Single-angle sensors typically use radiative transfer simulations based on Lambertian surface, even though the surface reflectance obtained exhibits directional characteristics. Fully accounting for the contribution of surface directional characteristics to the top of atmosphere (TOA) reflectances can further improve the accuracy of aerosol retrievals. In this study, we propose an enhanced aerosol retrieval algorithm for the Particulate Observing Scanning Polarimeter (POSP), by further considering the surface directional characteristics. Combined with an updated aerosol model, this approach achieves high-accuracy retrievals. We used historical bidirectional reflectance distribution function (BRDF) products to construct stable surface constraints. By exploring the strong empirical statistical relationships between adjacent blue bands, we have realized the joint inversion of multiple blue bands. In addition, we used an optimization algorithm that incorporates boundary constraints, simultaneously accounting for errors in the surface constraint model, and satellite observation errors. The global aerosol optical thickness (AOD) at 550 nm over land was retrieved from November 2021 to April 2022. Validation of POSP AOD versus AErosol RObotic NETwork (AERONET) data shows a high consistency, with correlation coefficient (R) of 0.93, root mean square error (RMSE) of 0.086, bias of 0.004, fraction within expected error (EE) of 80.8%, and fraction within Global Climate Observing System (GCOS) of 52.1%. Comparison with MODIS aerosol products shows that the accuracy of POSP AOD is better than that of MODIS AOD. According to the matching results, for DB, R of 0.936/0.907 and fraction within EE of 82.2%/74.9% (POSP/MODIS DB); for DT, R of 0.937/0.915 and fraction within EE of 83.5%/ 72.0% (POSP/MODIS DT). The spatial distribution difference between POSP AOD and DB AOD is small, indicating good consistency, and POSP AOD captured the intensity of aerosol pollution well. In summary, the enhanced aerosol algorithm achieves reliable high-precision AOD retrieval and because of its generality could also be applied to other sensors.

Numerous studies have been conducted on missing data recovery in remote sensing images, such as cloud removal and dead pixels restoration. Nevertheless, reconstructing continuous, extensive, and complete missing areas still poses a significant challenge. In this letter, we propose a new architecture named Extend-generative adversarial network (GAN), which leverages only a low-resolution image with relaxed requirements on spatial resolution and acquisition time as a condition to reconstruct a high-resolution image with large-scale missing areas. We equip Extend-GAN with learnable adaptive region normalization (LARN) to adjust the intensity distribution of pixels to reduce color distortion. We also introduce a new loss function into the training process of Extend-GAN, namely the structural similarity (SSIM)-based triplet loss, which helps to preserve the between missing parts and known regions. Gaofen-2 and Landsat-9 image pairs are used to validate the proposed method. Extend-GAN performs better when comprehensively evaluated on visual effect, quantitative metrics, processing speed, etc. Code is available athttps://github.com/yc-cui/Extend-GAN.

Pansharpening requires the fusion of a low-spatial-resolution multispectral (LRMS) image and a panchromatic (PAN) image with rich spatial details to obtain a high-spatial-resolution multispectral (HRMS) image. Recently, deep learning (DL)-based models have been proposed to tackle this problem and have made considerable progress. However, most existing methods rely on the conventional observation model, which treats LRMS as a blurred and downsampled version of HRMS. This observation model may lead to unsatisfactory performance and limited generalization ability at full-resolution evaluation, resulting in severe spectral and spatial distortion, as we observed that while DL-based models show significant improvement over traditional models on reduced-resolution evaluation, their performances deteriorate significantly at full resolution. In this article, we rethink the observation model and present a novel perspective from HRMS to LRMS and propose a pixel-wise ensembled masked autoencoder (PEMAE) to restore HRMS. Specifically, we consider LRMS as the result of pixel-wise masking on HRMS. Thus, LRMS can be seen as a natural input of a masked autoencoder. By ensembling the reconstruction results of multiple masking patterns, PEMAE obtains HRMS with both spectral information of LRMS and spatial details of PAN. In addition, we employ a linear cross-attention mechanism to replace the regular self-attention to reduce the computation to linear time complexity. Extensive experiments demonstrate that PEMAE outperforms state-of-the-art (SOTA) methods in terms of quantitative and visual performance at both reduced- and full-resolution evaluations. The codes are available athttps://github.com/yc-cui/PEMAE.

