DPIF-Net: a dual path network for rural road extraction based on the fusion of global and local information.

Background: Automatic extraction of roads from remote sensing images can facilitate many practical applications. However, thus far, thousands of kilometers or more of roads worldwide have not been recorded, especially low-grade roads in rural areas. Moreover, rural roads have different shapes and are influenced by complex environments and other interference factors, which has led to a scarcity of dedicated low level category road datasets.

Spatiotemporally continuous PM dataset in the Mekong River Basin from 2015 to 2022 using a stacking model.

With the potential to cause millions of deaths, PM pollution has become a global concern. In Southeast Asia, the Mekong River Basin (MRB) is experiencing heavy PM pollution and the existing PM studies in the MRB are limited in terms of accuracy and spatiotemporal coverage. To achieve high-accuracy and long-term PM monitoring of the MRB, fused aerosol optical depth (AOD) data and multi-source auxiliary data are fed into a stacking model to estimate PM concentrations.

Remote Sensing Image Scene Classification in Hybrid Classical-Quantum Transferring CNN with Small Samples.

The scope of this research lies in the combination of pre-trained Convolutional Neural Networks (CNNs) and Quantum Convolutional Neural Networks (QCNN) in application to Remote Sensing Image Scene Classification(RSISC). Deep learning (RL) is improving by leaps and bounds pretrained CNNs in Remote Sensing Image (RSI) analysis, and pre-trained CNNs have shown remarkable performance in remote sensing image scene classification (RSISC). Nonetheless, CNNs training require massive, annotated data as samples.

Spatio-Temporal Changes in Ecosystem Quality across the Belt and Road Region.

The Silk Road Economic Belt and the 21st Century Maritime Silk Road Initiative (BRI) proposed in 2013 by China has greatly accelerated the social and economic development of the countries along the Belt and Road (B&R) region. However, the international community has questioned its impact on the ecological environment and a comprehensive assessment of ecosystem quality changes is lacking. Therefore, this study proposes an objective and automatic method to assess ecosystem quality and analyzes the spatiotemporal changes in the B&R region.

A Joint Encryption and Compression Algorithm for Multiband Remote Sensing Image Transmission.

Due to the increasing capabilities of cybercriminals and the vast quantity of sensitive data, it is necessary to protect remote sensing images during data transmission with "Belt and Road" countries. Joint image compression and encryption techniques exhibit reliability and cost-effectiveness for data transmission. However, the existing methods for multiband remote sensing images have limitations, such as extensive preprocessing times, incompatibility with multiple bands, and insufficient security.

Chinese Soil Moisture Observation Network and Time Series Data Set for High Resolution Satellite Applications.

High-quality ground observation networks are an important basis for scientific research. Here, an automatic soil observation network for high-resolution satellite applications in China (SONTE-China) was established to measure both pixel- and multilayer-based soil moisture and temperature. SONTE-China is distributed across 17 field observation stations with a variety of ecosystems, covering both dry and wet zones.

Influences of environmental factors on the dissipation of green tides in the Yellow Sea, China.

Green tides attack the Yellow Sea every year since 2007 and have caused substantial financial loss. Based on Haiyang-1C/Coastal zone imager (HY-1C/CZI) and Terra/MODIS satellite images, the temporal and spatial distribution of green tides floating in the Yellow Sea during 2019 was extracted. The relationships between the growth rate of the green tides and the environmental factors including sea surface temperature (SST), photosynthetically active radiation (PAR), sea surface salinity (SSS), nitrate and phosphate during the green tides' dissipation phase has been detected.

QUantitative and Automatic Atmospheric Correction (QUAAC): Application and Validation.

The difficulty of atmospheric correction based on a radiative transfer model lies in the acquisition of synchronized atmospheric parameters, especially the aerosol optical depth (AOD). At the moment, there is no fully automatic and high-efficiency atmospheric correction method to make full use of the advantages of geostationary meteorological satellites in large-scale and efficient atmospheric monitoring. Therefore, a QUantitative and Automatic Atmospheric Correction (QUAAC) method is proposed which can efficiently correct high-spatial-resolution (HSR) satellite images.

Probing the dynamic characteristics of aerosol originated from South Asia biomass burning using POLDER/GRASP satellite data with relevant accessory technique design.

The dynamic characteristics of biomass burning aerosol originated from South Asia are investigated in this research using nearly 9 years of POLDER/GRASP satellite aerosol dataset. The POLDER/GRASP remote sensing data can provide global, repeatable, various, and sufficient real-world aerosol information even in the remote ocean region, which can't be offered by the ground measurement, laboratory observation or model simulation. The MODIS thermal anomalies/fire dataset and HYSPLIT backward trajectory are applied to search the aerosol originated from South Asia biomass burning.

Remote sensing algorithms for estimation of fractional vegetation cover using pure vegetation index values: A review.

Green fractional vegetation cover ( ) is an important phenotypic factor in the fields of agriculture, forestry, and ecology. Spatially explicit monitoring of via relative vegetation abundance (RA) algorithms, especially those based on scaled maximum/minimum vegetation index (VI) values, has been widely investigated in remote sensing research. Although many studies have explored the effectiveness of RA algorithms over the past 30 years, a literature review summarizing the corresponding theoretical background, issues, current state-of-the-art techniques, challenges, and prospects has not yet been published.

