NISAR Complementary Interferometric phase fusion of S & L band - A Simulation Study

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It will undergo a 90-day initial phase operation . The NISAR mission features two SAR sensors on a single satellite platform: one operating in the L-band from NASA and the other in the S-band from ISRO. NISAR will provide simultaneous data from two Interferometric SAR (InSAR) pairs—one in the S-band and one in the L-band—on a 12-day repeat pass orbit cycle. In this paper, we explore the potential for multi-frequency complementary interferometric phase fusion using simulated data from both the S-band and L-band. We propose a methodology aimed at generating a high-quality, improved fused S-band interferogram by combining the complementary phase information from the L-band interferogram. The methodology consists of three main processes. The first process for SAR Raw Data Simulation. This process generates S-band and L-band SAR interferometric pairs utilizing the NISAR sensor parameters. During this simulation, two datasets were created: the first simulated with short vegetation, and the second simulated with shrub vegetation surface backscattering coefficients. The second process is the InSAR Process. This step generates interferograms for both the S-band and L-band using the simulated datasets. The third process is the InSAR Phase Fusion Process. The final step involves fusing the InSAR phases to create a new, improved fused S-band interferogram. This fused S-band interferogram is designed to provide high-resolution unwrapped phase values while minimizing phase errors. The results are promising and show significant improvements compared to using a single-frequency InSAR pair. NISAR ISRO SAR InSAR Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 1. Introduction Synthetic Aperture Radar (SAR) records the electromagnetic energy from backscatter and the phase delay of the signal. It is used to map the scattering properties of the Earth's surface at specific wavelengths. Various physical and geometric parameters of the imaged scene contribute to the grey value of each pixel in a SAR image. SAR data collected at different wavelengths, polarizations, times, and incidence angles provide diverse backscatter information. To better utilize SAR data, multi-frequency SAR missions are essential. In this context, ISRO has launched a multi-frequency SAR mission called NISAR (NASA-ISRO Synthetic Aperture Radar). NISAR will have a minimum mission life of three years and will provide S- and L-band spaceborne SAR data with a repeat cycle of 12 days. It will offer high-resolution imagery ranging from 2 to 30 meters and a wide swath of 240 kilometers, with full polarimetric and interferometric operational capabilities. Interferometric phase measurements are used to quantify distance.The wavelength characteristics of SAR sensors play a significant role in this process. SAR sensors operate in different bands, specifically the L-band (~ 24 cm wavelength) and S-band (~ 9 cm wavelength). The imaging characteristics of these radar bands vary. Radar waves interact more strongly with structures that are similar in size to their wavelength. Consequently, surfaces appear rougher in images captured using shorter wavelengths (S-band) and smoother in those captured with longer wavelengths (L-band). Longer wavelengths can penetrate vegetation, dry soils, and ice to some extent, making phase measurements from these wavelengths less sensitive to small changes in surface conditions over time. Additionally, longer wavelengths exhibit reduced sensitivity to deformation per pixel compared to shorter wavelengths. Hence, S-band Differential Interferometric Synthetic Aperture Radar (DInSAR) provides higher-resolution gradient maps of surface deformation, while L-band SAR offers considerably less detail in both time and space. The characteristics of these two SAR bands complement each other effectively. Our primary focus is the synergistic utilization of S and L band multifrequency complementary interferometric phase fusion. The remote sensing community has already produced results on multifrequency repeat pass InSAR fusion, as documented in works by Lanari et al. ( 1996 ) and Zhang et al. ( 2014 ). In this paper, we present a methodology to enhance the interferogram of the S-band by incorporating the L-band interferogram. This proposed fusion process takes into account the coherence characteristics of both the S and L bands. High-frequency InSAR data often face challenges with poor coherence in areas covered by dense vegetation, while low-frequency InSAR data is less affected by vegetation (Zebker & Villasenor, 1992 ). For our research, we have simulated the SAR raw data for this mission. Simulating SAR raw data is beneficial for mission planning, SAR system design, processing algorithm testing, and inversion algorithm development. We simulated one InSAR pair for the S-band and one for the L-band. After processing the SAR raw data, InSAR was applied separately to the Single Look Complex (SLC) InSAR pairs of the S and L bands. The NISAR mission follows a 12-day repeat-pass orbital cycle, which means there is a 12-day temporal gap between two consecutive InSAR pair datasets. InSAR pairs can become decorrelated for several reasons, including spatial baseline decorrelation, the rotation of the target concerning the SAR look direction, and temporal decorrelation (Zebker & Villasenor, 1992 ). Temporal decorrelation occurs due to physical changes on the surface being observed over time. The S-band frequency (3.2 GHz) is higher than the L-band frequency (1.267 GHz), making high-frequency InSAR data more sensitive to vegetation compared to low-frequency InSAR data. As a result, the S-band InSAR pair experiences lower coherence in densely vegetated areas, while the L-band does not. We examined this concept using the simulated S and L-band InSAR pair data. The low coherence regions in the S-band interferogram were enhanced using the high coherence L-band interferogram, resulting in a fused S-band interferogram with improved coherence and lower phase errors. By using the improved fused S-band interferogram, we effectively address the issues related to phase unwrapping. The structure of this paper is as follows: Section 1 presents the introduction, Section 2 covers the methodology, Section 3 details the SAR Raw Data Simulation Process, Section 4 discusses the SAR Interferometry Process, Section 5 explains the S and L-band Complementary Interferometric Phase Fusion Process, and Section 6 provides the Results Analysis. Finally, Section 7 Concludes the paper. 2. Methodology The methodology consists of three significant processing steps, as illustrated in Fig. 1 . The first step involves generating L- and S-band SAR Raw Data Simulation for interferometric pairs using NISAR sensor parameters. The first dataset of L- and S-band InSAR pairs was simulated using backscattering coefficients from short vegetation surfaces. A second dataset was created using backscattering coefficients from shrub vegetation over a small Region of Interest (ROI). Different backscattering coefficients were applied to these two datasets to investigate how backscattering affects the InSAR phase at different wavelengths in the S- and L-bands. An indigenous-developed SAR Raw Data Simulator was utilized, capable of generating SAR raw data for various imaging modes, including spot, sliding spot, and strip map configurations along with InSAR pairs. The simulator can produce data for different frequency bands, polarizations, squint angles, and types of surface cover. The raw data simulation is based on sensor state vectors, a local terrain model obtained from the SRTM DEM at 1 arcsecond resolution, and other relevant sensor parameters. A local incidence angle map, created using the sensor orbit and SRTM DEM, is used to derive the backscattering values for the terrain. The simulation algorithm incorporates azimuth and range antenna patterns to account for the variation in signal strength based on squint, beam width, and pulse width. In the second step, the InSAR process was applied separately to the SLC InSAR pairs of the S- and L-bands. The S-band interferogram exhibited random phase errors and lost coherence over small shrub vegetation, while the L-band interferogram maintained continuous periodic phase and high coherence over the same region. The third step involved an InSAR phase fusion process applied to the S- and L-band interferograms. A new S-band fused interferogram was generated in the S-band phase resolution. 