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The new generation of remote sensing satellites improves the coverage of remote sensing imaging by improving the orbital surface and using the rotary scanning imaging technology. In this paper, a new technical scheme of rotating payload platform based on disturbance-free payload technology is proposed to adapt to the rotary scanning imaging satellite. The platform adopts the cubic Stewart configuration and uses the Lorentz force magnetic actuator as the actuator to form the satellite support module and the payload into a noncontact two-stage three-body. First, this paper uses Newton-Euler method to establish a six-degree-of-freedom dynamic model for the new rotating payload platform, and establishes a kinematic model by quaternion method. Then, according to the established kinematics and dynamics model, the control scheme of absolute pointing control of payload platform (upper platform) and relative follow-up control of kinetic energy container (middle platform) is designed. Furthermore, the six-degree-of-freedom decoupling control method of the actuator is designed. Based on this, the absolute pointing controller of the payload module and the relative position and attitude calculate controller of the kinetic energy container are designed. Finally, the research of simulation is carried out. The simulation results show that the new rotating payload platform has higher rotation pointing accuracy and stability than the traditional ultra-quiet platform, and the vibration isolation acting on the support module and the vibration absorption acting on the payload are more effective. [ABSTRACT FROM AUTHOR]

Hyperspectral target detection is challenging in scenarios where spectral variability is high due to noise, spectral redundancy, and mixing. In addition, this spectral variability also creates the need for target detection algorithms to be robust against variations in the detection threshold. To overcome these challenges, this paper proposes a novel two-stage process for improved target detection in hyperspectral data. In the first stage, coarse detection is performed using a detector with a high probability of detection to identify background samples. These background samples are then used for background learning using an adversarial autoencoder (AAE) network, having spectral angle mapper (SAM) and Huber loss functions to minimize the impact of target pixels' contamination. In the second stage, an inference is made using the spectral difference between the hyperspectral data and the output of the learned background model, which helps in reducing the false alarm rate of the first stage. The proposed approach is compared with seven other target detection techniques using multiple datasets and evaluated through several metrics, such as the area under the curve (AUC) and signal-to-noise probability ratio (SNPR). Results reveal that the proposed technique outperforms other detectors in terms of SNPR, indicating improved target detectability, background suppressibility, and more tolerance to variations in the detection threshold. [ABSTRACT FROM AUTHOR]

• The DTFA method showed higher extraction and classification accuracy compared to the SVM and k-means++ methods. • HY-1C has the most effective extraction, followed by Landsat 8. • Landsat 8 is able to extract new ice, grey ice, and grey-white ice with the best effect, while HY-1C has a better effect in extracting white ice. MODIS data has advantages in coverage and data updating. This paper presents the extraction and classification of sea ice in the Yellow and Bohai seas using multi-source remote sensing products, including Landsat 8, HY-1C, and MODIS. We propose a Dynamic Thresholding Fully Automated (DTFA) method for sea ice extraction and classification. The accuracy of sea ice extraction and classification is evaluated using four metrics: confusion matrix accuracy rate, overall accuracy, user accuracy, and kappa coefficient. The DTFA method showed higher extraction and classification accuracy compared to the SVM and k-means++ methods. HY-1C has the most effective extraction, followed by Landsat 8. Landsat 8 is able to extract new ice, grey ice, and grey-white ice with the best effect, while HY-1C has a better effect in extracting white ice. MODIS data has advantages in coverage and data updating. Finally, the fusion of multi-source remote sensing images was used to obtain the sea ice extent of the entire Chinese offshore. This method is also applicable to remote sensing monitoring of sea ice in other sea areas. [ABSTRACT FROM AUTHOR]

• Earthquake anomalies are analyzed for Mw 6.5 Idaho Earthquake America. • Significant anomalies occur in LST, AT, RH, AP and OLR. • Anomalies are studied using statistical, NARX, MLP and CWT methods. • NARX predicted anomalies are more clear than other methods. Remote sensing has became a powerful tool for identifying lithosphere and atmosphere anomalies associated with the impending Earthquakes (EQs) in the vicinity of seismic breeding zone. In this paper, Land Surface Temperature (LST) of both the daytime and nighttime from the Moderate Resolution Imaging Spectroradiometer (MODIS) along with Air Temperature (AT), Relative Humidity (RH), Air Pressure (AP) and Outgoing Longwave Radiations (OLR) are studied for the 2020 Idaho (USA) EQ of Mw 6.5. We found the EQ induced surface and atmospheric parameters anomalies just one day prior to EQ main shock using statistical analysis. Moreover, we observed a sharp increment in LST and AT followed by the drop in both AP and RH, which are responsible for cooling the hot gases emitted from epicenter during preparation period. Also, we observed a large increase in OLR on the same day confirming these anomalous variations to be related with the main shock. Furthermore, these abrupt variations are also confirmed using neural networks (nonlinear autoregressive network with exogenous inputs (NARX), multilayer perceptron (MLP) and continuous wavelet transformation (CWT). These multi-parameter and multi-technique analyses can contribute to assist in the main shock forecasting in future with an enhanced cluster of satellite observations. [ABSTRACT FROM AUTHOR]

Radio waves propagating the ionosphere are subject to a number of unpredictable and corrupting effects, the most significant of which are phase path reduction, Faraday rotation, and scintillation. A method of measuring these effects is necessary in order to study and understand the solar-terrestrial connections, as well as to anticipate their impact on radio systems. However, limitations in current measurement solutions, most notably the lack of direct ionospheric Faraday rotation measurement capability, restricts the ability to characterize the ionosphere to a sufficient degree for applications such as the detection of coronal mass ejections. The rise in popularity of small satellites presents a new cost-effective opportunity to study and observe the ionosphere in detail, however the challenge of realizing a beacon within the constraints of a small satellite system must be overcome. In this paper, we introduce a measurement technique that builds on past multi-frequency beacons, incorporating the well established differential phase technique, as well as introducing a new differential polarization technique. We achieve this by employing linearly polarized beacon signals, which enables us to separate phase path reduction and Faraday rotation into a phase and polarization error respectively. The introduction of linear polarization overcomes a key limitations in current ionospheric measurement techniques: eliminating errors associated with indirect Faraday rotation measurement. The additional polarization data also enables absolute unambiguous measurement of both parameters with fewer and lower frequencies than conventional solutions, as well as without the need for precise satellite clock, phase biases and positioning data. These characteristics correspond with a reduction in the size, power and complexity of the satellite payload, and as a result, our technique better suited to small satellite applications. To validate the capability of measuring the ionosphere within the constraints of a small satellite package, we establish the influence of system noise, weak scattering, clock errors and antenna characteristics on the measurement technique. We then use this to inform a starting point for a CubeSat beacon and receiver design solution which achieves unambiguous and high-resolution measurement of ionospheric parameters with practicable system requirements and constraints. This starting point demonstrates that a resolution of 12 mTECU and 0.03 rad/m2 for the electron density and Faraday rotation estimates respectively, as well as useful scintillation measurements dominated by scintillation noise may be achieved utilizing a tri-band 3U CubeSat beacon with frequencies 144.2 MHz, 432.6 MHz, and 1297.8 MHz, a transmitter power of no more than 4 watts, and antenna requirements consistent with a dipole type antenna. [ABSTRACT FROM AUTHOR]

As one of the central cities in the Wuling Mountains, Huaihua is regarded as the main body and core economic development area in the process of Wuling's future new urbanization, exerting a pivotal influence on the five provincial border areas (Hunan, Hubei, Chongqing, Guizhou and Guangxi). But studies on urban expansion in this city are few, so this paper makes efforts to simulate its urban land expansion by constructing the Cellular Automata model (the Weight-CA model) taking into account differences in land types conversion. Firstly, the paper obtains the time-series Landsat spectral bands (Red, NIR) and NDVI images using the time–space fusion model; then the optimal combination of classification features is screened based on J Bh distance and the object-oriented random forest algorithm is applied for land cover classification; the paper finally constructs the Weight-CA model for the simulation and validation of urban land expansion in Huaihua. Study results in the paper have shown that the Landsat spectral bands obtained with the ESTARFM have a relatively high correlation with real Landsat images. The land cover classification based on the optimal classification feature combination and the object-oriented random forest algorithm is high in accuracy, the total of which exceeding 84 % over 4 years. Meanwhile, based on the Weight-CA model, the simulation accuracy of urban land expansion in Huaihua in 2010 and 2015 reached 89.98 % and 89.60 % compared with the real values. It is operationally ready and practical and of strong practical significance to extract land cover types by Landsat and MODIS data fusion, and simulate urban land expansion using the Weight-CA method. [ABSTRACT FROM AUTHOR]

