China-Brazil Earth Resources Satellite Research Articles

Unscented Kalman filter and smoothing applied to attitude estimation of artificial satellites

This article uses the state smoothing methodology applied to nonlinear systems to refine the attitude of artificial satellites. In this paper, simulated data of telemetry and ephemeris of a satellite with the specifications of China Brazil Earth Resources Satellite are considered and the dynamic system is described by the set of kinematic equations in terms of the Euler angles and the bias vector of gyroscope. The estimator used to determine the forward estimates in time is the Unscented Kalman filter, while the Rauch–Tung–Striebel fixed interval estimator makes the estimate backward time. The results show that, although the time of the estimation process is slightly increased, the smoother presents estimated attitude and bias closer to the real values than the estimated values when using only the Unscented Kalman filter. Therefore, the smoother can be considered as a technique that provides refined measurements of the attitude and bias of the gyroscope that may serve to calibrate the Kalman filter for next estimates.

Sequential nonlinear estimation: regularized particle filter applied to the attitude estimation problem with real data

The aim of this work is to analyze the robustness and computational effort of particle filter when applied to the attitude and gyro bias estimation problem. The particle filter is based on the sampling method for sequential importance, in which the basic idea is to represent the density function posteriori by a set of random samples (particles) associated with their respective weights. However, after a few interactions, degeneration of particles may occur. To avoid degeneration, the regularized version of the particle filter was applied. In such estimator, the particles with lower weight are discarded and those with greater weight give rise to new particles close to the areas of the highest probability. For analysis, the attitude dynamical model is described by quaternions, and the observation vector, composed by real data of attitude sensors that are on board the CBERS-2 (China–Brazil Earth Resource Satellite). The attitude sensors available are digital sun sensor, infrared Earth sensor, and mechanical gyros. The attitude and bias of gyro estimated by the regularized particle filter are compared with the unscented Kalman filter and the results show that for systems that do not have a high degree of nonlinearity, and under consideration of real data, the unscented Kalman filter has shown better performance than the regularized particle filter.

Improvement of CBERS-3 satellite imaging camera components by plasma immersion ion implantation on SS304

Camera components made of stainless steel (SS304) and Ti-6Al-4V alloy are used as anti-vibrator stoppers (pin-pull) for the focusing imaging system embedded in satellites that are remotely operated. Vibration tests for the certification China-Brazil Earth Resources Satellite 3 (CBERS-3) generated galling problems in some parts of these components. Nitrogen plasma immersion ion implantation (N-PIII) was an useful technique to enhan-ce the surface properties of these components and consequently to overcome such problems. This work shows the characterization of implanted SS304 samples to explain the surface modifications that allow the improving of the mentioned components. X-ray diffraction (XRD) analysis of the samples indicated that N-PIII at 12 kV form expanded austenite phase with 0.7 ?m of thickness and atomic force microscopy (AFM) showed a typical nodular morphology at the surface. Hardness measurements revealed a significant increase in the surface hardness after N-PIII treatment. Wear tests using a pin-on-disc device showed an increase in wear resistance and a reduction in the friction coefficient. Such observed surface modifications successfully prevented galling in the pin-pull camera parts through the reduction of the adhesive mechanisms.

The use of CBERS (China-Brazil Earth Resources Satellite) to trace the dynamics of total suspended matter at an urbanized coastal area

ABSTRACT The distribution of organic and inorganic particles in the water column, or the total suspended matter (TSM), responds to local and remote oceanographic and meteorological processes, potentially impacting biogeochemical cycles. In shallow coastal areas, where particles have distinct origins and compositions and vary in different time scales, the use of remote sensing tools for monitoring and tracing this material is highly encouraged due to the high temporal and spatial data resolution. The objective of this work was to understand the variability of in situ TSM at Santos Bay (Southeastern Brazil) and its response to oceanographic and meteorological conditions. We also aimed to verify the applicability of the satellite data from CBERS-2 sensor in order to map the dynamics of TSM in this region. Our results have shown that the distribution of TSM in Santos Bay varied consistently with winds, currents and tidal cycles, with significant relationships emphasizing the role of south-western winds and spring tides. Neap tides and eastern winds, along with rainfall, play an important role in the input of organic matter into the bay. In conclusion, our analyses showed that the main patterns observed in situ regarding the responses of TSM to the ocean-meteorological processes could be reproduced in the CBERS-2 satellite data, after simple and standard methods of images processing. TSM data retrieval from CBERS-2 or other satellite sensors were shown to be feasible, becoming an essential tool for synoptic observations of the composition and quality of water, especially at urbanized and impacted coastal areas.

