Split-spectrum Method Research Articles

Heavy Rain Episodes Identified by L-band InSAR and Limitations of Split-Spectrum Method in Indonesia

Located in a tropical area with abundant precipitation, Indonesia is highly prone to heavy rain hazards, in particular landslides and floods. Thus, rainfall observation is vital. Nonetheless, the topography, the fund availability, as well as the archipelagic state of Indonesia may raise difficulties for in-situ observation, such as rain gauge and weather radar. Currently, the advance of radiometer satellites, such as the Global Precipitation Mission delivers rain estimation and has proven to show good association with in-situ observation on a monthly basis, not daily over the Indonesia area. Therefore, it is vital to have additional measurement methods. For the first time, we apply L-band Interferometric Synthetic Aperture Radar (InSAR) to observe heavy rain in Indonesia. From our three study cases, we successfully identified localized anomalies due to the dense water vapor during heavy rain in the InSAR images. The localized anomalies vary from 10.9 cm in West Java, 7.8 cm in East Kalimantan, and 7.7 cm in West Kalimantan. Furthermore, we utilize the split-spectrum method for our InSAR result; the high-water vapor occurrence in the troposphere associated with heavy rain should be identified in the non-dispersive term. Nevertheless, due to long temporal separation and thinner bandwidth, the split-spectrum method results display unsatisfactory results. We conclude that, while InSAR has the ability to identify heavy rain, having SSM to distinguish between non-dispersive and dispersive phases is not currently practical in Indonesia.

Ionospheric compensation in L-band InSAR time-series: Performance evaluation for slow deformation contexts in equatorial regions

Multi-temporal Synthetic Aperture Radar Interferometry (MT-InSAR) is the only geodetic technique allowing to measure ground deformation down to mm/yr over continuous areas. Vegetation cover in equatorial regions favors the use of L-band SAR data to improve interferometric coherence. However, the electron content of ionosphere, affecting the propagation of the SAR signal, shows particularly strong spatio-temporal variations near the equator, while the dispersive nature of the ionosphere makes its effect stronger on low-frequencies, such as L-band signals. To tackle this problem, range split-spectrum method can be implemented to compensate the ionospheric phase contribution. Here, we apply this technique for time-series of ALOS-PALSAR data, and propose optimizations for low-coherence areas. To evaluate the efficiency of this method to retrieve subtle deformation rates in equatorial regions, we compute time-series using four ALOS-PALSAR datasets in contexts of low to medium coherence, showing slow deformation rates (mm/yr to cm/yr). The processed tracks are located in Ecuador, Trinidad and Sumatra, and feature 15 to 19 acquisitions including very high, dominating ionospheric noise, corresponding to equivalent displacements of up to 2 m. The correction method performs well and allows to reduce drastically the noise level due to ionosphere, with significant improvement compared with a simple plane fitting method. This is due to frequent highly non-linear patterns of perturbation, characterizing equatorial TEC distribution. We use semivariograms to quantify the uncertainty of the corrected time-series, highlighting its dependence on spatial distance. Thus, using ALOS-PALSAR-like archive, one can expect a detection threshold on the Line-of-Sight velocity ranging between 3 and 6 mm/yr, depending on the spatial wavelength of the signal to be observed. These values are consistent with the accuracy derived from the comparison of velocities between two tracks in their overlapping area. In the case studies that we processed, the time-series corrected from ionosphere allows to retrieve accurately fault creep and volcanic signal but it is still too noisy for retrieving tiny long-wavelength signals such as slow (mm/yr) interseismic strain accumulation.

Ionospheric Correction of L-Band SAR Interferometry for Accurate Ice-Motion Measurements: A Case Study in the Grove Mountains Area, East Antarctica

