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Models for bathymetry retrieval from multispectral images have not considered the errors caused by tidal fluctuation. A rigorous bathymetric model that considers the variation in tide height time series, including the tide height calculation and instantaneous tide height correction at the epoch of satellite flight into the bathymetric retrieval model, is proposed in this paper. The model was applied on Weizhou Island, located in Guangxi Province, China, and its accuracy verificated with four check lines and seven checkpoints. A scene from the Landsat 8 satellite image was used as experimental data. The reference ("true") water depth data collected by a RESON SeaBat 7125 multibeam instrument was used for comparison analysis. When satellite-derived bathymetry is compared, it is found that maximum absolute error, mean absolute error, and RMSE have decreased 54, 45, and 30% relative to that of the traditional model in the entire test field. The accuracy of the water depths retrieved by our model increased 30 and 56% when validated using four check lines and seven checkpoints, respectively. Therefore, it can be concluded that the model proposed in this paper can effectively improve the accuracy of bathymetry retrieved from Landsat 8 images. [ABSTRACT FROM AUTHOR]

Due to the country's rapid economic growth, the problem of air pollution in China is becoming increasingly serious. In order to achieve a win-win situation for the environment and urban development, the government has issued many policies to strengthen environmental protection. PM2.5 is the primary particulate matter in air pollution, so an accurate estimation of PM2.5 distribution is of great significance. Although previous studies have attempted to retrieve PM2.5 using geostatistical or aerosol remote sensing retrieval methods, the current rough resolution and accuracy remain as limitations of such methods. This paper proposes a fine-grained spatiotemporal PM2.5 retrieval method that comprehensively considers various datasets, such as Landsat 8 satellite images, ground monitoring station data, and socio-economic data, to explore the applicability of different machine learning algorithms in PM2.5 retrieval. Six typical algorithms were used to train the multi-dimensional elements in a series of experiments. The characteristics of retrieval accuracy in different scenarios were clarified mainly according to the validation index, R2. The random forest algorithm was shown to have the best numerical and PM2.5-based air-quality-category accuracy, with a cross-validated R2 of 0.86 and a category retrieval accuracy of 0.83, while both maintained excellent retrieval accuracy and achieved a high spatiotemporal resolution. Based on this retrieval model, we evaluated the PM2.5 distribution characteristics and hourly variation in the sample area, as well as the functions of different input variables in the model. The PM2.5 retrieval method proposed in this paper provides a new model for fine-grained PM2.5 concentration estimation to determine the distribution laws of air pollutants and thereby specify more effective measures to realize the high-quality development of the city. [ABSTRACT FROM AUTHOR]

Validation is an essential and important step before the application of remote sensing products. This paper introduces a prototype of the validation network for remote sensing products in China (VRPC). The VRPC aims to improve remote sensing products at a regional scale in China. These improvements will enhance the applicability of the key remote sensing products in understanding and interpretation of typical land surface processes in China. The framework of the VRPC is introduced first, including its four basic components. Then, the basic selection principles of the observation sites are described, and the principles for the validation of the remote sensing products are established. The VRPC will be realized in stages. In the first stage, four stations that have improved remote sensing observation facilities have been incorporated according to the selection principles. Certain core observation sites have been constructed at these stations. Next the Heihe Station is introduced in detail as an example. The three levels of observation (the research base, pixel-scale validation sites, and regional coverage) adopted by the Heihe Station are carefully explained. The pixel-scale validation sites with nested multi-scale observation systems in this station are the most unique feature, and these sites aim to solve some key scientific problems associated with remote sensing product validation (e.g., the scale effect and scale transformation). Multi-year of in situ measurements will ensure the high accuracy and inter-annual validity of the land products, which will provide dynamic regional monitoring and simulation capabilities in China. The observation sites of the VRPC are open, with the goal of increasing cooperation and exchange with global programs. [ABSTRACT FROM AUTHOR]