3. SAR Raw Data Simulation Process Synthetic Aperture Radar (SAR) is an active side-looking microwave sensor. The combined response of all the targets within the antenna footprint is referred to as SAR raw data. This raw data is compressed using a technique called matched filtering. The steps involved in simulating SAR raw data are illustrated in Fig. 2 . The raw data was simulated for both L-band and S-band interferometric pairs in strip map imaging mode with zero squint. The sensor parameters used for this raw data simulation are listed in Table 1 . A backscattering model for short vegetation was applied to both InSAR pair datasets to achieve natural speckles in the data. For multi-frequency interferometry analysis, a shrub vegetation backscatter model was utilized in a small ROI in the second orbit dataset. The data was simulated using HH polarization (Fawwaz T. Ulaby, 2019 ). For terrain input, the Shuttle Radar Topography Mission (SRTM) one arc-second Digital Elevation Model (DEM) near Mount Fuji, Japan, was used. The raw data is processed using indigenously developed SAR raw data processor software. This SAR raw data simulation, processor, and interferometric processing software (S.K. Patra, 2013) are designed to support various SAR sensors, regardless of frequency, operational mode, or imaging squint. 3.1 Sensor state vectors estimation Sensor state vectors for every pulse repetition interval can be estimated by interpolating its orbit. The interpolation is needed because satellite state vectors are not available at particular imaging interval. Thus, prior information about starts state vectors, end state vectors and pulse repetition interval are needed. In case, orbit prior information is not available, NORAD SGP4 orbit propagation model can be used to simulate sensor’s orbit for a given epoch using Two Line Element (TLE). 3.2 Local Incidence map generation The local incidence angle is a very important parameter for SAR simulation. SRTM DEM was used for terrain information (latitude, longitude and height). These latitude, longitude and height parameters of terrain are converted into ECEF co-ordinate system. The angle between local normal and the line joining sensor position vector and ground object position vector is known as local incidence angle. 3.3 Back-Scattering Estimation Back-scattering of a terrain for a given sensor will depend on its local incidence angle, polarization, imaging band, type of terrain and its dielectric constant. Back-scattering is being calculated from parametric equations given in (Fawwaz T. Ulaby, 2019 ). 3.4 Simulation Procedure The flow diagram of Raw data simulation is explained in Fig. 3 . The demodulated baseband signal (Eq. (1)) given it was used to generate raw data (John C. Culander, 1992 ) (Elachi, 1988 ) (Wong, 2005 ). It is a function of range and azimuth time. Eq. (1), contains the information about backscatter and antenna pattern. (Eq. (2)) and (Eq. (3)) are Range and Azimuth windows respectively. In Eq. (1), τ represents the Range time of the emitted signal, R is the Slant Range, \(\:\:{f}_{\circ\:}\) is the Carrier Frequency, and \(\:\eta\:\) , \(\:{\eta\:}_{c}\:\) are Azimuth Time and Azimuth Time at Zero Doppler respectively. \(\:{\beta\:}_{bw}\) is range FM rate and \(\:{\theta\:}_{sq}\:\) represents squint angle. Figures 4 and 5 represent simulated and processed raw data for both interferometric orbits for S-band and L-band. $$\:{S}_{\circ\:}\left(\tau\:,\right)={A}_{\circ\:}{w}_{r}\left(\tau\:-\frac{2R\left(\eta\:\right)}{c}\right){w}_{a}\left(\eta\:-{\eta\:}_{c}\right)\text{exp}\left\{-\frac{j4\pi\:{f}_{\circ\:}R\left(\eta\:\right)}{c}\right\}\text{e}\text{x}\text{p}{\{{j\pi\:}{K}_{r}\left(\tau\:-\frac{2R\left(\eta\:\right)}{c}\right)}^{2}\:\:\:\:\:\left(1\right)$$ $$\:{w}_{r}=rect\left(\frac{t}{T}\right)\:\:\:\:\:\:\:\:\:\:\:\:\left(2\right)$$ $$\:{w}_{a}\left(\eta\:-{\eta\:}_{c}\right)={sinc}^{2}\left\{\frac{0.886\left({\theta\:}_{sq}-{\theta\:}_{sq,c}\right)}{{\beta\:}_{bw}}\right\}\:\:\:\:\:\:\:\:\:\left(3\right)$$ Table 1 Sensor parameters used for Simulation of Raw Data Parameters L-band S-band Polarization HH HH Carrier frequency 1.267 GHz 3.2 GHz Wavelength 23.622cm 9.375cm Pulse width 25 µs 25 µs Look Angle 38.683deg 38.683deg 4. SAR Interferometry Process SAR Interferometry is a specialized technique utilized for mapping the Earth's land surfaces, ice, seas, and topography, as well as for detecting changes and mapping surface displacements across extensive temporal and spatial scales with precision ranging from centimeters to millimeters (Wilkinson, 1998 ; Goblirsch, 1997 ). This method relies on the coherence of the SAR signal. By capturing two images of the same area from different positions across the track and leveraging the phase information from these SAR images, we can create phase difference images known as interferograms. We have developed an Interferometry software module that incorporates across-track and Differential Interferometry (DInSAR) processing for analyzing single or multiple time-series InSAR pairs (S.K. Patra, 2013). The general steps for Interferometry processing include fine co-registration, interferogram generation, coherence map generation, adaptive filtering, baseline estimation using the orbit method, flattening, phase unwrapping (Chaubey, 2016 ), and converting phase to height for Digital Surface Models (DSM), as well as generating displacement, deformation, and subsidence maps and performing geocoding (Goblirsch, 1997 ; B. Schättler, 1999 ; Chaubey, 2016 ; Richard M. Goldstein, 1988; Schwäbisch, 1998 ). Figure 6 presents a block diagram outlining the InSAR processing steps. In our study, we applied the InSAR process to simulated pairs of S and L bands, and the results are illustrated in Figs. 7 and 8 . We computed various InSAR parameters, including baseline and height ambiguity for the simulated S and L band InSAR pair, which are summarized in Table 2 . Table 2 InSAR parameters Parameters L-band S-band Baseline 276.3m 276.3m Normal Baseline 272.8m 272.8m Alpha 0.40565rad 0.40565rad Height Ambiguity 229.124m 90.9341m 5. S & L Band Complementary Interferometric Phase fusion process For any data fusion process, it's essential that the input data has the same spatial resolution and geometry. In the NISAR mission, both S-band and L-band SAR data will be collected simultaneously using a consistent acquisition geometry. Systematic corrections will be made to ensure geometric registration. Any misregistration errors will be addressed by aligning the simulated S-band interferogram with the L-band interferogram. An additional registration method, as detailed by Wegmüller ( 1999 ), may also be applied. High registration accuracy is crucial for clearly identifying low-coherence areas in the S-band interferogram. Low-coherence areas are initially identified using a coherence map and then updated directly with data from the simulated L-band interferogram. We assume that the S-band and L-band interferograms are perfectly co-registered. Both interferograms and coherence maps were analyzed and compared for each band. In the S-band interferogram, the fringes are closely spaced due to the short wavelength of the S-band. Phase errors were detected in the shrub vegetation backscatter region, resulting in a loss of coherence in this area, which is attributed to the limited penetration depth of the S-band. In contrast, the L-band interferogram displays widely spaced fringes due to its longer wavelengths. No phase errors were found in the shrub vegetation backscatter region of the L-band interferogram, and a high coherence value was observed there, again due to the longer wavelength. Figures 7 and 8 illustrate the interferograms and coherence maps for both bands, respectively. The multi-frequency interferometric phase fusion methodology is based on the works of Chaubey ( 2016 ) and Zhang et al. ( 2014 ). Figure 9 illustrates the process of complementary interferometric phase fusion, incorporating the S-band as a High-Frequency Interferogram (HFI) and the L-band as a Low-Frequency Interferogram (LFI). The steps for complementary interferometric phase fusion processing are outlined below: 1. The inputs consist of the flattened, unwrapped S-band HFI and the flattened, unwrapped L-band interferogram (LFI). 