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Wildfires exert strong influences on the environment, ecology, economy and public security. However, the existing hourly Himawari-8 fire product produced by the Japan Meteorological Agency (JMA) presents large uncertainties and is not suitable for reliable real-time fire monitoring in China. To fill this gap, the National Satellite Meteorological Center (NSMC) proposed an adaptive hourly Himawari-8 fire product for China based on the original Himawari-8 source by employing a dynamical threshold for fire extraction and a database of ground thermal sources. According to the visually extracted reference and consistency check, we found that the NSMC-Himawari-8 fire product effectively removed a majority of false fire alarms included in the original Himawari-8 fire product. Based on a rare field-collected ground reference dataset, we evaluated the reliability of original Himawari-8 and NSMC-Himawari-8 fire products across China. The overall accuracy of the raw Himawari-8 fire product was 54 % and 59 % (not considering the omission errors), respectively. As a comparison, by identifying more real fire pixels and avoiding a majority of false fire alarms, the overall accuracy of the NSMC-Himawari-8 fire product was 80 % and 84 % (not considering the omission errors), respectively, making it an ideal source for improved real-time fire monitoring across China. This research also provides a useful reference for employing a local dataset of underlying surfaces and thermal sources to enhance the accuracy of global fire products in specific regions. The NSMC-Himawari-8 fire product can be downloaded at http://figshare.com (last access: 17 April 2023) with the following DOI: https://doi.org/10.6084/m9.figshare.21550248 (Chen et al., 2022a).

The accuracy of grain yield estimation is critical for national food security. Because of the comprehensive influence of spatial differentiation conditions, such as temperature, precipitation, soil, rice variety, and irrigation, yield estimation requires integrated modeling that is based on dynamic conditions. These dynamic conditions include geographical background, biological factors, and human impact. Most existing studies focus on the observation and analysis of external factors; only a few reports on yield simulations are coupled with nature, management, and crop growth mechanism. Our study incorporates the crop growth mechanism of rice, along with data of rice varieties, soil, meteorology, and field management, to determine the rice yield in Jiangsu province, China. In addition, we have used a decision support system for the agrotechnology transfer model, along with Coupled Model Intercomparison Project data and geographic information system technology. Our results showed that: (1) A calibrated variety genetic coefficient could simulate rice biomass value (flowering stage, maturity stage, and yield) reasonably. The values of NRMSE (Normalized Root Mean Square Error) between the simulated and measured values after parameter calibration are all less than 10%, the values of d(index of agreement) are all close to 1, the simulated value of yield is in good agreement with the measured value. (2) A linear correlation between the meteorological elements and yield was observed. The linear correlation had regional differences. Notably, an increase in precipitation was conducive to the increase in yield. Except at the Huaiyin site, the other sites showed that the temperature rise could potentially lead to reduced production. We found that an increase in solar radiation was unfavorable to the production of rice in the northern and western sites in the Jiangsu province, whereas it was conducive in the southern and eastern sites. (3) Our study predicted the rice yield from typical sites in the Jiangsu province from 2019 to 2060 in the wake of climate change while excluding the extreme effects of diseases, pests, typhoons, and floods. The order of average yield per unit area is as follows: Xinghua site (8212.76 kg/ha) > Huaiyin site (7912.70 kg/ha) > Gaoyou site (7440.98 kg/ha) > Gaochun site (7512.29 kg/ha) > Ganyu site (7460.88 kg/ha) > Yixing site (7167.00 kg/ha). Notably, the average yields from the Xinghua and Huaiyin sites were higher than that from the Jiangsu province (7617.77 kg/ha). The fluctuation of the yield per unit area at each site was generally consistent with the fluctuation in the overall yield, showing a downward trend and tends to be stable. The dispersion of yield per unit area indicates that Gaochun had the most stable yield per unit area followed by Xinghua, Ganyu, Yixing, Huaiyin, and Gaoyou. The yield per unit area of the Huaiyin and Gaoyou sites was unstable and portrayed the biggest fluctuations. Future studies need to focus on how to deal with spatial variation and carry out adaptive verification to make the simulation results applicable to more dimensions.

Annual Land Use/Land Cover (LULC) change information at medium spatial resolution (i.e., at 30 m) is used in applications ranging from land management to achieving sustainable development goals related to food security. However, obtaining annual LULC information over large areas and long periods is challenging due to limitations on computational capabilities, training data, and workflow design. Using the Google Earth Engine (GEE), which provides a catalog of multi-source data and a cloud-based environment, we developed a novel methodology to generate a high accuracy 30-m LULC cover map collection of the Yangtze River Delta by integrating free and public LULC products with Landsat imagery. Our major contribution is a hybrid approach that includes three major components: 1) a high-quality training dataset derived from multi-source LULC products, filtered by k-means clustering analysis; 2) a yearly 39-band stack feature space, utilizing all available Landsat data and DEM data; and 3) a self-adaptive Random Forest (RF) method, introduced for LULC classification. Experimental results show that our proposed workflow achieves an average classification accuracy of 86.33% in the entire Delta. The results demonstrate the great potential of integrating multi-source LULC products for producing LULC maps of increased reliability. In addition, as the proposed workflow is based on open source data and the GEE cloud platform, it can be used anywhere by anyone in the world.

