In this study we systematically reviewed 1203 research papers published between 2008 and 2018 in China and recorded related data on eight kinds of soil heavy metals (Cr, Pb, Cd, Hg, As, Cu, Zn, and ...Ni). Based on that, the pollution levels, ecological risk and health risk caused by soil heavy metals were evaluated and the pollution hot spots and potential driving factors of different heavy metals in different provinces were also identified. Results indicated accumulation of heavy metals in soils of most provinces in China compared with background values. Consistent with previous findings, the most prevalent polluted heavy metals were Cd and Hg. Polluted regions are mainly located in central, southern and southwestern China. Hunan, Guangxi, Yunnan, and Guangdong provinces were the most polluted provinces. For the potential health risk caused by heavy metals pollution, children are more likely confront with non-carcinogenic risk than adults and seniors. And children in Hunan and Guangxi province were experiencing relatively larger non-carcinogenic risk. In addition, children in part of provinces were undergoing potentially carcinogenic risks due to soil heavy metals exposure. Furthermore, in our study the 31 provinces in mainland China were divided into six subsets according to corresponding potential driving factors for heavy metal accumulation. Our study provide more comprehensive and updated information for contributing to better soil management, soil remediation, and soil contamination control in China.
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•Cd and Hg were the most polluted heavy metals in soil across China.•The ER values for Cd in 30 provinces varied from moderate to very high risk.•Hunan, Guangxi, Yunnan and Guangdong were confirmed as priority control provinces.•Children are undergo larger health risk than adults and seniors in China.•Controlling factors of heavy metals accumulation in different provinces were mapped.
•Multi-source surface soil moisture datasets were merged.•Data error covariance was considered in calculating merging weights.•Merged dataset exhibited dramatically reduced uncertainties.•The impacts ...of cross error on data fusion quality were analyzed.•The relative strengths of individual parent datasets were identified.
Surface soil moisture (SSM) estimates from different sources have distinct error characteristics. Multi-source data combination is an efficient way to obtain improved SSM data with reduced uncertainties. Previous data merging studies based on the linear weight averaging scheme rarely considered the impacts of data error covariance (EC) and usually needed a reference dataset, which can lead to suboptimal merging weights. This study applied the quadruple collocation (QC) to estimate EC and combine four SSM datasets simultaneously without the need for a reference. Specifically, two passive microwave satellite datasets (the L3 Soil Moisture Active Passive (SMAP)-V7 and the L3 Soil Moisture and Ocean Salinity -INRA-CESBIO (SMOS-IC)-V2), one active microwave dataset from the Advanced Scatterometer (ASCAT), and one model dataset from the Modern-Era Retrospective analysis for Research and Application, Version 2 (MERRA2) were combined. Generally, QC-based data combination reduced SSM data uncertainties with significantly reduced unbiased Root Mean Square Error (ubRMSE) scores against in situ observations and globally decreased fMSE scores. Moreover, in situ evaluation showed that the QC-based fusion products exhibited better skills than the Tripe Collocation (TC)-based products without considering EC. There were statistically significant differences in Pearson correlation coefficients and ubRMSE metric between the QC and TC -based products. Ignoring the EC between SMAPV7 and SMOS-ICV2 caused overestimations in their relative contributions to fusion data and degraded fusion accuracy. Specifically, the QC-based merging weight was reduced averagely by 0.27 (0.28) for SMAP (IC) when their error cross-correlation increased roughly from −0.42 to 0.9. This study can provide guidance for the generation of improved merged datasets at a global scale.