COMPARING TWO ANALYTICAL MODELS FOR ORBIT DETERMINATION USING GPS NAVIGATION SOLUTIONS

This work presents the results of two analytical orbit models for orbit determination using the navigation solutions provided by on-board GPS receivers. Herein one proposes to analyze two analytical orbit models that can be used both on-board and on ground control centers for quick orbit determination. One model is the NORAD SGP8 elements (Hoots and Roehrich, 1980) where the two-line elements (TLE) are the orbit data needed to compute orbits. The second one is the analytical orbit model of the CBERS (China-Brazil Earth Resources Satellite) mission named COE (CBERS Orbit Elements). The orbit determination (OD) system to generate the TLE and COE datasets is being developed to be implemented in the INPE (Brazilian Institute for Space Research) ground control center. The TLE may be disseminated regularly to allow image receiving ground stations to track the CBERS-4 satellite. The COE can be used in the control center to monitor the orbit and compute maintenance maneuvers. The paper describes the basics of the two models, and implements such models to fit in the least squares sense the GPS long arc navigation solution measurements. Results will be shown based on actual GPS measurements from CBERS satellites, comparing aspects of the fittings and the models propagation. Keywords: Orbit determination, GPS Navigation Solution, CBERS Orbit Elements, Two-Line Elements

Image fusion of Sentinel-2 and CBERS-4 satellites for mapping soil cover in the Wetlands of Pantanal

ABSTRACTAdvances in remote-sensing technology and the release of new sensor systems have been providing a wide range of optical and radar satellite images. The availability of such images gives new options for mapping relatively remote and sparsely settled territories. Given this, the main goal of this research was to perform a quantitative assessment of the quality of a set of fused images obtained by China–Brazil Earth Resources Satellite (CBERS-4) and Sentinel-2 satellites over the region of the Brazilian Pantanal, considering a zonal quantitative approach. Through the applied methods, we combined the spectral resolution of Sentinel-2 images with the spatial resolution of the CBERS-4 panchromatic band. The quantitative evaluation of the spectral and spatial quality of the fused products based on a regionalised approach was essential in order to understand the spatial variability of the fusion quality, according to each fusion method. Based on the obtained results, we observed that CBERS-4 panchromatic band could be satisfactorily applied for refining the spatial resolution of Sentinel-2 multispectral (MS) images according to À trous wavelet transform and downscaling cokriging image fusion methods, allowing the generation of MS fused images with a 5-m spatial resolution. The resulting fused images were used to map the spatial distribution of saline and crystalline lakes at high spatial resolution and with unprecedented spectral and spatial accuracies, according to a semiautomatic classification approach.

A lake data set for the Tibetan Plateau from the 1960s, 2005, and 2014.

Long-term datasets of number and size of lakes over the Tibetan Plateau (TP) are among the most critical components for better understanding the interactions among the cryosphere, hydrosphere, and atmosphere at regional and global scales. Due to the harsh environment and the scarcity of data over the TP, data accumulation and sharing become more valuable for scientists worldwide to make new discoveries in this region. This paper, for the first time, presents a comprehensive and freely available data set of lakes’ status (name, location, shape, area, perimeter, etc.) over the TP region dating back to the 1960s, including three time series, i.e., the 1960s, 2005, and 2014, derived from ground survey (the 1960s) or high-spatial-resolution satellite images from the China-Brazil Earth Resources Satellite (CBERS) (2005) and China’s newly launched GaoFen-1 (GF-1, which means high-resolution images in Chinese) satellite (2014). The data set could provide scientists with useful information for revealing environmental changes and mechanisms over the TP region.

