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There has been controversy regarding the air pollutants emitted from sources closely related to the activities of daily life in China, such as cooking, setting off fireworks, sacrificial incense and joss paper burning, and barbecue (which have been named the five missing sources – FMSs), and the extent to which they impact the outdoor air quality. To date, due to the lack of both an activity dataset and emission factors, there have been no estimations of the emission of air pollutants from FMSs. In this work, we have attempted to combine questionnaire data, various statistical data, and data on points of interest to obtain a relatively complete set of activity data. The emission factors (EFs) of carbonaceous aerosols were tested in our lab. Emission inventories of carbonaceous aerosols with a high spatial–temporal resolution for FMSs were then established, and the spatial variation trend and driving forces were discussed. From 2000 to 2018, organic carbon (OC) emissions were in the range of 4268–4919 t. OC emissions from FMSs were between 1.5 ‰ and 2.2 ‰ of the total emissions in China. Black carbon (BC), elemental carbon (EC), and brown carbon (BrC) absorption cross-section (ACS BrC) emissions from FMSs were in the ranges of 22.6–43.9 t, 213–324 t, and 14.7–35.6 Gm 2 , respectively. Their emissions tended to be concentrated in certain periods and areas. The OC emission intensities in central urban areas were 3.85–50.5 times those in rural areas (due to the high density of human activity), while the ACS BrC emissions in rural regions accounted for 63.0–79.5 % of the total emissions (resulting from uncontrolled fireworks). A mass of fireworks led to much higher ACS BrC and EC emissions on Chinese New Year's Eve, with respective values that were 1444 and 262 times their corresponding yearly averages. Significant (p<0.01) correlations between the population's income and pollutant emissions were also found: these correlations were positive (r=0.94) and negative (r=-0.94) for urban and rural regions, respectively, indicating the necessity to regulate residents' lifestyles and increase residents' income in respective urban and rural regions. This study provides firsthand data to identify emissions, variation trends, and impacting factors from the FMSs. This is helpful for modeling works on air quality, the climate effect, and human health risks during specific periods or in specific regions as well as for modifying emission control policies. The data compiled in this work can found at 10.6084/m9.figshare.19999991.v2 (Cheng et al., 2022). [ABSTRACT FROM AUTHOR]

The New Baseline Surface Radiation (NBSR) system was established at the Shangdianzi (SDZ) regional Global Atmosphere Watch (GAW) station in 2013 to observe nine broadband radiation components, i.e. the global, direct, diffuse, and upwelling shortwave irradiance (GSWI, DSWI, DifSWI, and UpSWI); the photosynthetically active radiation (PAR); the ultraviolet irradiance (UVAI and UVBI); and the down- and upwelling longwave irradiance (DnLWI and UpLWI). To test the 1 min raw radiometric data, a Hybrid Algorithm for Radiation Data Quality Control (HARDQC) is presented in this study based on well-established methods, together with the solar irradiance dataset and the spectral features of the instrument bands. Subsequently, a NBSR dataset, which consists of radiation data at multiple timescales (i.e. 1 min, hourly, daily, monthly, monthly average hourly, and monthly average daily) over 2013–2022, is established and evaluated. Results show that more than 98.7 % of all radiation components passed the physical possibility test. The percentages of those that passed the extremely rare test are greater than 98.6 % for all radiation components except for the DnLWI (97.1 %). The percentages of those that passed the comparison test are greater than 83.3 % (GSWI), 78.3 % (DSWI), 81.7 % (DifSWI), 93.1 % (UpSWI), 88.9 % (PAR), 95.6 % (UVAI), 96.3 % (UVBI), 99.8 % (DnLWI), and 99.7 % (UpLWI), respectively. Due to data logger faults, removal of the instruments for calibration, and lightning strikes, some apparent data gaps in the upwelling radiation components (January 2015–August 2017) and all radiation components (December 2018; July to September 2021) were detected. Despite the existence of a few imperfections in the NBSR dataset, it is still reliable to apply it in many fields such as the validation of satellite products and numerical models, the investigation of relationships between radiation and atmospheric composition, and the detection of changes in the surface fluxes. The dataset described in this paper is available at 10.1594/PANGAEA.963330 (Quan et al., 2023b). [ABSTRACT FROM AUTHOR]

The Sheshan Geomagnetic Observatory (International Association of Geomagnetism and Aeronomy (IAGA) code SSH), China was built in Xujiahui, Shanghai in 1874 and moved to Sheshan, Shanghai at the end of 1932. So far, the SSH has a history of nearly 150 years. It is one of the earliest geomagnetic observatories in China and one of the geomagnetic observatories with the longest history in the world. In this paper, we present the rescue and quality control (QC) of the historical data at the SSH from 1933 to 2019. The rescued data are the absolute hourly mean values (AHMVs) of declination (D), horizontal (H), and vertical (Z) components. Some of these data are paper-based records and some are stored in a floppy disk in BAS, DBF, MDB, and other file storage formats. After digitization and format transformation, we imported the data into the Toad database to achieve the unified data management. We performed statistics of completeness, visual analysis, outliers detects, and data correction on the stored data. We then conducted the consistency test of daily variation and secular variation (SV) by comparing the corrected data with the data of the reference observatory, and the computational data of the COV-OBS model, respectively. The consistency test reveals good agreement. However, the individual data should be used with caution because these data are suspicious values, but there is not any explanation or change registered in the available metadata and logbooks. Finally, we present examples of the datasets in discriminating geomagnetic jerks and study of storms. The digitized and quality-controlled AHMVs data are available at: 10.5281/zenodo.7005471 (Zhang et al., 2022). [ABSTRACT FROM AUTHOR]

Large-scale and multi-annual maps of building rooftop area (BRA) are crucial for addressing policy decisions and sustainable development. In addition, as a fine-grained indicator of human activities, BRA could contribute to urban planning and energy modeling to provide benefits to human well-being. However, it is still challenging to produce a large-scale BRA due to the rather tiny sizes of individual buildings. From the viewpoint of classification methods, conventional approaches utilize high-resolution aerial images (metric or submetric resolution) to map BRA; unfortunately, high-resolution imagery is both infrequently captured and expensive to purchase, making the BRA mapping costly and inadequate over a consistent spatiotemporal scale. From the viewpoint of learning strategies, there is a nontrivial gap that persists between the limited training references and the applications over geospatial variations. Despite the difficulties, existing large-scale BRA datasets, such as those from Microsoft or Google, do not include China, and hence there are no full-coverage maps of BRA in China yet. In this paper, we first propose a deep-learning method, named the Spatio-Temporal aware Super-Resolution Segmentation framework (STSR-Seg), to achieve robust super-resolution BRA extraction from relatively low-resolution imagery over a large geographic space. Then, we produce the multi-annual China Building Rooftop Area (CBRA) dataset with 2.5 m resolution from 2016–2021 Sentinel-2 images. CBRA is the first full-coverage and multi-annual BRA dataset in China. With the designed training-sample-generation algorithms and the spatiotemporally aware learning strategies, CBRA achieves good performance with a F1 score of 62.55 % (+10.61 % compared with the previous BRA data in China) based on 250 000 testing samples in urban areas and a recall of 78.94 % based on 30 000 testing samples in rural areas. Temporal analysis shows good performance consistency over years and good agreement with other multi-annual impervious surface area datasets. STSR-Seg will enable low-cost, dynamic, and large-scale BRA mapping (https://github.com/zpl99/STSR-Seg , last access: 12 July 2023). CBRA will foster the development of BRA mapping and therefore provide basic data for sustainable research (Liu et al., 2023; 10.5281/zenodo.7500612). [ABSTRACT FROM AUTHOR]

Aviation emission accounting is the key to establishing market measures to control aviation pollutant emissions. Based on the fuel percentage method (FPM), this paper applies the improved BFFM2-FOA-FPM (Boeing Fuel Flow Method 2–First Order Approximation FPM) to calculate the emissions of six pollutants (CO 2 , CO, HC, NO x , SO 2 , and PM 2.5) between Chinese and foreign cities from 2014 to 2019, including CCD (climbing, cruising, and descending) emissions and LTO (landing and take-off) emissions. The error rate between the calculated results and the official data is about 2.75 %. The results show that the emissions of six pollutants changed before and after the proposal of the "Carbon Neutral Growth 2020" strategy (CNG2020 strategy). Although the total amount has increased, the average emission per tonne-kilometer of CO 2 , CO, HC, NO x , SO 2 , and PM 2.5 has decreased by 17.77 %, 17.26 %, 25.15 %, 14.32 %, 17.77 %, and 16.35 %, respectively. The results of this paper can provide a data basis and method reference for implementing the CNG2020 strategy and realizing global carbon emission reduction goals. The dataset is available from https://doi.org/10.6084/m9.figshare.20071751.v1 (Cui., 2022). [ABSTRACT FROM AUTHOR]

The alpine grassland ecosystem accounts for 53 % of the Qinghai–Tibet Plateau (QTP) area and is an important ecological protection barrier, but it is fragile and vulnerable to climate change. Therefore, continuous monitoring of grassland aboveground biomass (AGB) is necessary. Although many studies have mapped the spatial distribution of AGB for the QTP, the results vary widely due to the limited ground samples and mismatches with satellite pixel scales. This paper proposed a new algorithm using unmanned aerial vehicles (UAVs) as a bridge to estimate the grassland AGB on the QTP from 2000 to 2019. The innovations were as follows: (1) in terms of ground data acquisition, spatial-scale matching among the traditional ground samples, UAV photos, and MODIS pixels was considered. A total of 906 pairs between field-harvested AGB and UAV sub-photos and 2602 sets of MODIS pixel-scale UAV data (over 37 000 UAV photos) were collected during 2015–2019. Therefore, the ground validation samples were sufficient and scale-matched. (2) In terms of model construction, the traditional quadrat scale (0.25 m 2) was successfully upscaled to the MODIS pixel scale (62 500 m 2) based on the random forest and stepwise upscaling methods. Compared with previous studies, the scale matching of independent and dependent variables was achieved, effectively reducing the impact of spatial-scale mismatch. The results showed that the correlation between the AGB values estimated by UAV and MODIS vegetation indices was higher than that between field-measured AGB and MODIS vegetation indices at the MODIS pixel scale. The multi-year validation results showed that the constructed MODIS pixel-scale AGB estimation model had good robustness, with an average R2 of 0.83 and RMSE of 34.13 g m -2. Our dataset provides an important input parameter for a comprehensive understanding of the role of the QTP under global climate change. The dataset is available from the National Tibetan Plateau/Third Pole Environment Data Center (10.11888/Terre.tpdc.272587; H. Zhang et al., 2022). [ABSTRACT FROM AUTHOR]

