This PDF file contains the front matter associated with SPIE Proceedings Volume 10788, including the Title Page, Copyright information, Table of Contents, Author and Conference Committee lists.

Synthetic Aperture Radar (SAR) is an active microwave imaging radar to obtain target image. Due to the character of robust performance under all-weather conditions and features of reflection of radio waves from various surfaces, SAR is widely used in military and civilian fields. In contrast to optical images, due to the physical principle of operation of the radar, the multiplicative noise is present in the radar images. This noise, also called speckle, significantly complicates the classification of objects in the image. The object orientation normalization is applied in addition to filtration using traditional classifiers such as SVM. Comparative analysis of traditional classifiers with different filters and methods of normalizing the orientation of the object has been formed previously. The attempt to use neural networks in various areas, where traditional methods dominated, it is a high research trend now. Because of this, various architectures of neural networks have been proposed to enhance the efficiency on synthetic aperture radar automatic target recognition (SAR-ATR) and obtained state-of-art results on targets classification in many articles. Most of the results were obtained using a widely used Moving and Stationary Target Acquisition and Recognition (MSTAR) database. Therefore, MSTAR database is used in this paper for easy comparison of the results obtained. In this paper, we compared the effects of various methods for preprocessing radar images for traditional methods such as SVM, decision trees and methods based on neural networks.

Multi-temporal Synthetic Aperture Radar Interferometry (MTInSAR) techniques allow detecting and monitoring millimetric displacements occurring on selected targets exhibiting coherent radar backscattering properties. Because of the bandwidth limitation of Synthetic Aperture Radar images, pulse responses are significantly affected by sidelobes that should be identified and masked out before the selection of coherent targets candidates. In fact, sidelobes often exhibit a coherent backscattering behavior and can be hence erroneously identified as persistent scatterers (PS). These artifacts can be also clearly visible in the images, in case of strong scatterers embedded in a surrounding weak clutter environment, such as the echoes generated by ships or buildings. The actual large amount of Geospatial Big Data fosters the implementation of automatic tools for sidelobe detection and cancelation. A number of algorithms have been proposed in the scientific literature for sidelobe reduction in Single-Look-Complex images, ranging from apodization methods to target extraction techniques. In this study we deal in particular with the Spatially Variant Apodization (SVA), a nonlinear filter based on cosine-on-pedestal weighting functions. This approach is attractive because it is able – in theory – to achieve a total sidelobe cancelation, without degrading the original image resolution, and for its computational efficiency. However, as shown in recent works, SVA suffers from several drawbacks: (i) it modifies the statistics of speckle-dominated areas, (ii) a negative bias is introduced in the reflectivity images over homogeneous areas, (iii) the amplitude and phase of the original images could be distorted by the SVA filtering. In particular, it has been demonstrated that SVA filtering compromise the quality of MTInSAR outcomes. Therefore, recent works propose the use of SVA for just identifying and masking out the radar coordinates of the detected side-lobes in the PS candidates position map. In this work we apply SVA for proper coherent target selection in COSMO-SkyMed STRIPMAP co-polarized interferometric data takes, acquired in onshore and offshore environments. In order to highlight both advantages and limitations of this technique, we analyze the performances of SVA approaches in terms of computational efficiency and robustness to focusing artifacts (responsible of non-ideal pulse responses). In particular, we test the SVA filtering both in simulation and in complex real scenarios, where sidelobes of close strong scatterers are mixed and superimposed upon each other. We show the outcomes of MTInSAR techniques before and after the application of SVA filtering. Acknowledgments The research is partly co-funded by the Italian Space Agency in the framework of the FAST4MAP project (“Fast & Advanced SAR Techniques for Monitoring & Alerting Processes” - ASI Contract n. 2015-020-R.0). CSK® Products© of ASI delivered by ASI under a license to use.

Synthetic aperture radar (SAR) data can be acquired all daytime in all kinds of weather conditions and higher spatial resolution, but lack in image quality and wavebands information. Integrated optical and SAR data for land cover classification in Hong Kong Wetland Park with its surroundings where optical image data is hard to acquire is adopted. The integration focuses on the speckle noise effect in SAR intensity data, compared the classification result over original, and despeckled SAR intensity data. The adopted despeckling method is based on the research of multilayer perceptron (MLP) towards SAR intensity data despeckling. The despeckling result is more sharpen and deducted certain noise while remaining image details. Using the despeckled results, the optical data is fused with SAR data for further land cover classification. The integrated data is more sensitive to waterbodies and give corrections towards optical data in water areas. The misinformation in optical data classification over certain wetland area has been modified. Therefore, SAR data fusion with optical data could improve the analysis of waterbodies land cover classification and interpretation.

