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A new dual-frequency stratospheric–tropospheric (ST) and meteor radar has been built and installed at the Langfang Observatory in northern China. It utilizes a new two-frequency system design that allows interleaved operation at 53.8 MHz for ST mode and at 35.0 MHz for meteor mode, thus optimizing performance for both ST wind retrieval and meteor trail detection. In dedicated meteor mode, the daily meteor count rate reaches over 40 000 and allows wind estimation at finer time resolutions than the 1 h typical of most meteor radars. The root mean square uncertainty of the ST wind measurements is better than 2 m s -1 when estimating the line of best fit with radiosonde winds. Preliminary observation results for 1 month of winter gravity wave (GW) momentum fluxes in the mesosphere, lower stratosphere and troposphere are also presented. A case of waves generated by the passage of a cold front is found. [ABSTRACT FROM AUTHOR]

A new dual-frequency Stratospheric Tropospheric (ST) / Meteor radar has been built and installed at the Langfang Observatory in northern China. It utilizes a novel two-frequency system design that allows interleaved operation at 53.8 MHz for ST mode and at 35.0 MHz for Meteor mode, thus optimizing performance for both ST wind retrieval and meteor trail detection. In dedicated meteor mode, the daily meteor count rate reaches over 40,000 and allows wind estimation at finer time resolutions than the 1-hour typical of most meteor radars. The root mean square uncertainty of the ST wind measurements is better than 2 m/s when estimating the line of best fit with radiosonde winds. Preliminary observation results for one month of winter gravity wave (GW) momentum fluxes in the mesosphere, lower stratosphere and troposphere are also presented. A case of waves generated by the passage of a cold front is found. [ABSTRACT FROM AUTHOR]

Precipitation nowcasting has important implications for urban operation and flood prevention. Radar echo extrapolation is a common method in precipitation nowcasting. Using deep learning models to extrapolate radar echo data has great potential. The increase of lead time leads to a weaker correlation between the real rainfall evolution and the generated images. The evolution information is easily lost during extrapolation, which is reflected as echo attenuation. Existing models, including generative adversarial network (GAN)-based models, have difficulty curbing attenuation, resulting in insufficient accuracy in rainfall prediction. To solve this issue, a spatiotemporal process enhancement network (GAN-argcPredNet v2.0) based on GAN-argcPredNet v1.0 has been designed. GAN-argcPredNet v2.0 curbs attenuation by avoiding blurring or maintaining the intensity. A spatiotemporal image correlation (STIC) prediction network is designed as the generator. By suppressing the blurring effect of rain distribution and reducing the negative bias by STIC attention, the generator generates more accurate images. Furthermore, the discriminator is a channel–spatial (CS) convolution network. The discriminator enhances the discrimination of echo information and provides better guidance to the generator in image generation by CS attention. The experiments are based on the radar dataset of southern China. The results show that GAN-argcPredNet v2.0 performs better than other models. In heavy rainfall prediction, compared with the baseline, the probability of detection (POD), the critical success index (CSI), the Heidke skill score (HSS) and bias score increase by 18.8 %, 17.0 %, 17.2 % and 26.3 %, respectively. The false alarm ratio (FAR) decreases by 3.0 %. [ABSTRACT FROM AUTHOR]

With the continuous development of the China ocean dynamic environment satellite series (Haiyang-2, HY-2), it is urgent to explore the potential application of HY-2B in Arctic sea ice thickness retrievals. In this study, we first derive the Arctic radar freeboard and sea ice thickness during two cycles (from October 2019 to April 2020 and from October 2020 to April 2021) using the HY-2B radar altimeter and compare the results with the Alfred Wegener Institute (AWI) CryoSat-2 (CS-2) products. We evaluate our HY-2B sea ice freeboard and thickness products using Operation IceBridge (OIB) airborne data and Ice, Cloud, and land Elevation Satellite 2 (ICESat-2) products. Finally, we estimate the uncertainties in the HY-2B sea ice freeboard and sea ice thickness. Here, we derive the radar freeboard by calculating the difference between the relative elevation of the floe obtained by subtracting the mean sea surface (MSS) height and sea surface height anomaly (SSHA) determined by an average of the 15 lowest points method. The radar freeboard deviation between HY-2B and CS-2 is within 0.02 m, whereas the sea ice thickness deviation between HY-2B and CS-2 is within 0.2 m. The HY-2B radar freeboards are generally thicker than AWI CS-2, except in spring (March and April). A spring segment likely has more floe points than an early winter segment. We also find that the deviations in radar freeboard and sea ice thickness between HY-2B and CS-2 over multiyear ice (MYI) are larger than those over first-year ice (FYI). The correlation between HY-2B (CS-2) sea ice freeboard retrievals and OIB values is 0.77 (0.84), with a root mean square error (RMSE) of 0.13 (0.10) m and a mean absolute error (MAE) of 0.12 (0.081) m. The correlation between HY-2B (CS-2) sea ice thickness retrievals and OIB values is 0.65 (0.80), with an RMSE of 1.86 (1.00) m and an MAE of 1.72 (0.75) m. The HY-2B sea ice freeboard uncertainty values range from 0.021 to 0.027 m, while the uncertainties in the HY-2B sea ice thickness range from 0.61 to 0.74 m. The future work will include reprocessing the HY-2B L1 data with a dedicated sea ice retracker, and using the radar waveforms to directly identify leads to release products that are more reasonable and suitable for polar sea ice thickness retrieval. [ABSTRACT FROM AUTHOR]

The complex precipitation microphysics associated with super typhoon Lekima (2019) and its potential impacts on the consistency of multi-source datasets and radar quantitative precipitation estimation were disentangled using a suite of in situ and remote sensing observations around the waterlogged area in the groove windward slope (GWS) of Yandang Mountain (YDM) and Kuocang Mountain, China. The main findings include the following: (i) the quality control processing for radar and disdrometers, which collect raindrop size distribution (DSD) data, effectively enhances the self-consistency between radar measurements, such as radar reflectivity (ZH), differential reflectivity (ZDR), and the specific differential phase (KDP), as well as the consistency between radar, disdrometers, and gauges. (ii) The microphysical processes, in which breakup overwhelms coalescence in the coalescence–breakup balance of precipitation particles, noticeably make radar measurements prone to be breakup-dominated in radar volume gates, which accounts for the phenomenon where the high number concentration rather than the large size of drops contributes more to a given attenuation-corrected ZH (ZHC) and the significant deviation of attenuation-corrected ZDR (ZDRC) from its expected values (Z^DR) estimated by DSD-simulated ZDR – ZH relationships. (iii) The twin-parameter radar rainfall estimates based on measured ZH (ZHM) and ZDR (ZDRM), and their corrected counterparts ZHC and ZDRC , i.e., R (ZHM , ZDRM) and R (ZHC , ZDRC), both tend to overestimate rainfall around the GWS of YDM, mainly ascribed to the unique microphysical process in which the breakup-dominated small-sized drops above transition to the coalescence-dominated large-sized drops falling near the surface. (iv) The improved performance of R (ZHC , Z^DR) is attributed to the utilization of Z^DR , which equals physically converting breakup-dominated measurements in radar volume gates to their coalescence-dominated counterparts, and this also benefits from the better self-consistency between ZHC , Z^DR , and KDP , as well as their consistency with the surface counterparts. [ABSTRACT FROM AUTHOR]

