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Accurate cropland map serves as the cornerstone of effective agricultural monitoring. Despite the continuous enrichment of remotely sensed cropland maps, pervasive inconsistencies have impeded their further application. This issue is particularly evident in areas with limited valid observations, such as southwestern China, which is characterized by its complex topography and fragmented parcels. In this study, we constructed multi-sourced samples independent of the data producers, taking advantage of open-source validation datasets and sampling to rectify the accuracy of ten contemporary cropland maps in southwestern China, decoded their inconsistencies, and generated a refined cropland map (CroplandSyn) by leveraging ten state-of-the-art remotely sensed cropland maps released from 2021 onwards using the self-adaptive threshold method. Validations, conducted at both prefecture and county scales, underscored the superiority of the refined cropland map, aligning more closely with national land survey data. The refined cropland map and samples are publicly available to users. Our study offers valuable insights for improving agricultural practices and land management in under-monitored areas by providing high-quality cropland maps and validation datasets. [ABSTRACT FROM AUTHOR]

Mapping grazing intensity (GI) using satellites is crucial for developing adaptive utilization strategies according to grassland conditions. Here we developed a monitoring framework based on a paired sampling strategy and the classification probability of random forest algorithm to produce annual grazing probability (GP) and GI maps at 10-m spatial resolution from 2015 to 2021 for the largest temperate meadow in China (Hulun Buir grasslands), by harmonized Landsat 7/8 and Sentinel-2 images. The GP maps used values of 0–1 to present detailed grazing gradient information. To match widely used grazing gradients, annual GI maps with ungrazed, moderately grazed, and heavily grazed levels were generated from the GP dataset with a decision tree. The GI maps for 2015–2021 had an overall accuracy of more than 0.97 having significant correlations with the statistical data at city (r = 0.51) and county (r = 0.75) scales. They also effectively captured the GI gradients at site scale (r = 0.94). Our study proposed a monitoring approach and presented annual 10-m grazing information maps for sustainable grassland management. [ABSTRACT FROM AUTHOR]

Land surface phenology (LSP) is beneficial to understand ecosystem response to climate change, vegetation and crop type discrimination, and ecological modeling. However, the existing efforts based on coarse resolution data (≥500 m) cannot perform well in regions with higher spatial heterogeneity and multi-cropping system, such as China. Given that the majority of 10 m/30 m-based phenological research has focused on North America and Europe, developing spatiotemporally explicit LSP data in China is imperative. More importantly, the existing 30 m LSP products are mainly suitable for vegetation types with a single vegetation cycle, but cannot work well for biomes with complex seasonality (e.g., multiple growth cycles). Here we first harmonized three vegetation indices, i.e., the normalized difference vegetation index (NDVI), two-band enhanced vegetation index (EVI2), and land surface water index (LSWI) from Landsat-7/8 and Sentinel-2 imagery on the Google Earth Engine (GEE) platform. We then developed a new 30 m LSP algorithm that unified different phenological cycle-seeking processes per vegetation type and improved the existing algorithm. Furthermore, we used the algorithm to estimate the LSP product (LSP30CHN) for 2016–2019 across China, suitable for all vegetation types. The validation results showed a reasonably high accuracy (R2 > 0.6, RMSE < 15 days, mostly) of the LSP30CHN data against multi-sources in-situ observational (e.g., PhenoCam) and satellite-retrieved vegetation phenology data. Moreover, LSP30CHN data showed a consistent pattern but finer spatial details with the 500 m Moderate Resolution Imaging Spectroradiometer (MODIS) phenology product (MCD12Q2) at the homogenous area. We also found that phenological differences between LSP30CHN and MCD12Q2 increased with surface fragmentation, suggesting the potential of LSP30CHN to delineate phenological information on more fragmented landscapes. In contrast, the 500 m LSP data cannot provide such details in the regions with mixed cropping structures (e.g., corn, rice, and soybean) and multiple cropping index (e.g., single- and double-cropping systems). This study offers high accuracy of the LSP map for China, valuable for finer phenology-based services such as field-level crop management and agricultural phenology monitoring. It opens up new insights about exploring large-scale refined agricultural management and ecological assessment for other regions with complicated, fragmented landscapes and vegetation seasonality. [ABSTRACT FROM AUTHOR]

