State Key Laboratory of Resources and Environmental Information System

With the rapid expansion in the number of Unmanned Aircraft Vehicles (UAVs) available and the development of modern technologies, the commercial applications of UAVs in urban areas, such as urban remote sensing (RS), express services, urban road traffic monitoring, urban police security, urban air shows and so on, have increased greatly. However, most UAVs, especially light and small civil UAVs, have been operating in low-altitude airspace, and a conflict may exist between increasing the number of UAVs and the limited low airspace. To promote low-altitude airspace resource development and to standardize the operation and management of UAVs in urban regions, some global laws and regulations and key technologies for urban low-altitude applications of UAVs have been implemented. This paper reviews the development of current policies and key technologies concerning safe and efficient operations of the light-and-small civil UAVs in low altitude in urban areas. Discussions are made progressively on measures and methods of airspace restriction, airspace structuring and air route planning in China primarily and the rest of world. After surveying the practical industry tests and the initial studies of air routes, the survey results indicate that the construction of air route networks is a scientific and effective measure to standardize and improve the efficiency of low-altitude UAV operations. From the view point of safety and efficiency, the most valuable direction for UAV regulation in urban regions involves deepening the research which largely relies on urban RS and Geographic Information System (GIS) technology, and application demonstrations of low-altitude public air route networks.

Abstract Land degradation is a severe environmental problem on a regional and global scale that is often aggravated by intensive land‐use and climate change. The arid to semi‐arid Xilingol in Inner Mongolia, China, is an example of an area that has witnessed continuous land degradation for decades, in spite of numerous attempts to reverse this trend. In this study, land‐use and land‐cover change (LUCC) between 1975 and 2015 was investigated for Xilingol based on multi‐temporal remote sensing images. The aim of the study was to derive detailed information on LUCC over space and time as a basis for assessing ecological and social consequences of land degradation in a bid to develop better strategies for combating land degradation. Two main LUCC processes and two distinct phases were identified: During Phase 1 (1975–2000), the LUCC pattern was dominated by land degradation, affecting 11.4% (22,937 km 2 ) of the total area. During Phase 2 (2000–2015), land restoration increased (12.0% or 24,161 km 2 ) whereas degradation continued, resulting in a further 9.5% (19,124 km 2 ) of degraded land. The transition pattern changed accordingly. Our findings show that, in spite of notable restoration successes in the past, grassland degradation continues to be the main ecological and environmental problem in Xilingol, requiring the continued attention of decision‐makers. Strategic land‐use management has already had a significant influence on LUCC in this area, leading to the expectation that science‐based land‐use strategies can be developed to further reduce land degradation in Xilingol.

Aquaculture coasts have become widely distributed in coastal zones as human activities are intensified. Due to the complexity in this type of coast, it is difficult to extract the coastline with traditional automated mapping approaches. In this paper, we present an automated method—object-based region growing integrating edge detection (OBRGIE) for the extraction of this type of coastline. In this method, a new object feature named OMI (object merging index) is proposed to separate land and sea. The OBRGIE method was applied to Landsat Thematic Mapper (TM) (pixel size 30m) and Satellite Pour l’Observation de la Terre (SPOT-5) (pixel size 10 m) images of two coastal segments with lengths of 272.7 km and 35.5 km respectively, and the accuracy of the extracted coastlines was assessed in comparison with the manually delineated coastlines. The mean and RMSE (root mean square error) are 16.0 m and 16.4 m respectively for the TM images, and 8.0 m and 8.6 m, respectively, for the SPOT-5 images, indicating that the proposed method derives coastlines with pixel accuracy. The OBRGIE method is also found to be robust to the segmentation scale parameter, and the OMI feature is much more effective than the spectral attribute in separating land and sea in aquaculture coasts. This method may provide an inexpensive means of fast coastline mapping from remotely sensed imagery with relatively fine-to-moderate spatial resolution in coastal sectors with intense human interference.