Due to the fast progress of Deep Learning (DL) methods in remote sensing applications, many change detection (CD) algorithms have been recently proposed based on CNN networks and the mechanism of self-attention. These algorithms extract features without focusing on the temporal dependency between features. This shortcoming led to the introduction of the Transformer mechanism. In this paper, we design a convolutional transformer network with a Siamese U-shaped structure and name it CTS-Unet to solve the CD problem. We exploit the ability of the CNN to extract effective semantic features and that of the transformer to extract global information effectively. The Siamese architecture allows using CNN to simultaneously extract effective semantic features from low-resolution bi-temporal images. The transformer part contains an encoder and a decoder, all of which use the Swin transformer module as their basic unit. The encoder processes the features extracted from the CNN using patch merging and the Swin transformer module to produce semantic features. The encoder extracts the detailed information from the features using patch expansion, the Swin transformer module, and convolutional upsampling to create a CD map. The experiments were performed on the widely used LEVIR-CD and DSIFN-CD datasets. Compared with other state-of-the-art CD methods, CTS-Unet provides higher performance with F1-scores of 91.87% and 69.60% for LEVIR-CD and DSIFN-CD datasets, respectively. [ABSTRACT FROM AUTHOR]

Semantic Segmentation is an important problem in many vision related tasks. Land use and land cover classification involves semantic segmentation of satellite imagery and plays a vital role in many applications. In this paper, we propose an extended U-Net architecture with dense decoder connections and attention mechanism for pixel wise classification of satellite imagery named Attention Dense U-Net (ADU-Net). We further evaluate the effect of different upsampling strategies in the decoder part of the U-Net architecture. We evaluate our models on the Gaofen Image Dataset (GID) for landcover classification consisting of five classes: built-up, forest, farmland, meadow and water. The experiments on the GID dataset show better performance than the previous approaches. Our proposed architecture delivers more than 4% higher mIoU and F1-score than the baseline U-Net. Moreover, our proposed architecture achieves an F1-score of 87.21% and mIoU of 77.66% on the GID dataset. Our evaluations shows that data-dependent upsampling layer achieves higher accuracy than the Transposed Convolution, Pixel Shuffle and Bilinear upsampling layers. [ABSTRACT FROM AUTHOR]

Today, with the non-stop expansion of urbanization, mapping urban areas and monitoring their dynamic changes have become challenges for governments and also a hot topic for researchers. Remote sensing imageries play a key role in urban studies, the extraction of urban built-up areas, and monitoring their changes. A variety of studies have proposed methods for the extraction of regional, national, and global built-up areas. However, the majority of them used limited features and applied a manual sample selection strategy for classification, leading to time-consuming and low-efficient algorithms. This paper proposes a fully automatic procedure to real-time extract built-up areas by integrating the Luojia 1–01 nighttime lights (NTL) images, Sentinel-2 multispectral data, Sentinel-1 Radar images, and SRTM elevation data in cloud-computing Google Earth Engine. Firstly, potential built-up areas (PBA) and non-built-up areas (NBA) are obtained by applying Otsu and multi-level thresholding to some of the extracted spectral-textural-spatial (STS) features and by applying logical rules. Secondly, built-up and non-built-up samples are automatically selected and are used to train a Support Vector Machine (SVM) supervised classifier and to classify the hybrid feature set so that a preliminary classified map (PCM) can be obtained. Thirdly, the PCMs are automatically corrected using the non-built-up area, and morphological operations in the so-called post-classification to provide a refined classified map (RCM) and final built-up map. Four study areas in Northern America, Europe (Scandinavia), the Middle East, and Eastern Asia were selected to test the proposed method. Also, five state-of-the-art built-up products, accompanied by Google Earth images, were used as the reference data. The results indicate that the proposed method can accurately and automatically select samples and map built-up areas with a spatial resolution of 10 m. Its performance is validated with an average overall accuracy of 94.4% and an average Kappa coefficient of 0.89 and by visual comparison of our method results with other reference data. The proposed method has significant potential to be used in real-time extracting built-up areas and in monitoring their dynamic changes on national and global scales. [ABSTRACT FROM AUTHOR]

Mapping lithological units and conducting mineral exploration in a relatively short time and at a reduced cost, requires high-resolution satellite data and state-of-the-art image processing approaches and methods. In this regard, the current paper aims to test an integration of visible-near infrared imagery from WorldView-3 (WV-3) and object-based classification for mapping potential deposits of ocher in Hormuz Island, southern Iran. A combination of field observations, spectroscopy, microscopic mineralogy, and geochemical-based XRD and XRF analyses were conducted on the samples collected throughout the study area. The reflectance and absorption features of the ocher were extracted from spectroscopic measurements of samples from well-known ocher mines at the area, and were then convolved to the WV-3 bands. Results showed that the spectral characteristics of ocher are governed by iron oxides and clay minerals with a presence of distinct strong absorption and high reflectance features in the 510–625 nm and 630–745 nm wavelength regions, respectively. These features are complemented by molecular vibration processes of water O–H intramolecular stretching and H–O–H bending that generate absorption features in the 1440–1940 nm region. Additional absorption features in the 2210–2300 nm are most likely due to the Al-OH and CO3−2 vibrations. The absorptions centered at 480 nm, 540 nm, and 820 nm correspond to bands 2, 3, and 7 of the WV-3, respectively, whereas the high reflectance feature centered near 700 nm corresponds to band 6. These four bands, which were considered as index bands of ocher in this study, were used to assign the segmentation weights and to create the thresholds during image processing. Brightness, density, compactness, and homogeneity features were the primary factors for selecting the training areas in the index bands. The ocher-rich areas were enhanced by using a two-stepapproach of object-based image analysis (OBIA) and image classification, for suitable threshold selection. The information about the feature variables of the ocher within the image and lithological object hierarchy were obtained to evaluate the features of ocher deposits. Information extracted from the index bands provided an important description of the object features, including mean, standard deviation, minimum and maximum pixel values, hue, saturation, and intensity, while the training areas obtained information from the three known ocher mines. Considering the fixed numerical range of ocher mines as a reference, the fixed numerical range of general and specific features of ocher in index bands of WV-3 were achieved. The numerical ranges of pixels were used for creating threshold conditions when applying the "assign class" algorithm in bands 2, 3, and 7 are 815–975 nm, 930–1052 nm, and 1721–1904 nm, respectively. The presented OBIA approach shows a high potential with an overall accuracy of 88 % to discriminate deposits of ocher based on compatibility between ocher mines and identified ocher-bearing pixels. [ABSTRACT FROM AUTHOR]

• A method for extending polarimetric optimization of PS-InSAR and SBAS techniques on dual polarized Sentinel-1 data is proposed. • The phase quality and density of coherent pixels in both techniques are improved with the proposed method. • The potentials of the proposed method for differential land covers are investigated. The rationale of combining polarimetric SAR data with multi-temporal InSAR techniques, which is based on polarimetric optimization, is to improve their performance in identifying coherent pixels and increasing the accuracy of deformation monitoring. This paper extended the polarimetric optimization of Persistent Scatterer Interferometry (PSI) and Small Baseline Subset (SBAS) techniques on dual-polarized Sentinel-1 data. For this purpose, 20 dual-polarization (VV/VH) SAR images acquired by the Sentinel-1A sensor were used. The results indicated that by applying polarimetric optimization on dual-polarized data in the SBAS technique, the number of candidate and final coherent pixels increased 2.95 and 1.42 times, respectively. Similarly, the number of candidate and final coherent pixels (CP) increased 3.12 and 1.45 times in the PSI technique, respectively. Also, the effect of the polarimetric optimization on increasing the density of coherent points in different regions was investigated. In this framework, Sentinel-2 time-series images were classified into four classes (bare soil, vegetation, build-up, and others) using Google Earth Engine by applying remote sensing indices. This demonstrated that polarimetric optimization of the SBAS technique was more effective in bare soil and vegetated regions than in urban areas. However, polarimetric optimization of the PSI technique was more successful in non-urban regions than in urban areas. An external comparative analysis was also performed between the deformation results derived by PSI and SBAS techniques and GPS observation. The evaluation demonstrated that polarimetric optimization coupled with multi-temporal InSAR analysis yielded more reliable deformation results than conventional single-polarimetric data. [ABSTRACT FROM AUTHOR]

