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Mangroves are a key type of protected coastal wetland, with a range of benefits such as protection from wave damage, sand fixation, water purification and ecological tourism. As the academic knowledge of mangroves has gradually increased, the use of remote sensing to monitor their dynamic changes in China has become a hot topic of discussion and has received attention in academic circles. Remote sensing has also provided necessary auxiliary decision-making suggestions and data support for the scientific and rational conservation, restoration and management of mangrove resources. In this paper, we used Landsat satellite series data combined with the normalized difference vegetation index (NDVI) and adaptive threshold partitioning (OTSU method) to monitor mangrove dynamics in coastal China from 1986 to 2021 based on Google Earth Engine (GEE), with three main results. (1) Based on the massive data and efficient computational capability of the GEE platform, we achieved large-scale interannual mangrove distribution extraction. The overall classification accuracy for 2019 exceeded 0.93, and the mangrove distribution extraction effect was good. (2) The total mangrove area and the mean patch size in China showed overall increasing trends, and Guangdong and Guangxi were the top two provinces in China in terms of the largest mangrove area. (3) Except for Dongzhaigang National Nature Reserve, the mangrove areas in other national mangrove reserves mainly showed increasing trends, confirming the effectiveness of the reserves. Data on the spatial structure and area trends of mangroves in China can provide scientific references for mangrove conservation and development, and serve in the further restoration of mangrove ecosystems. [ABSTRACT FROM AUTHOR]

It is crucial for the protection and sustainable development of villages in China to effectively determine the changes that have occurred in traditional villages' architectural features and the causes of such changes in order to provide timely feedback, correct protection planning, and construction in these villages. This paper analyzed quantitative indexes at three different scales in Lingquan village, namely construction land, public infrastructure, and the form, material, and color of roofs, using remote sensing images at different time points, drone shooting, and a field survey. It was found that the rate of change of the architectural features at the village and street levels were low, with an overall trend of improvement observed. However, the residential roof features at the building level showed more changes, with a trend of deterioration observed. It was also found that the active participation of the government was the main reason for the substantial improvement observed in the village's street-level features. Further, for residential houses, the lack of standard guidance for their protection and renovation, as well as the lack of protection awareness and participation mechanisms, were the main reasons found for the deterioration of the village's building-level features. Finally, the study forwarded some suggestions in order to provide reference value for the timely revision of protection strategies for many villages in China similar to Lingquan village. [ABSTRACT FROM AUTHOR]

China's rapid urbanization has been accompanied by serious urban sprawl. Instead of measuring the physical urban boundaries (PUBs), most of existing studies in China rely on yearbook statistics to describe the growth of urbanized area; therefore, the understanding of the actual form and quantity of urban sprawl are restrained. As the statistical unit is generally at or above the county level, these studies tend to omit the lower-level "larger towns". This paper discusses the measurement of urban sprawl and compactness using multi-source data on the GIS platform through the case study of the Wen-Tai region in China. GlobeLand30 remote sensing image data, vector road network data, NPP/VIIRS nighttime light data, and points of interest (POIs) data are adopted. The new method enhances the identification of built-up areas in larger towns. Besides, the 2020s' PUBs of this region, data for 2010 and 2000 are retraced to assess the urban expansion rate, and two approaches are used to discuss the urban growth pattern. Additionally, a compactness model is constructed from four dimensions, i.e., the compactness of external contour, accessibility of road network, land-use intensity, and functional diversity, by which a high-resolution visual analysis tool is created for the provincial government to monitor urban sprawl. [ABSTRACT FROM AUTHOR]

Urbanization level is a key indicator for socioeconomic development and policy making, but the measurement data and methods need to be discussed further due to the limitation of a single index and the availability and accuracy of statistical data. China is urbanizing rapidly, but the urbanization level at the county scale remains a mystery due to its complexity and lack of unified and effective measurement indicators. In this paper, we proposed a new urbanization index to measure the Chinese urbanization level at the county scale by integrating population, land, and economic factors; by fusing remote sensing data and traditional demographic data, we investigated the multi-dimensional unbalanced development patterns and the driving mechanism from 1995 to 2015. Results indicate that: The average comprehensive urbanization level at the Chinese county scale has increased from 31.06% in 1995 to 45.23% in 2015, and the urbanization level in the permanent population may overestimate China's urbanization process. There were significant but different spatial and temporal dynamic patterns in population, land, and economic levels as well as at a comprehensive urbanization level. The comprehensive urbanization level shows the pattern of being high in the south-east and low in the north-west, divided by "Hu line". The urbanization of registered populations presents high in the northern border and the eastern coastal areas, which is further strengthened over time. Economic urbanization based on lighting data presents high in the east and low in the west. Land urbanization based on remote sensing data shows high in the south and low in the north. The registered population urbanization level is lower than economic and land urbanization. County urbanization was driven by large population size, reasonable industrial structure, and strong government capacity; 38% and 59% of urbanization levels can be regarded as the key nodes of the urbanization process. When the urbanization rate is lower than 38%, the secondary industry plays a strong role in powering urbanization; when the urbanization rate is higher than 38% but less than 59%, the promotion effect of the tertiary industry is more obvious, and the secondary industry is gradually weakened. When the urbanization rate exceeds 59%, the tertiary industry becomes the major driver. [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]

