Ground-based Research Articles

Research on Monitoring the Speed of Glacier Terminus Movement Based on the Time-Series Interferometry of a Ground-Based Radar System

The Tibetan Plateau (TP) is the largest glacier reserve outside the Antarctic and Arctic regions. Climate warming has affected the reserve of freshwater resources and led to frequent glacier disasters. However, due to its extreme environment of hypoxia and low pressure, it is extremely difficult to obtain data. Compared with other traditional monitoring methods such as makers and satellite remote sensing technology, Ground-Based (GB) radar systems have the advantages of convenient carrying and installation, sub-second level sampling, and sub-millimeter measurement accuracy. They can be used as an effective way to study the short-term rapid movement changes in glaciers. Based on a self-built GB radar system, monitoring experiments were conducted on two glacier termini on the TP. The movement speed of the Rongbuk glacier terminus on Mount Qomolangma was obtained through time-series interferometric measurement as 4.10 cm/day. When the altitude was about 5200 m, the glacier movement speed was 7.74 cm/day, indicating the spatial differences with altitude changes. And in another region, the movement speed of the Yangbulake glacier terminus on Mount Muztag Ata was 198.96 cm/day, indicating significant changes in glacier movement. The cross-validation of Sentinel-1 data during the same period proved the effectiveness of GB radar system interferometry in measuring glacier movement speed and also provided field validation data for remote sensing inversion.

Climatology and trends of cloudiness in a productive rice and vegetable region of South‐Eastern South America

AbstractClouds are a key element of the climate system. They play a crucial role in modulating solar radiative flux reaching the Earth's surface and influencing environmental conditions. In particular, total cloud cover (TCC) has both direct and indirect influence on agricultural production. Through dialogues with vegetable and rice producers of Northeastern Argentina (NEA), an important productive region in South‐Eastern South America, the key role of cloud cover on crop yields was brought to the forefront. In this study, we present the climatology and observed long‐term changes in TCC in NEA. The analyses are performed based on two independent datasets: TCC ground‐based (GB) observations and satellite‐based estimates from the International Satellite Cloud Climatology Project (ISCCP). The datasets cover a common period, from December 1983 to November 2016 (Satellite Period), while GB TCC observations extend further, from March 1961 to February 2021 (GB Period). To facilitate a more comprehensive examination of GB TCC, we introduce a novel Cloud Index, expressed in a familiar unit (%), which allows the study of its temporal variations. TCC exhibits a distinct annual cycle and substantial spatial variability over NEA, consistently evident in both satellite and GB datasets. Over the Satellite Period (GB Period), we observed a decrease in NEA's TCC in both datasets (GB dataset), with seasonal variations and spatial heterogeneity. We ensured the reliability of our results by comparing the two datasets, which showed similar temporal variability, although the ISCCP Cloud Amount values were larger than the GB TCC. The described TCC climatology and observed changes have considerable implications for NEA's agricultural production and provide a robust foundation for future research. This article generates robust methodological basis for the analysis of this complex variable, a necessary step to explore the forcing of climate variability that modulates cloudiness in future research, and that could facilitate future studies in other regions of South America and the world.

Innovative data clustering method improves drought prediction in heterogeneous landscapes using GEE-derived remote sensing indices