Nighttime light data offer a unique view of the Earth’s surface and can be used to estimate the spatial distribution of gross domestic product (GDP). Historically, using a simple regression function, the Defense Meteorological Satellite Program’s Operational Linescan System (DMSP/OLS) has been used to correlate regional and global GDP values. In early 2013, the first global Suomi National Polar-orbiting Partnership (NPP) visible infrared imaging radiometer suite (VIIRS) nighttime light data were released. Compared with DMSP/OLS, they have a higher spatial resolution and a wider radiometric detection range. This paper aims to study the suitability of the two nighttime light data sources for estimating the GDP relationship between the provincial and city levels in Mainland China, as well as of different regression functions. First, NPP/VIIRS nighttime light data for 2014 are corrected with DMSP/OLS data for 2013 to reduce the background noise in the original data. Subsequently, three regression functions are used to estimate the relationship between nighttime light data and GDP statistical data at the provincial and city levels in Mainland China. Then, through the comparison of the relative residual error (RE) and the relative root mean square error (RRMSE) parameters, a systematical assessment of the suitability of the GDP estimation is provided. The results show that the NPP/VIIRS nighttime light data are better than the DMSP/OLS data for GDP estimation, whether at the provincial or city level, and that the power function and polynomial models are better for GDP estimation than the linear regression model. This study reveals that the accuracy of GDP estimation based on nighttime light data is affected by the resolution of the data and the spatial scale of the study area, as well as by the land cover types and industrial structures of the study area.

Projecting the distribution of malaria vectors under climate change is essential for planning integrated vector control activities for sustaining elimination and preventing reintroduction of malaria. In China, however, little knowledge exists on the possible effects of climate change on malaria vectors. Here we assess the potential impact of climate change on four dominant malaria vectors (An. dirus, An. minimus, An. lesteri and An. sinensis) using species distribution models for two future decades: the 2030 s and the 2050 s. Simulation-based estimates suggest that the environmentally suitable area (ESA) for An. dirus and An. minimus would increase by an average of 49% and 16%, respectively, under all three scenarios for the 2030 s, but decrease by 11% and 16%, respectively in the 2050 s. By contrast, an increase of 36% and 11%, respectively, in ESA of An. lesteri and An. sinensis, was estimated under medium stabilizing (RCP4.5) and very heavy (RCP8.5) emission scenarios. in the 2050 s. In total, we predict a substantial net increase in the population exposed to the four dominant malaria vectors in the decades of the 2030 s and 2050 s, considering land use changes and urbanization simultaneously. Strategies to achieve and sustain malaria elimination in China will need to account for these potential changes in vector distributions and receptivity.

Triple collocation (TC) is a novel method for quantifying the uncertainties of three data sets with mutually independent errors and has been widely used over different geographical fields. Researches in recent years report that TC shows potential in merging multiple data sets from different sources, while the TC-based merging method has not been used over precipitation. Using the TC formulation, this study merges precipitation from the Climate Prediction Center's morphing technique (CMORPH), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), and the fifth-generation European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA5). The interim ECMWF Re-Analysis (ERA-Interim) is also involved to act as the substitute of ERA5 in some specific experiments for quality comparison between them. Merged data sets are produced at 0.25 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">°</sup> ×0.25 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">°</sup> and daily resolutions from March 2000 to December 2013 over Mainland China, using ground observations from more than 2000 rain gauges as the validation benchmark. First, the effectiveness of the TC-based method for precipitation merging is assessed. Then, two weighting methods using root-mean-square error (RMSE) in logarithmic scale (log-RMSE) and modified scale (mod-RMSE) are compared because previous studies show that mod-RMSE is more suitable for characterizing errors within estimated data. Meanwhile, two merging strategies are designed, that is, merging rainfall and snowfall separately (RS) and merging precipitation directly (P). The results show that 1) all the merged products are superior to any input product which proves that the TC method is effective in precipitation merging; 2) TC-based merging generally has a better performance than dynamic Bayesian model averaging (DBMA)-based merging; 3) mod-RMSE shows worse performance in weight estimation than log-RMSE because mod-RMSE will deteriorate the impact of the underestimated inputs; and 4) RS-based merging is superior to P-based merging, and the superiority is particularly notable in winter. The RS strategy will be very helpful in improving the accuracy of precipitation estimates in cold climate such as over mountainous and high-altitude regions. Finally, the limitations of the TC method and potential solutions are discussed. This study demonstrates the great potential of the TC-based merging method in precipitation and provides insights into its application and development.