It is an objective demand for sustainable agricultural development to realize fast and accurate cultivated land quality assessment. In this paper, Tengzhou city (county-scale hilly area: scale A), Shanghe county (county-scale plain area: scale B), and Huang-Huai-Hai region (including large-scale hilly and plain area: scale C and D) were taken as research areas. Through the conversion of evaluation systems, the inversion models at the county-scale were constructed. Then, the image scale conversion was carried out based on the numerical regression method, and the upscaling inversion was realized. The results showed that: (1) the conversion models of evaluation systems (CMES) are Y = 1.021x − 4.989 (CMESA−B), Y = 0.801x + 16.925 (CMESA−C), and Y = 0.959x + 3.458 (CMESC−D); (2) the booting stage is the best inversion phase; (3) the back propagation neural network model based on the combination index group (CI-BPNN) is the best inversion model, with the R2 are 0.723 (modeling set) and 0.722 (verification set). CI-BPNN and CI-BPNN-CMESA−B models are suitable for the hilly and plain areas at the county-scale, and the level area ratio difference is less than 4.87%. Furthermore, (4) the reflectance conversion model of short-wave infrared 2 is cubic, and the rest are quadratic. CI-BPNN-CMESA−C and CI-BPNN-CMESA−C-CMESC−D models realized upscaling inversion in the hilly and plain areas, with the maximum level area ratio difference being 1.60%. Additionally, (5) the wheat field quality has improved steadily since 2001 in the Huang-Huai-Hai region. This study proposes an upscaling inversion method of wheat field quality, which provides a scientific basis for cultivated land management and agricultural production in large areas. [ABSTRACT FROM AUTHOR]

Since the initiation of economic reforms and opening up, China has witnessed an unprecedented rate of development across all sectors. However, the country has also experienced severe ecological damage, surpassing that of many other nations. The rapid economic growth has come at the expense of the environment, revealing a significant lack of coordination between urbanization and eco-environmental protection in China. Consequently, there is an urgent need for a comprehensive and continuous historical dataset of China's eco-environmental quality (EEQ) based on remote sensing, allowing for the analysis of spatial and temporal changes. Such data would provide objective, scientific, and reliable support for China's eco-environmental protection and pollution prevention policies, while addressing potential ecological risks resulting from urbanization. To achieve this, the entropy value method is employed to integrate multi-source remote sensing data and construct an evaluation system for China's EEQ. Historical data from 2000 to 2017 is plotted to illustrate China's EEQ over time. The findings of this study are as follows: (1) The entropy method effectively facilitates the construction of China's eco-environmental quality assessment system. (2) From 2000 to 2017, approximately 39.7% of China's regions witnessed a decrease in EEQ, while 60.3% exhibited improvement, indicating an overall enhancement in EEQ over the past eighteen years. (3) The Yangtze and Yellow River basins experienced improved EEQ due to China's ecological restoration projects. (4) The future EEQ in China demonstrates a subtle positive trend across diverse contexts. This study departs from conventional approaches to EEQ evaluation by leveraging the advantages of multivariate remote sensing big data, including objectivity, timeliness, and accessibility. It provides a novel perspective for future eco-environmental quality evaluation. [ABSTRACT FROM AUTHOR]

Dust storm disasters have emerged as a significant environmental challenge in East Asia. However, relying on a single monitoring method to track dust storms presents limitations and can be variable. Therefore, it is necessary to use a combination of ground and remote sensing monitoring methods to explore the source and impact range of dust storms in order to fully characterize them. To achieve this, we examined the sources and impact ranges of dust storms in East Asia from 1980 to 2020 using both ground station data and remote sensing data. In addition, we focused on three specific dust storm events in the region. Our results indicate that the central source areas of dust storms are located in southern Mongolia and the Taklamakan Desert in China. Dust storms are mainly transported and spread in the northwestern region, while they are relatively rare in the southeastern region. The HYSPLIT model simulations reveal that the primary source directions of dust storms in East Asia are northwest, west, and north, the region involved includes Kazakhstan, southern Mongolia, and the Taklimakan Desert in China. The vertical structure of the dust storm layer depends on the source of the dust storm and the intensity of the dust storm event. Dust grain stratification probably occurs due to differences in dust storm sources, grain size, and regularity. These findings demonstrate that a combination of ground-based and remote sensing monitoring methods is an effective approach to fully characterize dust storms and can provide more comprehensive information for dust storm studies. [ABSTRACT FROM AUTHOR]

Despite the fact that gross primary productivity (GPP) and water use efficiency (WUE) have been widely used as indicators to evaluate the water-carbon cycle, uncertainties exist in the patterns of GPP and WUE responses to climate variability along different aridity gradients. In this study, the aridity index was used to divide China into four arid-humid zones. The spatiotemporal variability of multiple vegetation types GPP and WUE in response to climate change under different arid-humid zones were investigated based on remote sensing data. The results indicated that the increasing trend of WUE in the four arid-humid zones of China was less pronounced than GPP from 2001 to 2021. The GPP value decreased gradually from the humid to the arid zone, and the WUE value in the arid zone was slightly higher than in the semi-arid zone. The GPP of all vegetation types in China showed a tendency to increase, while shrubland and wetland WUE tended to decrease. The major vegetation types (e.g., forest, cropland and grassland) in each aridity gradient contributed to the changes in local GPP and WUE. However, in individual arid-humid zones, wetland and shrubland also exhibited high GPP and WUE values that were not inferior to forest and cropland. Temperature and precipitation were the main climatic factors responsible for the increase in vegetation GPP in different aridity gradients, with a higher positive correlation for temperature than precipitation. WUE showed a distinct positive and negative correlation with the thermal factors (temperature and net radiation) and the moisture factors (precipitation and relative humidity); this pattern was more pronounced in the humid and semi-humid zones. Net radiation and precipitation may be the main climatic factors causing a slight upward trend in WUE across the arid-humid zones, while the decrease in shrubland and wetland WUE may be related to relative humidity and precipitation. [ABSTRACT FROM AUTHOR]

