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Elucidating the influence mechanisms of seed germination and seedling growth is important for revealing the natural regeneration of forest plantations. We collected the seeds from 58-year-old Quercus acutissima Carruth. forest, and the seeds were further divided into three classes: large, medium, and small, and sown under the forest gaps (I, 197.82 m2; II, 91.85 m2, III, understory) to observe seed germination and early seedling growth. Precipitation in the study area and soil moisture content in the forest gaps were also observed during the trial period. The results showed that the precipitation in 2019 was similar to that in 2020; both were significantly lower than the precipitation in 2021. The difference in soil water content between gaps I and II was not significant, and both were significantly lower than III. The order of seedling emergence rate in gaps was II > III > I, but the minimum was almost close to zero in I. Large and medium seeds showed significantly greater emergence rate than small seeds. The seedlings of II had higher seedling height, ground diameter, ground diameter relative growth rate, seedling biomass, root surface area, and root volume than those of III. Large seeds had the highest ground diameter, ground diameter relative growth rate, biomass, root mass ratio, root shoot ratio, and root surface area. Correlation analysis showed that seedling biomass was significantly and positively correlated with root surface area and root volume, and significantly and negatively correlated with specific root length and specific root surface area. The regulation of soil moisture in the gap and the adaptability related to seed size were two key factors influencing the seed germination and early seedling growth of Q. acutissima.

Globally, flooding is the leading cause of natural disaster related deaths, especially in Bangladesh where approximately one third of national area gets flooded annually by overflowing rivers during the monsoon season, which drastically affects paddy rice agriculture and food security. However, existing studies on the pattern of floods and their impact on agriculture in Bangladesh are limited. Here we examined the spatiotemporal pattern of floods for the country during 2014–2018 using all the available Sentinel-1 Synthetic Aperture Radar (SAR) images and the Google Earth Engine (GEE) platform. We also identified the flood-affected paddy rice fields by integrating the flooding areas and remote sensing-based paddy rice planting areas. Our results indicate that flooding is frequent in northeastern Bangladesh and along the three major rivers, the Ganges, Brahmaputra, and Meghna. Between 2014 and 2018, the flood-affected paddy rice areas accounted for 1.61–18.17% of the total paddy rice area. The satellite-based detection of floods and flood-affected paddy rice fields advance our understanding of the annual dynamics of flooding in Bangladesh, which is essential for adaptation and mitigation strategies where severe annual floods threaten human lives, properties, and agricultural production.

How vegetation phenology responds to climate change is a key to the understanding of the mechanisms driving historic and future changes in regional terrestrial ecosystem productivity. Based on the 250-m and 8-day moderate resolution imaging spectroradiometer (MODIS) normalized difference vegetation index (NDVI) data for 2000–2014 in the Three-River Source Region (TRSR) of Qinghai Province, China, i.e., the hinterland of the Tibetan Plateau, we extracted relevant vegetation phenological information (e.g., start, end, and length of growing season) and analyzed the changes in the TRSR vegetation in response to climate change. The results reveal that, under the increasingly warm and humid climate, the start of vegetation growing season (SOS) advanced 1.03 day yr−1 while the end of vegetation growing season (EOS) exhibited no significant changes, which led to extended growing season length. It is found that the SOS was greatly affected by the preceding winter precipitation, with progressively enhanced precipitation facilitating an earlier SOS. Moreover, as the variations of SOS and its trend depended strongly on topography, we estimated the elevation break-points for SOS. The lower the elevations were, the earlier the SOS started. In the areas below 3095-m elevation, the SOS delay changed rapidly with increasing elevation; whereas above that, the SOS changes were relatively minor. The SOS trend had three elevation break-points at 2660, 3880, and 5240 m.

Data and knowledge of the spatial-temporal dynamics of surface water area (SWA) and terrestrial water storage (TWS) in China are critical for sustainable management of water resources but remain very limited. Here we report annual maps of surface water bodies in China during 1989–2016 at 30m spatial resolution. We find that SWA decreases in water-poor northern China but increases in water-rich southern China during 1989–2016. Our results also reveal the spatial-temporal divergence and consistency between TWS and SWA during 2002–2016. In North China, extensive and continued losses of TWS, together with small to moderate changes of SWA, indicate long-term water stress in the region. Approximately 569 million people live in those areas with deceasing SWA or TWS trends in 2015. Our data set and the findings from this study could be used to support the government and the public to address increasing challenges of water resources and security in China., The authors of this study compile data on spatial and temporal dynamics of surface water bodies across China, covering a time span from 1989 – 2016. The study describes hot-spot areas with strongly decreasing trends in surface water area and terrestrial water storage in North China and discusses implications of water resources and security in China.