China has experienced an increasing and spreading trend of ozone (O3) pollution in recent years, which can be of significant threat to human health. High-resolution full-coverage O3 data will be highly valuable for O3 pollution prevention and control. To this end, a spatiotemporally embedded deep residual learning model (STE-ResNet) is proposed in this study to obtain daily high-resolution surface O3 concentration data, by the integration of surface station O3 measurements, satellite O3 precursors, reanalysis data, and emissions data. The proposed model uses a novel temporal and spatial embedding technique to represent the temporal (year, month, and day) and spatial (latitude and longitude) information, rather than directly inputting the temporal and spatial information into the neural network. Meanwhile, a gap-filling approach is developed to reconstruct the missing data in the satellite-retrieved O3, so that daily full-coverage O3 data can thus be generated. The proposed approach was applied to a heavily polluted region of China, namely, the Guangdong-Hong Kong-Macao Greater Bay Area (denoted as GBA), where a world-class city agglomeration is being built. The sample-based cross validation (CV), spatial-based CV, temporal-based CV, and external validation demonstrate high consistency with the station measurements, with R2 values of 0.93, 0.90, 0.89, and 0.70, respectively, at a daily level. The spatiotemporal embedding promotes the estimation accuracy compared to directly inputting the temporal and spatial information, with the sample-based CV R2 value increasing from 0.91 to 0.93. The daily full-coverage surface O3 concentrations were obtained at a spatial resolution of 0.05°, and it was found the O3 pollution hotspot is located in the center of the GBA, with mean values of mostly > 80 μg/m3. In the worst case, about 60% of the days in autumn have O3 concentrations exceeding the standard (i.e., 160 μg/m3) in the city of Zhongshan. Furthermore, for a serious pollution incident that occurred on April 9, 2020, the reconstructed O3 data show superior monitoring capabilities, due to the full coverage, when compared with the station measurements and satellite retrievals. The approach proposed in this study will be of great value for the fine-scale and continuous monitoring of O3 pollution.

Accurate estimates of terrestrial evapotranspiration (ET) are critical and significant to the field for modeling water and energy transfer between the land surface and atmosphere. This paper evaluates the 8-day MOD16 actual ET product using the ground-based eddy covariance (EC) system and large aperture scintillometer (LAS) measurements collected from 2008 to 2011 at seven sites in North and Northwest China. Overall, the 8-day MOD16 ET reproduces the temporal patterns of both the LAS and EC measurements but tends to underestimate and overestimate these measurements at high and low ET levels, respectively. It is also of limited use when surface is irrigated because under such condition significant underestimation is observed. Using the LAS measurements that also include a large source area does not generate a better agreement than using the EC measurements which comprise a small source area. The 8-day MOD16 ET averaged over ${3} \times {3\;km}^{{2}}$ MODIS pixels agree better with both the EC and LAS measurements than that extracted at the ${1} \times {1}\;{km}^{{2}}$ MODIS pixel. For the EC validation, the relative bias and the relative root-mean-square error (RMSE) vary between 5% and $- 59\% $ and between 35% and 120%, respectively, for the LAS validation, the relative bias and the relative RMSE vary between 6% and $- 69\% $ and between 55% and 108%, respectively. The agreement between one site and another is not consistent and varies greatly. More validation work is recommended to testing the general applicability of the MOD16 ET product at a large number of sites worldwide.