Obtaining accurate atmospheric parameters, e.g., aerosol optical depth (AOD) and column water vapor (CWV), is important for the quantitative atmospheric correction (AC) of the high-spatial resolution remote sensing images. However, due to the strong temporal and spatial changes of the atmospheric parameters, it will be a challenge to ensure spatiotemporal registration of the satellite images given the AC parameters obtained separately from ground-based or other satellite products, which affects significantly the accuracy of the AC. The China National Space Administration launched a high resolution and multimode imaging satellite [Gao Fen Duo Mo (GFDM)] in July 2020, which has multifunctional observation modes and flexible mobility, with a high-spatial resolution imaging sensor (0.42 m in panchromatic and 1.6 m in multispectrum) and equipped the synchronization monitoring atmospheric corrector (SMAC) sensor. As the first atmospheric corrector with polarization detection capability on-board high-spatial resolution satellite, SMAC is designed to obtain multispectral intensity and polarized data and to retrieve synchronously AC parameters in the same field of view with main sensor. Based on the SMAC in-orbit test data, a lookup table method using the optimized inversion framework and a dual-channel ratio retrieval method are developed to derive AOD and CWV, respectively, in this article. The AOD and CWV results are validated against the AERosol RObotic NETwork (AERONET). The preliminary test of AC performance on the multispectral images of GFDM satellite indicates that SMAC is of great potential to improve the quality of the main sensor’s image.

As the successors of the overdue HuanjingJianzai-1 (HJ-1) satellites and new members in Chinese Environmental Protection and Disaster Monitoring Satellite Constellation, the first two of HuanjingJianZai-2 series satellites (HJ-2 A/B) have been launched on September 27, 2020. Each satellite carries four sensors, including the Polarized Scanning Atmospheric Corrector (PSAC), the charge-coupled device (CCD) camera, the hyperspectral imager (HSI) and the infrared spectroradiometer (IRS). Among them, PSAC is mainly used for the monitoring of atmospheric parameters to provide data support for atmospheric environmental monitoring and atmospheric correction of data from other sensors. To test the in-orbit performance of PSAC, we develop the “day-1” aerosol and water vapor retrieval algorithms. The preliminary validation results based on ground-based observations show that the aerosol optical depth (AOD) and columnar water vapor (CWV) datasets developed based on PSAC data have high accuracy and can effectively characterize the temporal trends of AOD and CWV. The accuracy of PSAC AOD dataset is better than the expected error ±(0.05 + 0.2 * AODAERONET), and the accuracy of PSAC CWV dataset is better than the expected error ±(0.5 + 0.15 * CWVAERONET). To eliminate the negative impact of the atmosphere on CCD data and expand its application range, aerosol and water vapor data developed based on PSAC are used for atmospheric correction of CCD data. Compared with L1 CCD data, the texture details and clarity of CCD data after atmospheric correction have been significantly improved.

Summary With the incessant expansion of applications and the frequent update of the software, Science Gateway for Massive Remote Sensing Image Processing (SGMRSIP), developed by client/server model or traditional browser/server model, has received more and more challenges. Fortunately, the Web 2.0 technologies, proposed in recent years, bring us a new user experience (UE) that has a fast response speed and a good interface. In particular, the remote sensing image can be processed smoothly in the absence of client software by Web 2.0 technologies. Hence, a Web 2.0‐based browser/server model is designed for SGMRSIP to enhance the UE in this paper. Firstly, functions of a parallel remote sensing image processing portal, based on high performance cluster and client/server model, are summarized. And then, a Web 2.0‐based interaction model is built, and all these functions are accomplished again on the basis of this model. Finally, the Web 2.0‐based Science Gateway is achieved. In addition, we design different workflows for different satellite data, and all the processing tasks are finished successfully to verify the feasibility of this Science Gateway. The experimental results showed that the software scalability and interaction were improved and a better UE was achieved, compared with the existing SGMRSIP. Copyright © 2013 John Wiley & Sons, Ltd.