Drought is a complex natural disaster characterized by unique features influenced by environmental factors, particularly at a regional scale. Remote Sensing (RS) indices have proven to be valuable in assessing drought conditions. In this study, we utilized the five most commonly used RS indices: Normalized Difference Vegetation Index (NDVI), Deviation/Anomaly of NDVI (NDVI-Dev/ANDVI), Temperature Condition Index (TCI), Vegetation Condition Index (VCI), and 2-band Enhanced Vegetation Index (EVI2). These indices were extracted over a 30-year period (1988–2017) using Google Earth Engine (GEE) for modeling the Standardized Precipitation Index (SPI) with various time-spans (1, 3, 6, 9, 12, and 24 months) as the Ground-Based (GB) drought indices. The models were trained from 1988 to 2014 and tested on data from 2015 to 2017. To enhance RS-based drought modeling over the complex and diverse study area of Iran, we proposed two different data clustering methods based on distinct environmental factors – climatic condition and land-cover type – and compared the results. The first method employed an enhanced-climate-based approach to improve upon the standard-climate-based method. This approach considered variations in the environment caused by seasonal changes in different climatic zones, which had received less attention in previous studies. Unique Support Vector Machine (SVM) models were trained using various clusters of inputs/outputs to assess the efficiency of the RS indices in predicting different drought conditions under various circumstances. Furthermore, we introduced an innovative data clustering method based on RS-derived land-cover similarity, which resulted in further improvements in drought modeling compared to agroclimatic zoning. The fusion-based drought modeling approach, using all RS indices as inputs in the enhanced-climate-based and land-cover-based methods, demonstrated an aggregated Overall Accuracy (OA) of 92.93% and 95.11% for predicting SPI-3, respectively. Notably, within the land-cover-based method, TCI exhibited the highest aggregated performance compared to other RS indices in predicting SPI-3 with an OA of approximately 95%.

Microstructure Analysis of Al-7 wt% Si Alloy Solidified on Earth Compared to Similar Experiments in Microgravity

During ground-based solidification, buoyancy flow can develop by the density difference in the hypoeutectic type of the alloys, such as Al-7 wt% Si alloy. Buoyancy flow can affect the thermal field, solute distribution in the melt, and the position and amount of the new grains. As solidification is a very complex process, it is not very easy to separate the different effects. Under microgravity conditions, natural convection does not exist or is strongly damped due to the absence of the buoyancy force. Therefore, experiments in microgravity conditions provide unique benchmark data for pure diffusive solidification conditions. Compared to the results of the ground-based and microgravity experiments, it is possible to get information on the effect of gravity (buoyancy force). In the framework of the CETSOL project, four microgravity solidification experiments were performed on grain refined (GF) and non-grain refined Al-7 wt% Si alloy onboard the International Space Station in the Materials Science Laboratory. These experiments aimed to study the effect of the solidification parameters (solid/liquid front velocity vSL, temperature gradient GSL) on the grain structure and dendritic microstructures. The microgravity environment eliminates the melt flow, which develops on Earth due to gravity. Four ground-based (GB) experiments were performed under Earth-like conditions with the same (similar) solidification parameters in a vertical Bridgman-type furnace having four heating zones. The detailed analysis of the grain structure, amount of eutectic, and secondary dendrite arm spacing (SDAS) for different process conditions is reported and compared with the results of the microgravity experiments. GB experiments showed that the microstructure was columnar in the samples that do not contain GF material or in case the solid/liquid (vSL front velocity was slow (0.02 mm/s)). In contrast, in the sample which contained GF material, progressive columnar/equiaxed transition (PCET) was observed at vSL = 0.077 mm/s and GSL = 3.9 K/mm. The secondary (SDAS) dendrite arm spacing follows the well-known power law, SDAS=K[t0]13, where K is a constant, and t0 is the local solidification time for both GB and µg experiments.

Long-Term and Emergency Monitoring of Zhongbao Landslide Using Space-Borne and Ground-Based InSAR

This work presents the ideal combination of space-borne and ground-based (GB) Interferometric Synthetic Aperture Radar (InSAR) applications. In the absence of early investigation reporting and specialized monitoring, the Zhongbao landslide unexpectedly occurred on 25 July 2020, forming a barrier lake that caused an emergency. As an emergency measure, the GB-InSAR system was installed 1.8 km opposite the landslide to assess real-time cumulative deformation with a monitoring frequency of 3 min. A zone of strong deformation was detected, with 178 mm deformation accumulated within 15 h, and then a successful emergency warning was issued to evacuate on-site personnel. Post-event InSAR analysis of 19 images acquired by the ESA Sentinel-1 from December 2019 to August 2020 revealed that the landslide started in March 2020. However, the deformation time series obtained from satellite InSAR did not show any signs that the landslide had occurred. The results suggest that satellite InSAR is effective for mapping unstable areas but is not qualified for rapid landslide monitoring and timely warning. The GB-InSAR system performs well in monitoring and providing early warning, even with dense vegetation on the landslide. The results show the shortcomings of satellite InSAR and GB-InSAR and a clearer understanding of the necessity of combining multiple monitoring methods.