Constructing a highly resolved comprehensive emission dataset for China is challenging due to limited availability of refined information for parameters in a unified bottom-up framework. Here, by developing an integrated modeling framework, we harmonized multi-source heterogeneous data, including several up-to-date emission inventories at national and regional scales and for key species and sources in China to generate a 0.1° resolution inventory for 2017. By source mapping, species mapping, temporal disaggregation, spatial allocation, and spatial–temporal coupling, different emission inventories are normalized in terms of source categories, chemical species, and spatiotemporal resolutions. This achieves the coupling of multi-scale, high-resolution emission inventories with the Multi-resolution Emission Inventory for China (MEIC), forming the high-resolution INTegrated emission inventory of Air pollutants for China (INTAC). We find that INTAC provides more accurate representations for emission magnitudes and spatiotemporal patterns. In 2017, China's emissions of sulfur dioxide (SO2), nitrous oxides (NOx), carbon monoxide (CO), non-methane volatile organic compounds (NMVOCs), ammonia (NH3), PM10 and PM2.5 (particulate matter), black carbon (BC), and organic carbon (OC) were 12.3, 24.5, 141.0, 27.9, 9.2, 11.1, 8.4, 1.3, and 2.2 Tg , respectively. The proportion of point source emissions for SO2 , PM10 , NOx , and PM2.5 increases from 7 %–19 % in MEIC to 48 %–66 % in INTAC, resulting in improved spatial accuracy, especially mitigating overestimations in densely populated areas. Compared with MEIC, INTAC reduces mean biases in simulated concentrations of major air pollutants by 2–14 µgm-3 across 74 cities, compared against ground observations. The enhanced model performance by INTAC is particularly evident at finer-grid resolutions. Our new dataset is accessible at http://meicmodel.org.cn/intac (last access: 15 April 2024) and 10.5281/zenodo.10459198 (Wu et al., 2024), and it will provide a solid data foundation for fine-scale atmospheric research and air-quality improvement. [ABSTRACT FROM AUTHOR]

High-quality gridded data on industrial water use are vital for research and water resource management. However, such data in China usually have low accuracy. In this study, we developed a gridded dataset of monthly industrial water withdrawal (IWW) for China, which is called the China Industrial Water Withdrawal (CIWW) dataset; this dataset spans a 56-year period from 1965 to 2020 at spatial resolutions of 0.1 and 0.25°. We utilized > 400 000 records of industrial enterprises, monthly industrial product output data, and continuous statistical IWW records from 1965 to 2020 to facilitate spatial scaling, seasonal allocation, and long-term temporal coverage in developing the dataset. Our CIWW dataset is a significant improvement in comparison to previous data for the characterization of the spatial and seasonal patterns of the IWW dynamics in China and achieves better consistency with statistical records at the local scale. The CIWW dataset, together with its methodology and auxiliary data, will be useful for water resource management and hydrological models. This new dataset is now available at 10.6084/m9.figshare.21901074 (Hou and Li, 2023). [ABSTRACT FROM AUTHOR]

The Long-term Gap-free High-resolution Air Pollutants (LGHAP) concentration dataset generated in our previous study has provided spatially contiguous daily aerosol optical depth (AOD) and fine particulate matter (PM 2.5) concentrations at a 1 km grid resolution in China since 2000. This advancement empowered unprecedented assessments of regional aerosol variations and their influence on the environment, health, and climate over the past 20 years. However, there is a need to enhance such a high-quality AOD and PM 2.5 concentration dataset with new robust features and extended spatial coverage. In this study, we present version 2 of a global-scale LGHAP dataset (LGHAP v2), which was generated using improved big Earth data analytics via a seamless integration of versatile data science, pattern recognition, and machine learning methods. Specifically, multimodal AODs and air quality measurements acquired from relevant satellites, ground monitoring stations, and numerical models were harmonized by harnessing the capability of random-forest-based data-driven models. Subsequently, an improved tensor-flow-based AOD reconstruction algorithm was developed to weave the harmonized multisource AOD products together for filling data gaps in Multi-Angle Implementation of Atmospheric Correction (MAIAC) AOD retrievals from Terra. The results of the ablation experiments demonstrated better performance of the improved tensor-flow-based gap-filling method in terms of both convergence speed and data accuracy. Ground-based validation results indicated good data accuracy of this global gap-free AOD dataset, with a correlation coefficient (R) of 0.85 and a root mean square error (RMSE) of 0.14 compared to the worldwide AOD observations from the AErosol RObotic NETwork (AERONET), outperforming the purely reconstructed AODs (R = 0.83, RMSE = 0.15), but they were slightly worse than raw MAIAC AOD retrievals (R = 0.88, RMSE = 0.11). For PM 2.5 concentration mapping, a novel deep-learning approach, termed the SCene-Aware ensemble learning Graph ATtention network (SCAGAT), was hereby applied. While accounting for the scene representativeness of data-driven models across regions, the SCAGAT algorithm performed better during spatial extrapolation, largely reducing modeling biases over regions with limited and/or even absent in situ PM 2.5 concentration measurements. The validation results indicated that the gap-free PM 2.5 concentration estimates exhibit higher prediction accuracies, with an R of 0.95 and an RMSE of 5.7 µg m -3 , compared to PM 2.5 concentration measurements obtained from former holdout sites worldwide. Overall, while leveraging state-of-the-art methods in data science and artificial intelligence, a quality-enhanced LGHAP v2 dataset was generated through big Earth data analytics by cohesively weaving together multimodal AODs and air quality measurements from diverse sources. The gap-free, high-resolution, and global coverage merits render the LGHAP v2 dataset an invaluable database for advancing aerosol- and haze-related studies as well as triggering multidisciplinary applications for environmental management, health-risk assessment, and climate change attribution. All gap-free AOD and PM 2.5 concentration grids in the LGHAP v2 dataset, as well as the data user guide and relevant visualization codes, are publicly accessible at https://zenodo.org/communities/ecnu%5flghap (last access: 3 April 2024, Bai and Li, 2023a). [ABSTRACT FROM AUTHOR]

Accurate, detailed, and up-to-date information on cropland extent is crucial for provisioning food security and environmental sustainability. However, because of the complexity of agricultural landscapes and lack of sufficient training samples, it remains challenging to monitor cropland dynamics at high spatial and temporal resolutions across large geographical extents, especially for regions where agricultural land use is changing dramatically. Here we developed a cost-effective annual cropland mapping framework that integrated time-series Landsat satellite imagery, automated training sample generation, as well as machine learning and change detection techniques. We implemented the proposed scheme to a cloud computing platform of Google Earth Engine and generated a novel dataset of China's annual cropland at a 30 m spatial resolution (namely CACD). Results demonstrated that our approach was capable of tracking dynamic cropland changes in different agricultural zones. The pixel-wise F1 scores for annual maps and change maps of CACD were 0.79 ± 0.02 and 0.81, respectively. Further cross-product comparisons, including accuracy assessment, correlations with statistics, and spatial details, highlighted the precision and robustness of CACD compared with other datasets. According to our estimation, from 1986 to 2021, China's total cropland area expanded by 30 300 km 2 (1.79 %), which underwent an increase before 2002 but a general decline between 2002 and 2015, and a slight recovery afterward. Cropland expansion was concentrated in the northwest while the eastern, central, and southern regions experienced substantial cropland loss. In addition, we observed 419 342 km 2 (17.57 %) of croplands that were abandoned at least once during the study period. The consistent, high-resolution data of CACD can support progress toward sustainable agricultural use and food production in various research applications. The full archive of CACD is freely available at 10.5281/zenodo.7936885 (Tu et al., 2023a). [ABSTRACT FROM AUTHOR]

Long time series and large-scale rice calendar datasets provide valuable information for agricultural planning and field management in rice-based cropping systems. However, current regional-level rice calendar datasets do not accurately distinguish between rice seasons in China, causing uncertainty in crop model simulation and climate change impact analysis. Based on satellite remote sensing data, we extracted transplanting, heading, and maturity dates of early-, middle-, and late-season rice across China from 2003 to 2022 and established a multi-season rice calendar dataset named ChinaRiceCalendar (10.7910/DVN/EUP8EY, Liu et al., 2023). Overall, the ChinaRiceCalendar dataset shows good agreement with field-observed phenological dates of early-, middle-, and late-season rice in Chinese agricultural meteorological stations (AMSs). According to the calendar data from 2003 to 2022 in China, the transplanting dates for early-, middle-, and late-season rice shifted by + 0.7, - 0.7, and - 5.1 DOY (day of year) per decade, respectively; the heading dates for early-, middle-, and late-season rice shifted by - 0.5, + 2.7, and - 0.6 DOY per decade, respectively; the maturity dates for early-, middle-, and late-season rice shifted by - 0.7, + 3.8, and - 1.6 DOY per decade, respectively. ChinaRiceCalendar can be utilized to investigate and optimize the spatiotemporal structure of rice cultivation in China under climate and land use change. [ABSTRACT FROM AUTHOR]

Trait-based approaches are of increasing concern in predicting vegetation changes and linking ecosystem structures to functions at large scales. However, a critical challenge for such approaches is acquiring spatially continuous plant functional trait maps. Here, six key plant functional traits were selected as they can reflect plant resource acquisition strategies and ecosystem functions, including specific leaf area (SLA), leaf dry matter content (LDMC), leaf N concentration (LNC), leaf P concentration (LPC), leaf area (LA) and wood density (WD). A total of 34 589 in situ trait measurements of 3447 seed plant species were collected from 1430 sampling sites in China and were used to generate spatial plant functional trait maps (∼1 km), together with environmental variables and vegetation indices based on two machine learning models (random forest and boosted regression trees). To obtain the optimal estimates, a weighted average algorithm was further applied to merge the predictions of the two models to derive the final spatial plant functional trait maps. The models showed good accuracy in estimating WD, LPC and SLA, with average R2 values ranging from 0.48 to 0.68. In contrast, both the models had weak performance in estimating LDMC, with average R2 values less than 0.30. Meanwhile, LA showed considerable differences between the two models in some regions. Climatic effects were more important than those of edaphic factors in predicting the spatial distributions of plant functional traits. Estimates of plant functional traits in northeastern China and the Qinghai–Tibetan Plateau had relatively high uncertainties due to sparse samplings, implying a need for more observations in these regions in the future. Our spatial trait maps could provide critical support for trait-based vegetation models and allow exploration of the relationships between vegetation characteristics and ecosystem functions at large scales. The six plant functional trait maps for China with 1 km spatial resolution are now available at 10.6084/m9.figshare.22351498 (An et al., 2023). [ABSTRACT FROM AUTHOR]

Multifaceted regime shifts of Earth's surface are ongoing dramatically and – in turn – considerably alter the global carbon budget, energy balance and biogeochemical cycles. Sustainably managing terrestrial ecosystems necessitates a deeper comprehension of the diverse and dynamic nature of multicomponent information within these environments. However, comprehensive records of global-scale fractional vegetation and soil information that encompass these structural and functional complexities remain limited. Here, we provide a globally comprehensive record of monthly vegetation and soil fractions during the period 2001–2022 using a spatiotemporally adaptive spectral mixture analysis framework. This product is designed to continuously represent Earth's terrestrial surface as a percentage of five physically meaningful vegetation and soil endmembers, including photosynthetic vegetation (PV), nonphotosynthetic vegetation (NPV), bare soil (BS), ice or snow (IS) and dark surface (DA), with high accuracy and low uncertainty compared to the previous vegetation index and vegetation continuous-field product as well as traditional fully constrained linear spectral mixture models. We also adopt nonparametric seasonal Mann–Kendall tested fractional dynamics to identify shifts based on interactive changes in these fractions. Our results – superior to previous portrayals of the greening planet – not only report a + 9.35 × 10 5 km 2 change in photosynthetic vegetation, but also explore decreases in nonphotosynthetic vegetation (- 2.19 × 10 5 km 2), bare soil (- 5.14 × 10 5 km 2) and dark surfaces (- 2.27 × 10 5 km 2). In addition, interactive changes in these fractions yield multifaceted regime shifts with important implications, such as a simultaneous increase in PV and NPV in central and southwestern China during afforestation activities, an increase in PV in cropland of China and India due to intensive agricultural development, a decrease in PV and an increase in BS in tropical zones resulting from deforestation. These advantages emphasize that our dataset provides locally relevant information on multifaceted regime shifts at the required scale, enabling scalable modeling and effective governance of future terrestrial ecosystems. The data about five fractional surface vegetation and soil components are available in the Science Data Bank (10.57760/sciencedb.13287, Sun and Sun, 2023). [ABSTRACT FROM AUTHOR]

