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A comprehensive, accurate, and timely expression of earth observation (EO) tasks is the primary prerequisite for the response to and the emergency monitoring of disasters, especially floods. However, the existing information model does not fully satisfy the demand for a fine-grain observation expression of EO task, which results in the absence of task process management. The current study proposed an EO task representation model based on meta-object facility to address this problem. The model not only describes the static information of a task, but it also defines the dynamics of an observation task by introducing a functional metamodel. This metamodel describes the full life cycle of a task; it comprises five process methods: birth, separation, combination, updating, and extinction. An earth observation task modeling and management prototype system (EO-TMMS) for conducting a remote sensing satellite sensor observation task representation experiment on flooding was developed. In accordance with the results, the proposed model can describe various EO tasks demands and the full life cycle process of an EO task. Compared with other typical observation task information models, the proposed model satisfies the dynamic and fine-grain process representation of EO tasks, which can improve the efficiency of EO sensor utilization. [ABSTRACT FROM AUTHOR]

When facing a specific emergent geographical environment observation task (GeoTask), people need to be able to handle reliable and comprehensive disaster information in the shortest possible time. The lack of effective cognition of multi-sensor collaborated observation capability is a hindrance to performance. By adopting the GIS object field concept as the bottom framework, we propose a sensor observation capability object field (SOCO-Field) with sensor observation capability particle (SOC-Particle) as its core. SOCO-Field integrates SOC-Objects and GeoField for the discovery and association of sensors. SOC-Particle objectively exists on every location point in the geospatial environment, and SOC-Particles in space-continuous areas can further aggregate into SOC-Particle cluster to represent single- or multi-sensor-associated observation capability information. SOCO-Field includes three basic association behaviours and four further association behaviours to solve associated observation capability, in which the dynamic GeoField is the influential factor. An experiment on flood monitoring in the lower reaches of Jinsha River Basin is conducted. The sensor planner can view any sensor combination's associated observation capability under a specific association mode and can effectively dispatch a multi-sensor for collaborated observation due to the effective modelling of associated sensor observation capability information (SOCInfo). [ABSTRACT FROM AUTHOR]

Abstract: Many achievements in web-based geoprocessing focus on logically chaining Open Geospatial Consortium (OGC) web services, for example using Business Process Execution Language to orchestrate web services that are interfaced through the OGC Web Processing Service. For e-Science application in a sensor web environment, how to internally integrate the sensor system, observation, and processes (physical and non-physical) as a geoprocessing e-Science workflow model is a critical issue. The OGC Sensor Model Language offers the possibility to construct a geoprocessing e-Science workflow model in the form of observation processes. We propose a construction method for a geoprocessing e-Science workflow model that integrates logical and physical processes into a composite process chain for sensor observations. The three phases of geoprocessing e-Science workflow creation are abstract process chain modeling, process chain instantiation, and process chain workflow execution. An experiment on chaining-related sub-processes for deriving the Normalized Difference Vegetation Index of Hubei Province (China) was conducted to verify the feasibility of the proposed workflow model. [Copyright &y& Elsevier]

AbstractFull-view coverage of a space area with a camera sensor network (CSN) is key to monitoring tasks like security monitoring. Unlike traditional CSN challenges that focus on mere target detection, the full-view area coverage problem (FVACP) demands recognition of targets irrespective of their locations or orientations. However, prior approaches often neglect real-world spatiotemporal context constraints like buildings and pedestrian dynamics, leading to inefficient CSN deployment. Moreover, FVACP’s complexity, being an NP-hard issue, underscores the need for effective optimisation strategies. Recently, quantum annealing (QA) has emerged as a promising solution, which potentially outperforms classical computing in optimisation tasks. Therefore, this study proposes a QA-based FVACP optimisation framework. It addresses spatial constraints by optimising candidate deployment points and tackles temporal constraints by optimising sensor orientations. These optimisation tasks are converted into quadratic unconstrained binary optimisation problems, which are suitable for QA techniques and benchmarking against classical methods. The effectiveness of the framework is validated through facial recognition-oriented experiments. Results demonstrate not only efficient CSN deployment with larger benefits and fewer cameras but also confirm the superiority of QA over classical computing given that it delivers approximate optimum outcomes across various scenarios. Consequently, CSN monitoring capabilities in real-world applications can be enhanced. [ABSTRACT FROM AUTHOR]