In the process of studying the spatiotemporal cause mechanism of urban heat island (UHI) effects, the classification method used will directly affect the robustness of urban surface heat classification. Applying five commonly used standard classification methods, we divided Beijing's urban surface temperatures in the summer of 2020 into five levels. We then compared the reliability of the five classification methods in resolving 12-period data and the seasonal average temperature in UHI patches, based on two indicators: UHI area and UHI intensity. The actual land-use composition of the UHI patches obtained with traditional methods was applied to confirm our results. The mean-standard deviation method and natural breaks (Jenks) method were more robust with regard to UHI classification and 12-period data reliability. For the UHI area index, the mean-standard deviation method produced the smallest total area of UHI patches for summer days and nights. For the UHI intensity index, the quantile method, mean-standard deviation method, and natural breaks (Jenks) method were associated with smaller errors. Considering the composition of land-use types in UHI patches, the mean-standard deviation method, and natural breaks (Jenks) method were more rigorous. Thus, our research results provide guidance for method selection when classifying UHI.

BACKGROUND: Previous research pointed to a close relationship between the incidence of tuberculosis (TB) in aging populations and socio-economic conditions, however there has been lack of studies focused on a region of unbalanced socio-economic development. The aim of this paper is to explore the spatio-temporal variation in TB incidence and examine risk determinants of the disease among aging populations in a typical region. METHODS: Data on TB-registered cases between 2009 and 2014, in addition to social-economic factors, were collected for each district/county in Beijing, Tianjin and Hebei, a region characterized by an aging population and disparities in social-economic development. A Bayesian space-time hierarchy model (BSTHM) was used to reveal spatio-temporal variation in the incidence of TB among the elderly in this region between 2009 to 2014. GeoDetector was applied to measure the determinant power (q statistic) of risk factors for TB among the elderly. RESULTS: The incidence of TB among the elderly exhibited geographical spatial heterogeneity, with a higher incidence in underdeveloped rural areas compared with that in urban areas. Hotspots of TB incidence risk among the elderly were mostly located in north-eastern and southern areas in the study region, far from metropolitan areas. Areas with low risk were distributed mainly in the Beijing-Tianjin metropolitan areas. Social-economic factors had a non-linear influence on elderly TB incidence, with the dominant factors among rural populations being income (q = 0.20) and medical conditions (q = 0.17). These factors had a non-linear interactive effect on the incidence of TB among the elderly, with medical conditions and the level of economic development having the strongest effect (q = 0.54). CONCLUSIONS: The findings explain spatio-temporal variation in TB incidence and risk determinants of elderly TB in the presence of disparities in social-economic development. High-risk zones were located mainly in rural areas, far from metropolitan centres. Medical conditions and the economic development level were significantly associated with elderly TB incidence, and these factors had a non-linear interactive effect on elderly TB incidence. The findings can help to optimize the allocation of health resources and to control TB transmission in the aging population in this region.

Previous studies on carbon storage simulation had ignored the difference of carbon intensity among various vegetation types inner the same land use. In this paper, The PLUS model was used to predict the land use change under multi-scenarios from 2030 to 2060, and the vegetation type data were supplemented by CA model to obtain the land cover-vegetation datasets from 2030-2060. Combined with the carbon density table of vegetation type, the future land use carbon storage during 2030-2060 under multi-scenarios in Beijing-Tianjin-Hebei region were analyzed. The main conclusions were as follows: (1) The spatial distribution of carbon storage in Beijing-Tianjin-Hebei region showed a pattern of ‘high in northeast-southwest and low in southeast-northwest’; (2) The carbon storage in Beijing-Tianjin-Hebei region during 1990-2020 showed a decreasing trend; (3) During 2030-2060, the carbon storage in Beijing-Tianjin-Hebei region showed a continuous decreasing trend in the absence of policy intervention, while that under the ecological protection and farmland protection scenarios showed an increasing trend; (4) Under different development scenarios, there were obvious significances of carbon storage in spatial distribution.