A high-resolution, spatially explicit forest age map is essential for quantifying forest carbon stocks and carbon sequestration potential. Prior attempts to estimate forest age on a national scale in China have been limited by sparse resolution and incomplete coverage of forest ecosystems, attributed to complex species composition, extensive forest areas, insufficient field measurements, and inadequate methods. To address these challenges, we developed a framework that combines machine learning algorithms (MLAs) and remote sensing time series analysis for estimating the age of China's forests. Initially, we identify and develop the optimal MLAs for forest age estimation across various vegetation divisions based on forest height, climate, terrain, soil, and forest-age field measurements, utilizing these MLAs to ascertain forest age information. Subsequently, we apply the LandTrendr time series analysis to detect forest disturbances from 1985 to 2020, with the time since the last disturbance serving as a proxy for forest age. Ultimately, the forest age data derived from LandTrendr are integrated with the result of MLAs to produce the 2020 forest age map of China. Validation against independent field plots yielded an R2 ranging from 0.51 to 0.63. On a national scale, the average forest age is 56.1 years (standard deviation of 32.7 years). The Qinghai–Tibet Plateau alpine vegetation zone possesses the oldest forest with an average of 138.0 years, whereas the forest in the warm temperate deciduous-broadleaf forest vegetation zone averages only 28.5 years. This 30 m-resolution forest age map offers crucial insights for comprehensively understanding the ecological benefits of China's forests and to sustainably manage China's forest resources. The map is available at 10.5281/zenodo.8354262 (Cheng et al., 2023a). [ABSTRACT FROM AUTHOR]

Water quality data represent a critical resource for evaluation of the well-being of aquatic ecosystems and assurance of clean water sources for human populations. While the availability of water quality datasets is growing, the absence of a publicly accessible national water quality dataset for both inland and the ocean in China has been notable. To address this issue, we utilized R and Python programming languages to collect, tidy, reorganize, curate, and compile three publicly available datasets, thereby creating an extensive spatiotemporal repository of surface water quality data for China. Distinguished as the most expansive, clean, and easily accessible water quality dataset in China to date, this repository comprised over 330 000 observations encompassing daily (3588), weekly (217 751), and monthly (114 954) records of surface water quality covering the period from 1980 to 2022. It spanned 18 distinct indicators, meticulously gathered at 2384 monitoring sites, which were further categorized as daily (244 sites), weekly (149 sites), and monthly (1991 sites), ranging from inland locations to coastal and oceanic areas. This dataset will support studies relevant to the assessment, modeling, and projection of water quality, ocean biomass, and biodiversity in China, and therefore make substantial contributions to both national and global water resources management. This water quality dataset and supplementary metadata are available for download from the figshare repository at 10.6084/m9.figshare.22584742 (Lin et al., 2023b). [ABSTRACT FROM AUTHOR]

Land surface temperature (LST) is a key variable within Earth's climate system and a necessary input parameter required by numerous land–atmosphere models. It can be directly retrieved from satellite thermal infrared (TIR) observations, which contain many invalid pixels mainly caused by cloud contamination. To investigate the spatial and temporal variations in LST in China, long-term, high-quality, and spatiotemporally continuous LST datasets (i.e., all-weather LST) are urgently needed. Fusing satellite TIR LST and reanalysis datasets is a viable route to obtain long time-series all-weather LSTs. Among satellite TIR LSTs, the MODIS LST is the most commonly used, and a few corresponding all-weather LST products have been reported recently. However, the publicly reported all-weather LSTs were not available during the temporal gaps of MODIS between 2000 and 2002. In this study, we generated a daily (four observations per day) 1 km all-weather LST dataset for China's landmass and surrounding areas, the Thermal and Reanalysis Integrating Moderate-resolution Spatial-seamless (TRIMS) LST, which begins on the first day of the new millennium (1 January 2000). We used the enhanced reanalysis and thermal infrared remote sensing merging (E-RTM) method to generate the TRIMS LST dataset with the temporal gaps being filled, which had not been achieved by the original RTM method. Specifically, we developed two novel approaches, i.e., the random-forest-based spatiotemporal merging (RFSTM) approach and the time-sequential LST-based reconstruction (TSETR) approach, respectively, to produce Terra/MODIS-based and Aqua/MODIS-based TRIMS LSTs during the temporal gaps. We also conducted a thorough evaluation of the TRIMS LST. A comparison with the Global Land Data Assimilation System (GLDAS) and ERA5-Land LST demonstrates that the TRIMS LST has similar spatial patterns but a higher image quality, more spatial details, and no evident spatial discontinuities. The results outside the temporal gap show consistent comparisons of the TRIMS LST with the MODIS LST and the Advanced Along-Track Scanning Radiometer (AATSR) LST, with a mean bias deviation (MBD) of 0.09/0.37 K and a standard deviation of bias (SD) of 1.45/1.55 K. Validation based on the in situ LST at 19 ground sites indicates that the TRIMS LST has a mean bias error (MBE) ranging from - 2.26 to 1.73 K and a root mean square error (RMSE) ranging from 0.80 to 3.68 K. There is no significant difference between the clear-sky and cloudy conditions. For the temporal gap, it is observed that RFSTM and TSETR perform similarly to the original RTM method. Additionally, the differences between Aqua and Terra remain stable throughout the temporal gap. The TRIMS LST has already been used by scientific communities in various applications such as soil moisture downscaling, evapotranspiration estimation, and urban heat island modeling. The TRIMS LST is freely and conveniently available at 10.11888/Meteoro.tpdc.271252 (Zhou et al., 2021). [ABSTRACT FROM AUTHOR]

Plant phenology refers to cyclic plant growth events, and is one of the most important indicators of climate change. Integration of plant phenology information is crucial for understanding the ecosystem response to global change and modeling the material and energy balance of terrestrial ecosystems. Utilizing 24 552 in situ phenological observations of 24 representative woody plant species from the Chinese Phenology Observation Network (CPON), we have developed maps delineating species phenology (SP) and ground phenology (GP) of forests over China from 1951 to 2020. These maps offer a detailed spatial resolution of 0.1 ∘ and a temporal resolution of 1 d. Our method involves a model-based approach to upscale in situ phenological observations to SP maps, followed by the application of weighted average and quantile methods to derive GP maps from the SP data. The resulting SP maps for the 24 woody plants exhibit a high degree of concordance with in situ observations, manifesting an average deviation of 6.9 d for spring and 10.8 d for autumn phenological events. Moreover, the GP maps demonstrate robust alignment with extant land surface phenology (LSP) products sourced from remote sensing data, particularly within deciduous forests, where the average discrepancy is 8.8 d in spring and 15.1 d in autumn. This dataset provides an independent and reliable phenology data source for China on a long-time scale of 70 years, and contributes to more comprehensive research on plant phenology and climate change at both regional and national scales. The dataset can be accessed at 10.57760/sciencedb.07995 (Zhu and Dai, 2023). [ABSTRACT FROM AUTHOR]

Quantifying the full-volatility organic emissions from cooking sources is important for understanding the causes of organic aerosol pollution. However, existing national cooking emission inventories in China fail to cover full-volatility organics and have large biases in estimating emissions and their spatial distribution. Here, we develop the first emission inventory of full-volatility organics from cooking in China, which covers emissions from individual commercial restaurants as well as residential kitchens and canteens. In our emission estimates, we use cuisine-specific full-volatility emission factors and provincial policy-driven purification facility installation proportions, which allows us to consider the significant impact of diverse dietary preferences and policy changes on China's cooking emissions. The 2021 emissions of volatile organic compounds (VOCs), intermediate-volatility organic compounds (IVOCs), semi-volatile organic compounds (SVOCs), and organic compounds with even lower volatility (xLVOCs) from cooking in China are 561 (317–891, 95 % confidence interval) kt yr -1 , 241 (135–374) kt yr -1 , 176 (95.8–290) kt yr -1 , and 13.1 (7.36–21.0) kt yr -1 , respectively. The IVOC and SVOC emissions from cooking account for 9 %–21 % and 31 %–62 % of the total emissions from all sources in the five most densely populated cities in China. Among all cooking types, commercial cooking dominates the emissions, contributing 54.5 %, 66.2 %, 68.5 %, and 46.7 % to the VOC, IVOC, SVOC, and xLVOC emissions, respectively. Sichuan–Hunan cuisine contributes the most to total cooking emissions among all commercial cuisines. Residential cooking emissions are also important, accounting for 22.2 %–47.1 % of the cooking organic emissions across the four volatility ranges, whereas canteens make minor contributions to each volatility range (<10%). In terms of spatial distribution, emission hotspots mainly occur in densely populated areas and regions with oily and spicy dietary preferences. From 2015 to 2021, national organic emissions from cooking increased by 25.2 % because of the rapid growth of the catering industry, despite being partly offset by the increased installation of purification facilities. Future control measures need to further promote the purification facilities in commercial restaurants and improve their removal efficiency as well as reduce emissions from residential cooking. Our dataset and generalizable methodology serve as valuable resources for evaluating the air quality, climate, and health impacts of cooking sources, and help to formulate effective emission control policies. Our national, multi-year, high-spatial-resolution dataset can be accessed from 10.6084/m9.figshare.23537673 (Li et al., 2023). [ABSTRACT FROM AUTHOR]

Evapotranspiration over crop growth period, also referred to as the consumptive water footprint of crop production (WFCP), is an essential component of the hydrological cycle. However, the existing high-resolution consumptive WFCP datasets do not distinguish between soil evaporation and crop transpiration and disregard the impacts of different irrigation practices. This restricts the practical implementation of existing WFCP datasets for precise crop water productivity assessments, agricultural water-saving evaluations, the development of sustainable irrigation techniques, cropping structure optimization, and crop-related interregional virtual water trade analysis. This study establishes a 5-arcmin gridded dataset of monthly green and blue WFCP, evaporation, transpiration, and associated unit WFCP benchmarks for 21 crops grown in China during 2000–2018. The data simulation was based on calibrated AquaCrop modelling under furrow-, sprinkler-, and micro-irrigated as well as rainfed conditions. Data quality was validated by comparing the current results with multiple public datasets and remote sensing products. The improved gridded WFCP dataset is available at 10.5281/zenodo.7756013 (Wang et al., 2023) and effectively compensated for the gaps in the existing datasets through (i) revealing the intensity, structure, and spatiotemporal evolution of both productive and non-productive blue and green water consumption on a monthly scale, and (ii) including crop-by-crop unit WFCP benchmarks according to climatic zones. [ABSTRACT FROM AUTHOR]

