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As Internet of Things (IoT) technology continues to advance at a rapid pace, smart devices have permeated daily life. Service providers are actively collecting copious numbers of user data, with the aim of refining machine learning models to elevate service quality and accuracy. However, this practice has sparked apprehensions amongst users concerning the privacy and safety of their personal data. Federated learning emerges as an evolution of centralized machine learning, enabling a collective training of machine learning models by multiple users on their respective devices. Crucially, this is achieved without the direct submission of data to a central server, thereby significantly mitigating the hazards associated with privacy infringements. Since the machine learning algorithms act locally in federated learning, passing just the local model back to the central server, the users’ data remain locally. However, current research work indicates that local models also include user data privacy-related components. Moreover, current privacy-preserving secure aggregation schemes either offer insufficient accuracy or need significantly high computing resources for training. In this work, we propose an efficient and secure aggregation scheme for privacy-preserving federated learning with lower computational costs, which is suitable for those weak IoT devices since the proposed scheme is robust and fault-tolerant, allowing some of the users to dynamically exit or join the system without restarting the federated learning process or triggering abnormal termination. In addition, this scheme with the property of result verification in the situation when the servers return incorrect aggregation results, which can be verified by the users. Extensive experimental evaluations, based on real-world datasets, have substantiated the high accuracy of our proposed scheme. Moreover, in comparison to existing schemes, ours significantly reduces computational and communication costs by at least 85% and 47%, respectively.

The assessment of ecological space quality (ESQ) and its spatio-temporal change monitoring are crucial for regional ecological management and sustainable development. However, there are few studies on how to construct a comprehensive ESQ assessment system to reveal the spatial and temporal change patterns of ESQ over a long time series. Therefore, this study constructs an ESQ evaluation model with comprehensive ecological characteristics to quantitatively assess the spatio-temporal dynamics of ESQ from 2001 to 2020 based on policy objectives and public demands, using the urban agglomeration in the middle reaches of the Yangtze River as an example. The results show that, in the past 20 years, the mean value of ESQ in urban agglomeration has decreased (−0.179·year−1), and the overall ESQ is dominated by a good level. The ESQ has shifted from improvement (2001–2010) to deterioration (2010–2020) and shows the spatial distribution characteristics of “high in the periphery and center, low in the interior”. From the trend of change, the degraded area of ESQ is greater than the improved area, and the degraded area of ESQ will increase in the future compared with the trend of 2001–2020. The distribution of ESQ has a significant spatial agglomeration and scale effect. The hot spots of ESQ at the town scale are mainly concentrated in the central part of urban agglomeration and mountainous areas in the periphery. The cold spots are mainly concentrated in the surrounding areas of central cities (Wuhan, Changsha, and Nanchang). The proposed assessment framework can be used to quantify spatial and temporal changes in ESQ and identify potential ecological space management issues, providing basic information for implementing ecological space protection, restoration, and developing adaptive ESQ management measures. The research results are of significant importance for ecosystem restoration and long-term development in the Yangtze River Basin.

Soil particle size fractions (PSFs) are important properties for understanding the physical and chemical processes in soil systems. Knowledge about the distribution of soil PSFs is critical for sustainable soil management. Although log-ratio transformations have been widely applied to soil PSFs prediction, the statistical distribution of original data and the transformed data given by log-ratio transformations is different, resulting in biased estimates of soil PSFs. Therefore, multivariate random forest (MRF) was utilized for the simultaneous prediction of soil PSFs, as it is able to capture dependencies and internal relations among the three components. Specifically, 243 soil samples collected across the Loess Plateau were used. Meanwhile, Landsat data, terrain attributes, and climatic variables were employed as environmental variables for spatial prediction of soil PSFs. The results depicted that MRF gave satisfactory soil PSF prediction performance, where the R2 values were 0.62, 0.53, and 0.73 for sand, silt, and clay, respectively. Among the environmental variables, nighttime land surface temperature (LST_N) presented the highest importance in predicting soil PSFs in the Loess Plateau, China. Maps of soil PSFs and texture were generated at a 30 m resolution, which can be utilized as alternative data for soil erosion management and ecosystem conservation.