COBIS: Opportunistic C-Band Bistatic SAR Differential Interferometry

Monitoring large built structures, landslides, volcanoes, or glaciers implies relative height measurements and line-of-sight (LOS) displacement measurements, which are typically performed with monostatic spaceborne synthetic aperture radar (SAR) sensors. In this case, for each satellite that illuminates a given location, we can usually exploit only the information available from one ascending/descending orbit, and if the satellite uses multiple subswaths, the information is obtained only from one subswath per orbit. A bistatic configuration with spaceborne transmitter and fixed ground-based (GB) receiver opens the possibility to exploit the information available from more than one orbit and one subswath per orbit. Additionally, such a configuration offers new perspectives for target tracking and characterization (e.g., multiple LOSs and bistatic scattering signatures). This article presents an opportunistic C-band bistatic SAR differential interferometry architecture that uses a multichannel GB stationary receiver and a separate transmitter, which can be either the Sentinel-1A/B satellite, used opportunistically, or a specially designed GB sliding transmitter. The combination of the operation modes based on spaceborne and GB transmitter allows monitoring a small critical area of interest with many acquisitions triggered by the GB transmitter and surveying of the whole surrounding area with a small number of acquisitions corresponding to the satellites passes. The hardware platforms are presented along with the bistatic interferometric processing flow, and the potential of the proposed architecture is assessed in the context of monitoring large built structures.

Long-term temporal and spatial analysis of SO2 over Gauteng and Mpumalanga monitoring sites of South Africa

This paper focused on the temporal and spatial variability of sulphur dioxide (SO2) from 2004 to 2013 over 36 ground-based (GB) stations located in Gauteng and Mpumalanga provinces of South Africa. The assessment was based on the in situ SO2 data, where the GB stations were sorted into different groups and in addition, kriging-based analysis and OMI SO2 data (2004–2013) were deployed to study their seasonal spatial variability. It was observed that Pretoria west, Witbank and Randwater had high SO2 levels and standard deviations. Although, winter played a major role in having peak SO2 values in most of the stations, it was surpassing that in spring the SO2 levels of Witbank and Pretoria west stations dropped and Randwater had the highest standard deviation of 11.2 ppbv in October. A prominent seasonal variation was seen in all the groups, excluding Mpumalanga power stations (MP) group. This was particularly evident in the Vaal Triangle group. Highveld group ranked topmost level with elevated SO2 values proceeded by the MP group in all the temporal time scales. The spatial based analysis further proved that the highest SO2 levels were centred around major industrial regions of Mpumalanga, regardless of all the seasons.

The use of QBO, ENSO, and NAO perturbations in the evaluation of GOME-2 MetOp A total ozone measurements

