1) In the first year a journal paper was completed, which was initiated during Dragon 3 within the project OPAC with the title “Oil spill detection by imaging radars: challenges and pitfalls” by Werner Alpers (University of Hamburg, Ben Holt (JPL/NASA), and Kan Zeng (Ocean University of China). It has been submitted to “Remote Sensing of Environment” and is presently undergoing a second review.

2) The Envisat and Sentinel-1 archives have been screened extensively for radar signatures of upwelling in Chinese waters and a large number of synthetic aperture radar (SAR) images showing such signatures have been identified.

3) For several prominent cases SAR images showing radar signatures of upwelling have been compared with SST, Chl-a images and with sea surface wind field maps retrieved from Quikscat and ASCAT.

4) Upwelling events at the east coast of Hainan have been compared with model results obtained by the HAMSON model.

5) Upwelling induced by the typhoon Soudelor (July/August 2015), which was the strongest tropical cyclone of the 2015 Pacific typhoon season, has been studied, which gave rise to plankton bloom and an eddy-like Chl-a concentration pattern.

6) The Chinese Ph. D. student Zheen Zhang, who is supervised by Thomas Pohlmann of the University of Hamburg, will carry out in the next year simulations with the MITgcm model with the aim to show that internal waves can be generated by upwelling. The focus will be on the upwelling area at the east coast of Hainan.

7) No exchange of European and Chinese partners could be arranged in the first year of the project.

Typhoons have very strong “Wind-Pump” effects on marine ecosystem, via inducing upwelling and vertical mixing. This paper introduces our recent related studies using satellite remote sensing data.

1, Typhoon wind-pump effects on air-sea CO2 flux

In-situ oceanographic measurements were made before and after the passage of Typhoon Wutip in September 2013 over the northern South China Sea (SCS). The surface geostrophic circulation over this region estimated from satellite altimetry data features a large-size anti-cyclonic eddy, a small-size cyclonic eddy, and smaller-size eddies during this period. Significant typhoon-induced changes occurred in the partial pressure of CO₂ at the sea surface (pCO_2sea) during Wutip. Before the passage of Wutip, pCO_2sea was about 392.92±1.83, 390.31±0.50, and 393.04±4.31 μatm over the cyclonic eddy water, the anti-cyclonic eddy water, and areas outside two eddies, respectively. The entire study region showed a carbon source (1.31±0.46 mmol CO₂ m^-2 d^-1) before Wutip. In the cyclonic eddy water after Wutip, high sea surface salinity (SSS), low sea surface temperature (SST), and high pCO_2sea (413.05±7.56 μatm) made this area to be a carbon source (3.30±0.75 mmol CO₂ m^-2 d^-1). In the anti-cyclonic eddy water after Wutip, both the SSS and SST were lower, pCO_2sea was also lower (383.03±3.72 μatm), and this area became a carbon sink (-0.11±0.55 mmol CO₂ m^-2 d^-1), in comparison with the pre-typhoon conditions. The typhoon-induced air-sea CO₂ flux reached about 0.03 mmol CO₂ m^-2 d^-1. Noticeable spatial variations in pCO_2sea were affected mainly by the Wind-pimp Effects - typhoon-induced mixing/upwelling and vertical stratifications.

Our study suggests that the impact of the typhoon Wind-pimp on the local air-sea CO₂ flux is highly correlated with the oceanographic conditions during the typhoon.

2. Upwelling effecting Distribution characteristics of phytoplankton size structure in the western SCS in summer

Driven by the southwest monsoon, an offshore jet is usually formed in western South China Sea (SCS) and sandwiched by a cyclonic eddy in the north and an anti-cyclonic eddy in the south, which effects ecosystem of the region. Using in-situ and satellite data in September 2014, the present study analyze the joint impact of this jet with two eddies on phytoplankton size structure in this region. The data showed that picophytoplankton (0.2-2µm) dominated the surface, taking average 76.7% of total chlorophyll. The contribution of nanophytoplankton (2-20µm) and microphytoplankton（20-200µm）in jet area was respectively higher and had a positive relationship with total chlorophyll. Comparatively higher percentage of microphytoplankton appeared in anti-cyclonic eddy in surface (av.10.3%) than in cyclonic eddy (av.3.6%). The results suggest that physical processes significantly influence summertime surface phytoplankton size structure in western South China Sea. Both jet and eddies can effect phytoplankton size structure by increasing the contribution of microphytoplankton. Surface horizontal advection of phytoplankton by northeastward jet form the coastal upwelling area is the main source of microphytoplankton in open sea. The interactions of convergence and divergence in eddies with jet form a chlorophyll front and increase the microphytoplankton component. Upwelling in the center cyclonic eddy bring up nutrients which raises microphytoplankton component.