2. Coherence maps for both the S-band and L-band are used as quality parameters to identify low-coherence areas in the S-band interferogram. 3. The flattened, unwrapped L-band interferogram is then mapped to the S-band interferogram. The phase resolution of each interferogram differs due to variations in their wavelengths. In this step, the L-band interferogram is scaled into the S-band interferogram by modifying the amplitude of the phase, utilizing the scale factor from Eq. ( 4 ), where, \(\:\lambda\:\) denotes the wavelength and \(\:\varDelta\:Ø\) represents the interferometric phase. The mapping of the L-band interferogram to the S-band interferogram is depicted in Fig. 10 . $$\:{\varDelta\:Ø}_{S-band}^{ʼ}=\frac{{\lambda\:}_{L-band}}{{\lambda\:}_{S-band}}\varDelta\:{Ø}_{L-band}$$ 4 4. Finally, the fusion of the S- and L-band interferograms involves replacing the phase errors in the shrub vegetation ROI in the S-band with the phase from the L-band interferogram. This fusion is guided by the coherence map values of both the L and S bands. Specifically, where the coherence value is high, the corresponding phase value is chosen from either the S-band or the L-band interferogram in the shrub vegetation ROI. The result is a fused and improved wrapped interferogram, which is shown in Figs. 11 and 12 . 6. Result Analysis In Figs. 10,11 and 12 flattened wrapped interferograms are shown as fringes giving better visual interpretation. A quality matrix of interferogram phase standard deviation and coherence values over shrub ROI region of fused improved S-band interferogram vs. original S and L band interferogram are shown in Table 3. The interferogram phase profile of the shrub ROI region is presented in Fig. 12. By visual inspection, we can observe that the original L-band interferogram phase profile (Top- Row in Fig. 12) is noise-free and periodic because of LFI. The original S-band interferogram phase profile (Middle-Row in Fig. 12) is noisy and random because of HFI. The fused improved S-band interferogram phase profile (Bottom-Row in Fig. 12) is less noisy and periodic compared to the original S-band interferogram. After the fusion of the L-band Interferogram phase in the S-band interferogram over the shrub ROI region, reduced phase error and improved coherence map values in the fused S-band interferogram were observed. Table 3 Comparative parameters of Fused S-band Interferogram vs. original S & L Interferogram Parameters for Shrub vegetation ROI region(512X512 pixels) S-band Interferogram L-band Interferogram S-band Fused Interferogram Standard Deviation of phase (radian) 1.82 1.09 1.02 Coherence mean value 0.274 0.897 0.878 7. Conclusion In this paper, we demonstrate the fusion of S-band and L-band interferometric phases using multi-frequency complementary interferometry with the NISAR mission. By combining these datasets, we can maximize the advantages of both S-band and L-band InSAR information, particularly in terms of phase data. The fused S-band interferogram provides high-resolution unwrapped phase values, which enhance the generation of detailed deformation maps. Therefore, the proposed fused S-band interferogram yields accurate and reliable outputs for interferometric applications. Conventional multi-frequency InSAR phase fusion often encounters several challenges, such as discrepancies arising from different acquisition geometries that require complex registration processes. Additionally, varying temporal resolutions lead to data being collected at different times and with different temporal baselines, introducing errors during phase fusion. The NISAR mission operate dual-frequency SAR in interferometric mode, acquiring S-band and L-band data at the same geometry, temporal resolution, and temporal baseline. This synchronization significantly reduces InSAR phase errors related to registration and temporal decorrelation, resulting in high-quality, high-resolution fused InSAR products. NISAR mission will provide large datasets comprising multi-frequency, multi-temporal, and interferometric-polarimetric SAR data for generating accurate and detailed surface deformation maps by integrating dual-frequency information. Statements and Declarations 1. Conflict of Interest: The Authors declared that they have no conflict of interest. Ethical Statement/ Ethics approval · We author (Nidhi Chaubey*, Sumit Pandey, Swati Upadhyay, B.Asha Rani, Neeraj Mishra, Dr. Chandrakanth R ) confirm that neither the manuscript nor any parts of its content are currently under consideration for publication with or published in another journal. · All authors have approved the manuscript and agree with its submission to Discover Geoscience. · the article's publication is approved by all authors and tacitly or explicitly by the responsible authorities where the work was carried out. · if accepted, the article will not be published elsewhere in the same form, in English or in any other language, including electronically, without the written consent of the copyright-holder. Conflicts of interest/Competing interests All authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript. Funding All the authors are associated with Advanced Data Processing Research Institute, Dept. of Space, India. This research work supported by Advanced Data Processing Research Institute Consent to participate Consent was obtained from all individual participants included in the study. Consent to publish The participant has consented to the submission of the research study to the journal. Author's Contribution 1. Conceptualization, Methodology, Software, Resources, Validation, Visualization Writing- Original Draft by Nidhi Chaubey 2. Conceptualization, Methodology, Software, Resources, Validation by Sumit Pandey 3. Resources, Validation, Visualization by Swati Upadhyay 4. Resources, Validation, Visualization, Formal Analysis, Writing- Review and Editing by Asha Rani B 5. Resources, Validation, Visualization, Formal Analysis, Supervision, Writing- Review and Editing by Neeraj Mishra 6. Resources, Validation, Visualization, Formal Analysis, Supervision, Writing- Review and Editing by Dr. Chandrakanth R References B. Schättler, M. E. K. a. M. H., 1999. Operational interferometric ERS tandem data processing. Toulouse, France, CEOS SAR CalibrationWorkshop. Chaubey, N., 2016. Improvement in InSAR Phase Unwrapping using External DEM. Melmaruvathur, India, International Conference on Communication and Signal Processing (ICCSP). Elachi, C., 1988. Spacebourne Radar Remote Sensing: Applications and Techniques. s.l.:IEEE Publications,U.S.. Fawwaz T. Ulaby, D. M. C. J. L. Á.-P., 2019. Handbook of Radar Scattering Statistics for Terrain. s.l.:Artech House. Goblirsch, W., 1997. The exact solution of the imaging equations for crosstrack interferometers. in Proc. IGARSS, Volume 1, pp. 43-441. John C. Culander, R. N. M., 1992. Synthetic Aperture Radar Systems and signal Processing. s.l.:John wiley& Sons.. Lanari, R. et al., 1996. Generation of digital elevation models by using SIR-C/X-SAR multifrequency two-pass interferometry: the Etna case study. IEEE Transactions on Geoscience and Remote Sensing, 34(5), pp. 1097 - 1114. Richard M. Goldstein, H. A. Z. a. C. L. W., 1988. Satellite radar interferometry : Two-dimensional phase unwrapping. in Radio Sci. , 23(4), p. 713 – 720. S.K.Patra, 2013. ShARP+ Data Products. Signatures, Newsletter of the ISRS-AC,, 25(2). Schwäbisch, M., 1998. A fast and efficient technique for SAR interferogram geocoding. in Proc. IGARSS, Volume 2, p. 1100–1102. Wegmüller, U., 1999. Automated terrain corrected SAR geocoding. s.l., IGARSS, vol. 3, 1999, pp. 1712–1714.. Wilkinson, A. J., 1998. Synthetic aperture radar interferometry: A model for the joint statistics in layover areas. Cape Town, South Africa, COMSIG’98. Wong, I. G. C. a. F. H., 2005. Digital processing of Synthetic Aperture Radar Data. s.l.:Artech House. Zebker, H. & Villasenor, J., 1992. Decorrelation in Interferometric Radar Echoes. IEEE Transactions on Geoscience and Remote Sensing, 30(5), pp. 950-959. Zhang, X., Zeng, Q., Jiao, J. & Xiong, S., 2014. Fusion of multi-frequency interferometric results by using Kalman filter to generate high quality DEM. Quebec City, QC, Canada, IEEE Geoscience and Remote Sensing Symposium. Additional Declarations No competing interests reported. 