China-Brazil Earth Resources Satellite Research Articles

Simultaneous estimation of five temporally regular land variables at seven spatial resolutions from seven satellite data using a multi-scale and multi-depth convolutional neural network

Various satellite sensors have provided a huge amount of observations of Earth's environment at variable spatial and temporal resolutions. Many global coarse-resolution land products have been generated from single-satellite data, but global temporally regular land products at fine spatial resolutions (say 10-30 m) are scarce because of infrequent observations. An ideal inversion framework can estimate multiple global continuous land variables at different spatial resolutions by combining all sources of satellite data. This paper proposes a new framework that can estimate five land variables simultaneously from the top-of-atmosphere (TOA) reflectance acquired by seven satellite sensors based on a multi-scale and multi-depth convolutional neural network (MSDCNN). This framework enables us to estimate temporally regular land variables at as fine as 10 m spatial resolution by transforming information from satellite data at coarser spatial resolutions. The estimated land variables include Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), shortwave albedo, visible albedo, and spectral reflectance. Seven sensors that acquire data at different spatial resolutions, include Visible Infrared Imaging Radiometer Suite (VIIRS) (750 m), Moderate Resolution Imaging Spectroradiometer (MODIS) (500 m), Fengyun-3 (FY-3) Medium Resolution Spectral Imager (MERSI) (250 m), China-Brazil Earth Resources Satellite 04 (CBERS-04) Wide Field Imager (WFI) (73 m), Landsat 8 Operational Land Imager (OLI) (30 m), Gaofen-1 (GF-1) Wide-Field-of-View (16 m) and Sentinel 2 A/B Multispectral Imager (MSI) (10 m). This framework is mainly composed of four steps. First, a Shuffled Complex Evolution (SCE) optimization method is adopted to estimate these variables from VIIRS TOA reference. Second, a joint-output random forest regression (RF) method is used to link the satellite observations and the estimated values from step 1. Third, the six-type multi-resolution satellite observations are used to downscale the VIIRS TOA reflectance to six different spatial resolutions by using the MSDCNN. Finally, the downscaled six-resolution VIIRS TOA reflectance is fed into the multiple-variable RF model to estimate land variables. The framework was validated, and the results based on high-resolution reference maps from ImagineS network and time series shortwave albedo field values from Surface Radiation (SURFRAD) and Integrated Carbon Observation System (ICOS) network show that the retrieved variables had high validation accuracy, with root mean square error (RMSE) ranges of 0.361–0.489 (LAI), 0.023–0.120 (FAPAR), 0.013–0.026 (snow-free shortwave albedo), respectively. Comparison results of the retrieved multi-scale variables with the Sentinel 2 A/B (10 m), Landsat 8 (30 m), Global LAnd Surface Satellite (GLASS, 250 m, 500 m), MODIS (500 m), and VIIRS (750 m) products show that their values were close, with RMSE ranges of 0.107–0.273 (LAI), 0.015–0.027 (FAPAR), 0.003–0.007 (shortwave albedo), 0.001–0.007 (visible albedo), and 0.003–0.025 (spectral reflectance), respectively. The results of the direct validation as well as the product intercomparison show that this novel framework has the potential to be used in estimating global land variables at various spatial scales using a variety of satellite data sources.

A Data-Centric Approach for Wind Plant Instance-Level Segmentation Using Semantic Segmentation and GIS

Wind energy is one of Brazil’s most promising energy sources, and the rapid growth of wind plants has increased the need for accurate and efficient inspection methods. The current onsite visits, which are laborious and costly, have become unsustainable due to the sheer scale of wind plants across the country. This study proposes a novel data-centric approach integrating semantic segmentation and GIS to obtain instance-level predictions of wind plants by using free orbital satellite images. Additionally, we introduce a new annotation pattern, which includes wind turbines and their shadows, leading to a larger object size. The elaboration of data collection used the panchromatic band of the China–Brazil Earth Resources Satellite (CBERS) 4A, with a 2-m spatial resolution, comprising 21 CBERS 4A scenes and more than 5000 wind plants annotated manually. This database has 5021 patches, each with 128 × 128 spatial dimensions. The deep learning model comparison involved evaluating six architectures and three backbones, totaling 15 models. The sliding windows approach allowed us to classify large areas, considering different pass values to obtain a balance between performance and computational time. The main results from this study include: (1) the LinkNet architecture with the Efficient-Net-B7 backbone was the best model, achieving an intersection over union score of 71%; (2) the use of smaller stride values improves the recognition process of large areas but increases computational power, and (3) the conversion of raster to polygon in GIS platforms leads to highly accurate instance-level predictions. This entire pipeline can be easily applied for mapping wind plants in Brazil and be expanded to other regions worldwide. With this approach, we aim to provide a cost-effective and efficient solution for inspecting and monitoring wind plants, contributing to the sustainability of the wind energy sector in Brazil and beyond.

