Spatiotemporal Distribution Research Articles

A Review: Potential of Earth Observation (EO) for Mapping Small-Scale Agriculture and Cropping Systems in West Africa

West Africa faces a complex range of challenges arising from climatic, social, economic, and ecological factors, which pose significant risks. The rapidly growing population, coupled with persistently low agricultural yield, further exacerbates these risks. A state-of-the-art monitoring and data derivation of agricultural systems are crucial for improving livelihoods and enhancing food security. Despite smallholder farming systems accounting for 80% of cultivated cropland area and providing about 42% of the total employment in West Africa, there exists a lack of a comprehensive overview of Remote Sensing (RS) products and studies specifically tailored to smallholder farming systems, which this review aims to address. Through a systematic literature review comprising 163 SCI papers sourced from the Web of Science database (Filter I), followed by a full-text review (Filter II), we analyze the RS sensors, spatiotemporal distribution, temporal scales, the crop types examined, and thematic foci employed in existing research. Our findings highlight the predominance of high to very high-resolution, multispectral sensors as the primary data source and we observe that a wide array of available sensors and datasets, along with increasing computing capacities, have shaped the field over the last years. By highlighting existing knowledge, this study identifies the potential of RS and pinpoints the key research gaps. This sets the stage for future investigations aimed at addressing critical challenges in West African smallholder agricultural systems.

3D fluid simulation of the propagation of a nanosecond discharge in air above a liquid surface: investigation of the pattern formation under various conditions and comparison with experiments

Abstract Plasma-liquid interaction remains one of the fundamental processes influencing the various applications. Understanding the influence of external parameters on discharge properties, particularly the discharge dynamic at the liquid surface, is therefore essential. In previous studies, we investigated the impact of voltage polarity, gap distance, and liquid dielectric permittivity and electrical conductivity on nanosecond discharges initiated in air at atmospheric pressure in a pin-to-liquid configuration. Herein, we present a 3D fluid model, improved with stochastic photoionization, to simulate the discharge dynamics under the previously mentioned conditions. The model outputs are compared with the discharge dynamics measured experimentally. For instance, filamentation and the homogeneous emission over solution’s surface measured in positive and negative discharges, respectively, are well reproduced by the simulation. Furthermore, the simulation allowed us to report other plasma properties not accessible experimentally such as the spatio-temporal distributions of electric field (E-field) and electron density. Notably, we observe that the E-field at the front of the negative surface ionization wave (SIW) is nearly four times lower than that of the positive SIW, which may explain the absence of filaments for negative discharges. Furthermore, we find that increasing solution conductivity or gap distance reduce the radial propagation velocity of the circular SIW front and stopping its expansion before a destabilization can occur. The simulation allowed investigating the influence of photoionization strength, and we find that increasing the number of ionizing photons leads to supress the filamentation while keeping the ionization front circular and propagating at high speed.

On the Ar I 419.8 nm/751.5 nm (3p5/2p5) line intensity ratio for electric field measurement in dielectric barrier discharge in argon at atmospheric pressure

Abstract Ar I 419.8 nm/751.5 nm line intensity ratio is suggested as a measure of the electric field in atmospheric pressure dielectric barrier discharges in argon. Using the electron distribution function calculated from the Boltzmann equation, electron impact excitation cross sections, quenching coefficients and other atomic data, theoretical dependencies of several line intensity ratios constructed from Ar I 3p5 4p and 3p5 5p argon lines on the electric field were calculated. Among them, the intensity ratio of 5p line at 419.8 nm (1s4–3p5 in Paschen notation) to 4p line at 751.5 nm line (1s4-2p5) was found to be sensitive in both the theory and the experiment.

The suggested line ratio was applied to the study of an atmospheric pressure volume dielectric barrier discharge in pure argon with sinusoidal voltage excitation. The spatiotemporal distribution of light emissions on 4p and 5p lines was measured using the time-correlated single photon counting technique (TCSPC) with high temporal (180 ps) and spatial (25 μm) resolution. The light emission on 419.8 nm line indicated the high electric field region in the propagating cathode-directed streamer due to a high excitation energy (14.58 eV) and a short effective lifetime (150 ps) of the 3p5 level. 