Ice motion is an essential element for accurately evaluating glacier mass balance. Interferometric synthetic aperture radar (InSAR) has been widely applied for monitoring ice motion with high precision and wide coverage in the Antarctic. However, the ionospheric effects can significantly impact InSAR-based ice-motion measurements. At low radar frequencies in particular, the ionospheric effects have been regarded as a serious source of noise in L-band SAR data. The split-spectrum method (SSM) is commonly used for correcting the ionospheric effects of the InSAR technique. However, it requires spatial filtering with the relatively large factors used to scale the sub-bands’ interferograms, which often results in an unwrapped phase error. In this paper, a reformulation of the split-spectrum method (RSSM) is introduced to correct the ionospheric effects in the Grove Mountains of East Antarctica, which have slow ice flow and frequent ionosphere changes. The results show that RSSM can effectively correct the ionospheric effects of InSAR-based ice-motion measurements. To evaluate the ability of ionospheric correction using RSSM, the result of ionospheric correction derived from SSM is compared with the results of RSSM. In addition, ionosphere-corrected ice motion is also compared with GPS and MEaSUREs. The results show that the ionosphere-corrected ice velocities are in good agreement with GPS observations and MEaSUREs. The average ice velocity from the InSAR time series is compared to that from MEaSUREs, and the average ionosphere-corrected ice velocity error reduces 43.9% in SSM and 51.1% in RSSM, respectively. The ionosphere-corrected ice velocity error is the most significant, reducing 86.9% in SSM and 90.4% in RSSM from 1 November 2007 to 19 December 2007. The results show that the ability of RSSM to correct ionospheric effects is slightly better than that of SSM. Therefore, we deduce that the RSSM offers a feasible way to correct ionospheric effects in InSAR-based ice-motion measurements in Antarctica.

Mitigation of Ionospheric Noise in Azimuth Offset Based on the Split-Spectrum Method

The pixel-offset method has been utilized as a powerful tool to measure large ground movements. However, L-band spaceborne synthetic aperture radar (SAR) data are often affected by the ionosphere, which produces serious noises in the azimuth component of the pixel offset field, called as azimuth streaks. Here, we propose a new method to mitigate azimuth streaks based on physical modeling. Azimuth streaks cannot be removed by simply combining the known relationship between ionospheric azimuth offset and the ionospheric phase delay with the phase delay obtained by the split-spectrum method. Thus, taking into account that image matching (coregistration) affects the measurement of azimuth offsets, we formulate a theoretical correction formula of azimuth streaks by subtracting the coregistration-induced effects approximated by a polynomial function from ionospheric azimuth offsets modeled using the split-spectrum method. Applying the method to two pairs of Advanced Land Observing Satellite-2 (ALOS-2)/Phased Array type L-band Synthetic Aperture Radar-2 (PALSAR-2) stripmap images which are severely affected by the ionosphere, we demonstrate effective mitigation of azimuth streaks. In the application to the case that no significant ground movement is detected by Global Navigation Satellite System (GNSS), while the standard deviation of azimuth pixel offset before the correction is 87.2 cm, the value after the correction is 29.2 cm, which is comparable to the theoretical measurement accuracy of the azimuth pixel offset. In the application to the 2016 Kumamoto earthquake, we substantially reduce the azimuth streaks and successfully extract the ground movements from the azimuth offset fields within an accuracy of about 20 cm. The result suggests the proposed method enables more accurate and operational estimation of the 3-D ground displacement.

Improving the Split-Spectrum Method for Sentinel-1 Differential TOPSAR Interferometry

Differential SAR interferometry (DInSAR) is a useful technique used to measure small movements and surface deformation. However, ionospheric phase screens are a major error source in multipass terrain observation by progressive scans sar (TOPSAR) interferograms. In this letter, an improved split-spectrum method is proposed. First, the burst used for ionospheric phase estimation is selected through coherence, and then, the ionospheric phase of the burst is estimated based on the split-spectrum method. Finally, the TOPSAR ionospheric space-variable phase in a large scene is obtained through 2-D space-variable fitting, which avoids the complicated processing of splicing between bursts of different periods and reduces the number of unwrapping calculations for large scenes after splicing. The method can ensure that the number of calculations is reduced without loss of accuracy. Sentinel-1 TOPSAR real data processing verifies the correctness of the proposed method.

Coherent Combining and Long Coherent Integration for BOC Signal Acquisition under Strong Interference

<h3>Abstract</h3> A coherent combining and long coherent integration (CCLCI) scheme is presented for standalone direct acquisition of binary offset carrier (BOC) signals under strong radio frequency interference (RFI). To mitigate the ambiguity of BOC signals, a split-spectrum method extracts the upper and lower sidebands of a BOC signal, treats them separately as two binary phase shift keying (BPSK) signals, and finally combines the results to recover the loss due to splitting. The CCLCI scheme burns through strong interference by building up the desired weak signal while averaging out noise and interference. It exploits all information available (L1 and L2, upper and lower sidebands, odd and even chips, and I- and Q-components) by applying coherent combining across signal components and long coherent integration over time, followed by noncoherent accumulation if necessary. Issues and enabling techniques are described. The results of an embedded implementation in demonstration with a GPS RF simulator are analyzed.