1.1 Definition of image fusion With the development of multiple types of biosensors, chemical sensors, and remote sensors on board satellites, more and more data have become available for scientific researches. As the volume of data grows, so does the need to combine data gathered from different sources to extract the most useful information. Different terms such as data interpretation, combined analysis, data integrating have been used. Since early 1990’s, “Data fusion” has been adopt and widely used. The definition of data fusion/image fusion varies. For example: Data fusion is a process dealing with data and information from multiple sources to achieve refined/improved information for decision making (Hall 1992)[1]. Image fusion is the combination of two or more different images to form a new image by using a certain algorithm (Genderen and Pohl 1994 )[2]. Image fusion is the process of combining information from two or more images of a scene into a single composite image that is more informative and is more suitable for visual perception or computer processing. (Guest editorial of Information Fusion, 2007)[3]. Image fusion is a process of combining images, obtained by sensors of different wavelengths simultaneously viewing of the same scene, to form a composite image. The composite image is formed to improve image content and to make it easier for the user to detect, recognize, and identify targets and increase his situational awareness. 2010. (http://www.hcltech.com/aerospace-and-defense/enhanced-vision-system/). Generally speaking, in data fusion the information of a specific scene acquired by two or more sensors at the same time or separate times is combined to generate an interpretation of the scene not obtainable from a single sensor [4]. Image fusion is a component of data fusion when data type is strict to image format (Figure 1). Image fusion is an effective way for optimum utilization of large volumes of image from multiple sources. Multiple image fusion seeks to combine information from multiple sources to achieve inferences that are not feasible from a single sensor or source. It is the aim of image fusion to integrate different data in order to obtain more information than can be derived from each of the single sensor data alone (`1+1=3’)[4].

NASA’s Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (IMERG) is a major source of precipitation data, having a larger coverage, higher precision, and a higher spatiotemporal resolution than previous products, such as the Tropical Rainfall Measuring Mission (TRMM). However, there rarely has been an application of IMERG products in flash flood warnings. Taking Yunnan Province as the typical study area, this study first evaluated the accuracy of the near-real-time IMERG Early run product (IMERG-E) and the post-real-time IMERG Final run product (IMERG-F) with a 6-hourly temporal resolution. Then the performance of the two products was analyzed with the improved Rainfall Triggering Index (RTI) in the flash flood warning. Results show that (1) IMERG-F presents acceptable accuracy over the study area, with a relatively high hourly correlation coefficient of 0.46 and relative bias of 23.33% on the grid, which performs better than IMERG-E; and (2) when the RTI model is calibrated with the gauge data, the IMERG-F results matched well with the gauge data, indicating that it is viable to use MERG-F in flash flood warnings. However, as the flash flood occurrence increases, both gauge and IMERG-F data capture fewer flash flood events, and IMERG-F overestimates actual precipitation. Nevertheless, IMERG-F can capture more flood events than IMERG-E and can contribute to improving the accuracy of the flash flood warnings in Yunnan Province and other flood-prone areas.

Leaf chlorophyll content (LCC) is an important plant physiological trait and is critical for accurate modeling of vegetation photosynthesis over time and space. To date, there is still a lack of a global long time-series dataset of LCC. In this study, we developed an algorithm to retrieve global LCC from MODIS surface reflectance data from 2000–2020. An essential requirement for generating LCC time series is to capture its seasonal dynamics. This issue was addressed by using a matrix system with two pairs of vegetation indices to minimize the impacts of leaf area index and canopy non-photosynthetic material on LCC estimation in different seasons. The matrix system algorithm was applied to Landsat data and MODIS data, respectively. The validation based on Landsat data and ground measurements reveals the algorithm has the ability to catch the seasonal variations of LCC in different plant functional types, and the MODIS-derived LCC shows good agreement with Landsat-upscaled LCC (R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> =0.77, RMSE=6.9 μg/cm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ). The global 8-day LCC data at 500-m resolution in 2000–2020 was generated using the matrix system from MODIS and presented distinct temporal and spatial variations, which provides a new opportunity for analyzing vegetation physiological dynamics in climate change studies.