We examine the geographical distribution and microphysical three-dimensional structure of convective overshooting over East China by matching the Global Precipitation Measurement Dual-frequency Precipitation Radar (GPM DPR) instrument with the European Centre for Medium-Range Weather Forecasts Fifth-Generation Reanalysis (ERA5). Convective overshooting mainly occurs over Northeast China (NC) and northern Middle and East China (MEC), and its frequency varies from 4×10-4 to 5.4×10-3. Radar reflectivity of convective overshooting over NC accounts for a higher proportion below the freezing level, while MEC and South China (SC) account for a higher proportion above the freezing level, indicating stronger upward motion and more ice crystal particles. The microphysical processes within convective overshooting are unique, leading to various properties of the droplets in precipitation. Droplets of convective overshooting are large but sparse, with an effective droplet radius of nearly 2.5 mm below 10 km, which is about twice that of non-overshooting precipitation. The findings of this study may have important implications for the microphysical evolution associated with convective overshooting and provide more accurate precipitation microphysical parameters as input for model simulations. [ABSTRACT FROM AUTHOR]

Precipitation nowcasting has important implications for urban operation and flood prevention. Radar echo extrapolation is the common method in precipitation nowcasting. Using deep learning models to extrapolate radar echo data has great potential. The increase of lead time leads to a weaker correlation between real rainfall evolution and generated images. The evolution information is easily lost during extrapolation, which is reflected as echoes attenuation. Existing models, including Generative Adversarial Network (GAN)-based models, are all difficult to reduce loss and curb attenuation, which results in insufficient rainfall prediction accuracy. Aim to the problem, a Spatiotemporal Process Intensification Network (GAN-argcPredNet v2.0) based on GAN-argcPredNet v1.0 is designed. GAN-argcPredNet v2.0 reduces the loss by intensifying the influence of the previously input evolution information. A Spatiotemporal Information Changes Prediction (STIC-Prediction) network is designed as generator. With the intensification of echo feature sequence, the generator focuses on the spatiotemporal variation and generates more accurate images. Furthermore, discriminator is a Channel-Spatial Convolution (CS-Convolution) network. The discriminator intensifies the discrimination of echoes information by strengthening spatial information of single image. Identification results are fed back to the generator, which reduces the loss of important evolutionary information. The experiments are based on the radar dataset of South China. The results show that GAN-argcPredNet v2.0 performs better than other models. In heavy rainfall prediction, compared with baseline, the Probability of Detection (POD), the Critical Success Index (CSI), and the Heidke Skill Score (HSS) increase by 24.8 %, 22.2 % and 21.5 % respectively. The False Alarm Ratio (FAR) decreases by 3.76 %. [ABSTRACT FROM AUTHOR]

Lidar at 1064 nm and Ka-band millimetre-wave cloud radar (MMCR) are powerful tools for detecting the height distribution of cloud boundaries and can monitor the entire life cycle of cloud layers. In this study, lidar and MMCR are employed to jointly detect cloud boundaries under different conditions. By enhancing the echo signal of lidar at 1064 nm and combining its signal-to-noise ratio (SNR), the cloud signal can be accurately extracted from the aerosol signals and background noise. The interference signal is eliminated from Doppler spectra of the MMCR by using the noise ratio of the smallest measurable cloud signal (SNR min⁡) and the spectral point continuous threshold (Nts). Moreover, the quality control of the reflectivity factor of MMCR obtained by the inversion is conducted, which improves the detection accuracy of the cloud signal. We analysed three typical cases studies; case one presents two interesting phenomena: (a) at 19:00–20:00 CST (China standard time), the ice crystal particles at the cloud top boundary are too small to be detected by MMCR, but they are well detected by lidar. (b) At 19:00–00:00 CST, the cirrus cloud changes to altostratus where the cloud particles eventually grow into large sizes, producing precipitation. Further, MMCR has more advantages than lidar in detection of the cloud top boundary within this period. Considering the advantages of the two devices, the change characteristics of the cloud boundary in Xi'an from December 2020 to November 2021 were analysed, with MMCR detection data as the main data and lidar data as the assistant data. The seasonal variation characteristics of clouds show that, in most cases, high clouds often occur in summer and autumn, and the low clouds are usually in winter. The normalized cloud cover shows that the maximum and minimum cloud cover occur in summer and winter, respectively. Furthermore, the cloud boundary frequency distribution results for the whole of the observation period show that the cloud bottom boundary below 1.5 km is more than 1 %, the frequency within the height range of 3.06–3.6 km is approximately 0.38 %, and the frequency above 8 km is less than 0.2 %. The cloud top boundary frequency distribution exhibits the characteristics of a bimodal distribution. The first narrow peak lies at approximately 1.0–3.1 km, and the second peak appears at 6.4–9.8 km. [ABSTRACT FROM AUTHOR]

The Tibetan Plateau stands as one of China's largest middle and low latitude permafrost regions. However, the effects of global warming and human activities have led to permafrost thawing, inducing surface instability and posing significant threats to infrastructure and indigenous communities. The deployment of Lu Tan-1 (LT-1), China's premier L-band synthetic aperture radar (SAR) satellite constellation, offers a novel opportunity to assess these changes. This paper evaluates the deformation of critical engineering corridors, such as the Qinghai-Tibet Railway (QTR) and the Qinghai-Tibet Highway (QTH), utilizing time-series InSAR techniques with LT-1 SAR constellation data. We introduce both Stacking InSAR and a multi-baseline persistent scatterer multitemporal (MT-InSAR) method to enhance permafrost and engineering corridor deformation detection capabilities. Results obtained through the MT-InSAR approach reveal line-of-sight (LOS) deformation velocities of permafrost in the Beiluhe region ranging from -90 mm/y to approximately 70 mm/y, with an average velocity amplitude of 0.06 m/y. Differential displacement between alpine meadows and alpine deserts across the Beiluhe region is successfully discerned using LT-1 SAR data. Deformation velocities of QTR, QTH were found to be lower than that of permafrost, with average velocities of 0.027 m/y. These findings underscore the LT-1 SAR constellation's potential to serve as a valuable SAR data source for monitoring engineering corridor deformation within the Tibetan Plateau permafrost region. [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]

