Modeling urban heat islands in heterogeneous land surface and its correlation with impervious surface area by using night-time ASTER satellite data in highly urbanizing city,Delhi-India

The present study is an assessment and identification of urban heat island (UHI) in the environment of one of the fastest urbanizing city of India, Delhi Metropolis, employing satellite image of ASTER and Landsat 7 ETM+ in the thermal infrared region 3–14 μm. Temporal (2001 and 2005) ASTER datasets were used to analyze the spatial structure of the thermal urban environment subsequently urban heat island (UHI) in relation to the urban surface characteristics and land use/land cover (LULC). The study involves derivation of parameters governing the surface heat fluxes, constructing statistics of ASTER thermal infrared images along with validation through intensive in situ measurements. The average images reveal spatial and temporal variations of land surface temperature (LST) of night-time and distinct microclimatic patterns. Central Business District (CBD) of Delhi, (Connaught Place, a high density built up area), and commercial/industrial areas display heat islands condition with a temperature greater than 4 °C compared to the suburbs. The small increase in surface temperature at city level is mainly attributed to cumulative impact of human activities, changes in LULC pattern and vegetation density. In this study the methodology takes into account spatially-relative surface temperatures and impervious surface fraction value to measure surface UHI intensity between the urban land cover and rural surroundings. Both the spatial and temporal variation in surface temperature associated with impervious surface area (ISA) has been evaluated to assess the effect of urbanization on the local climate.     

Object-based rice mapping using time-series and phenological data

Remote sensing techniques are often used in mapping rice, but high quality time-series remote sensing data are difficult to obtain due to the cloudy weather of rice growing areas and long satellite revisit interval. As such, rice mapping is usually based on mono-temporal Landsat TM/ETM+ data, which have large uncertainties due to the spectral similarity of different vegetation types. Moreover, conventional pixel-based classification method is unable to meet the required accuracy for rice mapping. Therefore, this study proposes a new strategy for mapping rice in cloud-prone areas using fused data of Landsat-8 OLI time-series and phenological parameters, based on the object-based method. We determine the critical growth stages of paddy rice from observed phenological data and MODIS-NDVI time-series data. The spatial and temporal adaptive reflectance fusion model (STARFM) is used to blend the MODIS and Landsat data to obtain a multi-temporal Landsat-like dataset for classification. Finally, an object-oriented algorithm is used to extract rice paddies from the Landsat-like, time-series dataset. The validation experiments show that the proposed method can provide high accuracy rice mapping, with an overall accuracy of 92.38% and a kappa coefficient of 0.85.     

Satellite remotely-sensed land surface parameters and their climatic effects for three metropolitan regions

By using both high-resolution orthoimagery and medium-resolution Landsat satellite imagery with other geospatial information, several land surface parameters including impervious surfaces and land surface temperatures for three geographically distinct urban areas in the United States – Seattle, Washington, Tampa Bay, Florida, and Las Vegas, Nevada, are obtained. Percent impervious surface is used to quantitatively define the spatial extent and development density of urban land use. Land surface temperatures were retrieved by using a single band algorithm that processes both thermal infrared satellite data and total atmospheric water vapor content. Land surface temperatures were analyzed for different land use and land cover categories in the three regions. The heterogeneity of urban land surface and associated spatial extents were shown to influence surface thermal conditions because of the removal of vegetative cover, the introduction of non-transpiring surfaces, and the reduction in evaporation over urban impervious surfaces. Fifty years of in situ climate data were integrated to assess regional climatic conditions. The spatial structure of surface heating influenced by landscape characteristics has a profound influence on regional climate conditions, especially through urban heat island effects.     

Substitution of satellite-based land surface temperature defective data using GSP method

