Many studies have demonsrated that nighttime light intensity has a strong correlation with various social parameters. Some social parameters are only correlated with the nighttime light intensity within a specific range of radiation intensity. However, traditional nighttime light index is established based on the total range of radiation intensity from nighttime light remote sensing images, which limits the application potential of the nighttime light remote sensing data. In this study, we proposed a method for constructing nighttime light index based on specific light radiation intensity ranges. The nighttime light index was calculated based on different light radiation sub-intervals. For each social parameter, an optimal sub-interval was determined when the nighttime light index on the sub-interval showed the strongest correlation with the social parameter. Based on the NPP-VIIRS nighttime light data from 2012 to 2020, the total light radiation values of 5050 light radiation intensity intervals in all provinces and 36 main cities of China were calculated. We conducted correlation analysis between these light radiation values and 39 and 24 social parameters at provincial and municipal spatial scales, respectively. The optimal light radiation intensity intervals for all social parameter were determined, except for one social parameter at provincial and two social parameters at municipal scales because of the failure to pass the significance test. Compared with the traditional total light radiation values from the total radiation interval, total light radiation values from the optimal light radiation intensity intervals showed stronger correlation with various social parameters at provincial and municipal scales. The average correlation coefficient was increased by 0.06 at provincial scale and 0.08 at municipal scale. Some social parameters that were not significantly correlated with the traditional total light radiation showed strong correlation with the total light radiation from optimal light radiation intensity intervals. The optimal light radiation intensity intervals for most social parameters were relatively stable with little fluctuation over time. The optimal light radiation intensity intervals for social parameters in different industries showed obvious differences, which provided insights for a more in-depth analysis of the relationship between social parameters and nighttime light remote sensing data and a better evaluation of the impact of background noise of nighttime light remote sensing data on social parameter analysis. The construction of nighttime light index considering light radiation intensity range can improve the application potential of nighttime light remote sensing in the field of social parameter research.

Most of the existing OD flow clustering methods adopt the strategy of dividing the OD flow into O point and D point or considering flow as the four-dimensional point to implement flow clustering, which ignores the effects caused by the length, direction and time information on the clustering process. In this paper, we proposedabrand-new spatio-temporal flow clustering method based on the similarity between flows with a strategy of merging flow clusters under different grading. Firstly, a reasonablespatio-temporal similarity measurement formula of OD flow was constructed to quantify the spatio-temporal similarity between OD flows on the basis of full stydy of OD flow's spatial information and temporal information. Then, with the purpose of optimizing the order of merging flow clusters, reducing the time consumption of clustering process, a strategy of merging flow clusters under different grading was used to complete flow clustering. In this method, both of time information and spatial information weretaken into consideration. By modifying the parameters of the spatio-temporal similarity measurement formula, our method can obtain clustering results for different time scales and spatial scales, which makes it possible to analyze the movement patterns from a multi-scale perspective. To verify the effective of our method, a series of experiments on real dataset was executed. The clustering results demonstrate that: ①flow clusters discovered by our method not only hadspatial characteristic but also hadtemporal characteristic; ② our method can discover different spatio-temporal OD flow cluster under different spatio-temporal parameters; ③ by comparingthe clustering results of our method with previous work of advanced technology level, it turnedout that our method hada better clustering performance, which was reflected in the fact that flows within the same flow cluster satisfied the similarity relationship and our method can not only find the obvious movements patterns but also capture inconspicuous movements patterns between non-hot zones. Thespatio-temporal joint OD flow clustering method proposed in this paper obtains new insights into motion from the perspective of joint temporal and spatial information, which is conducive to a reasonable and comprehensive study of residents' movement patterns, spatial linkage between regions, the determination of the known travel structure, and the exploration of the purpose of travel. The process of OD flow clutsering is the beginning of a series of subsequent analysis.

As the core technology of predictive policing, Spatial-Temporal (ST) prediction of crime has developed rapidly from around 2000 to the present. We introduce the basic theory of ST prediction of crime at the beginning. We regard the ST prediction method of crime as a process combining corresponding models to predict the ST distribution of crimes in the future and deconstruct it into relationships between three objects: case, ST backcloth, and individual behavior. Then, based on the input factors of prediction models, we sum up three current main methods, including ① the prediction method based on the information of cases' ST location, ② the prediction method based on the backcloth and the information of cases' ST location, and ③ the prediction method based on individual behavior, the backcloth, and the information of cases' ST location. We further summarize the mechanisms of different methods in detail respectively. In addition, we compare and analyze each method based on their applicable scenarios and predictive capacities. Finally, with the development of big data technology, we present solutions to improve current prediction methods, that are to construct a data-fusion system, refine data granularity, and integrate new types of data. For model optimization, we need to improve the ability of integrating heterogeneous data from multiple sources and balancing the interpretability and predictive ability of models.