A Bayesian optimization hyperband-optimized incremental deep belief network for online battery behaviour modelling for a satellite simulator

Simulation tools play crucial roles in the stable implementation of space missions during satellite operations; they are typically utilized for satellite behaviour monitoring by comparing obtained telemetry values and predicted values according to pretrained prediction models. However, as telemetry data streams arrive in a chunk-by-chunk manner, a common practice is to retrain the employed simulation tool based on the newly arrived data, which results in the consumption of many computing resources and time lags. Therefore, an incremental learning algorithm is required to achieve accurate and fast satellite behaviour prediction. This paper proposes a Bayesian optimization hyperband-optimized incremental learning-based deep belief network (BOHB-ILDBN) to reproduce battery voltage behaviours, where the BOHB algorithm is utilized to obtain a group of optimal hyperparameter configurations to initialize a DBN model, the DBN model is incrementally updated by a fine-tuning process, and the variance difference between the actual and forecasted values is taken as the criterion for determining the completion of model training. Finally, the effectiveness and robustness of the model are verified on telemetry data obtained from an on-orbit sun-synchronous remote sensing satellite, the China–Brazil Earth Resources Satellite (CBERS-4A).

Geometric accuracy assessment and a framework for automatic sub-pixel registration of WFI images from CBERS-4, CBERS-4A, and Amazonia-1 satellites over Brazil

Brazil faces the challenge of conciliating food production with natural vegetation preservation, and the government has been investing in Earth Observation satellites to assist in this task. In this context, images from the Wide-Field Imager (WFI) sensor onboard the China-Brazil Earth Resources Satellite (CBERS-4), CBERS-4A, and Amazonia-1 have been used in remote sensing applications in Brazil. However, the geometric accuracy of the WFI data has not yet been documented in the literature, as it was done for other satellites/sensors such as TERRA/MODIS, Landsat-8/OLI, and Sentinel-2/MSI. This work presents the first study to assess the geometric accuracy of WFI images along different spectral channels, from multi-temporal images, and taking Sentinel-2/MSI images as a reference. Surface reflectance WFI images, Level-4 (orthorectified) with spatial resolution of 55 m (CBERS-4A) and 64 m (CBERS-4 and Amazonia-1) were analysed. Thousands of control points (CPs) were used to quantify the geometric accuracy of more than 300 WFI images and to construct multiple mapping functions to correct the displacements found. The NIR band was taken as a reference in the inter-channel (band-to-band) registration evaluation. In this case, all tiles had an RMSE average of fewer than 0.44 pixels. For the multi-temporal case, the CBERS-4A/WFI had the highest average RMSE (0.87 ± 0.34 pixels). Considering the Sentinel-2/MSI as a reference, there were observed shifts up to 1.45 pixels for CBERS-4/WFI and Amazonia-1/WFI and 2.5 pixels for CBERS-4A/WFI. Given the displacements found, image registration is needed for further time series analysis of WFI images for several applications that requiring precise sub-pixel registration (i.e., land use cover change). As a result, we propose a framework for the automatic registration of WFI images to properly foster the use of WFI images. This framework substantially reduced the misregistrations, decreasing the RMSE average to less than 0.29 pixels for the WFI images from the three satellites. The use of the 2nd degree polynomial as a mapping function proved to be appropriate for registering the WFI data.