Accurate, timely, and fine-resolution crop phenology is essential for determining the optimal timing of agronomic management practices supporting precision agriculture and food security. Synthetic Aperture Radar (SAR) methods, unaffected by cloud occlusion, have been widely applied in monitoring maize phenology. Nonetheless, their reliance on manual threshold settings, which depend on the user's expertise, limits their applicability. Furthermore, the neglect of SAR's potential for monitoring other phenological periods (e.g., seven-leaves date (V7), jointing date (JD), tassel date (TD), and milky date (MID)) hinders their robustness, particularly for regional-scale applications. To address these issues, this study used an adaptive dynamic threshold to evaluate the ability of the Sentinel-1 cross-polarization ratio (CR) in detecting the three-leaves date (V3), V7, JD, TD, MID, and maturity date (MD) of maize. We analyzed the effect of incidence angle, precipitation, and wind speed on Sentinel-1 features to identify the optimal feature for time series fitting. Then, we employed linear regression to determine the optimal threshold and developed an adaptive dynamic threshold for phenology detection. This approach effectively mitigated the speckle noise of Sentinel-1 and minimized artificial interference caused by customary conventional thresholds. Finally, we mapped phenology across 8.3 million ha in Heilongjiang Province. The results indicated that the approach has a higher ability to detect JD (RMSE = 11.10 d), MID (RMSE = 10.31 d), and MD (RMSE = 9.41 d) than that of V3 (RMSE = 32.07 d), V7 (RMSE = 56.37 d), and TD (RMSE = 43.33 d) in Sentinel-1. Compared with Sentinel-2, the average RMSE of JD, MID, and MD decreased by 4.14%, 35.28%, and 26.48%. Moreover, when compared to different thresholds, the adaptive dynamic threshold can quickly determine the optimal threshold for detecting each phenological stage. CR is least affected by incident angle, precipitation, and wind speed, effectively suppressing noise to reflect phenological development better. This approach supports the rapid and feasible mapping of maize phenology across broad spatial regions with a few samples. [ABSTRACT FROM AUTHOR]

Scene classification of high-spatial resolution imagery (HSRI) includes various potential applications in various fields. Recently, deep convolutional neural networks (CNNs) have achieved competitive performance as a result of the powerful capability of feature extraction. In this paper, we propose a multi-level improved circle pooling (MICP) method with the pre-trained CNN-based model to enhance the discriminative power of CNN activations for scene classification. Specifically, an improved pooling strategy is presented to generate annular subregions without padding operations in traditional concentric circle pooling. Then, we extract the pooling features in these subregions under different levels and build a holistic representation by fusing these multi-level features. MICP is an effective and simple strategy enriching rotation insensitivity and multiscale spatial information. According to the experiments conducted on three challenging HRSI scene data sets, the proposed pooling method achieves similar or better classification accuracy compared to the other CNN-based scene classification methods. Moreover, a comprehensive discussion regarding the effect of data augmentation reveals that the proposed method can enhance the rotation insensitivity of CNNs for the HRSI scene classification. [ABSTRACT FROM AUTHOR]