Monitoring the spatiotemporal variations of the Earth Radiation Budget (ERB) could help to improve our understanding of global climate change. The Earth's reflected shortwave and emitted longwave radiation are important components of energy exchange between the Earth-Atmosphere system and outer space, and are main parameters to be measured by ERB sensors. The Earth radiation radiometer is intended for measuring the parameters of Earth radiation budget. The Angular Distribution Models (ADMs) refer to a series of factors for correcting the anisotropy of the Earth-Atmosphere radiation at the Top-of-Atmosphere (TOA), and it is an effective way to convert the broadband radiance measured by satellite-borne or Moon-based Earth radiation sensor to the Earth radiant flux. Therefore, the consistency between ADMs anisotropic factors and the anisotropy of TOA radiances would directly determine the accuracy of derived flux. This paper focused on the ADMs, reviewed the development progress of the ADMs over the past decades, introduced the current operational ADMs applied by the Clouds and the Earth's Radiant Energy System (CERES) onboard the Terra and Aqua satellites, and analyzed the advantages, problems, and potential of geostationary satellites and Moon-based Earth observations in the development of ADMs. Based on above reviews and analyses, the determinant factors for further improving the ADMs for satellite-borne and Moon-based Earth radiation measurements were discussed.

Hand, Foot and Mouth Disease (HFMD) is a common infectious disease in infants and children and has an important impact on their health. In order to reveal the spatiotemporal heterogeneity of HFMD in China and provide a scientific basis for the prevention and control of HFMD, we select HFMD from 2008 (when HFMD was listed as category C infectious disease) to 2018 as the study period and apply spatial statistical methods including Moran's I, Getis-Ord G_i ^*, emerging hot spots analysis, and standard deviational ellipse to analyze the general and local spatiotemporal variation and trend of HFMD in China. Results show that: ① from 2008 to 2018, HFMD exhibits a spatial clustering pattern and the intensity of the clustering increases significantly over time; ② the hot spots of HFMD mainly concentrate in the southeast coast and gradually expand towards inland and northern coastal areas. The cold spots mainly concentrate in the northwest inland and the northeast; ③ the emerging hot spots in mainland China mainly occur in Yunnan, Chongqing, and Sichuan provinces, while the emerging cold spots mostly locate in the same regions with the persistent cold spots. Stable hot spots mainly locate in Hainan province in southern China; and ④ high incidence rate of HFMD mainly occurs in the southwest during 2008 and 2018 and gradually occur in the north during 2008-2009, 2013-2014, and 2017-2018. In general, HFMD remains primarily in the south of China. This pattern remains relatively stable throughout the years of observation, indicating that public intervention should be strengthen in the south of China. However, the underlying mechanism of the spatiotemporal distribution of HFMD in China still needs further investigation. Combination of multiple scientific disciplines such as geography, spatial statistics, virology, molecular biology, and public health provides multi-perspectives that can aid the research on the underlying mechanism of HFMD transmission.

As a main way of conservation tillage, crop residue has important influence on the cycle of material and energy in farmland ecosystem. Acquisition of the biomass information of corn residues timely and accurately is of great significance for understanding the implementation of conservation tillage and evaluating the impact of residues quantitatively. However，compared with crops, residues have lower coverage and contain less water,which makes it more difficult to acquire the biomass. To address this issue, we developed a residue backscattering model based on AIEM-Oh model and the Water Cloud Model (WCM) to remove the soil scattering interference. Using Sentinel-1 SAR images as the main data source, a regression model of corn residue biomass was constructed based on radar features selected to retrieve and map the corn residue biomass in Lishu County. Results show that the residue backscattering model can eliminate the interference of soil backscattering contribution effectively, and the inversion model based on the residue backscattering coefficient can improve the biomass inversion accuracy. The autumn biomass inversion model based on the dual-polarized scattering product (Product) has an R² greater than 0.75, and an RMSE less than 85 g/m², showing an increase of at least 0.12 in R² and a decrease of 17 g/m² in RMSE compared to the biomass inversion model before soil scattering contribution removed. This study verifies the feasibility of residue biomass inversion model based on backscattering data with soil scattering interference removed, and provides an attempt for the dynamic monitoring of corn residue biomass using SAR remote sensing data in the future.