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(b) represents SRTM DEM, (c) represents back scattering map for a given sensor orbit (ALOS PALSAR ) and given terrain using DEM. (d) represents the simulated raw data using sensor parametrs of NISAR, SRTM DEM and back scattering map for the given terrain. (e) represents the final amplitude image \u003cem\u003ei.e., processed image\u003c/em\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7380126/v1/dff0a9033301a8d94e220f5d.png"},{"id":91858364,"identity":"89c71a54-f8f1-468a-9cf9-967887241ce7","added_by":"auto","created_at":"2025-09-22 12:22:25","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":408620,"visible":true,"origin":"","legend":"\u003cp\u003eS-band simulated raw data and processed data for interferometric pair processing\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7380126/v1/1c9826f27d80acc074445bc8.png"},{"id":91858363,"identity":"94188e41-8c6c-4301-8b46-67c68ef1de66","added_by":"auto","created_at":"2025-09-22 12:22:25","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":350039,"visible":true,"origin":"","legend":"\u003cp\u003eL-band simulated raw data and processed data for interferometric pair processing\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7380126/v1/df4e6ad59c333ce99b64fbec.png"},{"id":91858029,"identity":"805c5835-f6d6-423b-b710-c3711b1b04f0","added_by":"auto","created_at":"2025-09-22 12:14:25","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":35831,"visible":true,"origin":"","legend":"\u003cp\u003eBlock diagram of InSAR processing\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-7380126/v1/175085a88a05823208f2ed97.png"},{"id":91858368,"identity":"d37e0f42-f7c6-47e7-a111-392dfea54cc1","added_by":"auto","created_at":"2025-09-22 12:22:25","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":317256,"visible":true,"origin":"","legend":"\u003cp\u003eS-band Interferogram, Coherence Map (Shrub vegetation region marked with yellow colour ROI box)\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-7380126/v1/0a38572fe267948711d40dfc.png"},{"id":91859954,"identity":"3abc9cb6-b329-4d30-855d-73b6a3fe9a36","added_by":"auto","created_at":"2025-09-22 12:30:25","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":252867,"visible":true,"origin":"","legend":"\u003cp\u003eL-band Interferogram, Coherence Map\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-7380126/v1/144831a14d1513b03a7bf435.png"},{"id":91858032,"identity":"6a2ac975-7886-4765-be53-3259b33086a0","added_by":"auto","created_at":"2025-09-22 12:14:25","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":60126,"visible":true,"origin":"","legend":"\u003cp\u003eComplementary InSAR Phase Fusion Process\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-7380126/v1/3685655402dc5da11d538a54.png"},{"id":91858044,"identity":"964094bb-4cce-415c-a95e-e1c7e669907d","added_by":"auto","created_at":"2025-09-22 12:14:25","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":333863,"visible":true,"origin":"","legend":"\u003cp\u003eL-band wrap Interferogram (Left) mapped to S-band wrap Interferogram (Right)\u003c/p\u003e","description":"","filename":"10.png","url":"https://assets-eu.researchsquare.com/files/rs-7380126/v1/35ce4369a588d230f343d3de.png"},{"id":91859955,"identity":"7bb04543-db50-4d9d-8727-fea337c329d8","added_by":"auto","created_at":"2025-09-22 12:30:25","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":445646,"visible":true,"origin":"","legend":"\u003cp\u003e(Top-Left) S-band Interferogram \u0026amp; (Top-Right) L (mapped to S)-band Interferogram and (Below) S and L band Fused (Improved) S-band Interferogram.\u003c/p\u003e","description":"","filename":"11.png","url":"https://assets-eu.researchsquare.com/files/rs-7380126/v1/973f3ff30147bc0bd350da88.png"},{"id":91858373,"identity":"fb33b3ed-1a4e-4f5c-8d69-910a70088531","added_by":"auto","created_at":"2025-09-22 12:22:25","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":376118,"visible":true,"origin":"","legend":"\u003cp\u003e(Top Row-Left) L-band (mapped to S- band interferogram) and (Top Row-Right) phase profile of Shrub vegetation region, (Middle Row-Left) S-band Interferogram (Shrub vegetation region) and (Middle Row-Right) phase profile, (Below Row-Left) Fused (Improved) S-band Interferogram (Shrub vegetation region) and (Below Row-Right) Improved phase profile.\u003c/p\u003e","description":"","filename":"12.png","url":"https://assets-eu.researchsquare.com/files/rs-7380126/v1/782ec204c6e58ff5f1bd41a5.png"},{"id":91861195,"identity":"aaf8c02f-b2e2-43c1-be45-09ec81740962","added_by":"auto","created_at":"2025-09-22 12:39:48","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3302550,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7380126/v1/089bc7b3-32c2-445b-9111-294c53a70ccd.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"NISAR Complementary Interferometric phase fusion of S \u0026 L band - A Simulation Study","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eSynthetic Aperture Radar (SAR) records the electromagnetic energy from backscatter and the phase delay of the signal. It is used to map the scattering properties of the Earth's surface at specific wavelengths. Various physical and geometric parameters of the imaged scene contribute to the grey value of each pixel in a SAR image. SAR data collected at different wavelengths, polarizations, times, and incidence angles provide diverse backscatter information. To better utilize SAR data, multi-frequency SAR missions are essential. In this context, ISRO has launched a multi-frequency SAR mission called NISAR (NASA-ISRO Synthetic Aperture Radar). NISAR will have a minimum mission life of three years and will provide S- and L-band spaceborne SAR data with a repeat cycle of 12 days. It will offer high-resolution imagery ranging from 2 to 30 meters and a wide swath of 240 kilometers, with full polarimetric and interferometric operational capabilities.\u003c/p\u003e\u003cp\u003eInterferometric phase measurements are used to quantify distance.The wavelength characteristics of SAR sensors play a significant role in this process. SAR sensors operate in different bands, specifically the L-band (~\u0026thinsp;24 cm wavelength) and S-band (~\u0026thinsp;9 cm wavelength). The imaging characteristics of these radar bands vary. Radar waves interact more strongly with structures that are similar in size to their wavelength. Consequently, surfaces appear rougher in images captured using shorter wavelengths (S-band) and smoother in those captured with longer wavelengths (L-band). Longer wavelengths can penetrate vegetation, dry soils, and ice to some extent, making phase measurements from these wavelengths less sensitive to small changes in surface conditions over time. Additionally, longer wavelengths exhibit reduced sensitivity to deformation per pixel compared to shorter wavelengths. Hence, S-band Differential Interferometric Synthetic Aperture Radar (DInSAR) provides higher-resolution gradient maps of surface deformation, while L-band SAR offers considerably less detail in both time and space. The characteristics of these two SAR bands complement each other effectively.\u003c/p\u003e\u003cp\u003eOur primary focus is the synergistic utilization of S and L band multifrequency complementary interferometric phase fusion. The remote sensing community has already produced results on multifrequency repeat pass InSAR fusion, as documented in works by Lanari et al. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1996\u003c/span\u003e) and Zhang et al. (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). In this paper, we present a methodology to enhance the interferogram of the S-band by incorporating the L-band interferogram. This proposed fusion process takes into account the coherence characteristics of both the S and L bands. High-frequency InSAR data often face challenges with poor coherence in areas covered by dense vegetation, while low-frequency InSAR data is less affected by vegetation (Zebker \u0026amp; Villasenor, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e1992\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eFor our research, we have simulated the SAR raw data for this mission. Simulating SAR raw data is beneficial for mission planning, SAR system design, processing algorithm testing, and inversion algorithm development. We simulated one InSAR pair for the S-band and one for the L-band. After processing the SAR raw data, InSAR was applied separately to the Single Look Complex (SLC) InSAR pairs of the S and L bands.