Remote sensing applications in Digital Earth are overwhelmed with vast quantities of remote sensing (RS) image data. The intolerable I/O burden introduced by the massive amounts of RS data and the irregular RS data access patterns has made the traditional cluster based parallel I/O systems no longer applicable. We propose a RS data object-based parallel file system for remote sensing applications and implement it with the OrangeFS file system. It provides application-aware data layout policies, together with RS data object based data I/O interfaces, for efficient support of various data access patterns of RS applications from the server side. With the prior knowledge of the desired RS data access patterns, HPGFS could offer relevant space-filling curves to organize the sliced 3-D data bricks and distribute them over I/O servers. In this way, data layouts consistent with expected data access patterns could be created to explore data locality and achieve performance improvement. Moreover, the multi-band RS data with complex structured geographical metadata could be accessed and managed as a single data object. Through experiments on remote sensing applications with different access patterns, we have achieved performance improvement of about 30 percent for I/O and 20 percent overall.

SUMMARY Data intensive applications of remote sensing data processing are more and more widespread resulting from the evolutions in computer and network technologies. Especially, bags‐of‐tasks (BoTs) applications with a mass of sharing input files and directed acyclic graph (DAG) applications with data dependencies in a widely distributed computing environment bring new challenges. In this article, a strategy of partitioning group based on hypergraph (PGH) is introduced to formulate the model of sharing files. Within the PGH algorithm, BoTs applications would be partitioned into several groups to minimize the time of data transferring. We also adopted another scheduling policy, which is called optimized task tree (OTT) strategy to handle the DAG workflow of massive remote sensing data processing with data dependencies. A scheduling queue of DAG tasks would be updated according to the priorities changing. With the help of GridSim simulation environment, we designed the Gridlets within scheduler to test the performance of PGH and OTT. Copyright © 2013 John Wiley & Sons, Ltd.

Remote sensed imagery mosaicking at large scale has been receiving increasing attentions in regional to global research. However, when scaling to large areas, image mosaicking becomes extremely challenging for the dependency relationships among a large collection of tasks which give rise to ordering constraint, the demand of significant processing capabilities and also the difficulties inherent in organizing these enormous tasks and RS image data. We propose a task-tree based mosaicking for remote sensed imageries at large scale with dynamic DAG scheduling. It expresses large scale mosaicking as a data-driven task tree with minimal height. And also a critical path based dynamical DAG scheduling solution with status queue named CPDS-SQ is provided to offer an optimized schedule on multi-core cluster with minimal completion time. All the individual dependent tasks are run by a core parallel mosaicking program implemented with MPI to perform mosaicking on different pairs of images. Eventually, an effective but easier approach is offered to improve the large-scale processing capability by decoupling the dependence relationships among tasks from the complex parallel processing procedure. Through experiments on large-scale mosaicking, we confirmed that our approach were efficient and scalable.

Ecohydrological modeling is essential to assess impact of climate change and intense human activities (land use change) on hydrological process and ecosystem to support watershed management. The traditional ecohydrological models have the deficit in coupling ecological and hydrological processes, and parameterizing vegetation parameters. Recently, optimality hypothesis, proposed by Eagleson, has been introduced to ecohydrology research which has given rise to a novel framework for modeling ecohydrological process. However, as optimality-based model has just spring up in ecohydrology, it has not been fully tested and more application of this kind of model is needed. In this study, we tried to apply an optimality-model to simulate ecohydrological process so as to test the model and support watershed management. The model has been tested in the Walnut Gulch watershed. With collected data from the study area, the model was used to simulate hourly evaportranspiration and GPP and so on. The validation result showed that, the results produced by the model were in good agreement with observed values. The VOM model can effectively overcomes the problem of traditional watershed ecohydrological models in depict ecological and hydrological coupling, the haunting task of vegetation parameters calibration. This could come to a conclusion the optimality-based ecohydrological model could be a potential approach to simulate ecohydrological process.