High Mountain Asia provides water for more than a billion downstream users. Many catchments receive the majority of their yearly water budget in the form of snow - the vast majority of which is not monitored by sparse weather networks. We leverage passive microwave data from the SSMI series of satellites (SSMI, SSMI/S, 1987-2016), reprocessed to 3.125 km resolution, to examine trends in the volume and spatial distribution of snow-water equivalent (SWE) in the Indus Basin.

We find that the majority of the Indus has seen an increase in snow-water storage. There exists a strong elevation-trend relationship, where high-elevation zones have more positive SWE trends. Negative trends are confined to the Himalayan foreland and deeply-incised valleys which run into the Upper Indus. This implies a temperature-dependent cutoff below which precipitation increases are not translated into increased SWE. Earlier snowmelt or a higher percentage of liquid precipitation could both explain this cutoff.

Earlier work found a negative snow-water storage trend for the entire Indus catchment over the time period 1987-2009 (-4 × 10^-3 mm/yr). In this study based on an additional seven years of data, the average trend reverses to 1.4 × 10^-3. This implies that the decade since the mid-2000s was likely wetter, and positively impacted long-term SWE trends. This conclusion is supported by an analysis of snowmelt onset and end dates which found that while long-term trends are negative, more recent (since 2005) trends are positive (moving later in the year).

In this study, two different machine learning approaches, namely Artificial Neural Network (ANN) [1-2], and supported Vector Regressions (SVR) [3-4] have been implemented and tested for estimating the forest biomass (t/ha) from the ESA airborne SAR missions. The study was carried out in the framework of the BRIX exercise, aimed at intercomparing biomass retrieval algorithms for P-band full-polarimetric SAR sensors in view of the upcoming ESA BIOMASS mission (a P-band synthetic aperture polarimetric radar). Several strategies have been exploited, by developing “general” algorithms trained with data derived from the whole dataset and “specific” algorithms, trained with data derived from a single campaign, among Afrisar, Biosar and Tropisar. In all cases, the algorithms have been trained on a subset of the available data and validated on the remaining, obtaining correlation coefficients between R=0.82 and R= 0.94, with a RMSE between 15 t/ha and 70 t/ha, depending on the algorithm and on the dataset. In the case of ANN, the validation of the “general” and “specific” algorithms resulted in a correlation coefficient between R=0.78 and R=0.94, depending on the dataset, with a RMSE between 15 and 60 t/ha and negligible BIAS. The validation of the SVR algorithms resulted in a correlation coefficient between R=0.27 and R=0.90, depending on the dataset, with a corresponding RMSE between 25 and 77 t/ha and BIAS negligible in this case too. After validation, both ANN and SVR algorithms have been applied to the whole SAR images available for generating the corresponding biomass maps. References [1]. Santi E., S. Paloscia, S. Pettinato and G. Fontanelli. “Application of artificial neural networks for the soil moisture retrieval from active and passive microwave spaceborne sensors,” Int. J. Appl. Earth Observ. Geoinf., vol 48, pp. 61–73, Jun. 2016. [2]. Santi E., S. Paloscia, S. Pettinato, G. Fontanelli, M. Mura, C. Zolli, F. Maselli, M. Chiesi, L. Bottai, G. Chirici, 2017, The potential of multifrequency SAR images for estimating forest biomass in Mediterranean areas, Remote Sensing of Environment 200 (2017), pp. 63–73. [3]. Pasolli, L., Notarnicola, C., Bruzzone, L., Bertoldi, G., Della Chiesa, S., Hell, V., Niedrist, G., Tappeiner, U., Zebisch, M., Del Frate, F., Vaglio Laurin, G. 2011. Estimation of Soil Moisture in an Alpine Catchment with RADARSAT2 Images. Hindawi Publishing Corporation, Applied and Environmental Soil Science, Article ID 175473, 12 pages, doi:10.1155/2011/175473 [4]. Pasolli, L., Notarnicola, C., Bertoldi, G., Bruzzone, L., Remelgado R., Greifeneder, F., Niedrist, G., Della Chiesa, Tappeiner, U., Zebisch, M. 2015. Estimation of Soil Moisture in Mountain Areas Using SVR Technique Applied to Multiscale Active Radar Images at C-Band. IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, VOL. 8, NO. 1, JANUARY 2015, doi: 10.1109/JSTARS.2014.2378795.