During the satellite's operations, simulation tools perform an important role in ensuring the space mission success. In this sense, the models implemented in the context of an operational satellite simulator that enables analysis of health status and maintenance during operations shall reflect the current satellite behavior with high fidelity. Moreover, it is complicated to obtain all analytical models of a satellite's disciplines, considering its aging. This paper proposes an Artificial Neural Network (ANN) to reproduce the battery voltage behavior of a large sun-synchronous remote sensing satellite, the CBERS-4, currently in operation. Using the genetic algorithm to find the best network architecture of ANN, the neural model for this application presented an error of less than 1%, demonstrating its feasibility to obtain a high fidelity model for an operational simulator enabling extend analyses. The paper addresses advanced techniques aligned with the space industry's future, increasing the ability to analyze a large amount of data and improve the space system's operation. [ABSTRACT FROM AUTHOR]

Recently, the detection and extraction of geological lineaments have become an essential analytical technique to find relationships between the characteristics and occurrence of hydrogeology, and tectonic studies. The use of remote sensing, with the progressive development of image enhancement techniques, provides an opportunity to produce more reliable and comprehensive lineament maps. In this paper, semi-automatic approach based on Landsat 8 and Sentinel 1 radar data is proposed for lineaments extraction and validation. The combined method of linear filtering and automatic line module ensures a high degree of accuracy resulting in a lineament map. Based on identified lineaments, Sentinel1 is more capable of detecting edges than Landsat8, but the primary orientation lineaments extracted from Landsat8 and Sentinel1 were different. So, by combining band6 of Landsat8, and VV and VH polarization of Sentinel1, the area lineaments were extracted with high accuracy. Rose diagram showed the extracted lineaments' orientation is in good compliance with the region's existing faults. Also, the formations' lineament length density has good consistent with the density of the faults in the geological map. [ABSTRACT FROM AUTHOR]

• 390 major lineaments have been detected, mapped out and analysed in Kribi–Campo. • They are mostly oriented perpendicularly to the beach. • Their lengths range from 95 to 4910 m with an average of 1171 m. • They may affect inland intrusion of seawater in this coastal region. • For this, Landsat-8 image and geological data were used. This study detected, mapped and analysed lineaments from Landsat-8 satellite images of the coastal area of Kribi-Campo, Cameroon. Kribi, a fast-growing coastal city with a youthful population whose main industry is agriculture, is currently under great pressure due to the influx of internally displaced persons, victims of the current socio-political crisis in the western part of Cameroon. With the growing demand for food and water resources, it has been reported that saline soils and groundwater are affecting the agricultural productivity and domestic water supply of the area. This paper investigates the lineaments network in this area to understand its spatial distribution, its extent and the potential effect on inland intrusion of saline water. The datasets, including Landsat-8 imagery, geological data and a digital elevation model were processed using principal component analysis and filtering techniques. Linear features were enhanced based on textural changes of false colour composite images to describe fractures, faults and hydrology features. 390 effective lineaments were extracted automatically and manually from those images and analysed based on length, density and orientation using statistical analysis. But before this processing, non-effective lineaments that may correspond to terraces, linear valleys, cliffs, or tonal contrast as well as to human activities have been deleted. The effective lineaments range in length from 95 to 4910 m with an average of 1171 m. Although the lineaments are not isotropic, their perpendicular orientations to the coast suggest potential intrusion of saline water into regional groundwater resources and farmlands. The paper presents a combined technique of geospatial and statistical analysis in the interpretation of lineament effects which have the potential to inform on salinity intrusion in coastlands. The electrical conductivity of 50 groundwater samples collected in the study area are used to validate the impact of lineaments on seawater intrusion. [ABSTRACT FROM AUTHOR]

• Integrates Support Vector Machine with satellite remote sensing to detect dust. • Combines new and existing algorithm to improve the accuracy of dust detection. • The developed DSD_SVMS is more reliable and efficient than threshold-based method. • B7-B3, B20-B31 and B31/B32 are the optimum feature vectors in dust identification. Enhancing the dust storm detection is a key part for the environmental protection, human healthy and economic development. The goal of this paper is to propose a new Support Vector Machine (SVM)-based method to automatically detect dust storms using remote sensing data. Existing methods dealing with this problem are usually threshold-based that are of great complexity and uncertainty. In this paper we propose a simple and reliable method combining SVM with MODIS L1 data and explore the optimal band combinations used as the feature vectors of SVM. The developed method was evaluated by MODIS and OMI data qualitatively and quantitatively on three study sites located in the Arabian Desert, Gobi Desert and Taklimakan Desert, and it was also compared to three other traditional methods based on their accuracy, complexity, reliability and sensitivity to thresholds. The detection results demonstrated that the combination of (Band7 − Band3)/(Band7 + Band3) ((B7 − B3)/(B7 + B3)), Band20 − Band31 (B20 − B31), and Band31/Band32 (B31/B32) can detect the dust storms more precisely than other individual bands or their combination. The comparison among those cases indicated that the proposed automatic method exhibited an advantage of minimizing the uncertainty and complexity, which were the limits of defining thresholds based on the threshold-based methods. The conclusions can provide references for studies that focus on statistical-based dust storm detection. [ABSTRACT FROM AUTHOR]

Fengyun-4 is a second-generation Chinese geostationary quantitative remote sensing meteorological satellite. To operate continuously and steadily as well as achieve the high accuracy attitude determination requirement throughout the whole remote sensing operation period, the attitude control system is capable of autonomously locating and isolating its gyroscope faults. This paper introduces its practical strategy of gyroscope bias fault diagnosis, which is based on the correlated measurement equations among those redundancy gyroscopes. The correlated measurement equation, null space vector calculation, gyroscope bias fault detection algorithms as well as its practical bias fault diagnosis strategy are presented in detail. Numerical simulation suggests that the bias fault diagnosis in Fengyun-4 spacecraft could accurately detect and locate the bias fault among five redundancy gyroscopes. [ABSTRACT FROM AUTHOR]

Field spectroscopy data serve as a standard for the inter-comparisons, calibration and validation of remote sensing data. It also greatly assists in the detection, characterization and mapping of the earth features from the satellite imagery. Strategies and methods for field-based spectro-radiometric measurements are well-established for land and deep ocean applications, however, there are certain challenges and limitations with respect to the shallow water coastal ecosystems. This paper focuses on the background theory, concepts, and measurement techniques of in-vivo sensing of benthic reflectance in shallow water environment. We investigated various measurement methods, influences of water column, air–water interface, sources of errors (introduced by experimental and environmental factors), and the correction methods that are involved in the determination of benthic substrate reflectance. Special emphasis is provided using in-vivo measurements made in the estuarine and shelf regions, illustrating the benthic substrate reflectance at various platforms, i.e., within the water column, above the water surface, at the top-of-atmosphere, in the air, and in a controlled (laboratory) environment. As part of the correction procedure, a new water column correction method is theoretically formulated and examined for varying water optical properties and water depth. A new step correction method is also developed (based on the additive approach) to correct the spectral reflectance measured by a full range spectroradiometer (350–2500 nm) that is in-built with two or more detectors. This study will have direct implications for improving the data quality of field-based spectro-radiometric measurements that are essential for shallow water remote sensing applications. [ABSTRACT FROM AUTHOR]