Coastal wetlands, composed of coastal vegetation and non-vegetated tidal flats, play critical roles in biodiversity conservation, food production, and the global economy. Coastal wetlands in China are changing quickly due to land reclamation from sea, aquaculture, industrialization, and urbanization. However, accurate and updated maps of coastal wetlands (including vegetation and tidal flats) in China are unavailable, and the detailed spatial distribution of coastal wetlands are unknown. Here, we developed a new pixel- and phenology-based algorithm to identify and map coastal wetlands in China for 2018 using time series Landsat imagery (2,798 ETM+/OLI images) and the Google Earth Engine (GEE). The resultant map had a very high overall accuracy (98%). There were 7,474.6 km(2) of coastal wetlands in China in 2018, which included 5,379.8 km(2) of tidal flats, 1,856.4 km(2) of deciduous wetlands, and 238.3 km(2) of evergreen wetlands. Jiangsu Province had the largest area of coastal wetlands in China, followed by Shandong, Fujian, and Zhejiang Provinces. Our study demonstrates the high potential of time series Landsat images, pixel- and phenology-based algorithm, and GEE for mapping coastal wetlands at large scales. The resultant coastal wetland maps at 30-m spatial resolution serve as the most current dataset for sustainable management, ecological assessments, and conservation of coastal wetlands in China.

Timely and accurate information on crop planting areas is critical for estimating crop production, and earlier crop mapping can benefit decision-making related to crop insurance, land rental, supply-chain logistics, and food market. Previous efforts generally produce crop planting area maps after harvest and early season cropping information is rarely available. New opportunities emerge with rapid increase in satellite data acquisition and cloud computing platform such as Google Earth Engine (GEE) which can access and process a vast volume of multi-sensor images. Here we aimed to examine earliest identifiable timing (EIT) of major crops (rice, soybean, and corn) and generate early season crop maps independent of within-year field surveys in the Heilongjiang province, one most important province of grain production in China. The Random Forest classifiers were trained based on early season images and field samples in 2017, then were transferred (applied) to corresponding images in 2018 to obtain resultant maps. Six scenarios with different temporal intervals (10d, 15d, 20d, and 30d) and data integration (Sentinel-2 and Sentinel-1, a total of 16, 450 images) were compared to get the optimal crop maps. The results showed that the Sentinel-2 time series and 10-day composite outperformed in obtaining EITs and crop maps. We found various EITs for the three grain staples. Specifically, rice could be identified in the late transplanting stage (four months before harvest) with F1 score of 0.93, following by corn recognizable in the early heading stage (two months before harvest, with F1 score of 0.92) and soybean in the early pod setting stage (50 days before harvest, with F1 score of 0.91). The crop maps in the EITs based on the classifier transfer approach have comparable accuracies (overall accuracy = 0.91) comparing to the traditional post-season mapping approach based on current year’s all available images and samples (overall accuracy = 0.95). This study suggests the potential of growing fine resolution observations for timely monitoring of crop planting area within season, which provides valuable and timely information for different stakeholders and decision makers.

Teacher identity evolves over time and is often characterized by crisis and destabilization. Based on self-reflection journals and in-depth interviews, this narrative inquiry explores the identity evolution and the accompanying contributive factors of identity crisis of three experienced Chinese EFL teachers in a higher vocational college in China over ten years, aiming to unveil the interactions between teachers’ identity evolution trajectories and identity crisis factors. Findings show that, under the interplay of macro-level social factors, meso-level institutional factors, and micro-level personal factors, the three teachers’ professional identities underwent from honeymoon phase during their early career years, to confusion phase and dilemma phase in the mid-career years featured by identity crisis. It is suggested that relevant parties should attach more importance and make efforts to scaffold Chinese EFL teachers’ identity development during their mid-career phase in the particular L2 context of higher vocational college in China, so as to help them maintain high commitment to their professional identity and teaching throughout the whole career.

Natural regeneration is crucial for the development of sustainable forestry practices in light of the current global climate changes. In this paper, we compared the size distributions of Quercus acutissima seedlings in the understory of Q. acutissima forest plantations in Mount Tai in 2010 and 2017, studied the physiological and morphological responses of seedlings to the microenvironment, and explored the maintenance mechanisms of the seedling bank. The results showed that the density of understory seedlings in 2017 was only 61.63% of that in 2010, especially in the 20–40 cm height class. Between 2011 and 2016, the precipitation and soil water content were the highest in 2011, followed by 2013. The 2–4-year seedlings (height < 40 cm) were not significantly different in seedling biomass, biomass allocation, and root morphology (root total surface area, root volume, and root average diameter), and were significantly different in total root length, specific root length, specific root surface area, and nonstructural carbohydrate content of root, stem, and leaves. However, 5–6-year seedlings (height > 40 cm) showed the largest biomass. Principal component analysis indicated that altering root morphology, nonstructural carbohydrate, and biomass allocation played significant roles in the drought adaptation of seedlings in the understory. In conclusion, drought stress together with seedling adaptation influenced the dynamics of seedling bank in the understory of Q. acutissima plantations.