As drought vulnerability assessment is fundamental to risk management, it is urgent to develop scientific and reasonable assessment models to determine such vulnerability. A vulnerability curve is the key to risk assessment of various disasters, connecting analysis of hazard and risk. To date, the research on vulnerability curves of earthquakes, floods and typhoons is relatively mature. However, there are few studies on the drought vulnerability curve, and its application value needs to be further confirmed and popularized. In this study, on the basis of collecting historical disaster data from 52 drought events in China from 2009 to 2013, three drought remote sensing indexes were selected as disaster-causing factors; the affected population was selected to reflect the overall disaster situation, and five typical regional drought vulnerability curves were constructed. The results showed that (1) in general, according to the statistics of probability distribution, most of the normalized difference vegetation index (NDVI) and the temperature vegetation drought index (TVDI) variance ratios were concentrated between 0 and ~0.15, and most of the enhanced vegetation index (EVI) variance ratios were concentrated between 0.15 and ~0.6. From a regional perspective, the NDVI and EVI variance ratio values of the northwest inland perennial arid area (NW), the southwest mountainous area with successive years of drought (SW), and the Hunan Hubei Jiangxi area with sudden change from drought to waterlogging (HJ) regions were close and significantly higher than the TVDI variance ratio values. (2) Most of the losses (drought at-risk populations, DRP) were concentrated in 0~0.3, with a cumulative proportion of about 90.19%. At the significance level, DRP obeys the Weibull distribution through hypothesis testing, and the parameters are optimal. (3) The drought vulnerability curve conformed to the distribution rule of the logistic curve, and the line shape was the growth of the loss rate from 0 to 1. It was found that the arid and ecologically fragile area in the farming pastoral ecotone (AP) region was always a high-risk area with high vulnerability, which should be the focus of drought risk prevention and reduction. The study reduces the difficulty of developing the vulnerability curve, indicating that the method can be widely used to other regions in the future. Furthermore, the research results are of great significance to the accurate drought risk early warning or whether to implement the national drought disaster emergency rescue response. [ABSTRACT FROM AUTHOR]

Shoreline changes are of great importance for evaluating the interaction between humans and ecosystems in coastal areas. They serve as a useful metric for assessing the ecological costs of socioeconomic developmental activities along the coast. In this paper, we present an assessment of shoreline changes along the eastern coast of mainland China from ~1990 to 2019 by applying a novel method recently developed by us. This method which we call the Nearest Distance Method (NDM) is used to make a detailed assessment of shorelines delineated from Landsat Thematic Mapper (TM) and Operational Land Imager (OLI) images. The results indicate a dramatic decline in natural shorelines that correspond to the rapid increase in the construction of artificial shorelines, driven by China's economic growth. Of the entire coast of mainland China, the biggest change occurred along the Bohai Sea, where artificial shorelines expanded from 42.4% in ~1990 to 81.5% in 2019. Over this period, this study indicates that China lost > 60% of its biological shorelines, a trend that is especially worrisome because these include areas that were once biologically diverse and extremely rich. As anticipated, shoreline losses were greatest where regions of low economic value had been transformed to areas of higher economic value. Overall, this influence of human activities on shorelines in China is unprecedented. The repercussions of these changes on ecosystems, and the susceptibility of new shoreline developments to population growth and sea-level rise, need to be assessed urgently before additional changes are effected. [ABSTRACT FROM AUTHOR]

Accurately and precisely grasping the spatial distribution and changing trends of China's regional population is of great significance in new urbanization, economic development, public health, disaster assessment, and ecological environmental protection. To monitor and evaluate the long-term spatiotemporal characteristics of the population distribution in China, a population monitoring estimation model was proposed. Based on remote sensing data such as nighttime light (NTL) images, land use data, and data from the fifth, sixth, and seventh censuses of China, the population spatiotemporal distribution in China from 2000 to 2020 was analyzed with a random forest algorithm. This study obtained spatial distribution maps of population density at a 1 km x 1 km resolution in 2000, 2010, and 2020. The results revealed the trend of the spatiotemporal pattern of population change from 2000 to 2020. It shows that: the accuracy assessment using the 2020 census population of townships/streets as a reference shows an R2 of 0.67 and a mean relative error (MRE) of 0.44. The spatial pattern of the population in 2000 and 2010 is generally unchanged. In 2020, population agglomeration is evident in the east, with a slight increase in the proportion of the population in the west. The patterns of population agglomeration and urbanization also change over time. The population spatiotemporal distribution obtained in this study can provide a scientific reference for urban sustainable development and promote the rational allocation of urban resources. [ABSTRACT FROM AUTHOR]

