Temporal Variation Characteristics Research Articles

Comprehensive Evaluation of Water Resource Characteristics in the Northern Yangtze River Delta, China

The Yangtze River Delta is one of the most economically developed regions on the eastern coast of China. However, a local imbalance currently exists between its water resource environment and economic and social development due to its rapid urbanization. Thus, the reasonable evaluation and protection of local water resources are necessary. This study explores the northern Yangtze Delta, which is a more developed water system, as a pilot area. The temporal and spatial variation characteristics of rainfall and evaporation and their influencing factors were analyzed on the basis of 29 surface water sampling points, 16 rainfall stations, and three evaporation stations in the field from 1956 to 2019. Accordingly, the overall water supply quality of the river basin, the availability of different water resources, and the application of evaluation methods were assessed. Results show that local precipitation and evaporation are characterized by uneven spatial and temporal distributions in local areas, which, in turn, leads to the uneven temporal distribution of runoff, increasing the imbalance between the availability and demand of the limited local water resources. Nevertheless, the overall performance of local water quality is good. Surface water quality is mostly Ⅱ to Ⅲ, and locally Ⅳ. Most noncompliant months are during the non-flood season, and all values exceed the standard permanganate index. Groundwater is Class III or better, and the hydrochemistry type is predominantly calcium bicarbonate, sodium bicarbonate, and magnesium bicarbonate. By exploring the evaluation model of the Yangtze River Delta watershed characteristics, this study aims to provide a helpful reference for extending water resource evaluation in the Yangtze River Delta. Accordingly, this study can promote the sustainable development of the economic and social sectors of the Yangtze River Delta and the construction of its ecological environment.

Study on Atmospheric Water Resource Variation Characteristics in China and Influencing Factors of Precipitation Efficiency of Hydrometeors

Understanding the spatial and temporal variation characteristics of atmospheric water resources in each region of China is essential for their exploitation. In this study, we focus on the variation in atmospheric precipitable water (PW), cloud parameters, cloud water resources (CWR), and precipitation efficiency of hydrometeors (PEh) as well as their possible influencing factors in each region of China in summer using ERA5 and MODIS data. The results showed that: (1) In the past 42 years, PW increased significantly in the northwest and Tibetan Plateau regions, and both cloud cover and total column cloud water content decreased significantly in northern China, except for medium-cloud cover which significantly increased in the Tibetan Plateau. (2) In the past 20 years, annual PEh was significantly higher, with clear interdecadal variation in most regions, whereas CWR decreased in northern China, the southwest, central region, and southeast, and increased in northeast and Tibetan Plateau. (3) PEh in each region was greatly influenced by precipitation (correlation coefficient 0.55 < R < 0.82 *, * = p < 0.05), having a significant positive correlation in the northeast with cloud water path, cloud optical thickness, and aerosol optical depth (0.44 * < R < 0.54 *), and a significant negative correlation in the central region with the cloud effective radius (R = −0.54 *).

Temporal and spatial variation characteristics of surface water area in the Yellow River Basin from 1986 to 2021.

The Yellow River Basin is short of water resources. The dynamic monitoring of surface water area is helpful to clarify the distribution and change trend of water resources in this area. It is of great scientific significance to deeply understand the impacts of climate change and human activities on water resources and ensure the ecological security of the basin. Based on the Google Earth Engine (GEE) cloud platform, we analyzed the spatial variations of surface water area in the Yellow River Basin from 1986 to 2021 by using the mixed index algorithm, and revealed the driving factors of surface water area change in the Yellow River Basin. The results showed that the overall recognition accuracy of the water extraction algorithm based on mixing index was 97.5%. Compared with available water data products, the proposed algorithm can guarantee the integrity of the whole water area to a certain extent. The surface water area in the upper, middle, and lower reaches of the Yellow River Basin was 71.7%, 18.4%, and 9.9% of the total surface water area, respectively. From 1986 to 2021, the surface water area of the basin showed an overall upward trend, with a total increase of 3163.6 km2. The surface water area of the upper, middle, and downstream regions increased by 72.0%, 22.4%, and 5.6%, respectively. The increase of precipitation was the main reason for the increase of water area, with a contribution of 55%. Vegetation restoration and construction of water conservancy projects had increased the water area of the basin. The intensification of human water extraction activity reduced the water area of the basin.

