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Interferometric synthetic aperture radar (InSAR) technology has been widely applied to measure Earth surface motions related to natural and anthropogenic crustal deformation phenomena. With the widespread uptake of data captured by the European Space Agency’s Sentinel-1 mission and other recently launched or planned space-borne SAR missions, the usage of the InSAR technique to detect and monitor Earth surface displacements will increase even more in the coming years. However, InSAR can only measure a one-dimensional motion along the radar line of sight (LOS), which makes interpretation and communication of InSAR measurements challenging, and can add ambiguity to the modelling process. Within this paper, we investigate the implications of the InSAR LOS geometry using simulated and observed deformation phenomena and describe a methodology for multi-geometry data fusion of LOS InSAR measurements from many viewing geometries. We find that projecting LOS measurements to the vertical direction using the incidence angle of the satellite sensor (and implicitly assuming no horizontal motions are present) may result in large errors depending on the magnitude of horizontal motion and on the steepness of the incidence angle. We quantify these errors as the maximum expected error from simulated LOS observations based on a Mogi deformation model. However, we recommend to use LOS observations from several image geometries wherever data are available, in order to solve for vertical and E–W oriented horizontal motion. For an anthropogenic deformation phenomenon observed in seven independent InSAR analyses of Envisat SAR data from the Sydney region, Australia, we find that the strong horizontal motion present could lead to misinterpretation of the actual motion direction when projecting LOS measurements to vertical (uplift instead of subsidence). In this example, the difference between multi-geometry data fusion and vertical projection of LOS measurements (at an incidence angle of 33.8°) reach up to 67% of the maximum vertical displacement rate. Furthermore, the position of maximum vertical motion is displaced horizontally by several hundred metres when the LOS measurements are projected.

The applicability of optical and synthetic aperture radar (SAR) data for land cover classification to support REDD+ (Reducing Emissions from Deforestation and Forest Degradation) MRV (measuring, reporting and verification) services was tested on a tropical to sub-tropical test site. The 100 km by 100 km test site was situated in the State of Chiapas in Mexico. Land cover classifications were computed using RapidEye and Landsat TM optical satellite images and ALOS PALSAR L-band and Envisat ASAR C-band images. Identical sample plot data from Kompsat-2 imagery of one-metre spatial resolution were used for the accuracy assessment. The overall accuracy for forest and non-forest classification varied between 95% for the RapidEye classification and 74% for the Envisat ASAR classification. For more detailed land cover classification, the accuracies varied between 89% and 70%, respectively. A combination of Landsat TM and ALOS PALSAR data sets provided only 1% improvement in the overall accuracy. The biases were small in most classifications, varying from practically zero for the Landsat TM based classification to a 7% overestimation of forest area in the Envisat ASAR classification. Considering the pros and cons of the data types, we recommend optical data of 10 m spatial resolution as the primary data source for REDD MRV purposes. The results with L-band SAR data were nearly as accurate as the optical data but considering the present maturity of the imaging systems and image analysis methods, the L-band SAR is recommended as a secondary data source. The C-band SAR clearly has poorer potential than the L-band but it is applicable in stratification for a statistical sampling when other image types are unavailable.

Terrain surface displacements at a site can be induced by more than one geological process. In this work, we use advanced differential interferometry SAR (A-DInSAR) to measure ground deformation in Arcos de la Frontera (SW Spain), where severe damages related to landslide activity and subsidence have occurred in recent years. The damages are concentrated in two residential neighborhoods constructed between 2001 and 2006. One of the neighborhoods, called La Verbena, is located at the head of an active retrogressive landslide that has an extension of around 0.17 × 106 m2 and developed in weathered clayey soils. Landslide motion has caused building deterioration since they were constructed. After a heavy rainfall period in winter 2009–2010, the movement was accelerated, worsening the situation. The other neighborhood, Pueblos Blancos, was built over a poorly compacted artificial filling undergoing a spatially variable consolidation process which has also led to severe damage to buildings. For both cases, a short set of C-band data from the “ENVISAT 2010+” project has been used to monitor surface displacement for the period spanning April 2011–January 2012. In this work we characterize the mechanism of both ground deformation processes using in situ and remote sensing techniques along with a detailed geological interpretation and urban damage distribution.