Quantitative evaluation of the coupling coordination degree (CCD) between regional haze the disaster risk index (HRI) and urbanization development index level (UDI) is of great significance for the realization of regional sustainable development goals. Given the lack of the combination of remote sensing and statistical data to evaluate the CCD between two systems, the Chinese mainland's 31 provinces and autonomous regions were taken to evaluate their HRI and UDI by building index systems. Then, an entropy method and one improved coupling coordination model were used to calculate and analyze the spatiotemporal characteristics of CCD between HRI and UDI during 2000–2020. The results showed that: (1) From 2000 to 2020, the value of HRI in China showed a "W" type change trend with its value increased from 0.7041 in 2000 to 0.8859 in 2020, indicating that haze pollution level showed a fluctuating downward trend; (2) From 2000 to 2020, China's UDI values showed a gradual upward trend with its value increased from 0.1647 in 2000 to 0.4640 in 2020, with an average annual growth rate of 8.63%; (3) From 2000 to 2020, CCD values between HRI and UDI showed a fluctuating upward trend with its value increased from 0.5374 in 2000 to 0.7781 in 2020, with an average annual growth rate of 2.13%; the overall level of China's CCD had raised from low coordination to moderate coordination, and eastern coastal provinces had higher CCD values, while those of central and western provinces had lower CCD values; (4) HRI, UDI and CCD could be well fitted with the R2 of 0.9869. Specifically, UDI had a higher contribution to improving the CCD than the HRI. [ABSTRACT FROM AUTHOR]

(1) Background: Eco−spatial networks play an important role in enhancing ecosystem services and landscape connectivity. It is necessary to study landscape structure optimization to achieve synergistic gains in network connectivity and ecosystem functionality. (2) Method: Based on remote sensing data, RS and GIS were used to evaluate the spatiotemporal changes in ecosystem services in China. Combined with complex network theory, the spatiotemporal evolution of China's ecological spatial network and its topological structure from 2005 to 2020 is discussed. Network function–structure co−optimization was carried out using the edge augmentation strategy. (3) Result: The "three River resource" has high water conservation and high soil and water conservation in southeastern hilly areas. There is strong windbreak and sand fixation in southeastern Inner Mongolia. In the past 15 years, there have been about 8200 sources and about 14,000 corridors. The network has the characteristics of small−world and heterogeneity. After optimization, 18 sources and 3180 corridors are added, and the network connectivity and robustness are stronger. Finally, five regions are divided according to the network heterogeneity and corresponding protection and management countermeasures are proposed to provide scientific guidance for the country's territorial space planning. [ABSTRACT FROM AUTHOR]

Vegetation canopy interception (Ic) of precipitation is a considerable component of the global hydrological cycles. Although the measurement and modeling of canopy interception have been explored worldwide at the individual, stand or ecosystem scale, it is still unclear how to recognize this process at the regional or global scales within the context of global climate change. In this study, a revised Gash model was employed to estimate canopy interception based on remote sensing and meteorological data. The spatial and temporal variations in Ic were investigated and the main environmental factors were explored in China for the 2000–2018 period. The results showed that the revised Gash model performed well in modeling canopy interception at the regional scale compared with the PML_V2 dataset product and the in-situ measurements. The average annual Ic in China from 2000 to 2018 was 166.55 mm, with a significant decreasing spatial pattern from the Southeastern to the Northwestern regions. The ratio of canopy interception to precipitation (Ir) displayed a similar spatial pattern, with an average value of 22.30%. At the temporal scale, the mean annual Ic significantly increased at a rate of 1.79 mm yr−1 (p < 0.01) during the study period, and the increasing trend was more pronounced during the 2000–2009 period, at a rate of 3.34 mm yr−1 (p < 0.01). In most vegetation types, except for the deciduous broad-leaved forest and temperate desert, canopy interception showed a significant increasing trend (p < 0.01). Precipitation, temperature, and the normalized differential vegetation index (NDVI) were considered to be the main factors affecting the variations of Ic in China during the last two decades, with specific dominant factors varying in different areas. Specifically, precipitation was considered to control the variations of Ic in the Northwestern regions, temperature mainly influenced the Southern regions, and the NDVI was identified as the main factor in regions where significant ecological conservation projects are established, such as the Loess Plateau. Our findings are expected to not only contribute to the understanding of regional ecohydrological cycle but also provide valuable insights into the methodology of interception modeling at the regional and global scales. [ABSTRACT FROM AUTHOR]

Sustainable development is a common challenge for all global economies, and the assessment of the spatial distribution and development process of ecological resources is the basis of sustainable development. Considering the heterogeneity of regional ecological resource endowment, it is necessary to conduct a targeted assessment for different regions. In this study, we selected Guizhou Province, which is rich in ecological resources, but has a fragile environment, as our study area, and cultivated land, woodland, grassland, and water resources were selected as critical evaluation indicators. Notably, we applied Kernel density analysis methods, based on the remote sensing data of 2000, 2005, 2010, 2015, and 2020, to explore the spatiotemporal distribution characteristics and evolution pattern of regional ecological resources. The results indicated that the evolution of ecological resource development in Guizhou could be divided into three phases: degenerative (2000–2005), recovery (2005–2015), and development (2015–2020). The spatial distribution of Guizhou's ecological resource was shown to be heterogeneous in the north and south and more homogeneous in the middle regions. Guizhou has diverse land-use types, with obvious regional differences in land-use structure. Notably, even though the development of ecological resources in Guizhou has improved, the development and utilization degree of ecological resources is still low, and the ecological damage is serious. This study can be used as a scientific reference by policymakers and decisionmakers to develop new regulations for ecological resources protection and sustainable development in China. We suggest that different regions should adopt more detailed measures; particularly, it is important to establish a spatial governance system to promote ecological resources development in the context of local conditions. [ABSTRACT FROM AUTHOR]