Evaluation and prediction of ecological environment of karst world heritage sites based on google earth engine: a case study of Libo–Huanjiang karst

The assessment and prediction of ecological quality can help to quickly and systematically understand the ecological status of World Natural Heritage and assist with developing appropriate strategies to ensure the sustainable and healthy development of that heritage. Using the google earth engine platform, the remote sensing ecological index (RSEI) is rapidly constructed based on principal component analysis to evaluate the spatial and temporal distribution characteristics and the main impact indicators of the ecological environment, and the cellular automata (CA)-Markov model are used to simulate and predict the ecological quality, taking the Libo–Huanjiang karst world natural heritage site as the study area. The results show that: (a) the contribution rate of the four ecological indicators on the first principal component (PC1) are more than 90%. The construction of RSEI based on PC1 are applicable in karst world nature heritage sites and can be used to monitor and evaluate the spatial and temporal variation characteristics of the ecological environment. (b) The mean values of RSEI of the Libo–Huanjiang heritage site during 2000, 2007, 2013 and 2020 were 0.5435, 0.5465, 0.6009 and 0.5101, The overall ecological quality is mainly moderate and good, but in the eastern part of the heritage site the ecological quality is poor. (c) The evolution of the ecological environment quality during 20 years is mainly divided into the development trend of rapidly getting better, slowly getting better, and maintaining stability. (d) In the analysis of the relationship between RSEI and altitude, it is found that the ecological environment quality is mainly inferior, less favorable and moderate in areas with altitudes below 600 m, and there is a positive relationship between ecological quality level and altitude. (e) By analyzing the apparent spatial aggregation between the ecological environment quality, and then simulating the ecological grades in 2027 and 2033 using CA-Markov model, it is predicted that the area of medium and excellent ecological grades will increase in the future, but the ecological environment quality still needs to be improved in the eastern region due to the development of the tourism industry. Overall, the remote sensing ecological index is an effective model for evaluating and monitoring the ecological environment quality of karst heritage sites; the ecological environment quality of the Libo–Huanjiang heritage site is in a steady state of improvement, and the conservation measures of relevant departments are beginning to bear fruit; further coordination between conservation and development is needed to promote the sustainable development of heritage sites and to provide effective solutions for monitoring other karst-like heritage sites.

Temporal–Spatial Variations in the Economic Value Produced by Environmental Flows in a Water Shortage Area in Northwest China

Scientific and accurate assessments of the economic value produced by environmental flows are an important basis for the protection of environmental flows by means of economics. Because of the temporal and spatial variation characteristics of environmental flows, it is more appropriate to study the economic value produced by environmental flows using a temporal–spatial scale rather than static calculations. In the present study, we combine the major influencing factors to establish the temporal–spatial calculation methods of the economic value produced by environmental flows using the assessment techniques of resources and environmental economics. The results obtained for the Wei River show that the annual variation range of the total economic value is CNY 0.30–0.42 billion, and the unit economic value is 0.86–6.40 CNY/m3 during the non-flood season ranging from the years 1980 to 2017. In general, the monthly variation in the total economic value ranges from CNY 0.04 to 0.08 billion, and the unit economic value is 0.94–14.34 CNY/m3. Based on this result, the variation tendency of the total economic value is consistent with the changing trend of the environmental flows occurring in the river; however, the unit economic value presents a reverse pattern. Furthermore, the deficiency of environmental flows can lead to a significant increase in its unit economic value. This method presents a dynamic, small temporal–spatial scale assessment of the economic value produced by environmental flows. It can also provide theoretical support for the ecological compensation of environmental flow protection in rivers present in water shortage areas.