Arctic sea ice variations are sensitive to Arctic environmental changes and global changes. Freeboard and thickness are two important parameters in sea ice change research. Satellite altimetry can provide long-time and large-scale sea ice monitoring. We estimated the Arctic sea ice freeboard and its variations for the period from 2002 to 2012 from Envisat satellite altimetry data. To remove geoid undulations, we reprocessed the Envisat data using a newly developed mean sea surface (MSS) model, named DTU18. Residuals in the static geoid were removed by using the moving average technique. We then determined the local sea surface height and sea ice freeboard from the Envisat elevation profiles. We validated our freeboard estimates using two radar freeboard products from the European Space Agency (ESA) Climate Change Initiative (CCI) and the Alfred Wegener Institute (AWI), as well as the Operation IceBridge (OIB) sea ice freeboard product. The overall differences between our estimates and the CCI and AWI data were 0.11 ± 0.14 m and 0.12 ± 0.14 m, respectively. Our estimates show good agreement with the three products for areas of freeboard larger than 0.2 m and smaller than 0.3 m. For areas of freeboard larger than 0.3 m, our estimates correlate better with OIB freeboard than with CCI and AWI. The variations in the Arctic sea ice thickness are discussed. The ice freeboard reached its minimum in 2008 during the research period. Sharp decreases were found in the winters of 2005 and 2007.

When using microwave remote sensing for land use/land cover (LULC) classifications, there are a wide variety of imaging parameters to choose from, such as wavelength, imaging mode, incidence angle, spatial resolution, and coverage. There is still a need for further study of the combination, comparison, and quantification of the potential of multiple diverse radar images for LULC classifications. Our study site, the Qixing farm in Heilongjiang province, China, is especially suitable to demonstrate this. As in most rice growing regions, there is a high cloud cover during the growing season, making LULC from optical images unreliable. From the study year 2009, we obtained nine TerraSAR-X, two Radarsat-2, one Envisat-ASAR, and an optical FORMOSAT-2 image, which is mainly used for comparison, but also for a combination. To evaluate the potential of the input images and derive LULC with the highest possible precision, two classifiers were used: the well-established Maximum Likelihood classifier, which was optimized to find those input bands, yielding the highest precision, and the random forest classifier. The resulting highly accurate LULC-maps for the whole farm with a spatial resolution as high as 8 m demonstrate the beneficial use of a combination of x- and c-band microwave data, the potential of multitemporal very high resolution multi-polarization TerraSAR-X data, and the profitable integration and comparison of microwave and optical remote sensing images for LULC classifications.

Satellite remote sensing is a valuable tool for monitoring flooding. Microwave sensors are especially appropriate instruments, as they allow the differentiation of inundated from non-inundated areas, regardless of levels of solar illumination or frequency of cloud cover in regions experiencing substantial rainy seasons. In the current study we present the longest synthetic aperture radar-based time series of flood and inundation information derived for the Mekong Delta that has been analyzed for this region so far. We employed overall 60 Envisat ASAR Wide Swath Mode data sets at a spatial resolution of 150 meters acquired during the years 2007–2011 to facilitate a thorough understanding of the flood regime in the Mekong Delta. The Mekong Delta in southern Vietnam comprises 13 provinces and is home to 18 million inhabitants. Extreme dry seasons from late December to May and wet seasons from June to December characterize people’s rural life. In this study, we show which areas of the delta are frequently affected by floods and which regions remain dry all year round. Furthermore, we present which areas are flooded at which frequency and elucidate the patterns of flood progression over the course of the rainy season. In this context, we also examine the impact of dykes on floodwater emergence and assess the relationship between retrieved flood occurrence patterns and land use. In addition, the advantages and shortcomings of ENVISAT ASAR-WSM based flood mapping are discussed. The results contribute to a comprehensive understanding of Mekong Delta flood dynamics in an environment where the flow regime is influenced by the Mekong River, overland water-flow, anthropogenic floodwater control, as well as the tides.