Spatial and temporal variations of air quality and six air pollutants in China during 2015-2017.

Air pollution has aroused significant public concern in China, therefore, long-term air-quality data with high temporal and spatial resolution are needed to understand the variations of air pollution in China. However, the yearly variations with high spatial resolution of air quality and six air pollutants are still unknown for China until now. Therefore, in this paper, we analyze the spatial and temporal variations of air quality and six air pollutants in 366 cities across mainland China during 2015-2017 for the first time to the best of our knowledge.

Author Correction: A lake data set for the Tibetan Plateau from the 1960s, 2005, and 2014.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Biomass burning aerosol characteristics for different vegetation types in different aging periods.

Eighteen years of sun/sky photometer measurements at seven worldwide AErosol RObotic NETwork (AERONET) sites in typical biomass burning regions were used in this research. The AERONET measurements were analyzed with the help of Moderate-resolution Imaging Spectroradiometer (MODIS) fire products and the HYbrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model. The variation in the physicochemical and optical properties of biomass burning aerosols (BBAs), as well as their shortwave radiative forcing, was revealed for different vegetation types in different aging periods.

High-resolution inventory of mercury emissions from biomass burning in tropical continents during 2001-2017.

Mercury emissions from biomass burning contribute significantly to the atmospheric mercury budget and the interannual variation of mercury concentrations in the troposphere. This study developed a high-resolution (0.1° × 0.

Contribution of meteorological factors to particulate pollution during winters in Beijing.

Associated with its modernization, Beijing has experienced significant fine particulate matter (PM) pollution, especially in winter. In 2016, severe PM pollution (PM > 250 μg/m) lasted over 6 days and affected over 23 million people. A major challenge in dealing with this issue is the uncertainty regarding the influence of individual meteorological factors to the overall PM concentration in Beijing.

Underlying causes of PM-induced premature mortality and potential health benefits of air pollution control in South and Southeast Asia from 1999 to 2014.

Quantification of spatial and temporal variations in premature mortality attributable to PM has important implications for air quality control in South and Southeast Asia (SSEA). The number of PM-induced premature deaths during 1999-2014 in SSEA was estimated using an integrated exposure-response model based on 0.01° × 0.

Long-term trends and spatial patterns of PM-induced premature mortality in South and Southeast Asia from 1999 to 2014.

Fine particulate matter (PM) poses a potential threat to human health, including premature mortality under long-term exposure. Based on a long-term series of high-resolution (0.01°×0.

The impacts of regional transport and meteorological factors on aerosol optical depth over Beijing, 1980-2014.

Understanding the role of different sources that contribute to the aerosol extinction coefficient is an important aspect toward analyzing climate change and regional air quality. In Beijing specifically, the region has suffered severe air quality deterioration over the past three decades, but the magnitude of extraneous contributions to aerosol variation has remained uncertain. Therefore, we estimated trends of contributions to aerosol optical depth (AOD) for Beijing from 1980 to 2014 and built a seasonal regression model to decouple the extraneous contribution from the total emitted using ground-based aerosol and meteorological measurements, extended to the emissions of man-made and natural contribution.

Long-term trends and spatial patterns of satellite-retrieved PM concentrations in South and Southeast Asia from 1999 to 2014.

Fine particulate matter, or PM, is a serious air pollutant and has significant effects on human health, including premature death. Based on a long-term series of satellite-retrieved PM concentrations, this study analyzed the spatial and temporal characteristics of PM in South and Southeast Asia (SSEA) from 1999 to 2014 using standard deviation ellipse and trend analyses. A health risk assessment of human exposure to PM between 1999 and 2014 was then undertaken.

Leaf Area Index Estimation Using Chinese GF-1 Wide Field View Data in an Agriculture Region.

Leaf area index (LAI) is an important vegetation parameter that characterizes leaf density and canopy structure, and plays an important role in global change study, land surface process simulation and agriculture monitoring. The wide field view (WFV) sensor on board the Chinese GF-1 satellite can acquire multi-spectral data with decametric spatial resolution, high temporal resolution and wide coverage, which are valuable data sources for dynamic monitoring of LAI. Therefore, an automatic LAI estimation algorithm for GF-1 WFV data was developed based on the radiative transfer model and LAI estimation accuracy of the developed algorithm was assessed in an agriculture region with maize as the dominated crop type.

Assessing Spatial and Temporal Patterns of Observed Ground-level Ozone in China.

Elevated ground-level ozone (O), which is an important aspect of air quality related to public health, has been causing increasing concern. This study investigated the spatiotemporal distribution of ground-level O concentrations in China using a dataset from the Chinese national air quality monitoring network during 2013-2015. This research analyzed the diurnal, monthly and yearly variation of O concentrations in both sparsely and densely populated regions.

Assessment of PM2.5 concentrations and exposure throughout China using ground observations.

Exposure to PM2.5 results in negative effects on human health. However, PM2.