\u003c/p\u003e\u003cp\u003eThe NISAR mission follows a 12-day repeat-pass orbital cycle, which means there is a 12-day temporal gap between two consecutive InSAR pair datasets. InSAR pairs can become decorrelated for several reasons, including spatial baseline decorrelation, the rotation of the target concerning the SAR look direction, and temporal decorrelation (Zebker \u0026amp; Villasenor, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e1992\u003c/span\u003e). Temporal decorrelation occurs due to physical changes on the surface being observed over time.\u003c/p\u003e\u003cp\u003eThe S-band frequency (3.2 GHz) is higher than the L-band frequency (1.267 GHz), making high-frequency InSAR data more sensitive to vegetation compared to low-frequency InSAR data. As a result, the S-band InSAR pair experiences lower coherence in densely vegetated areas, while the L-band does not. We examined this concept using the simulated S and L-band InSAR pair data. The low coherence regions in the S-band interferogram were enhanced using the high coherence L-band interferogram, resulting in a fused S-band interferogram with improved coherence and lower phase errors. By using the improved fused S-band interferogram, we effectively address the issues related to phase unwrapping.\u003c/p\u003e\u003cp\u003eThe structure of this paper is as follows: Section 1 presents the introduction, Section 2 covers the methodology, Section 3 details the SAR Raw Data Simulation Process, Section 4 discusses the SAR Interferometry Process, Section 5 explains the S and L-band Complementary Interferometric Phase Fusion Process, and Section 6 provides the Results Analysis. Finally, Section 7 Concludes the paper.\u003c/p\u003e"},{"header":"2. Methodology","content":"\u003cp\u003eThe methodology consists of three significant processing steps, as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The first step involves generating L- and S-band SAR Raw Data Simulation for interferometric pairs using NISAR sensor parameters. The first dataset of L- and S-band InSAR pairs was simulated using backscattering coefficients from short vegetation surfaces. A second dataset was created using backscattering coefficients from shrub vegetation over a small Region of Interest (ROI). Different backscattering coefficients were applied to these two datasets to investigate how backscattering affects the InSAR phase at different wavelengths in the S- and L-bands.\u003c/p\u003e\u003cp\u003eAn indigenous-developed SAR Raw Data Simulator was utilized, capable of generating SAR raw data for various imaging modes, including spot, sliding spot, and strip map configurations along with InSAR pairs. The simulator can produce data for different frequency bands, polarizations, squint angles, and types of surface cover. The raw data simulation is based on sensor state vectors, a local terrain model obtained from the SRTM DEM at 1 arcsecond resolution, and other relevant sensor parameters. A local incidence angle map, created using the sensor orbit and SRTM DEM, is used to derive the backscattering values for the terrain. The simulation algorithm incorporates azimuth and range antenna patterns to account for the variation in signal strength based on squint, beam width, and pulse width.\u003c/p\u003e\u003cp\u003eIn the second step, the InSAR process was applied separately to the SLC InSAR pairs of the S- and L-bands. The S-band interferogram exhibited random phase errors and lost coherence over small shrub vegetation, while the L-band interferogram maintained continuous periodic phase and high coherence over the same region.\u003c/p\u003e\u003cp\u003eThe third step involved an InSAR phase fusion process applied to the S- and L-band interferograms. A new S-band fused interferogram was generated in the S-band phase resolution.\u003c/p\u003e"},{"header":"3. SAR Raw Data Simulation Process","content":"\u003cp\u003eSynthetic Aperture Radar (SAR) is an active side-looking microwave sensor. The combined response of all the targets within the antenna footprint is referred to as SAR raw data. This raw data is compressed using a technique called matched filtering. The steps involved in simulating SAR raw data are illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The raw data was simulated for both L-band and S-band interferometric pairs in strip map imaging mode with zero squint. The sensor parameters used for this raw data simulation are listed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. A backscattering model for short vegetation was applied to both InSAR pair datasets to achieve natural speckles in the data. For multi-frequency interferometry analysis, a shrub vegetation backscatter model was utilized in a small ROI in the second orbit dataset. The data was simulated using HH polarization (Fawwaz T. Ulaby, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eFor terrain input, the Shuttle Radar Topography Mission (SRTM) one arc-second Digital Elevation Model (DEM) near Mount Fuji, Japan, was used. The raw data is processed using indigenously developed SAR raw data processor software. This SAR raw data simulation, processor, and interferometric processing software (S.K. Patra, 2013) are designed to support various SAR sensors, regardless of frequency, operational mode, or imaging squint.\u003c/p\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Sensor state vectors estimation\u003c/h2\u003e\u003cp\u003eSensor state vectors for every pulse repetition interval can be estimated by interpolating its orbit. The interpolation is needed because satellite state vectors are not available at particular imaging interval. Thus, prior information about starts state vectors, end state vectors and pulse repetition interval are needed. In case, orbit prior information is not available, NORAD SGP4 orbit propagation model can be used to simulate sensor\u0026rsquo;s orbit for a given epoch using Two Line Element (TLE).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Local Incidence map generation\u003c/h2\u003e\u003cp\u003eThe local incidence angle is a very important parameter for SAR simulation. SRTM DEM was used for terrain information (latitude, longitude and height). These latitude, longitude and height parameters of terrain are converted into ECEF co-ordinate system. The angle between local normal and the line joining sensor position vector and ground object position vector is known as local incidence angle.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Back-Scattering Estimation\u003c/h2\u003e\u003cp\u003eBack-scattering of a terrain for a given sensor will depend on its local incidence angle, polarization, imaging band, type of terrain and its dielectric constant. Back-scattering is being calculated from parametric equations given in (Fawwaz T. Ulaby, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e3.4 Simulation Procedure\u003c/h2\u003e\u003cp\u003eThe flow diagram of Raw data simulation is explained in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The demodulated baseband signal (Eq.\u0026nbsp;(1)) given it was used to generate raw data (John C. Culander, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1992\u003c/span\u003e) (Elachi, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e1988\u003c/span\u003e) (Wong, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). It is a function of range and azimuth time. Eq.\u0026nbsp;(1), contains the information about backscatter and antenna pattern. (Eq.\u0026nbsp;(2)) and (Eq.\u0026nbsp;(3)) are Range and Azimuth windows respectively. In Eq.\u0026nbsp;(1), τ represents the Range time of the emitted signal, R is the Slant Range,\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\:{f}_{\\circ\\:}\\)\u003c/span\u003e\u003c/span\u003e is the Carrier Frequency, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\eta\\:\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\eta\\:}_{c}\\:\\)\u003c/span\u003e\u003c/span\u003eare Azimuth Time and Azimuth Time at Zero Doppler respectively. