Synthetic Aperture Radar (SAR) has been identified as a good candidate to provide high-resolution soil moisture information over extended areas. SAR data could be used as observations within a global Data Assimilation (DA) approach to benefit applications such as hydrology and agriculture. Prior to developing an operational DA system, one must tackle the following challenges of soil moisture estimation with SAR: (1) the dependency of the measured radar signal on both soil moisture and soil surface roughness which leads to an ill-conditioned inverse problem, and (2) the difficulty in characterizing spatially/temporally surface roughness of natural soils and its scattering contribution. The objectives of this project are (1) to develop a roughness measurement method to improve the spatial/temporal characterization of soil surface roughness, and (2) to investigate to what extent the inverse problem can be solved by combining multipolarization, multi-incidence, and/or multi-frequency radar measurements. The first objective is achieved with a measurement method based on Structure from Motion (SfM). It is tailored to monitor natural surface roughness changes which have often been assumed negligible although without evidence. The measurement method is flexible, a.ordable, straightforward and generates Digital Elevation Models (DEMs) for a SAR-pixel-size plot with mm accuracy. A new processing method based on band-filtering of the DEM and its 2D Power Spectral Density (PSD) is proposed to compute the classical roughness parameters. Time series of DEMs show that non-negligible changes in surface roughness can happen within two months at scales relevant for microwave scattering. The second objective is achieved using maximum likelihood fitting of the Oh backscattering model to (1) full-polarimetric Radarsat-2 data and (2) simulated multi-polarization / multi-incidence / multi-frequency radar data. Model fitting with the Radarsat-2 images leads to poor soil moisture retrieval which is related to inaccuracy of the Oh model. Model fitting with the simulated data quantifies the amount of multilooking for di.erent combinations of measurements needed to mitigate the critical e.ect of speckle on soil moisture uncertainty. Results also suggest that dual-polarization measurements at L- and C-bands are a promising combination to achieve the observation requirements of soil moisture. In conclusion, the SfM method along with the recommended processing techniques are good candidates to improve the characterization of surface roughness. A combination of multi-polarization and multi-frequency radar measurements appears to be a robust basis for a future Data Assimilation system for global soil moisture monitoring.

Synthetic aperture radar (SAR) is an imaging technique based on the radio reflectivity of the target being imaged. SAR instruments offer many advantages over optical imaging due to the ability to form coherent images in inclement weather, at night, and through ground cover. High resolution is achieved in azimuth through a synthesized aperture much larger than the physical antenna of the imaging device. Consequently, proper focusing requires accurate information about the relative motion between the antenna phase center and the scene. Any unknown target velocity, acceleration, rotation, or vibration will introduce errors in the image. This work addresses a novel method of focusing a moving target in a SAR image through the estimation of various motion parameters. The target azimuth position is determined through monopulse radar, at which point range velocity and acceleration are estimated across a series of overlapping sub-apertures. Cross-range velocity is then estimated through a search to optimize an image quality metric such as entropy or contrast. A final focused image is then generated based on this velocity vector. Methods of extending this work for a single phase center system are considered. This technique is demonstrated with real radar data from an experimental system, and the performance of this technique is compared both subjectively and with a variety of image metrics to the MITRE keystone technique. Finally, extensions to this current line of research are considered. / text

The objective of this research is a fast circular synthetic aperture radar (F-CSAR) algorithm. Slow-time imaging distinguishes synthetic aperture radar (SAR) from its predecessor imaging radars. SAR slow-time imaging is strongly rooted in Huygens-Fresnel principle and Kirchhoff's approximation based scalar diffraction theory. Slant-plane SAR Green's function and resultant Fourier integral, unlike some Fourier integrals, cannot be analyzed using residue theory from complex analysis and Cauchy-Riemann equations yield analyticity. The asymptotic expansion of 1D and 2D Fourier integrals renders a decomposition of the Green's function leading to SAR data focusing. The research unveils Fraunhofer diffraction patterns in 2D aperture synthesis formulation corresponding to various aperture shapes including circular aperture that appears to be an optimum aperture shape from the mathematical condition in the asymptotic expansion. It is shown that these diffraction patterns may be used for refocusing of defocused images. F-CSAR algorithm is demonstrated using Householder transform recently shown to have improved error bounds and stability. Research is also carried out into various interpolation schemes. Backprojection implementation of CSAR is compared to F-CSAR and elevation coverage renders 3D reconstruction. F-CSAR is also demonstrated using GTRI T-72 tank turntable data.