Several measured and calculated electron impact excitation cross sections were examined to interpret the measured line intensity ratio. Using the calculation based on a combination of the calculated (Zatsarinny 2014) and the measured cross section (Boffard 2007) for 2p5 and 3p5 levels, the electric field strengths reaching 110–120 Td were measured in the streamer head. 

The assumptions and limitations of this approach, as well as its instrumentation requirements, are discussed.

A Spatio-Temporal Deep Learning Model for Automatic Arctic Sea Ice Classification with Sentinel-1 SAR Imagery

Arctic sea ice has a significant effect on global climate change, ship navigation, Arctic ecosystems, and human activities. Therefore, it is essential to produce high-resolution sea ice maps that accurately represent the geographical distribution of various sea ice types. Based on deep learning technology, many automatic sea ice classification algorithms have been developed using synthetic aperture radar (SAR) imagery over the last decade. However, sea ice classification faces two vital challenges: (1) it is difficult to distinguish sea ice types with close developmental stages solely from SAR images and (2) an imbalanced sea ice dataset has a significantly negative effect on ice classification model performance. In this article, a spatio-temporal deep learning model—the Dynamic Multi-Layer Perceptron (MLP)—is utilized to classify 10 sea ice types automatically. It consists of a SAR image branch and a spatio-temporal branch, which extracts SAR image features and spatio-temporal distribution characteristics of sea ice, respectively. By projecting similar image features to different positions in the spatio-temporal feature space dynamically, the Dynamic MLP model effectively distinguishes between similar sea ice types. Furthermore, to reduce the impact of data imbalance on model performance, the dynamic curriculum learning (DCL) method is used to train the Dynamic MLP model. Experimental results demonstrate that our proposed method outperforms the long short-term memory (LSTM) network approach in distinguishing between sea ice types with similar developmental stages. Moreover, the DCL training method can also effectively improve model performance in identifying minority ice types.

Spatio-Temporal Distribution and Population Dynamics of Two Sympatric Species: The Rock Shrimps Sicyonia dorsalis Kingsley, 1878 and Sicyonia typica (Boeck, 1864) (Penaeoidea: Sicyoniidae) on the Coast of Ilhéus, Bahia, Northeastern Brazil

Rock shrimps (Sicyonia dorsalis and Sicyonia typica) are commonly caught as bycatch during shrimp trawling along the Brazilian coast, but are not commercially exploited due to their small size and hard carapace. This study evaluated their spatio-temporal distribution, size classes, and sex ratio near the Almada River Estuary, Ilhéus, Bahia, Northeastern Brazil, and tested correlations between environmental factors and species abundance. Samples were collected monthly using double-rig trawl nets in the estuary and along transects at depths of 5–35 m. Bottom water and sediment samples were obtained for analyses of environmental factors. In total, 5336 individuals of S. dorsalis and 303 individuals of S. typica were collected. No individuals were recorded in the estuary. Both species were significantly more abundant between 25 and 35 m, where fine sediment with high levels of organic matter occurred. Considering the temporal variation, their abundance decreased during the rainy season, coinciding with increased river flow. Organic matter content, salinity, and water transparency were the primary environmental factors influencing abundance. Females were generally larger and predominant compared to males, likely due to life cycle dynamics. Despite being congeneric and sympatric, the species exhibited distinct population patterns, possibly to avoid niche overlap and competition.

Assessing the impact of greenery on urban heat using opportunistic drive-by sensing

The urban heat island (UHI) phenomenon is recognized as a main urban sustainability problem in the face of a changing climate, affecting human health, energy consumption, and other socio-economic considerations. The UHI can be mitigated by urban greenery, but it needs further investigation of detailed impacts across the urban landscape. The aim was to study UHI and model the relation to greenery in combination with urban grey structures, at a high spatiotemporal resolution across the urban landscape, in Stockholm. Temperature data was collected through opportunistic drive-by sensors on electric three-wheeled taxis. Data on greenview and skyview factors were used to inform on greenery and building density along the roads. During night and morning hours, the surface temperature was in general higher than air temperature, indicating that some densely built-up environments stored heat overnight. Hot zones were unevenly distributed throughout the city, while greenery had a cooling effect, especially when combined with skyview as an inverse measure of building density. Our results provide information on the spatiotemporal distribution of heat that can be used to inform efforts to use greenery for mitigating impacts of UHI on urban residents.