• Develop a deep learning model (GWNN) for landslide susceptibility mapping. • Compare the performance of SVM, ANN, RF, GWR and GWNN models. • The proposed model can capture the spatial heterogeneity of landslides. • Reliable landslide susceptibility map in Yichang City using the proposed model. Landslides are among the most devastating natural disasters worldwide. Landslide susceptibility mapping (LSM) is a scientific approach for assessing landslides-prone areas. Despite the use of various statistical and machine learning (ML) models to predict landslide susceptibility, few have considered the effect of spatial location on landslides. Geographically weighted regression (GWR) is a common method for modeling spatial heterogeneity, but it may not be effective in solving complex nonlinear problems. This leads to inadequate prediction accuracy for landslide susceptibility at the local scale. This study introduces a geographically weighted neural network (GWNN) model to address this problem. The GWNN model combines the advantages of GWR and neural networks (NN) to assess the effect of landslide conditioning factors on landslides. The effectiveness of the proposed method was evaluated using landslide data collected from Yichang City, Hubei Province. The results show that GWNN exhibits the higher performance (AUC = 0.788) compared with traditional ML models and GWR. Furthermore, the superior ability of GWNN is demonstrated to capture spatial heterogeneity and predict landslide susceptibility accurately at local scales. The proposed method provides a valuable reference for modeling landslide-prone areas that exhibit spatial heterogeneity. [ABSTRACT FROM AUTHOR]

Spatiotemporal fusion (STF) is crucial for reconciling the conflict between temporal and spatial resolutions of remote sensing observations. However, fusing images in heterogeneous areas remains challenging under continuous missing values. Moreover, most current STF methods only consider temporal errors and disregard the spatial scale error in time variation. Therefore, we proposed a Quick Spatiotemporal Fusion with Coarse- and Fine-Resolution Scale Transformation Errors and Pixel-Based Synthesis Base Image Pair (STEPSBI). First, the optimal pixel-based image synthesis strategy was designed using all available fine- and coarse-resolution images. Then, the scale transformation error (STE) of coarse-resolution downscaling to fine-resolution in temporal variation was quantified. And a residual term was introduced to reduce the prediction error from the temporal variation. Finally, the spatial scale and temporal errors were corrected using the results of super-pixel segmentation as spatial weights. This model has two strengths: (1) Pixel-based image synthesis alleviates the absence of base images under continuous missing values; and (2) STE correction restores spatial details of heterogeneous areas in rapid land cover change. In scenarios of continuous missing images, abrupt land cover changes, and high-resolution heterogeneity, we evaluated STEPSBI, the Gap Filling and Savitzky–Golay filtering method (GF-SG), the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM), the Flexible Spatiotemporal DAta Fusion (FSDAF), the cross-attention-based adaptive weighting fusion network (CAFE), and the Multi-scene Spatiotemporal Fusion Network (MUSTFN). The results indicate that STEPSBI yields better overall performance than other models in cropland, woodland, grassland, and other land cover types. Furthermore, ablation experiments demonstrated that each component improved the model's performance. In addition, STEPSBI had higher fusion efficiency because it was developed on the Google Earth Engine cloud computing platform. Therefore, STEPSBI was feasible for advancing fine monitoring of spatiotemporal image fusion under continuous missing values and heterogeneous surfaces. The program of STEPBSI is freely available at: https://code.earthengine.google.com/684844aa42f64fa6b4eebe3bc0dd6483. [ABSTRACT FROM AUTHOR]

Observation schedules depend upon the accurate understanding of a single sensor’s observation capability and the interrelated observation capability information on multiple sensors. The general ontologies for sensors and observations are abundant. However, few observation capability ontologies for satellite sensors are available, and no study has described the dynamic associations among the observation capabilities of multiple sensors used for integrated observational planning. This limitation results in a failure to realize effective sensor selection. This paper develops a sensor observation capability association (SOCA) ontology model that is resolved around the task-sensor-observation capability (TSOC) ontology pattern. The pattern is developed considering the stimulus-sensor-observation (SSO) ontology design pattern, which focuses on facilitating sensor selection for one observation task. The core aim of the SOCA ontology model is to achieve an observation capability semantic association. A prototype system called SemOCAssociation was developed, and an experiment was conducted for flood observations in the Jinsha River basin in China. The results of this experiment verified that the SOCA ontology based association method can help sensor planners intuitively and accurately make evidence-based sensor selection decisions for a given flood observation task, which facilitates efficient and effective observational planning for flood satellite sensors. [ABSTRACT FROM AUTHOR]