Land surface temperature (LST) as an important environmental variable provides valuable information for earth environmental system modelling. Currently, LST is obtained through satellite thermal sensors at various spatial and temporal resolutions. Although spatially continuous satellite-based LST measurements are intended to overcome the shortcomings of sparse ground-based LST measurements, LST images often contain anomalous values due to the existence of clouds or sensor malfunctioning. The problem becomes more serious where the users deal with high spatial resolution characterized by low temporal resolution. This study examines the capability of a newly developed graph signal processing (GSP) method using two-dimensional single-date thermal data. For this purpose, four Landsat/TIRS datasets are analyzed. The data of five elliptical regions on thermal images are eliminated and then reconstructed through the GSP method and using the LST values of the enclosing rectangles containing the ellipsoids. The results indicate that the temperature variation determined by the GSP method generally conforms to the original image LST values. According to a correlation test conducted on the original image LST and those obtained through the GSP method, the values vary from 58% to 95%, which is an above-the-average rate (RMSE from 0.69 to 2.27). The statistical analysis of the original image LST in both the elliptical regions and the enclosing rectangles containing the ellipsoids indicates that an increase in the variance of LST data causes an increased error in the calculation of temperature by the GSP method, and vice versa. The results of the analysis of variance (ANOVA) and Duncan test indicated that an increase in the number of the non-zero spectral bins would result in increased RMSE values for all the dates and the regions. Moreover, the model errors were significant at the 0.05 level across all the image date and five elliptical study regions. Based on the results, the use of this method is recommended for the reconstruction of LST missing values, where dissimilarity of atmospheric conditions limits the use of other methods that depend on the time series data of various dates and a great deal of data calculation.     

Selection of classification techniques for land use/land cover change investigation

The concerns over land use/land cover (LULC) change have emerged on the global stage due to the realisation that changes occurring on the land surface also influence climate, ecosystem and its services. As a result, the importance of accurate mapping of LULC and its changes over time is on the increase. Landsat satellite is a major data source for regional to global LULC analysis. The main objective of this study focuses on the comparison of three classification tools for Landsat images, which are maximum likelihood classification (MLC), support vector machine and artificial neural network (ANN), in order to select the best method among them. The classifiers algorithms are well optimized for the gamma, penalty, degree of polynomial in case of SVM, while for ANN minimum output activation threshold and RMSE are taken into account. The overall analysis shows that the ANN is superior to the kernel based SVM (linear, radial based, sigmoid and polynomial) and MLC. The best tool (ANN) is then applied on detecting the LULC change over part of Walnut Creek, Iowa. The change analysis of the multi temporal images indicates an increase in urban areas and a major shift in the agricultural practices.     

An ensemble deep learning based shoreline segmentation approach (WaterNet) from Landsat 8 OLI images

Shorelines constantly vary due to natural, urbanization and anthropogenic effects such as global warming, population growth, and environmental pollution. Sustainable monitoring of coastal changes is vital in terms of coastal resource management, environmental preservation and planning. Publicly available Landsat 8 OLI (Operational Land Manager) images provide accurate, reliable, temporal and up-to-date information about coastal areas. Recently, the use of machine learning and deep learning algorithms have become widespread. In this study, we used our public Landsat 8 OLI satellite image dataset to create a majority voting method which is an ensemble automatic shoreline segmentation system (WaterNet) to obtain shorelines automatically. For this purpose, different deep learning architectures have been utilized namely as Standard U-Net, Dilated U-Net, Fractal U-Net, FC-DenseNet, and Pix2Pix. Also, we have suggested a novel framework to create labeling data from OpenStreetMap service to create a unique dataset called YTU-WaterNet. According to the results, IoU and F1 scores have been calculated as 99.59% and 99.79% for the WaterNet. The results indicate that the WaterNet method outperforms other methods in terms of shoreline extraction from Landsat 8 OLI satellite images.     

Study on extraction of crop information using time-series MODIS data in the Chao Phraya Basin of Thailand

In order to acquire the crop-related information in Chao Phraya Basin, time-series MODIS data were used in this paper. Although the spatial resolution of MODIS data is not very high, it is still useful for detecting very large-scale phenomenon, such as changes in seasonal vegetation patterns. After the data processing a general crop-related LULC (land use and land cover) map, cropping intensity map and cropping patterns map were produced. Analysis of these maps showed that the main land use type in the study area was farmland, most of which was dominated by rice. Rice fields mostly concentrated in the flood plains and double or triple rice-cropping system was commonly employed in this area. Maize, cassava, sugarcane and other upland crops were mainly distributed in the high alluvial terraces. Because these area often have water shortage problem particularly in the dry season which can support only one crop in a year, the cropping intensity was very low. However, some upland areas can be cultivated twice a year with crops which have short growing seasons. The crop information extracted from MODIS data sets were assessed by CBERS data, statistic data and so on. It was shown that MODIS derived crop information coincided well with the statistic data at the provincial level. At the same time, crop information extracted by MODIS data sets and CBERS were compared with each other which also showed similar spatial patterns.     