A combined Position-Reflectometry Galileo receiver on board a low cost unmanned aerial platform (UAV) was developed in the framework of the Combined Positioning-Reflectometry Galileo Code Receiver for Forest Management project (COREGAL) with the main aims of evaluating the accurate positioning in areas where no GNSS ground infrastructures are available and of investigating the forest biomass mapping.

In this context, the Soil and Vegetation Reflection Simulator (SAVERS) was employed to simulate GNSS-R data in order to analyze the GNSS-R sensitivity to biomass, to create a Look Up Table (LUT) of GNSS-R reflectivity and to carry out a biomass retrieval test.

SAVERS simulated the mean power of the reflected GNSS-R signals by applying the integral bistatic equation taking into account the scattering contributions from soil and vegetation. It was shown that the GNSS-R sensitivity to forest biomass can be enhanced by filtering out the incoherent component of the signal, i.e. by using a long coherent integration time. A LUT was created by running SAVERS for a realistic range of the input parameters. Moreover, a GNSS-R synthetic dataset was generated for the case of a cork oak open forest in Portugal and a retrieval test was carried out. A neural network with two hidden layers was trained on the LUT and the forest biomass was estimated from the synthetic data. The biomass mean retrieval error was approximately 10 t/ha.

The use of monitoring systems to detect wall movements in surface mining has emerged in the last ten years as a standard work and safety practice. The development and optimization of remote monitoring technologies like radar interferometry have greatly contributed to the anticipation of slope movements and to the reduction of incidents and fatalities related to slope failures. The same has not happened in the underground mining industry, where deformation monitoring of openings is in most cases still demanded to contact sensors like extensometers, micro-seismic gauges, strain gauges, fiber optic systems, which can provide only point information. The new HYDRA-U (HYper Definition Radar - Underground) radar system, designed by IDS GeoRadar, is a submillimeter accuracy radar interferometer operating at MMW (MilliMeter Wave) frequency; the system is able to produce an updated radar map every 30 seconds providing both range and angular resolution by performing FMCW Arc SAR acquisitions. In this research work a HYDRA-U unit has been extensively tested to measure the monitoring performances of the system and assess the actual applicability of this radar solution as a real-time ground fall early warning system. First tests have been performed in laboratory conditions using corner reflectors to assess the performance in terms of range resolution and angular resolution. Moreover, a motorized corner reflector with a micrometric movement control has been used to estimate the accuracy of the displacement measurement. The last part of the test consists in a long-term deployment in underground mining environment. The system has been deployed in an underground mine tunnel. During the test, the HYDRA-U system clearly picked up a moving area; the analysis of the interferometric data shows an evident developing trend with marked accelerations and periods of relative steadiness, which can be correlated with the intensity of the local mining activity.

Spaceborne synthetic aperture radars (SARs) operating at L-band and above are nowadays a well-established tool for Earth remote sensing. In this respect, a new frontier of technological and scientific progress is represented by satellite Ka-Band SARs. Since approximately 2010, a number of European Space Agency (ESA) studies have been funded in this direction. The main identified benefit of Ka-band systems is that the short wavelength allows the implementation on a single platform of single-pass interferometry, both cross-track and along-track, with adequate interferometric sensitivity. Ka-band is also interesting due to the low penetration in media such as ice, snow, and vegetation. In principle, the 500 MHz allocation also enables high-resolution measurements. Atmospheric effects represent a severe limitation to Ka-Band SARs. Gases and water particles introduce attenuation and path delay also in clear-sky condition; raindrops also depolarization. Finally, atmospheric turbulence causes scintillation effects. Unfortunately, very few studies and experiments exist at Ka Band. With this general context, the project KaBandSARApp aims to consolidate a Ka-band SAR mission concept, linking user (product-level) observation requirements to mission requirements, and evaluating and highlighting the expected performances for a set of relevant applications. This purpose will be pursued through the development of an End-to-End (E2E) performance tool, where atmospheric effects have been simulated through a Forward Model (FM) of SAR response. This work describes the developed forward model in the general context of atmospheric effects on SAR retrieved signal. A case study relative to a quite common and light cloud (alto-stratus) will be presented and discussed.