ACCURACY ASSESSMENT OF CLOUD MASK DETECTION ALGORITHMS FOR CBERS-4 WFI IMAGERY

Abstract. Clouds limit the potential use of optical images. Proper clouds and cloud shadows detection are necessary steps for optical image applications. Few algorithms are flexible in detecting clouds in images with a limited number of bands, such as the Wide-Field Imager (WFI) sensor on board the China-Brazil Earth Resources Satellite (CBERS-4), which has four spectral bands (blue, green, red, and near-infrared). Therefore, this work aims to assess the accuracy of two cloud detection algorithms: CMASK and ATSA, and evaluate the influence of the ATSA configuration parameters. We selected four regions in Brazil for our analysis. In all cases, ATSA presented overall accuracy (OA) superior to CMASK. While the ATSA obtained OA greater than 0.91 for all analyzes, the OA from CMASK did not exceed 0.84. CMASK presented commission errors for the No Clear class (combination of Cloud and Cloud shadow) and inclusion errors for the Clear class close to zero. However, many errors of omission of clouds misclassified as the Clear class was observed. The ATSA algorithm presented a better balance between inclusion errors and omission errors. Our results can be used as guidance for choosing a cloud mask algorithm for the CBERS-4 WFI images and for analysis considering the images from WFI on board CBERS-4A and Amazonia-1, as they have similar characteristics.

Active Fire Mapping on Brazilian Pantanal Based on Deep Learning and CBERS 04A Imagery

Fire in Brazilian Pantanal represents a serious threat to biodiversity. The Brazilian National Institute of Spatial Research (INPE) has a program named Queimadas, which estimated from January 2020 to October 2020, a burned area in Pantanal of approximately 40,606 km2. This program also provides daily data of active fire (fires spots) from a methodology that uses MODIS (Aqua and Terra) sensor data as reference satellites, which presents limitations mainly when dealing with small active fires. Remote sensing researches on active fire dynamics have contributed to wildfire comprehension, despite generally applying low spatial resolution data. Convolutional Neural Networks (CNN) associated with high- and medium-resolution remote sensing data may provide a complementary strategy to small active fire detection. We propose an approach based on object detection methods to map active fire in the Pantanal. In this approach, a post-processing strategy is adopted based on Non-Max Suppression (NMS) to reduce the number of highly overlapped detections. Extensive experiments were conducted, generating 150 models, as five-folds were considered. We generate a public dataset with 775-RGB image patches from the Wide Field Imager (WFI) sensor onboard the China Brazil Earth Resources Satellite (CBERS) 4A. The patches resulted from 49 images acquired from May to August 2020 and present a spatial and temporal resolutions of 55 m and five days, respectively. The proposed approach uses a point (active fire) to generate squared bounding boxes. Our findings indicate that accurate results were achieved, even considering recent images from 2021, showing the generalization capability of our models to complement other researches and wildfire databases such as the current program Queimadas in detecting active fire in this complex environment. The approach may be extended and evaluated in other environmental conditions worldwide where active fire detection is still a required information in fire fighting and rescue initiatives.

CBERS data cubes for land use and land cover mapping in the Brazilian Cerrado agricultural belt

ABSTRACT The agricultural frontier expansion in the Cerrado biome made Brazil a leader in commodity exports and is changing its landscape. Hence, efforts to accurate land use and land cover (LULC) monitoring in this region are strategic, due to its role in Brazil’s food, environmental, and economic security policy. Thinking on planning and technical sovereignty in the spatial sector, the China–Brazil Earth Resources Satellite (CBERS) Program was launched to provide useful data for decision-makers to manage the Brazilian territory independently of external policies. Their data, especially from CBERS-4 Wide-Field Imager (CBERS-4/WFI), are largely applied in deforestation monitoring by remote sensing specialists but less applied than data from other image providers for machine learning-based LULC mapping due to the small number of spectral bands and limitations related to clouds and shadows detection. However, with advances in orbital data analysis, data cubes enabled storing and accessing large spatio-temporal analysis-ready data. Within this scope, the Brazil Data Cube Project (BDC) creates multidimensional data cubes from orbital sensors’ data for all Brazilian territory. We applied BDC CBERS-4/WFI data cubes to generate LULC classifications for the Extremo Oeste Baiano agricultural belt correspondent to the 2017/2018 and 2019/2020 harvest periods, at two levels of detail: broad and crop type, incorporating ground truth samples, crop calendar knowledge, and vegetation indices to a dense time series analysis approach. Overall Accuracies were equal to 0.87 and 0.89 for broad, and 0.91 and 0.94 for crop type classifications. The results indicate CBERS-4/WFI data cubes as a useful tool for improving crop monitoring in the Cerrado biome based on machine learning.