Deep neural networks (DNN) with incomparably advanced performance have been extensively applied in diverse fields (e.g., image recognition, natural language processing, and speech recognition). Training a high-performance DNN model requires a lot of training data and intellectual and computing resources, which bring a high cost to the model owners. Therefore, illegal model abuse (model theft, derivation, resale or redistribution, etc.) seriously infringes model owners’ legitimate rights and interests. Watermarking is considered the main topic of DNN ownership protection. However, almost all existing watermarking works apply solely to image data. They do not trace the unique infringing model, and the adversary easily detects these ownership verification samples (trigger set) simultaneously. This paper introduces TADW, a dynamic watermarking scheme with tracking and antidetection abilities in the deep learning (DL) textual domain. Specifically, we propose a new approach to construct trigger set samples for antidetection and innovatively design a mapping algorithm that assigns a unique serial number (SN) to every watermarked model. Furthermore, we implement and detailedly evaluate TADW on 2 benchmark datasets and 3 popular DNNs. Experiment results show that TADW can successfully verify the ownership of the target model at a less than 0.5% accuracy cost and identify unique infringing models. In addition, TADW is excellently robust against different model modifications and can serve numerous users.

Recent experiments on vanadium-based nonmagnetic kagom\'e metals $A$V$_3$Sb$_5$ ($A=$ K, Rb, Cs) revealed evidence for possible spontaneous time-reversal symmetry (TRS) breaking in the charge density wave (CDW) ordered state. The long-sought-after quantum order of loop currents has been suggested as a candidate for the TRS breaking state. However, a microscopic model for the emergence of the loop-current CDW due to electronic correlations is still lacking. Here, we calculate the susceptibility of the real and imaginary bond orders on the kagom\'e lattice near van Hove filling, and reveal the importance of next-nearest-neighbor Coulomb repulsion $V_2$ in triggering the instability toward imaginary bond ordered CDW. The concrete effective single-orbital $t$-$V_1$-$V_2$ model on the kagom\'e lattice is then studied, where $t$ and $V_1$ are the hopping and Coulomb repulsion on the nearest-neighbor bonds. We obtain the mean-field ground states, analyze their properties, and determine the phase diagram in the plane spanned by $V_1$ and $V_2$ at van Hove filling. The region dominated by $V_1$ is occupied by a $2a_0 \times 2a_0$ real CDW insulator with the inverse of Star-of-David (ISD) bond configuration. Increasing $V_2$ indeed drives a first-order transition from ISD to stabilized loop-current insulators that exhibit four possible current patterns of different topological properties, leading to orbital Chern insulators. We then extend these results away from van Hove filling and show that electron doping helps the stabilization of loop currents, and gives rise to doped orbital Chern insulators with emergent Chern Fermi pockets carrying large Berry curvature and orbital magnetic moment. Our findings provide a concrete model realization of the loop-current Chern metal at the mean-field level for the TRS breaking normal state of the kagom\'e superconductors., Comment: 12 pages, 7 figures

Although rice cultivation is one of the most important agricultural sources of methane (CH4) and contributes ∼8% of total global anthropogenic emissions, large discrepancies remain among estimates of global CH4 emissions from rice cultivation (ranging from 18 to 115 Tg CH4 yr−1) due to a lack of observational constraints. The spatial distribution of paddy-rice emissions has been assessed at regional-to-global scales by bottom-up inventories and land surface models over coarse spatial resolution (e.g., > 0.5°) or spatial units (e.g., agro-ecological zones). However, high-resolution CH4 flux estimates capable of capturing the effects of local climate and management practices on emissions, as well as replicating in situ data, remain challenging to produce because of the scarcity of high-resolution maps of paddy-rice and insufficient understanding of CH4 predictors. Here, we combine paddy-rice methane-flux data from 23 global eddy covariance sites and MODIS remote sensing data with machine learning to 1) evaluate data-driven model performance and variable importance for predicting rice CH4 fluxes; and 2) produce gridded up-scaling estimates of rice CH4 emissions at 5000-m resolution across Monsoon Asia, where ∼87% of global rice area is cultivated and ∼ 90% of global rice production occurs. Our random-forest model achieved Nash-Sutcliffe Efficiency values of 0.59 and 0.69 for 8-day CH4 fluxes and site mean CH4 fluxes respectively, with land surface temperature, biomass and water-availability-related indices as the most important predictors. We estimate the average annual (winter fallow season excluded) paddy rice CH4 emissions throughout Monsoon Asia to be 20.6 ± 1.1 Tg yr−1 for 2001–2015, which is at the lower range of previous inventory-based estimates (20–32 CH4 Tg yr−1). Our estimates also suggest that CH4 emissions from paddy rice in this region have been declining from 2007 through 2015 following declines in both paddy-rice growing area and emission rates per unit area, suggesting that CH4 emissions from paddy rice in Monsoon Asia have likely not contributed to the renewed growth of atmospheric CH4 in recent years.