As an essential climate variable, soil moisture (SM) exerts an indispensable influence on numerous disciplines. However, various degrees of data gaps exist in current microwave SM products. ...Therefore, this article proposed a spatiotemporal attention-based residual deep network (STARN) to reconstruct gaps of the daily SM data from the Climate Change Initiative program of the European Space Agency (ESA CCI) over the Qinghai-Tibet Plateau (QTP) during unfrozen seasons (May to September) from 2001 to 2021. The developed model is an end-to-end residual network embedded with three attention modules to comprehensively consider the potential relationship between SM and surface variables. Evaluation results revealed that the proposed model could well reconstruct SM gaps with an overall median R and unbiased RMSE (ubRMSE) values of 0.52 and 0.054 m 3 /m 3 , while the overall median R and ubRMSE values for the ESA CCI SM were 0.41 and 0.058 m 3 /m 3 . Besides, comparison with five baseline methods (e.g., the artificial neural network, convolutional neural network, extreme gradient boosting, long-short term memory, and DCT-PLS model) indicated that the STARN model had certain advantages over the five baseline models with higher correlation and more reasonable distribution patterns. The R /ubRMSE values for the five models were 0.38/0.057, 0.34/0.058, 0.40/0.058, 0.41/0.056, and 0.41/0.058, respectively. The pretraining using the ERA5-Land SM data could further improve the accuracy of generated seamless SM data since the ERA5-Land and ESA CCI SM complemented each other to a certain extent on the QTP. In summary, by leveraging the spatiotemporal information and attention modules, the STARN model showed great potentials in SM gap filling.
ABSTRACTLong-term, high-resolution soil moisture (SM) is a vital variable for understanding the water-energy cycle and the impacts of climate change on the Qinghai-Tibet Plateau (QTP). However, most ...existing satellite SM data are only available at coarse scale (~25 km) and suffer a lot from data gaps due to satellite orbit coverage and snow cover, especially on the QTP. Although substantial efforts have been devoted to downscale SM utilizing multiple soil moisture indices (SMIs) or diverse machine learning (ML) methods, the potentials of different SMIs and ML approaches in SM downscaling on the complex plateau remain unclear, and there is still a necessity to obtain an accurate, long-term, high-resolution and seamless SM data over the QTP. To address this issue, this study generated the long-term, high-accuracy and seamless soil moisture dataset (LHS-SM) over the QTP during 2001–2020 using a two-step downscaling method (first downscaling then merging). Firstly, the daily SM data from the Climate Change Initiative program of the European Space Agency (ESA CCI) was downscaled to 1 km utilizing five ML approaches. Then, a dynamic data merging method that considers spatiotemporal nonstationary error was applied to derive the final LHS-SM data. The performance of fifteen SMIs was also assessed and the optimal indexes for downscaling were identified. Results indicated that the shortwave infrared band-based indices had better performance than the near infrared band-based and energy-based indices. The generated LHS-SM data exhibited satisfying accuracy (mean R = 0.52, ubRMSE = 0.047 m3/m3) and certain improvement to the ESA CCI SM data both at station and network scales. Compared with existing 1 km SM datasets, the LHS-SM data also showed the best performance (mean R = 0.62, ubRMSE = 0.047 m3/m3), while existing datasets either failed to fully characterize the spatial details or had some data gaps and unreasonable distributions. Strong spatial heterogeneity was observed in the SM dynamics during 2001–2020 with the southwest and northeast showing a “dry gets wetter” scheme and the southeast presenting a “wet gets drier” trend. Overall, the LHS-SM dataset gained its added values by compensating the drawbacks of existing 1 km SM products over the QTP and was much valuable for many regional applications.