In the absence of systematic structural monitoring to support adequate maintenance standards, many existing infrastructures may reach unacceptable quality levels during their life cycle, resulting in significant damage and even potential failure. The metropolitan area of the Gulf of Salerno (Italy), served by a complex multimodal transport network connecting the port area to the roads and railways surrounding the urban area, represents an important industrial and commercial hub at the local and international scale. This particular scenario, developed in a complex morphological and geological context, has led to the interference and overlapping of the transport network (highway, railway, main and secondary roads) that run through the piedmont area north of the port. Given the relevance of the area, our research aims to highlight the capabilities of the persistent scatterer interferometry (PSI) technique, belonging to the group of differential interferometric synthetic aperture radar (SAR), to extract space–temporal series of displacements on ground points or artifacts with millimeter accuracy useful to understand ongoing deformation processes. By using archived data from the European Space Agency missions, i.e., ERS1/2 (European remote-sensing satellite) and ENVISAT (environmental satellite), and the most recent data from COSMO-SkyMed constellations, it was possible to collect a 28-year dataset that was used to spatially analyze displacement patterns at a site-specific scale to check the stability of viaducts and embankments, and on a larger scale to understand the activity of the surrounding slopes. Despite the different resolution and subsequently the ground density, the analysis of the different datasets showed a spatiotemporal consistency in the displacement patterns that concerned two subareas showing significant annual velocity trends, one northeast of the city and the second in the port area. The analysis presented in this paper highlights how a complex geologic area, combining slope movements and various fault systems, could be a major concern for the stability of the overlying infrastructure and also the role that a PSI analysis can play in remotely monitoring their behavior over long periods of time.

Cyanobacterial blooms are widespread phenomena, expected to increase in frequency, magnitude, and duration due to a changing climate and increasing temperatures. We looked at remote sensing (optical, microwave, thermal) possibilities to enhance our understanding of bloom dynamics and its parameters, retrievable from satellite data in the shallow, eutrophic, transboundary lake Peipsi. Sentinel-3/OLCI (Ocean_and_Land_Colour_Instrument) and ENVISAT/MERIS (Medium_Resolution_Imaging_Spectrometer) data were used for Chlorophyll a (Chl a) detection, from which bloom parameters were retrieved. Pixels were categorized as “bloom” when Chl a concentration exceeded the long-term average by at least 5%. Maximal bloom coverage was about 100% in all parts of the lake, but on average, the blooms covered 96.5% of Lämmijärv, 85.0% of Peipsi sensu stricto ( s.s .), and 84.5% of Lake Pihkva. On average, the blooms lasted 101 days in Peipsi s.s . , 78 days in Lämmijärv, and 69 days in Lake Pihkva. Higher water levels resulted in significantly shorter blooms in Lämmijärv and Lake Pihkva. Traditional microscopy gave an overview of the main bloom-formers and their changes over a 20-years period. The importance of Gloeotrichia echinulata P.G.Richter has decreased in the last 10 years in Peipsi s.s and the importance of Aphanizomenon has increased in the entire lake.