3．Mixed layer depth responses to tropical cyclones in the northeastern SCS

Utilizing the vertical profiles of temperature and salinity data obtained by Argo floats and multi-source satellite remote sensing data, including sea surface temperature (SST) and sea surface wind fields, combined with the National Centers for Environmental Prediction (NCEP) Ⅱ reanalysis data, we analyzed changes of mixed layer depth (MLD) in the northeastern South China Sea (SCS) in responses to tropical cyclones Kalmaegi (typhoon) and Fung-Wong (tropical storm), which passed the SCS in succession in mid and late September 2014. The results indicate that the maximum net heat flux (upward into the air) increased from 170 to 400 W·m–2 at the air-sea interface, caused the maximum SST cooling of 3℃ by the “wind pump” effect after Kalmaegi and Fung-Wong passed through. The “cold wake” induced by Kalmaegi lasted for more than 10 days thanks to the following tropical storm Fung-Wong, indicating the effect of superposition in SST cooling. MLD was deepened from 23 to 50 m in the “cold wake” one day after Kalmaegi passed by. MLD was deepened from 31 to 91 m eight hours after Fung-Wong passed by, due to the coastal upwelling induced by offshore Ekman transport driven by wind stress at the southwestern of Taiwan Island. After the tropical cyclones passed by, salinity profile in the mixed layer showed uniformity later than temperature profile, and recovered earlier than temperature profile, revealing the time lag in mixed layer responses. For the spatial variation response to the two tropical cyclones, the changes of SST and MLD were larger on the right-hand side of the tropical cyclones (along the moving directions of tropical cyclones) than on the left-hand side. The uneven deepening even shallowing in MLD in the cold wake may reveal that different depths of deep cold water uplifted by the vertical current switch between upwelling and downwelling in the Ekman layer due to the change of Ekman pumping velocity

Sea Surface Temperature (SST) in South China Sea Retrieved from Chinese Satellite FY-3B VIRR Data

Chuqun CHEN^(1)(2)*, Quanjun HE^(3)**, Shilin TANG ^(1)(2)***,Haibin YE⁽¹⁾

(1) State key Lab of Tropical Oceanography，South China Sea Institute of Oceanology, Chinese Academy of Sciences,164 West Xingang Road, Guangzhou, China, 510301.

(2) University of Chinese Academy of Sciences，19A Yuquan Road, Beijing, China,100049.

(3) The Guangdong Ecological meteorological Center, 312 Dongguanzhuang Road, Guangzhou, China, 510080.

*cqchen@scsio.ac.cn，**hequanjunsx@163.com；***sltang@scsio.ac.cn

Abstract

In the surface layer of the ocean, Sea Surface Temperature (SST) is the most important parameter, which is widely applied for studying water masses, air-sea interaction, marine ecosystem and environment, and other subjects. In decades, a great many satellites with thermal infrared sensors have been launched and huge thermal infrared remote sensing data were collected for detection of SST. With the continuous improvement on accuracy, the satellite remote sensing technique has become the dominant approach for SST detection.

In this report, the thermal infrared data collected by FY-3B were employed for retrieval of SST in the South China Sea. FY-3B is one of the second generation of Chinese meteorological satellite on polar orbit, it has VIRR (Visible Infrared Radiometer) sensor with 10 bands, of which, band 4 covers 10.3~11.3um and band 5 covers 11.5~12.5um, similar to NOAA/AVHRR.