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}_{bw}\\)\u003c/span\u003e\u003c/span\u003e is range FM rate and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\theta\\:}_{sq}\\:\\)\u003c/span\u003e\u003c/span\u003erepresents squint angle. Figures\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e represent simulated and processed raw data for both interferometric orbits for S-band and L-band.\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:{S}_{\\circ\\:}\\left(\\tau\\:,\\right)={A}_{\\circ\\:}{w}_{r}\\left(\\tau\\:-\\frac{2R\\left(\\eta\\:\\right)}{c}\\right){w}_{a}\\left(\\eta\\:-{\\eta\\:}_{c}\\right)\\text{exp}\\left\\{-\\frac{j4\\pi\\:{f}_{\\circ\\:}R\\left(\\eta\\:\\right)}{c}\\right\\}\\text{e}\\text{x}\\text{p}{\\{{j\\pi\\:}{K}_{r}\\left(\\tau\\:-\\frac{2R\\left(\\eta\\:\\right)}{c}\\right)}^{2}\\:\\:\\:\\:\\:\\left(1\\right)$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:{w}_{r}=rect\\left(\\frac{t}{T}\\right)\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\left(2\\right)$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$\\:{w}_{a}\\left(\\eta\\:-{\\eta\\:}_{c}\\right)={sinc}^{2}\\left\\{\\frac{0.886\\left({\\theta\\:}_{sq}-{\\theta\\:}_{sq,c}\\right)}{{\\beta\\:}_{bw}}\\right\\}\\:\\:\\:\\:\\:\\:\\:\\:\\:\\left(3\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSensor parameters used for Simulation of Raw Data\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eParameters\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eL-band\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eS-band\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePolarization\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHH\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCarrier frequency\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.267 GHz\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.2 GHz\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWavelength\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e23.622cm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9.375cm\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePulse width\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e25 \u0026micro;s\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e25 \u0026micro;s\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLook Angle\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e38.683deg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e38.683deg\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4. SAR Interferometry Process","content":"\u003cp\u003eSAR Interferometry is a specialized technique utilized for mapping the Earth's land surfaces, ice, seas, and topography, as well as for detecting changes and mapping surface displacements across extensive temporal and spatial scales with precision ranging from centimeters to millimeters (Wilkinson, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Goblirsch, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). This method relies on the coherence of the SAR signal. By capturing two images of the same area from different positions across the track and leveraging the phase information from these SAR images, we can create phase difference images known as interferograms. We have developed an Interferometry software module that incorporates across-track and Differential Interferometry (DInSAR) processing for analyzing single or multiple time-series InSAR pairs (S.K. Patra, 2013). The general steps for Interferometry processing include fine co-registration, interferogram generation, coherence map generation, adaptive filtering, baseline estimation using the orbit method, flattening, phase unwrapping (Chaubey, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), and converting phase to height for Digital Surface Models (DSM), as well as generating displacement, deformation, and subsidence maps and performing geocoding (Goblirsch, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; B. Sch\u0026auml;ttler, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Chaubey, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Richard M. Goldstein, 1988; Schw\u0026auml;bisch, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). Figure\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e presents a block diagram outlining the InSAR processing steps. In our study, we applied the InSAR process to simulated pairs of S and L bands, and the results are illustrated in Figs.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e and \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e. We computed various InSAR parameters, including baseline and height ambiguity for the simulated S and L band InSAR pair, which are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eInSAR parameters\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eParameters\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eL-band\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eS-band\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBaseline\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e276.3m\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e276.3m\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNormal Baseline\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e272.8m\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e272.8m\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAlpha\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.40565rad\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.40565rad\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHeight Ambiguity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e229.124m\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e90.9341m\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"5. S \u0026 L Band Complementary Interferometric Phase fusion process","content":"\u003cp\u003eFor any data fusion process, it\u0026apos;s essential that the input data has the same spatial resolution and geometry. In the NISAR mission, both S-band and L-band SAR data will be collected simultaneously using a consistent acquisition geometry. Systematic corrections will be made to ensure geometric registration. Any misregistration errors will be addressed by aligning the simulated S-band interferogram with the L-band interferogram. An additional registration method, as detailed by Wegm\u0026uuml;ller (\u003cspan class=\"CitationRef\"\u003e1999\u003c/span\u003e), may also be applied. High registration accuracy is crucial for clearly identifying low-coherence areas in the S-band interferogram. Low-coherence areas are initially identified using a coherence map and then updated directly with data from the simulated L-band interferogram. We assume that the S-band and L-band interferograms are perfectly co-registered. Both interferograms and coherence maps were analyzed and compared for each band. In the S-band interferogram, the fringes are closely spaced due to the short wavelength of the S-band. Phase errors were detected in the shrub vegetation backscatter region, resulting in a loss of coherence in this area, which is attributed to the limited penetration depth of the S-band. In contrast, the L-band interferogram displays widely spaced fringes due to its longer wavelengths. No phase errors were found in the shrub vegetation backscatter region of the L-band interferogram, and a high coherence value was observed there, again due to the longer wavelength. Figures \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e and \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e illustrate the interferograms and coherence maps for both bands, respectively. The multi-frequency interferometric phase fusion methodology is based on the works of Chaubey (\u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e) and Zhang et al. (\u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e). Figure \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003e illustrates the process of complementary interferometric phase fusion, incorporating the S-band as a High-Frequency Interferogram (HFI) and the L-band as a Low-Frequency Interferogram (LFI). The steps for complementary interferometric phase fusion processing are outlined below:\u003c/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e1. The inputs consist of the flattened, unwrapped S-band HFI and the flattened, unwrapped L-band interferogram (LFI).