China's farmland landscape fragmentation follows a distinct evolutionary tendency along the spatial gradient “central urban areas → urban–rural transitional areas → rural areas”, because of its binary structure comprising urban and rural areas. This study explores the evolution of this spatiotemporal trend and its dynamic mechanisms in the case of Changzhou City. A landscape analysis model is constructed for farmland landscape fragmentation analysis at the pixel level, and a gradient analysis model is used for farmland landscape evolution analysis along the spatial gradient “urban → transitional → and rural areas”. During the study period, the net switching-in rates of edge patch farmland and discrete patch farmland were 4.58% and 3.63%, respectively, and farmland landscape tended to be somewhat structurally fragmented in the context of Changzhou's traditional agricultural environment. Nevertheless, contiguous farmland, dominated by core and edge farmland, still accounts for 82.53% of the total farmland area. Consequently, the farmland landscape remains relatively stable. In a non-farming environment, however, the net switching-out rate of core farmland reached 17.39%, and the total quantity of core farmland decreased significantly. Gradient analysis revealed that the core evolutionary driver for Changzhou's farmland landscape lies in policy factors relating to land utilisation and management, accounting for up to 79.7% and 60% of evolution in the farmland landscape within central urban areas and rural areas, respectively. Basic farmland preservation areas and spatial regulation of construction land positively affects farmland landscape fragmentation changes. Consequently, it is of great significance to formulate differentiated farmland management policies, and to place strict restrictions on the occupation of farmland resources for urban construction.

Urban vitality is the comprehensive form of regional development quality, sustainability, and attractiveness. Urban vitality of various regions within the cities has difference, and the quantitative evaluation of urban vitality within the cities can help guide to future city constructions. Evaluation of urban vitality needs the combination of multi-source data. Existing studies have developed index method and estimation models mainly based on geographic big data to evaluate urban vitality. This study aims to combine remote sensing data with geographic big data to evaluate urban vitality of Shenzhen at street block scale and build the estimation model by random forest method. Indexes and random forest model were built, and some further analyses were conducted. The results were: (1) urban vitality in Shenzhen was high in the coastal areas, business areas, and new towns; (2) compared to indexes, the estimation model had advantages of more accurate results, combination of various data, and the ability to analyze feature contributions; and (3) taxi trajectory, nighttime light, and housing rental data had the strongest influence on urban vitality.

Ozone formation regimes are closely related to the ratio of VOCs to NOx. Different ranges of HCHO/NO2 indicate three formation regimes, including VOCs-limited, transitional and NOx-limited regimes. Due to the unstable interactions between a diversity of precursors, the range of transitional regime, which plays a key role in identifying ozone formation regimes, remains unclear. To overcome the uncertainties from single models and the lack of reference data, we employed two models, polynomial simulation and Convergent Cross Mapping (CCM), to identify the ranges of HCHO/NO2 across China based on ground observations and remote sensing datasets. The ranges of transitional regime estimated by polynomial simulation and CCM were [1.0, 1.9] and [1.0, 1.8]. Since 2013, ozone formation regime has changed to the transitional and NOx-limited regime all over China, indicating ozone concentrations across China were mainly controlled by NOx. However, despite the NO2 concentrations, HCHO concentrations continuously exert a positive influence on ozone concentrations under transitional and NOx-limited regimes. Under the circumstance of national NOx-reduction policies, the increase of VOCs became the major driver for the soaring ozone pollution across China. For an effective management of ozone pollution across China, the emission-reduction of VOCs and NOx should be equally considered.

Additional file 1. Complete China Medical Resources Scoring Result.

Establishing a procedure which divides geographic environment into different spatial units with certain geographic homogeneity is the perquisite of resource management, but it is a challenging task owing to the complexity of geographic environment. Land-type research which aims to map patterns and interactions of geographic components (e.g., climate type, landform, soil type, and land cover etc.) lays foundation to division of geographic environment. Although national standard of land-type in China provided a framework for land survey, it weakened the importance of human activity. Combining multi-source data and geospatial analysis methods, this study identified agricultural land quality and urban functional areas as proxies for land-use intensities and further proposed a 1:100,000 hierarchical classification system that integrated landform type, soil type, land-use type, and land-use intensity. Factors in the classification system were represented by parameter layers. Using this classification system, Changzhou City land was classified into 3 classes, 37 subclasses, and 137 land units. The land-type map and analyses of human–land relationship could support current land-use planning in China which needs a comprehensively knowledge of geographic environment rather than just land-cover/land-use.