Satellite-based quantitative precipitation estimates (QPE) with a fine quality are of great importance to global water cycle and matter and energy exchange research. In this study, we firstly apply ...various statistical indicators to evaluate and compare the main current satellite-based precipitation products from Chinese Fengyun (FY)-2 and the Global Precipitation Measurement (GPM), respectively, over mainland China in summer, 2018. We find that (1) FY-2G QPE and Integrated Multi-satellitE Retrievals for GPM (IMERG) perform significantly better than FY-2E QPE, using rain gauge data, with correlation coefficients (CC) varying from 0.65 to 0.90, 0.80 to 0.90, and 0.40 to 0.53, respectively; (2) IMERG agrees well with rain gauge data at monthly scale, while it performs worse than FY-2G QPE at hourly and daily scales, which may be caused by its algorithms; (3) FY-2G QPE underestimates the precipitation in summer, while FY-2E QPE and IMERG generally overestimate the precipitation; (4) there is an interesting error phenomenon in that both FY-based and GPM-based precipitation products perform more poorly during the period from 06:00 to 10:00 UTC than other periods at diurnal scale; and (5) FY-2G QPE agrees well with IMERG in terms of spatial patterns and consistency (CC of ~0.81). These findings can provide valuable preliminary references for improving next generation satellite-based QPE retrieval algorithms and instructions for applying these data in various practical fields.
Soil moisture (SM) is a crucial driving variable for the global land surface-atmosphere water and energy cycle. There are now many satellite-based SM products available internationally and it is ...necessary to consider all available SM products under the same context for comprehensive assessment and inter-comparisons at the global scale. Moreover, product performances varying with dynamic environmental factors, especially those closely related to retrieval algorithms, were less investigated. Therefore, this study evaluated and identified the relative strengths of nine mainstream satellite-based SM products derived from the Advanced Microwave Scanning Radiometer 2 (AMSR2), Chinese Fengyun-3B (FY3B), the Soil Moisture Active Passive (SMAP), the Soil Moisture and Ocean Salinity (SMOS), and the European Space Agency (ESA) Climate Change Initiative (CCI) by using the Pearson correlation coefficient (R), R of SM seasonal anomalies (Ranom), unbiased Root Mean Square Error (ubRMSE), and bias metrics against ground observations from the International Soil Moisture Network (ISMN), as well as the Global Land Data Assimilation System (GLDAS) Noah model simulations, overall and under three dynamic (Land Surface Temperature (LST), SM, and Vegetation Optical Depth (VOD)) conditions. Results showed that the SMOS-INRA-CESBIO (IC) product outperformed the SMOSL3 product in most cases, especially in Australia, but it exhibited greater variability and higher random errors in Asia. ESA CCI products outperformed other products in capturing the spatial dynamics of SM seasonal anomalies and produced significantly high accuracy in croplands. Although the Chinese FY3B presented poor skills in most cases, it had a good ability to capture the temporal dynamics of the original SM and SM seasonal anomalies in most regions of central Africa. Under various land cover types, with the changes in LST, SM, and VOD, different products exhibited distinctly dynamic error characteristics. Generally, all products tended to overestimate the low in-situ SM content but underestimate the high in-situ SM content. It is expected that these findings can provide guidance and references for product improvement and application promotions in water exchange and land surface energy cycle.
Accurate recognition of the canopy is a prerequisite for precision orchard yield estimation. This paper proposed an enhanced LA-dpv3+ approach for the recognition of cherry canopies based on UAV ...image data, with a focus on enhancing feature representation through the implementation of an attention mechanism. The attention mechanism module was introduced to the encoder stage of the DeepLabV3+ architecture, which improved the network’s detection accuracy and robustness. Specifically, we developed a diagonal discrete cosine transform feature strategy within the attention convolution module to extract finer details of canopy information from multiple frequency components. The proposed model was constructed based on a lightweight DeepLabv3+ network architecture that incorporates a MobileNetv2 backbone, effectively reducing computational costs. The results demonstrate that our proposed method achieved a balance between computational cost and the quality of results when compared to competing approaches. Our model’s accuracy exceeded 89% while maintaining a modest model size of only 46.8 MB. The overall performance indicated that with the help of a neural network, segmentation failures were notably reduced, particularly in high-density weed conditions, resulting in significant increases in accuracy (ACC), F1-score, and intersection over union (IOU), which were increased by 5.44, 3.39, and 8.62%, respectively. The method proposed in this paper may be applied to future image-based applications and contribute to automated orchard management.