First in-Flight Radiometric Calibration of MUX and WFI on-Board CBERS-4

Brazil and China have a long-term joint space based sensor program called China-Brazil Earth Resources Satellite (CBERS). The most recent satellite of this program (CBERS-4) was successfully launched on 7 December 2014. This work describes a complete procedure, along with the associated uncertainties, used to calculate the in-flight absolute calibration coefficients for the sensors Multispectral Camera (MUX) and Wide-Field Imager (WFI) on-board CBERS-4. Two absolute radiometric calibration techniques were applied: (i) reflectance-based approach and (ii) cross-calibration method. A specific site at Algodones Dunes region located in the southwestern portion of the United States of America was used as a reference surface. Radiometric ground and atmospheric measurements were carried out on 9 March 2015, when CBERS-4 passed over the region. In addition, a cross-calibration between both MUX and WFI on-board CBERS-4 and the Operational Land Imager (OLI) on-board Landsat-8 was performed using the Libya-4 Pseudo Invariant Calibration Site. The gain coefficients are now available: 1.68, 1.62, 1.59 and 1.42 for MUX and 0.379, 0.498, 0.360 and 0.351 for WFI spectral bands blue, green, red and NIR, respectively, in units of (W/(m2·sr·μm))/DN. These coefficients were determined with uncertainties lower than 3.5%. As a validation of these radiometric coefficients, cross-calibration was also undertaken. An evaluation of radiometric consistency was performed between the two instruments (MUX and WFI) on-board CBERS-4 and with the well calibrated Landsat-7 ETM+. Results show that the reflectance values match in all the analogous spectral bands within the specified calibration uncertainties.

Unscented Kalman Filter for Determination of Spacecraft Attitude Using Different Attitude Parameterizations and Real Data

The non-linear estimators are certainly the most important algorithms applied to real problems, especially those involving the attitude estimation of spacecraft. The purpose of this paper was to use real data of sensors to analyze the behavior of Unscented Kalman Filter (UKF) in attitude estimation problems when it is represented in different ways and compare it with the standard estimator for non-linear estimation problems. The robustness of the estimation was performed when this was subjected to imprecise initial conditions. The attitude parametrization was described in Euler angles, quaternion and quaternion incremental. The satellite China-Brazil Earth Resources Satellite and measurements provided by the Satellite Control Center of the Instituto Nacional de Pesquisas Espaciais were considered in the study. The results indicate that the behaviors for both estimators were equivalent for such parameterizations under the same conditions. However, comparing the Unscented Kalman Filter with the standard filter for non-linear systems, Extended Kalman Filter (EKF), it was observed that, in the presence of inaccurate initial conditions, the Unscented Kalman Filter presented a fast convergence whereas Extended Kalman Filter had problems and only converged later on.

Cloud and Snow Discrimination for CCD Images of HJ-1A/B Constellation Based on Spectral Signature and Spatio-Temporal Context

It is highly desirable to accurately detect the clouds in satellite images before any kind of applications. However, clouds and snow discrimination in remote sensing images is a challenging task because of their similar spectral signature. The shortwave infrared (SWIR, e.g., Landsat TM 1.55–1.75 µm band) band is widely used for the separation of cloud and snow. However, for some sensors such as the CBERS-2 (China-Brazil Earth Resources Satellite), CBERS-4 and HJ-1A/B (HuanJing (HJ), which means environment in Chinese) that are designed without SWIR band, such methods are no longer practical. In this paper, a new practical method was proposed to discriminate clouds from snow through combining the spectral reflectance with the spatio-temporal contextual information. Taking the Mt. Gongga region, where there is frequent clouds and snow cover, in China as a case area, the detailed methodology was introduced on how to use the 181 scenes of HJ-1A/B CCD images in the year 2011 to discriminate clouds and snow in these images. Visual inspection revealed that clouds and snow pixels can be accurately separated by the proposed method. The pixel-level quantitative accuracy validation was conducted by comparing the detection results with the reference cloud masks generated by a random-tile validation scheme. The pixel-level validation results showed that the coefficient of determination (R2) between the reference cloud masks and the detection results was 0.95, and the average overall accuracy, precision and recall for clouds were 91.32%, 85.33% and 81.82%, respectively. The experimental results confirmed that the proposed method was effective at providing reasonable cloud mask for the SWIR-lacking HJ-1A/B CCD images. Since HJ-1A/B have been in orbit for over seven years and these satellites still run well, the proposed method is helpful for the cloud mask generation of the historical archive HJ-1A/B images and even similar sensors.