Access to food, water, and good air quality is indispensable for human life, as reflected in various United Nations Sustainable Development Goals (SDGs); however, pursuing food security may pose threats to water security and/or air quality. An important case is northwest India including the Punjab and Haryana states, which is the ‘breadbasket’ of India with a significantly increasing paddy rice area. The rapid expansion of rice farming has stressed groundwater resources and impacted air quality. Satellite observations have the potential to provide data for better decisions on food security, water storage, and air pollution, which would be vital for regional sustainable development. Based on observations from multiple satellites from 2001 to 2018, we found that paddy rice expansion (+22%) increased groundwater depletion (−1.50 cm/yr), residue burning (+500%), and air pollution (+29%, PM2.5) in the breadbasket of India. Moreover, satellite observations showed changes in these interactions after the enactment of a groundwater protection policy in 2009, which decelerated groundwater depletion (−1.20 cm/yr) due to delayed rice planting and harvest dates (∼15d); the latter elevated air pollution in November (+29%, PM2.5). Our finding stresses the need to reconcile the trade-offs and consider the interactions among SDGs 2 (food), 3 (good health), 6 (clean water), and 11 (air quality in cities), in policy-making for sustainable development. An efficient crop residue ultilization and management system, bottom-up groundwater use regulations, and cropping system shift towards less water-consuming crops are critically required to resolve the trade-offs of the food-water–air quality nexus in the northern India. Our study also showcases remote sensing approaches and methods to support and aid the achievement of the SDGs and track their progreses to support regional sustainable development.

Drought events have considerable direct and indirect economic, environmental, and social impacts, but few studies have analyzed and assessed future changes in drought disasters from a risk perspective to guide responses and adaptations thoroughly. Studying the potential climate-related impacts on future crop yield is therefore urgently needed. Intercomparison of the three Shared Socio-economic Pathway (SSP) scenarios based drought risks and yield loss of China was carried out using the climate models from the Coupled Model Intercomparison Project Phase 6 (CMIP6), and the hotspots of high drought risk regions were identified. This study found that the areas affected by severe maize drought (loss ratio larger than 0.2) accounted for 16.13 %, 20.79 %, and 18.87 % of the total national corn areas under three low, medium-to-high and high emission scenarios (SSP1-2.6, SSP3-7.0, SSP5-8.5) respectively. The northwest China maize region, the ecotone between agriculture and animal husbandry, and the western central northern China maize region have relatively high loss risk. Compared with SSP1-2.6, the yield loss rates increased with 70.73 % and 61.52 % of national corn areas for SSP3-7.0 and SSP5-8.5, respectively. There is a decrease in the areas with low-risk and a significant increase in the areas with high-risk for SSP3-7.0 and SSP5-8.5 compared to the SSP1-2.6. These results may provide theoretical support for agricultural drought risk reduction and adaptation planning to ensure food security under climate change.

Lakes are important water resources on the Mongolian Plateau (MP) for human's livelihood and production as well as maintaining ecosystem services. Previous studies, based on the Landsat-based analyses at epoch scale and visual interpretation approach, have reported a significant loss in the lake areas and numbers, especially from the late 1990s to 2010. Given the remarkable inter- and intra-annual variations of lakes in the arid and semi-arid region, a comprehensive picture of annual lake dynamics is needed. Here we took advantages of the power of all the available Landsat images and the cloud computing platform Google Earth Engine (GEE) to map water body for each scene, and then extracted lakes by post-processing including raster-to-vector conversion and separation of lakes and rivers. Continuous dynamics of the lakes over 1 km2 was monitored annually on the MP from 1991 to 2017. We found a significant shrinkage in the lake areas and numbers of the MP from 1991 to 2009, then the decreasing lakes on the MP have recovered since circa 2009. Specifically, Inner Mongolia of China experienced more dramatic lake variations than Mongolia. A few administrative regions with huge lakes, including Hulunbuir and Xilin Gol in Inner Mongolia and Ubsa in Mongolia, dominated the lake area variations in the study area, suggesting that the prior treatments on these major lakes would be critical for water management on the MP. The varied drivers of lake variations in different regions showed the complexity of factors impacting lakes. While both natural and anthropogenic factors significantly affected lake dynamics before 2009, precipitation played increasingly important role for the recovery of lakes on the MP after 2009.