Mapping recent agricultural developments in China from satellite data

Landsat data have been employed to study and map agricultural developments in three regions of China: 1) Pearl River delta; 2) Nen River basin; and 3) Xinjiang Autonomous Region. Manual interpretation procedures used in conjunction with multi-date Landsat images and collateral information permitted rice yields to be estimated for the Pearl River delta in 1978. A combination of manual and computer-assisted analyses of Landsat data of Northeast China revealed that more than 15,000 km² of agricultural land in a 184,500 km² study area had been reclaimed from rangeland and marshland. These analyses also indicated a shift in cropping practices, with the foodcrops wheat and corn replacing cash crops such as soybeans. In the arid west, Landsat image data provided valuable input to a geographic information system (GIS). It appears the GIS approach will prove useful for evaluating agricultural land potential in the remote areas of China.     

Landsat 8地表温度产品验证与分析

Landsat系列卫星热波段具有60~120m的空间分辨率，对各种环境监测起到了重要的作用。随着Landsat系列卫星在全球范围内地表温度（land surface temperature，LST）产品的发布，其验证工作也随之展开，然而对于长时间序列的精度验证工作仍然缺乏。以黑河流域中游为研究区，利用研究区内湿地站（SD）、戈壁站（GB）和大满超级站（CJZ）三个气象站的地面测量数据对2013-2016年清晰无云的31景Landsat 8地表温度产品进行了验证与分析，并将Landsat 8地表温度产品与广泛使用的普适性单通道算法（JMS）反演结果进行了对比。结果表明，Landsat 8地表温度产品与普适性单通道算法反演结果精度均较高，在各个站点处R2均优于0.949。基于所有站点分析，Landsat 8地表温度产品精度稍高于普适性单通道算法反演结果。     

Experiment for mapping land cover and it’s change in southeastern Sri Lanka utilizing 250 m resolution MODIS imageries

Utilizing freely available MODIS NDVI and Natural color imageries of 250 m spatial resolution produced by NASA, an experiment was made to map land-cover and its change with an emphasis on vegetation cover in southeastern Sri Lanka, which plays a vital role for control of green house gas. For the change detection purpose, 1987 land cover map made by present authors from Landsat MSS image and extensive ground truth survey data was used as the base map. The result of the experiment shows that MODIS data are useful to make a land cover map of 250 m spatial resolution for tropical areas with high cloud coverage like Sri Lanka. It was found that the forest cover decrease amounted as large as 21% in 19 years time span in southeastern Sri Lanka, the prominent forest region of the country. On the other hand homestead/vegetation and mixed vegetation/scrub dominant categories increased by 13.7% and 7.1%, respectively. These changes are considered due to a large clearance of forest areas for agriculture and building houses to accommodate increasing inhabitants.     

A comparison of different regression models for downscaling Landsat and MODIS land surface temperature images over heterogeneous landscape

Remotely sensed high spatial resolution thermal images are required for various applications in natural resource management. At present, availability of high spatial resolution (<200 m) thermal images are limited. The temporal resolution of such images is also low. Whereas, coarser spatial resolution (∼1000 m) thermal images with high revisiting capability (∼1 day) are freely available. To bridge this gap, present study attempts to downscale coarser spatial resolution thermal image to finer spatial resolution using relationships between land surface temperature (LST) and vegetation indices over a heterogeneous landscape of India. Five regression based models namely (i) Disaggregation of Radiometric Temperature (DisTrad), (ii) Temperature Sharpening (TsHARP), (iii) TsHARP with local variant, (iv) Least median square regression downscaling (LMS_DS) and (v) Pace regression downscaling (PR_DS) are applied to downscale LST of Landsat Thematic Mapper (TM) and Terra MODIS (Moderate Resolution Imaging Spectroradiometer) images. All the five models are first evaluated on Landsat image aggregated to 960 m resolution and downscaled to 480 m and 240 m resolution. The downscale accuracy is achieved using LMS_DS and PR_DS models at 240 m resolution at 0.61 °C and 0.75 °C respectively. MODIS data downscaled from 1000 m to 250 m spatial resolution results root mean square error (RMSE) of 1.43 °C and 1.62 °C for LMS_DS and PR_DS models, respectively. The LMS_DS model is less sensitive to outliers in heterogeneous landscape and provides higher accuracy when compared to other models. Downscaling model is found to be suitable for agricultural and vegetated landscapes up to a spatial resolution of 250 m but not applicable to water bodies, dry river bed sand sandy open areas.     