Rao-Blackwellized Particle Filter for the CBERS-4 attitude and gyros bias estimation

The Rao-Blackwellized Particle Filter (RaoBPF) and the Unscented Kalman Filter (UKF) were applied in this work to attitude and gyros bias estimation using simulated orbit and attitude measurement data for CBERS-4 (China Brazil Earth Resources Satellite 4) recently in operation. CBERS-4 was launched in 2014, controlled and operated in shifts by China (Xi’an Control Center) and Brazil (Satellite Control Center). Its orbit is heliosynchronous with an inclination of 98.504 degrees, a semi-major axis of 7148.865 km, eccentricity 1.1×10−3, crossing equador line at 10h30min in a descending direction with perigee frozen at 90 degrees, which establishes a commitment relationship between a satisfactory amount of solar irradiance, contrast between targets, and the presence of clouds. This configuration provides global coverage every 26 days. The real orbit and attitude measurements were provided by the Satellite Control Center of the National Institute for Space Research (CCS - INPE) from September 1st, 2015. The dynamic attitude model is described by quaternions. The available attitude sensors are two Digital Sun Sensors (DSS), two Infrared Earth Sensor (IRES) and a triad of mechanical gyroscopes. 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. The gyros furnish the angular measurements in the body frame reference system. Gyros provide direct incremental angles or angular velocities; however, they present several sources of error, and the drift is the most troublesome. Such drifts yield along time an accumulation of errors which must be accounted in the attitude determination process. The RaoBPF estimation method used to attitude and gyros bias estimation is a technique that exploits the state space structure in order to reduce the number of particles, decreasing the processing time, avoiding the computational effort common to the standard particle filter. The logical extension of the RaoBPF provides a more general model that can be divided into purely non-linear and conditionally linear-Gaussian aspects, which explores this structure, marginalizing the conditional linear parts and estimating them using exact filters, such as the Extended Kalman Filter (EKF). The results show that it is possible to achieve precision in determining attitudes within the prescribed requirements using the RaoBPF, with lower computational cost when compared to the standard particle filter and its branches, in addition to have competitive results such as the UKF.

Using Systems Architecture Views to Assess Integration Readiness Levels

Systems integration is a central element of the complex systems development effort. The integration readiness levels (IRL) scale complements technology readiness levels (TRL) to assess the system development maturity, representing risk levels for integrating technologies in a system. The increasing complexity of systems, due to multiple interfaces and an information-centric architecture, demands new systemic approaches to analyze the architecture and communicate IRL assessments. Therefore, this article developed a methodology to apply IRL using architecture views through the design structure matrix and domain mapping matrix. A case study analyzed technology introduction impacts and different integration strategies for space systems from China–Brazil Earth Resources Satellite program at the system and system-of-systems levels. Through the design science method, results show the pragmatic evaluation and validation for the proposed method and for the IRL scale itself, by professionals of the case study projects. The research draws suggestions for holistic system analysis and managerial communication applications using the proposed method and suggests a rationale for IRL assessment of legacy systems.

Monitoring Forest Change in the Amazon Using Multi-Temporal Remote Sensing Data and Machine Learning Classification on Google Earth Engine

Deforestation causes diverse and profound consequences for the environment and species. Direct or indirect effects can be related to climate change, biodiversity loss, soil erosion, floods, landslides, etc. As such a significant process, timely and continuous monitoring of forest dynamics is important, to constantly follow existing policies and develop new mitigation measures. The present work had the aim of mapping and monitoring the forest change from 2000 to 2019 and of simulating the future forest development of a rainforest region located in the Pará state, Brazil. The land cover dynamics were mapped at five-year intervals based on a supervised classification model deployed on the cloud processing platform Google Earth Engine. Besides the benefits of reduced computational time, the service is coupled with a vast data catalogue providing useful access to global products, such as multispectral images of the missions Landsat five, seven, eight and Sentinel-2. The validation procedures were done through photointerpretation of high-resolution panchromatic images obtained from CBERS (China–Brazil Earth Resources Satellite). The more than satisfactory results allowed an estimation of peak deforestation rates for the period 2000–2006; for the period 2006–2015, a significant decrease and stabilization, followed by a slight increase till 2019. Based on the derived trends a forest dynamics was simulated for the period 2019–2028, estimating a decrease in the deforestation rate. These results demonstrate that such a fusion of satellite observations, machine learning, and cloud processing, benefits the analysis of the forest dynamics and can provide useful information for the development of forest policies.