\u003c/p\u003e\u003cspan\u003e\n \u003cp\u003e2. Coherence maps for both the S-band and L-band are used as quality parameters to identify low-coherence areas in the S-band interferogram.\u003c/p\u003e\n\u003c/span\u003e\u003cspan\u003e\n \u003cp\u003e3. The flattened, unwrapped L-band interferogram is then mapped to the S-band interferogram. The phase resolution of each interferogram differs due to variations in their wavelengths. In this step, the L-band interferogram is scaled into the S-band interferogram by modifying the amplitude of the phase, utilizing the scale factor from Eq. (\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e), where, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\lambda\\:\\)\u003c/span\u003e\u003c/span\u003e denotes the wavelength and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varDelta\\:\u0026Oslash;\\)\u003c/span\u003e\u003c/span\u003e represents the interferometric phase. The mapping of the L-band interferogram to the S-band interferogram is depicted in Fig. \u003cspan class=\"InternalRef\"\u003e10\u003c/span\u003e.\u003c/p\u003e\n\u003c/span\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e$$\\:{\\varDelta\\:\u0026Oslash;}_{S-band}^{ʼ}=\\frac{{\\lambda\\:}_{L-band}}{{\\lambda\\:}_{S-band}}\\varDelta\\:{\u0026Oslash;}_{L-band}$$\u003c/div\u003e\n \u003cdiv class=\"EquationNumber\"\u003e4\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003e4. Finally, the fusion of the S- and L-band interferograms involves replacing the phase errors in the shrub vegetation ROI in the S-band with the phase from the L-band interferogram. This fusion is guided by the coherence map values of both the L and S bands. Specifically, where the coherence value is high, the corresponding phase value is chosen from either the S-band or the L-band interferogram in the shrub vegetation ROI. The result is a fused and improved wrapped interferogram, which is shown in Figs. \u003cspan class=\"InternalRef\"\u003e11\u003c/span\u003e and \u003cspan class=\"InternalRef\"\u003e12\u003c/span\u003e.\u003c/p\u003e"},{"header":"6. Result Analysis","content":"\u003cp\u003eIn Figs. 10,11 and 12 flattened wrapped interferograms are shown as fringes giving better visual interpretation. A quality matrix of interferogram phase standard deviation and coherence values over shrub ROI region of fused improved S-band interferogram vs. original S and L band interferogram are shown in Table 3. The interferogram phase profile of the shrub ROI region is presented in Fig. 12. By visual inspection, we can observe that the original L-band interferogram phase profile (Top- Row in Fig. 12) is noise-free and periodic because of LFI. The original S-band interferogram phase profile (Middle-Row in Fig. 12) is noisy and random because of HFI. The fused improved S-band interferogram phase profile (Bottom-Row in Fig. 12) is less noisy and periodic compared to the original S-band interferogram. After the fusion of the L-band Interferogram phase in the S-band interferogram over the shrub ROI region, reduced phase error and improved coherence map values in the fused S-band interferogram were observed.\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"1\" class=\"fr-table-selection-hover\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 3\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eComparative parameters of Fused S-band Interferogram vs. original S \u0026amp; L Interferogram\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eParameters for Shrub vegetation ROI region(512X512 pixels)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eS-band Interferogram\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eL-band Interferogram\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eS-band Fused Interferogram\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStandard Deviation of phase (radian)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCoherence mean value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.274\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.897\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.878\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e"},{"header":"7. Conclusion","content":"\u003cp\u003eIn this paper, we demonstrate the fusion of S-band and L-band interferometric phases using multi-frequency complementary interferometry with the NISAR mission. By combining these datasets, we can maximize the advantages of both S-band and L-band InSAR information, particularly in terms of phase data. The fused S-band interferogram provides high-resolution unwrapped phase values, which enhance the generation of detailed deformation maps. Therefore, the proposed fused S-band interferogram yields accurate and reliable outputs for interferometric applications.\u003c/p\u003e\u003cp\u003eConventional multi-frequency InSAR phase fusion often encounters several challenges, such as discrepancies arising from different acquisition geometries that require complex registration processes. Additionally, varying temporal resolutions lead to data being collected at different times and with different temporal baselines, introducing errors during phase fusion. The NISAR mission operate dual-frequency SAR in interferometric mode, acquiring S-band and L-band data at the same geometry, temporal resolution, and temporal baseline. This synchronization significantly reduces InSAR phase errors related to registration and temporal decorrelation, resulting in high-quality, high-resolution fused InSAR products.\u003c/p\u003e\u003cp\u003eNISAR mission will provide large datasets comprising multi-frequency, multi-temporal, and interferometric-polarimetric SAR data for generating accurate and detailed surface deformation maps by integrating dual-frequency information.\u003c/p\u003e"},{"header":" Statements and Declarations","content":"\u003cp\u003e1.\u0026nbsp;\u0026nbsp;Conflict of Interest: The Authors declared that they have no conflict of interest.\u003c/p\u003e\n\u003cp\u003eEthical Statement/\u0026nbsp;Ethics approval\u003c/p\u003e\n\u003cp\u003e· We author (Nidhi Chaubey*, Sumit Pandey, Swati Upadhyay, B.Asha Rani, Neeraj Mishra, Dr. Chandrakanth R\u003cstrong\u003e) confirm\u003c/strong\u003e that neither the manuscript nor any parts of its content are currently under consideration for publication with or published in another journal.\u003c/p\u003e\n\u003cp\u003e· All authors have approved the manuscript and agree with its submission to\u0026nbsp;Discover Geoscience.\u003c/p\u003e\n\u003cp\u003e· the article's publication is approved by all authors and tacitly or explicitly by the responsible authorities where the work was carried out.\u003c/p\u003e\n\u003cp\u003e· if accepted, the article will not be published elsewhere in the same form, in English or in any other language, including electronically, without the written consent of the copyright-holder.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of interest/Competing interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript.\u003c/p\u003e\n\u003cp\u003eFunding\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll the authors are associated with Advanced Data Processing Research Institute, Dept. of Space, India. This research work supported by Advanced Data Processing Research Institute\u003c/p\u003e\n\u003cp\u003eConsent to participate\u003c/p\u003e\n\u003cp\u003eConsent was obtained from all individual participants included in the study.\u003c/p\u003e\n\u003cp\u003eConsent to publish\u003c/p\u003e\n\u003cp\u003eThe participant has consented to the submission of the research study to the journal.\u003c/p\u003e\n\u003cp\u003eAuthor's Contribution\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e1.\u0026nbsp;\u0026nbsp;Conceptualization, Methodology, Software, Resources, Validation, Visualization Writing- Original Draft by Nidhi Chaubey\u003c/p\u003e\n\u003cp\u003e2.\u0026nbsp;\u0026nbsp;Conceptualization, Methodology, Software, Resources, Validation by Sumit Pandey\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e3.