The CryoSat-2 (CS-2) mission is an altimetric mission designed to meet the measurement requirements for ice-sheet elevation and sea-ice ‘freeboard’. In addition to Cryosphere applications, this mission is of great interest to the Hydrosphere and Oceanographic communities since it allows for quantitative assessment of expected enhanced altimetric capabilities in coastal monitoring, ocean floor topography, gravity field and inland water monitoring. The CS-2 main payload is a Synthetic aperture Interferometric Radar ALtimeter (SIRAL) operating in three different modes, including the SAR interferometric mode (SARin). In the work described in this paper, dedicated algorithms are developed for exploiting SARin mode for enhancing the sea level mapping at coastal regions. When operating in SARin mode the altimeter measurements allow for the derivation of the across-track Angle of Arrival (AoA), and so the reflection points at any range bin can be geolocated. In coastal altimetry, land contamination highly degrades the backscattered echo. This is sometimes, overcome with the SARM when tracks are perpendicular to the coast, since this mode provides an along-track resolution enhancement with respect to LRM mode due to the different footprints shapes (LRM is circular, while SARM is a stripe of the same wide across track but only around 300 m along track). However, across-track interferences still remain an issue in coastal altimetry and SARin mode can provide a solution for their mitigation. This paper proposes two different solutions for the improvement of SSH retrievals in coastal zones starting from L1b SARin products, developing post-L1b algorithms. The first solution is based in the use of the AoA. The second solution goes beyond SARin mode, and provides an algorithm for all operational modes, based in the historical window delay within a coastal track section. For both options, a sub-waveform approach is considered for the waveforms cleaning and retracking. In addition, a SAR L2 retracker inherited from the SAMOSA model is adapted to SARin. The results are analyzed and the potential improvements are assessed with respect to SARin data retracker by the CS-2 ground processor. A dedicated CS-2 SARin area (mask) covering the Cuban archipelago has been created for this investigation. A land-sea mask has been created from the Open Street Map high resolution catalogue for this study. A dataset has been produced including both ESA CS2 IPF Baseline B SSH and the SSH series coming from this study. [ABSTRACT FROM AUTHOR]

• The phenomenon of seawater intrusion makes aquifer vulnerable to salinisation. • Lineaments play the role of pathways for this intrusion. • Elevation and slope are aggravating factors for the salinity spreading. • Several new fractured zones are highlighted in the region. • The combined GIS-remote sensing has been effective. Coastal aquifers are vulnerable to salinisation due to seawater intrusion. Lineaments generally play the role of pathways for this intrusion. This paper investigates the lineament pattern in the region of southern-Cameroon with the main purpose of understanding the mechanism of the eventual contamination of groundwater by salinity. 472 lineaments are mapped in the study area using both Landsat-8 OLI and 7 ETM + images. 15 of those lineaments connect 19 contaminated boreholes to the shore. A total of 24 out of 58 boreholes are contaminated by seawater. A statistical analysis of this network depicts the responsibility of lineaments in seawater intrusion. Other aggravating factors are elevation (<100 m) and slope (<7°). A wide NE-SW-oriented fractured zone is detected within the flat area extending between Mount-Cameroon and Limbe-Mabeta Massif. Furthermore, several new fractured zones are highlighted, and will be considered when elaborating policy of local water management. This low-cost and time-saving approach which combines remote sensing and GIS has been effective, and recommended for similar investigations worldwide. A geophysical campaign with the purpose of characterizing local aquifer and upgrade hydrological, geological and electrical parameters of the local aquifer is scheduled. [ABSTRACT FROM AUTHOR]

Cloud detection is a necessary phase in satellite images processing to retrieve the atmospheric and lithospheric parameters. Currently, some cloud detection methods based on Random Forest (RF) model have been proposed but they do not consider both spectral and textural characteristics of the image. Furthermore, they have not been tested in the presence of snow/ice. In this paper, we introduce two RF based algorithms, Feature Level Fusion Random Forest (FLFRF) and Decision Level Fusion Random Forest (DLFRF) to incorporate visible, infrared (IR) and thermal spectral and textural features (FLFRF) including Gray Level Co-occurrence Matrix (GLCM) and Robust Extended Local Binary Pattern (RELBP_CI) or visible, IR and thermal classifiers (DLFRF) for highly accurate cloud detection on remote sensing images. FLFRF first fuses visible, IR and thermal features. Thereafter, it uses the RF model to classify pixels to cloud, snow/ice and background or thick cloud, thin cloud and background. DLFRF considers visible, IR and thermal features (both spectral and textural) separately and inserts each set of features to RF model. Then, it holds vote matrix of each run of the model. Finally, it fuses the classifiers using the majority vote method. To demonstrate the effectiveness of the proposed algorithms, 10 Terra MODIS and 15 Landsat 8 OLI/TIRS images with different spatial resolutions are used in this paper. Quantitative analyses are based on manually selected ground truth data. Results show that after adding RELBP_CI to input feature set cloud detection accuracy improves. Also, the average cloud kappa values of FLFRF and DLFRF on MODIS images (1 and 0.99) are higher than other machine learning methods, Linear Discriminate Analysis (LDA), Classification And Regression Tree (CART), K Nearest Neighbor (KNN) and Support Vector Machine (SVM) (0.96). The average snow/ice kappa values of FLFRF and DLFRF on MODIS images (1 and 0.85) are higher than other traditional methods. The quantitative values on Landsat 8 images show similar trend. Consequently, while SVM and K-nearest neighbor show overestimation in predicting cloud and snow/ice pixels, our Random Forest (RF) based models can achieve higher cloud, snow/ice kappa values on MODIS and thin cloud, thick cloud and snow/ice kappa values on Landsat 8 images. Our algorithms predict both thin and thick cloud on Landsat 8 images while the existing cloud detection algorithm, Fmask cannot discriminate them. Compared to the state-of-the-art methods, our algorithms have acquired higher average cloud and snow/ice kappa values for different spatial resolutions. [ABSTRACT FROM AUTHOR]

Due to the lack of information such as the space environment condition and resident space objects’ (RSOs’) body characteristics, current orbit predictions that are solely grounded on physics-based models may fail to achieve required accuracy for collision avoidance and have led to satellite collisions already. This paper presents a methodology to predict RSOs’ trajectories with higher accuracy than that of the current methods. Inspired by the machine learning (ML) theory through which the models are learned based on large amounts of observed data and the prediction is conducted without explicitly modeling space objects and space environment, the proposed ML approach integrates physics-based orbit prediction algorithms with a learning-based process that focuses on reducing the prediction errors. Using a simulation-based space catalog environment as the test bed, the paper demonstrates three types of generalization capability for the proposed ML approach: (1) the ML model can be used to improve the same RSO’s orbit information that is not available during the learning process but shares the same time interval as the training data; (2) the ML model can be used to improve predictions of the same RSO at future epochs; and (3) the ML model based on a RSO can be applied to other RSOs that share some common features. [ABSTRACT FROM AUTHOR]

• Multiple satellite formation flying reconfiguration for remote sensing application. • Low thrust manoeuvres via constrained convex optimal control problem. • Application of the control problem to a new mission concept for remote sensing. • Design of reconfiguration trajectories for remote sensing payload calibration. • Design of formation flying transition to safe mode for non-nominal operations. This paper presents a strategy for optimal manoeuvre design of multi-satellite formation flying in low Earth orbit environment, with the aim of providing a tool for mission operation design. The proposed methodology for formation flying manoeuvres foresees a continuous low-thrust control profile, to enable the operational phases. The design is performed starting from the dynamic representation described in the relative orbital elements, including the main orbital perturbations effects. It also exploits an interface with the classical radial-transversal-normal description to include the maximum delta-v limitation and the safety condition requirements. The methodology is applied to a remote sensing mission study, Formation Flying L-band Aperture Synthesis, for land and ocean application, such as a potential high-resolution Soil Moisture and Ocean Salinity (SMOS) follow-on mission, as part of a European Space Agency mission concept study. Moreover, the results are applicable to a wide range of low Earth orbit missions, exploiting a distributed system, and in particular to Formation Flying L-band Aperture Synthesis (FFLAS) as a follow-on concept to SMOS. [ABSTRACT FROM AUTHOR]

The evaluation of microvibrations effect is crucial for the success of remote sensing missions. The moving parts on the satellite such as reaction wheels can create microvibrations disturbances, which cause degradation of image quality. In our paper we present an experimental investigation methodology for on-ground quantification of wheel noise disturbance transmitted through the structure to the payload. This assessment was carried out for the ALSAT-1B satellite integrating a medium resolution payload. The angular motion along imager bore sight was measured using accelerometers and verified against image quality and mission requirement. The on ground experimental methodology suggests that the effect of microvibrations on a camera can be verified directly. [ABSTRACT FROM AUTHOR]