An accurate and comprehensive representation of an observation task is a prerequisite in disaster monitoring to achieve reliable sensor observation planning. However, the extant disaster event or task information models do not fully satisfy the observation requirements for the accurate and efficient planning of remote-sensing satellite sensors. By considering the modeling requirements for a disaster observation task, we propose an observation task chain (OTChain) representation model that includes four basic OTChain segments and eight-tuple observation task metadata description structures. A prototype system, namely OTChainManager, is implemented to provide functions for modeling, managing, querying, and visualizing observation tasks. In the case of flood water monitoring, we use a flood remote-sensing satellite sensor observation task for the experiment. The results show that the proposed OTChain representation model can be used in modeling process-owned flood disaster observation tasks. By querying and visualizing the flood observation task instances in the Jinsha River Basin, the proposed model can effectively express observation task processes, represent personalized observation constraints, and plan global remote-sensing satellite sensor observations. Compared with typical observation task information models or engines, the proposed OTChain representation model satisfies the information demands of the OTChain and its processes as well as impels the development of a long time-series sensor observation scheme. [ABSTRACT FROM AUTHOR]

Dynamic changes in traffic conditions cause spatiotemporal variation in traffic monitoring demand. It is, therefore, necessary to conduct efficient road monitoring to identify dynamic abnormal situations, especially in peak traffic periods. Recently, unmanned aerial vehicles (UAVs) have become an attractive solution to this problem. However, UAV monitoring routes suffer from time limitations during peak traffic hours. To optimize UAV monitoring routes during rush hours, we develop a route planning method incorporating spatiotemporal variations in monitoring demand, in which we introduce a team orienteering arc routing problem with time-varying profits (TOARP-TP) and construct a corresponding mathematical model. The TOARP-TP is an extension of an already existing routing problem, team orienteering arc routing problem (TOARP). An iterated local search (ILS)-based algorithm is designed to solve the large instances of this problem. To verify the proposed method, we conduct sets of numerical experiments with the Sioux Falls road network in South Dakota, US, and a case study is applied using Wuhan, Hubei, PRC. The results demonstrate the efficiency and practicality of our method in optimizing UAV traffic monitoring routes during rush hours. Furthermore, we discuss a strategy for scenario determination and method selection in UAV route planning. [ABSTRACT FROM AUTHOR]

Finding the right spatially aware web service in a heterogeneous distributed environment using criteria such as service type, version, time, space, and scale has become a challenge in the integration of geospatial information services. A new method for retrieving Open Geospatial Consortium (OGC) Web Service (OWS) that deals with this challenge using page crawling, link detection, service capability matching, and ontology reasoning, is described in this paper. Its major components are distributed OWS, the OWS search engine, the OWS ontology generator, the ontology-based OWS catalog service, and the ontology-based multi-protocol OWS client. Experimental results show that the execution time of this proposed method equals only 0.26 of that of Nutch's method. In addition, the precision is much higher. Moreover, this proposed method can carry out complex OWS reasoning-based queries. It is being used successfully for the Antarctica multi-protocol OWS portal of the Geo-Information Web Service Portal of the Polar. [ABSTRACT FROM AUTHOR]