Abstract. In this work we present evidence that quasi-cyclical perturbations in total ozone (quasi-biennial oscillation – QBO, El Niño–Southern Oscillation – ENSO, and North Atlantic Oscillation – NAO) can be used as independent proxies in evaluating Global Ozone Monitoring Experiment (GOME) 2 aboard MetOp A (GOME-2A) satellite total ozone data, using ground-based (GB) measurements, other satellite data, and chemical transport model calculations. The analysis is performed in the frame of the validation strategy on longer time scales within the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) Satellite Application Facility on Atmospheric Composition Monitoring (AC SAF) project, covering the period 2007–2016. Comparison of GOME-2A total ozone with ground observations shows mean differences of about -0.7±1.4 % in the tropics (0–30∘), about +0.1±2.1 % in the mid-latitudes (30–60∘), and about +2.5±3.2 % and 0.0±4.3 % over the northern and southern high latitudes (60–80∘), respectively. In general, we find that GOME-2A total ozone data depict the QBO–ENSO–NAO natural fluctuations in concurrence with the co-located solar backscatter ultraviolet radiometer (SBUV), GOME-type Total Ozone Essential Climate Variable (GTO-ECV; composed of total ozone observations from GOME, SCIAMACHY – SCanning Imaging Absorption SpectroMeter for Atmospheric CHartographY, GOME-2A, and OMI – ozone monitoring instrument, combined into one homogeneous time series), and ground-based observations. Total ozone from GOME-2A is well correlated with the QBO (highest correlation in the tropics of +0.8) in agreement with SBUV, GTO-ECV, and GB data which also give the highest correlation in the tropics. The differences between deseazonalized GOME-2A and GB total ozone in the tropics are within ±1 %. These differences were tested further as to their correlations with the QBO. The differences had practically no QBO signal, providing an independent test of the stability of the long-term variability of the satellite data. Correlations between GOME-2A total ozone and the Southern Oscillation Index (SOI) were studied over the tropical Pacific Ocean after removing seasonal, QBO, and solar-cycle-related variability. Correlations between ozone and the SOI are on the order of +0.5, consistent with SBUV and GB observations. Differences between GOME-2A and GB measurements at the station of Samoa (American Samoa; 14.25∘ S, 170.6∘ W) are within ±1.9 %. We also studied the impact of the NAO on total ozone in the northern mid-latitudes in winter. We find very good agreement between GOME-2A and GB observations over Canada and Europe as to their NAO-related variability, with mean differences reaching the ±1 % levels. The agreement and small differences which were found between the independently produced total ozone datasets as to the influence of the QBO, ENSO, and NAO show the importance of these climatological proxies as additional tool for monitoring the long-term stability of satellite–ground-truth biases.

Long-range transport of SO2 over South Africa: A case study of the Calbuco volcanic eruption in April 2015

South Africa (SA) is influenced by anthropogenic SO2 emissions, however, no studies exist detailing the atmospheric or climatic effects resulting from volcanic eruptions at this location. In this work, the Calbuco volcano situated in southern Chile (41.79°S, 73.13°W), was used as a case study to investigate the transport of volcanic SO2 plumes over SA in 2015. The Calbuco volcano underwent a series of eruptive phases starting from 22 April 2015. In this study, HYSPLIT forward trajectories and cluster analysis were carried out in order to estimate the altitude level of air parcels entering the SA subcontinent. These analyses along with Ozone Monitoring Instrument (OMI) assessments confirmed that trajectories beginning in Calbuco arrived in SA during the last few days of April (∼28 April 2015) and stayed until the 1st week of May 2015 (∼2 May 2015). In order to estimate the rise in SO2 columnar concentrations at various altitudes and surface SO2 levels, OMI data and 19 ground-based (GB) monitoring stations (situated throughout SA) were utilized. A comparative analysis employing SO2 data from pre-2014 and post-2016 volcanic eruption periods were also included in this study. Results showed that a major increase in SO2 concentration was observed at the Planetary Boundary Layer (∼ 2 DU) when compared to its upper layers during April and May 2015. Although some GB stations in North West province namely Damonsville, Mmabatho and Phola showed a moderate increase in SO2 values during April and May 2015, there was no significant rise in GB measurements at the surface in other locations However, we are not certain due to data gaps in the measurements and non-availability of ground-based measurements.