The seeding effect of upper clouds on lower clouds affects the evolution of clouds, especially the seeding from upper ice clouds on lower stratiform clouds or convective clouds, which can stimulate the precipitation of lower clouds and even produce extreme precipitation. Because when seeders of seeding cloud enter the feeding cloud, the interaction between cloud particles results in the change of macro and micro parameters of the feeding cloud. Based on the observation data of the ground-based Ka-band millimeter-wave cloud radar (MMCR) and microwave radiometer (MWR) in spring and autumn from 2020 to 2022, the seeder-feeder phenomenon among double-layer clouds in China Xi'an was studied. The study on 11cases of seeder-feeder processes shows that the processes can be divided into three categories by defining the height difference (HD) between the seeding cloud base and the feeding cloud top, and the effective seeding depth (ESD). Through the analysis on the reflectivity factor and the radial velocity of cloud particles detected by MMCR and on the retrieved cloud dynamics parameters (vertical velocity of airflow and falling velocity of cloud particles), it is shown that the reflectivity factor in the cloud are significantly enhanced during the seeder-feeder period for the three types of processes. But there are different enhancements among the reflectivity factor profiles for the three seeder-feeder processes. The results also showed the limited depth as seeders entering the top of the feeding cloud. The lower and thinner the HD height was, the lower and thicker the ESD height was. On the contrary, the higher the HD height, the higher and thinner the ESD height. [ABSTRACT FROM AUTHOR]

Spaceborne laser altimetry represents a novel active remote sensing technology applicable to earth observation, which together with imaging spectroscopy and synthetic aperture radar as a core technology for data acquisition in the earth observation systems. However, the accuracy of horizontal positioning for laser footprints from spaceborne laser altimeters declines due to various factors such as the changes in the orbital environment and the deterioration of performance. Moreover, the limited frequency of in-orbit calibration of the spaceborne laser altimeters and the non-disclosure of calibration parameters mean that users are heavily reliant on positioning accuracy of the altimetry data provided. To address this issue, a new algorithm is proposed in this study for enhancing the accuracy of horizontal positioning for laser footprints in the absence of satellite altimeter pointing and ranging parameters. In this algorithm, high-resolution DSM is taken as the reference terrain data to take advantage of the higher precision in elevation over horizontal positioning of the laser footprints. By adjusting the horizontal position of the laser footprint within a small area, the algorithm achieves the optimal alignment of laser elevation data with the reference terrain. Then, the resulting shift in the horizontal position of the laser footprints is referenced to correct their horizontal positioning during that period. Based on the high-accuracy DSM data collected from the Xinjiang autonomous region in China and the data collected by the GF-7 satellite, simulation experiments are performed in this study to analyze and validate the proposed algorithm. According to the experimental results, the horizontal accuracy of the laser footprints improves significantly from 12.56 m to 3.11 m after optimization by the proposed method. With the elimination of 9.45 m horizontal error, accuracy is improved by 75.23%. This method is demonstrated as effective in further optimizing the horizontal position of laser altimetry data products in the absence of altimeter parameters and original data, which promotes the application of spaceborne laser data. [ABSTRACT FROM AUTHOR]

Dams and reservoirs are human-made infrastructures that have attracted increasing attentions 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 water and material flowing into and out of reservoirs depends on their locations on the river network and the properties of 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% 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 full upstream 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 catchment, topography, climate, soil and geology, land cover and use, and anthropogenic activity). 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 data set on water level (data available for 20% of 3,254 reservoirs), water 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 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 https://doi.org/10.5281/zenodo. 7390715 (Shen et al., 2022a). [ABSTRACT FROM AUTHOR]

Estimating forest canopy height is crucial for assessing aboveground biomass and carbon sequestration. Light detection and ranging (lidar) is an important technology for its ability in capturing vertical structural information. However, due to instrument limitations and cost constraints, acquiring large-scale and continuous forest data solely through lidar is challenging. To compensate this, remote sensing images can be used to cover wide regions. Therefore, leveraging multi-source data for constructing canopy height models (CHMs) holds great promise in this field. The objective of this study is to evaluate and compare the contributions of multi-source remote sensing data and methods in estimating forest canopy height. In constructing the CHM, the commonly used random forest (RF) and fully convolutional network (FCN) are assessed. The canopy height obtained from GEDI was used as the reference data, and Landsat 8 and Sentinel-2 data were used for prediction. Multiple CHMs were constructed for the Dabie Mountains, Central China, in 2019 based on different data sources and methods, respectively, which are then comparatively analysed. The results showed that (1) the accuracy of the CHM using Sentinel-2 as input is marginally better than that using Landsat 8 based on RF, where the difference is insignificant; and (2) FCN is less accurate than RF despite domain-specific fine-tuning, although further improvement in accuracy is expected by weighing in more FCN models. [ABSTRACT FROM AUTHOR]

Interferometric Synthetic Aperture Radar (InSAR) technology is an important method to generate digital surface model (DSM). The past studies on space-borne derived DSM most focused on the elevation correction, due to the relative low resolution of DSM product. As a large number of high-resolution satellite data emerge, the horizontal discrepancies are needed to be considered. This paper proposes a DSM block adjustment method with horizontal constraints, aimed at eliminating the horizontal errors that exist between multiple DSM scenes with overlaps, achieving high precision and consistency in both the horizontal and vertical dimensions. Using ICESat-2 ATL08 point clouds as absolute elevation control and a reference DSM for horizontal control, the adjustment equations are constructed based on the constraint of tie points and controls. The experiment selects 7 image pairs of China TH2-01 SAR satellite, corresponding ICESat-2 ATL08 point and AW3D30 as reference DEM. The block adjustment results show that the proposed method improves the absolute vertical accuracy from 3.78 m to 2.56 m and reduces the average horizontal standard deviation between the InSAR derived DSMs and the reference AW3D30 from 15.31 m to 9.08 m. [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]