\u0026nbsp;\u0026nbsp;Resources, Validation, Visualization by Swati Upadhyay\u003c/p\u003e\n\u003cp\u003e4.\u0026nbsp;\u0026nbsp;Resources, Validation, Visualization, Formal Analysis, Writing- Review and Editing by Asha Rani B\u003c/p\u003e\n\u003cp\u003e5.\u0026nbsp;\u0026nbsp;Resources, Validation, Visualization, Formal Analysis, Supervision, Writing- Review and Editing by Neeraj Mishra\u003c/p\u003e\n\u003cp\u003e6.\u0026nbsp;\u0026nbsp;Resources, Validation, Visualization, Formal Analysis, Supervision, Writing- Review and Editing by Dr. Chandrakanth R\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eB. Sch\u0026auml;ttler, M. E. K. a. M. H., 1999. \u003cem\u003eOperational interferometric ERS tandem data processing.\u0026nbsp;\u003c/em\u003eToulouse, France, CEOS SAR CalibrationWorkshop.\u003c/li\u003e\n \u003cli\u003eChaubey, N., 2016. \u003cem\u003eImprovement in InSAR Phase Unwrapping using External DEM.\u0026nbsp;\u003c/em\u003eMelmaruvathur, India, International Conference on Communication and Signal Processing (ICCSP).\u003c/li\u003e\n \u003cli\u003eElachi, C., 1988. \u003cem\u003eSpacebourne Radar Remote Sensing: Applications and Techniques.\u0026nbsp;\u003c/em\u003es.l.:IEEE Publications,U.S..\u003c/li\u003e\n \u003cli\u003eFawwaz T. Ulaby, D. M. C. J. L. \u0026Aacute;.-P., 2019. \u003cem\u003eHandbook of Radar Scattering Statistics for Terrain.\u0026nbsp;\u003c/em\u003es.l.:Artech House.\u003c/li\u003e\n \u003cli\u003eGoblirsch, W., 1997. The exact solution of the imaging equations for crosstrack interferometers. \u003cem\u003ein Proc. IGARSS,\u0026nbsp;\u003c/em\u003eVolume 1, pp. 43-441.\u003c/li\u003e\n \u003cli\u003eJohn C. Culander, R. N. M., 1992. \u003cem\u003eSynthetic Aperture Radar Systems and signal Processing.\u0026nbsp;\u003c/em\u003es.l.:John wiley\u0026amp; Sons..\u003c/li\u003e\n \u003cli\u003eLanari, R. et al., 1996. Generation of digital elevation models by using SIR-C/X-SAR multifrequency two-pass interferometry: the Etna case study. \u003cem\u003eIEEE Transactions on Geoscience and Remote Sensing,\u0026nbsp;\u003c/em\u003e34(5), pp. 1097 - 1114.\u003c/li\u003e\n \u003cli\u003eRichard M. Goldstein, H. A. Z. a. C. L. W., 1988. Satellite radar interferometry : Two-dimensional phase unwrapping. \u003cem\u003ein Radio Sci. ,\u0026nbsp;\u003c/em\u003e23(4), p. 713 \u0026ndash; 720.\u003c/li\u003e\n \u003cli\u003eS.K.Patra, 2013. ShARP+ Data Products. \u003cem\u003eSignatures, Newsletter of the ISRS-AC,,\u0026nbsp;\u003c/em\u003e25(2).\u003c/li\u003e\n \u003cli\u003eSchw\u0026auml;bisch, M., 1998. A fast and efficient technique for SAR interferogram geocoding. \u003cem\u003ein Proc. IGARSS,\u0026nbsp;\u003c/em\u003eVolume 2, p. 1100\u0026ndash;1102.\u003c/li\u003e\n \u003cli\u003eWegm\u0026uuml;ller, U., 1999. \u003cem\u003eAutomated terrain corrected SAR geocoding.\u0026nbsp;\u003c/em\u003es.l., IGARSS, vol. 3, 1999, pp. 1712\u0026ndash;1714..\u003c/li\u003e\n \u003cli\u003eWilkinson, A. J., 1998. \u003cem\u003eSynthetic aperture radar interferometry: A model for the joint statistics in layover areas.\u0026nbsp;\u003c/em\u003eCape Town, South Africa, COMSIG\u0026rsquo;98.\u003c/li\u003e\n \u003cli\u003eWong, I. G. C. a. F. H., 2005. \u003cem\u003eDigital processing of Synthetic Aperture Radar Data.\u0026nbsp;\u003c/em\u003es.l.:Artech House.\u003c/li\u003e\n \u003cli\u003eZebker, H. \u0026amp; Villasenor, J., 1992. Decorrelation in Interferometric Radar Echoes. \u003cem\u003eIEEE Transactions on Geoscience and Remote Sensing,\u0026nbsp;\u003c/em\u003e30(5), pp. 950-959.\u003c/li\u003e\n \u003cli\u003eZhang, X., Zeng, Q., Jiao, J. \u0026amp; Xiong, S., 2014. \u003cem\u003eFusion of multi-frequency interferometric results by using Kalman filter to generate high quality DEM.\u0026nbsp;\u003c/em\u003eQuebec City, QC, Canada, IEEE Geoscience and Remote Sensing Symposium.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-geoscience","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Geoscience](https://www.springer.com/journal/44288)","snPcode":"44288","submissionUrl":"https://submission.nature.com/new-submission/44288","title":"Discover Geoscience","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"NISAR, ISRO, SAR, InSAR","lastPublishedDoi":"10.21203/rs.3.rs-7380126/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7380126/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eNASA and the Indian Space Research Organization (ISRO) are collaborating on the Synthetic Aperture Radar (SAR) mission known as NISAR, which was launched on 30 July 2025. It will undergo a 90-day initial phase operation . \u0026nbsp;The NISAR mission features two SAR sensors on a single satellite platform: one operating in the L-band from NASA and the other in the S-band from ISRO. NISAR will provide simultaneous data from two Interferometric SAR (InSAR) pairs—one in the S-band and one in the L-band—on a 12-day repeat pass orbit cycle. In this paper, we explore the potential for multi-frequency complementary interferometric phase fusion using simulated data from both the S-band and L-band. We propose a methodology aimed at generating a high-quality, improved fused S-band interferogram by combining the complementary phase information from the L-band interferogram. The methodology consists of three main processes. The first process for SAR Raw Data Simulation. This process generates S-band and L-band SAR interferometric pairs utilizing the NISAR sensor parameters. During this simulation, two datasets were created: the first simulated with short vegetation, and the second simulated with shrub vegetation surface backscattering coefficients. The second process is the InSAR Process. This step generates interferograms for both the S-band and L-band using the simulated datasets. The third process is the InSAR Phase Fusion Process. The final step involves fusing the InSAR phases to create a new, improved fused S-band interferogram. This fused S-band interferogram is designed to provide high-resolution unwrapped phase values while minimizing phase errors. The results are promising and show significant improvements compared to using a single-frequency InSAR pair.\u003c/p\u003e","manuscriptTitle":"NISAR Complementary Interferometric phase fusion of S \u0026amp; L band - A Simulation Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-22 12:14:20","doi":"10.21203/rs.3.rs-7380126/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-10-27T07:20:10+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-02T12:31:58+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-02T06:13:15+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-28T10:12:34+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-24T20:53:37+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"206024328942092396114959045531670257921","date":"2025-09-23T19:35:49+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"222342259761218018029401242797916711870","date":"2025-09-22T05:22:05+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"149869711992148387700275590879340325178","date":"2025-09-13T10:49:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"276342813205913827122127756557594003471","date":"2025-09-13T10:16:09+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-13T02:40:02+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-09-09T12:08:24+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-01T05:14:36+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-31T14:26:59+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Geoscience","date":"2025-08-31T14:24:17+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-geoscience","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Geoscience](https://www.springer.com/journal/44288)","snPcode":"44288","submissionUrl":"https://submission.nature.com/new-submission/44288","title":"Discover Geoscience","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0282229e-745a-4f2a-b549-7350fb1516fc","owner":[],"postedDate":"September 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-12-23T05:24:00+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-22 12:14:20","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7380126","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7380126","identity":"rs-7380126","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}