Deep convolutional neural networks (DCNNs) have shown significant improvements in remote sensing image scene classification for powerful feature representations. However, because of the high variance and volume limitations of the available remote sensing datasets, DCNNs are prone to overfit the data used for their training. To address this problem, this paper proposes a novel scene classification framework based on a deep Siamese convolutional network with rotation invariance regularization. Specifically, we design a data augmentation strategy for the Siamese model to learn a rotation invariance DCNN model that is achieved by directly enforcing the labels of the training samples before and after rotating to be mapped close to each other. In addition to the cross-entropy cost function for the traditional CNN models, we impose a rotation invariance regularization constraint on the objective function of our proposed model. The experimental results obtained using three publicly-available scene classification datasets show that the proposed method can generally improve the classification performance by 2~3% and achieves satisfactory classification performance compared with some state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Sea surface temperature (SST) is one of the most important parameters in the global ocean-atmospheric system, changes of which can have profound effects on the global climate and may lead to extreme weather events such as droughts and floods. Therefore, predicting the dynamics of future SSTs is of vital importance which can help identify these extreme events and alleviate the losses they cause. In this paper, a machine learning method combining the long short-term memory (LSTM) deep recurrent neural network model and the AdaBoost ensemble learning model (LSTM-AdaBoost) is proposed to predict the short and mid-term daily SST considering that LSTM is good at modelling long-term dependencies but suffers from overfitting, while AdaBoost has strong prediction capability and is not easily overfitted. By combining these two strong and heterogeneous models, the prediction errors related to variance may cancel out each other and the final results can be improved. In this method, the historical time-series satellite data of SST anomaly (SSTA) is used instead of SST itself considering that the fluctuations of SSTs are very small compared to their absolute magnitudes. The seasonality of the SSTA time series is first modelled using polynomial regression and then removed. Then, the deseasonalized time series are used to train the developed LSTM model and AdaBoost model independently. Daily SSTA predictions are made using these two models, and eventually, their predictions are combined as final predictions using the averaging strategy. A case study in the East China Sea that predicts the daily SSTA 10 days ahead shows that the proposed LSTM-AdaBoost combination model outperforms the LSTM and AdaBoost separately, as well as the optimized support vector regression (SVR) model, the optimized feedforward backpropagation neural network model (BPNN), and the stacking LSTM-AdaBoost model (S_LSTM-AdaBoost), when judged using multiple error statistics and from different perspectives. The results suggest that the LSTM-AdaBoost combination model using the averaging strategy is highly promising for short and mid-term daily SST predictions. • A LSTM-AdaBoost combination method is proposed to predict short and mid-term SST. • 36-year satellite-derived time series daily SST data are used. • A case study has been demonstrated in the East China Sea. • The proposed method outperforms LSTM, AdaBoost, SVR, BPNN and S_LSTM-AdaBoost. [ABSTRACT FROM AUTHOR]

Sea surface temperature (SST) is a vitally important parameter of the global ocean, which can profoundly affect the climate and marine ecosystems. To achieve an accurate and holistic prediction of the short and mid-term SST field, a spatiotemporal deep learning model is proposed which can capture the correlations of SST across both space and time. The model uses the convolutional long short-term memory (ConvLSTM) as the building block and is trained in an end-to-end manner. Experiments using 36-year satellite-derived SST data in a subarea of the East China Sea demonstrate that the proposed model outperforms the persistence model, the linear support vector regression (SVR) model, and two LSTM models with different settings, when judged using multiple statistics and from different perspectives. The results suggest that the proposed model is highly promising for short and mid-term daily SST field prediction accurately and conveniently. • A spatiotemporal deep learning model is proposed to predict SST field. • The model can capture spatiotemporal correlations of time-varing SST fields. • 36-year satellite-derived time series daily SST data are used. • The model outperforms persistence, SVR, and LSTM models on multiple statistics. [ABSTRACT FROM AUTHOR]

Seasonal precipitation forecasts at regional or local areas can help guide agricultural practice and urban water resource management. The North American multi-model ensemble (NMME) is a seasonal forecasting system providing precipitation forecasts globally. Bias correction and downscaling of the NMME is a critical step before applied at local scales. Here, the machine learning methods coupling with wavelet are used to correct the precipitation forecasts in NMME for 518 meteorological stations in China for eight models at 0.5–8.5 months leads. Compared with the traditional quantile mapping (QM) approach, the wavelet support vector machine (WSVM) and wavelet random forest (WRF) methods exhibit obvious advantage in downscaling, with an overall average improvement of Pearson's correlation coefficient increasing by 0.05–0.3 and root mean square error (RMSE) reducing by 18–40 mm (21–33%) for individual models. Both the spatial and seasonal patterns of downscaled results demonstrate the superiority of wavelet machine learning methods over QM. A spatial analysis indicates that the corrected NMME precipitation forecasts show the best skill in South China, with an average RMSE of about 30 mm, while the worst skill in Central and Southwest China with a RMSE of 80 mm. In spite of the correction, the uncertainties of seasonal precipitation forecasts in summer and extreme wet cases are still large. However, the WSVM and WRF methods may serve as an effective tool in the bias correction of NMME precipitation forecasts. [ABSTRACT FROM AUTHOR]