The STEAM (SaTellite Earth observation for Atmospheric Modelling) project, funded by the European Space Agency, aims at investigating new areas of synergy between high-resolution numerical weather prediction (NWP) models and data from spaceborne remote sensing sensors. An example of synergy is the incorporation of high-resolution remote sensing data products in NWP models. The rationale is that NWP models are presently able to produce forecasts with a spatial resolution in the order of 1 km, but unreliable surface information or poor knowledge of the initial state of the atmosphere may imply an inaccurate simulation of the weather phenomena. It is expected that forecast inaccuracies could be reduced by ingesting high resolution Earth Observation derived products into models operated at cloud resolving grid spacing. In this context, the Copernicus Sentinel satellites represent an important source of data, because they provide a set of high-resolution observations of physical variables (e.g. soil moisture, land/sea surface temperature, wind speed, columnar water vapor) used NWP models runs. This paper presents the first results of the experiments carried out in the framework of the STEAM project, regarding the ingestion/assimilation of surface information derived from Sentinel data into a NWP model. The experiments concern a flood event occurred in Tuscany (Central Italy) in September 2017. Moreover, in view of the assimilation of water vapor maps obtained by applying the SAR Interferometry technique to Sentinel-1 data, the results of the assimilation of Zenith total delay data derived from global navigation satellite system (GNSS) are also presented.

Worldwide, about 2.1 PgC are released every year from biomass burning. Due to its importance for climate modelling, several products were developed to map burned areas (BA) at global levels. Most of these products are based on medium or low-resolution optical sensors which are rather insensitive to small size fires. Moreover, frequent cloud cover and smog may hinder BA detection using optical sensors. To mitigate such shortcomings, BA may be derived from high resolution radar backscatter time-series. This study analyses the results of a locally adaptive BA detection algorithm based on data acquired by the ESA’s C-band synthetic aperture radar (SAR) Sentinel-1 A and B satellites. Sentinel-1 time series were analysed to understand the backscatter coefficient variation over burned and unburned areas. In addition, the analysis was extended to areas where the detection algorithm was affected by commission and omission errors. The study was carried out at 17 sites globally distributed. The analysis revealed shortcomings of the proposed algorithm particularly over areas where fire does not significantly decrease the cross-polarized backscatter. Over such areas, the backscatter change in the co-polarized channel could provide additional information that may overcome the lack of pre to post fire dynamic range of the VH polarization. In fact, for more than 50% of the study sites the change in VV polarization was peaked over undetected burned areas (omission errors). Furthermore, the VV polarization may help reducing commission errors as similar backscatter changes over burned and unburned areas was observed over less tiles (47%) when compared to the VH polarization (70%).

This study focuses on testing the SAR coherence changes from Sentinel-1 data to detect burned areas and to compare the results with optical Sentinel-2 derived burned area product to be used as validation. Visible Infrared Imaging Radiometer Suite (VIIRS) data at 350 m resolution was used to identify active fires locations.

We focused on a sequence of wildfires that affected the Sila mountain area during the summer of the 2017. This area of the Calabria region (southern Italy) was interested by a range of fires for the second half of July and the whole month of August ([1], [2]) due also to an extremely dry and hot summer. We used a pair of optical images acquired from Sentinel- 2 satellites on 24 July 2017 (pre-events) and 23 August 2017 (post-events).

Firstly, we computed the Normalized Difference Vegetation Index (NDVI) for both images and calculated the difference between these two (dNDVI) at 10m resolution; the results put in evidence several areas characterized by vegetation reduction, with dNDVI values up to 0.3-0.4. Concerning the SAR data, we evaluated the coherence changes by exploiting two pairs of Sentinel-1 SAR data over the same area. Both pairs were acquired along descending orbit, respectively before (on July, 19th and 31st) and after (on September, 5th and 17th) the fires occurred in the Sila mountain area. The coherence was computed separately for the first (γ_pre) and the second pair (γ_post) and the difference γ_post - γ_pre was calculated. In this way, we evaluated the difference in coherence between September, i.e. post-fires, and July, i.e. pre-fires expecting a higher coherence after burning, due to the vegetation reduction. In several areas, the coherence seems to be consistent with the fire events showing increments up to 0.20-0.25. However, the increasing of coherence difference could also be due to other reasons such as the soil moisture variations in the proximity of lakes/rivers or the seasonal cultivation changes.