As the world’s largest developing country, China has played an important role in the achievement of the global CO2 emissions mitigation goal. The monitoring and analysis of CO2 emissions in the Yangtze River Economic Belt (YREB) urban agglomerations is strategic to the carbon peak and carbon neutrality in China. In this paper, we revealed the spatial and temporal variations of CO2 emissions in Cheng-Yu urban agglomeration (CY-UA), Yangtze River Middle-Reach urban agglomeration (YRMR-UA), and Yangtze River Delta urban agglomeration (YRD-UA) in YREB and investigated the carbon emission development stage of YREB urban agglomerations. Particularly, a carbon emission development stage framework that considered the relationship between economic growth and carbon emissions was built based on Environmental Kuznets Curves (EKCs). Meanwhile, multiscale geographically weighted regression (MGWR) was used to analyze the impact of different influencing factors, including population (POP), GDP per capita (GDPPC), the proportion of secondary industry (SI), carbon emission intensity (CI), and urbanization (UR), on the CO2 emissions of three urban agglomerations. The results illustrate the following: (1) The CO2 emissions of YREB urban agglomerations decreased, with YRD-UA having the highest CO2 emissions among the three urban agglomerations and contributing 41.87% of YREB CO2 emissions in 2017. (2) CY-UA, YRMR-UA, and YRD-UA reached the CO2 emissions peak in 2012, 2011, and 2020, respectively, all of which are at the low-carbon stage. (3) POP and GDPPC show the greatest impact on the CO2 emissions of the three YREB urban agglomerations.

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Water quality grade is an intuitive element for people to understand the condition of water quality. However, in situ water quality grade measurements are often labor intensive, which makes measurement over large areas very costly and laborious. In recent years, numerous studies have demonstrated the effectiveness of remote sensing techniques in monitoring water quality. In order to automatically extract the water quality information, machine learning technologies have been widely applied in remote sensing data interoperation. In this study, Landsat-8 data and deep neural networks (DNN) were employed to identify the water quality grades of lakes in two cities, Wuhan and Huangshi, in the middle reach of the Yangtze River, central China. Additionally, linear support vector machine (L-SVM), random forest (RF), decision tree (DT), and multi-layer perceptron (MLP) were selected as comparative methods. The experimental results showed that DNN achieved the most promising performance compared to the other approaches. For the lakes in Wuhan, DNN gave water quality results with overall accuracy (OA) of 93.37% and Kappa of 0.9028. For the lakes in Huangshi, OA and kappa given by DNN were 96.39% and 0.951, respectively. The results show that the use of remote sensing images for water quality grade monitoring is effective. In the future, our method can be used for water quality monitoring of lakes in large areas at a low cost.

As an effective approach to obtaining agricultural information, the remote sensing technique has been applied in the classification of crop types. The unmanned aerial vehicle (UAV)-manned hyperspectral sensors provide imagery with high spatial and high spectral resolutions. Moreover, the detailed spatial information, as well as abundant spectral properties of UAV-manned hyperspectral imagery, opens a new avenue to the fine classification of crops. In this manuscript, multiscale superpixel-based approaches are proposed for the fine identification of crops in the UAV-manned hyperspectral imagery. Specifically, the multiscale superpixel segmentation is performed and a series of superpixel maps can be obtained. Then, the multiscale information is integrated into image classification by two strategies, namely pre-processing and post-processing. For the pre-processing strategy, the superpixel is regarded as the minimum unit for image classification, whose feature is obtained by using the average of spectral values of pixels within it. At each scale, the classification is performed on the basis of the superpixel. Then, the multiscale classification results are combined to generate the final map. For the post-processing strategy, the pixel-wise classification is implemented to obtain the label and posterior probabilities of each pixel. Subsequently, the superpixel-based voting is conducted at each scale, and these obtained voting results are fused to generate the multiscale voting result. To evaluate the effectiveness of the proposed approaches, three open-sourced hyperspectral UAV-manned datasets are employed in the experiments. Meanwhile, seven training sets with different numbers of labeled samples and two classifiers are taken into account for further analysis. The results demonstrate that the multiscale superpixel-based approaches outperform the single-scale approaches. Meanwhile, the post-processing strategy is superior to the pre-processing strategy in terms of higher classification accuracies in all the datasets.