The ship-measured SST dataset in 2011 and 2012 were collected and totally 20607 (of which 11419 in daytime and 9188 in nighttime) of the ship-measured SSTs were selected on consideration of the quality, the measurement time and the measurement location matching with cloudy-free Fy-3B data. Based on the well matched ship-measured SST and FY-3B VIRR data, a non-linear SST (NLSST) algorithm was developed and applied for retrieval of SST in the South China Sea. The monthly mean SST distribution image maps of South China Sea were integrated. The monthly mean SST image maps show that the maximum monthly mean SST occurs in June, although in July and August there is a stronger solar heating. It possibly due to the monsoon-induced mixing, which results in lower SST.

Keywords: FY-3B satellite, Visible Infrared Radiometer (VIRR), Sea Surface Temperature (SST), South China Sea.

利用中国风云卫星（FY-3B）数据反演南海海面温度

陈楚群^{（1）（2）（*）}，何全军^{（3）（**）}，唐世林^{（1）（2）（***）}，叶海彬^（1）

（1） 中科院南海所热带海洋环境国家重点实验室，广州市新港西路164号，510301

（2） 中国科学院大学，北京市玉泉路19号甲，100049.

（3）广东省生态气象中心，广州市东莞庄路312号，510080

（*） cqchen@scsio.ac.cn）；（**）hequanjunsx@163.com；(***)sltang@scsio.ac.cn

摘要

海面温度（SST）是上层海洋最重要参数，在水团研究、海气相互作用研究、海洋生态环境研究等众多领域具有非常广泛的应用。利用卫星热红外遥感数据反演海面温度已经成为探测海面温度的主要手段，大量的热红外卫星传感器用于探测地面（海面）温度。

本报告介绍利用中国第二代极轨气象卫星“ 风云三号气象卫星”的第二颗卫星（FY-3B）数据反演南海海面温度。FY-3B于2010年11月发射，其可见红外辐射计（Visible Infrared Radiometer-VIRR）具有与NOAA/AVHRR传感器类似10个波段，其中的热红外波段（4）和（5）的波段范围分别为10.3~11.3（μm）和11.5~12.5（μm）。

收集了2011-2012两年与FY-3B卫星数据准同步的南海及西太平洋海域的船舶站观测资料，经过对FY-3B数据与船舶实测数据的严格筛选，获得与FY-3B数据匹配的船舶测量温度有效数据共有20607组，其中白天数据数11419组，夜间数据9188组，利用这些匹配数据组建立了反演南海SST的非线性算法，反演了南海SST，并得到南海月平均SST分布图。从南海月平均SST分布图看，南海月平均SST最大值出现在6月，而不是太阳辐射强度更大的7月或8月，这可能与7月和8月南海受季风搅拌引起表层水温降低有关。

关键词：风云气象卫星三号，可见红外辐射计，海面温度，南海

Sentinel-1 A & B perform acquisitions over Taiwan island. To date, this data are not routinely processed up to Level-2 OCN product by Sentinel-1 ESA PDGS. This paper considers the complete archive of Sentinel-1 data acquired over Taiwan and proposes a new algorithm for wind inversion in coastal areas. It relies on a two steps process. The first one aims at extracting wind direction and filtering out non-wind related signatures in both co- and cross- polarized channels. The second one combines these information and the radar cross-section from the two channels to retrieve wind speed and direction at high resolution.

The complete Sentinel-1 SAR archive is processed with this new algorithm. Results are presented and will be made available to the public. The buoys network around Taiwan island is used as reference for the ocean surface wind. SAR wind speed and direction are compared to in-situ buoys and atmospheric model winds. The choice of the GMF to be used for wind inversion is discussed.

The occurrence and the location of the non-wind signature are also presented and tentatively related to other oceanic or meteorological phenomena such as rain or bloom.