Multivariate time series (MTS) prediction has been widely adopted in various scenarios. Recently, some methods have employed patching to enhance local semantics and improve model performance. However, length-fixed patch are prone to losing temporal boundary information, such as complete peaks and periods. Moreover, existing methods mainly focus on modeling long-term dependencies across patches, while paying little attention to other dimensions (e.g., short-term dependencies within patches and complex interactions among cross-variavle patches). To address these challenges, we propose a pure MLP-based HDMixer, aiming to acquire patches with richer semantic information and efficiently modeling hierarchical interactions. Specifically, we design a Length-Extendable Patcher (LEP) tailored to MTS, which enriches the boundary information of patches and alleviates semantic incoherence in series. Subsequently, we devise a Hierarchical Dependency Explorer (HDE) based on pure MLPs. This explorer effectively models short-term dependencies within patches, long-term dependencies across patches, and complex interactions among variables. Extensive experiments on 9 real-world datasets demonstrate the superiority of our approach. The code is available at https://github.com/hqh0728/HDMixer.

Soils typically exhibit complex spatial variation of multi‐categorical variables such as soil types and soil textural classes. Quantifying and assessing soil spatial variation is necessary for land management and environmental research, especially for accurately assessing the water and solute transport processes in watershed scales. This study describes an efficient Markov chain model for two‐dimensional modeling and simulation of spatial distribution of soil types (or classes). The model is tested through simulations of a simplified soil map. The application of the model for predictive soil mapping with parameters estimated from survey lines is explored. Analyses of both simulated maps and associated semi‐variograms show that the model can effectively reproduce observed spatial patterns of soil types and their spatial autocorrelation given an adequate number of survey lines. This indicates that the model is a feasible method for modeling spatial distributions of soil types (or classes) and the transition probability matrices of soil types in different directions can adequately capture the spatial interdependency relationship of soil types. The model is highly efficient in terms of computer time and storage. The model also provides an approach for assessing the uncertainty of soil type spatial distribution in areas where detailed survey data are lacking. The major constraint on applications of the model at this stage is that the minor soil types are relatively underestimated when survey lines are too sparse.

Abstract The oasis, a special landscape with the integration of nature and humanity in the arid region, has undergone an enormous transformation during the past decades. To gain a better understanding of the tradeoff between economic growth and oases stability in the arid land, we took the oases in the Hexi Corridor as a case to explore the constraints of oases development and the driving factors of oases expansion. The dynamic changes and spatial distribution patterns underwent by the oases were examined using multispectral remote sensing imagery. The constraints of oasis development in arid land were investigated by the grid-transformed model, as well as the index system of driving forces was analyzed using the grey incidence model based on the data from statistics yearbooks. The oasis area in the Hexi Corridor had tremendous changes expanded 40% from 1986 to 2015, the stable oasis area was 9062 km 2 , while the maximum area reached 16,374 km 2 . The constraints for oases of topography, hydrology and heat condition are as follow: The elevation of oasis ranged from 1000 to 1800 m, peaked in 1500 m; the slope of oasis distribution was flatter than 3 degrees; the aspect of oases on slope land concentrated in northeast and north, accounting for more than 60%. The main driving forces of oasis spatial expansion in the arid region were population, water resource, economy, policies, and other factors. These results are expected to (1) improve the rationality of oasis development, and (2) promote the sustainable planning and management of oases in the arid land.

Discrete global grid systems (DGGSs) are considered to be promising structures for global geospatial information representation. Square and triangular DGGSs have had the advantage over hexagonal ones in geospatial data processing over the past few decades. Despite a significant body of research supporting hexagonal grids as the superior alternative, the application thereof has been hindered partly owing to the lack of a hierarchy. This study presents an original perspective to combine two types of aperture 4 hexagonal discrete grid systems into a hierarchy. Each cell of the hierarchy is assigned a unique code using a linear quadtree that constructs the hexagonal quaternary balanced structure (HQBS). The mathematical system described by HQBS addressing and the vector operations, including addition, subtraction, multiplication, and division, are defined. Essential spatial operations for HQBS cell retrieval, transformation between HQBS codes and other coordinate systems, and arrangement of HQBS cells on spherical surfaces were studied and implemented. The accuracy and efficiency of algorithms were validated through experiments. The results indicate that the average efficiency of cell retrieval using the HQBS is higher than that using other schemes, thus proving it to be more efficient.

