In the present study, the total concentrations of three cyclic methylsiloxanes (ΣCMSs), including octamethylcyclotetrasiloxane (D4), decamethylcyclopentasiloxane (D5) and ...dodecamethylcyclohexasiloxane (D6), in surface water and sediment samples of five main rivers draining into the Bohai Sea were in the range of 1.62–1.39 × 103 ng/L and 1.92–1.69 × 103 ng/g dw, respectively. Riverine input had great influence on the coastal distribution of siloxanes in the Bohai Sea. The concentrations of ΣCMSs in coastal sediments farthest away (40–50 Km) from the estuaries were only 4–33% of those close to the estuaries. But surprisingly, compared with those in coastal sediments (1.03–1.44 × 103 ng/g dw), the concentrations of CMSs (1.56–2.67 × 103 ng/g dw) in some deep-sea sediments were higher, and certain positive correlation existed between sediment ΣCMSs in this area with the total petroleum hydrocarbons concentration (R2 = 0.92, p < 0.05) suggested offshore oil exploitation as one important emission source of siloxanes. Overall, calculated based on their sediment concentrations, D4–D6 had negligible ecological risks to the benthic organisms in river-Bohai Sea system, i.e. HQs < 1. However, sediment-accumulation of siloxanes should be paid attention, especially for some deep-sea sediments nearby drilling platforms, where it will take only less than 1 year for D4 to reach its threshold.
•Siloxanes were widely found in five main rivers draining into the Bohai Sea.•Riverine input had great influence on the coastal siloxanes in the Bohai Sea.•Offshore oil exploitation was important emission source of siloxanes in deep sea.•D4-D6 had negligible ecological risks to the benthos in river-Bohai Sea system.
In remote sensing, red edge bands are important indicators for monitoring vegetation growth. To examine the application potential of red edge bands in forest canopy closure estimation, three types of ...commonly used models—empirical statistical models (multiple stepwise regression (MSR)), machine learning models (back propagation neural network (BPNN)) and physical models (Li–Strahler geometric-optical (Li–Strahler GO) models)—were constructed and verified based on Sentinel-2 data, DEM data and measured data. In addition, we set up a comparative experiment without red edge bands. The relative error (ER) values of the BPNN model, MSR model, and Li–Strahler GO model with red edge bands were 16.97%, 20.76% and 24.83%, respectively. The validation accuracy measures of these models were higher than those of comparison models. For comparative experiments, the ER values of the MSR, Li–Strahler GO and BPNN models were increased by 13.07%, 4% and 1.22%, respectively. The experimental results demonstrate that red edge bands can effectively improve the accuracy of forest canopy closure estimation models to varying degrees. These findings provide a reference for modeling and estimating forest canopy closure using red edge bands based on Sentinel-2 images.
ABSTRACTUrban green space (UGS) is important to the urban ecological environment. It has physical characteristics and social function characteristics and plays an important role in urban climate ...change, sustainable development goals (SDG) and residents’ health. However, existing researches mostly focus on the extraction of UGS physical features, neglecting the importance of UGS social functions, resulting in the unresolved problem of multi-type and fine-grained functional mapping of UGS. Therefore, based on natural language processing (NLP) and multi-source data fusion, this paper proposes a multi-type and fine-grained UGS function mapping method. First, the social functional standards of UGS have been re-established, with a total of 19 categories. Second, the semantic information in the POI data name text is extracted using the deep learning model, and the reclassification of the UGS type of POI data is realized. Then, combined with multi-source data, 18 types of UGS are extracted. Finally, combining multi-source data to extract urban road green spaces (GS), a fine-grained UGS functional map of Shanghai is created. The results show that the overall accuracy rate of the method is 93.6%, and the Kappa coefficient is 0.93, which proves that the method has good performance in large-scale spatial UGS classification.