During summer 2016, ESA set up SHOC (for Satellite Hurricane Observations Campaign) campaign dedicated to hurricane observations with Sentinel-1 SAR in both VV and VH polarizations acquired in wide swath modes. Among the 70 Sentinel-1 passes scheduled by ESA mission planning team, more than 20 observations over hurricane eyes were acquired and Tropical Cyclones (TC) were captured at different development stages. The sensitivity difference of VH and VV Normalized Radar Cross Section (NRCS) to the response of the ocean surface to tropical cyclones is analyzed. In particular, during Lester hurricane most intense phase (when the track files indicates wind speeds of 120 knots), the sensitivity of the VH-NRCS computed at 3-km resolution is found to be more than 3.5 times larger than in VV. In addition, taking opportunity of SAR high resolution, we also show that the decrease in resolution (up to 25 km) does not change dramatically the sensitivity difference between VV and VH polarizations. This clearly opens perspectives for MetOp-SG SCA, the next generation C-band scatterometer with co- and cross-polarization capability. VV and VH channels are combined to get ocean surface wind vectors from Sentinel-1 dual-polarized Level-1 products. SAR winds are compared at 40-km resolution against L-Band SMAP radiometer winds with co-locations less than 30 minutes. The wind speed obtained using both VH and VV polarization is found to be more consistent with SMAP than wind obtained in co-polarization for wind speeds larger than 25 m/s. Based on radiometer winds, a new GMF (MS1A) is proposed. It improves the consistency between 40-km SAR winds and SMAP winds. The method is applied to Megi and Lyonrock Typhoon and results presented in this paper.

This paper presents a new water and energy budget-based glacier mass balance model. Enthalpy, rather than temperature, is used in the energy balance equations to simplify the computation of the energy transfers through the water phase change and the movement of liquid water in the snow. A new parameterization for albedo estimation and state-of-the-art parameterization schemes for rainfall/snowfall type identification and surface turbulent heat flux calculations are implemented in the model. This model was driven with meteorological data and evaluated using mass balance and turbulent flux data collected during a field experiment implemented in the ablation zone of the Parlung No. 4 Glacier on the Southeast Tibetan Plateau during 2009 and 2015–2016. The evaluation shows that the model can reproduce the observed glacier ablation depth, surface albedo, surface temperature, sensible heat flux, and latent heat flux with high accuracy. Comparing with a traditional energy budget-based glacier mass balance model, this enthalpy-based model shows a superior capacity in simulation accuracy. Therefore, this model can reasonably simulate the energy budget and mass balance of glacier melting in this region and be used as a component of land surface models and hydrological models.

Most estimates of melt and surface sublimation rates for the glaciers of the Tibetan Plateau have been obtained at the point scale, and it is not entirely established how ablation components vary across the entire extent of a glacier in this environment. Energy balance models can provide accurate simulations of energy fluxes and ablation components, thus fostering understanding of the main processes at the glacier-atmosphere interface, but need a high number of meteorological and surface input variables that can be available at the point scale of Automatic Weather Stations but are difficult to extrapolate across the distributed domain of an entire glacier.

Here, we simulate the distributed energy and mass balance of Parlung No. 4 Glacier, for the ablation season 2016, using a distributed energy balance model. We use input data at one AWS and two novel methods, which combine in-situ and reanalysis data, to generate fields of near-surface air temperature and wind speed that are needed to force the model. We calculate the spatial distribution of energy fluxes and ablation rates over the glacier surface at a high spatial resolution (50 m), which allows the inclusion of small-scale processes, such as katabatic and valley winds, refreezing and topographic shading, and knowledge of the local variability of topographic parameters, such as sky-view factors and local slopes.

We also compare our model results with the simulations of a point-scale energy-balance model based on enthalpy calculations which includes advanced schemes for calculation of albedo and turbulent fluxes; and with large-scale, regional energy balance simulations driven by satellite input data. The model is validated with in-situ and satellite observations of ablation, surface temperature and turbulent fluxes.

Our main objectives are to: i) advance our understanding of glacier ablation in the Tibetan Plateau by providing one of the first spatially-distributed quantifications of energy fluxes and ablation rates on a glacier in this region, including understanding the role that surface sublimation plays; ii) explore the use of two relatively new methods to generate fields of air temperature and wind speed, which provide alternatives to solve some of the interactions between large-scale meteorological forcing and the atmospheric boundary layer over a mountain glacier during the summer period; and iii) understand the level of model complexity that is needed for accurate simulations of energy fluxes and ablation components at different spatial scales.