A review of methods for mitigating ionospheric artifacts in differential SAR interferometry

Interferometric synthetic aperture radar (InSAR) has been widely used to measure ground displacements related to geophysical and anthropic activities over the past three decades. Satellite SAR systems use microwave signals that interact with the ionosphere when they travel through it during the imaging processes. In this context, ionospheric variations can significantly contaminate SAR imagery, which in turn affects spaceborne InSAR measurements. This bias also leads to a decrease in the coherence and accuracy of InSAR measurements, especially for the low-frequency SAR systems. In this paper, we give an overview of the latest methods for mitigating the ionospheric contributions in InSAR, including Faraday rotation method, azimuth shift method, and range split-spectrum method, and only focus on the single pair of InSAR interferograms. The current challenges and future perspectives are outlined at the end of this paper.

Tropospheric dispersive phase anomalies during heavy rain detected by L-band InSAR and their interpretation

The split-spectrum method (SSM) can largely isolate and correct for the ionospheric contribution in the L-band interferometric synthetic aperture radar (InSAR). The standard SSM is performed on the assumption of only the first-order ionospheric dispersive effect, which is proportional to the total electron content (TEC). It is also known that during extreme atmospheric events, either originated from the ionosphere or in the troposphere, other dispersive effects do exist and potentially provide new insights into the dynamics of the atmosphere, but there have been few detection reports of such signals by InSAR. We apply L-band InSAR into heavy rain cases and examine the applicability and limitation of the standard SSM. Since no events such as earthquakes to cause surface deformation took place, the non-dispersive component is apparently attributable to the large amount of water vapor associated with heavy rain, whereas there are spotty anomalies in the dispersive component that are closely correlated with the heavy rain area. The ionosonde and Global Navigation Satellite System (GNSS) rate of total electron content index (ROTI) map both show little anomalies during the heavy rain, which suggests few ionospheric disturbances. Therefore, we interpret that the spotty anomalies in the dispersive component of the standard SSM during heavy rain are originated in the troposphere. While we can consider two physical mechanisms, one is runaway electron avalanche and the other is the dispersive effect due to rain, comparison with the observations from the ground-based lightning detection network and rain gauge data, we conclude that the rain dispersive effect is spatiotemporally favorable. We further propose a formulation to examine if another dispersive phase than the first-order TEC effect is present and apply it to the heavy rain cases as well as two extreme ionospheric sporadic-E events. Our formulation successfully isolates the presence of another dispersive phase during heavy rain that is in positive correlation with the local rain rate. In comparison with other dispersive phases during Sporadic-E episodes, the dispersive heavy rain phases seem to have the same order of magnitude with the ionospheric higher order effects.

Ionospheric Phase Compensation for InSAR Measurements Based on the Faraday Rotation Inversion Method.

The ionospheric error can significantly affect the synthetic aperture radar (SAR) signals, particularly in the case of L band and lower frequency SAR systems. The ionospheric distortions are mixed with terrain and ground deformation signals, lowering the precision of the interferometric measurements. Moreover, it is often difficult to detect the small-scale ionospheric structure due to its rapid changes and may have more influence on ionospheric phase compensation for InSAR measurements. In this paper, we present a Faraday rotation (FR) inversion method and corresponding procedure to compensate the ionospheric error for SAR interferograms and to detect the variations of small-scale ionospheric disturbances. This method retrieves the absolute total electron content (TEC) based on the FR estimation and corrects the ionospheric error for synthetic aperture radar interferometry (InSAR) measurements by transforming the differential TEC into the ionospheric phase. In two selected study cases, located in high latitude and equatorial regions where ionospheric disturbances occur frequently, we test the method using the Phased Array L-band Synthetic Aperture Radar (PALSAR) full-polarimetric SAR images. Our results show that the proposed procedure can effectively compensate the ionospheric phase. In order to validate the results, we present the results of ionospheric phase compensation based on the split-spectrum method as a comparison to the proposed method. To analyze the ability of our proposed method in detecting small-scale ionospheric disturbances, TEC derived from FR estimation are also compared with those derived from the global ionosphere maps (GIM). Our research provides a robust choice for the correction of ionospheric error in SAR interferograms. It also provides a powerful tool to measure small-scale ionospheric structure.