This paper presents data from two campaigns at the High Frequency Active Auroral Research Program facility (HAARP) in 2011 and 2012. The measurements of stimulated radio emissions (often called stimulated electromagnetic emissions or SEE) were conducted 15 km from the HAARP site. The potential of Narrowband SEE (NSEE) as a new diagnostic tool to monitor artificial irregularities excited during HF-pump heating of the ionosphere is the main goal of this paper. This has been investigated using well established diagnostics including the Modular UHF Ionospheric Radar (MUIR) and Kodiak SuperDARN radars as well as Wideband SEE (WSEE). The measured data using these three diagnostics were compared to characterize the ionospheric parameters and study the plasma irregularities generated in the interaction region. Variation of the wideband/narrowband SEE features, SuperDARN echoes, and HF-enhanced ion lines (EHIL) were studied with pump power variation, pump frequency stepping near the third electron gyro-frequency (3 f ce ) as well as changing beam angle relative to the magnetic zenith. In particular, electrostatic plasma waves and associated irregularities excited near the reflection resonance layer as well as the upper-hybrid resonance layer are investigated. The time evolution and growth rate of these irregularities are studied using the experimental observations. Close alignment of narrowband SEE (NSEE) with wideband SEE (WSEE) and EHIL was observed. SuperDARN radar echoes and WSEE also showed alignment as in previous investigations. Correlations between these three measurements underscore potential diagnostics by utilizing the NSEE spectrum to estimate ionospheric parameters such as electron temperature. [ABSTRACT FROM AUTHOR]

Earthquake is one of the most devastating natural disasters that its prediction has not materialized comprehensive. Remote sensing data can be used to access information which is closely related to an earthquake. The unusual variations of lithosphere, atmosphere and ionosphere parameters before the main earthquakes are considered as earthquake precursors. To date the different precursors have been proposed. This paper examines one of the parameters which can be derived from satellite imagery. The mentioned parameter is Aerosol Optical Depth (AOD) that this article reviews its relationship with earthquake. Aerosol parameter can be achieved through various methods such as AERONET ground stations or using satellite images via algorithms such as the DDV (Dark Dense Vegetation), Deep Blue Algorithm and SYNTAM (SYNergy of Terra and Aqua Modis). In this paper, by analyzing AOD’s time series (derived from MODIS sensor on the TERRA platform) for 16 major earthquakes, seismic anomalies were observed before and after earthquakes. Before large earthquakes, rate of AOD increases due to the pre-seismic changes before the strong earthquake, which produces gaseous molecules and therefore AOD increases. Also because of aftershocks after the earthquake there is a significant change in AOD due to gaseous molecules and dust. These behaviors suggest that there is a close relationship between earthquakes and the unusual AOD variations. Therefore the unusual AOD variations around the time of earthquakes can be introduced as an earthquake precursor. [ABSTRACT FROM AUTHOR]

This paper explores the impact of the individual components of a coastal ocean observing system on estimates of the circulation derived from a state-of-the-art analysis and forecast system for the Mid-Atlantic Bight and Gulf of Maine. The foundation of these activities is the Regional Ocean Modeling System 4-dimensional variational (4D-Var) data assimilation platform, which is run in support of the Mid-Atlantic Regional Association Coastal Ocean Observing System as part of the U.S. Integrated Ocean Observing System. The specific focus of this study is on the impact of remote sensing observations from both space- and land-based platforms on estimates of cross-shelf transport in the vicinity of the National Science Foundation Ocean Observatories Initiative Pioneer array. Sea surface temperature (SST) and sea surface height (SSH) were found to have, on average, a similar impact on the transport estimates. However, during a typical 3-day 4D-Var assimilation cycle, approximately two orders of magnitude more observations of SST than SSH are used in the model, and closer analysis shows that each altimeter measurement has approximately 50 times more impact on the transport estimates than an individual SST observation. This highlights the value of altimetry data for ocean state estimation, and the significance of expanding the altimeter constellation. The observations that are most impactful of all are in situ measurements of temperature and salinity, which have typically 3–4 times more impact than an individual SSH datum. A robust geographical distribution of the observation impacts emerges across a range of transport metrics which results from the combined influence of space-time dynamical interpolation and error covariance information within the 4D-Var system. The observation impact calculations suggest that High Frequency (HF) radar estimates of surface currents have relatively little direct influence on cross-shelf transport estimates. However, quantification of the sensitivity of these same estimates to changes in the observing system indicate that HF radar observations indirectly provide important information. This is understood in the current system by appealing to the idea of borrowing strength from the field of statistics in which some observations (satellite remote sensing in the case considered here) can borrow strength from other, seemingly less important observations. [ABSTRACT FROM AUTHOR]

This paper describes the scientific objectives and payloads of Tianwen-1, China's first exploration mission to Mars. An orbiter, carrying a lander and a rover, lifted-off in July 2020 for a journey to Mars where it should arrive in February 2021. A suite of 13 scientific payloads, for in-situ and remote sensing, autonomously commanded by integrated payload controllers and mounted on the orbiter and the rover will study the magnetosphere and ionosphere of Mars and the relation with the solar wind, the atmosphere, surface and subsurface of the planet, looking at the topography, composition and structure and in particular for subsurface ice. The mission will also investigate Mars climate history. It is expected that Tianwen-1 will contribute significantly to advance our scientific knowledge of Mars. [ABSTRACT FROM AUTHOR]

LiDAR (light detection and ranging) is one of the most important techniques used in remote sensing and photogrammetry to extract high quality features, objects, and models. In this paper, we present an innovative and simple method for ground point filtering from LiDAR point clouds. This method is inspired by nature, where tornadoes destroy buildings and other manmade objects and uproot trees. This simulates the removing of points of building, trees and other objects from LiDAR point clouds. We have imposed that the tornado is a vertical cone with a down vertex and up base. The vertical cone moves on the ground surface so that a network of points is selected as vertex points for the vertical cone. Points that are located inside the cone are removed as non ground points. In other words, the distances between the points and the cone axis are measured, then these distances are compared with calculated distances relative to the cone equation, finally, the points within the cone are removed. To evaluate the proposed method, the datasets provided by the International Society for Photogrammetry and Remote Sensing (ISPRS) are used. The results show that this method can adapt to complex environments and achieve high filtering accuracy. Furthermore, by comparing our method with other methods tested by the ISPRS, the proposed method achieves an average total error of 6.63%, which is smaller than that of the most methods. [ABSTRACT FROM AUTHOR]

Recently launched hyper-spectral instrumentation with ever-increasing data return capabilities deliver the remote-sensing data to characterize planetary soils with increased precision, thus generating the need to classify the returned data in an efficient way for further specialized analysis and detection of features of interest. This paper investigates how lunar near-infrared spectra generated by the SIR-2 on Chandrayaan-1 can be classified into distinctive groups of similar spectra with automated feature extraction algorithms. As common spectral parameters for the SIR-2 spectra, two absorption features near 1300 nm and 2000 and their characteristics provide 10 variables which are used in two different unsupervised clustering methods, the mean-shift clustering algorithm and the recently developed graph cut-based clustering algorithm by Müller et al. (2012). The spectra used in this paper were taken on the lunar near side centering around the Imbrium region of the Moon. More than 100,000 spectra were analyzed. [ABSTRACT FROM AUTHOR]

Road detection and road extraction are important and challenging issues in the fields of photogrammetry and remote sensing. Researchers have conducted wide research in this regard based on multispectral images and achieved relatively useful results. Image-data driven methods have some shortcomings such as shadows, eliminating small and long vehicles, geometric distortions, and occlusions. In recent years, in order to overcome the above limitations and complexities, several attempts have been done based on LIDAR data. The present paper proposes an automatic hierarchical road detection and extraction method. The main goal of this research is to increase the level of continuity of the road detection and extraction processes. This method includes the preprocessing of intensity data using a local minima filter, applying the Mean Shift segmentation to the refined intensity data, and finally integrating it with various nDSM-based Products. The proposed method involves not only considering both small and long vehicles as road features but also neglecting some parts of large parking lots based on the nearby neighborhood of parked vehicles as much as possible as non-road features. The next step was the process of road centerline extraction by adopting a Voronoi-diagram based approach and then removing dangle lines in several iterations. The proposed method was applied to the Vaihingen and Toronto datasets (ISPRS). The completeness of the two datasets is 95.85% and 88%, and the correctness of these datasets are 83.68% and 72.2%, respectively. The results were indicative of the great potential of the proposed method for effective road centerline extraction in urban areas. [ABSTRACT FROM AUTHOR]