Background: Surface pathogens in the ICU pose a global public health threat, especially to elderly patients who are immunocompromised. To detect these pathogens, unbiased methods such as metagenomic next-generation sequencing (mNGS) are increasingly utilized for environmental microbiological surveillance.Methods: In a six-month study from January to July 2022, we investigated microbial communities in Chinese geriatric ICUs by regularly monitoring multiple surfaces at three-month intervals. Using mNGS sequencing, we analyzed microorganisms present at eight specific locations within the ICU. Additionally, we compared pathogen profiles and drug resistance genes between patient cultures and environmental samples collected during the same period.Results: The microbial composition remained relatively stable over time, but significant differences in alpha diversities were observed among various surfaces such as floors, hands, pumps, trolleys, and ventilator inlets/outlets. Surfaces with high contact frequency for healthcare workers, including workstations, ventilator panels, trolleys, pumps, and beds, harbored pathogenic microorganisms such as Acinetobacter baumannii, Cutibacterium acnes, Staphylococcus haemolyticus, Pseudomonas aeruginosa, and Enterococcus faecium. Acinetobacter baumannii, particularly the carbapenem-resistant strain (CRAB), was the most frequently identified pathogen in geriatric ICU patients regardless of testing method used. The mNGS approach enabled detection of viruses, fungi, and parasites that are challenging to culture. Additionally, an abundance of drug resistance genes was found in almost all environmental samples.Conclusion: The microbial composition and abundance in the ICU remained relatively constant over time. The floor exhibited the highest microbial diversity and abundance in the ICU environment. Drug-resistant genes in the ICU environment may migrate between patients. Overall, mNGS is an emerging and powerful tool for microbiological monitoring of the hospital environment. [ABSTRACT FROM AUTHOR]

• Updated forest biomass carbon sinks and density of China from 1977 to 2013. • Used carbon fraction coefficients of 46 tree species replaced 0.5. • The spatiotemporal variation of forest carbon storage and density is discussed; • Analysis of natural and planted forests in the past two inventories. • Compared the carbon estimation of forest by different methods. Forests are a major contributor of terrestrial ecosystem carbon pools. Accurate estimates of forest biomass carbon sinks can improve our understanding of carbon cycles and help in developing sustainable forest management policies in the face of climate change. In this study, we update estimates of the biomass carbon stocks of China's forests based on seven forest inventory datasets from 1977 to 2013 and carbon fraction coefficients of 46 tree species in the continuous biomass expansion factor (CBEF) model. Our findings suggest that: (1) China's forest stands acted as an average biomass carbon sink of 99.07 Tg C year−1; and (2) biomass carbon stocks increased by 72.62% from 1977 to 2013 and recently reached 7.27 Pg C, driven by forest area expansion and forest growth. The biomass carbon density of forest stands (canopy coverage >20%) increased from 38.18 to 44.52 Mg C ha−1 during the study period, with higher carbon densities in natural compared to planted forests, and the gap increasing with forest age. The largest increases in the biomass of carbon stocks of forest stands occurred in the eastern and northern regions. Our results suggest that biomass carbon stocks of natural forests account for about 85% in the most recent inventory and that the total biomass carbon stocks of forest stands in China will keep increasing in the future because of the large area of planted forests with young and middle-aged forest growth. The results from this study can help with comprehensive investigations of forest carbon budgets and the calibration and validation of simulation model results. [ABSTRACT FROM AUTHOR]