Chlorophyll content is an important indicator of plant photosynthesis, which directly affects the growth and yield of crops. Using hyperspectral imaging technology to quickly and non-destructively estimate the soil plant analysis development (SPAD) value of pepper leaf and its distribution inversion is of great significance for agricultural monitoring and precise fertilization during pepper growth. In this study, 150 samples of pepper leaves with different leaf positions were selected, and the hyperspectral image data and SPAD value were collected for the sampled leaves. The correlation coefficient, stability competitive adaptive reweighted sampling (sCARS), and iteratively retaining informative variables (IRIV) methods were used to screen characteristic bands. These were combined with partial least-squares regression (PLSR), extreme gradient boosting (XGBoost), random forest regression (RFR), and gradient boosting decision tree (GBDT) to build regression models. The developed model was then used to build the inversion map of pepper leaf chlorophyll distribution. The research results show that: (1) The IRIV-XGBoost model demonstrates the most comprehensive performance in the modeling and inversion stages, and its Rcv2, RMSEcv, and MAEcv are 0.81, 2.76, and 2.30, respectively; (2) The IRIV-XGBoost model was used to calculate the SPAD value of each pixel of pepper leaves, and to subsequently invert the chlorophyll distribution map of pepper leaves at different leaf positions, which can provide support for the intuitive monitoring of crop growth and lay the foundation for the development of hyperspectral field dynamic monitoring sensors.

The rapidly increasing world population and human activities accelerate the crisis of the limited freshwater resources. Water quality must be monitored for the sustainability of freshwater resources. Unmanned aerial vehicle (UAV)-borne hyperspectral data can capture fine features of water bodies, which have been widely used for monitoring water quality. In this study, nine machine learning algorithms are systematically evaluated for the inversion of water quality parameters including chlorophyll-a (Chl-a) and suspended solids (SS) with UAV-borne hyperspectral data. In comparing the experimental results of the machine learning model on the water quality parameters, we can observe that the prediction performance of the Catboost regression (CBR) model is the best. However, the prediction performances of the Multi-layer Perceptron regression (MLPR) and Elastic net (EN) models are very unsatisfactory, indicating that the MLPR and EN models are not suitable for the inversion of water quality parameters. In addition, the water quality distribution map is generated, which can be used to identify polluted areas of water bodies.

Hyperspectral imagery has been widely used in precision agriculture due to its rich spectral characteristics. With the rapid development of remote sensing technology, the airborne hyperspectral imagery shows detailed spatial information and temporal flexibility, which open a new way to accurate agricultural monitoring. To extract crop types from the airborne hyperspectral images, we propose a fine classification method based on multi-feature fusion and deep learning. In this research, the morphological profiles, GLCM texture and endmember abundance features are leveraged to exploit the spatial information of the hyperspectral imagery. Then, the multiple spatial information is fused with the original spectral information to generate classification result by using the deep neural network with conditional random field (DNN+CRF) model. Specifically, the deep neural network (DNN) is a deep recognition model which can extract depth features and mine the potential information of data. As a discriminant model, conditional random field (CRF) considers both spatial and contextual information to reduce the misclassification noises while keeping the object boundaries. Moreover, three multiple feature fusion approaches, namely feature stacking, decision fusion and probability fusion, are taken into account. In the experiments, two airborne hyperspectral remote sensing datasets (Honghu dataset and Xiong’an dataset) are used. The experimental results show that the classification performance of the proposed method is satisfactory, where the salt and pepper noise is decreased, and the boundary of the ground object is preserved.

Significant improvement in the fire resistance of polyethylene terephthalate (PET) while ensuring its mechanical properties is a tremendous challenge. A novel flame retardant (GO-HCCP, graphene oxide-hexachlorocyclotriphosphazene) was synthesized by nucleophilic substitution of the graphene oxide (GO) and hexachlorocyclotriphosphazene (HCCP) and then applied in PET by an in situ polymerization technique. The scanning electron microscope (SEM) showed a better dispersion of GO-HCCP than GO in the PET matrix. The char yield at 700 °C increased by 32.5% with the addition of GO-HCCP. Moreover, the peak heat release rate (pHRR), peak smoke produce rate (pSPR)and carbon monoxide production (COP)values significantly decreased by 26.0%, 16.7% and 37.5%, respectively, which indicates the outstanding fire and smoke suppression of GO-HCCP. In addition, the composites exhibited higher elastic modulus and tensile strength without compromising the toughness of PET matrix. These significantly reduced fire hazards properties are mainly attributed to the catalytic carbonation of HCCP and the barrier effect of GO. Thus, PET composites with good flame-retardant and mechanical properties were prepared, which provides a new strategy for further flame retardant PET preparation.