Suitability of Sentinel-3 chlorophyll a products based on optical water types

ABSTRACT Analysis of the suitability of water colour products for the reliability of subsequent applications, or improvements of the algorithm at later stages, is important. We used three types of water bodies: Type 1 (Southern Ocean and Qinghai Lake) clean oceanic water bodies dominated by chlorophyll and clean inland water bodies dominated by chlorophyll and coloured dissolved organic matter; Type 2 (Taihu Lake and Chagan Lake) turbid inland water bodies dominated by suspended matter and chlorophyll and Type 3 (Xiaoxingkai Lake and Daxingkai Lake) turbid inland water bodies dominated by suspended matter. The accuracies of two Chl a concentration products data for synchronous Sentinel-3 OLCI Neural Network (NN) and ocean colour 4 band ratio (OC4Me) algorithms were verified using two metrics combined with in situ data (to compare the applicability of Chl a concentration algorithms for the three types of water bodies). The optimal Chl-a concentration algorithm was selected to analyse the spatial and temporal variation characteristics of monthly concentrations of Chl a. Based on the analysis, (1) the NN algorithm had better applicability to Type 1 water bodies ; the NN algorithm had better applicability to Type 2 water bodies, ; and both algorithms had poor applicability to Type 3 water bodies, (2) Limitations: An overall low derivation value was obtained for the Sentinel-3 OLCI NN algorithm with water concentrations of Chl a, higher than 25 mg m−3. The OC4Me Chl a concentration product only applies to the concentrations of Chl a in Type 2 during the summer. (3) When the Sentinel-3 OLCI Chl a concentration product was compared with the moderate-resolution imaging spectroradiometer (MODIS) and visible infrared imaging radiometer (VIIRS) Chl a concentration products, the distribution trends of the Chl a concentrations were consistent. Furthermore, the Sentinel-3 OLCI Chl a concentration product was more dominant in spatial resolution.

Study on the influence of mining disturbance on the variation characteristics of vegetation index: A case study of Lingwu Mining Area

The vegetation growth environment has been seriously affected by the high-intensity mining of coal resources in ecologically fragile mining areas in the Western China. To understand the spatial and temporal variation characteristics of vegetation index in mining area is the precondition of exploring the protective mining methods of surface vegetation. Based on the Landsat-8OLI and Sentinel-2A remote sensing image data from 2016 to 2020, the SBAS-InSAR technology and Arcgis spatial analysis tool were employed in this study; the spatial-temporal evolution of normalized differential vegetation index (NDVI) was analyzed from the mining area scale by regression slope and correlation coefficient. The 10 mm subsidence contour range on the typical working face was extracted as the mining disturbance range. At the scale of the working face, the influence law of mining technical parameters and occurrence conditions on surface vegetation index was revealed. The results show that: (1) Lingwu mining area has relatively low surface vegetation coverage, which conforms to the partial normal distribution; the average NDVI in the southwest of the mining area is greater than that in the northeast. (2) The vegetation growth process in the mining area presents a phenological periodic variation trend, and mining activities have a certain negative impact on the growth of surface vegetation. However, after regional vegetation restoration, the NDVI in the mining area shows a fluctuating growth trend. (3) In the pre-mining stage of the working face, the NDVI change trend of vegetation in the mining area is basically the same; in the mining stage of the working face, the mining influence is relatively small due to the lagging effect of mining subsidence; in the post-mining stage (1–2 years after mining), the growth of vegetation is seriously damaged, and NDVI of the mining area decreases significantly. (4) The mining area of working face is positively correlated with the decline scope of NDVI; the ratio of mining depth to mining thickness and bedrock thickness are negatively related to surface subsidence, which slows down the decline of NDVI; the loose layer on the surface has large thickness, which can also effectively slow down the destruction of vegetation in the subsidence area. The research results are of great significance for further understanding the influence of coal mining on surface vegetation elements in ecologically fragile mining areas in Northwest China.

Spatial and temporal variations of grassland vegetation on the Mongolian Plateau and its response to climate change