In China, the demand for a more precise perception of the national land surface has become most urgent given the pace of development and urbanization. Constructing a very-high-resolution (VHR) land-cover dataset for China with national coverage, however, is a nontrivial task. Thus, this has become an active area of research that is impeded by the challenges of image acquisition, manual annotation, and computational complexity. To fill this gap, the first 1 m resolution national-scale land-cover map of China, SinoLC-1, was established using a deep-learning-based framework and open-access data, including global land-cover (GLC) products, OpenStreetMap (OSM), and Google Earth imagery. Reliable training labels were generated by combining three 10 m GLC products and OSM data. These training labels and 1 m resolution images derived from Google Earth were used to train the proposed framework. This framework resolved the label noise stemming from a resolution mismatch between images and labels by combining a resolution-preserving backbone, a weakly supervised module, and a self-supervised loss function, to refine the VHR land-cover results automatically without any manual annotation requirement. Based on large-storage and computing servers, processing the 73.25 TB dataset to obtain the SinoLC-1 covering the entirety of China, ∼ 9 600 000 km 2 , took about 10 months. The SinoLC-1 product was validated using a visually interpreted validation set including over 100 000 random samples and a statistical validation set collected from the official land survey report provided by the Chinese government. The validation results showed that SinoLC-1 achieved an overall accuracy of 73.61 % and a κ coefficient of 0.6595. Validations for every provincial region further indicated the accuracy of this dataset across the whole of China. Furthermore, the statistical validation results indicated that SinoLC-1 conformed to the official survey reports with an overall misestimation rate of 6.4 %. In addition, SinoLC-1 was compared with five other widely used GLC products. These results indicated that SinoLC-1 had the highest spatial resolution and the finest landscape details. In conclusion, as the first 1 m resolution national-scale land-cover map of China, SinoLC-1 delivered accuracy and provided primal support for related research and applications throughout China. The SinoLC-1 land-cover product is freely accessible at 10.5281/zenodo.7707461 (Li et al., 2023). [ABSTRACT FROM AUTHOR]

Accurate assessment of soil nitrogen (N) storage and carbon (C) : N stoichiometry under impervious surface areas (ISAs) is key to understanding the impact of urbanization on soil health and the N cycle. Based on 888 soil profiles from 148 sampling sites in 41 cities across China, we estimated the country's N stock (100 cm depth) in the ISA soil to be 98.74±59.13 Tg N with a mean N density (N ISA) of 0.59±0.35 kg m -2 , which was significantly lower (at all depths) than the soil N density (N PSA=0.83±0.46 kg m -2) under the reference permeable surface areas (PSAs). The N ISA was also only about 53 %–69 % of the reported national mean soil N density, indicating that ISA expansion caused soil N loss. The C:N ratio of ISA (10.33±2.62) was 26 %–34 % higher than that of natural ecosystems (forests, grasslands, etc.) but close to the C:N of PSA. Moreover, there was a significant C–N correlation in ISA soil, showing no signs of C–N decoupling as suggested by the previous studies. The ISA had smaller variances in the C:N ratio than did the PSA at regional scale, indicating convergence of soil C:N stoichiometry due to ISA conversion. The eastern subregion of China had the highest N ISA , although its natural soil N density was among the lowest in the country. Unlike the vertical pattern in natural permeable soils, whose N density declined faster in the upper soil layers than in the lower layers, N ISA decreased linearly with depth. Similarly to natural soil N, N ISA was negatively correlated with temperature; but unlike natural soil C:N which was positively correlated with temperature, the C:N ISA was negatively correlated with temperature. N ISA was not correlated with net primary productivity, but was significantly correlated with the soil N density of adjacent PSA and the urbanization rate. These findings indicate the ISA soil had a unique N distribution pattern, possibly as the result of intensive disturbances during land conversion. The dataset "Observations of soil nitrogen and soil organic carbon to soil nitrogen stoichiometry under the impervious surfaces areas (ISA) of China" is available from the National Cryosphere Desert Data Center (10.12072/ncdc.socn.db2851.2023) (Ding et al., 2023). [ABSTRACT FROM AUTHOR]

Generating spatial crop yield information is of great significance for academic research and guiding agricultural policy. Existing public yield datasets have a coarse spatial resolution, spanning from 1 to 43 km. Although these datasets are useful for analyzing large-scale temporal and spatial change in yield, they cannot deal with small-scale spatial heterogeneity, which happens to be the most significant characteristic of the Chinese farmers' economy. Hence, we generated a 30 m Chinese winter wheat yield dataset (ChinaWheatYield30m) for major winter-wheat-producing provinces in China for the period 2016–2021 with a semi-mechanistic model (hierarchical linear model, HLM). The yield prediction model was built by considering the wheat growth status and climatic factors. It can estimate wheat yield with excellent accuracy and low cost using a combination of satellite observations and regional meteorological information (i.e., Landsat 8, Sentinel 2 and ERA5 data from the Google Earth Engine (GEE) platform). The results were validated using in situ measurements and census statistics and indicated a stable performance of the HLM based on calibration datasets across China, with a correlation coefficient (r) of 0.81 and a relative root mean square error (rRMSE) of 12.59 %. With regards to validation, the ChinaWheatYield30m dataset was highly consistent with in situ measurement data and statistical data (p<0.01), indicated by an r (rRMSE) of 0.72** (15.34 %) and 0.69** (19.16 %). The ChinaWheatYield30m is a sophisticated dataset with both high spatial resolution and excellent accuracy; such a dataset will provide basic knowledge of detailed wheat yield distribution, which can be applied for many purposes including crop production modeling and regional climate evaluation. The ChinaWheatYield30m dataset generated from this study can be downloaded from 10.5281/zenodo.7360753 (Zhao et al., 2022b). [ABSTRACT FROM AUTHOR]

Young forest age mapping at a fine spatial resolution is important for increasing the accuracy of estimating land–atmosphere carbon fluxes and guiding forest management practices. In recent decades, China has actively conducted afforestation and forest protection projects, thereby laying the foundation for the realization of carbon neutrality. However, very few studies have been conducted which map the ages of young forests for the whole of China at a fine spatial resolution. In this research, a continuous change detection and classification (CCDC)-based method suitable for large-scale forest age mapping is proposed and used to estimate young forest ages across China in 2020 at a spatial resolution of 30 m. First, a 10 m spatial-resolution land cover dataset (WorldCover2020) from the European Space Agency (ESA) was used to determine the forest cover areas in 2020. Then, the CCDC algorithm was used to identify stand-replacing disturbances to determine the stand age based on 436 967 Landsat tiles across China from 1990 to 2020. A validation sample set composed of multiple land use and land cover (LULC) products was used to calculate the overall accuracy (OA) of the 2020 young forest age (1–31-year) map of China, and the OA was 90.28 %. The reliability and applicability of the proposed CCDC-based forest age mapping method were validated by comparing the forest age map with Hansen's forest change dataset, Max Planck Institute for Biogeochemistry (MPI-BGC) 1 km global forest age datasets, and field measurements. The CCDC-based method has strong application potential in real-time mapping of the age of young forests at the global scale. The produced forest age map provides a basic dataset for research on the forest carbon cycle and forest ecosystem services as well as important guidance for government departments, such as the National Forestry and Grassland Administration and the National Development and Reform Commission in China. Data presented in this study is available at 10.6084/m9.figshare.21627023.v7 (Xiao, 2022). [ABSTRACT FROM AUTHOR]

High-quality, freely accessible, long-term precipitation estimates with fine spatiotemporal resolution play essential roles in hydrologic, climatic, and numerical modeling applications. However, the existing daily gridded precipitation datasets over China are either constructed with insufficient gauge observations or neglect topographic effects and boundary effects on interpolation. Using daily observations from 2839 gauges located across China and nearby regions from 1961 to the present, this study compared eight different interpolation schemes that adjusted the climatology based on a monthly precipitation constraint and topographic characteristic correction, using an algorithm that combined the daily climatology field with a precipitation ratio field. Results from these eight interpolation schemes were validated using 45 992 high-density daily gauge observations from 2015 to 2019 across China. Of these eight schemes, the one with the best performance merges the Parameter-elevation Regression on Independent Slopes Model (PRISM) in the daily climatology field and interpolates station observations into the ratio field using an inverse-distance weighting method. This scheme had median values of 0.78 for the correlation coefficient, 8.8 mm d -1 for the root-mean-square deviation, and 0.69 for the Kling–Gupta efficiency for comparisons between the 45 992 high-density gauge observations and the best interpolation scheme for the 0.1 ∘ latitude × longitude grid cells from 2015 to 2019. This scheme had the best overall performance, as it fully considers topographic effects in the daily climatology field and it balances local data fidelity and global fitting smoothness in the interpolation of the precipitation ratio field. Therefore, this scheme was used to construct a new long-term, gauge-based gridded precipitation dataset for the Chinese mainland (called CHM_PRE, as a member of the China Hydro-Meteorology dataset) with spatial resolutions of 0.5, 0.25, and 0.1 ∘ from 1961 to the present. This precipitation dataset is expected to facilitate the advancement of drought monitoring, flood forecasting, and hydrological modeling. Free access to the dataset can be found at 10.6084/m9.figshare.21432123.v4 (Han and Miao, 2022). [ABSTRACT FROM AUTHOR]

Paddy rice is the second-largest grain crop in China and plays an important role in ensuring global food security. However, there is no high-resolution map of rice covering all of China. This study developed a new rice-mapping method by combining optical and synthetic aperture radar (SAR) images in cloudy areas based on the time-weighted dynamic time warping (TWDTW) method and produced distribution maps of single-season rice in 21 provincial administrative regions of China from 2017 to 2022 at a 10 or 20 m resolution. The accuracy was examined using 108 195 survey samples and county-level statistical data. On average, the user's, producer's, and overall accuracy values over all investigated provincial administrative regions were 73.08 %, 82.81 %, and 85.23 %, respectively. Compared with the statistical data from 2017 to 2019, the distribution maps explained 83 % of the spatial variation of county-level planting areas on average. The distribution maps can be obtained at 10.57760/sciencedb.06963 (Shen et al., 2023). [ABSTRACT FROM AUTHOR]

Dams and reservoirs are human-made infrastructures that have attracted increasing attention because of their societal and environmental significance. Towards better management and conservation of reservoirs, a dataset of reservoir-catchment characteristics is needed, considering that the amount of water and material flowing into and out of reservoirs depends on their locations on the river network and the properties of the upstream catchment. To date, no dataset exists for reservoir-catchment characteristics. The aim of this study is to develop the first database featuring reservoir-catchment characteristics for 3254 reservoirs with storage capacity totaling 682 595 km3 (73.2 % of reservoir water storage capacity in China) to support the management and conservation of reservoirs in the context of catchment level. To ensure a more representative and accurate mapping of local variables of large reservoirs, reservoir catchments are delineated into full catchments (their full upstream contributing areas) and intermediate catchments (subtracting the area contributed by upstream reservoirs from the full upstream part of the current reservoir). Using both full catchments and intermediate catchments, characteristics of reservoir catchments were extracted, with a total of 512 attributes in six categories (i.e., reservoir and catchment body characteristics, topography, climate, soil and geology, land cover and use, and anthropogenic activity characteristics). Besides these static attributes, time series of 15 meteorological variables of catchments were extracted to support hydrological simulations for a better understanding of drivers of reservoir environment change. Moreover, we provide a comprehensive and extensive reservoir dataset on water level (data available for 20 % of 3254 reservoirs), water surface area (99 %), storage anomaly (92 %), and evaporation (98 %) from multisource satellites such as radar and laser altimeters and images from Landsat and Sentinel satellites. These products significantly enhance spatial and temporal coverage in comparison to existing similar products (e.g., 67 % increase in spatial resolution of water level and 225 % increase in storage anomaly) and contribute to our understanding of reservoir properties and functions within the Earth system by incorporated national or global hydrological modeling. In situ data of 138 reservoirs are employed in this study as a valuable reference for evaluation, thus enhancing our confidence in the data quality and enhancing our understanding of the accuracy of current satellite datasets. Along with its extensive attributes, the Reservoir dataset in China (Res-CN) can support a broad range of applications such as water resources, hydrologic/hydrodynamic modeling, and energy planning. Res-CN is on Zenodo through 10.5281/zenodo.7664489 (Shen et al., 2022c). [ABSTRACT FROM AUTHOR]