Diss. -- Espoo -- Teknillinen korkeakoulu. / Myös verkkojulkaisuna.

Thesis (Ph. D.)--University of Florida, 2001. / Title from first page of PDF file. Document formatted into pages; contains xi, 124 p.; also contains graphics. Vita. Includes bibliographical references (p. 120-123).

Thesis (MA)--Stellenbosch University, 2015. / ENGLISH ABSTRACT: Image classification has long been used in earth observation and is driven by the need for accurate maps to develop conceptual and predictive models of Earth system processes. Synthetic aperture radar (SAR) imagery is used ever more frequently in land cover classification due to its complementary nature with optical data. There is therefore a growing need for reliable, accurate methods for using SAR and optical data together in land use and land cover classifications. However, combining data sets inevitably increases data dimensionality and these large, complex data sets are difficult to handle. It is therefore important to assess the benefits and limitations of using multi-temporal, dual-sensor data for applications such as land cover classification. This thesis undertakes this assessment through four main experiments based on combined RADARSAT-2 and SPOT-5 imagery of the southern part of Reunion Island. In Experiment 1, the use of feature selection for dimensionality reduction was considered. The rankings of important features for both single-sensor and dual-sensor data were assessed for four dates spanning a 6-month period, which coincided with both the wet and dry season. The mean textural features produced from the optical bands were consistently ranked highly across all dates. In the two later dates (29 May and 9 August 2014), the SAR features were more prevalent, showing that SAR and optical data have complementary natures. SAR data can be used to separate classes when optical imagery is insufficient. Experiment 2 compared the accuracy of six supervised and machine learning classification algorithms to determine which performed best with this complex data set. The Random Forest classification algorithm produced the highest accuracies and was therefore used in Experiments 3 and 4. Experiment 3 assessed the benefits of using combined SAR-optical imagery over single-sensor imagery for land cover classifications on four separate dates. The fused imagery produced consistently higher overall accuracies. The 29 May 2014 fused data produced the best accuracy of 69.8%. The fused classifications had more consistent results over the four dates than the single-sensor imagery, which suffered lower accuracies, especially for imagery acquired later in the season. In Experiment 4, the use of multi-temporal, dual-sensor data for classification was evaluated. Feature selection was used to reduce the data set from 638 potential training features to 50, which produced the best accuracy of 74.1% in comparison to 71.9% using all of the features. This result validated the use of multi-temporal data over single-date data for land cover classifications. It also validated the use of feature selection to successfully inform data reduction without compromising the accuracy of the final product. Multi-temporal and dual-sensor data shows potential for mapping land cover in a tropical, mountainous region that would otherwise be challenging to map using single-sensor data. However, accuracies Stellenbosch University https://scholar.sun.ac.za iv generally remained lower than would allow for transferability and replication of the current methodology. Classification algorithm optimisation, supervised segmentation and improved training data should be considered to improve these results. / AFRIKAANSE OPSOMMING: Beeld-klassifikasie word al ‘n geruime tyd in aardwaarneming gebruik en word gedryf deur die behoefte aan akkurate kaarte om konseptuele en voorspellende modelle van aard-stelsel prosesse te ontwikkel. Sintetiese apertuur radar (SAR) beelde word ook meer dikwels in landdekking klassifikasie gebruik as gevolg van die aanvullende waarde daarvan met optiese data. Daar is dus 'n groeiende behoefte aan betroubare, akkurate metodes vir die gesamentlike gebruik van SAR en optiese data in landdekking klassifikasies. Die kombinasie van datastelle bring egter ‘n onvermydelike verhoging in data dimensionaliteit mee, en hierdie groot, komplekse datastelle is moeilik om te hanteer. Dus is dit belangrik om die voordele en beperkings van die gebruik van multi-temporale, dubbel-sensor data vir toepassings soos landdekking-klassifikasie te evalueer. Die waarde van gekombineerde (versmelte) RADARSAT-2 en SPOT-5 beelde word in hierdie tesis deur middel van vier eksperimente geevalueer. In Eksperiment 1 is die gebruik van kenmerk