We present vertically resolved thermospheric temperatures and NO abundances in terms of volume mixing ratio retrieved simultaneously from spectrally resolved 5.3 μm emissions recorded by the Michelson Interferometer for Passive Atmospheric Spectroscopy (MIPAS) in its upper atmospheric observation mode during 2005–2009. These measurements are unique since they represent the first global observations of temperature and NO for both day and night conditions taken from space. A retrieval scheme has been developed which accounts for vibrational, rotational and spin‐orbit non‐LTE distributions of NO. Retrieved polar temperature and NO profiles have a vertical resolution of 5–10 km for high Ap values, and degrade to 10–20 km for low Ap conditions. Though retrieved NO abundances depend strongly on the atomic oxygen profile used in the non‐LTE modeling, observations can be compared to model results in a consistent manner by applying a simple correction. Apart from this, total retrieval errors are dominated by instrumental noise. The typical single measurement precision of temperature and NO abundances are 5–40 K and 10–30%, respectively, for high Ap values, increasing to 30–70 K for Tk and 20–50% for NO VMR for low Ap conditions. Temperature and NO profiles observed under auroral conditions are rather insensitive to smoothing errors related to the mapping of a priori profile shapes. However, for extra‐polar and low Ap conditions, a potential systematic bias in the retrieved nighttime temperature and NO profiles related to smoothing errors has been identified from a comparison to Thermosphere Ionosphere Mesosphere Electrodynamics General Circulation Model (TIME‐GCM) simulations. We have constructed a solar minimum monthly climatology of thermospheric temperature and NO from MIPAS observations taken during 2008–2009. MIPAS temperature distributions agree well, on average, with the Mass Spectrometer and Incoherent Scatter radar model (NRLMSISE‐00), but some systematic differences exist. MIPAS temperatures are generally colder than NRLMSISE‐00 in the polar middle thermosphere (mainly in the summer polar region) by up to 40 K; and are warmer than NRLMSISE‐00 in the lower thermosphere around 120–125 km by 10–40 K. Thermospheric NO daytime distributions agree well with the Nitric Oxide Empirical Model (NOEM), based on Student Nitric Oxide Explorer (SNOE) observations. A comparison of MIPAS NO number density with the previous climatology for the declining phases of the solar cycle based on HALOE and SME data shows that MIPAS is generally larger with values ranging from 10 to 40%, except in the auroral region and at the equatorial latitudes above 130 km where the MIPAS/HALOE+SME ratio varies from 1.6 to 2. Day‐night differences in MIPAS NO show daytime enhancements of up to 140% in the tropical and midlatitudes middle thermosphere. In the lower thermosphere, the diurnal amplitude is smaller and NO concentrations are generally higher during night by about 10–30%, particularly in the auroral regions. Key Points The retrieval of NO and Tk from MIPAS upper atmospheric observation is described NO and Tk climatology is compared to models Diurnal variations of thermospheric NO are analyzed

Sea ice thickness (SIT), which is a crucial and sensitive indicator of climate change in the Antarctic, has a substantial impact on atmosphere-sea-ice-ocean interactions. Despite the slight thinning in SIT and reduction in sea ice volume (SIV) in the Antarctic in the recent decade, challenges remain in quantifying their changes, primarily because of the limited availability of high-quality long-term observational data. Therefore, it is crucial to accurately simulate Antarctic SIT and to assess the SIT simulation capability of state-of-the-art climate models. In this study, we evaluated historical simulations of SIT by 51 climate models of the Coupled Model Intercomparison Project Phase 6 (CMIP6) using Envisat (ES) and CryoSat-2 (CS2) observations. Results revealed that most models can capture the seasonal cycles in SIV and that the CMIP6 multimodel mean (MMM) can reproduce the increasing and decreasing trends in the SIV anomaly based on ES and CS2 data, although the magnitudes of the trends in the SIV anomaly are underestimated. Additionally, the intermodel spread in simulations of SIT and SIV was found to be reduced (by 43%) from CMIP5 to CMIP6. Nevertheless, based on the CMIP6 MMM, substantial underestimations in SIV of 57.52% and 59.66% were found compared to those derived from ES and CS2 observations, respectively. The most notable underestimation in SIT was located in the sea ice deformation zone surrounding the northwestern Weddell Sea, coastal areas of the Bellingshausen and Amundsen seas, and the eastern Ross Sea. The substantial bias in the simulated SIT might result from deficiencies in simulating critical physical processes such as ocean heat transport, dynamic sea ice processes, and sea ice-ocean interactions. Therefore, increasing the model resolution and improving the representation of sea ice dynamics and the physical processes controlling sea ice-ocean interactions are essential for improving the accuracy of Antarctic sea ice simulation.