Allium triquetrum (Linnaeus, 1753) is a bulbous flowering plant of the genus Allium (Amaryllidaceae), native to the Mediterranean basin, and is now widespread and invasive in different parts of the ...world via ornamental horticultural trade. However, to date, the genomic study of A. triquetrum has lagged, which impedes the development of appropriate utilization and management practices for this species. Here, we report the complete chloroplast genome sequence of A. triquetrum. The chloroplast genome size of A. triquetrum was 153,298 bp, consisting of a pair of inverted repeat regions (26,547 bp), separated by a large single-copy (82,875 bp) region and a small single-copy (17,329 bp) region. Genome annotation predicted 133 genes, including 87 protein-coding genes, 38 tRNA genes, and eight rRNA genes. Phylogenetic analysis based on 60 whole chloroplast genome sequences of Allium species suggested that A. triquetrum and A. moly are sister to each other along with the clade of A. fasciculatum, A. hookeri, and A. macranthum.
Abstract
Disturbances in vegetated land could dramatically affect the process of vegetation growth and reshape the land cover state. The overall greenup of vegetation on the Tibetan Plateau (TP) has ...almost served as a consensus to date. However, we still lack consistent acquisitions on the timing, the spatial patterns, and the temporal frequency of vegetation disturbance over the TP, limiting the capacity for planning land management strategies. Therefore, we explored the spatiotemporal pattern and variation of vegetation disturbances across the TP during the past decades and analyzed the disturbance agents. We utilized 37-year Landsat time series images and field observations coupled with a temporal segmentation approach to characterize the spatiotemporal pattern of vegetation disturbances across the TP for the period 1986–2018. The results from this study revealed that 75.71 M ha (accounting for 29.34% of TP’s area) vegetation area underwent at least one disturbance, of which 8.44 M ha area ever experienced large-scale disturbances (disturbance area greater than 0.9 ha and disturbance magnitude (the difference between the spectral value of pre-disturbance and that of post-disturbance) over 0.2). Further, the spatial distributions of these large-scale disturbances varied over time: before 2002, the disturbed sites were evenly distributed over the southeast part of the TP probably induced by overgrazing and unscientific livestock management, while after 2002, most disturbances were concentrated in the south of the Yarlung Tsangpo, mainly caused by anthropogenic activities, such as urban area, roadways, railway, and water control projects. This study presents an effort to characterize vegetation disturbances and their variations over the past decades on the TP, which provides crucial insights toward a complete understanding of vegetation dynamics and its causal relationship with human activities.
Precipitation estimates with high accuracy and fine spatial resolution play an important role in the field of meteorology, hydrology, and ecology. In this study, support vector machine (SVM) and ...back-propagation neural network (BPNN) machine learning algorithms were used to downscale the Integrated Multi-satellite Retrievals for Global Precipitation Measurement (GPM) (IMERG) data at daily scale through four events selected from 2017 and 2018 by establishing the relationships between precipitation and six environmental variables over Zhejiang, Southeastern China. The downscaled results were validated by ground observations, and we found that (1) generally, the SVM-based products had better performance and finer spatial textures than the BPNN-based products, the multiple linear regression (MLR)-based products, and the original IMERG; (2) all downscaled products decreased the degree of overestimation of the original IMERG at heavy-precipitation regions to a certain extent; (3) for heavy-precipitation events in the plum rain season, the downscaled products based on SVM and BPNN both improved prediction accuracy compared to the MLR-based products and the original IMERG considering the validations against ground observations. R2 maximally increased from 0.344 to 0.615 for the SVM-based products and from 0.344 to 0.435 for the BPNN-based products compared to the original IMERG; and (4) for typhoon precipitation events, the SVM-based products still showed better accuracy with R2 maximally increased from 0.492 to 0.615 compared to the original IMERG. In contrast, the performance of BPNN-based products was not satisfying and showed no significant differences with the performance of MLR-based products. This study provided a potential solution for generating downscaled satellite-based precipitation products at meteorological scales with finer accuracy and spatial resolutions.
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