A substantial amount of CO 2 is released into the atmosphere from the process of the high-temperature decomposition of limestone to produce lime. However, during the lifecycle of lime production, the alkaline components of lime will continuously absorb CO 2 from the atmosphere during use and waste disposal. Here, we adopt an analytical model describing the carbonation process to obtain regional and global estimates of carbon uptake from 1930 to 2020 using lime lifecycle use-based material data. The results reveal that the global uptake of CO 2 by lime increased from 9.16 Mt C yr -1 (95 % confidence interval, CI: 1.84–18.76 Mt C) in 1930 to 34.84 Mt C yr -1 (95 % CI: 23.50–49.81 Mt C) in 2020. Cumulatively, approximately 1444.70 Mt C (95 % CI: 1016.24–1961.05 Mt C) was sequestered by lime produced between 1930 and 2020, corresponding to 38.83 % of the process emissions during the same period, mainly contributed from the utilization stage (76.21 % of the total uptake). We also fitted the missing lime output data of China from 1930 to 2001, thus compensating for the lack of China's lime production (cumulative 7023.30 Mt) and underestimation of its carbon uptake (467.85 Mt C) in the international data. Since 1930, lime-based materials in China have accounted for the largest proportion (about 63.95 %) of the global total. Our results provide data to support including lime carbon uptake into global carbon budgets and scientific proof for further research of the potential of lime-containing materials in carbon capture and storage. The data utilized in the present study can be accessed at 10.5281/zenodo.7896106 (Ma et al., 2023). [ABSTRACT FROM AUTHOR]

China is facing the challenge of synergistic reduction of air pollutants and CO 2 emissions. However, the studies on its historical progress and future priorities are insufficient. This study compiled China's emission inventory of air pollutants and CO 2 from 2005 to 2021 (ABaCAS-EI v2.0 dataset) based on a unified emission-source framework by considering the influences of activity level, technology evolution, and emission control policies. The characteristics of air pollutants and CO 2 emissions were comprehensively analyzed from multiple dimensions such as time, space, sector, and synergies between air pollutants and CO 2 emissions. Mitigation policies have decoupled the emissions of air pollutants and CO 2 with economic development in China since 2013. In the context of growing activity levels, energy structure adjustment and energy and material saving reduced the average annual increase rate of CO 2 emissions by 7 % after 2011. Based on this, end-of-pipe control contributed 51 %–98 % of air pollutant emission reductions after 2013. Industrial boilers and residential fossil fuel combustion sectors in seven provinces (Beijing, Tianjin, Shanghai, Jilin, Henan, Sichuan, and Qinghai) achieved emission reductions in both air pollutants and CO 2 during 2013–2021. The declining trends in both the sectoral and regional emission ratios of air pollutants to CO 2 indicated that the potential for synergistic emission reduction in China declined from 2013 to 2021. The emission ratios in 2021 showed that residential fossil fuel combustion, iron and steel industry, and transportation exhibited relatively higher co-benefits of SO 2 , PM 2.5 , NOx , and VOC emission reductions when CO 2 emissions were reduced. Most cities with a higher potential to synergistically reduce NOx , VOC, and CO 2 emissions were within the Yangtze River Economic Belt, while those with a higher potential to co-control SO 2 and CO 2 , and PM 2.5 and CO 2 were in southern and northeast China, respectively. Further deconstruction of the sectoral emissions in 2021 suggested future reduction measures: for example, controlling coal consumption in the energy field; promoting innovative technologies with low air pollutant emission intensities and coal-saving measures in the iron and steel industry; combining coal and carbonate replacement technologies with separated particle control measures in the cement industry; and controlling light-duty passenger vehicles, heavy-duty trucks, agricultural machinery, and inland water transport in the transportation sector. Our dataset and findings provide insights into the co-control of air pollutants and CO 2 emissions in the future in China and other countries with the same demand. Our ABaCAS-EI v2.0 dataset can be accessed from 10.6084/m9.figshare.21777005.v1 (S. Li et al., 2022) by species, sector, and province. [ABSTRACT FROM AUTHOR]

Wildfires exert strong influences on the environment, ecology, economy and public security. However, the existing hourly Himawari-8 fire product produced by the Japan Meteorological Agency (JMA) presents large uncertainties and is not suitable for reliable real-time fire monitoring in China. To fill this gap, the National Satellite Meteorological Center (NSMC) proposed an adaptive hourly Himawari-8 fire product for China based on the original Himawari-8 source by employing a dynamical threshold for fire extraction and a database of ground thermal sources. According to the visually extracted reference and consistency check, we found that the NSMC-Himawari-8 fire product effectively removed a majority of false fire alarms included in the original Himawari-8 fire product. Based on a rare field-collected ground reference dataset, we evaluated the reliability of original Himawari-8 and NSMC-Himawari-8 fire products across China. The overall accuracy of the raw Himawari-8 fire product was 54 % and 59 % (not considering the omission errors), respectively. As a comparison, by identifying more real fire pixels and avoiding a majority of false fire alarms, the overall accuracy of the NSMC-Himawari-8 fire product was 80 % and 84 % (not considering the omission errors), respectively, making it an ideal source for improved real-time fire monitoring across China. This research also provides a useful reference for employing a local dataset of underlying surfaces and thermal sources to enhance the accuracy of global fire products in specific regions. The NSMC-Himawari-8 fire product can be downloaded at http://figshare.com (last access: 17 April 2023) with the following DOI: 10.6084/m9.figshare.21550248 (Chen et al., 2022a). [ABSTRACT FROM AUTHOR]

The South China Sea (SCS) is the largest marginal sea of the North Pacific Ocean, where intensive field observations, including mappings of the sea surface partial pressure of CO 2 (p CO 2), have been conducted over the last 2 decades. It is one of the most studied marginal seas in terms of carbon cycling and could thus be a model system for marginal sea carbon research. However, the cruise-based sea surface p CO 2 datasets are still temporally and spatially sparse. Using a machine-learning-based method facilitated by empirical orthogonal function (EOF) analysis, this study provides a reconstructed dataset of the monthly sea surface p CO 2 in the SCS with a reasonably high spatial resolution (0.05 ∘ × 0.05 ∘) and temporal coverage between 2003 and 2020. The data input to our model includes remote-sensing-derived sea surface salinity, sea surface temperature, and chlorophyll, the spatial pattern of p CO 2 constrained by EOF, atmospheric p CO 2 , and time labels (month). We validated our reconstruction with three independent testing datasets that are not involved in the model training. Among them, Test 1 includes 10 % of our in situ data, Test 2 contains four independent in situ datasets corresponding to the four seasons, and Test 3 is an in situ monthly dataset available from 2003–2019 at the South East Asia Time-series Study (SEATs) station located in the northern basin of the SCS. Our Test 1 validation demonstrated that the reconstructed p CO 2 field successfully simulated the spatial and temporal patterns of sea surface p CO 2 observations. The root mean square error (RMSE) between our reconstructed data and in situ data in Test 1 averaged ∼10 µatm , which is much smaller (by ∼50 %) than that between the remote-sensing-derived data and in situ data. Test 2 verified the accuracy of our retrieval algorithm in months lacking observations, showing a relatively small bias (RMSE of ∼8 µatm). Test 3 evaluated the accuracy of the reconstructed long-term trend, showing that, at the SEATs station, the difference between the reconstructed p CO 2 and in situ data ranged from -10 to 4 µatm (-2.5 % to 1 %). In addition to the typical machine learning performance metrics, we assessed the uncertainty resulting from reconstruction bias and its feature sensitivity. These validations and uncertainty analyses strongly suggest that our reconstruction effectively captures the main spatial and temporal features of sea surface p CO 2 distributions in the SCS. Using the reconstructed dataset, we show the long-term trends of sea surface p CO 2 in five subregions of the SCS with differing physicobiogeochemical characteristics. We show that mesoscale processes such as the Pearl River plume and China coastal currents significantly impact sea surface p CO 2 in the SCS during different seasons. While the SCS is overall a weak source of atmospheric CO 2 , the northern SCS acts as a sink, showing a trend of increasing strength over the past 2 decades. The data used in this article are available at 10.57760/sciencedb.02050 (Wang and Dai, 2022). [ABSTRACT FROM AUTHOR]

To quantify the ecological consequences of recent nationwide restoration efforts in China, spatially explicit information on forest biomass carbon stock changes over the past 20 years is critical. However, long-term biomass tracking at the national scale remains challenging as it requires continuous and high-resolution monitoring. Here, we characterize the changes in the above- and belowground biomass carbon (AGBC and BGBC) of forests in China between 2002 and 2021 at 1 km spatial resolution by integrating multiple types of remote sensing observations with intensive field measurements through regression and machine learning approaches. On average, 8.6 ± 0.6 and 2.2 ± 0.1 PgC were stored in above- and belowground live forests in China. Over the last 20 years, the total forest biomass carbon pool in China has increased at a rate of 114.5 ± 16.3 TgC yr -1 (approximately 1.1 % yr -1). The most pronounced forest biomass carbon stock gains occurred in central to southern China, including the southern Loess Plateau, Qinling mountains, southwestern karsts and southeastern forests. While the combined use of multi-source remote sensing data provides a powerful tool to assess the forest biomass carbon changes, future research is also needed to explore the drivers of the observed woody biomass trends and to evaluate the degree to which biomass gains will translate into biodiverse, healthy ecosystems that are sustainable. Annual forest above- and belowground biomass maps for China are now available at 10.6084/m9.figshare.21931161.v1 (Chen, 2023). [ABSTRACT FROM AUTHOR]

Urban green space (UGS) is an important component in the urban ecosystem and has great significance to the urban ecological environment. Although the development of remote sensing platforms and deep learning technologies have provided opportunities for UGS mapping from high-resolution images (HRIs), challenges still exist in its large-scale and fine-grained application due to insufficient annotated datasets and specially designed methods for UGS. Moreover, the domain shift between images from different regions is also a problem that must be solved. To address these issues, a general deep learning (DL) framework is proposed for UGS mapping in the large scale, and fine-grained UGS maps of 31 major cities in mainland China are generated (UGS-1m). The DL framework consists of a generator and a discriminator. The generator is a fully convolutional network designed for UGS extraction (UGSNet), which integrates attention mechanisms to improve the discrimination to UGS, and employs a point-rending strategy for edge recovery. The discriminator is a fully connected network aiming to deal with the domain shift between images. To support the model training, an urban green space dataset (UGSet) with a total number of 4544 samples of 512×512 in size is provided. The main steps to obtain UGS-1m can be summarized as follows: (a) first, the UGSNet will be pre-trained on the UGSet in order to obtain a good starting training point for the generator. (b) After pre-training on the UGSet, the discriminator is responsible for adapting the pre-trained UGSNet to different cities through adversarial training. (c) Finally, the UGS results of 31 major cities in China (UGS-1m) are obtained using 2179 Google Earth images with a data frame of 7 ′ 30 ′′ in longitude and 5 ′ 00 ′′ in latitude and a spatial resolution of nearly 1.1 m. An evaluation of the performance of the proposed framework by samples from five different cities shows the validity of the UGS-1m products, with an average overall accuracy (OA) of 87.56 % and an F1 score of 74.86 %. Comparative experiments on UGSet with the existing state-of-the-art (SOTA) DL models proves the effectiveness of UGSNet as the generator, with the highest F1 score of 77.30 %. Furthermore, an ablation study on the discriminator fully reveals the necessity and effectiveness of introducing the discriminator into adversarial learning for domain adaptation. Finally, a comparison with existing products further shows the feasibility of the UGS-1m and the great potential of the proposed DL framework. The UGS-1m can be downloaded from 10.57760/sciencedb.07049. [ABSTRACT FROM AUTHOR]