Scheduling algorithms for rapid imaging using agile Cubesat constellations

Distributed Space Missions such as formation flight and constellations, are being recognized as important Earth Observation solutions to increase measurement samples over space and time. Cubesats are increasing in size (27U, ～40?kg in development) with increasing capabilities to host imager payloads. Given the precise attitude control systems emerging in the commercial market, Cubesats now have the ability to slew and capture images within short notice. We propose a modular framework that combines orbital mechanics, attitude control and scheduling optimization to plan the time-varying, full-body orientation of agile Cubesats in a constellation such that they maximize the number of observed images and observation time, within the constraints of Cubesat hardware specifications. The attitude control strategy combines bang-bang and PD control, with constraints such as power consumption, response time, and stability factored into the optimality computations and a possible extension to PID control to account for disturbances. Schedule optimization is performed using dynamic programming with two levels of heuristics, verified and improved upon using mixed integer linear programming. The automated scheduler is expected to run on ground station resources and the resultant schedules uplinked to the satellites for execution, however it can be adapted for onboard scheduling, contingent on Cubesat hardware and software upgrades. The framework is generalizable over small steerable spacecraft, sensor specifications, imaging objectives and regions of interest, and is demonstrated using multiple 20?kg satellites in Low Earth Orbit for two case studies – rapid imaging of Landsat’s land and coastal images and extended imaging of global, warm water coral reefs. The proposed algorithm captures up to 161% more Landsat images than nadir-pointing sensors with the same field of view, on a 2-satellite constellation over a 12-h simulation. Integer programming was able to verify that optimality of the dynamic programming solution for single satellites was within 10%, and find up to 5% more optimal solutions. The optimality gap for constellations was found to be 22% at worst, but the dynamic programming schedules were found at nearly four orders of magnitude better computational speed than integer programming. The algorithm can include cloud cover predictions, ground downlink windows or any other spatial, temporal or angular constraints into the orbital module and be integrated into planning tools for agile constellations.     

A method in near-surface estimation of air temperature (NEAT) in times following the satellite passing time using MODIS images

Air temperature is one of the most important parameters in environmental, agricultural and water resources studies. This information is not usually always available at the required temporal and spatial resolution. The air temperature is measured at a fixed point in the meteorological stations which are dispersed and may not have the appropriate spatial resolution needed for many applications. On the other hand, MODIS satellite images have relatively acceptable spatial resolution specially for use in environmental studies. There is a methodology with which the near surface air temperature can be extracted from MODIS images at the satellite passing time with an acceptable accuracy. The goal in this study is to find a way to predict the air temperature in times after/before the satellite passing time. The procedure consists of two steps. In the first step, the relationship between the air temperature at a time in a synoptic station and the air temperature in other times up to 5 h later were modeled. In the second step, using these built up relationships, the air temperature extracted from the satellite image at the passing time was extrapolated to the next hours. Finally, the results of this extrapolation method were evaluated using the air temperatures measured at those hours and in the pixels containing some other meteorological stations. The error of the method when applied to a relatively homogeneous surface cover was about 1.5 °C. This error when applied to the next hours, was below 2 °C up to 5 h after satellite passing time. This method can be useful in some agricultural and horticultural applications in which both the spatial and temporal resolution are needed simultaneously. This product is a useful tool for frost prediction, a phenomenon that usually happens at night or early in the morning.     

Water surface temperature estimation from Landsat 7 ETM+ thermal infrared data using the generalized single-channel method: Case study of Embalse del Río Tercero (Córdoba,Argentina)

Monitoring of warm distribution in water is fundamental to understand the performance and functioning of reservoirs and lakes. Surface water temperature is a key parameter in the physics of aquatic systems processes since it is closely related to the energy fluxes through the water–atmosphere interface. Remote sensing applied to water quality studies in inland waterbodies is a powerful tool that can provide additional information difficult to achieve by other means. The combination of good real-time coverage, spatial resolution and free availability of data makes Landsat system a proper alternative. Many papers have developed algorithms to retrieve surface temperature (principally, land surface temperature) from at-sensor and surface emissivity data. The aim of this study is to apply the single-channel generalized method (SCGM) developed by Jiménez-Muñoz and Sobrino (2003) for the estimation of water surface temperature from Landsat 7 ETM+ thermal bands. We consider a constant water emissivity value (0.9885) and we compare the results with radiative transfer classic method (RTM).     