Field surveys and questionnaires are a cornerstone of rural socioeconomic research, providing invaluable firsthand data regarding on-the-ground situations. However, cost-effective and efficient methods for validating the accuracy of self-reported data in such questionnaires are lacking. Biased data are likely to lead to incorrect conclusions. In this study, we propose a new index, the survey bias index (SBI), for evaluating the degree of survey bias in field surveys. This index was obtained by comparing the data recorded in questionnaires with those from portable unmanned aerial vehicles (UAVs). In a case study, we employed SBI to reveal the degree of survey bias of questionnaires in field surveys on rural homesteads. The SBI of self-reported areas of rural homesteads reached 0.439, implying that 43.9% of data were significantly different from those collected using UAVs. A greater SBI was obtained in the pre-urban zone (0.515) than in the pure rural zone (0.258). These results indicate that homestead areas in the pre-urban zone have more incentive to expand than those in the pure rural zone. UAV remote sensing can strongly support research in the field of social economy, which reveals key information hidden in field surveys and questionnaires. [ABSTRACT FROM AUTHOR]

To explore the fast, accurate, and efficient remote sensing identification method of cultivated land quality, this study took Shandong Province as the study area, and used measured data to carry out the soil quality evaluation based on conventional GIS. On this basis, MODIS sequence images were used as remote sensing data sources, and multi-source data such as topography, meteorology, and statistical yearbook were fused. Then, according to the Pressure-State-Response framework, we constructed three kinds of characteristic indicators through distinguishing crop rotation types and fusing remote sensing data. Finally, the soil quality grade was identified by the random forest method, and the accuracy analysis was carried out. The results showed that the NDVI peak values of double-season crops are in mid-April and mid-August, and one-season crops are in mid-August. Through evaluation, soil quality was divided into three categories, with six grades. Through principal component analysis, each soil status indicator contains two to three principal components, and each principal component contains five to eight temporal crop remote sensing information. After distinguishing crop rotation types and fusing remote sensing images, the identification accuracy of soil quality is significantly improved. The overall accuracy is 79.18%, 86.12%, and 93.65%, and the Kappa coefficient is 0.66, 0.77, and 0.90, respectively. This research developed an automatic identification method for cultivated land quality grade, and it proved that distinguishing crop rotation types and fusing multi-temporal crop remote sensing information are effective ways to improve identification accuracy. [ABSTRACT FROM AUTHOR]

As the second most abundant greenhouse gas after carbon dioxide (CO2), methane not only plays an important role in global and regional photochemical reactions, but also has an important impact on energy balance and climate change. To explore the long-term trends and spatiotemporal variation of methane concentration over China, we verified the accuracy of the column-averaged, dry air-mixing ratio of CH4 (abbreviated as XCH4 hereafter) merged by SCIAMACHY and GOSAT products, utilizing the data of six surface observation stations in China and the surrounding areas. The root mean square error (RMSE) was mostly less than 2.5%, and the correlation coefficients (r) were 0.77, 0.84, 0.66, 0.42, 0.62 and 0.75. Furthermore, we analyzed the temporal and spatial variation patterns of the XCH4 concentration over China from 2003 to 2020. The results showed that the XCH4 concentration had an increasing trend over China from 2003 to 2020; the average growth rate was 6.64 ppb·a−1, and the value range of the increase rate was from 4.66 ppb·a−1 to 8.46 ppb·a−1. The lowest XCH4 concentration was located over Tibet (1764.03 ppb), and the high values were located in the Sichuan Basin, Central China (Hunan, Hubei, and Henan) and East China (Anhui and Jiangxi) (1825–1845 ppb). The XCH4 concentration was higher in autumn and summer, low in winter and spring, and had obvious seasonal variations. Human factors such as population density, GDP and energy consumption have a significant impact on the XCH4 concentration over China. [ABSTRACT FROM AUTHOR]

The strong economic growth in China in recent decades, together with meteorological factors, has resulted in serious air pollution problems, in particular over large industrialized areas with high population density. To reduce the concentrations of pollutants, air pollution control policies have been successfully implemented, resulting in the gradual decrease of air pollution in China during the last decade, as evidenced from both satellite and ground-based measurements. The aims of the Dragon 4 project "Air quality over China" were the determination of trends in the concentrations of aerosols and trace gases, quantification of emissions using a top-down approach and gain a better understanding of the sources, transport and underlying processes contributing to air pollution. This was achieved through (a) satellite observations of trace gases and aerosols to study the temporal and spatial variability of air pollutants; (b) derivation of trace gas emissions from satellite observations to study sources of air pollution and improve air quality modeling; and (c) study effects of haze on air quality. In these studies, the satellite observations are complemented with ground-based observations and modeling. [ABSTRACT FROM AUTHOR]