Comparison between GNSS ground-based and GPS radio occultation precipitable water observations over ocean-dominated regions

Precipitable water (PW) inferred from GPS (Global Positioning System) radio occultation (RO) and ground-based (GB) Global Navigation Satellite System (GNSS) observations are compared between years 2007 and 2014. As previous studies were mainly performed over continental areas we now focus over ocean-dominated geographical areas. Our analysis is done in order to find out how the reliability level of RO results over oceanic areas compares to land. As RO soundings usually miss some information close to the ground, we also assess different methods to complete the lacking data. We found 47 terrestrial stations that lie in islands small and far away from continental areas where the weather might be governed by the sea conditions. From comparisons of almost 5000 collocated samples, PW from RO and GB exhibit a global mean difference around 1 mm, root-mean-square deviation about 5 mm and a correlation above 0.9. The 2007–2014 timeseries and the monthly mean RO and GB PW were also compared to reanalyses per hemisphere, latitude regions and oceans. In each zone it was found that PW from RO, GB and reanalyses all exhibit in general consistent seasonal qualitative behavior. However, quantitative differences exist between reanalyses on one side and RO and GB on the other side. It is shown that PW from reanalyses lacks reliability in areas where the island topography is poorly represented by them. We also conclude that RO and GB seem to be more sensitive for the detection of features that depart from the regular annual cycle.

SO2 seasonal variation and assessment of Ozone Monitoring Instrument (OMI) measurements at Sharpeville (27.86°E; 26.68°S) a South African ground-based station

ABSTRACTIn this study, seasonal, inter-annual variations of sulphur dioxide (SO2) were analysed for Sharpeville, South Africa (27.86°E; 26.68°S) for the period 2007–2013. Sharpeville is a residential site located within a highly industrialized region. Inter-annual variations were investigated by analysis of data collated by a ground-based (GB) instrument as well as the SO2 retrievals recorded by the Ozone Monitoring Instrument (OMI), a satellite-based sensor. The planetary boundary layer (PBL) SO2 vertical column data recorded by OMI were converted to surface SO2 volume mixing ratios using the pressure difference between surface and PBL. Accordingly, the OMI-derived SO2 measurements overestimated GB values and showed a good correlation with GB data in the austral winter. Besides this, the overall comparisons i.e. their daily, seasonal, and yearly correlation studies found that OMI-derived measurements was in better agreement with GB regardless of its moderate relative percentage of difference with GB. The seasonal variations of SO2 demonstrated that GB and OMI-derived measurements followed a general pattern of increasing trend from autumn until late winter and decreased from the onset of spring; however, the latter showed unique high SO2 levels in summer. Together with this, the inter-annual variations of both computations displayed a small decrement in SO2 values during the period 2011–2013. This however proved to be a stable variation when compared to other countries.

Intercomparison of stratospheric nitrogen dioxide columns retrieved from ground-based DOAS and FTIR and satellite DOAS instruments over the subtropical Izana station

Abstract. A 13-year analysis (2000–2012) of the NO2 vertical column densities derived from ground-based (GB) instruments and satellites has been carried out over the Izaña NDACC (Network for the Detection of the Atmospheric Composition Change) subtropical site. Ground-based DOAS (differential optical absorption spectroscopy) and FTIR (Fourier transform infrared spectroscopy) instruments are intercompared to test mutual consistency and then used for validation of stratospheric NO2 from OMI (Ozone Monitoring Instrument) and SCIAMACHY (SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY). The intercomparison has been carried out taking into account the various differences existing in instruments, namely temporal coincidence, collocation, sensitivity, field of view, etc. The paper highlights the importance of considering an “effective solar zenith angle” instead of the actual one when comparing direct-sun instruments with zenith sky ones for a proper photochemical correction. Results show that NO2 vertical column densities mean relative difference between FTIR and DOAS instruments is 2.8 ± 10.7 % for a.m. data. Both instruments properly reproduce the NO2 seasonal and the interannual variation. Mean relative difference of the stratospheric NO2 derived from OMI and DOAS is −0.2 ± 8.7 % and from OMI and FTIR is −1.6 ± 6.7 %. SCIAMACHY mean relative difference is of 3.7 ± 11.7 and −5.7 ± 11.0 % for DOAS and FTIR, respectively. Note that the days used for the intercomparison are not the same for all the pairs of instruments since it depends on the availability of data. The discrepancies are found to be seasonally dependent with largest differences in winter and excellent agreement in the spring months (AMJ). A preliminary analysis of NO2 trends has been carried out with the available data series. Results show increases in stratospheric NO2 columns in all instruments but larger values in those that are GB than that expected by nitrous oxide oxidation. The possible reasons for the discrepancy between instruments and the positive trends are discussed in the text.