We present the first gridded and temporally continuous quantitative pollen-based plant-cover reconstruction for temperate and northern subtropical China over the Holocene (11.7 ka to present) obtained by applying the Regional Estimates of VEgetation Abundance from Large Sites (REVEALS) model. The objective is to provide a dataset of pollen-based land cover for the last ca. 12 millennia that is suitable for palaeoclimate modelling and for the evaluation of simulated past vegetation cover from dynamic vegetation models and anthropogenic land-cover change (ALCC) scenarios. The REVEALS reconstruction was achieved using 94 selected pollen records from lakes and bogs at a 1 ∘ × 1 ∘ spatial scale and a temporal resolution of 500 years between 11.7 and 0.7 ka and in three recent time windows (0.7–0.35 ka, 0.35–0.1 ka, and 0.1 ka to present). The dataset includes REVEALS estimates of cover and their standard errors (SEs) for 27 plant taxa in 75 1 ∘ × 1 ∘ grid cells distributed within the study region. The 27 plant taxa were also grouped into 6 plant functional types and 3 land-cover types (coniferous trees CT, broadleaved trees BT, and C 3 herbs/open land (C 3 H/OL)), and their REVEALS estimates of cover and related SEs were calculated. We describe the protocol used for the selection of pollen records and the REVEALS application (with parameter settings) and explain the major rationales behind the protocol. As an illustration, we present, for eight selected time windows, gridded maps of the pollen-based REVEALS estimates of cover for the three land-cover types (CT, BT, and C 3 H/OL). We then discuss the reliability and limitations of the Chinese dataset of Holocene gridded REVEALS plant cover, and its current and potential uses. The dataset is available at the National Tibetan Plateau Data Center (TPDC; Li, 2022; 10.11888/Paleoenv.tpdc.272292). [ABSTRACT FROM AUTHOR]

Soil moisture (SM) is a key variable of the regional hydrological cycle and has important applications for water resource and agricultural drought management. Various global soil moisture products have been mostly retrieved from microwave remote sensing data. However, currently there is rarely spatially explicit and time-continuous soil moisture information with a high resolution at the national scale. In this study, we generated a 1 km soil moisture dataset for dryland wheat and maize in China (ChinaCropSM1 km) over 1993–2018 through a random forest (RF) algorithm based on numerous in situ daily observations of soil moisture. We independently used in situ observations (181 327 samples) from the agricultural meteorological stations (AMSs) across China for training (164 202 samples) and others for testing (17 125 samples). An irrigation module was first developed according to crop type (i.e., wheat, maize), soil depth (0–10, 10–20 cm) and phenology. We produced four daily datasets separately by crop type and soil depth, and their accuracies were all satisfactory (wheat r 0.93, ubRMSE 0.033 m 3 m -3 ; maize r 0.93, ubRMSE 0.035 m 3 m -3). The spatiotemporal resolutions and accuracy of ChinaCropSM1 km were significantly better than those of global soil moisture products (e.g., r increased by 116 %, ubRMSE decreased by 64 %), including the global remote-sensing-based surface soil moisture dataset (RSSSM) and the European Space Agency (ESA) Climate Change Initiative (CCI) SM. The approach developed in our study could be applied to other regions and crops in the world, and our improved datasets are very valuable for many studies and field management, such as agricultural drought monitoring and crop yield forecasting. The data are published in Zenodo at https://doi.org/10.5281/zenodo.6834530 (wheat 0–10) (Cheng et al., 2022a), https://doi.org/10.5281/zenodo.6822591 (wheat 10–20) (Cheng et al., 2022b), https://doi/org/10.5281/zenodo.6822581 (maize 0–10) (Cheng et al., 2022c) and https://doi.org/10.5281/zenodo.6820166 (maize 10–20) (Cheng et al., 2022d). [ABSTRACT FROM AUTHOR]

Human-perceived thermal comfort (known as human-perceived temperature) measures the combined effects of multiple meteorological factors (e.g., temperature, humidity, and wind speed) and can be aggravated under the influences of global warming and local human activities. With the most rapid urbanization and the largest population, China is being severely threatened by aggravating human thermal stress. However, the variations of thermal stress in China at a fine scale have not been fully understood. This gap is mainly due to the lack of a high-resolution gridded dataset of human thermal indices. Here, we generated the first high spatial resolution (1 km) dataset of monthly human thermal index collection (HiTIC-Monthly) over China during 2003–2020. In this collection, 12 commonly used thermal indices were generated by the Light Gradient Boosting Machine (LightGBM) learning algorithm from multi-source data, including land surface temperature, topography, land cover, population density, and impervious surface fraction. Their accuracies were comprehensively assessed based on the observations at 2419 weather stations across the mainland of China. The results show that our dataset has desirable accuracies, with the mean R2 , root mean square error, and mean absolute error of 0.996, 0.693 ∘ C, and 0.512 ∘ C, respectively, by averaging the 12 indices. Moreover, the data exhibit high agreements with the observations across spatial and temporal dimensions, demonstrating the broad applicability of our dataset. A comparison with two existing datasets also suggests that our high-resolution dataset can describe a more explicit spatial distribution of the thermal information, showing great potentials in fine-scale (e.g., intra-urban) studies. Further investigation reveals that nearly all thermal indices exhibit increasing trends in most parts of China during 2003–2020. The increase is especially significant in North China, Southwest China, the Tibetan Plateau, and parts of Northwest China, during spring and summer. The HiTIC-Monthly dataset is publicly available from Zenodo at 10.5281/zenodo.6895533 (Zhang et al., 2022a). [ABSTRACT FROM AUTHOR]

Reservoirs and dams are essential infrastructure in water management; thus, information of their surface water area (SWA), water surface elevation (WSE), and reservoir water storage change (RWSC) is crucial for understanding their properties and interactions in hydrological and biogeochemical cycles. However, knowledge of these reservoir characteristics is scarce or inconsistent at the national scale. Here, we introduce comprehensive reservoir datasets of 338 reservoirs in China, with a total of 470.6 km 3 storage capacity (50 % Chinese reservoir storage capacity). Given the scarcity of publicly available gauged observations and operational applications of satellites for hydrological cycles, we utilize multiple satellite altimetry missions (SARAL/AltiKa, Sentinel-3A and Sentinel-3B, CroySat-2, Jason-3, and ICESat-2) and imagery data from Landsat and Sentinel-2 to produce a comprehensive reservoir dataset on the WSE, SWA, and RWSC during 2010–2021. Validation against gauged measurements of 93 reservoirs demonstrates the relatively high accuracy and reliability of our remotely sensed datasets. (1) Across gauge comparisons of RWSC, the median statistics of the Pearson correlation coefficient (CC), normalized root mean square error (NRMSE), and root mean square error (RMSE) are 0.89, 11 %, and 0.021 km 3 , with a total of 91 % validated reservoirs (83 of 91) having good RMSE from 0.002 to 0.31 km 3 and NRMSE values smaller than 20 %. (2) Comparisons of WSE retracked by six satellite altimeters and gauges show good agreement. Specifically, the percentages of reservoirs having good and moderate RMSE values smaller than 1.0 m for CryoSat-2 (validated in 30 reservoirs), SARAL/AltiKa (9), Sentinel-3A (34), Sentinel-3B (25), Jason-3 (11), and ICESat-2 (26) are 77 %, 75 %, 79 %, 87 %, 81 %, and 82 %, respectively. By taking advantages of six satellite altimeters, we are able to densify WSE observations across spatiotemporal scales. Statistically, around 96 % of validated reservoirs (71 of 74) have RMSE values below 1.0 m, while 57 % of reservoirs (42 of 74) have good data quality with RMSE values below 0.6 m. Overall, our study fills such a data gap with regard to comprehensive reservoir information in China and provides strong support for many aspects such as hydrological processes, water resources, and other studies. The dataset is publicly available on Zenodo at https://doi.org/10.5281/zenodo.7251283 (Shen et al., 2021). [ABSTRACT FROM AUTHOR]

Accurate high-resolution actual evapotranspiration (ET) and gross primary production (GPP) information is essential for understanding the large-scale water and carbon dynamics. However, substantial uncertainties exist in the current ET and GPP datasets in China because of insufficient local ground measurements used for model constraint. This study utilizes a water–carbon coupled model, Penman–Monteith–Leuning Version 2 (PML-V2), to estimate 500 m ET and GPP at a daily scale. The parameters of PML-V2(China) were well calibrated against observations of 26 eddy covariance flux towers across nine plant functional types in China, indicated by a Nash–Sutcliffe efficiency (NSE) of 0.75 and a root mean square error (RMSE) of 0.69 mm d -1 for daily ET, respectively, and a NSE of 0.82 and a RMSE of 1.71 g C m -2 d -1 for daily GPP. The model estimates get a small Bias of 6.28 % and a high NSE of 0.82 against water-balance annual ET estimates across 10 major river basins in China. Further evaluations suggest that the newly developed product is better than other typical products (MOD16A2, SEBAL, GLEAM, MOD17A2H, VPM, and EC-LUE) in estimating both ET and GPP. Moreover, PML-V2(China) accurately monitors the intra-annual variations in ET and GPP in the croplands with a dual-cropping system. The new data showed that, during 2001–2018, the annual GPP and water use efficiency experienced a significant (p<0.001) increase (8.99 g C m -2 yr -2 and 0.02 g C mm -1 H 2 O yr -1 , respectively), but annual ET showed a non-significant (p>0.05) increase (0.43 mm yr -2). This indicates that vegetation in China exhibits a huge potential for carbon sequestration with little cost in water resources. The PML-V2(China) product provides a great opportunity for academic communities and various agencies for scientific studies and applications, freely available at 10.11888/Terre.tpdc.272389 (Zhang and He, 2022). [ABSTRACT FROM AUTHOR]

High-quality gridded soil moisture products are essential for many Earth system science applications, while the recent reanalysis and remote sensing soil moisture data are often available at coarse resolution and remote sensing data are only for the surface soil. Here, we present a 1 km resolution long-term dataset of soil moisture derived through machine learning trained by the in situ measurements of 1789 stations over China, named SMCI1.0 (Soil Moisture of China by in situ data, version 1.0). Random forest is used as a robust machine learning approach to predict soil moisture using ERA5-Land time series, leaf area index, land cover type, topography and soil properties as predictors. SMCI1.0 provides 10-layer soil moisture with 10 cm intervals up to 100 cm deep at daily resolution over the period 2000–2020. Using in situ soil moisture as the benchmark, two independent experiments were conducted to evaluate the estimation accuracy of SMCI1.0: year-to-year (ubRMSE ranges from 0.041 to 0.052 and R ranges from 0.883 to 0.919) and station-to-station experiments (ubRMSE ranges from 0.045 to 0.051 and R ranges from 0.866 to 0.893). SMCI1.0 generally has advantages over other gridded soil moisture products, including ERA5-Land, SMAP-L4, and SoMo.ml. However, the high errors of soil moisture are often located in the North China Monsoon Region. Overall, the highly accurate estimations of both the year-to-year and station-to-station experiments ensure the applicability of SMCI1.0 to study the spatial–temporal patterns. As SMCI1.0 is based on in situ data, it can be a useful complement to existing model-based and satellite-based soil moisture datasets for various hydrological, meteorological, and ecological analyses and models. The DOI link for the dataset is http://dx.doi.org/10.11888/Terre.tpdc.272415 (Shangguan et al., 2022). [ABSTRACT FROM AUTHOR]