The data, information and knowledge on the tropical forest area and its dynamics in the Brazilian Amazon remain contentious. We use time-series satellite images to quantify annual forest area, loss and gain in the Brazilian Amazon during 2000–2017. We find that forest area was ~15% higher than the estimate by the official Brazilian forest dataset (PRODES), but annual forest-loss rates were twice the PRODES estimates (~0.027 × 106 km2 yr–1 during 2001–2016). Forest-loss rates increased again after 2013. The El Nino and drought year (2015/2016) drove large forest area loss. The cumulative forest-loss area within the protected areas (which include ~50% of forests in the region) was ~11% of the total forest-loss area, which highlights the roles of protected areas in forest conservation. A new estimation of forest cover and loss finds more area but also double the loss rates in recent years, relative to previous estimates. Deforestation increased after a low peak in 2013.

Changes of land systems are largely a consequence of human decision making at multiple scales, from local land management to national land use planning and global trade agreements. Improved understanding of the status, trend, and consequences of land changes is relevant to the engagement of Land System Science (LSS) in transformations for sustainability. While remote sensing has been a major tool for documenting land cover change, new opportunities are emerging to understand land as a socio-ecological system by integrating crowd-sourced social sensing data (e.g. through mobile technologies). Increased access to remote sensing time series and social sensing data are improving mapping of land and providing new information on the coupled social-ecological system for better land governance. Given advances in data availability and machine learning algorithms, land mapping efforts have evolved to more sophisticated analysis strategies. In addition, the emergence of planetary-scale geospatial analysis platforms facilitates regional and global land change monitoring in a rapid, scalable, and convenient way. This review aims to document current status and prospects on data, algorithms, and processing platforms in the era of ‘Big Earth Data’, providing examples of how it can contribute to LSS at the interface of normative and policy concerns, including those in support of internationally agreed environmental goals and land governance.

Changes in Earth’s albedo due to vegetation dynamics, snow cover, and land cover change have attracted much attention. However, the effects of vegetation dynamics on albedo have not been comprehensively documented according to its spatial (regional), temporal (within growing season), and spectral (visible, near-infrared, and shortwave) characteristics. This study examined the effects of vegetation greenness on albedo from 2000 to 2014 in China’s grasslands, which have considerable intra- and inter-annual variations, using remote sensing-based albedo and two-band Enhanced Vegetation Index (EVI2) data. Generally, we found an insignificant negative correlation between the shortwave (SW) albedo and EVI2 for grasslands in China. However, the visible (VIS) albedo was more sensitive to changes in vegetation greenness than near-infrared (NIR) albedo in China’s grasslands. The relationship between the NIR albedo and EVI2 was more complicated, especially in the Tibetan Plateau (TP), where the correlation was negative in the early growing season and positive in the late growing season; while the correlation between the NIR albedo and EVI2 was always negative in main part of Inner Mongolia (IM). The different albedo-EVI2 relationships in IM and TP may be related to differences in soil albedos. The higher sensitivity of the SW albedo to vegetation greenness change in IM, the stronger effect on land surface radiation budget. Our finding about vegetation-induced changes in albedo differ in space, time and spectral bands is expected to contribute to the improvement of land surface models.

Interpreting the relationship between urban heat island (UHI) and urban vegetation is a basis for understanding the impacts of underlying surfaces on UHI. The calculation of UHI intensity (UHII) requires observations from paired stations in both urban and rural areas. Due to the limited number of paired meteorological stations, many studies have used remotely sensed land surface temperature, but these time-series land surface temperature data are often heavily affected by cloud cover and other factors. These factors, together with the algorithm for inversion of land surface temperature, lead to accuracy problems in detecting the UHII, especially in cities with weak UHII. Based on meteorological observations from the Oklahoma Mesonet, a world-class network, we quantified the UHII and trends in eight cities of the Great Plains, USA, where data from at least one pair of urban and rural meteorological stations were available. We examined the changes and variability in urban temperature, UHII, vegetation condition (as measured by enhanced vegetation index, EVI), and evapotranspiration (ET). We found that both UHI and urban cold islands (UCI) occurred among the eight cities during 2000–2014 (as measured by impervious surface area). Unlike what is generally considered, UHII in only three cities significantly decreased as EVI and ET increased (p