The growing population and northward shifts in the center of grain production collectively contribute to the arising farmland use intensity of the farming–pastoral ecotone of Northern China (FPENC). Consequently, it poses a great threat to the vulnerable ecosystem of FPENC. Thus, farmland use intensity monitoring is a top priority to practice sustainable farming. In this study, we establish an indicator system designed to evaluate farmland use intensity in Ulanqab, located in the central part of FPENC. This system includes three single-year indicators (the degree of coupling between effective rainfall and crop water requirement (Dcrr), irrigation intensity (Iri) and crop duration (Cd)) and two multi-year indicators (the frequency of adopting the green-depressing cropping system (Gf) and rotation frequency (Rf)). We mapped five farmland use intensity indicators in Ulanqab from 2010 to 2019 using satellite imagery and other ancillary data. Then, the farmland use patterns were recognized by applying the self-organizing map algorithm. Our results suggest that the mapping results of crop types, center pivot irrigation (CPI), and irrigated areas are reasonably accurate. Iri, Cd, and Rf experienced an increase of 31 m3/hm2, 1 day, and 0.06 in Ulanqab from 2010 to 2019, respectively, while Dcrr and Gf witnessed a decrease of 0.002 and 0.004, respectively. That is, farmers are progressively inclined to higher farmland use intensity. Moreover, spatial heterogeneity analysis shows that Northern Ulanqab owned higher Dcrr, Iri, Cd, and Rf, and lower Gf than the southern part. We conclude the paper by discussing the implications of the results for areas with different farmland use intensity patterns. [ABSTRACT FROM AUTHOR]

Agricultural greenhouses (AGs) are an important component of modern facility agriculture, and accurately mapping and dynamically monitoring their distribution are necessary for agricultural scientific management and planning. Semantic segmentation can be adopted for AG extraction from remote sensing images. However, the feature maps obtained by traditional deep convolutional neural network (DCNN)-based segmentation algorithms blur spatial details and insufficient attention is usually paid to contextual representation. Meanwhile, the maintenance of the original morphological characteristics, especially the boundaries, is still a challenge for precise identification of AGs. To alleviate these problems, this paper proposes a novel network called high-resolution boundary refined network (HBRNet). In this method, we design a new backbone with multiple paths based on HRNetV2 aiming to preserve high spatial resolution and improve feature extraction capability, in which the Pyramid Cross Channel Attention (PCCA) module is embedded to residual blocks to strengthen the interaction of multiscale information. Moreover, the Spatial Enhancement (SE) module is employed to integrate the contextual information of different scales. In addition, we introduce the Spatial Gradient Variation (SGV) unit in the Boundary Refined (BR) module to couple the segmentation task and boundary learning task, so that they can share latent high-level semantics and interact with each other, and combine this with the joint loss to refine the boundary. In our study, GaoFen-2 remote sensing images in Shouguang City, Shandong Province, China are selected to make the AG dataset. The experimental results show that HBRNet demonstrates a significant improvement in segmentation performance up to an IoU score of 94.89%, implying that this approach has advantages and potential for precise identification of AGs. [ABSTRACT FROM AUTHOR]

The official method of collecting county-level GDP values in the Chinese Mainland relies mainly on administrative reporting data and suffers from high costs of time, money, and human labor. To date, a series of studies have been conducted to generate fine-grained maps of socioeconomic indicators from the easily accessed remote sensing data and achieved satisfactory results. This paper proposes a transfer learning framework that regards nightlight intensities as a proxy of economic activity degrees to estimate county-level GDP around the Chinese Mainland. In the framework, paired daytime satellite images and nightlight intensity levels were applied to train a VGG-16 architecture, and the output features at a specific layer, after dimensional reduction and statistics calculation, were fed into a simple regressor to estimate county-level GDP. We trained the model with data of 2017 and utilized it to predict county-level GDP of 2018, achieving an R-squared of 0.71. Furthermore, the results of gradient visualization confirmed the validity of the proposed framework qualitatively. To the best of our knowledge, this is the first time that county-level GDP values around the Chinese Mainland have been estimated from both daytime and nighttime remote sensing data relying on attention-augmented CNN. We believe that our work will shed light on both the evolution of fine-grained socioeconomic surveys and the application of remote sensing data in economic research. [ABSTRACT FROM AUTHOR]