Abstract: It is known that a wireless sensor network uses some sort of sensors to detect a physical quantity of interest, in general. The wireless sensor network is a potential tool for exploring the difficult-to-access area on the earth and the concept may be extended to space applications in future. Recently, lunar water has been detected by a few lunar missions using remote sensing techniques. The lunar water is expected to be in the form of ice at very low temperatures of permanently dark regions on the moon. To support the remote observations and also to find out potential ice bearing sites on the moon, in-situ measurement of the lunar ice is essential. However, a rover may not be able to reach the permanently shadowed regions due to terrain irregularity. One possibility to access such areas is to use a wireless sensor network on the lunar surface. In this paper, we have investigated a possibility of in-situ exploration of lunar ice by a wireless sensor network. The research issues related to the lunar wireless sensor network and a few possible solutions have been reviewed for the sake of completeness. A key component in the system is an ice sensor, which can measure the permittivity of the ice at appropriate frequency to differentiate with the soil. We suggest an impedance based sensor for this purpose, whose design aspects were reported earlier. We have successfully tested pure ice sample made from Milli-Q water in the laboratory environment and the results are shown in this paper. [Copyright &y& Elsevier]

Abstract: The GNSS signal along its more than 20,000km line of sight is bended, attenuated and delayed. These effects are results of the Earth’s atmosphere, and Sun radiation. Amongst all mentioned effects the one considered in this paper is a signal phase delay in troposphere. This paper presents investigations in the field of GNSS ground tomography, a remote sensing technique able to capture distribution of water vapour partial pressure in the troposphere. In this method, observations are Slant Wet Delays (SWD), the phase delay of GNSS signal due to the presence of water vapour in the troposphere. One of the most important limitation in GNSS tomography is so far mandatory usage of constraints to improve design matrix rank score and in consequence the ability to obtain meaningful troposphere parameters. Solution proposed here is based on combination of SWDs from several consecutive observation epochs to increase rank score. Another important goal is to perform careful selection of singular values in the process of pseudo inverse, to limit the observation noise influence. Three different methods to select singular values are investigated: functional analysis, significant testing and L-curve fitting. The obtained results are validated by means of internal uncertainties propagation, and comparison with external model. With the simulated data the unconstrained stacked GNSS produce reliable results with uncertainties on the level of in terms of water vapour partial pressure and with mean discrepancies on the level of . The same measures transform to the 0.03% and 0.01% of relative error on the ground level and 100% and more, above 8km. Moreover all instabilities inside profiles were resolved properly. The simulations shows that to attain such optimistic results at least 10 observations epoch have to be stacked. [Copyright &y& Elsevier]

Abstract Air temperature is an important parameter affecting biosphere processes, air quality, regional climate, and human health, but is difficult to estimate, especially on a nationwide scale. Remote sensing provides high spatial resolution data that influence air temperature and can help overcome this problem in spatially heterogeneous environments. Generally, the simple linear relationships between air temperature, land surface temperature (LST), and auxiliary data, seldom consider the complex spatial variations in air temperature. The random forest (RF) regression model can characterize the complex temperature distribution by establishing the non-linear relationships among them. The main purpose of this paper is to present an RF regression method to estimate monthly average temperatures in China, using MODIS LST, normalized differential vegetation index (NDVI), night-time light, and a digital elevation model (DEM). Results based on cross-validation show that mean absolute error (MAE) and root mean square error (RMSE) at national level in different months ranged from 1.15 to 1.44 °C (mean ± standard deviation: 1.30 ± 0.10 °C) and from 1.57 to 1.99 °C (1.79 ± 0.13 °C), respectively. Potential sources of error and uncertainty were analyzed and investigated. Results show that regression model performs better in eastern (MAE: 0.68 ± 0.79 °C; RMSE: 0.75 ± 0.81 °C) and central China (MAE: 0.68 ± 0.41 °C; RMSE: 0.74 ± 0.41 °C). The model performance was strongly dependent on elevation, slope, land cover, and satellite data quality. In addition, the percentage increase in mean squared error from RF regression model was used to quantify the importance of variables. Positive importance scores indicated an improvement in accuracy with the assistance of NDVI, night-time lights, and the DEM. LST, and more auxiliary data sources, when integrated into an RF regression model, may be ways in which to improve the modeling of air temperature variations in the future. [ABSTRACT FROM AUTHOR]

Abstract To identify policies that will promote positive effects and mitigate negative ones of grazing is a major challenge in the Silvo-pastoral system. This paper presents the role of examining land-cover change trajectories by remote sensing imagery in grazing policy monitoring. The study was conducted for Duzlercami forest ecosystem located in the Mediterranean geographical region of Turkey and administrated by the General Directorate of Forestry (GDF) of the Ministry of Forestry and Water Affairs. Time series land-cover datasets from Landsat images between 1988 and 2016 were collected and classified. To link the conversions among trajectories and grazing policy, class level landscape metrics derived from the classified images were used. To validate the approach, yearly grazing-plans managed by GDF and populations of livestock were used. Results of this research have indicated that even though there is a yearly grazing plan, overgrazing can happen on the pilot site, and it can be easily identified by the destruction of woody vegetation. The notable correlation (r2 = 0.89) between degraded woody vegetation and cattle population has occurred in the last 30 years in the landscape, and Landsat imagery can effectively support the grazing policy mapping and monitoring. [ABSTRACT FROM AUTHOR]

Abstract: Theses days, many nano- and micro-satellites are applied to several astronomy and remote sensing missions. In order to achieve mission requirements, these satellites must control the attitude precisely. A magnetic disturbance is one of the dominant sources of attitude disturbances. Therefore, this disturbance should be canceled in-orbit or on the ground to achieve the attitude strict requirements. This paper presents the effect of the magnetic disturbance to the attitude in nano- and micro-satellite missions and the sources of the residual magnetic moment of the satellites, which causes the magnetic disturbance. Then, the paper proposes a method to compensate the residual magnetic moment both in-orbit and in the design phase of the satellites. The research also focused on a time-varying residual magnetic moment. Finally, the method is applied to a micro-astrometry satellite as an example. [Copyright &y& Elsevier]

Abstract Geological lineaments are important reactant of geological structure on the surface, and control the distribution of regional groundwater, geohazards, geothermal and earthquakes. The technology of geological lineaments extraction is of great significance for the analysis of regional plate movement and ore forming prognosis. However, the traditional methods are mainly based on the semi-automatic or manual visual interpretation, which consuming time and labor for the dependence on the rich professional experience and knowledge of the interpreter experts, and involving less in the special geomorphological regions. Taking Loess areas in northern Baoji as an example, this paper proposed a lineaments extraction algorithm based on tensor voting coupled Hough Transform, with the help of DEM and Landsat 8 OLI remote sensing images. Firstly, the best independent band combination of Landsat 8 OLI images for lineaments extraction were selected by using principal component analysis. Secondly, Gaussian high-pass filter was applied to sharpen the edge in the DEM and composite Landsat 8 OLI images. Linear boundary was extracted by tensor voting according to the conspicuousness of the vector sum superposition feature based on edge points. Finally, the Hough Transform was employed to search the edges and extracted the geological lineaments in this region. The experimental results showed that the orientation of lineaments was dominated by NW-SE and NE- SW, supplemented by NNW-SSE. Under the influence of the uplift of the eastern foot of Liupan Mountains and the southern margin of Ordos, the lineaments were mainly distributed over linear landforms, which were better consistent with previous studies about the direction of tectonics in this region. Compared with the segment tracing algorithm, this method has more applicability, efficiency, high practical value and scientific significance in the analysis of tectonic movement and evolution in special landform area. [ABSTRACT FROM AUTHOR]

Abstract This paper proposes an algorithm to find the smallest satellite constellation satisfying a given set of Earth observation requirements. This methodology is exemplified with the Satellites Observing Lakes and Vegetation Environments (SOLVE) study, which aims at deploying a fleet of small satellites carrying miniaturized hyperspectral spectrometers. A key requirement of this mission is a high temporal resolution through which the ground target can be observed several times a day. Hourly observations are required in this mission in order to capture diurnal changes in water quality and vegetation environments. Given sensor specifications and observation requirements, the proposed algorithm determines orbital parameters of an optimal constellation design via a semi-analytical approach. This approach reveals trade-offs amongst performance metrics and deployment cost, providing better physical intuition for decision-making compared to stochastic optimization. [ABSTRACT FROM AUTHOR]