seleksie vir dimensionaliteit-vermindering toegepas. Die ranglys van belangrike kenmerke vir beide enkel-sensor en 'n dubbel-sensor data is beoordeel vir vier datums wat oor 'n tydperk van 6 maande strek. Die gemiddelde tekstuur kenmerke uit die optiese lae is konsekwent hoog oor alle datums geplaas. In die twee later datums (29 Mei en 9 Augustus 2014) was die SAR kenmerke meer algemeen, wat dui op die aanvullende aard van SAR en optiese data. SAR data dus gebruik kan word om klasse te onderskei wanneer optiese beelde onvoldoende daarvoor is. Eksperiment 2 het die akkuraatheid van ses gerigte en masjien-leer klassifikasie algoritmes vergelyk om te bepaal watter die beste met hierdie komplekse datastel presteer. Die random gorest klassifikasie algoritme het die hoogste akkuraatheid bereik en is dus in Eksperimente 3 en 4 gebruik. Eksperiment 3 het die voordele van gekombineerde SAR-optiese beelde oor enkel-sensor beelde vir landdekking klassifikasies op vier afsonderlike datums beoordeel. Die versmelte beelde het konsekwent hoër algehele akkuraathede as enkel-sensor beelde gelewer. Die 29 Mei 2014 data het die hoogste akkuraatheid van 69,8% bereik. Die versmelte klassifikasies het ook meer konsekwente resultate oor die vier datums gelewer en die enkel-sensor beelde het tot laer akkuraathede gelei, veral vir die later datums. In Eksperiment 4 is die gebruik van multi-temporale, dubbel-sensor data vir klassifikasie ge-evalueer. Kenmerkseleksie is gebruik om die data stel van 638 potensiële kenmerke na 50 te verminder, wat die beste akkuraatheid van 74,1% gelewer het. Hierdie resultaat bevestig die belangrikheid van multi-temporale data vir grond dekking klassifikasies. Dit bekragtig ook die gebruik van kenmerkseleksie om data vermindering suksesvol te rig sonder om die akkuraatheid van die finale produk te belemmer. Stellenbosch University https://scholar.sun.ac.za vi Multi-temporale en dubbel-sensor data toon potensiaal vir die kartering van landdekking in 'n tropiese, bergagtige streek wat andersins uitdagend sou wees om te karteer met behulp van enkel-sensor data. Oor die algemeen het akkuraathede egter te laag gebly om vir oordraagbaarheid en herhaling van die huidige metode toe te laat. Klassifikasie algoritme optimalisering, gerigte segmentering en verbeterde opleiding data moet oorweeg word om hierdie resultate te verbeter.

abstract: For synthetic aperture radar (SAR) image formation processing, the chirp scaling algorithm (CSA) has gained considerable attention mainly because of its excellent target focusing ability, optimized processing steps, and ease of implementation. In particular, unlike the range Doppler and range migration algorithms, the CSA is easy to implement since it does not require interpolation, and it can be used on both stripmap and spotlight SAR systems. Another transform that can be used to enhance the processing of SAR image formation is the fractional Fourier transform (FRFT). This transform has been recently introduced to the signal processing community, and it has shown many promising applications in the realm of SAR signal processing, specifically because of its close association to the Wigner distribution and ambiguity function. The objective of this work is to improve the application of the FRFT in order to enhance the implementation of the CSA for SAR processing. This will be achieved by processing real phase-history data from the RADARSAT-1 satellite, a multi-mode SAR platform operating in the C-band, providing imagery with resolution between 8 and 100 meters at incidence angles of 10 through 59 degrees. The phase-history data will be processed into imagery using the conventional chirp scaling algorithm. The results will then be compared using a new implementation of the CSA based on the use of the FRFT, combined with traditional SAR focusing techniques, to enhance the algorithm's focusing ability, thereby increasing the peak-to-sidelobe ratio of the focused targets. The FRFT can also be used to provide focusing enhancements at extended ranges. / Dissertation/Thesis / M.S. Electrical Engineering 2011