Drastic rice paddy expansion and rapid upland crop loss have occurred over high latitudes in China, which would affect national food security. Different agro-ecosystems (i.e., state farms guided by the central government for agriculture and private farms guided by individual farmers for agriculture) could lead to different agricultural land use patterns; but this topic has not been investigated, which has limited our understanding of the dynamics of cropping patterns (i.e., rice paddies and upland crops) under different agro-ecosystems and their effect on total grain production. Thus, this study examined these issues over high latitudes in China. The results showed that: the developed methodology for determining cropping patterns presented high accuracy (over 90%). Based on the cropping pattern data, first, a satellite evidence of substantial increase in rice paddies with the loss of upland crops was found, and the large-scale conversion from upland crops to rice paddies has become the principal land use changes during the period of 2000–2015. Second, the new phenomenon was observed with rice paddies in state farms expanding at faster rates (at proportions of 12.98%–70.11%) than those in private farms (4.86%–30.48%). Third, the conversion of upland crops into rice paddies contributed 10.69% of the net increase in grain, which played a significant role in ensuring food security. The study provided new evidence of different changes in cropping patterns under different agro-ecosystems, thereby affecting rice cropping pattern and total grain production. This information is important for understanding and guiding the response to food sustainability and environmental issues.

Water status is an important indicator of ecosystem health. Based on the spectral signatures of liquid water and other land surface features (e.g., soil and vegetation), many water-related spectral reflectance indices (WIs) have been developed to characterize the presence of liquid water. These WIs are widely used in agricultural and ecological applications including surface water body characterization, vegetation water status estimation, soil water content assessment, and wetland (e.g., paddy rice) monitoring. However, choosing the correct WIs for specific studies is confusing due to arbitrary selections of WIs in respective applications and the mixing of WIs names in different studies. To increase the clarity of appropriate usage of WIs in specific conditions, this study reviewed the principles, developments, and applications of various WIs to identify the suitability of WIs for different applications. We started with the discussion of the underlying principles and developments (e.g., from ratio to normalized difference of two bands) of different WIs, based on the spectrum features of water, vegetation, and soil from widely used spectral libraries. Applications from different studies, in each category, were then compared to show the performances of different WIs. This is followed by a brief summary about the terminology of WIs in different studies to further reduce the confusion caused by the mixed use of WIs terms. A field experiment was designed to investigate the dynamics of various WIs in response to different mixing ratios of water and soil or plant. The effectiveness of combining WIs and greenness indices for detecting surface water body and wetland were evaluated using the field experiment data. Finally, we identified the major gaps and pointed out the potential improvements for the future.

A Correction to this paper has been published: https://doi.org/10.1038/s41597-021-00854-6.

China launched the Three-North Shelterbelt Forest Program (TNSFP) in 1978 in northern China to combat desertification and dust storms, but it is still controversial in ecologically fragile arid and semi-arid areas, which is partly due to the uncertainties of monitoring of the spatial-temporal changes of forest distribution. In this study, we aim to provide an overall retrospect of the forest changes (i.e., forest gain and forest loss) in northern China during 2007–2017, and to analyze the forest changes in different precipitation zones. We first generated annual forest maps at 30 m spatial resolution during 2007–2017 in northern China through integrating Landsat and PALSAR/PALSAR-2 data. We found the PALSAR/Landsat-based forest maps outperform other four existing products (i.e., JAXA F/NF, FROM-GLC, GlobeLand30, and NLCD-China) from either PALSAR or Landsat data, with a higher overall accuracy 96% ± 1%. The spatial-temporal analyses of forests showed a substantial forest expansion from 316,898 ± 34,537 km2 in 2007 to 384,568 ± 35,855 km2 in 2017 in the central and eastern areas. We found a higher forest loss rate (i.e., 35%) in the precipitation zones with the annual mean precipitation less than 400 mm (i.e., the arid and semi-arid areas) comparing to that (i.e., 25%) in the zones with more than 400 mm (i.e., the humid areas), which suggests the lower surviving rate in the drylands. This study provides satellite-based evidence for the forest changes in different precipitation zones, and suggests that the likely impacts of precipitation on afforestation effectiveness should be considered in future implementation of ecological restoration projects like TNSFP.

Cropland redistribution to marginal land has been reported worldwide; however, the resulting impacts on environmental sustainability have not been investigated sufficiently. Here we investigated the environmental impacts of cropland redistribution in China. As a result of urbanization-induced loss of high-quality croplands in south China (∼8.5 t ha–1), croplands expanded to marginal lands in northeast (∼4.5 t ha–1) and northwest China (∼2.9 t ha–1) during 1990–2015 to pursue food security. However, the reclamation in these low-yield and ecologically vulnerable zones considerably undermined local environmental sustainability, for example increasing wind erosion (+3.47%), irrigation water consumption (+34.42%), fertilizer use (+20.02%) and decreasing natural habitats (−3.11%). Forecasts show that further reclamation in marginal lands per current policies would exacerbate environmental costs by 2050. The future cropland security risk will be remarkably intensified because of the conflict between food production and environmental sustainability. Our research suggests that globally emerging reclamation of marginal lands should be restricted and crop yield boost should be encouraged for both food security and environmental benefits., Widespread loss of high-quality cropland induced by urbanization and increasing shift of crop production to low-quality marginal land threaten food security and environmental sustainability.