The Impact of SAR Parameter Errors on the Ionospheric Correction Based on the Range-Doppler Model and the Split-Spectrum Method

Interferometric synthetic aperture radar (InSAR) products may be significantly distorted by microwave signals traveling through the ionosphere, especially with long wavelengths. The split-spectrum method (SSM) is used to separate the ionospheric and the nondispersive phase terms with lower and higher spectral sub-band interferogram images. However, the ionospheric path delay phase is very delicate to the synthetic aperture radar (SAR) parameters including orbit vectors, slant range, and target height. In this paper, we get the impact of SAR parameter errors on the ionospheric phase by two steps. The first step is getting the derivates of geolocation with reference to SAR parameters based on the range-Doppler (RD) imaging model and the second step is calculating the derivates of the ionospheric phase delay with respect to geometric positioning. Through the numerical simulation, we demonstrate that the deviation of ionospheric phase has a linear relationship with SAR parameter errors. The experimental results show that the estimation of SAR parameters should be accurate enough since the parameter errors significantly affect the performance of ionospheric correction. The root mean square error (RMSE) between the corrected differential interferometric SAR (DInSAR) phase with SAR parameter errors and the corrected DInSAR phase without parameter errors varies from centimeter to decimeter level with the L-band data acquired by the Advanced Land Observing Satellite (ALOS) Phased Array type L-band SAR (PALSAR) over Antofagasta, Chile. Furthermore, the effectiveness of SSM can be improved when SAR parameters are accurately estimated.

A Framework for Correcting Ionospheric Artifacts and Atmospheric Effects to Generate High Accuracy InSAR DEM

Repeat-pass interferometric synthetic aperture radar is a well-established technology for generating digital elevation models (DEMs). However, the interferogram usually has ionospheric and atmospheric effects, which reduces the DEM accuracy. In this paper, by introducing dual-polarization interferograms, a new approach is proposed to mitigate the ionospheric and atmospheric errors of the interferometric synthetic aperture radar (InSAR) data. The proposed method consists of two parts. First, the range split-spectrum method is applied to compensate for the ionospheric artifacts. Then, a multiresolution correlation analysis between dual-polarization InSAR interferograms is employed to remove the identical atmospheric phases, since the atmospheric delay is independent of SAR polarizations. The corrected interferogram can be used for DEM extraction. Validation experiments, using the ALOS-1 PALSAR interferometric pairs covering the study areas in Hawaii and Lebanon of the U.S.A., show that the proposed method can effectively reduce the ionospheric artifacts and atmospheric effects, and improve the accuracy of the InSAR-derived DEMs by 64.9% and 31.7% for the study sites in Hawaii and Lebanon of the U.S.A., respectively, compared with traditional correction methods. In addition, the assessment of the resulting DEMs also includes comparisons with the high-precision Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) altimetry data. The results show that the selection of reference data will not affect the validation results.

Ionospheric Correction of InSAR Time Series Analysis of C-band Sentinel-1 TOPS Data

The Copernicus Sentinel-1A/B satellites operating at C-band in terrain observation by progressive scans (TOPS) mode bring unprecedented opportunities for measuring large-scale tectonic motions using interferometric synthetic aperture radar (InSAR). Although the ionospheric effects are only about one-sixteenth of those at L-band, the measurement accuracy might still be degraded by long-wavelength signals due to the ionosphere. We implement the range split-spectrum method for correcting ionospheric effects in InSAR with C-band Sentinel-1 TOPS data. We perform InSAR time series analysis and evaluate these ionospheric effects using data acquired on both ascending (dusk-side of the Sentinel-1 dawn–dusk orbit) and descending (dawn-side) tracks over representative midlatitude and low-latitude (geomagnetic latitude) areas. We find that the ionospheric effects are very strong for data acquired at low latitudes on ascending tracks. For other cases, ionospheric effects are not strong or even negligible. The application of the range split-spectrum method, despite some implementation challenges, largely removes ionospheric effects, and thus improves the InSAR time series analysis results.