The United Nations adopted 17 Sustainable Development Goals (SDGs) to address societal, economic and environmental sustainability issues. The efficiency of SDGs monitoring could be improved by ...essential variables (EVs), which can help to better deal with massive data, interdisciplinary knowledge and workloads. However, in practice, effectively combining EVs with SDGs monitoring remains challenging. In this paper, we proposed a refining method of essential SDGs variables (ESDGVs) to land degradation. Firstly, we selected northwest China as our experimental region and extracted a group of variables related to land degradation from SDG indicators based on the DPSIR framework. Next, we identify the essential ones using a combined qualitative and quantitative methods with the criteria of feasibility, spatialization, and relevance which considered the issues of data acquisition, monitoring scale, and closeness to the land degradation. Finally, we analysed the monitoring role of ESDGVs. Results show that, compared to conventional observations, ESDGVs facilitate the monitoring and evaluation of regional SDGs with reduced efforts. And both climate and human activities have a facilitating or inhibiting effect on land degradation processes. In the future, we hope to have more mature data sets and consider adding more SDG indicators for ESDGVs' refinement.
Rapid population growth has had a significant impact on society, economy and environment, which will challenge the achievement of the United Nations Sustainable Development Goals (SDGs). Spatially ...accurate and detailed population distribution data are essential for measuring the impact of population growth and tracking progress on the SDGs. However, most population data are evenly distributed within administrative units, which seriously lacks spatial details. There are scale differences between the population statistical data and geospatial data, which makes data analysis and needed research difficult. The disaggregation method is an effective way to obtain the spatial distribution of population with greater granularity. It can also transform the statistical population data from irregular administrative units into regular grids to characterize the spatial distribution of the population, and the original population count is preserved. This paper summarizes the research advances of population disaggregation in terms of methodology, ancillary data, and products and discusses the role of spatial disaggregation of population statistical data in monitoring and evaluating SDG indicators. Furthermore, future work is proposed from two perspectives: challenges with spatial disaggregation and disaggregated population as an Essential SDG Variable (ESDGV).
ABSTRACTThe increasing availability of global observational data has sparked a demand for deep learning algorithms on spherical grids to enable intelligent analysis at a global scale. However, a ...spherical surface cannot be subdivided into completely identical grid cells through recursive division, and its nonuniformity and irregular deformations lead to uncertainties in the spherical convolutional neural network (SCNN). This paper proposes a multimetric evaluation method to assess the impact of the icosahedral diamond grid quality on the performance of the SCNN by introducing the random forest algorithm to establish nonlinear relationships between multiple grid quality metrics and the SCNN performance and using feature importance analysis to assign impact weights to each grid quality metric considering the SCNN performance. The results show an R2 score of 0.80 for the evaluation method, with four indicators having different weights: cell wall midpoint ratio (0.47), distance between grid points and neighbouring points (0.29), zone standardized compactness (0.13), and angle between a grid point and its two neighbours (0.11). The cell wall midpoint ratio indicator has the most significant impact on the SCNN performance among all grid indicators.
Synthetic aperture radar (SAR) data have significant potential for soil moisture monitoring because of their high spatial resolution and independence from cloud coverage. However, it is challenging ...to retrieve soil moisture from SAR data over vegetated areas, as vegetation significantly affects backscattered radar signals. Auxiliary vegetation information obtained from optical images, such as the normalized difference vegetation index (NDVI) and the leaf area index (LAI), is commonly used to correct vegetation effects. However, it is generally difficult to obtain SAR and optical data in the same area simultaneously, because of the discrepancies in satellite coverage and the effects of cloud coverage. This study focuses on whether vegetation descriptors obtained directly from radar data at L-band can adequately parameterize the semi-empirical backscattering water cloud model (WCM) to support soil moisture retrieval. Four vegetation descriptors (three based on radar images and one based on optical images), were chosen to assess the parameterization and calibration of the WCM and the retrieval accuracy of soil moisture. The results showed that the vegetation descriptor of backscattering at VH polarization outperformed the other three vegetation descriptors (NDVI-derived vegetation water content, radar vegetation index, and the ratio of cross-polarization to VV polarization) in the investigation of four crop types (canola, corn, bean, and wheat) based on the Soil Moisture Active Passive Validation Experiment in 2012 (SMAPVEX12) in Canada. For the vegetation descriptor of VH, the overall accuracy of retrieved soil moisture was promising by separating into two growth stages, with unbiased root mean squared errors of 0.056, 0.053, 0.098, and 0.079 cm
3
/cm
3
for canola, corn, bean, and wheat, respectively. The results also confirmed that variations in vegetation growth affect the accuracy of soil moisture retrieval. In addition, the retrieval performance was undermined when the vegetation changed dramatically, leading to variations or uncertainty in the vegetation structure. This study provides new insights into soil moisture retrieval methods with active L-band microwave observations.