abstract: Inverse problems model real world phenomena from data, where the data are often noisy and models contain errors. This leads to instabilities, multiple solution vectors and thus ill-posedness. To solve ill-posed inverse problems, regularization is typically used as a penalty function to induce stability and allow for the incorporation of a priori information about the desired solution. In this thesis, high order regularization techniques are developed for image and function reconstruction from noisy or misleading data. Specifically the incorporation of the Polynomial Annihilation operator allows for the accurate exploitation of the sparse representation of each function in the edge domain. This dissertation tackles three main problems through the development of novel reconstruction techniques: (i) reconstructing one and two dimensional functions from multiple measurement vectors using variance based joint sparsity when a subset of the measurements contain false and/or misleading information, (ii) approximating discontinuous solutions to hyperbolic partial differential equations by enhancing typical solvers with l1 regularization, and (iii) reducing model assumptions in synthetic aperture radar image formation, specifically for the purpose of speckle reduction and phase error correction. While the common thread tying these problems together is the use of high order regularization, the defining characteristics of each of these problems create unique challenges. Fast and robust numerical algorithms are also developed so that these problems can be solved efficiently without requiring fine tuning of parameters. Indeed, the numerical experiments presented in this dissertation strongly suggest that the new methodology provides more accurate and robust solutions to a variety of ill-posed inverse problems. / Dissertation/Thesis / Doctoral Dissertation Mathematics 2018

Climate-induced landscape evolution is resulting in changes to biogeochemical and hydrologi- cal cycling. In the Arctic and sub-Arctic permafrost zones, rising air temperatures are warming, and in some regions even thawing, the frozen ground. Permafrost is a carbon sink. The thermal state of the ground therefore has important implications on carbon exchange with the atmo- sphere. Permafrost thaw mobilises previously sequestered carbon stocks, potentially turning these high latitude regions into a net carbon source. Borehole temperature and active layer depth measurements are the traditional means for monitoring permafrost, however these point measurements cannot easily be extrapolated to the landscape-scale; Earth Observation (EO) data may be used for such purposes. It is widely recognised that changes in the thermal state of permafrost may be associated with longterm changes in surface hydrology. As the ground shifts from a frozen to a thawed state, Arctic lakes display changes in surface extent. Therefore, it has become common practice to explore lake dynamics, using these as indicators of permafrost change; dynamics being the keyword. Surface hydrology is a dynamic process. Discharge studies in the Arctic and sub-Arctic regions are associated with flashy hydrographs. Currently, however, remote sensing of permafrost lake change is done on the scale of decades without explicitly taking seasonality and rapid hydrolog- ical phenology into consideration. To examine the seasonal changes in Arctic surface hydrology on the landscape scale high temporal resolution data are necessary. Synthetic aperture radar instruments are exemplary for such a task. The PhD research focuses on establishing operational techniques for mapping open surface water using synthetic aperture radar data, investigating straightforward raster classification methods and exploring their feasibility by undertaking map accuracy and sensitivity studies (chapter 3). The results are then used to justify error propagation when developing an auto- mated procedure that creates temporal composites of water body extent. These temporal water body classifications are the main EO product used to identify and image seasonal surface water change in Arctic permafrost environments (chapter 4). Furthermore, a terrain-based hydrolog- ical study is undertaken to explore the context of the detected changes and possible links to relief and stream channel network (chapter 5). The aim of this PhD is to demonstrate a new method of dynamic monitoring using the Euro- pean Space Agency’s Envisat Advanced Synthetic Aperture Radar, recommending its incorpo- ration in longterm lake change studies. Technical feasibility is explored, the inherent trade-off vii between spatial and temporal resolution discussed. An automated surface water change de- tection algorithm is developed and its applicability to monitoring spring floods is assessed; noting possible modifications to the drainage system given present-day land-use and land- cover changes that are taking place in the study area, the hydrocarbon-rich Yamalo-Nenets Autonomous District in the North of West Siberia (chapter 6). The key significance of this research is to improve the current knowledge of Arctic lake change by including spring flood events and seasonality in the equation. Therefore, it is strongly believed that this research is of benefit to the entire permafrost community.