With the continuous application of arsenic-containing chemicals, arsenic pollution in soil has become a serious problem worldwide. The detection of arsenic pollution in soil is of great significance to the protection and restoration of soil. Hyperspectral remote sensing is able to effectively monitor heavy metal pollution in soil. However, due to the possible complex nonlinear relationship between soil arsenic (As) content and the spectrum and data redundancy, an estimation model with high efficiency and accuracy is urgently needed. In response to this situation, 62 samples and 27 samples were collected in Daye and Honghu, Hubei Province, respectively. Spectral measurement and physical and chemical analysis were performed in the laboratory to obtain the As content and spectral reflectance. After the continuum removal (CR) was performed, the stable competitive adaptive reweighting sampling algorithm coupled the successive projections algorithm (sCARS-SPA) was used for characteristic band selection, which effectively solves the problem of data redundancy and collinearity. Partial least squares regression (PLSR), radial basis function neural network (RBFNN), and shuffled frog leaping algorithm optimization of the RBFNN (SFLA-RBFNN) were established in the characteristic wavelengths to predict soil As content. These results show that the sCARS-SPA-SFLA-RBFNN model has the best universality and high prediction accuracy in different land-use types, which is a scientific and effective method for estimating the soil As content.

Soil organic matter (SOM) refers to all carbon-containing organic matter in soil and is one of the most important indicators of soil fertility. The hyperspectral inversion analysis of SOM traditionally relies on laboratory chemical testing methods, which have the disadvantages of being inefficient and time-consuming. In this study, 69 soil samples were collected from the Honghu farmland area and a mining area in northwest China. After pretreatment, 10 spectral indicators were obtained. Ridge regression, kernel ridge regression, Bayesian ridge regression, and AdaBoost algorithms were then used to construct the SOM hyperspectral inversion model based on the characteristic bands, and the accuracy of the models was compared. The results showed that the AdaBoost algorithm based on a grid search had the best accuracy in the different regions. For the mining area in northwest China, R p 2 = 0.91, R M S E p = 0.22, and M A E p = 0.2. For the Honghu farmland area, R p 2 = 0.86, R M S E p = 0.72, and M A E p = 0.56. The detection of SOM content using hyperspectral technology has the characteristics of a high detection precision and high speed, which will be of great significance for the rapid development of precision agriculture.

Soil total arsenic (TAs) contamination caused by human activities—such as mining, smelting, and agriculture—is a problem of global concern. Visible/near-infrared (VNIR), X-ray fluorescence spectroscopy (XRF), and laser-induced breakdown spectroscopy (LIBS) do not need too much sample preparation and utilization of chemicals to evaluate total arsenic (TAs) concentration in soil. VNIR with hyperspectral imaging has the potential to predict TAs concentration in soil. In this study, 59 soil samples were collected from the Daye City mining area of China, and hyperspectral imaging of the soil samples was undertaken using a visible/near-infrared hyperspectral imaging system (wavelength range 470–900 nm). Spectral preprocessing included standard normal variate (SNV) transformation, multivariate scatter correction (MSC), first derivative (FD) preprocessing, and second derivative (SD) preprocessing. Characteristic bands were then identified based on Spearman’s rank correlation coefficients. Four regression models were used for the modeling prediction: partial least squares regression (PLSR) (R2 = 0.71, RMSE = 0.48), support vector machine regression (SVMR) (R2 = 0.78, RMSE = 0.42), random forest (RF) (R2 = 0.78, RMSE = 0.42), and extremely randomized trees regression (ETR) (R2 = 0.81, RMSE = 0.38). The prediction results were compared with the results of atomic fluorescence spectrometry methods. In the prediction results of the models, the accuracy of ETR using FD preprocessing was the highest. The results confirmed that hyperspectral imaging combined with Spearman’s rank correlation with machine learning models can be used to estimate soil TAs content.