Abstract High‐quality precipitation data are vital for hydrological applications and climate change research. In this study, we evaluated two satellite‐based precipitation products from Chinese Fengyun (FY) 2G (G is one of the third batch of operational geostationary satellites in the Chinese FY‐2 series) and the Global Precipitation Measurement (GPM) against the rain gauge‐based precipitation data, respectively, over the Yunnan‐Kweichow Plateau, China, at different temporal scales (e.g., monthly, daily, and hourly). And the main findings of this study were as follows: (1) FY2G Quantitative Precipitation Estimation (QPE) and Integrated Multisatellite Retrievals for GPM (IMERG) shared similar spatial precipitation patterns with those of rain gauge data, while the FY2G QPE general underestimated the precipitation (bias, −30%–0%), and IMERG obviously overestimated the precipitation (bias, 0%–60%) at monthly scale over the Yunnan‐Kweichow Plateau; (2) the FY2G QPE correlated significantly better (Correlation coefficient, CC, ~0.80) than IMERG (CC ~0.20) against rain gauge data at daily scale, meanwhile, the average daily bias value of IMERG (~30%) was around 6 times larger than that of FY2G QPE (~5%); (3) The FY2G QPE (CC ~0.7, bias ~ − 8%, root‐mean‐square error ~2.0 mm/hr, mean absolute error ~ 0.6 mm/hr) also generally outperformed the IMERG (CC ~0.1, bias ~13%, root‐mean‐square error ~3.0 mm/hr, mean absolute error ~ 1.0 mm/hr), at hourly scale; (4) The FY2G QPE significantly underestimated the precipitation in the period between 16:00 and 20:00 with the probability of detection (POD) decreased from 0.8 to 0.5, while the IMERG significantly overestimated the precipitation in the period between 4:00 and 12:00, at diurnal scale; and (5) one of the reasons resulting the significant overestimations of IMERG might due to its weak abilities in detecting the precipitation events with POD ~0.2 and false alarm ratio (FAR) ~0.7 at diurnal scale, which performed worse than those of the FY2G QPE (POD ~0.8 and FAR ~0.4). These findings would provide valuable recommendations for using these satellite‐based precipitation products in various application fields, such as hydrology, agriculture, and meteorology over the YKP, and also for improving the algorithms of GPM.

BACKGROUND: Bacillary dysentery remains a major public health concern in China. The Beijing-Tianjin-Tangshan urban region is one of the most heavily infected areas in the country. This study aimed to analyze epidemiological features of bacillary dysentery, detect spatial-temporal clusters of the disease, and analyze risk factors that may affect bacillary dysentery incidence in the region. METHODS: Bacillary dysentery case data from January 2011 to December 2011 in Beijing-Tianjin-Tangshan were used in this study. The epidemiological features of cases were characterized, then scan statistics were performed to detect spatial temporal clusters of bacillary dysentery. A spatial panel model was used to identify potential risk factors. RESULTS: There were a total of 28,765 cases of bacillary dysentery in 2011. The results of the analysis indicated that compared with other age groups, the highest incidence (473.75/105) occurred in individuals <5 years of age. The incidence in males (530.57/105) was higher compared with females (409.06/105). On a temporal basis, incidence increased rapidly starting in April. Peak incidence occurred in August (571.10/105). Analysis of the spatial distribution model revealed that factors such as population density, temperature, precipitation, and sunshine hours were positively associated with incidence rate. Per capita gross domestic product was negatively associated with disease incidence. CONCLUSIONS: Meteorological and socio-economic factors have affected the transmission of bacillary dysentery in the urban Beijing-Tianjin-Tangshan region of China. The success of bacillary dysentery prevention and control department strategies would benefit from giving more consideration to climate variations and local socio-economic conditions.