The hallmark of development in the Yangtze River Delta (YRD) of East China has been sprawling urbanization. However, air pollution is a significant problem in these urban areas. In this paper, we investigated and analyzed the air pollution index (API) in four cities (Shanghai, Nanjing, Hangzhou and Ningbo) in the YRD from 2001 to 2012. We attempted to empirically examine the relationship between meteorological factors and air quality in the urban areas of the YRD. According to the monitoring data, the API in Shanghai, Nanjing, Hangzhou slightly declined and that in Ningbo increased over the study period. We analyzed the inter-annual, seasonal, and monthly variations of API, from which we found that the air quality had different temporal changes in the four cities. It was indicated that air quality was poor in winter and spring and best in summer. Furthermore, different weather conditions affected air quality level. The wind direction was considered as an important and influential factor to air pollution, which has an impact on the accumulating or cleaning processes of pollutants. The air quality was influenced by the different wind directions that varied with seasons and cities.

The delineation of permanent basic farmland will safeguard the production baseline of China’s agricultural development by securing easily appropriated, high-quality farmland surrounding urban areas, thereby strictly controlling the use of farmland (especially high-quality farmland surrounding urban areas) facing accelerated urban expansion. This study researched the delineation of permanent basic farmland in a typical region undergoing rapid urbanization. By constructing a systematic classification model, farmland was classified into matrix, edge, and island farmlands in order to analyze farmland contiguity and fragmentation. Based on the indicator requirements of various plans related to farmland, an evaluation indicator system was constructed in order to develop an evaluation model for comprehensive farmland productivity. From the perspective of farmland spatial contiguity and highly efficient productivity, a delineation model for permanent basic farmland was proposed to delimit the permanent protection and utilization boundaries for high-quality farmland around urban areas. The results show the following: (1) matrix and edge farmlands can intuitively display farmland contiguity characteristics; (2) comprehensive farmland productivity was closely related to farmland spatial patterns, supporting infrastructure, and policy management and protection; and (3) there was a high degree of spatial overlap between contiguous and highly productive farmland. The model took both comprehensive farmland productivity and spatial clustering into consideration in order to delineate permanent basic farmland, which is a beneficial factor in protecting farmland quality and safeguarding sustainable farmland utilization. It can also be used as a control line to limit urban sprawl, guide urban cluster development, and improve economical and intensive urban land use.

Farmland landscape systems are crucial to sustainable agriculture, but they can readily be disturbed by non-agricultural activities. However, the conclusion that agricultural landscape system would affect farmland productivity remains uncertain and lacks quantitative evidence. To construct a healthy farmland ecosystem to avoid farmland fragmentation and support a sustainable agricultural production, we built a structural classification model for the farmland landscape system to analyse spatiotemporal evolutionary trends in China's farmland landscape system at the pixel level. Then, long-term grain yield monitored by stations and potential productivity of farmland calculated using a global agro-ecological zone (GAEZ) model were applied to explore the relationship between agriculture production and farmland landscape system variations. The results showed that China's farmland landscape tends to be fragmented. Model analyses showed that the fragmentation of farmland will cause an average 55.33 % cumulative decline in potential farmland productivity and that fragmentation of core farmland landscape will cause a 0.85 % decline in potential productivity. Based on these findings, we offer a farmland protection strategy based on the spatial optimum allocation of natural and human landscapes.

The purpose of this study was to analyze the spatial and temporal patterns of farmland loss and fragmentation and to explore factors that may influence that loss and fragmentation. The study examined changes to farmland in a context of rapid urbanization in Changzhou City, China. It also examined farmland changes in the suburban areas outside the city and in the exurban regions. Time series data from 2004 to 2011 were used to conduct spatial analysis using landscape indices and to perform a redundancy analysis. The results found that farmland was steadily declining during the study period. Farmland also became increasingly fragmented and it moved from low elevation and a gentle slope terrain to higher elevation and a steep slope terrain while its center of gravity generally moved northward. China’s farmland protection policy, agricultural production level, and terrain conditions contributed most to farmland loss and fragmentation in the city. Farmland loss and fragmentation in the suburbs and exurbs were influenced mostly by prime farmland area, total population, and per capita net income of rural residents. To control farmland loss and fragmentation, laws regarding its conversion to built-up environments should be enforced. China’s overall land-use plan and laws of farmland protection are presently the best ways to effectively protect farmland loss and fragmentation.