Spaceborne formaldehyde (HCHO) measurements constitute an excellent proxy for the sources of non-methane volatile organic compounds (NMVOCs). Past studies suggested substantial overestimation of NMVOC emissions in state-of-the-art inventories over major source regions. Here, the QA4ECV (Quality Assurance for Essential Climate Variables) retrieval of HCHO columns from OMI (Ozone Monitoring Instrument) are evaluated against (1) FTIR (Fourier-transform infrared) column observations at 26 stations worldwide, and (2) aircraft in situ HCHO measurements from campaigns conducted over the U.S. in 2012–2013. Both validation exercises show that OMI underestimates high columns and overestimates low columns. The linear regression of OMI and aircraft-based columns gives ΩOMI = 0.651 Ωairc + 2.95×1015 molec.cm-2, with ΩOMI and Ωairc the OMI and aircraft-derived vertical columns, whereas the regression of OMI and FTIR data givesΩOMI = 0.659 ΩFTIR + 2.02×1015 molec.cm-2. Inverse modelling of NMVOC emissions with a global model based on OMI columns corrected for biases based on those relationships leads to much-improved agreement against FTIR data and HCHO concentrations from 11 aircraft campaigns. The optimized global isoprene emissions (~445 Tg yr-1) are 25 % higher than those obtained without bias correction. The optimized isoprene emissions bear both striking similarities and differences with recently published emissions based on spaceborne isoprene columns from the CrIS (Cross-track Infrared Sounder) sensor. Although the interannual variability of OMI HCHO columns is well understood over regions where biogenic emissions are dominant, and the HCHO trends over China and India clearly reflect anthropogenic emission changes, the observed HCHO decline over Southeastern U.S. remains imperfectly elucidated. [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]

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 10 or 20-m resolution. The accuracy was examined by using 108195 survey samples and countylevel statistical data. On average, the user's, producer's, and overall accuracy 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 map explained 83 % of the spatial variation of county-level planting areas on average. The distribution maps can be obtained at https://doi.org/10.57760/sciencedb.06963 (Shen et al., 2023). [ABSTRACT FROM AUTHOR]

Accurate monitoring of atmospheric carbon dioxide (CO 2) and its distribution is of great significance for studying the carbon cycle and predicting future climate change. Compared to the ground observational sites, the airborne observations cover a wider area and simultaneously observe a variety of surface types, which helps with effectively monitoring the distribution of CO 2 sources and sinks. In this work, an airborne experiment was carried out in March 2019 over the Shanhaiguan area, China (39–41 ∘ N, 119–121 ∘ E). An integrated path differential absorption (IPDA) light detection and ranging (lidar) system and a commercial instrument, the ultraportable greenhouse gas analyser (UGGA), were installed on an aircraft to observe the CO 2 distribution over various surface types. The pulse integration method (PIM) algorithm was used to calculate the differential absorption optical depth (DAOD) from the lidar data. The CO 2 column-averaged dry-air mixing ratio (XCO 2) was calculated over different types of surfaces including mountain, ocean, and urban areas. The concentrations of the XCO 2 calculated from lidar measurements over ocean, mountain, and urban areas were 421.11 ± 1.24, 427.67 ± 0.58, and 432.04 ± 0.74 ppm, respectively. Moreover, through the detailed analysis of the data obtained from the UGGA, the influence of pollution levels on the CO 2 concentration was also studied. During the whole flight campaign, 18 March was the most heavily polluted day with an Air Quality Index (AQI) of 175 and PM 2.5 of 131 µ g m -3. The aerosol optical depth (AOD) reported by a sun photometer installed at the Funing ground station was 1.28. Compared to the other days, the CO 2 concentration measured by UGGA at different heights was the largest on 18 March with an average value of 422.59 ± 6.39 ppm, which was about 10 ppm higher than the measurements recorded on 16 March. Moreover, the vertical profiles of Orbiting Carbon Observatory-2 (OCO-2) and CarbonTracker were also compared with the aircraft measurements. All the datasets showed a similar variation with some differences in their CO 2 concentrations, which showing a good agreement among them. [ABSTRACT FROM AUTHOR]

The time series of normalized difference vegetation index (NDVI) and interferometric coherence extracted from optical and Synthetic Aperture Radar (SAR) images, respectively, have strong responses to sudden landslide failures in vegetated regions, which is expressed by a sudden increase or decrease in the values of NDVI and coherence. Compared with optical sensors, SAR sensors are not affected by cloud and daylight conditions and can detect the occurrence time of failure in near real-time. The purpose of this paper is to automatically determine the time of failure occurrence using time series coherence values. We propose, based on some existing anomaly detection algorithms, a deep neural network-based anomaly detection strategy that combines supervised and unsupervised learning without a priori knowledge about failure time. Our experiment using July 21, 2020 Shaziba landslide in China shows that in comparison to widely used unsupervised methodology, the use of our algorithm leads to a more accurate detection of the timing of the landslide failure. [ABSTRACT FROM AUTHOR]

Top-down inversions of China's terrestrial carbon sink are known to be uncertain because of errors related to the relatively coarse resolution of global transport models and the sparseness of in situ observations. Taking advantage of regional chemistry transport models for mesoscale simulation and spaceborne sensors for spatial coverage, the Greenhouse Gases Observing Satellite (GOSAT) retrievals of column-mean dry mole fraction of carbon dioxide (XCO2) were introduced in the Models-3 (a flexible software framework) Community Multi-scale Air Quality (CMAQ) and ensemble Kalman smoother (EnKS)-based regional inversion system to constrain China's biosphere sink at a spatiotemporal resolution of 64 km and 1 h. In general, the annual, monthly, and daily variation in biosphere flux was reliably delivered, attributable to the novel flux forecast model, reasonable CMAQ background simulation, well-designed observational operator, and Joint Data Assimilation Scheme (JDAS) of CO2 concentrations and natural fluxes. The size of the assimilated biosphere sink in China was - 0.47 PgCyr-1 , which was comparable with most global estimates (i.e., - 0.27 to - 0.68 PgCyr-1). Furthermore, the seasonal patterns were recalibrated well, with a growing season that shifted earlier in the year over central and south China. Moreover, the provincial-scale biosphere flux was re-estimated, and the difference between the a posteriori and a priori flux ranged from - 7.03 TgCyr-1 in Heilongjiang to 2.95 TgCyr-1 in Shandong. Additionally, better performance of the a posteriori flux in contrast to the a priori flux was statistically detectable when the simulation was fitted to independent observations, indicating sufficient to robustly constrained state variables and improved fluxes estimation. This study serves as a basis for future fine-scale top-down carbon assimilation. [ABSTRACT FROM AUTHOR]