A Method to Determine Appropriate Spatial Resolution for Hard Image Classification

This study analyzed the relationship between the spatial resolution and the hard classification effect based on pixel-based image classification, and then discussed how to determine appropriate spatial resolution. Thematic maps of winter wheat derived from 250 m MODIS image, 19.5 m China-Brazil Earth Resources Satellite (CBERS) image, and 2.44 m QuickBird image were used to examine the classification effect as a case study. It indicated that the “Pareto Boundaries” and the “within-class variability” could be used to determine the coarsest and the highest resolution for hard classification, respectively. The methods proposed in this study should be useful to guide how to select appropriate spatial resolution for land cover mapping.

A remote sensing image fusion method based on feedback sparse component analysis

We propose a new remote sensing image (RSI) fusion technique based on sparse blind source separation theory. Our method employs feedback sparse component analysis (FSCA), which can extract the original image in a step-by-step manner and is robust against noise. For RSIs from the China–Brazil Earth Resources Satellite, FSCA can separate useful surface feature information from redundant information and noise. The FSCA algorithm is therefore used to develop two RSI fusion schemes: one focuses on fusing high-resolution and multi-spectral images, while the other fuses synthetic aperture radar bands. The experimental results show that the proposed method can preserve spectral and spatial details of the source images. For certain evaluation indexes, our method performs better than classical fusion methods.

Nonlinear Least Squares Method for Gyros Bias and Attitude Estimation Using Satellite Attitude and Orbit Toolbox for Matlab

The knowledge of the attitude determination is essential to the safety and control of the satellite and payload, and this involves approaches of nonlinear estimation techniques. Here one focuses on determining the attitude and the gyros drift of a real satellite CBERS-2 (China Brazil Earth Resources Satellite) using simulated measurements provided by propagator PROPAT Satellite Attitude and Orbit Toolbox for Matlab. The method used for the estimation was the Nonlinear Least Squares Estimation (NLSE). The attitude dynamical model is described by nonlinear equations involving the Euler angles. The attitude sensors available are two DSS (Digital Sun Sensor), two IRES (Infra-Red Earth Sensor), and one triad of mechanical gyros. The two IRES give direct measurements of roll and pitch angles with a certain level of error. The two DSS are nonlinear functions of roll, pitch, and yaw attitude angles. Gyros are very important sensors, as they provide direct incremental angles or angular velocities. However gyros present several sources of error of which the drift is the most troublesome. Results show that one can reach accuracies in attitude determination within the prescribed requirements, besides providing estimates of the gyro drifts which can be further used to enhance the gyro error model.

Unscented Kalman filter for spacecraft attitude estimation using modified Rodrigues parameters and real data

In this paper, a sigma-point Kalman filter formulation for attitude estimation is derived using the modified Rodrigues parameters and real data of attitude sensors. The unscented Kalman filter algorithm is used for attitude estimation and the gyro-based model is considered for attitude propagation. In this study, the attitude of satellite is estimated using real data supplied by gyros, Earth sensors and Sun sensors that are on board of the CBERS-2 (China Brazil Earth Resources Satellite). The results show that even with a sparse set of measures, the UKF with MRP shows results similar to those obtained when directly uses the Euler angles.