Correction of Ionospheric Artifacts in SAR Data: Application to Fault Slip Inversion of 2009 Southern Sumatra Earthquake

Interferometric synthetic aperture radar (InSAR) is one of the most popular geodetic techniques for studying earthquake-related crustal displacements. Satellite SAR signals interact with the ionosphere when they travel through it during the synthetic aperture time. The condition of the ionosphere and its variation can significantly affect spaceborne InSAR measurements. In this letter, we use the Advanced Land Observation Satellite Phase Array-Type L-band SAR data from the 2009 southern Sumatra earthquake to evaluate the effects of the ionospheric artifacts on the slip distribution inversion of earthquake. The split-spectrum method is used to estimate and correct the ionospheric artifacts in the InSAR results. This letter shows that the long-wavelength ionospheric artifacts in the coseismic interferograms can be effectively mitigated. The slip distribution of the earthquake derived from the interferograms corrected for the ionospheric artifacts is presented. The slip distribution pattern and the magnitude of the slip are significantly refined after correcting the ionospheric artifacts.

COMPENSATION OF THE IONOSPHERIC EFFECTS ON SAR INTERFEROGRAM BASED ON RANGE SPLIT-SPECTRUM AND AZIMUTH OFFSET METHODS – A CASE STUDY OF YUSHU EARTHQUAKE

Abstract. InSAR technique can measure the surface deformation with the accuracy of centimeter-level or even millimeter and therefore has been widely used in the deformation monitoring associated with earthquakes, volcanoes, and other geologic process. However, ionospheric irregularities can lead to the wavy fringes in the low frequency SAR interferograms, which disturb the actual information of geophysical processes and thus put severe limitations on ground deformations measurements. In this paper, an application of two common methods, the range split-spectrum and azimuth offset methods are exploited to estimate the contributions of the ionosphere, with the aim to correct ionospheric effects in interferograms. Based on the theoretical analysis and experiment, a performance analysis is conducted to evaluate the efficiency of these two methods. The result indicates that both methods can mitigate the ionospheric effect in SAR interferograms and the range split-spectrum method is more precise than the other one. However, it is also found that the range split-spectrum is easily contaminated by the noise, and the achievable accuracy of the azimuth offset method is limited by the ambiguous integral constant, especially with the strong azimuth variations induced by the ionosphere disturbance.

Reformulating the Split-Spectrum Method to Facilitate the Estimation and Compensation of the Ionospheric Phase in SAR Interferograms

Spatial and temporal variation of the free electron concentration in the ionosphere affects SAR interferograms, in particular at low radar frequencies. The split-spectrum method permits separating the ionospheric and the non-dispersive phase terms using spectral sub-band images. In this work a reformulation of the split-spectrum method facilitates the necessary processing steps. Reformulating the split-spectrum method permits determining the ionospheric phase component based on a split-spectrum double-difference interferogram and the full-bandwidth differential interferogram. In this way differing a pair of unwrapped phase images, each one scaled by a large factor, can be avoided, making the method more robust. The applicability of the proposed method is demonstrated with examples including cases with very strong ionospheric effects.

Midlatitude sporadic-E episodes viewed by L-band split-spectrum InSAR

Sporadic-E (Es) is a layer of ionization that irregularly appears within the E region of the ionosphere and is known to generate an unusual propagation of very high frequency waves over long distances. The detailed spatial structure of Es remains unclear due to the limited spatial resolution in the conventional ionosonde observations. We detect midlatitude Es by interferometric synthetic aperture radar (InSAR), which can clarify the spatial structure of Es with unprecedented resolution. Moreover, we use the range split-spectrum method (SSM) to separate dispersive and nondispersive components in the InSAR image. While InSAR SSM largely succeeds in decomposing into dispersive and nondispersive signals, our results indicate that small-scale dispersive signals due to the total electron content anomalies are accompanied by nondispersive signals with similar spatial scale at the same locations. We also examine the effects of higher-order terms in the refractive index for dispersive media. Both of these detected Es episodes indicate that smaller-scale dispersive effects originate from higher-order effects. We interpret that the smaller-scale nondispersive signals could indicate the emergence of nitric oxide (NO) generated by the reactions of metals, Mg and Fe, with nitric oxide ion (NO+) during the Es.