The Integrated Multi-satellitE Retrievals for Global Precipitation Measurement Final Run (IMERGF) product has now been upgraded to Version 4 (V4), which has been available since March 2017. Therefore, it is desirable to evaluate the characteristic differences between the V4 and the previous V3 products. A comprehensive performance evaluation of the errors of the successive V3 and V4 IMERGF products is performed with a comparison of the China daily Precipitation Analysis Products (CPAP) from March 2014 to February 2015. The version 6 Global Satellite Mapping of Precipitation (GSMaP) research product (which is another Global Precipitation Measurement (GPM) based precipitation product) is also used as a comparison in this study. Overall, the IMERGF-V4 product does not exhibit the anticipated improvement for China compared to the IMERGF-V3 product. An analysis of the metrics of annual daily average precipitation over China for the IMERGF-V3 and IMERGF-V4 products indicates a decrease of the relative bias (RB) from 3.70% to -7.18%, a decrease of the correlation coefficient (CC) from 0.91 to 0.89, an increase of the fractional standard error (FSE) from 0.49 to 0.56, and an increase of the root-mean-square error (RMSE) from 0.63 mm to 0.72 mm. Compared to the IMERGF-V3 product, the IMERGF-V4 product exhibits a significant underestimation of precipitation in the Qinghai-Tibetan plateau with a much lower RB of -60.91% (-58.19%, -65.30%, and -63.74%) based on the annual (summer, autumn, and winter) daily average precipitation and an even worse performance during winter (-72.33% of RB). In comparison, the GSMaP product outperforms the IMERGF-V3 and IMERGF-V4 products and has the smallest RMSE (0.47 mm/day), highest CC (0.95), lowest FSE (0.37), and best performance of the RB (-2.39%) in terms of annual daily precipitation over China. However, the GSMaP product underestimates the precipitation more than the IMERGF-V3 product for the arid XJ region. [ABSTRACT FROM AUTHOR]

Wildfire emissions in the boreal forests yield an important contribution to the chemical budget of the troposphere. To assess the contribution of wildfire to the emissions of atmospheric trace species in the Great Xing'an Mountains (GXM), which is also the most severe fire-prone boreal forest region in China, we estimated various wildfire activities by combining explicit spatio-temporal remote sensing data with fire-induced emission models. We observed 9998 fire scars with 46,096 km² in the GXM between the years 1986 and 2010. The years 1987 and 2003 contributed 33.2% and 22.9%, respectively, in burned area during the 25 years. Fire activity is the strongest in May. Most large fires occurred in the north region of the GXM between 50° N and 54° N latitude due to much drier weather and higher fire danger in the northern region than in the southern region of the study domain. Evergreen and deciduous needleleaf forest and deciduous broadleaf forest are the main sources of emissions, accounting for 84%, 81%, 84%, 87%, 89%, 86%, 85% and 74% of the total annual CO2, CH4, CO, PM10, PM2.5, SO2, BC and NOx emissions, respectively. Wildfire emissions from shrub, grassland and cropland only account for a small fraction of the total emissions level (approximately 4%-11%). Comparisons of our results with other published estimates of wildfire emissions show reasonable agreement. [ABSTRACT FROM AUTHOR]

The monitoring capability of a polarized instrument (POLDER) under high aerosol loading conditions over China is investigated. The aerosol optical thickness (AOT), which infers the aerosol burden, is used to measure the satellite monitoring capabilities. AOT products retrieved from POLDER on low aerosol loading days, and products from a radiometric instrument (MODIS) on high and low aerosol loading days, are presented for comparison. Our study reveals that for high aerosol days, the monitoring capability of the polarized instrument is lower than that of the traditional instrument. The accuracy of matched POLDER fine-AOTs is lower than that of MODIS-matched AOTs. On low aerosol loading days, the performance of the polarized instrument is good when monitoring the aerosol optical thickness. Further analysis reveals that for the high aerosol loading days, the mean relative errors of matched POLDER fine AOTs and MODIS AOTs with respect to AERONET measurements are 44% and 16%, respectively. For the low aerosol loading days, the mean relative errors of POLDER and MODIS measurements with respect to AERONET measurements are 41% and 40%, respectively. During high aerosol days, POLDER-retrieved fine-AOTs reveal a poor accuracy with only 14% of matches falling within the error range, which is nearly one fourth of the MODIS regression results (51.59%). For the low aerosol loading days, the POLDER regression results are good. Approximately 62% of the POLDER measurements fall within the expected error range ±(0.05 + 15%) compared with the AERONET observed values. [ABSTRACT FROM AUTHOR]

With the high resolution of optical data and the lack of weather effects of passive microwave data, we developed an algorithm to map daily cloud-free fractional snow cover (FSC) based on the Moderate Resolution Imaging Spectroradiometer (MODIS) standard daily FSC product, the Advanced Microwave Scanning Radiometer (AMSR2) snow water equivalent (SWE) product and digital elevation data. We then used the algorithm to produce a daily cloud-free FSC product with a resolution of 500 m for regions in China. In addition, we produced a high-resolution FSC map using a Landsat 8 Operational Land Imager (OLI) image as a true value to test the accuracy of the cloud-free FSC product developed in this study. The analysis results show that the daily cloud-free FSC product developed in this study can completely remove clouds and effectively improve the accuracy of snow area monitoring. Compared to the true value, the mean absolute error of our product is 0.20, and its root mean square error is 0.29. Thus, the synthesized product in this study can improve the accuracy of snow area monitoring, and the obtained snow area data can be used as reliable input parameters for hydrological and climate models. The land cover type and terrain factors are the main factors that limit the accuracy of the daily cloud-free FSC product developed in this study. These limitations can be further improved by improving the accuracy of the MODIS standard snow product for complicated underlying surfaces. [ABSTRACT FROM AUTHOR]