Satellite aerosol optical depth (AOD) products have been widely used in estimating fine particulate matter (PM2.5) concentrations near the surface at a regional scale, and perform well compared with ground measurements. However, the influence of limitations such as retrieval frequency and the spatial resolution of satellite AODs on the applicability of predicted PM2.5 values has been rarely considered. With three widely used MODIS AOD products, including Multi-Angle Implementation of Atmospheric Correction (MAIAC), Deep Blue (DB) and Dark Target (DT), here we evaluate the influence of their spatial resolution and sampling frequency by estimating daily PM2.5 concentrations in the Beijing-Tianjin-Hebei (BTH) region of northern China during 2017 utilizing a mixed effects model. The daily concentrations of PM2.5 derived from MAIAC, DB and DT AOD all have high correlations (R2: 0.78, 0.8, and 0.78) with the observed values, but the predicted annual PM2.5 exhibits a distinct spatial distribution. DT estimation obviously underestimates annual PM2.5 in polluted areas due to lower sampling of heavy pollution events. By contrast, the retrieval frequency (~40-60%) of MAIAC and DB AOD can represent well annual PM2.5 in nearly all 83 sites tested. However, MAIAC and DB-derived PM2.5 have a larger bias compared with observed values than DT, indicating that the large spatial variation of aerosol properties can exert an influence on the reliability of the statistical AOD-PM2.5 relationship. Also, there is notable difference between MAIAC and DB PM2.5 due to their different cloud screening methods. The MAIAC PM2.5 with high spatial resolution at 1 km can capture much finer hotpots than DB and DT at 10 km. Our results suggest that it is crucial to consider the applicability of satellite-predicted PM2.5 values derived from different aerosol products according to the specific requirements besides modeling the AOD-PM2.5 relationship.

The sustainable development of water resources is always emphasized in China, and a set of perfect standards for the division of inland water environment quality have been established to monitor water quality. However, most of the 24 indicators that determine the water quality level in the standards are non-optically active parameters. The weak optical characteristics make it difficult to find significant correlations between the single parameters and the remote sensing imagery. In addition, traditional on-site testing methods have been unable to meet the increasingly extensive water-quality monitoring requirements. Based on the above questions, it’s meaningful that the supervised classification process of a detail-preserving smoothing classifier based on conditional random field (CRF) and Landsat-8 data was proposed in the two study areas around Wuhan and Huangshi in Hubei Province. The random forest classifier was selected to model the association potential of the CRF. The results (the first study area: OA = 89.50%, Kappa = 0.841; the second study area: OA = 90.35%, Kappa = 0.868) showed that the water-quality monitoring based on CRF model is feasible, and this approach can provide a reference for water-quality mapping of inland lakes. In the future, it may only require a small amount of on-site sampling to achieve the identification of the water quality levels of inland lakes across a large area of China.

The formation of black-odor water in urban rivers has a long history. It not only seriously affects the image of the city, but also easily breeds germs and damages the urban habitat. The prevention and treatment of urban black-odor water have long been important topics nationwide. “Action Plan for Prevention and Control of Water Pollution” issued by the State Council shows Chinese government’s high attention to this issue. However, treatment and monitoring are inextricably linked. There are few studies on the large-scale monitoring of black-odor water, especially the cases of using unmanned aerial vehicle (UAV) to efficiently and accurately monitor the spatial distribution of urban river pollution. Therefore, in order to get rid of the limitations of traditional ground sampling to evaluate the point source pollution of rivers, the UAV-borne hyperspectral imagery was applied in this paper. It is hoped to grasp the pollution status of the entire river as soon as possible from the surface. However, the retrieval of multiple water quality parameters will lead to cumulative errors, so the Nemerow comprehensive pollution index (NCPI) is introduced to characterize the pollution level of urban water. In the paper, the retrieval results of six regression models including gradient boosting decision tree regression (GBDTR) were compared, trying to find a regression model for the retrieval NCPI in the current scenario. In the first study area, the retrieval accuracy of the training dataset (adjusted_R2 = 0.978), and test dataset (adjusted_R2 = 0.974) was higher than that of the other regression models. Although the retrieval effect of random forest is similar to that of GBDTR in both training accuracy and image inversion, it is more computationally expensive. Finally, the spatial distribution graphs of NCPI and its technical feasibility in monitoring pollution sources were investigated, in combination with field observations.