The Mongolian Plateau is an arid and semi-arid region with grassland as its main vegetation. It has a fragile ecosystem and is a sensitive area for global warming. The study is based on MODIS NDVI data and growth season meteorological data from 2000 to 2018, this study examined the spatial and temporal variation characteristics of grassland vegetation on the Mongolian Plateau during the growing season using trend analysis, partial correlation analysis, and residual analysis, and it explores the dual response of NDVI changes to climate and human activities. The study’s findings demonstrated that the growing season average NDVI of grassland vegetation on the plateau gradually increased from southwest to northeast during the growing season; the growing season average NDVI demonstrated a significant overall increase of 0.023/10a (p < 0.05) from 2000 to 2018, with an increase rate of 0.030/10a in Inner Mongolia and 0.019/10a in Mongolia; the area showing a significant increase in NDVI during the growing season accounted for 91.36% of the entire study area. In Mongolian Plateau grasslands during the growing season of 2000–2018, precipitation and downward surface shortwave radiation grew significantly at rates of 34.83mm/10a and 0.57 W/m2/10a, respectively, while average air temperature decreased slightly at a rate of −0.018°C/10a. Changes in meteorological factors of grassland vegetation varied by region as well, with Inner Mongolia seeing higher rates of precipitation, lower rates of average air temperature, and lower rates of downward surface shortwave radiation than Mongolia. On the Mongolian Plateau, the NDVI of grassland vegetation in the growing season showed a significant positive correlation with precipitation (0.31) and a significant negative correlation with average air temperature (−0.09) and downward surface shortwave radiation (−0.19), indicating that increased in NDVI was driven by an increase in precipitation paired with a decrease in air temperature and a decrease in surface shortwave radiation. The overall increase in NDVI caused by human activity in the grasslands of the Mongolian Plateau was primarily positive, with around 18.37% of the region being beneficial. Climate change and human activity both affect NDVI variations in Mongolian Plateau grasslands, which are spatially heterogeneous. Moderate ecological engineering and agricultural production activities are crucial for vegetation recovery. This work is crucial to further understanding surface–atmosphere interactions in arid and semi-arid regions in the context of global climate change.

Regional/Single Station Zenith Tropospheric Delay Combination Prediction Model Based on Radial Basis Function Neural Network and Improved Long Short-Term Memory

Atmospheric water vapor is an essential source of information that predicts global climate change, rainfall, and disaster-natured weather. It is also a vital source of error for Earth observation systems, such as the global navigation satellite system (GNSS). The Zenith Tropospheric Delay (ZTD) plays a crucial role in applications, such as atmospheric water vapor inversion and GNSS precision positioning. ZTD has specific temporal and spatial variation characteristics. Real-time ZTD modeling is widely used in modern society. The conventional back propagation (BP) neural network model has issues, such as local, optimal, and long short-term memory (LSTM) model needs, which help by relying on long historical data. A regional/single station ZTD combination prediction model with high precision, efficiency, and suitability for online modeling was proposed. The model, called K-RBF, is based on the machine learning algorithms of radial basis function (RBF) neural network, assisted by the K-means cluster algorithm (K-RBF) and LSTM of real-time parameter updating (R-LSTM). An online updating mechanism is adopted to improve the modeling efficiency of the traditional LSTM. Taking the ZTD data (5 min sampling interval) of 13 international GNSS service stations in southern California in the United States for 90 consecutive days, K-RBF, R-LSTM, and K-RBF were used for regions, single stations, and a combination of ZTD prediction models regarding research, respectively. Real-time/near real-time prediction results show that the root-mean-square error (RMSE), mean absolute error (MAE), coefficient of determination (R2), and training time consumption (TTC) of the K-RBF model with 13 station data are 8.35 mm, 6.89 mm, 0.61, and 4.78 s, respectively. The accuracy and efficiency of the K-RBF model are improved compared with those of the conventional BP model. The RMSE, MAE, R2, and TTC of the R-LSTM model with WHC1 station data are 6.74 mm, 5.92 mm, 0.98, and 0.18 s, which improved by 67.43%, 66.42%, 63.33%, and 97.70% compared with those of the LSTM model. The comparison experiments of different historical observation data in 24 groups show that the real-time update model has strong applicability and accuracy for the time prediction of small sample data. The RMSE and MAE of K-RBF with 13 station data are 4.37 mm and 3.64 mm, which improved by 47.70% and 47.20% compared to K-RBF and by 28.48% and 31.29% compared to R-LSTM, respectively. The changes in the temporospatial features of ZTD are considered, as well, in the combination model.