Arctic sea ice is a major element of the Earth’s climate system. It acts to regulate regional heat and freshwater budgets and subsequent atmospheric and oceanic circulation across the Arctic and at lower latitudes. Satellites have observed a decline in Arctic sea ice extent for all months since 1979. However, to fully understand how changes in the Arctic sea ice cover impact on our global weather and climate, long-term and accurate observations of its thickness distribution are also required. Such observations were made possible with the launch of the European Space Agency’s (ESA’s) CryoSat-2 satellite in April 2010, which provides unparalleled coverage of the Arctic Ocean up to 88°N. Here we provide an end-to-end, comprehensive description of the data processing steps employed to estimate Northern Hemisphere sea ice thickness and subsequent volume using CryoSat-2 radar altimeter data and complementary observations. This is a sea ice processor that has been under constant development at the Centre for Polar Observation and Modelling (CPOM) since the early 1990s. We show that there is no significant bias in our satellite sea ice thickness retrievals when compared with independent measurements. We also provide a detailed analysis of the uncertainties associated with our sea ice thickness and volume estimates by considering the independent sources of error in the retrieval. Each month, the main contributors to the uncertainty are snow depth and snow density, which suggests that a crucial next step in Arctic sea ice research is to develop improved estimates of snow loading. In this paper we apply our theory and methods solely to CryoSat-2 data in the Northern Hemisphere. However, they may act as a guide to developing a sea ice processing system for satellite radar altimeter data over the Southern Hemisphere, and from other Polar orbiting missions. [ABSTRACT FROM AUTHOR]

Abstract: This paper describes the CryoSat satellite mission, due for launch in 2005, whose aim is to accurately determine the trends in Earth’s continental and marine ice fields. The paper’s purpose is to provide scientific users of the CryoSat data with a description of the design and operation of the SIRAL radar and the CryoSat platform, the data products, and the expected error budget. The ‘low-resolution mode’ (LRM), ‘synthetic aperture mode’ (SARM) and “synthetic aperture interferometric mode’ (SARInM) of the SIRAL radar are described, together with its system parameters, its antenna gain pattern and interferometer phase difference pattern, and its calibration modes. The orbit is described, together with the platform attitude and altitude control law and control systems, and the expected pointing and altitude knowledge. The geographical masks that are used to determine acquisitions in the three SIRAL modes are described. The SIRAL data products, and the processing applied to produce them, are described. Level 1b, level 2 and higher-level products are described in turn, with a particular emphasis on the new procedures applied to the SARInM and SARM processing over ice surfaces. The beam forming and multi-looking is summarised, and a description is given of the behaviour of the SARM and SARInM echoes over idealised surfaces. These inform descriptions of the elevation retrievals of the level 2 processing, including the SARInM retrieval of interferometric phase. The combination of these data, through cross-over analysis over continental ice sheets, and through averaging over sea-ice, to determine areal averages of ice sheet elevation change or sea-ice thickness, is described. The error budget in these higher-level products is described, together with its breakdown into errors arising from the instrument and errors arising from the retrievals. The importance of the co-variance of these errors in determining the final error is stressed. The description of the errors also includes a summary of the experiments required following the launch to validate the CryoSat mission data. An estimate of the mission performance over ice surfaces is made at various spatial scales, and it is concluded that even the relatively short, three-year duration of the CryoSat mission will allow it to make an important scientific contribution, particularly when combined with results from earlier satellite missions. [Copyright &y& Elsevier]

A nonparametric method termed as manifold structure preservative (MSP) is proposed in this paper for hyperspectral target detection. MSP transforms the feature space of data to maximize the separation between target and background signals. Moreover, it minimizes the reconstruction error of targets and preserves the topological structure of data in the projected feature space. MSP does not need to consider any distribution for target and background data. So, it can achieve accurate results in real scenarios due to avoiding unreliable assumptions. The proposed MSP detector is compared to several popular detectors and the experiments on a synthetic data and two real hyperspectral images indicate the superior ability of it in target detection. [ABSTRACT FROM AUTHOR]

The Borowiec Satellite Laser Ranging station (BORL 7811, Borowiec) being a part of the Space Research Centre of the Polish Academy of Sciences (SRC PAS) went through modernization in 2014–2015. One of the main tasks of the modernization was the installation of a high-energy laser module dedicated to space debris tracking. Surelite III by Continuum is a Nd:YAG pulse laser with 10 Hz repetition rate, a pulse width of 3–5 ns and a pulse energy of 450 mJ for green (532 nm). This new laser unit was integrated with the SLR system at Borowiec performing standard satellite tracking. In 2016 BORL 7811 participated actively to the observational campaigns related to the space debris targets from LEO region managed by the Space Debris Study Group (SDSG) of the International Laser Ranging Service (ILRS). Currently, Borowiec station regularly tracks 36 space debris from the LEO regime, including typical rocket bodies (Russian/Chinese) and cooperative targets like the inactive TOPEX/Poseidon, ENVISAT, OICETS and others. In this paper the first results of space debris laser measurements obtained by the Borowiec station in period August 2016 – January 2017 are presented. The results gained by the SRC PAS Borowiec station confirm the rotation of the defunct TOPEX/Poseidon satellite which spins with a period of approximately 10 s. The novelty of this work is the presentation of the sample results of the Chinese CZ-2C R/B target (NORAD catalogue number 31114) which is equipped (probably) with retroreflectors. Laser measurements to space debris is a very desirable topic for the next years, especially in the context of the Space Surveillance and Tracking (SST) activity. Some targets are very easy to track like defunct ENVISAT or TOPEX/Poseidon. On the other hand, there is a big population of different LEO targets with different orbital and physical parameters, which are challenging for laser ranging like small irregular debris and rocket boosters. [ABSTRACT FROM AUTHOR]

The present paper has used a comprehensive approach to study atmosphere pollution sources including the study of vertical distribution characteristics, the epicenters of occurrence and transport of atmospheric aerosol in North-West China under intensive dust storm registered in all cities of the region in April 2014. To achieve this goal, the remote sensing data using Moderate Resolution Imaging Spectroradiometer satellite (MODIS) as well as model-simulated data, were used, which facilitate tracking the sources, routes, and spatial extent of dust storms. The results of the study have shown strong territory pollution with aerosol during sandstorm. According to ground-based air quality monitoring stations data, concentrations of PM 10 and PM 2.5 exceeded 400 μg/m 3 and 150 μg/m 3 , respectively, the ratio PM 2.5 /PM 10 being within the range of 0.123–0.661. According to MODIS/Terra Collection 6 Level-2 aerosol products data and the Deep Blue algorithm data, the aerosol optical depth (AOD) at 550 nm in the pollution epicenter was within 0.75–1. The vertical distribution of aerosols indicates that the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) 532 nm total attenuates backscatter coefficient ranges from 0.01 to 0.0001 km −1  × sr −1 with the distribution of the main types of aerosols in the troposphere of the region within 0–12.5 km, where the most severe aerosol contamination is observed in the lower troposphere (at 3–6 km). According to satellite sounding and model-simulated data, the sources of pollution are the deserted regions of Northern and Northwestern China. [ABSTRACT FROM AUTHOR]

The paper has developed and proposed a statistical analysis method based on the Robust Satellite data analysis technique to detect seismic anomalies within the NOAA OLR dataset based on spatial/temporal continuity analysis. The proposed methods has been applied to statistical analyze about 3376 earthquake cases from September 01, 2007 to May 23, 2015. For statistical purposes, all these events have been divided into different types on the basis of the seismic parameters, including Southern or Northern Hemisphere earthquakes, earthquakes at different magnitude levels, earthquakes at different depth levels. The results show that the intensity of the anomalies increased with the magnitude increasing; anomalies are more easily observed during shallow earthquakes than the deep ones; more obvious anomalies could be detected for the earthquakes occurring in the Northern Hemisphere and the anomalies significant increases near the epicenter one day before and on the day of the earthquake. A similar anomaly shows that there are anomalies near the epicenters before earthquakes and the anomalies have some relation with the earthquake preparation on all seismic activity. All these statistical results can help create a better understanding of the preparation process of the earthquakes. [ABSTRACT FROM AUTHOR]