Artificial impervious surface area (ISA) documents human footprints. Accurate, timely, and detailed ISA datasets are therefore essential for global climate change and urban planning. However, due to the lack of sufficient training samples and operational mapping methods, global ISA mapping at 10-m resolution is 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 the crowdsourcing OpenStreetMap (OSM), 58 million training samples were extracted via a series of temporal, spatial, spectral, and geometric rules. Combined with over 2.7 million Sentinel optical and radar images on the Google Earth Engine, we produced the 10 m global ISA dataset (GISA-10m). Based on the test samples that are independent to the training set, GISA- 10m embraced an overall accuracy greater than 86%. In addition, the GISA-10m was comprehensively compared with the existing global ISA datasets, and the superiority of GISA-10m was demonstrated. It was found that China and the United States embraced the largest ISA and road area. The global rural ISA was 2.2 times that of urban while rural road area was 1.5 times larger than that of urban region. The global road area accounted for 14.2% of the global ISA, 57.9% of which was located in the top ten countries. Generally, the produced GISA-10m dataset and the proposed sampling and mapping method are able to achieve rapid and efficient global mapping, and have potential for detecting other land covers. It was also indicated that global ISA mapping can be improved by incorporating refined OSM data. GISA-10m can be used as a fundamental parameter for Earth system science, and provide valuable support for of urban planning and water cycle study. [ABSTRACT FROM AUTHOR]

We examine geographical distribution pattern of convective overshooting and its internal microphysical three-dimensional structure of precipitation over East China by matching Global Precipitation Measurement Dual-frequency Precipitation Radar instrument (GPM DPR) with European Centre for Medium-Range Weather Forecasts 5th Reanalysis (ERA5). Convective overshooting events mainly occur over NC (Northeast China) and northern MEC (Middle and East China), with a magnitude of only 10−3; Radar reflectivity of convective overshooting over NC accounts for a higher proportion below the zero level, while MEC and SC (South China) account for a higher proportion above the zero level, indicating stronger upward motion and more ice crystal particles; The microphysical processes within convective overshooting are unique, leading to various properties of the droplets in precipitation. Droplets of convective overshooting are large, but sparse. And its effective radius of droplet, below 10 km altitude, is almost exceeding 2.5 mm, which is about twice than normal precipitation. Convective overshooting humidifies air below the cloud top and obviously increases the ozone near tropopause as a result of influx of ozone from lower troposphere and sinking of air with high concentration ozone in the stratosphere. Findings of this study may have important implications for the microphysical evolution associated with convective overshooting, and provide more accurate precipitation microphysical parameters as the input of the model simulation. [ABSTRACT FROM AUTHOR]

The peripheral circulation of typhoon forms sustained ozone episodes. However, how it impacts the day-to-day ozone pollution levels during the episodes has not been clearly studied, which is crucial for better prediction of the daily ozone variation. In this study, the analysis of ground observation, wind profile data, and model simulation is integrated. By analysing the wind profile radar observations, we found a weak wind deepening (WWD; vertical depth of the weak winds increased), more correlated with the ground-level ozone variation than surface weak wind. Long-term statistical analyses showed that the WWD is a common weather phenomenon in the peripheral subsidence region of typhoons and is generally accompanied by ozone pollution episodes. The Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) with process analysis simulation showed that the peripheral subsidence chemical formation (CHEM) and vertical mixing (VMIX) effects are two major contributors to the enhancement of ozone levels to form the episode, while the advection (ADV) showed negative values. However, the day-to-day variation of the daytime ozone levels during the episode is not determined by the daily variation of daytime CHEM and VMIX but is dominated by the ADV terms. Therefore, the ozone and its precursors accumulation, including the enhancement during the night-time, contribute to the daytime ozone increase in the following day. A detail day-to-day process analysis showed that in additional to decrease of negative ADV values (e.g. the weakened advection outflow or dispersion) on the ground, the integrated effect of the daily variation of the accumulative CHEM and ADV above the ground throughout the planetary boundary layer (PBL) together determined the overall day-to-day daytime ozone variation on the ground through the VMIX process. The results indicate that the peripheral characteristics of approaching typhoon not only form the ozone episode by the enhanced photochemical reactions, but also could increase the day-to-day daytime ozone levels via pollution accumulation throughout the PBL due to the WWD up to 3–5 km. These results illustrate the important role of the WWD in the lower troposphere for the formation of sustained ozone episodes due to the peripheral circulation of the typhoon, which helps to better predict the daily changes of daytime ozone levels. [ABSTRACT FROM AUTHOR]

Looting is one of the biggest threats to archaeology and cultural heritage and archaeologists had been struggling with this problem for decades. Help to deal with this threat, we carried out an experiment in Wuhan / China and created our own artifical looting holes by digging two holes and shaping them in different measurements while collecting TerraSAR-X images from the experiment area. Our study aims creating simulated SAR images of looting holes, in the light of the information we obtained from our experiment. Parameters of collected SAR images and 3D models of the artificial holes will be foundation of the work with SAR simulation. Since field work is money and time consuming duty, simulating SAR images of looting hole is advantageous and benefitial since we can play with the 3D models and change their geometry. In this study we obtain SAR simulation images by using our 3D models of the looting holes. By using simulation, we are able to change the geometry of the hole and even possible to implement natural effects to the 3D model which is effective on results. Thus, assessment of the looting holes will become much more simple work and it will help to protection of cultural heritage. [ABSTRACT FROM AUTHOR]

In recent years, urban floods occur frequently in China, and urban flood disaster has become one of the important reasons for the sustainable development of cities. Photogrammetry and remote sensing technology has the advantages of large observation range, large amount of information, fast speed, good real-time performance and strong dynamic. It plays an increasingly important role in flood control and disaster reduction, and has become an important support means for flood control and flood reduction in China. This paper mainly reviews the research status and development trend of photogrammetry and remote sensing technology in urban floods, summarizes the monitoring and evaluation of flood disasters, numerical simulation of flood disasters, and acquisition of hydrological characteristics, and prospects the future development of photogrammetry and remote sensing technology in flood disaster management in China. [ABSTRACT FROM AUTHOR]