Marine oil spills is one of the most serious sea pollution which has a horrible effect on environment, economy, and quality of life for coastal inhabitants. How to reduce the risk of oil spill disasters has become one of the principal problems faced with marine environment management. Oil spill observation and spill processes simulation are two main parts for oil spill accident controlling and management. Traditionally, the oil spill information detection and spill simulation is disjoined without any feedback. The modeling approach is all conducted with fixed structure and static data input while the observation system is always static with fixed monitoring scheme. In such a circumstance, neither the observation system nor the simulation can provide highly accurate information. This paper propose a new framework combining oil spill monitoring and simulation as a symbiotic feedback control system based on the theory of Dynamic Data Drive Application System (DDDAS), a new paradigm dynamically integrated simulations, measurements, and applications. The numerical oil spill model can accepts real time data from remote sensing monitoring which assure modeling a more accurate and more reliable outcomes. Multiple simulations will be executed with different remote sensing monitoring scheme and the feedback from simulation guide and determine how to gather the data. For mathematical modeling of the DDDAS based marine oil spill management system, we built a multi-stage optimization model. Such system could promise more accurate prediction and more reliable outcomes with real time oil spill input, which will improve modeling technologies, advance prediction capabilities of simulation systems, and enhance oil spill monitoring.

Stream flow series analysis is of great importance for watershed management such as water conservation, water quality control etc. How to describe the characteristic of stream flow, particularly quantify stream flow variability, has been the subject of numerous studies. Due to the fact that stream flow process usually suffers from strong natural and anthropogenic disturbances, it is hardly to measure the chaotic characteristic by pure statistic method. This paper applied fractal concept for steam flow series analysis. The fractal dimension of the daily stream flow of Malian Basin was estimated using box-counting method. The result showed that the fractal dimension of the main stream and downstream gauging stations is higher than that of the tributary, headwater gauging stations. From the correlative and regress analyze of stream flow fractal dimension and soil erosion we found that the two has distinct relativity, which can bring forward a new method for the study of watershed management and so on. This could come to a conclusion stream flow series fractal dimension analysis could be a potential approach for watershed management.

We reconstruct the multispectral image based on compressive sensing theory. Both spatial domain regularization and transform domain regularization are employed in the proposed objective function. Bregman split method is used to optimize the proposed objective function. In order to making use of the correlation features between different channels of multispectral image, principal component analysis (PCA) is introduced into the shrinkage step of the spatial domain regularization. For further enhance the performance of CS reconstruction, the similarity of wavelet coefficients between different channels are also explored in the shrinkage step of transform domain. We compare the proposed method with some other methods. Experiments validate the better performances of the proposed method, and it is attributed to combine two regularizations and employ the spectral correlation between channels.

SUMMARY The remotely sensed images continuously acquired by satellite and airborne sensors are increasing dramatically. Remote sensing applications are overwhelmed with tons of remote sensing data with complex data structures. Efficient programming in parallel systems for data‐intensive applications like massive remote sensing data processing will be a challenge. We propose a generic data‐structure oriented programming template to support massive remote sensing data processing in high‐performance clusters. These templates provide distributed abstractions for large remote sensing image data with complex data structure and allow these distributed data to be accessed as a global one. Through data serialization and one‐sided message passing primitives provided by message passing interface, the distributed remote sensing data template whose sliced data blocks are scattered among nodes could offer a simple and effective way to distribute and communicate massive remote sensing data. Efficient parallel input/output directly to and from the distributed data structure will also be offered to address the input/output bottleneck caused by massive image data. Developers can take the advantage of our templates to program efficient parallel remote sensing algorithms without dealing with data slicing and communication through low‐level message passing interface APIs. Through experiments on remote sensing applications, we confirmed that our templates were productive and efficient. Copyright © 2012 John Wiley & Sons, Ltd.