Temperature Is a Key Factor Affecting Total Phosphorus and Total Nitrogen Concentrations in Northeastern Lakes Based on Sentinel-2 Images and Machine Learning Methods

Nitrogen and phosphorus are limiting nutrients in freshwater ecosystems, and the remote estimation of total phosphorus (TP) and total nitrogen (TN) in eutrophic waters is of great significance. This study utilized machine learning algorithms based on Sentinel-2 satellite imagery for remote estimation of TP and TN concentrations in Lake Xingkai, Chagan and Songhua. Results indicate that random forest (RF) and XGBoost regression algorithms perform better. The performance of the GBDT algorithm was slightly lower than that of the RF and XGBoost regression algorithms, the BP algorithm had overfitting, and the SVR algorithm had poor fitting performance. Results showed that the TN concentration inversion model based on the RF algorithm had the highest accuracy (R2 = 0.98, RMSE = 0.09, MAPE = 19.74%). The Extreme Gradient Boosting (XGB) model also performed well, though slightly less accurately than RF (R2 = 0.97, RMSE = 0.14, MAPE = 20.67%). For TP concentration, the XGB model’s performance (R2 = 0.82, RMSE = 0.08, MAPE = 24.89%) was comparable to that of the RF model (R2 = 0.82, RMSE = 0.07, MAPE = 29.55%). The RF algorithm was applied to all cloud-free Sentinel-2 satellite images of these typical lakes in northeastern China during the non-glacial period from 2017 to 2023, generating spatiotemporal distribution maps of TP and TN concentrations. Between 2017 and 2023, TP concentrations in Lake Xingkai, Chagan and Songhua showed increasing, decreasing, and initially decreasing then increasing patterns, respectively. A positive correlation between temperature and TP concentration was observed, as higher temperatures enhance biological activity. In contrast, a negative correlation was found with TN concentration, as higher temperatures promote phytoplankton growth and reproduction. This study not only offers a new method for monitoring eutrophication in lakes but also provides valuable support for sustainable water resource management and ecological protection goals.

A Systematic Review on Atmospheric Ozone Pollution in a Typical Peninsula Region of North China: Formation Mechanism, Spatiotemporal Distribution, Source Apportionment, and Health and Ecological Effects

A Systematic Review on Atmospheric Ozone Pollution in a Typical Peninsula Region of North China: Formation Mechanism, Spatiotemporal Distribution, Source Apportionment, and Health and Ecological Effects

Spatiotemporal Distribution of the Impact of Climate Change and Human Activities on NDVI in China

Spatiotemporal Distribution of the Impact of Climate Change and Human Activities on NDVI in China

Percolation analysis of spatiotemporal distribution of population in Seoul and Helsinki

Percolation analysis of spatiotemporal distribution of population in Seoul and Helsinki

Exceptional Uptake, Limited Protein Expression: Liver Macrophages Lost in Translation of Synthetic mRNA.

Most gene therapies exert their actions via manipulation of hepatocytes (parenchymal cells) and the reasons behind the suboptimal performance of synthetic mRNA in non-parenchymal cells (NPC) such as Kupffer cells (KC), and liver macrophages, remain unclear. Here, the spatio-temporal distribution of mRNA encoding enhanced green fluorescent protein (Egfp), siRNA, or both co-encapsulated into lipid nanoparticles (LNP) in the liver in vivo using real-time intravital imaging is investigated. Although both KC and hepatocytes demonstrate comparable high and rapid uptake of mRNA-LNP and siRNA-LNP in vivo, the translation of Egfp mRNA occurs exclusively in hepatocytes during intravital imaging. Despite attempts such as inhibiting intracellular ribonuclease, substituting uridine bases in mRNA with pseudouridine, and using a different ionizable lipid in the LNP mixture, no substantial increase in Egfp translation by NPC is possible. The investigation reveals that hepatocytes, which are distinct from other liver cells due to their polyploidy, exhibit significantly elevated levels of total RNA and protein, along with a higher proportion of ribosomal protein per individual cell. Consequently, fundamental cellular differences account for the low mRNA translation observed in NPC. The findings therefore suggest that cellular biology imposes a natural limitation on synthetic mRNA translation that is strongly influenced by cellular ploidy.