Large amounts of dust aerosols are lifted to the upper troposphere every year and play a major role in cirrus formation by acting as efficient ice nuclei. However, the relative importance of heterogeneous nucleation and spontaneous homogenous nucleation in dust-related cirrus clouds is still not well evaluated globally. Here, based on spaceborne observations, we propose a method to identify two ice-nucleating regimes of dust-related cirrus clouds, i.e., (1) the sole presence of heterogeneous nucleation and (2) competition between heterogeneous and homogeneous nucleation, by characterizing the relationship between dust ice-nucleating particle concentrations (INPCs) calculated from the Cloud-Aerosol LIdar with Orthogonal Polarization (CALIOP) using the POlarization LIdar PHOtometer Networking (POLIPHON) method and in-cloud ice crystal number concentration (ICNC) from the DARDAR (lidar–radar) dataset. Two typical cirrus cases over central China are shown as a demonstration. In the first case, the upper part (near the cloud top) of a series of cirrus clouds successfully realized the INPC–ICNC closure, meaning that solely heterogeneous nucleation takes place, while the lower part of cirrus clouds showed the possible competition between heterogeneous and homogeneous nucleation. In the second case, the ICNCs in the cirrus cloud dramatically exceeded the dust INPCs in the vicinity by more than an order of magnitude, revealing that besides dust-induced heterogeneous nucleation, homogeneous nucleation also participated in ice formation and produced additional ice crystals. The proposed identification method is anticipated to apply in the evaluation of the influence of upper-troposphere dust on global cirrus formation and the investigation of the potential positive role of cirrus cloud thinning in the offset of climate warming. [ABSTRACT FROM AUTHOR]

The construction of terraces is a key soil conservation practice on agricultural land in China providing multiple valuable ecosystem services. Accurate spatial information on terraces is needed for both management and research. In this study, the first 30 m resolution terracing map of the entire territory of China is produced by a supervised pixel-based classification using multisource and multi-temporal data based on the Google Earth Engine (GEE) platform. We extracted time-series spectral features and topographic features from Landsat 8 images and the Shuttle Radar Topography Mission digital elevation model (SRTM DEM) data, classifying cropland area (cultivated land of Globeland30) into terraced and non-terraced types through a random forest classifier. The overall accuracy and kappa coefficient were evaluated by 10 875 test samples and achieved values of 94 % and 0.72, respectively. For terrace class, the producer's accuracy (PA) was 79.945 %, and the user's accuracy (UA) was 71.149 %. The classification performed best in the Loess Plateau and southwestern China, where terraces are most numerous. Some northeastern, eastern-central, and southern areas had relatively high uncertainty. Typical errors in the mapping results are from the sloping cropland (non-terrace cropland with a slope of ≥ 5 ∘), low-slope terraces, and non-crop vegetation. Terraces are widely distributed in China, and the total terraced area was estimated to be 53.55 Mha (i.e., 26.43 % of China's cropland area) by pixel counting (PC) method and 58.46 ± 2.99 Mha (i.e., 28.85 % ± 1.48 % of China's cropland area) by error-matrix-based model-assisted estimation (EM) method. Elevation and slope were identified as the main features in the terrace/non-terrace classification, and multi-temporal spectral features (such as percentiles of NDVI, TIRS2, and BSI) were also essential. Terraces are more challenging to identify than other land use types because of the intra-class feature heterogeneity, interclass feature similarity, and fragmented patches, which should be the focus of future research. Our terrace mapping algorithm can be used to map large-scale terraces in other regions globally, and our terrace map will serve as a landmark for studies on multiple ecosystem service assessments including erosion control, carbon sequestration, and biodiversity conservation. The China terrace map is available to the public at 10.5281/zenodo.3895585 (Cao et al., 2020). [ABSTRACT FROM AUTHOR]

This paper reveals the dynamic movement characteristics of thunderstorms using total lightning data obtained from very low- and low-frequency (VLF/LF) location measurements. Eight thunderstorms, which were evenly distributed in the morning, midday, afternoon and evening, are selected to compare the different kinematic features over the Pearl River Delta (PRD) region in the south of China from 17 to 23 May 2014. The connected-neighbourhood labelling method is used to identify lightning clusters and obtain the centroids. Significant characterisation parameters are put forward as metrics to reveal the kinematic features of thunderstorms, including the duration time, valid area (VA), movement velocity, movement direction and farthest distance in longitude and latitude during the life cycle of the storm. A common trend is that the storms initiate in the west of the PRD region, moving to the east and disappearing after the thunderstorm travels around 106.5 km in longitude. There are two kinds of distributions to depict the property of the valid area, which are one-peak distribution with the maximum in the mature stage and two-peak distribution with a relatively smaller peak in the early time of the storm. The velocity does not show the same trend as the variation in VA which shows a steady increase or decrease during the lifetime of thunderstorms. The biggest VA and highest velocity are 891 km 2 occurring on the evening of 17 May and 204.8 km h -1 occurring on the morning of 20 May. The 19 May evening storm was the weakest, with the maximum VA and velocity being 253 km 2 and 115.3 km h -1 , respectively. The motion of eight storms shows a distinct pattern as the spread of direction distributes tightly in the range of 0–180 ∘. The movement characteristics of thunderstorms and the associated parameters may help to improve the nowcasting and forecasting system of thunderstorms in the warm season. [ABSTRACT FROM AUTHOR]

From the end of the "Eleventh Five-Year Plan", China had implemented a major project for a high-resolution Earth observation system, and completed all satellite launch tasks by 2020. CHEOS aims to build an Earth observation system with high spatial, temporal and spectral resolution in an all-round way, and actively meet the major national strategic needs of China's national defense, natural resource survey, disaster response and prevention. CHEOS has promoted the continuous maturity of high-resolution Earth observation technology in China and the wide application of high-resolution images. This paper reviews the development history of CHEOS, analyzes the technical characteristics and application status of the GF (means high resolution in Chinese) series of civil satellites, discusses the current problems and future development trends of CHEOS, and provides a reference for the subsequent promotion and application of the GF series satellites. [ABSTRACT FROM AUTHOR]