The project is focusing on monitoring water resources in the Red River basin using microwave remote sensing. The Red River, also known as the Yuan River in Chinese, is a trans-boundary river basin with its total area of 169,000km2 shared by Vietnam (51%), China (48%) and Laos (1%). The Red River basin has a tropical or subtropical climate, dominated by the southwest monsoon from May to September and the northeast monsoon from October to April. The monsoon results in massive flow volume fluctuations. Flooding is a significant problem during the rainy season, particularly in July and August. At the same time, large area of karst landform causes the river flow loss and water shortage. One of the greatest challenges for flood prediction and integrated water management in the Red River basin is a lack of information on reservoir management as a consequence it is not easy to estimate the water resources. Since it is a trans-boundary river, there are difficulties to manage the area as a whole, and the information might not be in time for flood and drought early warning.

Hydrological data on major rivers, lakes and wetlands can often be difficult to obtain due to a region's inaccessibility, sparse distribution of gauge stations or the slow dissemination of data. Remote sensing technologies can be used to overcome such shortcomings.

The main objective of this project is to develop the algorithms and synergies between different Microwave Remote Sensing sensors to be able to monitoring water resources in the Red River Basin. For that purpose, the water elevation information, precipitation, soil moisture and evapotranspiration by remote sensing will be integrated in a hydrological model to improve its accuracy.

In this presentation we will be showing the first results of the variables monitored during this first year.

The discomfort index (DI) is an important indicator that measures human heat sensation for different climatic conditions. Currently, the DI of a city is usually calculated using a few weather stations and hence does not accurately represent various thermal discomfort states of the city as a whole. This is a considerable drawback taken the importance of the index for assessing the quality of life within urban agglomerations and thus facilitating measures for smart urbanization. In this study a technique to produce fine-scale DI maps is proposed and applied accordingly. The technique is based on the combination of Sentinel-2 and Landsat 8 images with in-situ measured meteorological data. The DI map clearly reveals the spatial details of the DI in different locations of the city and thus supports focused interventions with the potential to support the operation of a city as smart.

Urban land surface temperature (LST) is an important parameter. However, due to the contradiction between the temporal and spatial resolution of thermal infrared remote sensing data, it’s difficult to obtain high temporal- spatial resolution LST data simultaneously. In order to solve this problem, this author considered multiple city characteristical parameters and used a simple model that can adjust the spatial distribution of scaling factors, combining with the SVM model to establish a new surface temperature spatial downscaling method which was suitable for city surface, named A-SVM.

In this paper, Beijing was chose as research area. The author divided the study into four steps. Firstly, correlation analysis and PCA (Principal Component Analysis) were used to select the parameters strongly related-to LST but independent respectively as the regression kernels (scaling factors) to do downscaling regression. The result shows that there are 6 factors were selected to do SVM regression, namely NDVI, UI, MNDWI, VAP, BAP and WAP. Screening of scale factors is the basis of SVM and A-SVM methods.

Secondly, three methods were used to verify the accuracy of the A-SVM model, which are direct resampling, TsHARP method and SVM model. The direct resampling method is a strategy that does not use any auxiliary data to directly resample a low-resolution image to a high spatial resolution. The TsHARP method is an improvement to the DisTrad quadratic model, using the negative correlation between NDVI and LST, and its basic assumption is that the linear relationship between LST and NDVI is scale invariant. SVM is a newer statistical learning theory proposed by Vapnik. Using the SVM model for low resolution to high resolution, replacing the low-resolution scale factors to high-resolution scale factors and further adding the model estimation error，high-resolution LST can be obtained.

There is a simple model to dynamically adjust the scale factor spatial distribution according to the Standard Deviation of scale factors from high resolution and low resolution LST. A-SVM model was built based on this method and SVM model. By considering the relationship between high-resolution and low-resolution images, the purpose of the model-building is to make the remote-sensing indices of the high spatial resolution image have the same spatial distribution as those of the low spatial resolution image, which will reduce the downscaling estimation error of different resolutions.