Joint response of surface subsidence and strong mine earthquake under high-positioned and thick-hard strata in deep coal mine

Multiple active mining faces and extensive excavations under thick-hard strata in deep coal mines result in frequent strong mine earthquakes, often accompanied by significant surface subsidence deformation. Understanding the specific law of surface movement and the spatiotemporal distribution response to intense mine earthquakes is crucial for effectively preventing and mitigating dynamic disasters in deep mines. Utilizing the key layer theory, the intricate strata of the Yingpanhao Coal Mine are systematically delineated, drawing upon the engineering context of working faces 2201 and 2202 within the Ordos Chemical Co., Ltd., a subsidiary of the Shandong Energy Group. Field investigations are conducted to analyze the law of surface subsidence associated with multi-working face extraction within deep thick-hard strata, as well as to elucidate the spatiotemporal distribution characteristics of strong mine earthquakes. Furthermore, the interplay between law of surface subsidence and the spatial distribution of strong mine earthquakes is investigated, revealing a cohesive relationship between these phenomena. The research findings of this study provide certain references for the pre-control of surface subsidence and strong mine earthquakes during multiple working face and large space mining under thick-hard strata in deep coal mine with similar engineering geological conditions.

Spatiotemporal distribution of global peatland area during the Holocene

Peatlands are a key component of terrestrial ecosystems, and their development has an important impact on global carbon cycle and climate change. However, the long-term evolution of global peatlands remains uncertain, particularly their spatial distribution. We compiled 4700 basal peatland data during Holocene, and 669 pollen data of Sphagnum with basal and end ages, to allow a more robust reconstruction of the spatial distribution of peatlands. Using buffer analysis (BA) and inverse distance weighted (IDW) interpolation of peat data, we reconstructed spatiotemporal changes in global peatland area at a spatial resolution of 0.5° × 0.5° for every 1,000 years period during Holocene. The results show that peatland area have expanded substantially in North America, Europe, and Western Siberia during early-Holocene, and increased rapidly from 2.18(0.32) Mkm2 to 4.03(3.08) Mkm2 during 12-6 ka BP, then slowly to 4.15(4.23) Mkm2 after 6 ka BP according to BA (IDW) methods. The database will be useful for analyzing the global/regional terrestrial carbon cycle and climate change during Holocene, especially for modeling peatland methane emissions.

Spatiotemporal Distribution and Potential Source Areas of PM2.5 Pollution in Xianyang City, Guanzhong Basin

This study employed PM2.5 concentration data for Xianyang City spanning the years 2014 to 2021, in conjunction with the global data assimilation system （GDAS）. Various analytical techniques, including backward trajectory clustering analysis, potential source contribution function （PSCF）, concentration weighted trajectory analysis （CWT）, and relevant statistical methods, were employed to investigate the temporal and spatial variations in PM2.5 pollution. Furthermore, this research aimed to elucidate the source characteristics and potential areas contributing to PM2.5 pollution within Xianyang City. The results revealed a fluctuation in PM2.5 pollution concentration in Xianyang City from 2014 to 2021, with an initial increase followed by a subsequent decrease. The peak average annual concentration, reaching 81.25 μg·m-3, was recorded in 2016. Seasonal variations indicated higher PM2.5 concentrations in autumn and winter, contrasting with lower levels in spring and summer. Winter exhibited the highest PM2.5 concentration at 116 μg·m-3, while the lowest was recorded in summer at 31.58 μg·m-3. Spatially, the annual mean distribution of PM2.5 in Xianyang City demonstrated heightened pollution in the southern and central regions, juxtaposed with lower pollution in the northern areas. Cluster analysis highlighted that Xianyang City experienced substantial influence from northwest airflow during spring, autumn, and winter, while short-distance transport dominated during the summer months. PSCF and CWT analyses indicated that the high-value potential source contribution areas were most extensive during winter, followed by spring and autumn. Conversely, the high-value area in summer was the smallest. The potential source areas were concentrated within Xianyang City and extended northwest to southeast, encompassing regions in western Inner Mongolia, central and eastern Gansu Province, central and southern Ningxia, and central Henan. In analyzing periods of heavy pollution, the study demonstrated that PM2.5 pollution in Xianyang City was primarily induced by westerly airflow. High PM2.5 concentrations were influenced by the surrounding areas of Xianyang City and the source zone, akin to regions with high potential source values during the winter. As a consequence, mitigating PM2.5 pollution in Xianyang City necessitates stringent control measures for both local and regional pollution sources. Additionally, regional collaborative efforts should be emphasized to prevent external pollution sources from exacerbating persistent pollution episodes in the region.