While aqueous-phase processing is known to contribute to the formation of nitrogen-containing organic compounds (NOCs), the specific pathways involved remain poorly understood. In this study, we aimed to characterize the NOCs present in both pre-fog aerosols and fog water collected at a suburban site in northern China. Fourier-transform ion cyclotron resonance mass spectrometry was utilized to analyze the molecular composition of NOCs in both negative and positive modes of electrospray ionization (ESI - and ESI +). In both pre-fog aerosols and fog water samples, NOCs constituted a significant portion, accounting for over 60 % of all assigned formulas in ESI - and more than 80 % in ESI +. By comparing the molecular composition of NOCs originating from biomass burning, coal combustion, and vehicle emissions, we identified that 72.3 % of NOCs in pre-fog aerosols were attributed to primary anthropogenic sources (pNOCs), while the remaining NOCs were categorized as secondary NOCs formed within the aerosols (saNOCs). Unique NOCs found in fog water were classified as secondary NOCs formed within the fog water (sfNOCs). Through a comprehensive "precursor–product pair" screening involving 39 reaction pathways, we observed that the nitration reaction, the amine pathway, and the intramolecular N-heterocycle pathway of NH 3 addition reactions contributed 43.6 %, 22.1 %, and 11.6 % of saNOCs, respectively. In contrast, these pathways contributed 26.8 %, 28.4 %, and 29.7 % of sfNOCs, respectively. This disparity in formation pathways is likely influenced by the diverse precursors, the aqueous acidity, and the gas-phase species partitioning. Correspondingly, saNOCs were found to contain a higher abundance of carbohydrate-like and highly oxygenated compounds with two nitrogen atoms compared to pNOCs. Conversely, sfNOCs exhibited a higher content of lipid-like compounds with fewer oxygen atoms. These results underscore the distinct secondary processes contributing to the diversity of NOCs in aerosols and fog water, which may lead to their different climate effects. [ABSTRACT FROM AUTHOR]

Storm surges are a common natural hazard in China's southern coastal area which usually cause a great loss of human life and financial damages. With the economic development and population concentration of coastal cities, storm surges may result in more impacts and damage in the future. Therefore, it is of vital importance to conduct risk assessment to identify high-risk areas and evaluate economic losses. However, quantitative study of storm surge risk assessment in undeveloped areas of China is difficult, since there is a lack of building character and damage assessment data. Aiming at the problem of data missing in undeveloped areas of China, this paper proposes a methodology for conducting storm surge risk assessment quantitatively based on deep learning and geographic information system (GIS) techniques. Five defined storm surge inundation scenarios with different typhoon return periods are simulated by the coupled FVCOM–SWAN (Finite Volume Coastal Ocean Model–Simulating WAves Nearshore) model, the reliability of which is validated using official measurements. Building footprints of the study area are extracted through the TransUNet deep learning model and remote sensing images, while building heights are obtained through unoccupied aerial vehicle (UAV) measurements. Subsequently, economic losses are quantitatively calculated by combining the adjusted depth–damage functions and overlaying an analysis of the buildings exposed to storm surge inundation. Zoning maps of the study area are provided to illustrate the risk levels according to economic losses. The quantitative risk assessment and zoning maps can help the government to provide storm surge disaster prevention measures and to optimize land use planning and thus to reduce potential economic losses in the coastal area. [ABSTRACT FROM AUTHOR]

Root zone soil moisture (RZSM) is critical for water resource management, drought monitoring and sub-seasonal flood climate prediction. While RZSM is not directly observable from space, several RZSM products are available and widely used at global and continental scales. This study conducts a comprehensive and quantitative evaluation of eight RZSM products using observations from 58 in situ soil moisture stations over the Huai River basin (HRB) in China. Attention is drawn to the potential factors that contribute to the uncertainties of model-based RZSM, including the errors in atmospheric forcing, vegetation parameterizations, soil properties and spatial scale mismatch. The results show that the Global Land Data Assimilation System Catchment Land Surface Model (GLDAS_CLSM) outperforms the other RZSM products with the highest correlation coefficient (R= 0.69) and the lowest unbiased root mean square error (ubRMSE = 0.018 m 3 m -3), while SMOS Level 4 (L4) RZSM shows the worst performance among eight RZSM products. The RZSM products based on land surface models generally perform better in the wet season than in the dry season due to the enhanced ability to capture of the temporal dynamics of in situ observations in the wet season and the inertia of remaining high soil moisture values even in the dry season, while the SMOS L4 RZSM product, derived from SMOS L3 surface moisture (SSM) combined with an exponential filter method, performs better in the dry season due to the attenuated ground microwave radiation signal caused by the increased water vapour absorption and scattering in the wet season. The underestimated SMOS L3 SSM triggers the underestimation of RZSM in SMOS L4. The overestimated RZSM products based on land surface models could be associated with the overestimated precipitation amounts and frequency, the underestimated air temperature, and the underestimated ratio of transpiration to the total terrestrial evapotranspiration. In addition, the biased soil properties and flawed vegetation parameterizations affect the hydrothermal transport processes represented in different land surface models (LSMs) and lead to inaccurate soil moisture simulation. The scale mismatch between point and footprint also introduces representative errors. The comparison of frequency of normalized soil moisture between RZSM products and in situ observations indicates that the LSMs should focus on reducing the frequency of wet soil moisture, increasing the frequency of dry soil moisture and the ability to capture the frequency peak of soil moisture. The study provides some insights into how to improve the ability of land surface models to simulate the land surface states and fluxes by taking into account the issues mentioned above. Finally, these results can be extrapolated to other regions located in similar climate zones, as they share similar precipitation patterns that dominate the terrestrial water cycle. [ABSTRACT FROM AUTHOR]

A larger antecedent effective precipitation (AEP) indicates a higher probability of a debris flow (Pdf) being triggered by subsequent rainfall. Scientific topics surrounding this qualitative conclusion that can be raised include what kinds of variation rules they follow and whether there is a boundary limit. To answer these questions, Jiangjia Gully in Dongchuan, Yunnan Province, China, is chosen as the study area, and numerical calculation, a rainfall scenario simulation, and the Monte Carlo integration method have been used to calculate the occurrence probability of debris flow under different AEP conditions and derive the functional relationship between Pdf and AEP. The relationship between Pdf and AEP can be quantified by a piecewise function. Pdf is equal to 15.88 %, even when AEP reaches 85 mm, indicating that debris flow by nature has an extremely small probability compared to the rainfall frequency. Data from 1094 rainfall events and 37 historical debris flow events are collected to verify the reasonability of the functional relationship. The results indicate that the piecewise functions are highly correlated with the observation results. Our study confirms the correctness of the qualitative description of the relationship between AEP and Pdf , clarifies that debris flow is a small-probability event compared to rainfall frequency, and quantitatively reveals the evolution law of debris flow occurrence probability with AEP. All the above discoveries can provide a clear reference for the early warning of debris flows. [ABSTRACT FROM AUTHOR]