Validation of Satellite Estimated Solar Ultraviolet Radiation Data with Ground Based data in Kathmandu, Nepal

The main objective of this study is to validate the satellite estimated solar Ultraviolet radiation data with ground based data in Kathmandu (27.72 N, 85.32 E), located at an elevation of 1350m, from the sea level. The ground based measurement and the satellite estimation were performed by NILU-UV irradiance meter and EOS Aura OMI satellite respectively. The NILU-UV irradiance meter is a six channel 0 0 radiometer designed to measure hemispherical irradiances on a fat surface. Meanwhile the Ozone Monitoring Instrument (OMI) on board, the NASA EOS Aura space craft is a nadir viewing spectrometer that measures solar refected and back scattered light in ultraviolet and visible spectrum. The study is based on OMI and ground based (GB), Ultraviolet Radiation (UVR) data. Considering these data the relative differences between predicted OMI and ground based Ultraviolet Index (UVI) assuming normal distribution ±1σ was found to be 24.8±13.7% in July. Further study showed that the ratio of predicted OMI, UVI to that determined from ground based measurement is 1.14. Also the relative difference in UVI in corrected condition in summer season was found to be 5.8%. The correlation between predicted UVI and ground based UVI was found to be signifcant. Journal of the Institute of Engineering, 2015, 11(1): 101-107

Space-Borne and Ground-Based InSAR Data Integration: The Åknes Test Site

This work concerns a proposal of the integration of InSAR (Interferometric Synthetic Aperture Radar) data acquired by ground-based (GB) and satellite platforms. The selected test site is the Åknes rockslide, which affects the western Norwegian coast. The availability of GB-InSAR and satellite InSAR data and the accessibility of a wide literature make the landslide suitable for testing the proposed procedure. The first step consists of the organization of a geodatabase, performed in the GIS environment, containing all of the available data. The second step concerns the analysis of satellite and GB-InSAR data, separately. Two datasets, acquired by RADARSAT-2 (related to a period between October 2008 and August 2013) and by a combination of TerraSAR-X and TanDEM-X (acquired between July 2010 and October 2012), both of them in ascending orbit, processed applying SBAS (Small BAseline Subset) method, are available. GB-InSAR data related to five different campaigns of measurements, referred to the summer seasons of 2006, 2008, 2009, 2010 and 2012, are available, as well. The third step relies on data integration, performed firstly from a qualitative point of view and later from a semi-quantitative point of view. The results of the proposed procedure have been validated by comparing them to GPS (Global Positioning System) data. The proposed procedure allowed us to better define landslide sectors in terms of different ranges of displacements. From a qualitative point of view, stable and unstable areas have been distinguished. In the sector concerning movement, two different sectors have been defined thanks to the results of the semi-quantitative integration step: the first sector, concerning displacement values higher than 10 mm, and the 2nd sector, where the displacements did not exceed a 10-mm value of displacement in the analyzed period.