Aerosols are complex compounds that greatly affect the global radiation balance and climate system and even human health; in addition, aerosols are currently a large source of uncertainty in the numerical simulation process. The arid and semi-arid areas have fragile ecosystems with abundant dust but lack related high-accuracy aerosol data. To solve these problems, we use the bagging trees ensemble model, based on 1 km aerosol optical depth (AOD) data and multiple environmental covariates, to produce a monthly advanced-performance, full-coverage, and high-resolution (250 m) AOD product (named FEC AOD, fusing environmental covariates AOD) covering the arid and semi-arid areas. Then, based on the FEC AOD products, we analyzed the spatiotemporal AOD pattern and further discussed the interpretation of environmental covariates to AOD. The results show that the bagging trees ensemble model has a good performance, with its verification R2 values always remaining at 0.90 and the R2 being 0.79 for FEC AOD compared with AERONET AOD product. The high-AOD areas are located in the Taklimakan Desert and on the Loess Plateau, and the low-AOD areas are concentrated in southern Qinghai province. The higher the AOD, the stronger the interannual variability. Interestingly, the AOD reflected a dramatic decrease on the Loess Plateau and an evident increase in the south-eastern Taklimakan Desert, while the southern Qinghai province AODs showed almost no significant change between 2000 and 2019. The annual variation characteristics show that the AOD was largest in spring (0.267±0.200) and smallest in autumn (0.147±0.089); the annual AOD variation pattern showed different features, with two peaks in March and August over Gansu province but only one peak in April in other provinces/autonomous regions. Farmlands and construction lands have high AOD levels compared to other land cover types. Meteorological factors demonstrate the maximum interpretation ability of the AODs on all set temporal scales, followed by the terrain factors, while surface properties have the smallest explanatory abilities; the corresponding average contributions are 77.1 %, 59.1 %, and 50.4 %, respectively. The capability of the environmental covariates to explain the AOD varies seasonally in the following sequence: winter (86.6 %) > autumn (80.8 %) > spring (79.9 %) > summer (72.5 %). In this research, we provide a pathbreaking high spatial resolution (250 m) and long time series (2000–2019) FEC AOD dataset covering arid and semi-arid regions to support atmospheric and related studies in northwest China; the full dataset is available at 10.5281/zenodo.5727119 (Chen et al., 2021b). [ABSTRACT FROM AUTHOR]

China ranks in the highest position for nitrogen (N) fertilizer consumption in the world. Although N fertilizer use has greatly contributed to the China's food production, this has also caused an unprecedented alteration in the biogeochemical cycles and endangered terrestrial and aquatic ecosystems. Existing use of N fertilizers in China, as shown by digital maps, is usually coarse in resolution and intermittently covered with a biased gridded dataset. Here, we have reconstructed a historical, annual N fertilizer use dataset in China and resampled it to 5km×5km resolution, covering the period from 1952 to 2018 by integrating improved cropland maps. Results showed that most of the N input was directly applied as N-only fertilizer, while the contribution from compound fertilizers has ranged between 16 % and 24 % since 1980. The national total N fertilizer input increased from 0.06 Tg N yr -1 (0.05 g N m -2 yr -1) in 1952 to 31.15 Tg N yr -1 (18.83 g N m -2 yr -1) in 2014 and then decreased to 28.31 Tg N yr -1 (17.06 g N m -2 yr -1) in 2018. Despite the total N input decreasing by 9.1 % (2.84 Tg N yr -1) from 2014 to 2018, the N input from compound fertilizers has increased by 6 % (0.43 Tg N yr -1) during the corresponding period. The previous Food and Agriculture Organization (FAO) data-based N fertilizer products in China overestimated N use in low cropland coverage areas but underestimated N use in high cropland coverage areas. However, our newly reconstructed data have not only corrected the existing biases and improved the spatial distribution but have also shown that vegetable and other crops (e.g., orchards), but not grain crops, are the most intensively fertilized crops in China, implying the importance of quantifying greenhouse gas (GHG) emissions from these croplands. We argue that the reconstructed, spatially explicit N fertilizer use data in this study are expected to contribute to better understanding of biogeochemical cycles, including the simulations of GHG emissions and food production in China. The spatially explicit N fertilizer use and the crop-specific N fertilizer use datasets are available via an open data repository (10.6084/m9.figshare.21371469.v1; Yu, 2022). [ABSTRACT FROM AUTHOR]

The thermal state of permafrost in the present and future is fundamental to ecosystem evolution, hydrological processes, carbon release and infrastructure integrity in cold regions. In 2011, we initiated a permafrost monitoring network along the China–Russia crude oil pipeline (CRCOP) route at the eastern flank of the northern Da Xing'anling Mountains in Northeast China. We compiled an integrated dataset of the ground thermal state along the CRCOP route consisting of meteorological data near the southern limit of latitudinal permafrost, ground temperature (GT) data in 20 boreholes with depths of 10.0–60.6 m, soil volumetric liquid water contents (VWCs) and 2D electrical resistivity tomography (ERT) data at different sites. Results demonstrate a permafrost warming during 2011–2020 in the vicinity of the southern limit of latitudinal permafrost, as manifested by rising GTs at almost all depths in response to climate warming. Local thermal disturbances triggered by the construction and operation of CRCOPs have resulted in significant permafrost warming and subsequent thawing on the right-of-way (ROW) of the pipelines. This permafrost thaw will persist, but it can be alleviated by adopting mitigative measures, such as an insulation layer and thermosyphons. The in situ observational dataset is of great value for assessing the variability of permafrost under the linear disturbances of the CRCOPs and related environmental effects, for understanding hydro–thermal–mechanical interactions between the buried pipelines and permafrost foundation soils, and for evaluating the operational and structural integrity of the pipeline systems in the future. The dataset is available at the National Tibetan Plateau/Third Pole Environment Data Center (10.11888/Cryos.tpdc.272357; Li, 2022). [ABSTRACT FROM AUTHOR]

Accurate long-term temperature and precipitation estimates at high spatial and temporal resolutions are vital for a wide variety of climatological studies. We have produced a new, publicly available, daily, gridded maximum temperature, minimum temperature, and precipitation dataset for China with a high spatial resolution of 1 km that covers a long-term period (1961 to 2019). It has been named the HRLT, and the dataset is publicly available at 10.1594/PANGAEA.941329 (Qin and Zhang, 2022). In this study, the daily gridded data were interpolated using comprehensive statistical analyses, which included machine learning methods, the generalized additive model, and thin plate splines. It was based on the 0.5 ∘ × 0.5 ∘ gridded dataset from the China Meteorological Administration, together with covariates for elevation, aspect, slope, topographic wetness index, latitude, and longitude. The accuracy of the HRLT daily dataset was assessed using observation data from meteorological stations across China. The maximum and minimum temperature estimates were more accurate than the precipitation estimates. For maximum temperature, the mean absolute error (MAE), root mean square error (RMSE), Pearson's correlation coefficient (Cor), coefficient of determination after adjustment (R2), and Nash–Sutcliffe modeling efficiency (NSE) were 1.07 ∘ C, 1.62 ∘ C, 0.99, 0.98, and 0.98, respectively. For minimum temperature, the MAE, RMSE, Cor, R2 , and NSE were 1.08 ∘ C, 1.53 ∘ C, 0.99, 0.99, and 0.99, respectively. For precipitation, the MAE, RMSE, Cor, R2 , and NSE were 1.30 mm, 4.78 mm, 0.84, 0.71, and 0.70, respectively. The accuracy of the HRLT was compared to those of three other existing datasets, and its accuracy was either greater than the others, especially for precipitation, or comparable in accuracy, but with higher spatial resolution or over a longer time period. In summary, the HRLT dataset, which has a high spatial resolution, covers a longer period of time and has reliable accuracy. [ABSTRACT FROM AUTHOR]

Millet agriculture, i.e., broomcorn millet (Panicum miliaceum) and foxtail millet (Setaria italica), initially originated in northern China and provided the basis for the emergence of the first state in the Central Plains. However, owing to the lack of a comprehensive archaeobotanical dataset, when, where, and how these two millet types evolved across different regions and periods remains unclear. Here, we presented a dataset of archaeobotanical macroremains (n=538) spanning the Neolithic and Bronze ages in northern China and suggested a significant spatiotemporal divergence of millet agriculture in the subhumid mid-lower Yellow River (MLY) and semiarid agro-pastoral ecotone (APE). The key timing of the diffusion and transition of millet agriculture occurred around 6000 cal. a BP, coinciding with the Holocene Optimum (8000–6000 cal. a BP) and Miaodigou Age (6200–5500 cal. a BP). It spread westward and northward from the MLY to APE and underwent a dramatic transition from low-yield broomcorn millet to high-yield foxtail millet. The combined influence of warm-wet climate, population pressure, and field management may have promoted the intensification, diffusion, and transition of millet agriculture around 6000 cal. a BP. Thereafter, the cropping patterns in the MLY were predominated by foxtail millet (∼ 80 %), while those in APE focused on both foxtail (∼ 60 %) and broomcorn millet under a persistent drying trend since the mid-Holocene. This study provided the first quantitative spatiotemporal cropping patterns during the Neolithic and Bronze ages in northern China, which can be used for evaluating prehistoric human subsistence, discussing past human–environment interaction, and providing a valuable perspective of agricultural sustainability for the future. The dataset is publicly available at https://doi.org/10.5281/zenodo.6669730 (He et al., 2022). [ABSTRACT FROM AUTHOR]

With rapid population growth and socioeconomic development over the last century, a great number of dams/reservoirs have been constructed globally to meet various needs. China has strong economical and societal demands for constructing dams and reservoirs. The official statistics reported more than 98 000 dams/reservoirs in China, including nearly 40 % of the world's largest dams. Despite the availability of several global-scale dam/reservoir databases (e.g., the Global Reservoir and Dam database (GRanD), the GlObal geOreferenced Database of Dams (GOODD), and the Georeferenced global Dams And Reservoirs (GeoDAR)), these databases have insufficient coverage of the reservoirs in China, especially for small or newly constructed ones. The lack of reservoir information impedes the estimation of water budgets and the evaluation of dam impacts on hydrologic and nutrient fluxes for China and its downstream countries. Therefore, we presented the China Reservoir Dataset (CRD), which contains 97 435 reservoir polygons and fundamental attribute information (e.g., name and storage capacity) based on existing dam/reservoir products, national basic geographic datasets, multi-source open map data, and multi-level governmental yearbooks and databases. The reservoirs compiled in the CRD have a total maximum water inundation area of 50 085.21 km 2 and a total storage capacity of about 979.62 km 3 (924.96–1060.59 km 3). The quantity of reservoirs decreases from the southeast to the northwest, and the density hotspots mainly occur in hilly regions and large plains, with the Yangtze River basin dominating in reservoir count, area, and storage capacity. We found that these spatial accumulations of reservoirs are closely related to China's socioeconomic development and the implementation of major policies. Finally, we presented the comparison of the CRD with GOODD, GeoDAR, and GRanD databases. The CRD has significantly increased the reservoir count, area, and storage capacity in China, especially for reservoirs smaller than 1 km 2. The CRD database provides more comprehensive reservoir spatial and attribute information and is expected to benefit water resources managements and the understanding of ecological and environmental impacts of dams across China and its affected transboundary basins. The CRD database is publicly available at 10.5281/zenodo.6984619 (Song et al., 2022). [ABSTRACT FROM AUTHOR]