Analysis on temporal variation law of ozone pollution and meteorological driving factors in Pudong New Area of Shanghai

AbstractIn order to deeply understand the influence of meteorological drivers on ozone (O3) pollution during the high season and explore O3 pollution control options, we analyzed the temporal variation characteristics of O3 pollution based on the ambient air observation data of Pudong New Area, Shanghai from 2020 to 2021, studied the influence of meteorological drivers on O3 pollution, and explored seasonal emission reduction options based on the results of EKMA (Empirical Kinetics Modeling Approach) curves. The results show that the seasonal O3 concentration variation is spring > summer > autumn > winter; relative humidity, temperature and wind speed are the main meteorological drivers of O3 pollution; the cross‐regional transport of high O3 concentrations and precursors from the south and southwest has a strong influence on the local O3 concentration; Pudong New Area is in the “VOCs control zone” during the O3 pollution period in May. Therefore, it is important to control the production and emission of local VOCs and to strengthen the synergistic control of O3 and its precursors between regions for the control of spring O3 in Pudong New Area.

Transformer Based Water Level Prediction in Poyang Lake, China

Water level is an important indicator of lake hydrology characteristics, and its fluctuation significantly affects lake ecosystems. In recent years, deep learning models have shown their superiority in the long-time range prediction of hydrology processes, while the application of deep learning models with the attention mechanism for lake water level prediction is very rare. In this paper, taking Poyang Lake as a case study, the transformer neural network model is applied to examine the model performance in lake water level prediction, to explore the effects of the Yangtze River on lake water level fluctuations, and to analyze the influence of hyper-parameters (window size and model layers) and lead time on the model accuracy. The result indicated that the transformer model performs well in simulating the lake water level variations and can reflect the temporal water level variation characteristics in Poyang Lake. In the testing stage, the RMSE values were recorded in the range of 0.26–0.70 m, and the NSE values are higher than 0.94. Moreover, the Yangtze River inflow has a great influence on the lake water level fluctuation of Poyang Lake, especially in flood and receding periods. The contribution rate of the Yangtze River in RMSE and NSE is higher than 80% and 270%, respectively. Additionally, hyper-parameters, such as window size and model layers, significantly influence the transformer model simulation accuracy. In this study, a window size of 90 d and a model layer of 6 are the most suitable hyper-parameters for water level prediction in Poyang Lake. Additionally, lead time may affect the model accuracy in lake water level prediction. With the lead time varied from one to seven days, the model accuracy was high and RMSE values were in the range of 0.46–0.73 m, while the RMSE value increased to 1.37 m and 1.82 m with the lead time of 15 and 30 days, respectively. The transformer neural network model constructed in this paper was the first to be applied to lake water forecasting and showed high efficiency in Poyang Lake. However, few studies have tried to use transformer model coupling with the attention mechanism for forecasting hydrological processes. It is suggested that the model can be used for long sequence time-series forecasting in hydrological processes in other lakes to test its performance, providing further scientific evidence for the control of lake floods and management of lake resources.

Evaluation and Prediction of Water Yield Services in Shaanxi Province, China

The water yield module of the InVEST model was used to estimate the water yield and its temporal and spatial variation characteristics in Shaanxi Province from 2000 to 2020. Moreover, the influences of future precipitation changes and land use changes on water yield in Shaanxi Province were discussed in the 2030s and 2050s. The results showed that: (1) from 2000 to 2020, the multi-year average water yields in northern Shaanxi, Guanzhong and southern Shaanxi were 33.23 × 108 m3, 73.75 × 108 m3, and 280.63 × 108 m3, respectively; (2) the spatial pattern of water yield depth displayed a characteristic of gradually increasing from north to south; (3) under the precipitation change scenario, the water yield under different emission scenarios wa s in the order of RCP (Representative Concentration Pathways) 8.5 > RCP2.6 > RCP4.5; under the land use change scenario, the water yield depth of Shaanxi Province as a whole and in the three regions in the 2030s and 2050s showed a decline. The research results can provide scientific support for water ecological security, water resources, and regional high-quality sustainable development in Shaanxi Province.

Spatial and temporal variation characteristics of extreme hydrometeorological events in the Yellow River Basin and their effects on vegetation

Spatial and temporal variation characteristics of extreme hydrometeorological events in the Yellow River Basin and their effects on vegetation

Spatial and Temporal Variation Characteristics and Driving Mechanisms of Multidimensional Socio-Economic Development Levels in Resource-Based Cities