Development and testing of a statistical texture model for land cover classification of the Black Sea region with MODIS imagery

A statistical model is proposed for analysis of the texture of land cover types for global and regional land cover classification by using texture features extracted by multiresolution image analysis techniques. It consists of four novel indices representing second-order texture, which are calculated after wavelet decomposition of an image and after texture extraction by a new approach that makes use of a four-pixel texture unit. The model was applied to four satellite images of the Black Sea region, obtained by Terra/MODIS and Aqua/MODIS at different spatial resolution. In single texture classification experiments, we used 15 subimages (50 × 50 pixels) of the selected classes of land covers that are present in the satellite images studied. These subimages were subjected to one-level and two-level decompositions by using orthonormal spline and Gabor-like spline wavelets. The texture indices were calculated and used as feature vectors in the supervised classification system with neural networks. The testing of the model was based on the use of two kinds of widely accepted statistical texture quantities: five texture features determined by the co-occurrence matrix (angular second moment, contrast, correlation, inverse difference moment, entropy), and four statistical texture features determined after the wavelet transformation (mean, standard deviation, energy, entropy). The supervised neural network classification was performed and the discrimination ability of the proposed texture indices was found comparable with that for the sets of five GLCM texture features and four wavelet-based texture features. The results obtained from the neural network classifier showed that the proposed texture model yielded an accuracy of 92.86% on average after orthonormal wavelet decomposition and 100% after Gabor-like wavelet decomposition for texture classification of the examined land cover types on satellite images.     

Surface temperature estimation in Singhbhum Shear Zone of India using Landsat-7 ETM+ thermal infrared data

Land surface temperature (LST) is an important factor in global change studies, heat balance and as control for climate change. A comparative study of LST over parts of the Singhbhum Shear Zone in India was undertaken using various emissivity and temperature retrieval algorithms applied on visible and near infrared (VNIR), and thermal infrared (TIR) bands of high resolution Landsat-7 ETM+ imagery. LST results obtained from satellite data of October 26, 2001 and November 2, 2001 through various algorithms were validated with ground measurements collected during satellite overpass. In addition, LST products of MODIS and ASTER were compared with Landsat-7 ETM+ and ground truth data to explore the possibility of using multi-sensor approach in LST monitoring. An image-based dark object subtraction (DOS3) algorithm, which is yet to be tested for LST retrieval, was applied on VNIR bands to obtain atmospheric corrected surface reflectance images. Normalized difference vegetation index (NDVI) was estimated from VNIR reflectance image. Various surface emissivity retrieval algorithms based on NDVI and vegetation proportion were applied to ascertain emissivities of the various land cover categories in the study area in the spectral range of 10.4–12.5 μm. A minimum emissivity value of about 0.95 was observed over the reflective rock body with a maximum of about 0.99 over dense forest. A strong correlation was established between Landsat ETM+ reflectance band 3 and emissivity. Single channel based algorithms were adopted for surface radiance and brightness temperature. Finally, emissivity correction was applied on ‘brightness temperature’ to obtain LST. Estimated LST values obtained from various algorithms were compared with field ground measurements for different land cover categories. LST values obtained after using Valor’s emissivity and single channel equations were best correlated with ground truth temperature. Minimum LST is observed over dense forest as about 26 °C and maximum LST is observed over rock body of about 38 °C. The estimated LST showed that rock bodies, bare soils and built-up areas exhibit higher surface temperatures, while water bodies, agricultural croplands and dense vegetations have lower surface temperatures during the daytime. The accuracy of the estimated LST was within ±2 °C. LST comparison of ASTER and MODIS with Landsat has a maximum difference of 2 °C. Strong correlation was found between LST and spectral radiance of band 6 of Landsat-7 ETM+. Result corroborates the fact that surface temperatures over land use/land cover types are greatly influenced by the amount of vegetation present.     

Assimilation of endmember variability in spectral mixture analysis for urban land cover extraction

Variable Endmember Constrained Least Square (VECLS) technique is proposed to account endmember variability in the linear mixture model by incorporating the variance for each class, the signals of which varies from pixel to pixel due to change in urban land cover (LC) structures. VECLS is first tested with a computer simulated three class endmember considering four bands having small, medium and large variability with three different spatial resolutions. The technique is next validated with real datasets of IKONOS, Landsat ETM+ and MODIS. The results show that correlation between actual and estimated proportion is higher by an average of 0.25 for the artificial datasets compared to a situation where variability is not considered. With IKONOS, Landsat ETM+ and MODIS data, the average correlation increased by 0.15 for 2 and 3 classes and by 0.19 for 4 classes, when compared to single endmember per class.     