AMSR-E and MODIS are two EOS (Earth Observing System) instruments on board the Aqua satellite. A regression analysis between the brightness of all AMSR-E bands and the MODIS land surface temperature product indicated that the 89 GHz vertical polarization is the best single band to retrieve land surface temperature. According to simulation analysis with AIEM, the difference of different frequencies can eliminate the influence of water in soil and atmosphere, and also the surface roughness partly. The analysis results indicate that the radiation mechanism of surface covered snow is different from others. In order to retrieve land surface temperature more accurately, the land surface should be at least classified into three types: water covered surface, snow covered surface, and non-water and non-snow covered land surface. In order to improve the practicality and accuracy of the algorithm, we built different equations for different ranges of temperature. The average land surface temperature error is about 2–3°C relative to the MODIS LST product.

With the wide use of high-resolution remotely sensed imagery, the object-oriented remotely sensed informa- tion classification pattern has been intensively studied. Starting with the definition of object-oriented remotely sensed information classification pattern and a literature review of related research progress, this paper sums up 4 developing phases of object-oriented classification pattern during the past 20 years. Then, we discuss the three aspects of method- ology in detail, namely remotely sensed imagery segmentation, feature analysis and feature selection, and classification rule generation, through comparing them with remotely sensed information classification method based on per-pixel. At last, this paper presents several points that need to be paid attention to in the future studies on object-oriented RS in- formation classification pattern: 1) developing robust and highly effective image segmentation algorithm for multi-spectral RS imagery; 2) improving the feature-set including edge, spatial-adjacent and temporal characteristics; 3) discussing the classification rule generation classifier based on the decision tree; 4) presenting evaluation methods for classification result by object-oriented classification pattern.

In developing country, most of cities are expanding rapidly, and many projects of infrastructure construction are implementing. Hence, the remits of regional planning and monitoring are growing steadily. Planning data and status information need to made available in ever more up-to-date form and with high spatial resolution. Given the constraints on public funding and technique, real-time and high spatial resolution data is difficult to acquire with a user-defined frequency. However, Unmanned Aerial Vehicles (UAVs) technology provides potential for very high spatial resolution (

Land covers in urban areas trend to change more drastically over a short period of time than elsewhere because of incessant urbanization. These changes are ideally monitored and detected from remotely sensed images as they are relatively up-to-date and give a panoramic view. In this paper we propose a new method based on New Built-up Index (NBI) to automate the process of mapping residential areas. The unique spectral response of land covers is most distinctive from one another for each cover in band 3, 4, 5. It takes advantage of the unique spectral response of residential areas and other land covers. This method has been successfully applied to extract the residential areas in Changzhou City, Jiangsu Province of China as a representative of plain areas by manipulating the spectral bands of TM imagery in 2007. The mapping results at a high accuracy. It acquires a high accuracy than NDBI. So it can serve as a worthwhile alternative for quickly extracting.

Spatial neighborhood relation detecting is the basis of organization, query, analysis and reasoning of spatial data. For the spatial neighborhood relations of the geographic entities are contained in the Delaunay triangulation, in this paper, the spatial neighborhood relations between multi-factors (including points, lines and polygons) are intelligently detected based on the Constrained Delaunay Triangulation (CDT). This approach consists of steps listed below: A matched candidate points index is set up in order to reorganize the related data of multi-factors. Then the CDT, which is constrained by edges of lines and polygons, is established with the source of coordinates in point index. After coding the CDT according to a rule this paper proposing, neighborhood relation of geographic entities can be searched automatically. And a global neighborhood relation (including separation and neighbor relation) among multi-factors is automatically established by spatial reasoning. This intelligent method of spatial neighborhood relation detecting, which is no need for manual intervention and not limited between two kinds of geographic entities, has high precision and great feasibility in practice.

A method for extracting road from high spatial resolution remote sensing imagery (HSRRSI) based on Fast Marching Method and Mean Shift Method is proposed in this paper. Firstly, the user inputs a set of nodes of the road. Secondly, preliminary partition on the imagery is achieved by Mean Shift Method. Last, the roads are extracted between the nodes user has set by the implementation of Fast Marching Method The test results for HSRRSI of IKONOS demonstrates that the novel method is robust and is capable of coping with the partial occlusion and false connectivity.

Urban fringe is a special area with interaction of urban and rural areas. So far how to identify urban fringe automatically from urban area and rural areas is one of the major fields that urban geography concern. In this paper, remote sensing images of Nanjing district in 2003 is the calculating base. At the same time, the ERDAS Imagine and Visual C++ software are applied to program and recognize the image. The Breakpoint method, considering ratios of urban land-use as the only index, is used to divide the urban fringe of Nanjing city. Then this paper concludes a comparatively boundary between urban area and urban fringe. However, this paper also points out that the boundary of urban fringe is dynamic and in fact, subjective distinguishing with experience is needed in this process, this method is new effective way to divide and supervise urban fringe with mutation detection of land-use ratio and has great feasibility in practice.