Multi‐angular ground‐based SAR system for soil surface roughness characterisation

Developped in the Institut of Electronics and Telecommunication of Rennes (IETR), a Ground Based (GB) SAR system is proposed for soil surface roughness discrimination at C-band. By using this GB-SAR system, the multi-angular SAR measurement is realized at different resolution cell sizes. The specific levels of surface roughness are set deliberately to analyze the roughness contribution on multi-angular SAR signature in a controlled experimental condition. Based on the GB-SAR measurement, the effect of incidence angle on SAR sensitivity is studied under two resolution cell sizes for retrieving soil surface roughness. The results show that, by using our GB-SAR system, high incidence angle configuration (e.g. > 40 •) is optimal to discriminate surface roughness, which is more significant at small resolution cell size (e.g. 30 cm). This study indicates the effectivity of GB-SAR system to validate the SAR backscattering mechanisms and will be helpful for future spatial missions [1]. Introduction: With high spatial and temporal resolution, the remote sensing techniques play an important role in monitoring the soil status. Compared with optical remote sensing, the synthetic aperture radar (SAR) in independent of weather and light constraint, owing to the active microwave generation and the penetration ability of microwave. Nevertheless, the mechanisms of SAR backscattering from bare soils are suffered from the coherent superposition of surface roughness contribution with soil moisture. Thus, discrimination of surface roughness effect on SAR signature is crucial for further soil monitoring. Recently, the multi-angular SAR measurements increase the observing dimension [2], making it possible to improve the robustness of soil parameter retrievals. However, the temporal interval of spaceborne multi-angular image acquisitions induces several problems in the multi-angular SAR study: i) the soil characteristics may vary during the multi-angular measurements; ii) it is challenged to accomplish the consistent ground truth measurements over large areas simultaneously with satellite pass. To solve the aforementioned issues, this study proposes a Ground Based (GB) SAR system for bare soil monitoring. This kind of GB-SAR system was also developed for terrain mapping [3], vegetation monitoring [4] and snow pack imaging [5]. However, our study concentrates on the bare soil characterization under multi-angular configuration and the influence of resolution cell size on the multi-angular SAR sensitivity. GB-SAR system for soil monitoring: Our GB-SAR system is mainly comprised of three components: i) A vector network analyzer; ii) Four wide band horn antennas (2-12 GHz); iii) A three-meter long metal rail with an accurate positioning motor. This GB-SAR system is capable of measuring the backscattering matrix of targets under controlled experimental condition. Repeatable measurements can be realized by the GB-SAR system as the motion of the platform is controlled accurately, which is essential for multi-angular SAR measurements. Furthermore, different soil status can be set deliberately in the scene to analyze the induced response in SAR signature. The SAR back projection algorithm [6] makes possible to obtain the absolute backscattering coefficients through several calibration procedures: i) The antenna patterns are compensated along both azimuth and range axis; ii) The methodology proposed by [7] is used to accomplish the polarimetric calibration; iii) The radiometric calibration is realized by comparing the measured RCS (Radar Cross Section) of a canonical reference target with its theoretical RCS value.

Estimation of Mangrove Carbon Stocks by Applying Remote Sensing and GIS Techniques

This paper presents a methodological framework for predicting C stock in Avicennia marina stands in the Thane creek of Mumbai. This methodology combines ground-based (GB) and remote sensing (RS) approaches for C stock estimation. RS based approach use Normalized differential vegetation index (NDVI), Light use efficiency (LUE) and Photo-synthetically Active Radiation (PAR) as the most important parameters for C stock estimation. The sensitivity of NDVI values to aerosols, water vapor and ozone was removed using the 6S radiative transfer code. The difference in NDVI values before and after atmospheric correction was assessed using student’s t-test and was found to be statistically significant. The total carbon stock of the area was observed to be about 39.7188 t/ha. The bias estimation between C stock calculated using allometry and RS approaches confirmed the prediction accuracy and validated both the techniques (R 2 = 0.964 and bias = 0.915 %). The paper, thus, reports a statistically robust framework, which is a combination of the RS and GB approaches, and can be used for estimating the biomass and carbon stock of any ecosystem. This framework is especially effective where forest inventory data is unavailable, the site is geographically inaccessible or harvesting of mangroves or other trees is prohibited.