In this study, in order to solve the difficulty of the inversion of soil arsenic (As) content using laboratory and field reflectance spectroscopy, we examined the transferability of the prediction method. Sixty-three soil samples from the Daye city area of the Jianghan Plain region of China were taken and studied in this research. The characteristic wavelengths of soil As content were then extracted from the full bands based on iteratively retaining informative variables (IRIV) coupled with Spearman’s rank correlation analysis (SCA). Firstly, the IRIV algorithm was used to roughly select the original spectral data. Gaussian filtering (GF), first derivative (FD) filtering, and gaussian filtering again (GFA) pretreatments were then used to improve the correlation between the spectra and soil As content. A subset with absolute correlation values greater than 0.6 was then retained as the optimal subset after each pretreatment. Finally, partial least squares regression (PLSR), Bayesian ridge regression (BRR), ridge regression (RR), kernel ridge regression (KRR), support vector machine regression (SVMR), eXtreme gradient boosting (XGBoost) regression, and random forest regression (RFR) models were used to estimate the soil As values using the different characteristic variables. The results showed that, compared with the traditional method based on IRIV, using the characteristic bands selected by the IRIV-SCA method can effectively improve the prediction accuracy of the models. For the laboratory spectra experiment stage, the six most representative characteristic bands were selected. The performance of IRIV-SCA-SVMR was found to be the best, with the coefficient of determination (R2), root-mean-square error (RMSE), and mean absolute error (MAE) in the validation set being 0.97, 0.22, and 0.11, respectively. For the field spectra experiment stage, the 12 most representative characteristic bands were selected. The performance of IRIV-SCA-XGBoost was found to be the best, with the R2, RMSE, and MAE in the validation set being 0.83, 0.35, and 0.29, respectively. The accuracy and stability of the inversion of soil As content are significantly improved by the use of the proposed method, and the method could be used to provide accurate data for decision support for the treatment and recovery of As pollution over a large area.

The precise classification of crop types is an important basis of agricultural monitoring and crop protection. With the rapid development of unmanned aerial vehicle (UAV) technology, UAV-borne hyperspectral remote sensing imagery with high spatial resolution has become the ideal data source for the precise classification of crops. For precise classification of crops with a wide variety of classes and varied spectra, the traditional spectral-based classification method has difficulty in mining large-scale spatial information and maintaining the detailed features of the classes. Therefore, a precise crop classification method using spectral-spatial-location fusion based on conditional random fields (SSLF-CRF) for UAV-borne hyperspectral remote sensing imagery is proposed in this paper. The proposed method integrates the spectral information, the spatial context, the spatial features, and the spatial location information in the conditional random field model by the probabilistic potentials, providing complementary information for the crop discrimination from different perspectives. The experimental results obtained with two UAV-borne high spatial resolution hyperspectral images confirm that the proposed method can solve the problems of large-scale spatial information modeling and spectral variability, improving the classification accuracy for each crop type. This method has important significance for the precise classification of crops in hyperspectral remote sensing imagery.

Suspended solids concentration (SSC) is an important indicator of the degree of water pollution. However, when using an empirical or semi-empirical model adapted to some of the inland waters to estimate SSC on unmanned aerial vehicle (UAV)-borne hyperspectral images, the accuracy is often not sufficient. Thus, in this study, we attempted to use the particle swarm optimization (PSO) algorithm to find the optimal parameters of the least-squares support vector machine (LSSVM) model for the quantitative inversion of SSC. A reservoir and a polluted riverway were selected as the study areas. The spectral data of the 36-point and 29-point 400−900 nm wavelength range on the UAV-borne images were extracted. Compared with the semi-empirical model, the random forest (RF) algorithm and the competitive adaptive reweighted sampling (CARS) algorithm combined with partial least squares (PLS), the accuracy of the PSO-LSSVM algorithm in predicting the SSC was significantly improved. The training samples had a coefficient of determination ( R 2 ) of 0.98, a root mean square error (RMSE) of 0.68 mg/L, and a mean absolute percentage error (MAPE) of 12.66% at the reservoir. For the polluted riverway, PSO-LSSVM also performed well. Finally, the established SSC inversion model was applied to UAV-borne hyperspectral remote sensing (HRS) images. The results confirmed that the distribution of the predicted SSC was consistent with the observed results in the field, which proves that PSO-LSSVM is a feasible approach for the SSC inversion of UAV-borne HRS images.