Estimation of the number and geo-location of oil wells is important for policy holders considering their impact on energy resource planning. With the recent development in optical remote sensing, it is possible to identify oil wells from satellite images. Moreover, the recent advancement in deep learning frameworks for object detection in remote sensing makes it possible to automatically detect oil wells from remote sensing images. In this paper, we collected a dataset named Northeast Petroleum University–Oil Well Object Detection Version 1.0 (NEPU–OWOD V1.0) based on high-resolution remote sensing images from Google Earth Imagery. Our database includes 1192 oil wells in 432 images from Daqing City, which has the largest oilfield in China. In this study, we compared nine different state-of-the-art deep learning models based on algorithms for object detection from optical remote sensing images. Experimental results show that the state-of-the-art deep learning models achieve high precision on our collected dataset, which demonstrate the great potential for oil well detection in remote sensing. [ABSTRACT FROM AUTHOR]

Mangrove forests are important ecosystems in the coastal intertidal zone, but China's mangroves have experienced a large reduction in area from the 1950s, and the remaining mangrove forests are exhibiting increased fragmentation. A detailed mangrove dataset of China is crucial for mangrove ecosystem management and protection, but the fragmented mangrove patches are hardly mapped by medium resolution satellite imagery. To overcome these difficulties, we presented a fine-scale mangrove map for 2018 using the 2-meter resolution Gaofen-1 and Ziyuan-3 satellite imagery together with field data. We employed a hybrid method of object-based image analysis (OBIA), interpreter editing, and field surveying for mangrove mapping. The field survey route reached 9500 km, and 2650 patches were verified during the field work. Accuracy assessment by confusion matrix showed that the kappa coefficient reached 0.98, indicating a highly thematic accuracy of the mangrove dataset. Results showed the total area of mangrove forest in China for 2018 was 25,683.88 hectares, and approximately 91% of mangroves were found in the three provinces of Guangdong, Guangxi, and Hainan. About 64% of mangroves were distributed in or near the nature reserves established by national or local governments, which indicated that China's mangroves were well protected in recent years. The new fine-scale mangrove dataset was freely shared together with this paper, and it can be used by local authorities and research groups for mangrove management and ecological planning. [ABSTRACT FROM AUTHOR]

Water body extraction can help eco-environmental policymakers to intuitively grasp surface water resources. Remote sensing technology can accurately and quickly extract surface water information, which is of great significance for monitoring surface water changes. Fengyun satellite images have the advantages of high time resolution and multispectral bands. This provides important image data suitable for high-frequency surface water monitoring. Based on Fengyun 3 medium resolution spectral imager (FY-3/MERSI) data, 7 methods were applied in this study, which include single-band threshold method, water body index method, knowledge decision tree classification method, supervised classification method, unsupervised classification method, spectral matching based on discrete particle swarm optimization (SMDPSO), and improved spectral matching based on discrete particle swarm optimization with linear feature enhancement (SMDPSO+LFE). These methods were used to extract the land surface water of Poyang Lake, check the samples from the Landsat image with similar times to the FY-3 images, and calculate the classification accuracy via the confusion matrix. The results showed that the overall classification accuracy (OA) of the SMDPSO+LFE is 97.64%, and the Kappa coefficient is 0.95. To analyze the stability of the surface water extracted by SMDPSO+LFE in different regions, this paper selected eight test sites with different surface water types, landscapes, and terrains to extract surface water. Based on an analysis of the land surface water results at the eight test sites, every OA in the eight sites was higher than 94.5%, the Kappa coefficient was greater than 0.88. In conclusion, the SMDPSO+LFE is found to be the most suitable method among the 7 methods and effectively distinguish between different surface water bodies and backgrounds with good stability. [ABSTRACT FROM AUTHOR]

Stereo photogrammetric survey used to be used to extract the height of buildings, then to convert the height to number of stories through certain rules to estimate the number of stories of buildings by means of satellite remote sensing. In contrast, we propose a new method using deep learning to estimate the number of stories of buildings from monocular optical satellite image end to end in this paper. To the best of our knowledge, this is the first attempt to directly estimate the number of stories of buildings from monocular satellite images. Specifically, in the proposed method, we extend a classic object detection network, i.e., Mask R-CNN, by adding a new head to predict the number of stories of detected buildings from satellite images. GF-2 images from nine cities in China are used to validate the effectiveness of the proposed methods. The result of experiment show that the mean absolute error of prediction on buildings whose stories between 1–7, 8–20, and above 20 are 1.329, 3.546, and 8.317, respectively, which indicate that our method has possible application potentials in low-rise buildings, but the accuracy in middle-rise and high-rise buildings needs to be further improved. [ABSTRACT FROM AUTHOR]