Further analysis integrating more information such as the SAR amplitude signal and the cross-polarized backscattering coefficient will be conducted in order to better evaluate and discriminate any contributions.

The main objective of current study is to investigate the potential of decomposition methods for monitoring crops and discrimination of other land cover targets using c-band hybrid polarimetric Risat-1 SAR images. There are two study areas namely Burdwan and Bharatpur in India chosen for analysing various existing decomposition methods in this paper. The Risat-1 hybrid polarimetric SAR Single look complex (SLC) data by ascending observation mode were utilized in our experiment and acquired in the month of December 22nd, 2014 and August 3rd, 2016 from parts of Burdwan and Bharatpur area respectively. The Stokes classical parameters G₀, G₁, G₂ and G₃ are derived from hybrid SAR images for further analysis. From these Stokes parameters, the relative phase, degree of polarization, orientation, ellipticity and polarization angle are calculated. Furthermore, four decomposition techniques namely m-δ, m-α, m-χ and modified m-χ are performed and expressed in the form of odd bounce, even bounce and volume components for monitoring crops and surrounding land cover targets in our study areas. The preliminary results have been observed from supervised classification on the basis of decomposition methods for identification of various crops, barren land, urban areas and waterbodies in the study sites. It has been shown that volume component among all decompositions is over estimated in comparison to odd and even bounce components. Risat-1 hybrid SAR data is found to be more suitable, convenient and cost-effective for discrimination of various land cover targets whereas cloud free optical data is a prime hindrance to the crop inventory.

The local magnitude (ML) scale 6.22 earthquake struck eastern Taiwan on 6 February 2018. The epicenter was on the coastline near Hualien and caused a maximum shaking intensity of 7 in Hualien City, which was the most severely affected area. The crushed buildings and main surface cracks are distributed along the Milun fault, which is a oblique-slip fault. The earthquake is the largest of a sequence of events that affected the area over a period of days. In order to identify the surface deformation from Milun fault to coastal mountain range of Taiwan, we processed C-band Sentinel-1 and L-band ALOS-2 interferometric pairs acquired across the earthquake date, from both ascending and descending orbits. The Generic Mapping Tools Synthetic Aperture Radar (GMTSAR), and SNAPHU tools were used. According to the interferometric line-of-sight (LOS) displacement maps, the northwestern Milun fault moved of about 40 cm in the descending geometry, and about 0-10 cm in the ascending one, while the southwestern part of the fault moved 15-20 cm in the ascending geometry and is stable in the descending one. By combining interferometric LOS displacements derived from both ascending and descending acquisition geometries, just E-W and vertical displacement components can be derived. However, GPS and field survey results show northwards movements ranging from 50 and 70 cm over the Milun tableland, which is located on the eastern side of Milun fault. In order to provide SAR-based displacement measurements along north-south direction, we adopted the pixel-off set technique. The offsets along the azimuth direction were derived by computing the maximum of the amplitude cross-correlation of two SAR images, by using estimation windows of 128 x 128 pixel size. The 3D displacement was then computed by combining through least-square method the interferometric LOS displacements from ascending and descending acquisition geometry, and azimuth displacement component derived though pixel offset. Results indicate that, in the northern part of Milun tableland the maximum of E-W displacement reaches 60 cm, and that the whole Milun tableland moves northwards (up to about 60 cm) and upwards (up to about 40 cm). Moreover, the western side of Milun fault shows westwards movement, while Hualien city is affected by subsidence. The correlation coefficients in three directions between GPS data and 3D displacement are 0.81 (E-W), 0.89 (N-S), and 0.91 (U-D).