Finally, the author got the results of retrieved LST from four methods and the scatter diagram of estimated 120m LST from four methods against the LST received from TM. The 120m LST which was directly retrieved from TM thermal infrared band was selected as the real LST and verification data. Results shows that the improved method could obtain ideal downscaling results, whose RMSE was 1.82 K, R² was 0.85, and ERGAS index achieved 0.07, and it was superior to the direct resampling method, the classical method TsHARP and the SVM method whose scaling factors were unadjusted.

Through this research, this author got two important conclusions:

(1)To reflect the complex characteristics of urban surface, multiple related parameters with LST including NDVI, UI, MNDWI,VAP,BAP,WAP were chosen as the scaling factors based on the correlation and PCA analysis, which can partly show the change of urban LST and avoid redundancy phenomenon between each other.

(2) Based on the 6 kinds of scaling factors, a new adjusted-SVM spatial downscaling method for urban land surface (A-SVM) was developed combining the SVM model with a space distribution adjustment model, which could avoid the ideal assumptions that the relation between scaling factors and LST was invariant. The validation result shows A-SVM method can get the better prediction precision.

Urbanization affects considerably the thermal environment of cities and influence the spatial and temporal consumption of energy for heating and cooling. The increase of impervious surfaces alongside with the reduction of vegetated areas lead to increased air and surface temperatures. Remote sensing data is suitable for up-to-date urban land use mapping and for the assessment of the thermal environment of urban areas. In this study a statistical approach is developed on the basis of satellite data, in order to identify the areas where maximum and minimum temperature values are observed; the approach is tested for the city of Athens, Greece. The recognition of such areas is important as they reflect areas where immediate interventions are necessary to ameliorate the thermal environment, whereas the knowledge of their spatial distribution and temporal variations is needed for smart urbanization.

This abstract presents a detailed application of feature extraction methods on PolSAR records for built-up area identification. This work aims at providing a thorough description of the multivariate patterns insisting on roll-variant and roll-invariant quad-POLSAR features.

Indeed, the detection and classification of urban areas in the instantaneous field of view can take a great advantage from processing the whole amount of records and parameters obtained from POLSAR images. In fact, multivariate processing is used to deliver a thorough understanding of the relationships and regularities that can be retrieved over the selected areas for each target class. Hence, the rationale for employing both roll-variant and roll-invariant features is the intimate involvement of all the attributes acquired by POLSAR remote sensing with the anthropogenic extents distribution, which is used to provide a complete characterization of the built-up areas and of the hidden regularities lying within the records as well.

In order to provide such a multivariate analysis for feature extraction, two methods which aim at retrieving a solid recognition of significant patterns within the dataset have been taken into account. First, we employed a method named Hierarchical Binary Decision Tree (HBDT) [1], which combines different processing chains (made by a feature selection and a classification step) and automatically adapts to the spatial (and spectral) properties of the classes available in the scene. Typically, in homogeneous decision trees, at each node the same algorithm is used for separation between groups of classes. In our case, the most useful processing chain, composed by the most suitable feature set and the most efficient classifier, is selected per each node. This selection is performed by computing an intermediate accuracy assessment for each processing chain. Only the feature set (or its most noiseless subset) and classifier pair that produces the best result is selected and assigned to the node. The procedure is then repeated removing the already identified class/classes until all the nodes are identified.

Moreover, we considered a method based on mutual information maximization to explore the dataset in order to detect relevant patterns for built-up area extraction [2]. Specifically, the method that has been developed aims at providing a identifying affinity patterns, which better describe each class within the considered dataset. Good classification performances are obtained by selecting patterns fulfilling the Pareto optimality and by properly modeling a combination of the information provided by each pattern. Since the proposed approach is completely data driven and relies on information theory-based quantities, it is very flexible and totally independent from the statistics of the classes, and allows exploring datasets consisting of heterogeneous features.