As a prominent part of global and regional terrestrial carbon (C) pools, increases in forest biomass C sinks can be attributed to either forest areal expansion (FAE) or increased biomass C density (IBCD). Accurate estimates of the relative contributions of FAE and IBCD to forest C sequestration can improve our understanding of forest C cycling processes and will help to formulate rational afforestation policies to cope with global warming. In this study, the Continuous Biomass Expansion Factor (CBEF) model and Forest Identity concept were used to map the spatiotemporal variation of the relative contribution of FAE and IBCD to the C sequestration of forest (natural and planted forests) in China and seven regions during the past 40 years. Our results suggest that: (1) total forest biomass C density and stocks of forest increased from 35.41 Mg C ha−1 and 4128.50 Tg C to 43.95 Mg C ha−1 and 7906.23 Tg C in China from 1977 to 2018, respectively; (2) for all forests, the IBCD has been a smaller contributor to C sinks than FAE in China from 1977 to 2018 (33.27 vs. 66.73%); (3) the contribution of FAE to C sinks is greater than that of IBCD in planted forests (63.99 vs. 36.01%), while in natural forests, IBCD has a larger contribution than FAE (57.82 vs. 42.18%) from 1977 to 2018 and the relative contribution of FAE has exceeded IBCD in the last decade; and (4) these patterns varied at the regional level such that the relative contribution of FAE increased for planted forests in most regions but for natural forests, IBCD gradually reached saturation and C stocks declined in northern regions in the last decade. The results from this study suggest that total biomass C sinks will keep increasing because of the increased forest area contributed by afforestation and the relatively young trees in planted forests. This study facilitates a more comprehensive assessment of forest C budgets and improves our understanding of ecological mechanisms of forest biomass carbon stock and dynamics. • Updated forest biomass carbon sinks and density of China from 1977 to 2018. • The spatiotemporal variation of relative contributions is discussed. • Calculated the proportion to C sinks attributed of natural and planted forests. • Discuss the effects of afforestation on C sinks in the past four decades. [ABSTRACT FROM AUTHOR]

Quantification of the spatial pattern of forest carbon (C) sinks in high resolution is helpful to reveal the factors that affect the C cycle and provides valuable information for developing sustainable forest management policies. Here we developed a method using the data of long-term forest inventories (1977–2018) and spatially-explicit remotely sensed information from land-use maps and the Normalized Difference Vegetation Index (NDVI) datasets, to estimate the spatial and temporal variation of forest biomass C in China. At first, we calculated forest biomass C stocks using the refined Continuous Biomass Expansion Factor (CBEF) model with parameters for each forest type based on eight national forest inventories. Secondly, based on multi-temporal land-use remote sensing and national forest inventory datasets, we obtained forest coverage datasets with high resolution (1 km*1 km). Thirdly, we downscaled the forest biomass C density using the calibrated forest coverage maps and the maximum NDVI values derived from GIMMS-NDVI3g imagery. Our results showed that China's forest functioned as a C sink of 3777.73 Tg C, and the C density of forest stands increased from 35.41 Mg C ha−1 during 1977–1981 to 43.95 Mg C ha−1 during 2014–2018. In addition, the validation results for most of the provinces based on published inventory estimates during the eight periods showed that the forest area at the pixel scale was successfully calibrated. From this, we produced the maps with a finer resolution for a series of spatially continuous forest biomass carbon density distribution and carbon sinks. Notably eight major forest projects have accounted for 44%–51% of the forest C stocks added in China from 1977 to 2018. Our research provides new insights for understanding and monitoring the spatiotemporal variations in of forest biomass and key information to support the development of new afforestation policies moving forward. • Calibrated forest area using inventory and land-use datasets from 1977 to 2018. • Downscaled the forest biomass using calibrated forest area and NDVI data. • Examined the effects of ecological programs on C sinks in the past four decades. [ABSTRACT FROM AUTHOR]