PCA-based sea-ice image fusion of optical data by HIS transform and SAR data by wavelet transform

Sea ice as a disaster has recently attracted a great deal of attention in China. Its monitoring has become a routine task for the maritime sector. Remote sensing, which depends mainly on SAR and optical sensors, has become the primary means for sea-ice research. Optical images contain abundant sea-ice multi-spectral information, whereas SAR images contain rich sea-ice texture information. If the characteristic advantages of SAR and optical images could be combined for sea-ice study, the ability of sea-ice monitoring would be improved. In this study, in accordance with the characteristics of sea-ice SAR and optical images, the transformation and fusion methods for these images were chosen. Also, a fusion method of optical and SAR images was proposed in order to improve sea-ice identification. Texture information can play an important role in sea-ice classification. Haar wavelet transformation was found to be suitable for the sea-ice SAR images, and the texture information of the sea-ice SAR image from Advanced Synthetic Aperture Radar (ASAR) loaded on ENVISAT was documented. The results of our studies showed that, the optical images in the hue-intensity-saturation (HIS) space could reflect the spectral characteristics of the sea-ice types more efficiently than in the red-green-blue (RGB) space, and the optical image from the China-Brazil Earth Resources Satellite (CBERS-02B) was transferred from the RGB space to the HIS space. The principal component analysis (PCA) method could potentially contain the maximum information of the sea-ice images by fusing the HIS and texture images. The fusion image was obtained by a PCA method, which included the advantages of both the sea-ice SAR image and the optical image. To validate the fusion method, three methods were used to evaluate the fused image, i.e., objective, subjective, and comprehensive evaluations. It was concluded that the fusion method proposed could improve the ability of image interpretation and sea-ice identification.

Least Squares Method for Attitude Determination Using the Real Data of CBERS-2 Satellite

his work is applied to the dynamics of rotational motion of artificial satellites, that is, itsorientation (attitude) with respect to an inertial reference system. The attitude determination involvesapproaches of nonlinear estimation techniques, which knowledge is essential to the safety and controlof the satellite and payload. Here one focuses on determining the attitude of a real satellite: CBERS-2(China Brazil Earth Resources Satellite). This satellite was launched in 2003 and were controlled andoperated in turns by China (Xi’an Control Center) and Brazil (Satellite Control Center). Its orbit isnear polar sun-synchronous with an altitude of 778km, crossing Equator at 10:30am in descendingdirection, frozen perigee at 90 degrees, and providing global coverage of the world every 26 days.The attitude dynamical model is described by nonlinear equations involving the Euler angles. Theattitude sensors available are two DSS (Digital Sun Sensor), two IRES (Infra-Red Earth Sensor), andone triad of mechanical gyros. The two IRES give direct measurements of roll and pitch angles with acertain level of error. The two DSS are nonlinear functions of roll, pitch, and yaw attitude angles. Thegyros furnish the angular measurements in the body frame reference system. Gyros are very importantsensors, as they provide direct incremental angles or angular velocities. They can sense instantaneousvariations of nominal velocities. An important feature is that it allows the replacement of complexmodels (different torques acting on the space environment) by using their measurements to turn thedynamical equations into simple kinematic equations. However gyros present several sources of errorof which the drift is the most troublesome. Such drifts yield along time an accumulation of errorswhich must be accounted for in the attitude determination process. Herein one proposes to estimatethe attitude and the drift of the gyros using the Least SquaresMethod. Results show that one can reachaccuracies in attitude determination within the prescribed requirements, besides providing estimatesof the gyro drifts which can be further used to enhance the gyro error model.

Landslide spatial susceptibility mapping by using GIS and remote sensing techniques: a case study in Zigui County, the Three Georges reservoir, China