There is inconsistency between the existing remote sensing cropland products, whose accuracy of estimated cropland area and spatial positioning needs to be improved. The existing generalized methods of generating synergy cropland products for improving the accuracy of existing products do not consider the overall consistency difference between the different products in each grid cell in the fusion process. To reduce the impact of the abnormal estimated cropland areas of the individual cropland products on the results, this paper proposes a method of generating a synergy cropland product by fusing the multiple existing cropland products, based on the overall consistency difference. In the proposed method, the process of fusing the multiple existing cropland products is based on the overall consistency difference of the estimated cropland area of all the cropland products in each grid cell. The synergy cropland product is then generated after determining the best combination level with the cropland statistics. In this study, we set 2010 as the base year, and used the proposed method to conduct experiments with four remote sensing cropland products: GlobCover 2009, MODIS Cropland, MCD12Q1, and FROM-GLC within China, and national cropland statistics. The results show that the synergy cropland product generated by the proposed method has a higher accuracy of cropland area estimation and spatial positioning than the results obtained by the generalized model, as well as the original products.

Hyperspectral remote sensing can be used to effectively identify contaminated elements in soil. However, in the field of monitoring soil heavy metal pollution, hyperspectral remote sensing has the characteristics of high dimensionality and high redundancy, which seriously affect the accuracy and stability of hyperspectral inversion models. To resolve the problem, a gradient boosting regression tree (GBRT) hyperspectral inversion algorithm for heavy metal (Arsenic (As)) content in soils based on Spearman’s rank correlation analysis (SCA) coupled with competitive adaptive reweighted sampling (CARS) is proposed in this paper. Firstly, the CARS algorithm is used to roughly select the original spectral data. Second derivative (SD), Gaussian filtering (GF), and min-max normalization (MMN) pretreatments are then used to improve the correlation between the spectra and As in the characteristic band enhancement stage. Finally, the low-correlation bands are removed using the SCA method, and a subset with absolute correlation values greater than 0.6 is retained as the optimal band subset after each pretreatment. For the modeling, the five most representative characteristic bands were selected in the Honghu area of China, and the nine most representative characteristic bands were selected in the Daye area of China. In order to verify the generalization ability of the proposed algorithm, 92 soil samples from the Honghu and Daye areas were selected as the research objects. With the use of support vector machine regression (SVMR), linear regression (LR), and random forest (RF) regression methods as comparative methods, all the models obtained a good prediction accuracy. However, among the different combinations, CARS-SCA-GBRT obtained the highest precision, which indicates that the proposed algorithm can select fewer characteristic bands to achieve a better inversion effect, and can thus provide accurate data support for the treatment and recovery of heavy metal pollution in soils.

The fine classification of crops is critical for food security and agricultural management. There are many different species of crops, some of which have similar spectral curves. As a result, the precise classification of crops is a difficult task. Although the classification methods that incorporate spatial information can reduce the noise and improve the classification accuracy, to a certain extent, the problem is far from solved. Therefore, in this paper, the method of spatial–spectral fusion based on conditional random fields (SSF-CRF) for the fine classification of crops in UAV-borne hyperspectral remote sensing imagery is presented. The proposed method designs suitable potential functions in a pairwise conditional random field model, fusing the spectral and spatial features to reduce the spectral variation within the homogenous regions and accurately identify the crops. The experiments on hyperspectral datasets of the cities of Hanchuan and Honghu in China showed that, compared with the traditional methods, the proposed classification method can effectively improve the classification accuracy, protect the edges and shapes of the features, and relieve excessive smoothing, while retaining detailed information. This method has important significance for the fine classification of crops in hyperspectral remote sensing imagery.

Data authenticated aggregation is always a significant issue for wireless sensor networks (WSNs). The marine sensors are deployed far away from the security monitoring. Secure data aggregation for marine WSNs has emerged and attracted the interest of researchers and engineers. A multi-signature enables the data aggregation through one signature to authenticate various signers on the acknowledgement of a message, which is quite fit for data authenticated aggregation marine WSNs. However, most of the previous multi-signature schemes rely on the technique of bilinear pairing involving heavy computational overhead or the management of certificates, which cannot be afforded by the marine wireless sensors. Combined with the concept of identity-based cryptography, a few pairing-free identity-based multi-signature (IBMS) schemes have been designed on the basis of the integer factorization problem. In this paper, we propose two efficient IBMS schemes that can be used to construct provably secure data authenticated aggregation protocols under the cubic residue assumption, which is equal to integer factorization. We also employ two different methods to calculate a cubic root for the cubic residue number during the signer’s private key extraction. The algorithms are quite efficient compared to the previous work, especially for the algorithms of the multi-signature generation and its verification.