Stable nighttime light (NTL) data, derived from the Defense Meteorological Satellite Program's Operational Linescan System (DMSP/OLS), are typically considered a proxy measure of the dynamics of human settlements and have been extensively used to quantitative estimates of demographic variables, economic activity, and land-use change in previous studies at both regional and global scales. The utility of DMSP data for characterizing spatio-temporal trends in urban development at a local scale, however, has received less attention. In this study, we utilize a time series of DMSP data to examine the spatio-temporal characteristics of urban development in 285 Chinese cities from 1992 to 2009, at both the local and national levels. We compare linear models and piecewise linear models to identify the turning points of nighttime lights and calculate the trends in nighttime light growth at the pixel level. An unsupervised classification is applied to identify the patterns in the nighttime light time series quantitatively. Our results indicate that nighttime light brightness in most areas of China exhibit a positive, multi-stage process over the last two decades; however, the average trends in nighttime light growth differ significantly. Through the piecewise linear model, we identify the saturation of nighttime light brightness in the urban center and significant increases in suburban areas. The maps of turning points indicate the greater the distance to the city center or sub-center, the later the turning point occurs. Six patterns derived from the classification illustrate the various characteristics of the nighttime light time series from the local to the national level. The results portray spatially explicit patterns and conspicuous temporal trends of urbanization dynamics for individual Chinese cities from 1992 to 2009. [ABSTRACT FROM AUTHOR]

Evaluating and exploring regional eco-environmental quality (EEQ), economic development equality (EDE) and the coupling coordination degree (CCD) at multiple scales is important for realizing regional sustainable development goals. The CCD can reflect both the development level and the interaction relationship of two or more systems. However, relevant previous studies have ignored non-statistical data, lacked multiscale analyses, misused the coupling coordination degree model or have not sufficiently considered economic development equality. In response to these problems, this study integrated multisource remote sensing datasets to calculate and analyse the remote sensing ecological index (RSEI) and then used nighttime light data and population density data to calculate the proposed nighttime difference index (NTDI). Next, a modified coupling coordination degree (MCCD) index was proposed to analyse the MCCD between EEQ and EDE. Then, spatiotemporal and multiscale analyses at the county, city, province, urban agglomeration and country levels were performed. Global and local spatial autocorrelation and trend analyses were performed to evaluate the spatial aggregation degree and change trends from 2001 to 2020. The main conclusions are as follows: (1) The EEQ of China displayed a fluctuating upwards trend (0.0048 a−1), with average RSEI values of 0.5950, 0.6277, 0.6164, 0.6311 and 0.6173; the EDE of China showed an upwards trend (0.0298 a−1), with average NTDI values of 0.1271, 0.1635, 0.1642, 0.2181 and 0.2490; and China's MCCD indicated an upwards trend (0.0220 a−1), with values of 0.4614, 0.5027, 0.4978, 0.5401 and 0.5525. (2) The highest global Moran's I of NTDI and MCCD was achieved at the city scale, while the highest RSEI was achieved at the county scale. From 2001 to 2020, the spatial agglomeration effect of the RSEI decreased, while that of the NTDI and MCCD increased. (3) A power function relationship occurred between NTDI and MCCD at different scales. Furthermore, the NTDI had a higher contribution to improving the MCCD than the RSEI and the R2 of the fitted curve at different scales ranged from 0.8183 to 0.9915. [ABSTRACT FROM AUTHOR]

Through the integrated approach of remote sensing and geographic information system (GIS) techniques, four Landsat TM/ETM+ imagery acquired during 1979 and 2008 were used to quantitatively characterize the patterns of land use and land cover change (LULC) and urban sprawl in the fast-growing Shanghai Metropolis, China. Results showed that, the urban/built-up area grew on average by 4,242.06 ha yr-1. Bare land grew by 1,594.66 ha yr-1 on average. In contrast, cropland decreased by 3,286.26 ha yr-1 on average, followed by forest and shrub, water, and tidal land, which decreased by 1,331.33 ha yr-1, 903.43 ha yr-1, and 315.72 ha yr-1 on average, respectively. As a result, during 1979 and 2008 approximately 83.83% of the newly urban/built-up land was converted from cropland (67.35%), forest and shrub (9.12%), water (4.80%), and tidal land (2.19%). Another significant change was the continuous increase in regular residents, which played a very important role in contributing to local population growth and increase in urban/builtup land. This can be explained with this city's huge demand for investment and qualified labor since the latest industrial transformation. Moreover, with a decrease in cropland, the proportion of population engaged in farming decreased 13.84%. Therefore, significant socio-economic transformation occurred, and this would lead to new demand for land resources. However, due to very scarce land resources and overload of population in Shanghai, the drive to achieve economic goals at the loss of cropland, water, and the other lands is not sustainable. Future urban planning policy aiming at ensuring a win-win balance between sustainable land use and economic growth is urgently needed. [ABSTRACT FROM AUTHOR]

As a local environmental issue with global importance, land use/land cover change (LUCC) has always been one of the key issues in geography and environmental studies with the expansion of regional case studies. While most of LUCC studies in China have focused on urban land use change, meanwhile, compared with the rapid change of urban land use in the coastal areas of eastern China, slow but distinct rural land use changes have also occurred in the mountainous areas of western China since the late 1980s. In this case through a study in Lijiang County of Yunnan Province, with the application of remote sensing data and geographic information system techniques, the process of rural land use change in mountain areas of western China was monitored through extensive statistical analysis of detailed regional data. The results showed significant increases in construction land, paddy field and dry land, and a decrease in dense forest land and waste grassland between 1986 and 2002. The conversions between dense forest land and sparse forest land, grassland, waste grassland and dry land were the primary processes of rural land use change. Sparse forest land had the highest rate of land use change, with glacier or snow-capped land the lowest; while human settlement and rural economic development were found to be the main driving forces of regional difference in the integrated land use change rate among the 24 towns of Lijiang County. Quantified through landscape metrics, spatial patterns of rural land use change were represented as an increase in landscape diversity and landscape fragmentation, and the regularization of patch shapes, suggesting the intensification of human disturbances and degradation of ecological quality in the rural landscape. [ABSTRACT FROM AUTHOR]