Two datasets acquired by Radarsat-2 Quad-PolSAR sensor over the San Francisco area have been considered for testing. Specifically, they consist of 28 and 113 roll-invariant and roll-variant features, respectively. The aforementioned algorithms have been used to investigate the POLSAR records in order to obtain a precise characterization of the patterns that describe anthropogenic extents in the considered area. In order to accurately assess the detection and classification performance of the aforesaid methods, we analyzed the considered datasets by means of algorithms relying on a different approach. Specifically, we used methods based on ensemble learning [3] (such as random cluster ensemble and recursive feature elimination scheme) in order to explore the relationships among the data. Results show that the methods relying on the multiple feature pattern recognition are able to provide accurate extraction of built-up areas over both the considered datasets. Indeed, they are able to outperform existing algorithms while guaranteeing acceptable computational complexity costs, so that they represent a valid option for POLSAR feature extraction applied to identification of built-up areas.

Land subsidence induced by excessive groundwater withdrawal has caused serious social, geological, and environmental problems in Beijing. Rapid increases in population and economic development have aggravated the situation. Monitoring and prediction of ground settlement is important to mitigate these hazards. In this study, we combined persistent-scatterer interferometric synthetic aperture radar (PS-InSAR) with Grey system theory to predict the evolution of land subsidence in the Beijing plain. Land subsidence during 2003–2014 in the Beijing plain was determined based on 39 ENVISAT Advanced Synthetic Aperture Radar (ASAR) images and 27 RadarSat-2 images. Results were consistent with global positioning system (GPS), leveling measurements at the point level and TerraSAR-X subsidence maps at the regional level. It was demonstrated that the land surface in the Beijing plain is settling at an accelerating rate. The average deformation rate in the line-of-sight (LOS) was from −124 mm/year to 7 mm/year during 2003–2014; accumulative displacement was up to 1426 mm by the end of 2014. To predict future subsidence, the evolution of deformation was used to build a prediction model based on an improved Grey-Markov model (IGMM), which adapted the conventional GM(1,1) model by utilizing rolling mechanism and integrating a k-means clustering method in Markov-chain state interval partitioning. Evaluation of the IGMM at three representative points showed good accuracy of simulated subsidence values (root-mean-square error <3 mm). Simulated deformation during 2013 and 2014 agreed well with the observed deformation during each year based on PS-InSAR (R² = 0.94 and 0.91). Finally, InSAR measurements from 2003–2014 were used to predict subsidence in 2015–2016. It was calculated that the maximum cumulative deformation will reach 1717 mm by the end of 2016 in Beijing plain. The promising results indicate that this method provides an alternative to the conventional numerical and empirical models in order to predict short-term deformation when there is lack of detailed geological or hydraulic information.

Change detection of urban extents is now a very important topic for urban remote sensing mapping at regional and global scales. Indeed, since more and more single-date and global data sets are becoming available, change detection of urban areas is feasible. However, so far it ahs been focused on binary changes, i.e. changes that affect (either positively or negatively) the urban area extents, by considering new urbanized areas or zones given back to the natural environment.

This work aims at providing a few preliminary results of a more accurate analysis, where the density of built-up areas, and the change from low-rise to high-rise buildings (and vice versa) is also considered. The rationale for this analysis is that these situation correspond to change that mostly go unnoticed when urban extents only are considered. Still, these changes are extremely important to understand phenomena related to change in population density, leading often to overcrowding effects, and subsequently to risk management issues.

To find a sound methodology and extract this more complex changes, we explore the use of SAR, and specifically polarimetric SAR, data, under the assumption that the electromagnetic field backscattered from built-up area is affected by the geometric properties of the illuminated structures, and therefore it is different according to the type of built-up elements located in a specific geographical area. Accordingly, a segmentation of the SAR image is first performed at the first date of thr temporal sequence to be analyzed, and then multiple polarimetric parameters (in addition to the backscattered intensity) are considered, to understand which are the parameters that are more useful to analyze to track not only 2D, but also 3D changes and density changes in urban areas.

The test area for this work is the city of Nanjing, and the data sets that were used are obtained from Radarsat-2 fully polarimetric images. A complete analysis of segmentation using different object size, applied to different polarimetric decomposition parameters, and validate using both positive and negative 2D and 3D changes in urban areas have been performed. They show that it is possible to extract a richer analysis for urban changes than the simple detection of changes in urban extents in a bi-dimensional sense. However, a clear and quantitative description of the 3D change is still to be obtained, and the methodology needs to be improved to reach this goal.