SUMMARY Earth observation applications are now facing the challenges of managing and processing massive data sets from multiple sources from large‐scale distributed data centers (DCs). To solve this research problem, this paper presents an infrastructure of multiple data centers (MDC) for managing and processing massive remote sensing images. The proposed system is built on both groups of distributed DCs/clusters, which are equipped with DC or cluster resource manager. Access security and information service are introduced to support this architecture of MDC. We collaboratively organized the algorithm, and data belonged to the MDC in the manner of workflow. In practice, we succeeded in working out the concrete problems regarding procedures in processing applications collaboratively and transfer the massive remote sensing dataset fast and with stable cross‐MDC. On the basis of the previously mentioned research work, we will investigate the platform integration of MDC. Copyright © 2012 John Wiley & Sons, Ltd.

Remote Sensing (RS) data processing is characterized by massive remote sensing images and increasing amount of algorithms of higher complexity. Parallel programming for data-intensive applications like massive remote sensing image processing on parallel systems is bound to be especially trivial and challenging. We propose a C++ template mechanism enabled generic parallel programming skeleton for these remote sensing applications in high performance clusters. It provides both programming templates for distributed RS data and generic parallel skeletons for RS algorithms. Through one-side communication primitives provided by MPI, the distributed RS data template could provide a global view of the big RS data whose sliced data blocks are scattered among the distributed memory of cluster nodes. Moreover, by data serialization and RMA (Remote Memory Access), the data templates could also offer a simple and effective way to distribute and communicate massive remote sensing data with complex data structures. Furthermore, the generic parallel skeletons implement the recurring patterns of computation, performance optimization and pass the user-defined sequential functions as parameters of templates for type genericity. With the implemented skeletons, Developers without extensive parallel computing technologies can implement efficient parallel remote sensing programs without concerning for parallel computing details. Through experiments on remote sensing applications, we confirmed that our templates were productive and efficient.

HPC analytics has become increasingly vital to analyze the large volumes of data produced by sophisticated computing instruments. Meanwhile, with the successful development of cloud computing, more and more scientists are devoted to deploy HPC analytics in the ever-popular clouds, which poses new challenges mainly caused by different storage architectures, resource management mechanisms and programming APIs. Firstly, there exists a ``data semantics" gap between the way data are stored by Cloud platform and the way data will be accessed by the HPC Analytics. Secondly, data are mostly distributed across data nodes for in-house data-intensive clusters to achieve co-located computation and storage, however, it is challenging for the public clouds to mimic because their data are stored centrally. In this paper, we develop a new HPC analytics framework called NOHAA, to provide 1) a semantics-aware intelligent data upload interface and 2) a locality-aware hierarchical storage system in support of co-located computation and storage on Windows Azure. Our extensive real world experiments show that NOHAA significantly reduces the average data access time by up to 85% and accelerates the HPC analytics execution time by a factor of 2 to 7.

Up to now, it still remains a big challenge for us to build a high performance geo-computing system with high processing speed and also be easy of use by domain researchers. The unprecedented scale data and various complex algorithms pose many computational and management challenges. To properly settle these main issues above, a new system framework for high performance geo-computing is presented in this paper. A High Performance Geo-data Object Storage System (HPGOSS) base on parallel file System is used for eliminating I/O performance bottleneck and deal with the data managing problem result from the close relevancy between geo-information and remote sensing image data. Parallel programming models for fast parallelization of geo-computing algorithms are proposed. In addition, the job scheduling strategy and workflow engine are also discussed. Finally, such system could provide a parallel geo-computing environment with high performance, easy to use, optimal resource utilization, and high scalability.