As resources are depleted, resource-based cities face unique challenges in the process of socio-economic development. We constructed a multidimensional socio-economic development level model by adopting Entropy Value Method, Analytical Hierarchy Process, time series weighting method, and Game Theory approach for the data of 10 indicators in 4 dimensions of 115 resource-based cities in China from 2004 to 2019 to explore the spatial and temporal divergence characteristics of multidimensional socio-economic development level and the driving mechanism of its pattern of evolution. The results show that: (1) the overall socio-economic development level of resource-based cities has improved from 2004 to 2019, but the overall level is low. Large differences exist in the spatial distribution of socio-economic development levels between cities with more significant regional spatial aggregation characteristics. (2) Secondary industry, tertiary industry, retail trade goods sales, urban construction land area, and total freight transport have a significant positive impact on socio-economic development; the correlation coefficient between the number of schools and the socio-economic development level index is negative. (3) Retail trade merchandise sales contribute the most to the Gini coefficient, where the percentage of secondary industry and urban construction land area have a higher cumulative contribution to growing cities (55.02%), the percentage of secondary industry has the lowest contribution to regenerating cities (10.94%), and the percentage of tertiary industry has an increasing contribution to declining cities year by year. Based on the above findings, some specific suggestions are provided to provide reference for resource-based city development planning.

Dataset of temperature and precipitation over the major Belt and Road Initiative regions under different temperature rise scenarios

ABSTRACT Changes in temperature and precipitation have a profound effect on the ecological environment and socioeconomic systems. In this study, we focus on the major Belt and Road Initiative (BRI) regions and develop a dataset of temperature and precipitation at global temperature rise targets of 1.5°C, 2°C, and 3°C above pre-industrial levels under the Representative Concentration Pathway (RCP) 8.5 emission scenario using 4 downscaled global model datasets data at a fine spatial resolution of 0.0449147848° (~5 km) globally from EnviDat. The temperature variables include the daily maximum (Tmax), minimum (Tmin) and average (Tmp) surface air temperatures, and the diurnal temperature range (DTR). We first evaluate the performance of the downscaled model data using CRU-observed gridded data for the historical period 1986–2005. The results indicate that the downscaled model data can generally reproduce the pattern characteristics of temperature and precipitation variations well over the major BRI regions for 1986–2005. Furthermore, we project temperature and precipitation variations over the major BRI regions at global temperature rise targets of 1.5°C, 2°C, and 3°C under the RCP8.5 emission scenario based on the dataset by adopting the multiple-model ensemble mean. Our dataset contributes to understanding detailed the characteristics of climate change over the major BRI regions, and provides data fundamental for adopting appropriate strategies and options to reduce or avoid disadvantaged consequences associated with climate change over the major BRI regions. The dataset is available at https://doi.org/10.57760/sciencedb.01850.

Evaluating the impact of power station regulation on the suitability of drifting spawning fish habitat based on the fuzzy evaluation method

Ecological regulation is an important means of reservoir adaptive management, but its effective evaluation faces two major difficulties: the response mechanism of fish spawning behavior is not completely clear, and how to establish a feedback regulation relationship of hydrological processes to improve the river environment is unknown. Based on a long-term series of early fish resources, hydrology, water temperature, and meteorology data, this research clarifies the fish spawning habitat requirements in the power station regulation environment, determines a habitat suitability evaluation index system and evaluation criteria, reveals the temporal and spatial variation characteristics of fish habitat suitability under power station regulation based on the fuzzy logic method, provides feedback to the existing regulation scheme, and proposes suggestions for sustainable adaptive management of the reservoir. The temporal and spatial variation characteristics of the spawning river sections habitat suitability are the comprehensive differences among multiple objectives and factors. The habitat suitability of each river section decreases after impoundment, especially in May, which is related to the delayed of water temperature changes under reservoir regulation. The reduced suitability of the Yibin(YB) river section is most affected by the impoundment regulation of the Xiluodu Reservoir (XLDR) and Xiangjiaba Reservoir (XJBR), while the Luzhou(LZ) river section is affected by the inflow of the Minjiang River (MJ) tributary, which reduces the suitability difference before and after impoundment. The Jiangjin(JJ) river section is less affected by the regulation of the XJBR and is greatly affected by tributaries and rainfall. How to adjust the regulation strategies under the new boundaries and new situations in the future, which are affected by the cumulative impact of the sustainable development of upstream cascades, is the focus of reservoir adaptive management. This research can provide technical support for the management of cascade reservoirs under future scenarios.