Roadside noise barriers (RNBs) are important urban infrastructures to ensure that cities remain liveable. However, the absence of accurate and large-scale geospatial data on RNBs has impeded the increasing progress of rational urban planning, sustainable cities, and healthy environments. To address this problem, this study creates a vectorized RNB dataset in China using street view imagery and a geospatial artificial intelligence framework. First, intensive sampling is performed on the road network of each city based on OpenStreetMap, which is used as the georeference for downloading 6×106 Baidu Street View (BSV) images. Furthermore, considering the prior geographic knowledge contained in street view images, convolutional neural networks incorporating image context information (IC-CNNs) based on an ensemble learning strategy are developed to detect RNBs from the BSV images. The RNB dataset presented by polylines is generated based on the identified RNB locations, with a total length of 2667.02 km in 222 cities. Last, the quality of the RNB dataset is evaluated from two perspectives, i.e., the detection accuracy and the completeness and positional accuracy. Specifically, based on a set of randomly selected samples containing 10 000 BSV images, four quantitative metrics are calculated, with an overall accuracy of 98.61 %, recall of 87.14 %, precision of 76.44 %, and F1 score of 81.44 %. A total length of 254.45 km of roads in different cities are manually surveyed using BSV images to evaluate the mileage deviation and overlap level between the generated and surveyed RNBs. The root mean squared error for the mileage deviation is 0.08 km, and the intersection over union for overlay level is 88.08 % ± 2.95 %. The evaluation results suggest that the generated RNB dataset is of high quality and can be applied as an accurate and reliable dataset for a variety of large-scale urban studies, such as estimating the regional solar photovoltaic potential, developing 3D urban models, and designing rational urban layouts. Besides that, the benchmark dataset of the labeled BSV images can also support more work on RNB detection, such as developing more advanced deep learning algorithms, fine-tuning the existing computer vision models, and analyzing geospatial scenes in BSV. The generated vectorized RNB dataset and the benchmark dataset of labeled BSV imagery are publicly available at https://doi.org/10.11888/Others.tpdc.271914 (Chen, 2021). [ABSTRACT FROM AUTHOR]

Photovoltaic (PV) technology, an efficient solution for mitigating the impacts of climate change, has been increasingly used across the world to replace fossil fuel power to minimize greenhouse gas emissions. With the world's highest cumulative and fastest built PV capacity, China needs to assess the environmental and social impacts of these established PV power plants. However, a comprehensive map regarding the PV power plants' locations and extent remains scarce on the country scale. This study developed a workflow, combining machine learning and visual interpretation methods with big satellite data, to map PV power plants across China. We applied a pixel-based random forest (RF) model to classify the PV power plants from composite images in 2020 with a 30 m spatial resolution on the Google Earth Engine (GEE). The resulting classification map was further improved by a visual interpretation approach. Eventually, we established a map of PV power plants in China by 2020, covering a total area of 2917 km2. We found that most PV power plants were situated on cropland, followed by barren land and grassland, based on the derived national PV map. In addition, the installation of PV power plants has generally decreased the vegetation cover. This new dataset is expected to be conducive to policy management, environmental assessment, and further classification of PV power plants. The dataset of photovoltaic power plant distribution in China by 2020 is available to the public at 10.5281/zenodo.6849477 (Zhang et al., 2022). [ABSTRACT FROM AUTHOR]

Data and knowledge of surface water bodies (SWB), including large lakes and reservoirs (surface water areas > 1 km 2), are critical for the management and sustainability of water resources. However, the existing global or national dam datasets have large georeferenced coordinate offsets for many reservoirs, and some datasets have not reported reservoirs and lakes separately. In this study, we generated China's surface water bodies, Large Dams, Reservoirs, and Lakes (China-LDRL) dataset by analyzing all available Landsat imagery in 2019 (19 338 images) in Google Earth Engine and very-high spatial resolution imagery in Google Earth Pro. There were ∼ 3.52 × 10 6 yearlong SWB polygons in China for 2019, only 0.01 × 10 6 of them (0.43 %) were of large size (> 1 km 2). The areas of these large SWB polygons accounted for 83.54 % of the total 214.92 × 10 3 km 2 yearlong surface water area (SWA) in China. We identified 2418 large dams, including 624 off-stream dams and 1794 on-stream dams, 2194 large reservoirs (16.35 × 10 3 km 2), and 3051 large lakes (73.38 × 10 3 km 2). In general, most of the dams and reservoirs in China were distributed in South China, East China, and Northeast China, whereas most of lakes were located in West China, the lower Yangtze River basin, and Northeast China. The provision of the reliable, accurate China-LDRL dataset on large reservoirs/dams and lakes will enhance our understanding of water resources management and water security in China. The China-LDRL dataset is publicly available at 10.6084/m9.figshare.16964656.v3 (Wang et al., 2021b). [ABSTRACT FROM AUTHOR]

Artificial impervious surface area (ISA) documents the human footprint. Accurate, timely, and detailed ISA datasets are therefore essential for global climate change studies and urban planning. However, due to the lack of sufficient training samples and operational mapping methods, global ISA datasets at a 10 m resolution are still lacking. To this end, we proposed a global ISA mapping method leveraging multi-source geospatial data. Based on the existing satellite-derived ISA maps and crowdsourced OpenStreetMap (OSM) data, 58 million training samples were extracted via a series of temporal, spatial, spectral, and geometric rules. We then produced a 10 m resolution global ISA dataset (GISA-10m) from over 2.7 million Sentinel optical and radar images on the Google Earth Engine platform. Based on test samples that are independent of the training set, GISA-10m achieves an overall accuracy of greater than 86 %. In addition, the GISA-10m dataset was comprehensively compared with the existing global ISA datasets, and the superiority of GISA-10m was confirmed. The global road area was further investigated, courtesy of this 10 m dataset. It was found that China and the US have the largest areas of ISA and road. The global rural ISA was found to be 2.2 times that of urban while the rural road area was found to be 1.5 times larger than that of the urban regions. The global road area accounts for 14.2 % of the global ISA, 57.9 % of which is located in the top 10 countries. Generally speaking, the produced GISA-10m dataset and the proposed sampling and mapping method are able to achieve rapid and efficient global mapping, and have the potential for detecting other land covers. It is also shown that global ISA mapping can be improved by incorporating OSM data. The GISA-10m dataset could be used as a fundamental parameter for Earth system science, and will provide valuable support for urban planning and water cycle study. The GISA-10m can be freely downloaded from 10.5281/zenodo.5791855 (Huang et al., 2021a). [ABSTRACT FROM AUTHOR]

The currently available long-term snow depth data sets are either from point-scale ground measurements or from gridded satellite/modeled/reanalysis data with coarse spatial resolution, which limits the applications in climate models, hydrological models, and regional snow disaster monitoring. Benefitting from its unique advantages of cost-effective and high spatiotemporal resolution (∼ 1000 m 2 , hourly in theory), snow depth retrieval using the Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) technique has become a popular topic in recent years. However, due to complex environmental and observation conditions, developing robust and operational technology to produce long-term snow depth data sets using observations from various GNSS station networks is still challenging. The two objectives of this study are (1) to propose a comprehensive framework using raw data of the complex GNSS station networks to retrieve snow depth and control its quality automatically; and (2) to produce a long-term snow depth data set over northern China (i.e., GSnow-CHINA v1.0, 12 h or 24 h, 2013–2022) using the proposed framework and historical data from 80 stations. The data set has high internal consistency with regards to different GNSS constellations (mean r=0.98, RMSD = 0.99 cm, and nRMSD (snow depth > 5 cm) = 0.11), different frequency bands (mean r = 0.97, RMSD = 1.46 cm, and nRMSD (snow depth > 5 cm) = 0.16), and different GNSS receivers (mean r = 0.62). The data set also has high external consistency with the in situ measurements and the passive microwave (PMW) product, with a consistent illustration of the interannual snow depth variability. Additionally, the result show the potential of GNSS to derive hourly snow depth observations for better monitoring of snow disasters. The proposed framework to develop the data set provides comprehensive and supportive information for users to process raw data of ground GNSS stations with complex environmental conditions and various observation conditions. The resulting GSnow-CHINA v1.0 data set is distinguished from the current point-scale in situ data or coarse-gridded data, which can be used as an independent data source for validation purposes. The data set is also useful for regional climate research and other meteorological and hydrological applications. The algorithm and data files will be maintained and updated as more data become available in the future. The GSnow-CHINA v1.0 data set is available at the National Tibetan Plateau/Third Pole Environment Data Center via 10.11888/Cryos.tpdc.271839 (Wan et al., 2021). [ABSTRACT FROM AUTHOR]

An accurate spatially continuous air temperature data set is crucial for multiple applications in the environmental and ecological sciences. Existing spatial interpolation methods have relatively low accuracy, and the resolution of available long-term gridded products of air temperature for China is coarse. Point observations from meteorological stations can provide long-term air temperature data series but cannot represent spatially continuous information. Here, we devised a method for spatial interpolation of air temperature data from meteorological stations based on powerful machine learning tools. First, to determine the optimal method for interpolation of air temperature data, we employed three machine learning models: random forest, support vector machine, and Gaussian process regression. A comparison of the mean absolute error, root mean square error, coefficient of determination, and residuals revealed that a Gaussian process regression had high accuracy and clearly outperformed the other two models regarding the interpolation of monthly maximum, minimum, and mean air temperatures. The machine learning methods were compared with three traditional methods used frequently for spatial interpolation: inverse distance weighting, ordinary kriging, and ANUSPLIN (Australian National University Spline). Results showed that the Gaussian process regression model had higher accuracy and greater robustness than the traditional methods regarding interpolation of monthly maximum, minimum, and mean air temperatures in each month. A comparison with the TerraClimate (Monthly Climate and Climatic Water Balance for Global Terrestrial Surfaces), FLDAS (Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System), and ERA5 (ECMWF, European Centre for Medium-Range Weather Forecasts, Climate Reanalysis) data sets revealed that the accuracy of the temperature data generated using the Gaussian process regression model was higher. Finally, using the Gaussian process regression method, we produced a long-term (January 1951 to December 2020) gridded monthly air temperature data set, with 1 km resolution and high accuracy for China, which we named GPRChinaTemp1km. The data set consists of three variables: monthly mean air temperature, monthly maximum air temperature, and monthly minimum air temperature. The obtained GPRChinaTemp1km data were used to analyse the spatiotemporal variations of air temperature using Theil–Sen median trend analysis in combination with the Mann–Kendall test. It was found that the monthly mean and minimum air temperatures across China were characterised by a significant trend of increase in each month, whereas monthly maximum air temperatures showed a more spatially heterogeneous pattern, with significant increase, non-significant increase, and non-significant decrease. The GPRChinaTemp1km data set is publicly available at 10.5281/zenodo.5112122 (He et al., 2021a) for monthly maximum air temperature, at 10.5281/zenodo.5111989 (He et al., 2021b) for monthly mean air temperature, and at 10.5281/zenodo.5112232 (He et al., 2021c) for monthly minimum air temperature. [ABSTRACT FROM AUTHOR]