Urban flooding is one of the most costly and destructive natural hazards worldwide. Remote-sensing images with high temporal resolutions have been extensively applied to timely inundation monitoring, assessing and mapping, but are limited by their low spatial resolution. Sub-pixel mapping has drawn great attention among researchers worldwide and has demonstrated a promising potential of high-accuracy mapping of inundation. Aimed to boost sub-pixel urban inundation mapping (SUIM) from remote-sensing imagery, a new algorithm based on spatial attraction models and Elman neural networks (SAMENN) was developed and examined in this paper. The Elman neural networks (ENN)-based SUIM module was developed firstly. Then a normalized edge intensity index of mixed pixels was generated. Finally the algorithm of SAMENN-SUIM was constructed and implemented. Landsat 8 images of two cities of China, which experienced heavy floods, were used in the experiments. Compared to three traditional SUIM methods, SAMENN-SUIM attained higher mapping accuracy according not only to visual evaluations but also quantitative assessments. The effects of normalized edge intensity index threshold and neuron number of the hidden layer on accuracy of the SAMENN-SUIM algorithm were analyzed and discussed. The newly developed algorithm in this study made a positive contribution to advancing urban inundation mapping from remote-sensing images with medium-low spatial resolutions, and hence can favor urban flood monitoring and risk assessment. [ABSTRACT FROM AUTHOR]

As the largest developing country, China has experienced dramatic urban expansion since the "reform and opening-up" policy started at the end of the 1970s. In this paper, we monitor three decades of urban expansion in China's 36 major cities, based on the spectral mixture analysis of remotely sensed satellite images. The results demonstrated that these major cities have expanded by 5.85 times from 1986 to 2015, with 15.51 km2 average expansion area per city per year. We found the urban expansion trajectories showed three different modes, i.e., exponential, linear and s-shaped, which were closely related to the city development level. In the old city zones, however, there was an interesting common tendency of the impervious surface area (ISA) first increasing and then decreasing, which could be largely attributed to the phenomenon of urban village reconstruction in China's cities. Based on the Granger Causality Test (GCT), the interaction between urban ISA and Gross Domestic Product (GDP) per capita (GDPPC) suggested that the former was the driver of the latter. Meanwhile, taking the Yangtze River as the division between north and south China, there exists a north–south territorial differentiation for the interaction between ISA and total population at the year-end (TP). [ABSTRACT FROM AUTHOR]

Building extraction plays a significant role in many high-resolution remote sensing image applications. Many current building extraction methods need training samples while it is common knowledge that different samples often lead to different generalization ability. Morphological building index (MBI), representing morphological features of building regions in an index form, can effectively extract building regions especially in Chinese urban regions without any training samples and has drawn much attention. However, some problems like the heavy computation cost of multi-scale and multi-direction morphological operations still exist. In this paper, a multi-scale filtering building index (MFBI) is proposed in the hope of overcoming these drawbacks and dealing with the increasing noise in very high-resolution remote sensing image. The profile of multi-scale average filtering is averaged and normalized to generate this index. Moreover, to fully utilize the relatively little spectral information in very high-resolution remote sensing image, two scenarios to generate the multi-channel multi-scale filtering index (MMFBI) are proposed. While no high-resolution remote sensing image building extraction dataset is open to the public now and the current very high-resolution remote sensing image building extraction datasets usually contain samples from the Northern American or European regions, we offer a very high-resolution remote sensing image building extraction datasets in which the samples contain multiple building styles from multiple Chinese regions. The proposed MFBI and MMFBI outperform MBI and the currently used object based segmentation method on the dataset, with a high recall and F-score. Meanwhile, the computation time of MFBI and MBI is compared on three large-scale very high-resolution satellite image and the sensitivity analysis demonstrates the robustness of the proposed method. [ABSTRACT FROM AUTHOR]