Community Dynamics of Fish Larvae in Coastal Zhejiang: Seasonal Variations in Spatiotemporal Distribution and Environmental Driving Factors

Community Dynamics of Fish Larvae in Coastal Zhejiang: Seasonal Variations in Spatiotemporal Distribution and Environmental Driving Factors

Spatio-temporal distribution and socioeconomic inequality of low birthweight rate in China from 1992 to 2021 and its predictions to 2030.

This paper aims to investigate the trend, spatio-temporal distribution, and socioeconomic inequality of the low birthweight rate (LBWR) in China from 1992 to 2021 and to project the LBWR to 2030. We performed a secondary analysis of data from the China Health Statistics Yearbook. LBWR refers to the ratio of the number of infants born with a birth weight less than 2,500 grams to the number of live births in a given year. We used joinpoint regression models to estimate LBWR trends from 1992 to 2021 for the whole country and from 2002 to 2021 for the three regions (eastern, central, and western regions) and each province. The slope index of inequality (SII) and relative index of inequality (RII) were calculated for each year from 2002 to 2021 based on provincial data. LBWR increased from 2.52% (1992) to 3.70% (2021), and the average annual percentage change (AAPC) (95% confidence interval [CI]) was 1.35% (0.22%, 2.49%) in China. The overall LBWR from 2002 to 2021 was greatest in the Eastern region, but LBWR had the fastest increase in the Western region, with an AAPC (95% CI) of 3.15% (2.59%, 3.12%). There were spatio-temporal differences in the LBWR and trends between provinces. The SII and RII increased linearly from -0.15 and 0.94 to 0.53 (B = 0.035%, p < 0.001) and 1.16 (B = 0.011, p < 0.01), respectively, over the past 20 years. The results of the ARIAM model showed that the National LBWR will be increasedfrom 3.70% in 2021 to 5.28% in 2030. The LBWRs in the eastern, central and western regions in 2030 will be 4.93%, 6.02% and 5.82%, respectively. National and local governments must prioritize disadvantaged groups to mitigate the rapid prevalence of LBWR, reduce regional disparities, and improve perinatal and infant health and health equity in China.

Spatiotemporal Variation and Driving Factors of Carbon Sequestration Rate in Terrestrial Ecosystems of Ningxia, China

This study investigates the spatiotemporal variation characteristics and influencing factors of an ecosystem’s carbon sequestration rate (CSR) in the Ningxia region from 2001 to 2023, providing scientific evidence for assessing the regional carbon sequestration capacity and formulating carbon neutrality policies. Based on ground observation data and multimodal datasets, the optimal machine learning model (EXT) was used to invert a 30 m high-resolution vegetation and soil carbon density dataset for Ningxia from 2000 to 2023. Annual variation analysis and geographical detector methods were employed to assess the spatiotemporal distribution characteristics of the CSR from 2001 to 2023 and identify the primary influencing factors. The results show that from 2001 to 2023, the CSR of the Ningxia ecosystem exhibits a spatial distribution pattern characterized by higher values in the south and lower values in the north, with a mean value of 21.95 gC·m−2, and an overall fluctuating increasing trend, with an annual growth rate of 0.53 gC·m−2 a−1. Significant differences in the CSR exist across different ecological regions. In terms of land use types, the ranking of carbon sequestration capacity is forest &gt; farmland &gt; grassland &gt; barren, while the ranking of the carbon sequestration enhancement capacity is farmland &gt; forest &gt; grassland &gt; barren. Among land use change types, the carbon sequestration enhancement capacity significantly increased when grassland was converted to forest or shrubland, farmland to forest–grassland, and bare land to forest–grassland, with increases of 42.9%, 9.2%, and 34.6%, respectively. The NDVI is the primary driver of CSR spatiotemporal variation, while the interaction between the Enhanced Vegetation Index (EVI) and soil bulk density has a more significant explanatory power for CSR spatial differentiation. This study shows that ecological restoration projects, such as the conversion of cropland to forest (or grassland) and protective farmland measures, play a significant role in enhancing the carbon sequestration capacity in Ningxia.