Submitted by ULISSES SILVA GUIMARÃES null (ulissesguimaraes21@yahoo.com.br) on 2017-04-23T14:46:14Z No. of bitstreams: 1 PPGCC_Tese_Guimaraes_mar2017.pdf: 11881192 bytes, checksum: b616aff4dd851b26e6151f6a753a3e62 (MD5) / Rejected by Luiz Galeffi (luizgaleffi@gmail.com), reason: Solicitamos que realize uma nova submissão seguindo a orientação abaixo: O arquivo submetido não contém o certificado de aprovação. Corrija esta informação e realize uma nova submissão com o arquivo correto. Agradecemos a compreensão. on 2017-04-25T19:47:14Z (GMT) / Submitted by ULISSES SILVA GUIMARÃES null (ulissesguimaraes21@yahoo.com.br) on 2017-04-27T00:35:55Z No. of bitstreams: 1 PPGCC_DR_UG.pdf: 11945196 bytes, checksum: 6a00a3b24776e561994802637366de42 (MD5) / Approved for entry into archive by Luiz Galeffi (luizgaleffi@gmail.com) on 2017-05-03T13:21:12Z (GMT) No. of bitstreams: 1 guimaraes_us_dr_prud.pdf: 11945196 bytes, checksum: 6a00a3b24776e561994802637366de42 (MD5) / Made available in DSpace on 2017-05-03T13:21:12Z (GMT). No. of bitstreams: 1 guimaraes_us_dr_prud.pdf: 11945196 bytes, checksum: 6a00a3b24776e561994802637366de42 (MD5) Previous issue date: 2017-03-06 / A Zona Costeira Amazônica (ZCA) é marcada por uma alta descarga de sedimentos e água doce sob a influência do rio Amazonas, possui ampla plataforma continental, extensas planícies de inundação e planaltos rebaixados. É uma região de clima tropical, caracterizando-se por chuvas e nebulosidade severas, além da influência de macromarés. Este estudo propõe-se a avaliar a precisão de Modelos Digitais de Superfícies (MDSs), elaborados a partir de dados de radar de abertura sintética (SAR) Cosmo-SkyMed (CSK) e TerraSAR-X (TSX), utilizando abordagens de reconstrução tridimensional por interferometria e radargrametria, para caracterizar esse relevo plano e dinâmico da costa amazônica. O estudo foi desenvolvido em quatro experimentos contemplando: i) as variações de linha de costa por meio de detecção de mudanças a partir de imagens ópticas; ii) mapeamento de ambientes costeiros; iii) elaboração e análise de MDSs interferométricos e iv) radargramétricos, por meio das suas respectivas cadeias de processamento SAR. A ZCA teve forte dinâmica nos últimos 15 anos, com acresção total de 5.582,18 km2 e sob a taxa de 372,15 km2.ano-1, erosão total de 5.475,90 km² e sob taxa de 365,06 km2.ano-1, resultando no balanço sedimentar de 106,27 km², com taxa de 7,08 km2.ano-1. O setor Insular Estuarino apresentou a maior dinâmica de linha de costa, com mudanças costeiras de 213,17±56,46 km2 e balanço sedimentar de 20,65±73,59 km2. Os ambientes costeiros amazônicos foram descritos pelo retroespalhamento e pela coerência, os quais compartilharam alta ambiguidade e dispersão elevada, sendo o pior caso de separabilidade e baixa coerência registrado para Planície Costeira. O mapeamento dos diferentes ambientes costeiros resultou em coeficiente Kappa entre 0,46 a 0,51, apontando os ângulos de incidência rasantes e o período seco como mais apropriados para o estudo. Os MDSs interferométricos e radargramétricos foram elaborados em passagens múltiplas de única revisita com compromissos entre ângulos de incidência, linha de base espacial e descorrelação temporal. A acurácia vertical foi realizada por testes estatísticos pareados com levantamentos de campo que resultou em discrepâncias, viés e precisão compatíveis com o Padrão de Exatidão Cartográfica Brasileiro para Produtos Cartográficos Digitais (PEC-PCD), em adição, os MDS foram comparados por meio dos diagramas de Taylor e Alvo. Os MDSs interferométricos alcançaram RMSE entre 9,57 e 25,18 m, com melhor desempenho para o MDS CSK, adquirido com 1 dia de revisita, ângulo de incidência íngreme, no período chuvoso e compatível a escala de 1:50.000, classe A. Entretanto, a abordagem interferométrica não foi capaz de solucionar a reconstrução tridimensional de ambientes que se mostraram incoerentes. Os modelos radargramétricos obtidos pelas abordagens do SARscape e Toutin alcançaram RMSE entre 4,34 e 7,76 m, com melhor desempenho para os modelos de Toutin, que foram compatíveis com a escala 1:50.000, classe A. A radargrametria permitiu a reconstrução tridimensional contínua, incluindo a Planície Costeira de comportamento incoerente. A comparação dos MDSs por meio dos diagramas de Taylor e Alvo, mostrou variações de precisão entre os sistemas CSK e TSX, e suas respectivas condições de aquisição e modelos, com destaque à menor variabilidade e ajuste da correlação para MDSs do sistema TSX, em incidências rasantes, no período seco e gerados pelo