Landslides are one of the most destructive phenomena in nature and damage both property and lives every year. In this paper, a logistic regression model with datasets developed via a geographic information system and remotely sensed data was used to create a landslide spatial susceptibility map for the Three Gorges Project reservoir region on the Yangtze River in Zigui County. The five causative factors used in the logistic regression model were evaluated in different ways: topographic slope and topographic aspect were derived from a topographical map at 1:50,000 scale; bed rock-slope relationship and lithology were obtained from a geological map at 1:50,000 scale; and fractional vegetation cover (FVC), which represents the reduced frequency of landslides due to the vegetation canopy and ground cover and is also one of the most difficult parameters to estimate over broad geographic areas, was generated using a back propagation neural network (BPNN) method based on CBERS (China–Brazil Earth Resources Satellite) data, the results of which were compared with values measured in the field. The obtained Pearson correlation coefficient (r) was 0.899. Then, the FVC factor and the other four factors were used as the input to a logistic regression model. By integrating the five factor maps in the geographical information system (GIS) via pixel-based computing, the landslide spatial susceptibility map was obtained. The study area was reclassified into four categories of landslide susceptibility: severe, moderate, low, and very low. Approximately 15.0 % of the study area was identified as severe susceptibility, and very low, low, and moderate susceptibility zones covered 21.8, 41.7, and 21.5 % of the area, respectively. These results have an accuracy of 78.90 %. Thus, by using a logistic regression model in a GIS environment, a spatial susceptibility map of landslides can be obtained, and the regions in Zigui County that are susceptible to landslides and need immediate protective and mitigation measures can be identified.

Dynamic remote sensing interpretation of geological disasters in Nanping City of Fujian Province using CBERS serial data

It is important to expand the application of domestic satellite data such as China- Brazil Earth Resource Satellite( CBERS) images for backtracking interpretation research in such areas year- by- year. In this paper,with the Nanping City in northwestern Fujian Province as the study area,the authors collected the related eleven CBERS images acquired during 1999- 2008,and established the image interpretation keys to the disasters. The visual interpretation method based on GIS platform was used in combination with the field studies,and a total of 2 059 interpreted disaster points were obtained during the past 8 years,with no data obtained in 2001 and 2002. The results of dynamic analysis show that the geological disasters in he study area were mainly the shallow landslides that concentratedly happened in 2000 and 2005. They reached 582 and 766 sites respectively,possessed 65% of the total landslides,and were distributed near residential and traffic districts,showing heterogeneous temporal and spatial characteristics. It is held that CBERS images can be employed for dynamic survey of geological disasters.

On the effects of each term of the geopotential perturbation along the time I: Quasi-circular orbits

This paper provides a useful new method to determine minimum and maximum range of values for the degree and order of the geopotential coefficients required for simulations of orbits of satellites around the Earth. The method consists in a time integration of the perturbing acceleration coming from each harmonic of the geopotential during a time interval T. More precisely, this integral represents the total velocity contribution of a specific harmonic during the period T. Therefore, for a pre-fixed minimum contribution, for instance 1×10-8m/s during the period of time T, any harmonic whose contribution is below this value can, safely, be neglected. This fact includes some constraints in the degree and order of the terms which are present in the geopotential formula, saving computational efforts compared to the integration of the full model. The advantage of this method is the consideration of other perturbations in the dynamics (we consider the perturbations of the Sun, the Moon, and the direct solar radiation pressure with eclipses), since these forces affect the value of the perturbation of the geopotential, because these perturbations depend on the trajectory of the spacecraft, that is dependent on the dynamical model used. In this paper, we work with quasi-circular orbits and we present several simulations showing the bounds for the maximum degree and order (M) that should be used in the geopotential for different situations, e. g., for a satellite near 500km of altitude (like the GRACE satellites at the beginning of their mission) we found 35⩽M⩽198 for T=1day. We analyzed the individual contribution of the second order harmonic (J2) and we use its behavior as a parameter to determine the lower limit of the number of terms of the geopotential model. In order to test the accuracy of our truncated model, we calculate the mean squared error between this truncated model and the “full” model, using the CBERS (China-Brazil Earth Resources Satellite) satellite in this test.

Earth Observation Applications in Brazil with Focus on the CBERS Program

Abstract-Brazil is a large and diverse country and almost all remote sensing application fields can be developed there: oceanographic, agricultural, environmental, urban, etc. Some applications are instrumental for environmental governance, some are very useful for business and others are devoted to research. As part of its remote sensing development, Brazil counts on the CBERS (China-Brazil Earth Resources Satellite) Program. Applications and accomplishments of the CBERS Program are presented in this article.