The effects of the ionosphere on spaceborne synthetic aperture radar (SAR) systems have received attention since the development of ALOS PALSAR (L-band). One of them is the Faraday rotation (FR) due to the dispersive nature of ionosphere and the existence of Earth’s magnetic field. The FR error is obviously embedded in polarimetric data of PALSAR systems, which destroys the scattering matrix. Nevertheless, distorted echoes contain abundant ionospheric information, the ionospheric sounding based on the scattering matrix data can become possible if the mechanisms of ionospheric interference can be understood and accurately modeled. SAR systems are generally characterized by high spatial resolution, this powerful technique can detect kilometer-scale ionospheric information where such unprecedented spatial resolution was previously inaccessible (e.g., such resolution is 1 to 2 orders of magnitude higher than that obtained by GPS). In this paper, by using the ALOS PALSAR full-polarization data sets, we quantitatively evaluate the reliability and accuracy of retrieved TEC information. We have used the observation results of incoherent scattering radar (ISR) to verify the accuracy of our results, given that ISR is currently the most powerful ground means for ionospheric monitoring and the ideal means for ionospheric data verification. In our work, the AMISR data for the Alaskan region in the United States is selected. After screening the data, we have selected and compared three sets of SAR and ISR results obtained at the same observation time (universal time: 8/6/2010, 21:6:25; 3/19/2011, 7:32:50; 3/31/2011, 7:28:16) and place. Our results show that the deviation between the results of SAR and ISR is only 0.1-0.35 TECU accounting for different factors, such as system and geographical deviations. However, the accuracy of the most widely used GPS data can only up to 1-2 TECU. Both accuracy and resolution of ionospheric sounding using fullpolarization SAR are therefore superior to those of ionospheric sounding using GPS.

Landslide events occur annually induced by heavy rain or an earthquake in the world. Remote sensing technique is an effective for landslide mapping and monitoring. Synthetic aperture radar (SAR) has a great potential due to its all-weather day and night imaging capabilities. Therefore, the utilization of SAR data for rapid damage assessment is expected. In a previous study, we demonstrated that landslide detection derived from correlation coefficient using pre- and post-event COSMO-SkyMed HH single polarization data. On the other hand, fully-polarimetric SAR (PolSAR) data contain various information compared to single polarization SAR data. In this study, we demonstrated the applicability of polarimetric analysis from SAR images for detection of the landslide area. The 2016 Kumamoto earthquake in Japan caused landslide damage in Kumamoto prefecture, Japan. Rapid damage assessment after natural disasters is crucial to fast crisis response. Three ALOS-2/PALSAR-2 polarimetric data acquired on 3 December, 2015, 21 April, 2016 and 5 May, 2016 were used. Entropy/α angle/anisotropy were calculated from each PolSAR data. Yamaguchi four-component decomposition analysis of PolSAR was also conducted. The polarimetric coherence (γ_{HH – V}) was calculated from the correlation between HH and VV polarization from pre- and post-event PolSAR data. In this study, we deal with the detection of landslides using pre- and post-event polarimetric parameters from PolSAR data. The largest landslide area in Minami-Aso village was clearly showed the surface scattering because the landslide induced by earthquake removed forested vegetation on the ground surface. The extent of the landslides was detected using pre- and post-event PolSAR data with Random forest (RF) classifier. It is clarified that pre- and post-event alpha angle, entropy and γ_{HH – V} from PolSAR data with the RF classifier is effective for landslide detection.

Terrestrial Oil spills pose serious health risk to human lives from indirect ingestion of contaminated drinking water, aquatic products and on contaminated farmland produce. Spill impact mapping is essential in establishing the extent of damage by oil spills to inland vegetation. It also helps ascertaining the type of environmental remediation required for a particular landcover. Various detection methods have been reported in the literature's ranging from radiative transfer models, vegetation health indices, canopy water use efficiency and UAVSAR Polarimetric Backscatter Decomposition among several others. These approach in isolation cannot be effectively used in wide area mapping and discrimination of oil free from oil polluted landcover. Similarly, it is also necessary to develop transferable techniques that will leverage freely accessible space borne satellite data for consistent spill impact mapping in near real time. In this study, we integrated derivatives from the freely accessible sentinel 2 spectral reflectance and vegetation health indices, with sentinel 1 retrieved backscatter, alpha (α) and entropy (Η) scatter to examine and map oil spill impacted areas within terrestrial habitat. The utility of machine learning random forest was explored to assess classification accuracies. Initial results suggest that the Red Edge and SWIR Bands of Sentinel 2 are vital discriminators of oil polluted and oil free landcover.