Coupling Land Use Analysis and Ecological Risk Assessment: A Study of the Three Gorges Reservoir Area, China

Exploring the ecological response of land use change is important for regional sustainable development and assists in understanding the profound consequences of this change. This paper proposes coupling a study of the evolution of land use with an assessment of ecological risk, based on a typical watershed in the hinterland of China's Three Gorges Reservoir area. Land use data for the Caotangxi watershed derived from Landsat images from 1990, 2000, 2004, and China–Brazil Earth Resource Satellite data from 2010 and 2016 were used to identify ecological risk based on land use changes. The spatial interaction process between land use and ecological risk changes from 2 spatial scales of grid and terrain was analyzed using geographic information system technology and an ecological risk index. The study area's ecological risk index demonstrated at first a worsening trend and then improvement during the period from 1990 to 2016. During 2004, there was a turning point in ecological risk evolution; this was most apparent in zones with a slope of 15–25 and >25°, as well as in the elevation zone between 300 and 1000 m. The proportion of serious-risk areas decreased sharply after 2010, and land function transformation resulted in improvement of ecological risk in the watershed during the 26 years from 1990 to 2016. Our study provides support for evaluating ecological risk trends and the development of land use transition theory in the Three Gorges Reservoir area. It is a typical description of land remediation and ecological restoration in this area.

Innovation path and contingencies in the China-Brazil Earth Resources Satellite program

The innovation paths in Complex Products and Systems (CoPS) industries are negotiated ex-ante among stakeholders, in projects or small batches, unlike mass production industries. Organizations dealing with CoPS must sharp their project portfolio to secure their operations and deploy their development strategy while facing diverse natures of project uncertainties. Literature is still evolving with empirical studies to master this critical challenge. The diamond approach for innovation and project management appraises uncertainty dimensions in projects to infer their contingencies and to examine portfolios for innovation. This research applied the diamond approach in a case study to reveal how the China-Brazil Earth Resources Satellite (CBERS) program overcome the above challenge. Results show the categorization for each space mission, a comparative analysis of how they contributed to the program objectives, the identification of strategic and operational projects, and depicted innovation patterns across the satellite product families. The research explored a market innovation of a sensor conceived specially to enhance a deforestation detection alert system to the Amazon biome. The diamond approach was valuable to perform this analysis and could be improved to represent the specificities of innovation types in complex systems. Organizations may benefit from this research by using successful strategies and lessons learned to address the above challenge.

CBERS DATA CUBE: A POWERFUL TECHNOLOGY FOR MAPPING AND MONITORING BRAZILIAN BIOMES

Abstract. Currently, the overwhelming amount of Earth Observation data demands new solutions regarding processing and storage. To reduce the amount of time spent in searching, downloading and pre-processing data, the remote Sensing community is coming to an agreement on the minimum amount of corrections satellite images must convey in order to reach the broadest range of applications. Satellite imagery meeting such criteria (which usually include atmospheric, radiometric and topographic corrections) are generically called Analysis Ready Data (ARD). Furthermore, ARD is being assembled into multidimensional data cubes, minimising preprocessing tasks and allowing scientists and users in general to focus on analysis. A particular instance of this is the Brazil Data Cube (BDC) project, which is processing remote sensing images of medium spatial resolution into ARD datasets and assembling them as multidimensional cubes of the Brazilian territory. For example, BDC users are released from performing tasks such as image co-registration , aerosol interference correction. This work presents a BDC proof of concept, by analysing a BDC data cube made with images from the fourth China-Brazil Earth Resources Satellite (CBERS-4) of one of the largest biodiversity hotspot in the world, the Cerrado biome. It also shows how to map and monitor land use and land cover using the CBERS data cube. We demonstrate that the CBERS data cube is effective in resolving land use and and land cover issues to meet local and national needs related to the landscape dynamics, including deforestation, carbon emissions, and public policies.

