS-InSAR) technique has overcome some of the difficulties by focusing only on the temporally coherent radar targets in a time series of synthetic aperture radar (SAR) images. This paper presents an improved PS-InSAR technique by introducing PS-neighborhood networking and empirical mode decomposition (EMD) approaches in the PS-InSAR solution. Linear deformation rates and topographic errors are estimated based on a least squares method, while the nonlinear deformation and atmospheric signals are computed by singular value decomposition and the EMD method. An area in Phoenix, AZ, is used as a test site to determine its historical subsidence with 39 C-band SAR images acquired by European Remote Sensing 1 and 2 satellites from 1992 to 2000.

This paper analyzes the relationship between the noise level in interferometric phases at permanent scatters (PS) and the accuracy in deformation measurements with the PS-based differential SAR interferometry (PS-DInSAR). The study is carried out based on interferograms that are simulated with parameters of 26 ERS-1/2 SAR scenes over Shanghai. The results show that in the cases of high phase signal to noise ratio (noise level lower than plusmn0.5 rad), the accuracy of the deformation rates estimated with PS-DInSAR can be up to about plusmn2 mm/a, and the accuracy of the estimated elevations is about plusmn1 m. The accuracies decrease with the increase of the noise level of the phase data. When the noise level is plusmn0.8 rad, the accuracy of deformation measurements decreases to plusmn1 cm/a and that of elevation measurements decreases to plusmn3 m.