Compressed Sensing for Millimeter-wave Ground Based SAR/ISAR Imaging

Millimeter-wave (MMW) ground based (GB) synthetic aperture radar (SAR) and inverse SAR (ISAR) imaging are the powerful tools for the detection of foreign object debris (FOD) and concealed objects that requires wide bandwidths and highly frequent samplings in both slow-time and fast-time domains according to Shannon/Nyquist sampling theorem. However, thanks to the compressive sensing (CS) theory GB-SAR/ISAR data can be reconstructed by much fewer random samples than the Nyquist rate. In this paper, the impact of both random frequency sampling and random spatial domain data collection of a SAR/ISAR sensor on reconstruction quality of a scene of interest was studied. To investigate the feasibility of using proposed CS framework, different experiments for various FOD-like and concealed object-like targets were carried out at the Ka and W band frequencies of the MMW. The robustness and effectiveness of the recommend CS-based reconstruction configurations were verified through a comparison among each other by using integrated side lobe ratios (ISLR) of the images.

Satellite Estimation and Ground Based Measurements of Solar UV Index in Kathmandu

The study is based on the satellite estimation and ground measurements of solar UV index in Kathmandu for the year 2009. Kathmandu (27.720N, 85.320E) is located at an elevation of 1350m from the sea level. The ground based measurement and satellite estimation was performed by NILU-UV irradiance meter and EOS Aura OMI spacecraft. The NILU-UV irradiance meter measures UV radiation in different spectral range. The Ozone Monitoring Instrument (OMI) is a spectrometer designed to monitor solar radiation in spectral range of (270-500) nm. This study shows that satellite overestimates the ground based (GB) UV Index (UVI) before monsoon by 103.6 % whereas during the monsoon period overestimation comes down to 70.8 %. The correlation coefficient (r) between ozone column from satellite and ground based measurement before monsoon and after monsoon is also studied. It is observed that the correlation between satellite estimated ozone column and ground based ozone column before and after monsoon is 0.83. The study showed that the estimation of OMI before monsoon is high than after monsoon. DOI: http://dx.doi.org/10.3126/jie.v9i1.10664Journal of the Institute of Engineering, Vol. 9, No. 1, pp. 18–26

Short-range ground-based synthetic aperture radar imaging: performance comparison between frequency-wavenumber migration and back-projection algorithms

Two popular synthetic aperture radar (SAR) reconstruction algorithms, namely the back-projection (BP) and the frequency wavenumber (ω−k ) algorithms, were tested and compared against each other, especially for their use in ground-based (GB) SAR applications directed to foreign object debris removal. For this purpose, an experimental setup in a semi-anechoic chamber room was accomplished to obtain near-field SAR images of objects on the ground. Then, the 90 to 95 GHz scattering data were acquired by using a stepped frequency continuous-wave radar operation. The performances of the setup and the imaging algorithms were then assessed by exploiting various metrics including point spread function, signal-to-clutter ratio, integrated side-lobe ratio, and computational complexity. Results demonstrate that although both algorithms produce almost accurate images of targets, the BP algorithm is shown to be superior to the ω−k algorithm due to its some inherent advantages specifically suited for short-range GB-SAR applications.

Efficient Wavenumber Domain Focusing for Ground-Based SAR

We present an efficient focusing algorithm for synthetic aperture radar (SAR) data acquired in short bursts by a geometry with a large range spread, i.e., the case of ground-based (GB) SAR. The usual approach for focusing GB SAR data is the polar formatting algorithm, whose computational complexity, however, is quite relevant due to the nonseparability of the kernel. In this letter, we introduce a different format for the focused data, namely, the range-angular domain. Such format keeps the benefit of the polar format that samples data close to the resolution, but allows for the design of a separable kernel in the wavenumber domain. The proposed kernel exploits a modified Stolt interpolation and an efficient space-varying resampling. Results of processing on both simulated and real data are presented.