Unveiling spatiotemporal temperature distribution at the skin in contact with hot solid surfaces

Unveiling spatiotemporal temperature distribution at the skin in contact with hot solid surfaces

Spatiotemporal distribution prediction for PM2.5 based on STXGBoost model and high-density monitoring sensors in Zhengzhou High Tech Zone, China.

The increasing demand for air pollution control has driven the application of low-cost sensors (LCS) in air quality monitoring, enabling higher observation density and improved air quality predictions. However, the inherent limitations in data quality from LCS necessitate the development of effective methodologies to optimize their application. This study established a hybrid framework to enhance the accuracy of spatiotemporal predictions of PM2.5, standard instrument measurements were employed as reference data for the remote calibration of LCS. To account for local emission characteristics, the calibration model was trained using statistical values from LCS during periods of reduced anthropogenic emissions. This calibration approach significantly improved data quality, increasing R2 values of LCS data from 0.60 to 0.85. Subsequently, an advanced predictive model, STXGBoost, was developed, combining Kriging interpolation technology with high-density LCS data to integrate temporal trends and geographic spatial correlations. The STXGBoost model effectively captured the spatiotemporal variability of PM2.5 data, producing accurate and high spatiotemporal resolution PM2.5 prediction maps, with R2 values of 0.96, 0.92, and 0.89 for 1-h, 4-h, and 48-h predictions, respectively. These findings demonstrate the feasibility of generating high-resolution urban air pollution maps by integrating high-density ground monitoring data with advanced computational approaches. This framework provides valuable support for precise management and informed decision-making in urban atmospheric environments.

Improving ozone estimation during rainy-warm seasons from the perspective of weather systems based on machine learning.

Surface ozone pollution in eastern China is increasingly serious during summer, coinciding with distinct stages of the rainy seasons in this region. This study focuses on the spatiotemporal distribution of ozone concentrations, their synoptic driving factors and estimation during the Meiyu periods from 2015 to 2022. Results show that high ozone levels mainly occur during the interval of Meiyu season (HOP), accounting for 15.1% of the total Meiyu period, with MDA8 O3 concentrations in HOP exceeding those in non-HOP (NHOP) by over 20μgm-3 at >60% of sites. Compared to NHOP, HOP is characterized by lower precipitation (-0.17mm), higher temperatures (0.25K), increased solar radiation (3.16kJm-2), and reduced relative humidity (-8.56%). Using the Lindeman, Merenda, and Gold method, it is found that the meteorological factors with the greatest impact on MDA8 O3 during the 2015-2022 Meiyu season are relative humidity (30.4%), surface 2m temperature (28.6%), and total precipitation (23.2%). These favorable conditions for ozone production are along with specific state synoptic-scale and large-scale atmospheric circulation systems. On a daily scale, the further south the position of the western Pacific subtropical high pressure ridge (72.2%) and the stronger the intensities of El Niño-Southern Oscillation and East Asian summer monsoon (19.0%) will lead to higher O3 concentration in the Meiyu-affected area. The results of machine learning prove that instead of conventional consideration of meteorological conditions, the introduction of daily atmospheric circulation indices can effectively improve the accuracy of ozone concentration estimation (R2: 0.72, NRMSE: 0.15). These findings provide valuable insights for predicting ozone pollution during the rainy interval period.