Construction of lunar DEMs based on reflectance modelling

Existing lunar DEMs obtained based on laser altimetry or photogrammetric image analysis are characterised by high large-scale accuracies while their lateral resolution is strongly limited by noise or interpolation artifacts. In contrast, image-based photometric surface reconstruction approaches reveal small-scale surface detail but become inaccurate on large spatial scales. The framework proposed in this study therefore combines photometric image information of high lateral resolution and DEM data of comparably low lateral resolution in order to obtain DEMs of high lateral resolution which are also accurate on large spatial scales. Our first approach combines an extended photoclinometry scheme and a shape from shading based method. A novel variational surface reconstruction method further increases the lateral resolution of the DEM such that it reaches that of the underlying images. We employ the Hapke IMSA and AMSA reflectance models with two different formulations of the single-particle scattering function, such that the single-scattering albedo of the surface particles and optionally the asymmetry parameter of the single-particle scattering function can be estimated pixel-wise. As our DEM construction methods require co-registered images, an illumination-independent image registration scheme is developed. An evaluation of our framework based on synthetic image data yields an average elevation accuracy of the constructed DEMs of better than 20 m as long as the correct reflectance model is assumed. When comparing our DEMs to LOLA single track data, absolute elevation accuracies around 30 m are obtained for test regions that cover an elevation range of several thousands of metres. The proposed illumination-independent image registration method yields subpixel accuracy even in the presence of 3D perspective distortions. The pixel-wise reflectance parameters estimated simultaneously with the DEM reflect compositional contrasts between different surface units. Specifically, the detected variations of the parameter of the single-particle scattering function indicate small-scale variations of the regolith particle size, possibly as a result of differences in soil maturity.     

Sensor data for forest policy information

NASA-Ames Research Center has investigated the role and performance capabilities of the Landsat Multi-Spectral Scanner (MSS) for forest policy analysis for the past four years in cooperation with the California Department of Forestry. A thorough series of studies, from a statewide land cover map to smaller, highly detailed studies including collateral data, have been conducted with a view to comprehensive forest policy needs. The strengths and limitations of MSS data have been evaluated. Some observations about the information needed from new satellite sensors such as the Thematic Mapper are discussed against this background.     

Monitoring of long-term land subsidence from 2003 to 2017 in coastal area of Semarang,Indonesia by SBAS DInSAR analyses using Envisat-ASAR,ALOS-PALSAR,and Sentinel-1A SAR data

Land subsidence is a critical issue that large cities located in coastal areas, such as Semarang, Indonesia, must address. The monitoring of land subsidence is vital for predicting and mitigating the disasters that such subsidence may cause. Therefore, an economical and effective monitoring method, which can continuously provide accurate measurements over extensive areas, is highly required. Differential Interferometry Synthetic Aperture Radar (DInSAR) has the potential to be a powerful technique that can meet the above demands. Actually, DInSAR has been applied to monitor the subsidence in Semarang, but it was for a limited period before 2012.In order to clarify the transition of the long-term subsidence behavior in Semarang, the Small Baseline Subset (SBAS) method, which is one type of time-series DInSAR, is employed in this research. The sets of data of Envisat-ASAR (2003–2007), ALOS-PALSAR (2007–2011), and Sentinel-1A (2015–2017) are employed for the analyses. Then, the validity of the SBAS results is discussed from the viewpoints of both spatial distribution and temporal transition using GPS displacement measurement results and the geological conditions of the ground.On the other hand, as the lifespan of SAR satellites is commonly designed to be around 5–7?years, an appropriate method, which can connect the subsidence provided independently by the unlinked time-series data sets of the three different SAR satellite data, is required. This study uses the Hyperbolic Method (HM) to connect the above unlinked SBAS results. The HM is often used to fit the monitored subsidence in practice as a geotechnical engineering tool. Using this method, 14?years of the temporal behavior of the subsidence in Semarang is evaluated.It is found that the transition of the subsidence is different depending on the location, and that the subsidence rate is still increasing in the north and northeast parts of the coastal area. This study shows that SBAS DInSAR can be a useful tool for long-term continuous subsidence monitoring.