The changing climate is shifting the seasonality of photosynthesis in vegetation, including the start (SOS), end (EOS), and length (LOS) of the growing season, and the peak photosynthesis timing (PPT). While the SOS, EOS, and LOS have been widely investigated, the PPT of grasslands – as a proxy for the response of seasonal plant photosynthesis to climate change – has been overlooked. In this study, we propose a hybrid generalized additive model (HGAM) method to extract PPT using the Vegetation Photosynthesis Model (VPM)-based gross primary production (GPP) product, and we examine the dynamics, drivers, and consequences of PPT changes in temperate and alpine grasslands in China over 2000–2016. We found that the PPTs in temperate and alpine grasslands have exhibited advancing (with −0.68 days yr−1, p < 0.05) and delaying (with 0.29 days yr−1, p = 0.158) trends, respectively. In addition, preseason precipitation and soil moisture were positively correlated with the PPT in temperate and alpine grasslands, respectively, while the preseason temperature consistently controlled the PPT changes in both grasslands. Furthermore, we found that an earlier PPT was associated with higher annual production in the temperate grasslands but not in the alpine grasslands in China. The divergent PPT patterns indicated the varied adaptation characteristics to climatic constraints in the temperate and alpine grasslands and also caused different consequences on carbon uptake. This study highlights the importance of PPT in understanding the spatiotemporal dynamics of vegetation photosynthesis and the carbon cycle under a changing climate. • Identification of peak photosynthesis timing (PPT) by a hybrid generalized additive model (HGAM). • Divergent PPT trends exist between temperate and alpine grasslands in China. • Different climatic factors affect the PPTs in temperate and alpine grasslands. • The correlation between an earlier peak and higher production exists only in temperate grasslands. [ABSTRACT FROM AUTHOR]

The global climate change-induced advanced spring green-up dates (GUDs) of vegetation in the past decades have been well documented. However, anthropogenic effects on GUDs are still unclear, and the impacts of widespread cropland expansion on GUDs are rarely quantified. Here, we examine the differences in GUDs between agroecosystems and natural ecosystems using both satellite and in-situ observations across China's ecosystems with a latitude ≥ 30°N and explore the potential effects of cropland expansion on vegetation spring phenology. We find that agroecosystems had significantly later GUDs than adjacent natural ecosystems according to satellite observations, with a delay of 22.26 ± 16.32 (Mean ± 1STD) days and 18.11 ± 11.75 days compared with woodland and grassland ecosystems, respectively. The later GUDs for agroecosystems is more distinct in the Northeast China Plain and Xinjiang Oasis region (>30 days), where land reclamation was remarkable. We also find a distinct delayed GUD trend (> 5 day/yr, p < 0.001) during 2001–2014 in a cropland expansion region in Xinjiang Province, which is much higher than the usual advancement of GUDs due to warming (usually ≤ 1 day/yr). In-situ and satellite observations of delayed GUDs agree better in agroecosystems than in forest and grassland ecosystems. More attention is needed to better understand the important role of cropland expansion in driving inter-annual change of vegetation spring phenology in addition to climate change, and integration of realistic crop green-up timing into climate models is also needed for accurate climate simulations. • Differences in the vegetation green-up dates (GUDs) between agroecosystems and natural ecosystems are quantified. • Cropland expansion delayed vegetation GUDs greatly according to satellite and in-situ observations. • Effects of cropland expansion on spring phenology change trends cannot be overlooked. [ABSTRACT FROM AUTHOR]

Objective: To assess impact of personal protective equipment (PPE) on healthcare providers (HCPs) in caring for COVID-19 patients., Methods: A cross-sectional survey was conducted over 50 hospitals in China. Descriptive analyses and Chi-square tests were performed on the collected data., Results: All 104 frontline HCPs report negative impacts of PPE on their clinical performance, 97% of them experienced discomfort and injuries caused by wearing PPE for long hours. Frontline HCPs provided suggestions to alleviate the negative impacts and to enhance communication between healthcare staff and patients. Two hundred eighty two non-frontline HCPs also revealed similar problems; however, we recorded a few discrepancies between answers given by frontline and non-frontline HCPs., Conclusions: Wearing PPE for long hours degrades health performance. Measures were suggested to improve the design of PPE for protecting HCPs and enhancing their services to COVID patients., Competing Interests: Zheng, Duan, Sun, He, Yang, Li, and Taparia have no relationships/conditions/circumstances that present potential conflict of interest., (Copyright © 2021 American College of Occupational and Environmental Medicine.)