modelo de Toutin. O Tabuleiro Costeiro e Terraço Fluviomarinho apresentaram menor erro vertical entre 3,89 e 28,59 m, e entre 3,79 e 20,33 m, respectivamente, enquanto que a Planície Costeira teve maior RMSE entre 4,16 e 26,24 m. O Tabuleiro Costeiro foi o ambiente costeiro mais adequado para estimar altura, com posições plotadas próximas as referências de campo. Os dados CSK e TSX permitiram mapear o relevo plano e dinâmico da ZCA, por meio da banda X, alta resolução espacial e revisita, o que demostrou o suporte para cartografar em detalhe de escala espacial (1:50.000) e frequente atualização (semestral a anual). / The Amazon Coastal Zone (ACZ) is marked by a high discharge of sediments and fresh water under the influence of the Amazon River, which has a wide continental shelf, extensive flood plain and lowered plateaus. It is a region of tropical climate, rainfall, severe cloudiness and macrotidal influence. This study proposes to assess the performance of Digital Surface Models (DSM) based on Cosmo-SkyMed (CSK) and TerraSAR-X (TSX) dataset, using threedimensional reconstruction by interferometry and radargrammetry approaches on the flat and dynamic relief of Amazonian coast. The method focused on four experiments: i) shoreline variations through change detection of optical images, ii) mapping of coastal environments; iii) elaboration and analyses of interferometric DMSs and iv) radargrammetric through their respective Synthetic Aperture Radar (SAR) processing chains. The ACZ had high dynamic in the last 15 years with total sediment deposition of 5,582.18 km2 and under a rate of 372.15 km2 .yr -1 , and with total erosion of 5,475.90 km² and under a rate of 365.06 km2 .yr-1 . Besides, it was obtained a sedimentary balance of 106.27 km² and under a rate of 7.08 km2 .yr-1 . The Estuarine Insular sector presented the greater dynamics of shoreline, registering coastal changes of 213.17 ± 56.46 km2 and sedimentary balance of 20.65 ± 73.59 km2 . The Amazonian coastal environments were described by backscattering and coherence which shared ambiguity and high dispersion, with the lowest separability and coherence noted for Coastal Flat. The mapping of the coastal environments obtained Kappa coefficients between 0.46 and 0.51, indicating the shallow incidence angles during the dry season as more appropriated for the study. The interferometric and radargrammetric DSMs were elaborated in multi-pass and single revisit with commitment between incidence angles, spatial baseline and temporal decorrelation. A vertical accuracy assessment was performed with paired statistical tests at surveyed elevations in the field that resulted in discrepancies, bias and precision, in accordance to the Brazilian Cartographic Accuracy Standard for Digital Cartographic Products (PEC-PCD), in addition, the DSMs were compared throughout Taylor and Target diagrams. The interferometric DMSs achieved RMSE between 9.57 and 25.18 m, with better performance for the DMS CSK, acquired with 1 day of revisit, steeper incidence, in rainy season and compatible at a scale of 1: 50,000, class A. However, the interferometric approach was not able to solve the threedimensional reconstruction in incoherent environments. The radargrammetric models of SARscape and Toutin achieved a RMSE between 4.34 and 7.76, and the best performances were for the Toutin’s models compatible at a scale of 1: 50,000, class A. The advantage of radargrammetry was to provide continuous three-dimensional reconstruction, including the Coastal Flat of incoherent behavior. The comparison of the DMSs through the Taylor and Target diagrams showed fluctuations of precision between CSK and TSX systems and their respective acquisition conditions and models, but it is remarkable the stability of the lowest variability and the correlation well fitted for the DMSs given by TSX system, shallow incidences, dry season and Toutin model. The Coastal Plateau and Fluvial Marine Terrace had the lowest vertical error between 3.89 and 28.59 m, and between 3.79 and 20.33 m, respectively. On the contrary, the Coastal Flat had the highest RMSE between 4.16 and 26.24 m. The Coastal Plateau was the most suitable coastal environment to estimate the height following the Taylor and Target diagrams, with the plotted positions close to the field references. The CSK and TSX data allowed to map the ZCA precisely, based on X-band perspective, high spatial resolution and revisit, which has demonstrated the support for detailed cartography of spatial scale (1: 50,000) and frequent updating (semiannual up to annual).