The symplectic multi-resolution time-domain(SMTD) scheme with cylindrical grids is developed for electromagnetic simulation. Based on Daubechies' scaling functions and sympletic propagation technique, equations are implemented. In cylindrical coordinates, some singular areas are analyzing and solving. In different stencil sizes, the stability and dispersion property is investigated and it turns out that SMTD is better than FDTD for stability restriction and medium discretization. Perfectly matched layer(PML) absorbing boundary conditions (ABC's) are derived for cylindrical SMTD grids. The numerical simulations validate that SMTD and PML are effective, so this paper may provide a new solution for electromagnetic simulation of cylindrical objects.

Fire is considered an essential climate variable (ECV) by the Global Climate Observing System (GCOS). Remote sensing is often used to detect the burned areas and subsequently estimate CO2 emissions from wildfires. Most burned area mapping approaches are based on optical images. However, cloud cover independent radar datasets are increasingly employed for burned area detection. This study presents results related to a burned area detection algorithm based on temporal series of backscatter images. The algorithm was developed in the frame of the ESA’s Fire cci project. During the development, it was observed that large temporal differences exist between the fire date and the date when significant changes of the C-band backscatter coefficient occurred. In this contribution we analysed this temporal decorrelation. The aim was to quantify it and try to understand the reasons behind it and thus improve the C-band SAR based mapping algorithm.

Prescribed burning is a technique applied to control fire risk, and it has been used in the forests of Western Australia since the 1960s. Synthetic Aperture Radar (SAR) data are sensitive to vegetation structural changes and may detect changes in understory vegetation particularly when the upper forest canopy remains largely unaffected, as it is often the case for prescribed burns. In this study, the ability of the Radar Burn Ratio (RBR), a SAR index that measures the degree of change between pre- and post-event radar backscatter, to appraise fire efficiency in prescribed burns was assessed. Data acquired by the L-band PALSAR-2 sensor, onboard the ALOS-2 satellite, were analysed to study the relationship between radar backscatter coefficient and prescribed burns carried out in eucalypt forests in Western Australia. A previously proposed framework was adapted to evaluate burn impacts in different environmental conditions (dry, wet and mean) using HV and HH polarizations as well as the RFDI (Radar Forest Degradation Index). A linear relationship between RBR and fire severity was found for HV polarization and RFDI confirming previous results observed for wildfires. RBR_HV in dry environmental conditions yielded the most accurate estimates of fire impact (OA = 77.8 %; k = 0.67). RBR_HH showed higher ability to differentiate between severity classes in wet conditions while the RBR_RFDI showed an intermediate behavior. HH polarization and RFDI showed special ability for burn area detection in wet conditions. The results showed that it is possible to estimate the impact of prescribed burns using SAR data. As such, SAR data could contribute and assess the effectiveness of fire policy in Western Australia and similar environments.

Miniaturized microwave radiometers deployed on nanosatellites in Low Earth Orbit are now demonstrating cost-effective weather monitoring capability, with increased temporal and spatial resolution compared to larger weather satellites. MicroMAS-2A is a 3U CubeSat that launched on January 11, 2018 with a 1U 10-channel passive microwave radiometer with channels near 90, 118, 183, and 206 GHz for moisture and temperature profiling and precipitation imaging. The Time-Resolved Observations of Precipitation structure and storm Intensity with a Constellation of Smallsats (TROPICS) mission is projected to launch in 2020, and its 1U 12-channel passive microwave radiometer is based on the current CubeSat mission MicroMAS-2A. TROPICS will provide rapid-refresh measurements over the tropics and measure environmental and inner-core conditions for tropical cyclones. In order to effectively use small satellites such as MicroMAS-2A and TROPICS as a weather monitoring platform, calibration must ensure consistency with state of the art measurements, such as the Advanced Technology Microwave Sounder (ATMS), which has a noise equivalent delta temperature (NEDT) at 300 K of 0.5 - 3.0 K. In this work, we present initial analysis from the MicroMAS-2A radiometric bias validation, which compares MicroMAS-2A measured brightness temperatures to simulated brightness temperatures calculated by the Community Radiative Transfer Model (CRTM) using input from GPS radio occultation (GPSRO), radiosonde, and numerical weather prediction (NWP) atmospheric profiles. We also model solar and lunar intrusions for TROPICS, and show that the frequency of intrusions with a scanning payload allows for the novel opportunity of using the solar and lunar intrusions as a calibration source.