The two major rainfall observation techniques, ground-based measurements and remote sensing, have distinct coverage characteristics. Large-scale spatial coverage and long-term temporal coverage cannot be achieved simultaneously by using only ground-based precipitation stations or space-borne sensors. Given the temporal discontinuity of satellite coverage and limited ground-based observation resources, we propose a method for siting precipitation stations in conjunction with satellite-based rainfall sensors to maximize the total spatial-temporal coverage of weighted demand in a continuous observation period. Considering the special principles of deploying precipitation stations and the requirement for continuous coverage in space and time, a time-continuous maximal covering location problem (TMCLP) model is introduced. The maximal spatial coverage range of a precipitation station is determined based on the minimum density required and the site-specific terrain conditions. The coverage of a satellite sensor is calculated for each time period when it passes overhead. The polygon intersection point set (PIPS) is refined to identify finite candidate sites. By narrowing the continuous search space to a finite dominating set and discretizing the continuous observation period to sequential sub-periods, the siting problem is solved using the TMCLP model and refined PIPS. According to specific monitoring purposes, different weighting schemes can be used to evaluate the coverage priority of each demand object. The Jinsha River Basin is selected as the study region to test the proposed method. Satellite-borne precipitation radar is used to evaluate the satellite coverage. The results show that the proposed method is effective for precipitation station configuration optimization, and the model solution achieves higher coverage than the real-world deployment. The applicability of the proposed method, site selection criteria, deployment strategies in different observation modes, and the impacts of different weighting schemes are also discussed. [ABSTRACT FROM AUTHOR]

Sensor capability information can be used as a basis for the integrated management of vast and heterogeneous remote sensing sensors in Global Earth Observation System of Systems environment. However, the existing representation of this information shows an inconsistent pattern, incomplete capability aspects, and casual expression forms, resulting in information silos among different systems. A sensor capability representation model (SCRM) is proposed in this study. Based on the Meta Object Facility architecture, a five-tuple hierarchical SCRM framework is formulated. Five specific representation element collections for typical remote sensing sensor types are developed to satisfy the requirements of detailed capability expression. The Open Geospatial Consortium Sensor Model Language is used as the expression form of the proposed SCRM. A prototype system is developed and a case study is conducted for a soil moisture monitoring application in Baoxie Town. The SCRM can also be extensively utilised for other environmental monitoring and modelling situations. [ABSTRACT FROM AUTHOR]

Root zone soil moisture (RZSM) is a vital variable for vegetation growth, drought monitoring and agricultural water management. Satellite remote sensing measures soil moisture at the surface layer, while RZSM is derived usually by model-based simulations. Here, we provide the first comprehensive evaluation of eight RZSM products at a global scale, including GLDAS NOAH, ERA-5, MERRA-2, NCEP R1, NCEP R2, JRA-55, SMAP level 4 and SMOS level 4 datasets. An in-situ validation based on the stations from the International Soil Moisture Network (ISMN) and a triple collocation (TC) evaluation are both conducted to assess the accuracy of these RZSM products. SMAP exhibits the median highest correlation and the median lowest RMSE with in-situ stations over North America. In the TC analysis, MERRA-2 shows the highest median correlation and the median lowest error standard deviation with the unknown truth, followed by GLDAS, SMAP, JRA-55 and ERA-5. A temporal pattern analysis indicates that SMOS has a dry bias relative to other datasets and NCEP R1 has larger seasonal variations relative to other datasets over Asia and North America. The TC analysis indicates that MERRA-2, SMAP, GLDAS, JRA-55, and ERA-5 have better performance relative to other datasets. SMAP is not as good as GLDAS, MERRA-2 and JRA-55 in RZSM estimation over forest areas. The possible factors influencing RZSM performance are discussed, including precipitation forcing, assimilated observations, radio frequency interference issue and validation methods. These results and conclusions may provide new insights for the improvement of model-based RZSM estimation. • Evaluation of eight RZSM products by ISMN stations and triple collocation globally. • SMAP has highest correlation and lowest RMSE with in-situ stations in North America. • MERRA-2 performs better than other products in TC, followed by GLDAS and SMAP. • Triple collocation and in-situ validation could complement each other. [ABSTRACT FROM AUTHOR]