The authors would like to change the authors’ affiliation in the recent published paper [...]

Background: Natural regeneration is critically important for the sustainable management of artificial forests. Studies have been investigated for the effects of seedlings height distribution and ontogenetic adaptability in a 60-year-old Quercus acutissima artificial pure forest in Mount Tai. Results: The results showed that the height distribution of seedlings under the forest was pyramidal-shape from the year 2010 to 2017. In 2017, the density of seedlings under the forest was only 61.63% of that in 2010, and the number of individuals at each height class was lower than that in 2010, especially the individuals at 20-40cm height class only 32% of that in 2010. With the increase of seedling height under the forest, seedling biomass (SB), ground diameter (GD), root mass ratio (RMR), total root length (TRL), root volume (RV), root average diameter (RAD) and nonstructural carbohydrates (NSC) increased significantly, but leaf mass ratio (LMR), leaf area ratio (LAR), photosynthetic/non-photosynthetic tissue ratio (P/NP), specific root length (SRL) and specific root surface area (SRA) decreased significantly. Correlation analysis showed that SB was negatively correlated with LMR, LAR, P/NP, SRL and SRA, and positively correlated with RMR, TRL, RV, RAD and NSC. NSC was negatively correlated with LMR, LAR, P/NP, SRL and SRA. Conclusions: Therefore, with the increase of seedlings height, the increased carbon storage was helpful for the seedlings of Q. acutissima to survive under the forest for a long time, but the reduced ability of light interception, photosynthetic tissue ratio, water and nutrient absorption capacity contributed to their low survival ratio under the forest.

Tidal flats (non-vegetated area), along with coastal vegetation area, constitute the coastal wetlands (intertidal zone) between high and low water lines, and play an important role in wildlife, biodiversity and biogeochemical cycles. However, accurate annual maps of coastal tidal flats over the last few decades are unavailable and their spatio-temporal changes in China are unknown. In this study, we analyzed all the available Landsat TM/ETM+/OLI imagery (~44,528 images) using the Google Earth Engine (GEE) cloud computing platform and a robust decision tree algorithm to generate annual frequency maps of open surface water body and vegetation to produce annual maps of coastal tidal flats in eastern China from 1986 to 2016 at 30-m spatial resolution. The resulting map of coastal tidal flats in 2016 was evaluated using very high-resolution images available in Google Earth. The total area of coastal tidal flats in China in 2016 was about 731,170 ha, mostly distributed in the provinces around Yellow River Delta and Pearl River Delta. The interannual dynamics of coastal tidal flats area in China over the last three decades can be divided into three periods: a stable period during 1986–1992, an increasing period during 1993–2001 and a decreasing period during 2002–2016. The resulting annual coastal tidal flats maps could be used to support sustainable coastal zone management policies that preserve coastal ecosystem services and biodiversity in China.

Human land use intensity affects the surface energy balance by changing the biogeophysical parameters. This study used Moderate Resolution Imaging Spectroradiometer remote sensing data and surface energy balance algorithms to quantify changes in surface energy budgets corresponding to changes in land use in Beijing from 2000 to 2015. Land use was reclassified by considering land use intensity. The difference in the latent heat flux (LE) and net radiation (Rn) (LE&minus, Rn) expressed the warming or cooling effect. The results showed that: (i) The increasing trend of net longwave radiation in Beijing offset the decreasing trend of net shortwave radiation. The Rn changed slightly, while the LE and LE&minus, Rn showed a significant increase of 0.55 and 0.56 W/(m&sup2, ∙year), respectively. The findings indicated that considering only radiative forcing, or even Rn, was not enough to measure the impacts of land use change on the energy budget. (ii) The order of Rn, LE, and LE&minus, Rn values from high to low were natural and seminatural areas, cropland, mixed pixel areas, urban expansion areas, and old urban areas. Compared with natural and seminatural areas, the changing LE&minus, Rn trend in the other four land use types decreased with the increase in human impact intensity, indicating that human activities weakened the positive change trend of LE&minus, Rn and increased the warming effect. (iii) Although the temporal trend of LE increased in Beijing from 2000 to 2015, the effect of Rn on LE&minus, Rn was greater than that of LE, especially in the four land use types affected by human activities. The results for surface temperature in various land use types confirmed this point. This study highlights the energy budget differences of various land use types affected by human activities. It makes an important contribution to understanding the urban heat island effect from a biogeophysical perspective.