Spectral mixing models for minerals can be complex, and choosing the right unmixing model is indispensable to ensure the accuracy of spectral unmixing. Continuum removal (CR) and natural log ...operation have the potential to eliminate nonlinear effects in spectral mixing, and have already been used in nonlinear spectral unmixing applications. In this study, the newly proposed log and CR (LCR) model and four other spectral unmixing models for mineral analysis (the linear model, CR model, natural log model, and simplified Hapke model) were summarized and applied to both laboratory mineral powder spectra and hyperspectral data of Cuprite, Nevada, USA. This paper summarizes the concept of mixing reflectance reconstruction (MRR), along with a comprehensive method to determine accuracy based on MRR results, which can be performed in different dimensions, such as spatial dimension and spectral dimension. The LCR model performed the best in both laboratory experiments and image data analysis, indicating its strong potential for practical application. The level of atmospheric correction's influence on unmixing accuracy varied for different spectral unmixing models, among which LCR model also had the best performance.
Fine-scale population spatial distribution plays an important role in urban microcosmic research, influencing infrastructure placement, emergency evacuation management, business decisions, and urban ...planning. In the past, nighttime light (NTL) data were generally used to map the spatial distribution of the population at a large scale because of their low spatial resolution. The new generation of Luojia1-01 NTL data can be used for fine-scale social and economic analysis with its high spatial resolution and quantitative range. However, due to the geometry and background noise of the data themselves, the accuracy of the original NTL data is still low. Points-of-interest (POI) also can be used to map the population spatialization, but the indicative relationship between the POI and population is not clear, especially in rural and urban areas with different landscape structures. To solve the above-mentioned problems, this study proposes an improved nighttime light (INTL) index to better use the Luojia1-01 NTL data. Meanwhile, a zonal classification model based on INTL and impervious surface area is proposed to distinguish urban and rural areas. Compared with previous research and existing datasets, our result had the highest accuracy ( R ² = 0.86). This study explains that the INTL index is applicable to population spatialization research with the emergence of high-resolution and multispectral NTL satellite data. Moreover, the zonal classification model in this research can significantly improve the accuracy of population spatialization in rural areas. This study provides a possible way to use NTL and POI data in other social and economic spatialization research.
The distribution and dynamic changes of regional or national population data with long time series are very important for regional planning, resource allocation, government decision-making, disaster ...assessment, ecological protection, and other sustainability research. However, the existing population datasets such as LandScan and WorldPop all provide data from 2000 with limited time series, while GHS-POP only utilizes land use data with limited accuracy. In view of the limited remote sensing images of long time series, it is necessary to combine existing multi-source remote sensing data for population spatialization research. In this research, we developed a nighttime light desaturation index (NTLDI). Through the cross-sensor calibration model based on an autoencoder convolutional neural network, the NTLDI was calibrated with the same period Visible Infrared Imaging Radiometer Suite Day/Night Band (VIIRS-DNB) data. Then, the geographically weighted regression method is used to determine the population density of China from 1990 to 2020 based on the long time series NTL. Furthermore, the change characteristics and the driving factors of China's population spatial distribution are analyzed. The large-scale, long-term population spatialization results obtained in this study are of great significance in government planning and decision-making, disaster assessment, resource allocation, and other aspects.
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