Temporal and Spatial Variation Characteristics of Snow Cover Area in the Pamirs from 2010 to 2020

Scientific and comprehensive monitoring of snow cover changes in the Pamirs is of great significance to the prevention of snow disasters around the Pamirs and the full utilization of water resources. Utilize the 2010-2020 snow cover product MOD10A2, Synthesis by maximum, The temporal and spatial variation characteristics of snow cover area in the Pamirs in the past 11 years have been obtained. Research indicates: In terms of interannual changes, the snow cover area of the Pamir Plateau from 2010 to 2020 generally showed a slight decrease trend. The average snow cover area in 2012 was the largest, reaching 54.167% of the total area. In 2014, the average snow cover area was the smallest, accounting for only 44.863% of the total area. In terms of annual changes, there are obvious changes with the change of seasons. The largest snow area is in March, and the smallest snow area is in August. In the past 11 years, the average snow cover area in spring and summer showed a slow decreasing trend, and there was almost no change in autumn and winter. In terms of space, the snow cover area of the Pamirs is significantly affected by altitude, and the high snow cover areas are mainly distributed in the Karakoram Mountains and other areas with an altitude greater than 5000 meters.

The Application of Compact Polarization Decomposition in the Construction of a Dual-Polarization Radar Index and the Effect Evaluation of Rape Extraction

The rape planting plots are fragmented and have the same temporal variation characteristics as other land types, the rape planting area is cloudy and rainy, and the optical remote sensing images are limited, all make it difficult to extract the rape planting areas. Aiming at the above-mentioned problems, this study used microwave remote sensing data combined with the compact polarimetric technique to construct a dual-polarization radar index to classify rape planting areas and analyzed the influence of time series combinations on the extraction accuracy of rape in South China. First, based on <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m-χ</i> compact polarimetric decomposition, a dual-polarization radar vegetation index (RVI) was constructed. Then, based on Sentinel-1 data, the dynamic time warping (DTW) threshold classification method was used to extract rape planting areas in six counties in the main rape-producing areas in the middle and lower reaches of the Yangtze River, China. Finally, a random forest algorithm was used to analyze and screen the optimal time series combination of the extracted regional rape planting areas. Among the 94 rape samples obtained in the field, 74 samples were accurately classified as rape, and the overall accuracy was 78.72%. Six ground samples were used to verify the accuracy of the rape planting area extraction results, and the F-1 score was 81.00%. The above-mentioned results indicated that the rape planting area extraction approach based on the RVI <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">_m-χ</i> and DTW threshold classification methods yields high accuracy in regional rape planting area extraction from dual-polarization SAR data, and RVI <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">_m-χ</i> is characterized by certain regional scalability and stability.

Temporal and spatial variation characteristics of air quality trends in China

Temporal and spatial variation characteristics of air quality trends in China

Spatial and Temporal Evolution of Population-Weighted PM2.5 Concentration and Its Influencing Factors in China from 2000 to 2021

The repeated occurrences of severe haze in China in recent years have drawn increased attention from the public and government departments to the impact of pollutants on public health. In order to investigate the spatial and temporal distribution patterns and risks of PM2.5 population exposure levels in various regions of China, based on population-weighted PM2.5 concentration data, spatial autocorrelation analysis and geographic detector methods are used to reveal its overall spatial and temporal evolution patterns and local variation characteristics. The specific influences and interactions of population-weighted PM2.5 concentrations in China are also studied in four dimensions: socio-economic, climatic, geographic environment and policy. Finally, projections of population-weighted PM2.5 concentrations in China from 2019 to 2021 are made based on the main influencing factors. The study shows that (1) China's population-weighted PM2.5 concentrations decreased year by year from 2000 to 2018. (2) Spatial clustering of population-weighted PM2.5 concentrations in China is evident. (3) The population-weighted PM2.5 concentration in China showed a spatially heterogeneous pattern between 2000 and 2018. (4) At the national scale, the factor with the greatest explanatory power of China's population-weighted PM2.5 concentration is the average temperature. The interactions between the factors mainly show two types of interactions: two-factor enhancement and non-linear enhancement. (5) A prediction model of China's population-weighted PM2.5 concentration was established to predict the population-weighted PM2.5 concentration from 2019 to 2021 with high accuracy.