Bayesian Data Combination for the Estimation of Ionospheric Effects in SAR Interferograms

The ionospheric propagation path delay is a major error source in synthetic aperture radar (SAR) interferograms and, therefore, has to be estimated and corrected. Various methods can be used to extract different kinds of information about the ionosphere from SAR images, with different accuracies. This paper presents a general technique, based on a Bayesian inverse problem, that combines various information sources in order to increase the estimation accuracy, and thus the correction. A physically realistic fractal modeling of the ionosphere turbulence and a data-based estimation of the model parameters allow the avoidance of arbitrary filtering windows and coefficients. To test the technique, the differential ionospheric phase screen was estimated by combining the split-spectrum method with the azimuth mutual shifts between interferometric pair images. This combination is convenient since it can benefit from the strengths of both sources: range and azimuth variations from the split-spectrum method and small-scale azimuth variations from more sensitive azimuth shifts. Therefore, the two methods can recover the long and short wavelength components of the ionospheric phase screen, respectively. A theoretical comparison between the Faraday rotation method and the split-spectrum method is also reported. For the use in the combination, precedence was then given to the split-spectrum method because of the comparable precision level, lower susceptibility to biases, and wider applicability. Finally, Advanced Land Observing Satellite Phased Array type L-band SAR L-band images are used to show how the combined result is more accurate than that obtained with the simple split-spectrum method.

Measuring Azimuth Deformation With L-Band ALOS-2 ScanSAR Interferometry

We analyze the methods for measuring azimuth deformation with the L-band Advanced Land Observing Satellite-2 (ALOS-2) scanning synthetic aperture radar (ScanSAR) interferometry. To implement the methods, we extract focused bursts from the ALOS-2 full-aperture product, which is the only product available for ScanSAR interferometry at present. The extracted bursts are properly processed to measure azimuth deformation using interferometric phase. We apply the range split-spectrum method to ScanSAR to estimate the differential ionospheric phase of the interferogram, and take the azimuth derivative of the differential ionospheric phase to mitigate the relative azimuth shift caused by ionosphere. For the first time, azimuth deformation of a large earthquake (April 25, 2015 Nepal earthquake) is nearly completely measured by the L-band ScanSAR interferometry with moderate precision. The result is validated by the azimuth deformation measured by incoherent cross correlation using a pair of high-resolution RADARSAT-2 images. In addition to the final azimuth deformation, we show the possibility of processing full-aperture ScanSAR product using a burst-by-burst approach to form regular interferograms. We also show the recent strong large-scale ionospheric effects on the L-band ALOS-2 ScanSAR interferograms. Other possible applications of this paper include measuring the movement of glaciers.

Ionospheric Phase Screen Compensation for the Sentinel-1 TOPS and ALOS-2 ScanSAR Modes

Variations of the ionosphere can significantly disrupt synthetic aperture radar (SAR) acquisitions and interferometric measurements of ground deformation. In this paper, we show how the ionosphere can also strongly modify C-band interferograms despite its smaller influence at higher frequencies. Thus, ionospheric phase screens should not be neglected: their compensation improves the estimation of ground deformation maps. The split-spectrum method is able to estimate the dispersive ionospheric component of the interferometric phase; we describe the implementation of this method for the burst modes TOPS and ScanSAR to estimate and remove ionospheric phase screens. We present Sentinel-1 interferograms of the 2016 Taiwan earthquake and ALOS-2 interferograms of the 2015 Nepal earthquake, which show strong ionospheric phase gradients, and their corrected versions. Finally, to validate the results and better understand the origin of these ionospheric variations, we compare the estimated differential ionosphere with global Total Electron Content maps and local Global Positioning System measurements.

Toward Operational Compensation of Ionospheric Effects in SAR Interferograms: The Split-Spectrum Method

The differential ionospheric path delay is a major error source in L-band interferograms. It is superimposed to topography and ground deformation signals, hindering the measurement of geophysical processes. In this paper, we proceed toward the realization of an operational processor to compensate the ionospheric effects in interferograms. The processor should be robust and accurate to meet the scientific requirements for the measurement of geophysical processes, and it should be applicable on a global scale. An implementation of the split-spectrum method, which will be one element of the processor, is presented in detail, and its performance is analyzed. The method is based on the dispersive nature of the ionosphere and separates the ionospheric component of the interferometric phase from the nondispersive component related to topography, ground motion, and tropospheric path delay. We tested the method using various Advanced Land Observing Satellite Phased-Array type L-band synthetic aperture radar interferometric pairs with different characteristics: high to low coherence, moving and nonmoving terrains, with and without topography, and different ionosphere states. Ionospheric errors of almost 1 m have been corrected to a centimeter or a millimeter level. The results show how the method is able to systematically compensate the ionospheric phase in interferograms, with the expected accuracy, and can therefore be a valid element of the operational processor.