In remote sensing petrology fields, studies have mainly concentrated on spectroscopy remote sensing research, and methods to identify minerals and rocks are mainly based on the analysis and enhancement of spectral features. Few studies have reported the application of thermodynamics for lithology identification. This paper aims to establish a thermal characteristic index ( TCI ) to explore rock thermal behavior responding to defined environmental systems. The study area is located in the northern Qinghai Province, China, on the northern edge of the Qinghai-Tibet Plateau, where mafic-ultramafic rock, quartz-rich rock, alkali granite rock and carbonate rock are well exposed; the pixel samples of these rocks and vegetation were obtained based on relevant indices and geological maps. The scatter plots of TCI indicate that mafic-ultramafic rock and quartz-rich rock can be well extracted from other surface objects when interference from vegetation is lower. On account of the complexity of environmental systems, three periods of TCI were used to construct a three-dimensional scatter plot, named the multi-temporal thermal feature space (MTTFS) model. Then, the Bayes discriminant analysis algorithm was applied to the MTTFS model to extract rocks quantitatively. The classification accuracy of mafic-ultramafic rock is more than 75% in both training data and test data, which suggests TCI can act as a sensitive indicator to distinguish rocks and the MTTFS model can accurately extract mafic-ultramafic rock from other surface objects. We deduce that the use of thermodynamics is promising in lithology identification when an effective index is constructed and an appropriated model is selected. [ABSTRACT FROM AUTHOR]

A new digital low-frequency receiver system has been developed at Wuhan University for sensitive reception of low-latitude broadband Extremely Low Frequency (ELF) and Very Low Frequency (VLF) radio waves originating from either natural or artificial sources. These low-frequency radio waves are useful for ionospheric remote sensing, geospace environment monitoring, and submarine communications. This paper presents the principle and architecture of the system framework, including magnetic loop antenna design, low-noise analog front-end and digital receiver with data sampling and transmission. A new structure is adopted in the analog front end to provide high common-mode rejection and to reduce interference. On basis of field programmable gate array (FPGA) device and Universal Serial Bus (USB) architecture, the digital receiver is developed along with time keeping and synchronization module. The validity and feasibility of the self-developed ground-based ELF/VLF receiver system is evaluated by first results of experimental data that show the temporal variation of broadband ELF/VLF wave spectral intensity in Wuhan (30.54 °N, 114.37 °E). In addition to the acquisition of VLF transmitter signals at various frequencies, tweek atmospherics are also clearly captured to occur at multiple modes up to n = 6. [ABSTRACT FROM AUTHOR]

Desertification is a serious ecological, environmental, and socio-economic threat to the world, and there is a pressing need to develop a reasonable and reproducible method to assess it at different scales. In this paper, the Hogno Khaan protected area in Mongolia was selected as the study area, and a quantitative method for assessing land cover change and desertification assessment was developed using Landsat TM/ETM+ data on a local scale. In this method, NDVI (Normalized Difference Vegetation Index), TGSI (Topsoil Grain Size Index), and land surface albedo were selected as indicators for representing land surface conditions from vegetation biomass, landscape pattern, and micrometeorology. A Decision Tree (DT) approach was used to assess the land cover change and desertification of the Hogno Khaan protected area in 1990, 2002, and 2011. Our analysis showed no correlation between NDVI and albedo or TGSI but high correlation between TGSI and albedo. Strong correlations (0.77–0.92) between TGSI and albedo were found in the non-desertification areas. The TGSI was less strongly correlated with albedo in the low and non desertification areas, at 0.70 and 0.92; respectively. The desertification of the study area is increasing each year; in the desertification map for 1990–2002, there is a decrease in areas of zero and low desertification, and an increase in areas of high and severe desertification. From 2002 to 2011, areas of non desertification increased significantly, with areas of severe desertification also exhibiting increase, while areas of medium and high desertification demonstrated little change. [ABSTRACT FROM AUTHOR]

This paper reports a study of spatial and temporal variations of columnar averaged concentration of CO 2 and CH 4 over India using SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) and Greenhouse gas Observing SATellite (GOSAT) data. Comparison of these data with the global view National Oceanic and Atmospheric Administration (NOAA) land data and also location specific flask data is made. The temporal variation in column averaged global CO 2 is similar to that over India and it is also similar to the NOAA surface flask data and global view. The variation in NOAA surface CH 4 is location dependent and its global view appears to vary seasonally in opposite phase with the column averaged CH 4 values from satellites, reflecting the limited comparability of surface and column averaged data. Over India the CO 2 maximum is in May and minimum in August/September while for CH 4 the maximum is in September and minimum in February/March. The seasonal variation of CH 4 over India is correlated with the eastern coastal rice cultivation. [ABSTRACT FROM AUTHOR]

This paper describes the development and validation of a transportable active transponder designed for the image calibration of Korea Multi-Purpose Satellite-5 (KOMPSAT-5) with a synthetic aperture radar (SAR). Ground targets are essential in SAR image calibration. The environment for the deployment of ground targets for SAR image calibration should provide uniformity and minimum interference. The Amazon or deserts are regarded as desirable environments. However, such environments for SAR image calibration are difficult to find in Korea. Thus, it will be advantageous to have an active transponder whose performance will not be severely limited by the absence of such uniform environment. We have therefore developed an active transponder which has an adjustable internal delay and into which the orbit data of an arbitrary satellite can be loaded. The stored obit data with the aid of an internal global positioning system (GPS) receiver and gyroscope enables the active transponder to point to a selected satellite. In addition, a virtual deployment of the active transponder is possible due to its adjustable internal delay. Thus, the developed active transponder can be deployed at any place without environmental constraint. The performance of the developed active transponder is validated using the satellite TerraSAR-X, which is already in operation. The test results show that the active transponder is successfully compliant with the requirements for KOMPSAT-5 image calibration. [ABSTRACT FROM AUTHOR]

There are extensive reports of ionospheric disturbances before the great 2008 Wenchuan earthquake, which are possibly explained by seismogenic electric field hypotheses linked with the aerosols injected in atmosphere. This paper attempts to investigate the possible change of atmospheric aerosol optical depth (AOD) associated with this earthquake by using MODIS data from both Terra and Aqua satellites. The result shows a clear enhancement of AOD along the Longmenshan faults 7 days before the quake, which is 1 day and 4 days earlier than the reported negative and positive ionospheric disturbances, respectively, and is 1 day earlier than or quasi-synchronism with other reported atmospheric anomalies including air temperature, outgoing longwave radiation and relative humidity. Particularly, the spatial distribution of AOD enhancement is very local and it is correlated well with the active faults and surface ruptures. We suggest that this unique enhancement could be associated with the Lithosphere–Atmosphere–Ionosphere coupling process during the preparation of the Wenchuan earthquake. [ABSTRACT FROM AUTHOR]

Nowadays, GNSS-Reflectometry (GNSS-R) can be a new promising remote sensing tool in the ocean, snow/ice and land surfaces, e.g., vegetation biomass monitoring. Although GNSS-R provides a potentially special L-band multi-angular and multi-polarization measurement, the theoretical vegetation scattering properties and mechanisms for GNSS-R are not understood clearly. In this paper, the GNSS-R vegetation polarization scattering properties are studied and modeled at different incidence angles (specular direction). The bistatic scattering model Bi-mimics is employed, which is the first-order radiative transfer equation. As a kind of forest stand, the Aspen's crown layer is composed of entire leaves, and its parameters in Mimics handbook are used as model input. The specular circular polarizations (co-polarization RR and cross-polarization LR) are simulated. For cross-polarization, the received polarization is assumed as a linear (horizontal and vertical) polarizations and ±45° linear polarizations. Therefore, the HR VR, +45R and −45R polarizations are simulated here. Contributions from different scattering components at RR, LR and VR polarization are also presented. For co-polarization, it is large in the whole specular angles (10-80°). The scattering trends of the other cross polarization (HR, LR, +45R and −45R) are a little similar when compared to the RR and RV. Therefore, the RHCP and V polarizations are more favorable to collect the reflected signals. The trunk heights and crown depths do not affect the scattering trends of RR, RV and RL, while the trunk height has some effect on the scattering amplitude of different polarizations. The azimuth angle has more effects on RR, RL and RV scattering, especially in lower than 50°. The observation angles and polarization combinations are extremely important for GNSS-R remote sensing. [ABSTRACT FROM AUTHOR]