Coal fire detection and evolution of trend analysis based on CBERS-04 thermal infrared imagery

Coal fire hazards not only causes loss of coal resources but also induces serious geological hazards and environmental degeneration. Wuda Coalfield (China), known for its widespread coal fires, was used as the study area for this study. Owing to an inherent noise problem, CBERS (China & Brazil Earth Resource Satellite)-04 thermal infrared images have not been explored and applied in the field of coal fire detection. An adaptive-edge-threshold (AET) method was proposed to delineate coal fire maps based on nighttime CBERS-04 data from 2015 to 2020. Iterative Self-Organizing Data Analysis Technique Algorithm (ISODATA) was employed to cluster coal fire spots into different classes, then coal fire propagation directions were generated by connecting the clustering centers of the same class in chronological order. The results of quantitative analysis of coal fires showed that AET method achieved the highest accuracy among four coal fire recognition algorithms; the accuracy of AET in field verification reached 81.25%. The total area of coal fires increased by 137%, while the total intensity of coal fires decreased by 18% from 2015 to 2020. Overall accuracies of ISODATA clustering coal fire spots were between 90.25% and 100.00%, the Kappa coefficients were between 0.82 and 1.00. The evolution trend of coal fires obtained based on this clustering accuracy is reliable. The evolution trend of coal fires is as follows: coal fires of Class I spread to the northwest; coal fires of Class II first propagated to the northwest, then to the south and then to the west; coal fires of Class III essentially stayed in the same place; coal fires of Class IV spread to the northwest; coal fires of Class V spread to the southeast and then to the northeast.

Information items to improve Integration Readiness Levels evaluation

Abstract Systems integration is critical to the engineering of successful complex products and systems. The Technology Readiness Levels (TRL) scale assists in making decisions about the infusion of new technologies into complex systems. This scale presents challenges to represent the integration between technologies in a system. The Integration Readiness Levels (IRL) scale aims to address these challenges. A good practice for objective technology readiness assessments is to rely on evidence-based documentation. The TRL scale shows the evidence required for each level of the scale and the appropriate types of documents to collect this evidence, while the IRL scale proposes only the necessary evidence for each level. This research proposes a set of information items to document the required evidence for each level of the IRL scale. The results show the main stages of investigation and the successful tests of the proposal in a case study with spacecraft developed in the binational program China-Brazil Earth Resources Satellite (CBERS). This article contributes to the research in integration readiness assessments, aims to support professionals who carry out evidence-based evaluations and, consequently, may contribute to improving the accuracy of decision-making in the systems integration by using this scale.

Torrential rainfall-triggered shallow landslide characteristics and susceptibility assessment using ensemble data-driven models in the Dongjiang Reservoir Watershed, China

This study investigated the characteristics of rainfall-triggered landslides during the Typhoon Bilis in the Dongjiang Reservoir Watershed, China. The comparative shallow landslide susceptibility mappings (LSMs) were produced by the ensemble data-driven statistical models in a GIS environment. At first, the landslide inventory for the study area was prepared from the high-resolution QuickBird images, and China–Brazil Earth Resources Satellite images, and field survey. Other necessary data for landslide susceptibility analysis such as the amount of rainfall, geology, and topography were also collected from the respective agencies. Twelve predisposing factors were then prepared using this available dataset. To reduce the subjectivity of models and caution in the selection of predisposing factors, and to avoid the spatial autocorrelation redundancy, certainty factor approach was attempted to optimize these twelve set of parameters. For validating the accuracy of the model, the original landslide data were randomly divided into two parts: 70% (1545 landslides) for training the model and the remaining 30% (662 landslides) for validation. The verified results showed that using the optimized predisposing factors has a higher performance than using all the original twelve factors. The results of ensemble models also showed that LSM maps prepared using binary logistic regression (accuracy is 0.848) model are more accurate than those prepared using bivariate statistical analysis (accuracy is 0.837) model. Additionally, our analysis concludes that the short duration and high-intensity rainfall, drainage density, lithology, and curvature are the major influencing factors for landslide occurrences in this case study area. This research provides an improved understanding of the mechanism of landslides caused by the typhoons for the adjoining watersheds nearby the reservoir. The preliminary understandings and approach could also be applied in similar geological and rainfall-triggered case study sites in the other parts of the world for risk mitigation.