The impact of human activities on vegetation has been the focus of much research, but the impact on radiation energy through surface albedo associated with vegetation greenness and length of the growth season is still not well documented. Based on the land cover data for the years 2000 and 2015, this study first divided the land cover change in Beijing from 2000 to 2015 into five types according to the impact of human activities and vegetation resilience, namely, old urban areas (OU), urban expansion areas (UE), cropland (CP), mixed pixel areas (MP, which means the land covers other than urban expansion which had changed from 2000 to 2015), and the residual vegetation cover areas (pure pixels (PP), dominated by natural and seminatural vegetation, such as grassland, forest, and wetland). Then, we calculated the direct radiative forcing from the albedo change from 2000 to 2015 and analyzed the effect of vegetation on the albedo under different land cover types based on multi-resource Moderate Resolution Imaging Spectroradiometer (MODIS) products of vegetation, albedo, and solar radiation. The results showed that the most typical changes in land cover were from urban expansion. By comparing the PP with the four human-affected land cover types (OU, UE, MP, and CP), we confirmed that the radiative forcing increment between 2001&ndash, 2003 and 2013&ndash, 2015 in PP (0.01 W/m2) was much smaller than that in the four human-affected land cover types (the mean increment was 0.92 W/m2). This study highlights that human activities affected vegetation growth. This, in turn, brought changes in the albedo, thereby enhancing radiative forcing in Beijing during 2000&ndash, 2015.

Agriculture (e.g., rice paddies) has been considered one of the main emission sources responsible for the sudden rise of atmospheric methane concentration (XCH4) since 2007, but remains debated. Here we use satellite-based rice paddy and XCH4 data to investigate the spatial–temporal relationships between rice paddy area, rice plant growth, and XCH4 in monsoon Asia, which accounts for ~87% of the global rice area. We find strong spatial consistencies between rice paddy area and XCH4 and seasonal consistencies between rice plant growth and XCH4. Our results also show a decreasing trend in rice paddy area in monsoon Asia since 2007, which suggests that the change in rice paddy area could not be one of the major drivers for the renewed XCH4 growth, thus other sources and sinks should be further investigated. Our findings highlight the importance of satellite-based paddy rice datasets in understanding the spatial–temporal dynamics of XCH4 in monsoon Asia., The role of paddy rice agriculture in the spatial and temporal dynamics of atmospheric methane concentration remains unclear. Here, Zhang et al. show that regions with dense rice paddies have high satellite-based column averaged CH4 concentrations (XCH4), and that seasonal dynamics of XCH4 mirror those of paddy rice growth.

Urban heat island (UHI) plays an important role in urban sustainability under climate change. Urbanization is the driving force of UHI. However, the quantification of UHI's response to urbanization is still challenging due to the lack of robust and continuous temperature and urbanization datasets and reliable quantification methods. This study developed a framework to quantify the response of surface UHI (SUHI) to urban expansion using the annual temperate cycle model. We developed a continuous annual SUHI series at the buffer level from 2003 to 2018 in the Jing-Jin-Ji region of China using MODIS land surface temperature and imperviousness derived from a high-resolution urban map. We then investigated the spatiotemporal dynamic of SUHI under urban expansion and examined the underlying mechanism. Spatially, the largest SUHI interannual variations occurred in suburban areas compared to the urban center and rural areas. Temporally, the increase in SUHI under urban expansion was more significant in daytime compare to nighttime. We found that the seasonal variation of SUHI was largely affected by the seasonal variations of vegetation in rural areas and the interannual variation was mainly attributed to urban expansion in urban areas. Additionally, urban greening led to the decrease in summer daytime SHUI in central urban areas. These findings deepen the understanding of the long-term spatiotemporal dynamic of UHI and the quantitative relationship between UHI and urban expansion, providing a scientific basis for prediction and mitigation of future UHI.

Urban green space (UGS) is an indispensable component of urban environmental systems and is important to urban residents. Both physical features (e.g., shrubs, trees) and social functions (e.g., public parks, green buffers) are important in UGS mapping. Most UGS studies rely solely on remote sensing data to conduct UGS mapping of physical features, and few studies have focused on UGS mapping from a social function perspective. Due to the limitations of remote sensing in identifying social features; social sensing, which can reflect socioeconomic characteristics, is needed. As a result, a novel methodological framework for integrating these two different data sources to conduct the social functional mapping of UGS has been required. Consequently, we first extracted vegetation patches from an area in Beijing, via the Hyperplanes for Plant Extraction Methodology (HPEM) and considered the parcels segmented by the OpenStreetMap (OSM) road networks as the basic analytical units. Then, near-convex-hull analysis (NCHA) and text-concave-hull analysis (TCHA) were performed to integrate the multi-source data. The results show that the Level I and Level II (refer to Table 3) social function types of UGS had overall accuracies of 92.48% and 88.76%, respectively. Our study provides an improved understanding of UGS and can assist government departments in urban planning. It can also help researchers broaden their research scope by acting as a freely available data source for their work.