Surface Soil Moisture (SM) plays an important role in the land-atmosphere interaction and hydrological cycle. Low spatiotemporal resolution (i.e., 25~40 km and 2~3 days) microwave-based SM products such as the Soil Moisture and Ocean Salinity (SMOS) and the Advanced Microwave Scanning Radiometer for EOS (AMSR-E) limit their application in regional scale studies. The Soil Moisture Active Passive (SMAP) and Copernicus Sentinel 1A/B microwave active-passive surface soil moisture product (L2_SM_SP) has a higher spatial resolution (3 km), but its temporal resolution is coarse from 4 to 20 days due to the narrow overlapped swath width. In this study, we developed a machine learning algorithm using the General Regression Neural Network (GRNN) to improve the spatiotemporal resolution of the L2_SM_SP product based on multi-source remote sensing data. Land Surface Temperature (LST), Multi-band Reflectance, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Elevation, Slope, Longitude (Lon), and Latitude (Lat) were selected as input variables to simulate the L2_SM_SP soil moisture in GRNN model. Results show that: (1) GRNN-estimated soil moisture and the original estimates of L2_SM_SP were strongly correlated (r=0.7392, RMSE=0.0757 cm³/cm³); (2) the correlation between GRNN estimates and original L2_SM_SP product at typical dates of different seasons varied a lot. The correlation in spring was the lowest (r_Spr=0.6152, RMSE_Spr=0.0653 cm3/cm3). While the correlation in winter was the strongest (r_Win=0.8214, and RMSE_Win=0.0367 cm³/cm³). The correlation in summer and autumn was close to each other (r_Sum=0.6957, r_Aut=0.7053, RMSE_Sum=0.0754 cm3/cm3, and RMSE_Aut=0.0694 cm3/cm3); and (3) in 2016, the soil moisture in summer and autumn of the study area was significantly higher than that that in other seasons. In terms of spatial distribution, the soil moisture in the Bashang plateau area was low, while the soil moisture along coastal areas was obviously higher. In this study, we successfully improved the spatiotemporal resolution of L2_SM_SP product over Beijing-Tianjin-Hebei region from 3 km, and 4~20 days to 1 km, and 1 day. Its spatial coverage was also extended. The improved soil moisture product is of great significance for future eco-hydrological assessment, climate prediction, and drought monitoring in Beijing-Tianjin-Hebei region.

In recent years, with the continuous exploitation and utilization of marine resources, marine spatial planning has become more and more important, among which fishery resources account for the main proportion. In order to provide auxiliary information for the monitoring and planning of fishery resources, this paper obtained the 2018 Automatic Identification System data of the South China Sea and surrounding countries, extracted the activity intensity of fishing vessels and carried out preprocessing, sampling processing, and GIS spatial analysis, and then mathematically analyzed the spatial and temporal characteristics. The results showed that, firstly, in 2018, fishing vessels in the South China Sea and surrounding countries were mainly distributed regionally, concentrated in areas within 100 km of the coast of China and Vietnam. Fishing activities were frequent in autumn and November. The average activity intensity of fishing vessels was higher during the day than at night, with the maximum activity intensity at 16:00 PM; Secondly, the intensity of fishing activities in main ports of Guangdong, Guangxi, and Hainan provinces is clustered as dots, with the intensity of fishing activities bigger than 100. The sea area near some ports is striped, and the intensity of activities of other fishing vessels in the South China Sea is larger than that of other islands in the Paracel Islands. The activity intensity of fishing vessels is smaller than 2; Thirdly, the regional distribution of Vietnamese fishing activities is obvious, showing a stable mass clustering distribution in Ho Chi Minh Port, with little change in activity intensity throughout the year. The activity intensity of nearshore fishing vessels remains at 50~100. Vietnamese fishing vessels are banned in the South China Sea. There are two areas with strong activity in the southwestern part of Hainan Province within the fishing line. In 2018, days with fishing activity accounted for 87.71% of the total sampling days, with on average 7~10 fishing boats every hour in the area. During the moratorium period, the average number of boats every hour is bigger than 5, which poses a great threat to China's south China sea fishery resources. In this paper, AIS data research and analysis of fishing vessel activities can provide data support for marine spatial planning and relevant government departments.

The ecotourism suitability assessment is the basis and a crucial reference for evaluating development potential, formulating plans, and implementing exploitation in ecotourism. In this study, we first analyze the feasibility of machine learning methods for modeling ecotourism suitability, and the Random Forest (RF) algorithm is selected for conducting an empirical study in the Wuling Mountain area in Hunan Province. In the study area, there are abundant tourism resources with an urgent need for ecotourism development, which can not only consolidate and expand the achievements of poverty alleviation, but also effectively connect with rural revitalization, thereby promoting sustainable development of tourism. The results show that: (1) Machine learning, as a new regional ecotourism suitability assessment approach, provides new insights and solutions for further improvement of suitability assessment; (2) The RF algorithm as a typical machine learning method can be effectively applied in the regional ecotourism suitability assessment. The optimized RF model achieves an average testing accuracy of 86.49%, with an area under the curve (AUC) of 0.95. These results also indicate the ecotourism suitability of the Wuling Mountain area in Hunan Province; (3) The ranking of feature importance reveals that land use type contributes most to the model, accounting for 28.98%, followed by other significant factors including population density (16.34%), distance from scenic spots (12.2%), and biological richness (10.65%). The above factors should be all considered in ecotourism development efforts; (4) The ecotourism suitability results show a high proportion of highly and moderately suitable areas, suggesting significant potential for ecotourism development in the study area. Based on the ecotourism suitability assessment, different development directions are proposed: A protective pattern and experiential education-oriented ecotourism are well-suited in highly suitable areas; a joint pattern and supportive ecotourism are appropriate for moderately suitable areas; a restrictive pattern is recommended for marginally suitable areas; and for unsuitable areas, the development should be prohibited. Finally, we present a new development strategy known as "two centers, one belt, and one plate," providing theoretical and technical guidance for ecotourism development and the consolidation of poverty alleviation achievements in the Wuling Mountain area of Hunan Province.