Nonlinear filtering for sequential spacecraft attitude estimation with real data: Cubature Kalman Filter, Unscented Kalman Filter and Extended Kalman Filter

This article compares the attitude estimated by nonlinear estimator Cubature Kalman Filter with results obtained by the Extended Kalman Filter and Unscented Kalman Filter. Currently these estimators are the subject of great interest in attitude estimation problems, however, mostly the Extended Kalman Filter has been applied to real problems of this nature. In order to evaluate the behavior of the Extended Kalman Filter, Unscented Kalman Filter and Cubature Kalman Filter algorithms when submitted to realistic situations, this paper uses real data of sensors on-board the CBERS-2 remote sensing satellite (China Brazil Earth Resources Satellite). It is observed that, for the case studied in this article, the filters are very competitive and present advantages and disadvantages that should be dealt with according to the requirements of the problem.

Continental-scale surface reflectance product from CBERS-4 MUX data: Assessment of atmospheric correction method using coincident Landsat observations

A practical atmospheric correction algorithm, called Coupled Moderate Products for Atmospheric Correction (CMPAC), was developed and implemented for the Multispectral Camera (MUX) on-board the China-Brazil Earth Resources Satellite (CBERS-4). This algorithm uses a scene-based processing and sliding window technique to derive MUX surface reflectance (SR) at continental scale. Unlike other optical sensors, MUX instrument imposes constraints for atmospheric correction due to the absence of spectral bands for aerosol estimation from imagery itself. To overcome this limitation, the proposed algorithm performs a further processing of atmospheric products from Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) sensors as input parameters for radiative transfer calculations. The success of CMPAC algorithm was fully assessed and confirmed by comparison of MUX SR data with the Landsat-8 OLI Level-2 and Aerosol Robotic Network (AERONET)-derived SR products. The spectral adjustment was performed to compensate for the differences of relative spectral response between MUX and OLI sensors. The results show that MUX SR values are fairly similar to operational Landsat-8 SR products (mean difference < 0.0062, expressed in reflectance). There is a slight underestimation of MUX SR compared to OLI product (except the NIR band), but the error metrics are typically low and scattered points are around the line 1:1. These results suggest the potential of combining these datasets (MUX and OLI) for quantitative studies. Further, the robust agreement of MUX and AERONET-derived SR values emphasizes the quality of moderate atmospheric products as input parameters in this application, with root-mean-square deviation lower than 0.0047. These findings confirm that (i) CMPAC is a suitable tool for estimating surface reflectance of CBERS MUX data, and (ii) ancillary products support the application of atmospheric correction by filling the gap of atmospheric information. The uncertainties of atmospheric products, negligence of the bidirectional effects, and two aerosol models were also identified as a limitation. Finally, this study presents a framework basis for atmospheric correction of CBERS-4 MUX images. The utility of CBERS data comes from its use, and this new product enables the quantitative remote sensing for land monitoring and environmental assessment at 20 m spatial resolution.

Comparison of CBERS-04, GF-1, and GF-2 Satellite Panchromatic Images for Mapping Quasi-Circular Vegetation Patches in the Yellow River Delta, China.

Vegetation in arid and semi-arid regions frequently exists in patches, which can be effectively mapped by remote sensing. However, not all satellite images are suitable to detect the decametric-scale vegetation patches because of low spatial resolution. This study compared the capability of the first Gaofen Satellite (GF-1), the second Gaofen Satellite (GF-2), and China-Brazil Earth Resource Satellite 4 (CBERS-04) panchromatic images for mapping quasi-circular vegetation patches (QVPs) with K-Means (KM) and object-based example-based feature extraction with support vector machine classification (OEFE) in the Yellow River Delta, China. Both approaches provide relatively high classification accuracy with GF-2. For all five images, the root mean square errors (RMSEs) for area, perimeter, and perimeter/area ratio were smaller using the KM than the OEFE, indicating that the results from the KM are more similar to ground truth. Although the mapped results of the QVPs from finer-spatial resolution images appeared more accurate, accuracy improvement in terms of QVP area, perimeter, and perimeter/area ratio was limited, and most of the QVPs detected only by finer-spatial resolution imagery had a more than 40% difference with the actual QVPs in these three parameters. Compared with the KM approach, the OEFE approach performed better for vegetation patch shape description. Coupling the CBERS-04 with the OEFE approach could suitably map the QVPs (overall accuracy 75.3%). This is important for ecological protection managers concerned about cost-effectiveness between image spatial resolution and mapping the QVPs.