Land use/cover change (LUCC), which results from the complex interaction of social, ecological and geophysical processes, is a major issue and the main cause of global environmental change. This study analyzed the land use/cover dynamics and their environmental and socio-political forces in the upper reaches of the Yangtze River from 1980 to 2000 by using remote sensing, climatic and socio-economic data from both research institutes and government departments. The results indicated that there had been significant land use/cover changes between 1980 and 2000 in the study area, which were characterized by a severe replacement of cropland and woodland with grassland and builtup land. The transition matrices highlight the dominant dynamic events and the internal conversions between land use/cover types during the study period and reveal two distinct transition phases. Land use/cover changes in the upper reaches of the Yangtze River during 1980 to 2000, while restricted by environmental attributes, were strongly driven by socio-political factors. However, excessively pursuing higher land use benefits likely results in serious environmental degradation. This study suggests that the restructuring of land use should be based on land suitability and sustainable protection of fragile environment in the upper reaches of the Yangtze River. A thorough comprehension of historical changes will enhance our capability to predict future land use change and contribute to effective management strategies and policies for the rational land use. [ABSTRACT FROM AUTHOR]

China implemented an aggressive nationwide lockdown procedure immediately after the COVID-19 outbreak in January 2020. As China emerges from the impact of COVID-19 on national economic and industrial activities, it has become the site of a large-scale natural experiment to evaluate the impact of COVID-19 on regional air quality. However, ground measurements of fine particulate matters (PM2.5) concentrations do not offer comprehensive spatial coverage, especially in suburban and rural regions. In this study, we developed a machine learning method with satellite aerosol remote sensing data, meteorological fields and land use parameters as major predictor variables to estimate spatiotemporally resolved daily PM2.5 concentrations in China. Our study period consists of a reference semester (1 November 2018–30 April 2019) and a pandemic semester (1 November 2019–30 April 2020), with six modeling months in each semester. Each period was then divided into subperiod 1 (November and December), subperiod 2 (January and February) and subperiod 3 (March and April). The reference semester model obtained a 10-fold cross-validated R2 (RMSE) of 0.79 (17.55 μg/m3) and the pandemic semester model obtained a 10-fold cross-validated R2 (RMSE) of 0.83 (13.48 μg/m3) for daily PM2.5 predictions. Our prediction results showed high PM2.5 concentrations in the North China Plain, Yangtze River Delta, Sichuan Basin and Xinjiang Autonomous Region during the reference semester. PM2.5 levels were lowered by 4.8 μg/m3 during the pandemic semester compared to the reference semester and PM2.5 levels during subperiod 2 decreased most, by 18%. The southeast region was affected most by the COVID-19 outbreak with PM2.5 levels during subperiod 2 decreasing by 31%, followed by the Northern Yangtze River Delta (29%) and Pearl River Delta (24%). [ABSTRACT FROM AUTHOR]

Producing gridded electric power consumption (EPC) maps at a fine geographic scale is critical for rational deployment and effective utilization of electric power resources. Brightness of nighttime light (NTL) has been extensively adopted to evaluate the spatial patterns of EPC at multiple geographical scales. However, the blooming effect and saturation issue of NTL imagery limit its ability to accurately map EPC. Moreover, limited sectoral separation in applying NTL leads to the inaccurate spatial distribution of EPC, particularly in the case of industrial EPC, which is often a dominant portion of the total EPC in China. This study pioneers the separate estimation of spatial patterns of industrial and nonindustrial EPC over mainland China by jointly using points of interest (POIs) and multiple remotely sensed data in a random forests (RF) model. The POIs provided fine and detailed information about the different socioeconomic activities and played a significant role in determining industrial and nonindustrial EPC distribution. Based on the RF model, we produced industrial, non-industrial, and overall EPC maps at a 1 km resolution in mainland China for 2011. Compared against statistical data at the county level, our results showed a high accuracy (R2 = 0.958 for nonindustrial EPC estimation, 0.848 for industrial EPC estimation, and 0.913 for total EPC). This study indicated that the proposed RF-based method, integrating POIs and multiple remote sensing data, can markedly improve the accuracy for estimating EPC. This study also revealed the great potential of POIs in mapping the distribution of socioeconomic parameters. [ABSTRACT FROM AUTHOR]

Urban green space (UGS) plays a pivotal role in improving urban ecosystem services and building a livable environment for urban dwellers. However, remotely sensed investigation of UGS at city scale is facing a challenge due to the pixels' mosaics of buildings, squares, roads and green spaces in cities. Here we developed a new algorithm to unmix the fraction of UGS derived from Landsat TM/ETM/8 OLI using a big-data platform. The spatiotemporal patterns and dynamics of UGSs were examined for 70 major cities in China between 2000 and 2018. The results showed that the total area of UGS in these cities grew from 2780.66 km2 in 2000 to 6764.75 km2 in 2018, which more than doubled its area. As a result, the UGS area per inhabitant rose from 15.01 m2 in 2000 to 18.09 m2 in 2018. However, an uneven layout of UGS occurred among the coastal, western, northeastern and central zones. For example, the UGS percentage in newly expanded urban areas in the coastal zone rose significantly in 2000–2018, with an increase of 2.51%, compared to the decline in UGS in cities in the western zone. Therefore, the effective strategies we have developed should be adopted to show disparities and promote green infrastructure capacity building in those cities with less green space, especially in western China. [ABSTRACT FROM AUTHOR]