Crop residue burning is the major biomass burning activity in China, strongly influencing the regional air quality and climate. As the cultivation pattern in China is rather scattered and intricate, it is a challenge to derive an accurate emission inventory for crop residue burning. In this study, we proposed a remote sensing-based method to estimate nitrogen oxide (NOx) emissions related to crop residue burning at the field level over Hubei, China. The new method considers differences in emission factors and the spatial distribution for different crop types. Fire radiative power (FRP) derived from moderate-resolution imaging spectroradiometer (MODIS) was used to quantify NOx emissions related to agricultural biomass combustion. The spatial distribution of different crops classified by multisource remote sensing data was used as an a priori constraint. We derived a new NOx emission database for Hubei from 2014 to 2016 with spatial resolution of 1 × 1 km. Significant seasonal patterns were observed from the NOx emission database. Peak NOx emission occurring in October was related to the residue burning in late autumn harvesting. Another peak was observed between January and April, which was due to the frequent burning of stubble before spring sowing. Our results were validated by comparing our emission inventory with geostationary satellite observations, previous studies, global fire emission database (GFED), NO2 vertical column densities (VCDs) from ozone monitoring instrument (OMI) satellite observations, and measurements from environmental monitoring stations. The comparisons showed NOx emission from GFED database was 47% lower than ours, while the evaluations from most of the statistical studies were significantly higher than our results. The discrepancies were likely related to the differences of methodology and data sources. The spatiotemporal variations of NOx emission in this study showed strong correlations with NO2 VCDs, which agreed well with geostationary satellite observations. A reasonable correlation between in situ NO2 observations and our results in agricultural regions demonstrated that our method is reliable. We believe that the new NOx emission database for crop residue burning derived in this study can potentially improve the understanding of pollution sources and can provide additional information for the design of pollution control measures. [ABSTRACT FROM AUTHOR]

Traditional location problems usually focus on spatial and temporal impacts of facilities, but few studies have focused on sensor siting under satellite-borne monitoring in a space-ground integrated sensor network. Given the partial coverage and the requirement for continuous coverage in space and time, a time-weighted maximal covering location problem with partial coverage (TMCLP-PC) model is introduced. This model is solved using a modified genetic algorithm (GA)-based approach that utilizes the spatio-temporal characteristics of potential facility sites for faster convergence. The performance of the new GA is tested on a precipitation station siting problem in the Jinsha River Basin on the upper reaches of the Yangtze River in China. The results demonstrate that the proposed GA can significantly reduce the computation time compared with CPLEX, a commercial integer programming solver, and can outperform the greedy algorithm and the GAs with one-point, two-point, fusion, and uniform crossover operators. The applicability of the proposed method and exploration of the design in the new GA are also discussed. • The problem is to site stationary sensors under time-varying satellite monitoring. • It is a time-weighted maximal coverage problem for which a new GA is proposed. • The algorithm considers the importance of each site in crossover and mutation. • The proposed method is tested on siting rain gauges in the Jinsha River Basin. • The method can be extended to serve other spatio-temporal location problems. [ABSTRACT FROM AUTHOR]