Correção Geométrica de Imagens CBERS-4/PAN com Modelos Generalizados Usando como Referência Dados do Sistema Nacional de Gestão Fundiária

As imagens orbitais vêm sendo amplamente empregadas em diversas aplicações no contexto de observação da Terra, as quais exigem diferentes níveis de detalhamento e de acurácia posicional. O Programa CBERS (China–Brazil Earth Resources Satellite Program) originou-se de uma parceria entre Brasil e China no setor técnico-científico espacial. O satélite CBERS-4 é o quinto satélite do Programa CBERS e contém o sensor PAN, que coleta imagens pancromáticas com elemento de resolução espacial (GSD – Ground Sample Distance) de 5 m. Os estudos relacionados à análise da confiabilidade posicional e correção geométrica de imagens CBERS-4 ainda são limitados. Estudos prévios com imagens CBERS-4 PAN com diferentes níveis de processamento indicam deslocamentos posicionais significativos das imagens georreferenciadas, disponibilizadas pelo INPE (Instituto Nacional de Pesquisas Espaciais), incompatíveis com o seu GSD. Nesse sentido, o objetivo desse trabalho consistiu em investigar a aplicação de modelos matemáticos generalizados na correção geométrica de imagens CBERS-4 PAN usando limites das propriedades rurais do INCRA (Instituto Nacional de Colonização e Reforma Agrária) como pontos de controle. Destaca-se que esses limites estão disponíveis para propriedades de todo o Brasil, o que possibilita a replicação do trabalho em nível nacional. Foram consideradas imagens com diferentes níveis de correção prévia (níveis 1 e 2). As imagens de nível 1 são resultantes apenas da aplicação de procedimentos de calibração radiométrica, enquanto as de nível 2 são imagens de nível 1 corrigidas geometricamente a partir de informações de dados orbitais do satélite. Nos experimentos, considerou-se 3 (três) imagens no nível 1 e 1 (uma) imagem no nível 2. Adotaram-se os seguintes modelos generalizados: Polinomiais de ordem 1, 2 e 3; Projetiva e; Thin Plate Spline (TPS). Os modelos generalizados têm por vantagem não exigir o conhecimento dos parâmetros de aquisição do sistema, como a distância focal, o tamanho do sensor, dentre outras. Entretanto, exigem quantidade significativa de pontos de controle com distribuição uniforme ao longo da imagem. Para o processo de correção geométrica utilizou-se diferentes configurações de pontos de controle (30, 25, 20, 15 e 10) coincidentes com os limites das propriedades rurais georreferenciadas no Estado de Mato Grosso do Sul, que apresentam acurácia superior a 50 cm. A validação da correção geométrica foi realizada a partir do REQM (Raiz quadrado do Erro Quadrático Médio) nos pontos de verificação. A transformação Polinomial de ordem 1 apresentou valores elevados (maior que 10 GSD – 50 metros) de REMQ quando comparado aos demais modelos matemáticos, mesmo considerando 30 pontos de controle. O modelo Polinomial de ordem 2 apresentou comportamento mais consistente se comparado aos demais modelos. Mesmo ao considerar apenas 10 GCP apresentou REQM entre 1 e 2 GSD. Nesse modelo não há melhora significativa nos resultados mesmo aumentando a quantidade de pontos de controle. Nos demais modelos (TPS, Projetiva e Polinomial de ordem 3), verificou-se um aumento significativo no REQM ao se reduzir a quantidade de pontos. As imagens utilizadas nesse trabalho recobrem parte do Estado de Mato Grosso do Sul, o qual engloba a maior parte do Pantanal, considerado patrimônio natural da humanidade. Essas imagens orbitais, portanto, podem contribuir para o mapeamento e monitoramento de seus recursos naturais e, consequentemente, na proteção desse patrimônio.