Black carbon (BC) plays an important role in air quality, public health, and climate, but its long-term variations in emissions and health effects have been insufficiently understood for China. Here, we present the spatiotemporal evolution of BC emissions and the associated premature mortality in China during 2000–2020 based on an integrated framework combining satellite observations from the Ozone Monitoring Instrument (OMI), an extreme gradient boosting (XGBoost) algorithm, a "top-down" inversion approach, and an exposure–response model. We found that the "bottom-up" approach most likely underestimated BC emissions, particularly in less developed western and remote areas. Pollution controls were estimated to reduce the annual BC emissions by 26 % during 2010–2020, reversing the 8 % growth during 2000–2010. BC emissions in the main coal-producing provinces declined by 2010 but rebounded afterwards. By contrast, provinces with higher economic and urbanization levels experienced emission growth (0.05–0.10 Mg km -2 yr -1) by 2010 and declined greatly (0.07–0.23 Mg km -2 yr -1) during 2010–2020. The national annual BC-associated premature mortality ranged between 733 910 (95 % confidence interval: 676 790–800 250) and 937 980 cases (864 510–1 023 400) for different years. The changing BC emissions contributed to an increase of 78 590 cases (72 520–85 600) during 2000–2005 and a decrease of 133 360 cases (123 150–145 180) during 2010–2015. Strategies differentiated by region are needed for further reducing BC emissions and their health and climate impacts. [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]

Accurate flood event simulation and prediction, enabled by effective models and reliable data, are critical for mitigating the potential risk of flood disaster. This study aims to investigate the impacts of spatiotemporal resolutions of precipitation on flood event simulation in a large-scale catchment of China. We use high-spatiotemporal-resolution Integrated Multi-satellite Retrievals for Global Precipitation Measurement (IMERG) products and a gauge-based product as precipitation forcing for hydrologic simulation. Three hydrological models (HBV, SWAT and DHSVM) and a data-driven model (long short-term memory (LSTM) network) are utilized for flood event simulation. Two calibration strategies are carried out, one of which targets matching of the flood events, with peak discharge exceeding 8600 m 3 s -1 between January 2015 and December 2017, and the other one is the conventional strategy for matching the entire streamflow time series. The results indicate that the event-based calibration strategy improves the performance of flood event simulation compared with a conventional calibration strategy, except for DHSVM. Both hydrological models and LSTM yield better flood event simulation at a finer temporal resolution, especially in flood peak simulation. Furthermore, SWAT and DHSVM are less sensitive to the spatial resolutions of IMERG, while the performance of LSTM obtains improvement when degrading the spatial resolution of IMERG-L. Generally, LSTM outperforms the hydrological models in most flood events, which implies the usefulness of the deep learning algorithms for flood event simulation. [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]

Accurate estimation of the wind profile, especially in the lowest few hundred meters of the atmosphere, is of great significance for the weather, climate, and renewable energy sector. Nevertheless, the Monin–Obukhov similarity theory fails above the surface layer over a heterogeneous underlying surface, causing an unreliable wind profile to be obtained from conventional extrapolation methods. To solve this problem, we propose a novel method called the PLM-RF method that combines the power-law method (PLM) with the random forest (RF) algorithm to extend wind profiles beyond the surface layer. The underlying principle is to treat the wind profile as a power-law distribution in the vertical direction, with the power-law exponent (α) determined by the PLM-RF model. First, the PLM-RF model is constructed based on the atmospheric sounding data from 119 radiosonde (RS) stations across China and in conjunction with other data such as surface wind speed, land cover type, surface roughness, friction velocity, geographical location, and meteorological parameters from June 2020 to May 2021. Afterwards, the performance of the PLM-RF, PLM, and RF methods over China is evaluated by comparing them with RS observations. Overall, the wind speed at 100 m from the PLM-RF model exhibits high consistency with RS measurements, with a determination coefficient (R2) of 0.87 and a root mean squared error (RMSE) of 0.92 m s -1. By contrast, the R2 and RMSE of wind speed results from the PLM (RF) method are 0.75 (0.83) and 1.37 (1.04) m s -1 , respectively. This indicates that the estimates from the PLM-RF method are much closer to observations than those from the PLM and RF methods. Moreover, the RMSE of the wind profiles estimated by the PLM-RF model is relatively large for highlands, while it is small for plains. This result indicates that the performance of the PLM-RF model is affected by the terrain factor. Finally, the PLM-RF model is applied to three atmospheric radiation measurement sites for independent validation, and the wind profiles estimated by the PLM-RF model are found to be consistent with Doppler wind lidar observations. This confirms that the PLM-RF model has good applicability. These findings have great implications for the weather, climate, and renewable energy sector. [ABSTRACT FROM AUTHOR]

Assessing the role of physical processes in the stratosphere under climate change has been one of the hottest topics over the past few decades. However, due to the limitations of detection techniques, stratospheric disturbance information from in situ observations is still relatively scarce. The round-trip intelligent sounding system (RTISS) is a new detection technology, developed in recent years, that can capture atmospheric fine-structure information about the troposphere and stratosphere via three-stage (rising, flat-floating, and falling) detection. Based on the structure function and singular measure relationships, we quantify stratospheric small-scale gravity waves (SGWs) over China, using the Hurst and intermittency parameters, and discuss their relationship with inertia-gravity waves (IGWs). The results show that the enhancement of SGWs in the stratosphere is accompanied by weakening of the IGWs below, which is related to the Kelvin–Helmholtz instability (KHI), and is conducive to the transport of ozone to higher altitudes from lower stratosphere. The parameter space (H1 , C1) shows sufficient potential in the analysis of stratospheric disturbances and their role in material transport and energy transfer. [ABSTRACT FROM AUTHOR]

Airborne dust aerosols impact negatively the climate, ecosystems, air quality, and human health. To mitigate these impacts, it is crucial to identify their three–dimensional spatiotemporal distribution, transport pathways and driving factors. In this study, the three–dimensional spatiotemporal variations and distribution of dust aerosols in China from 2007 to 2021 were first analyzed using multiple dust datasets, including Modern Era Retrospective Analysis for Research and Applications version 2 (MERRA–2) dust aerosol optical depth (DAOD) data, ultraviolet aerosol index (UVAI) data from the Ozone Monitoring Instrument (OMI), and the Vertical Feature Mask (VFM) product of Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP). Also, the transport pathways and potential source regions for dust haze in principle provincial capital cities of West and North China in spring were identified using the Hybrid Single–Particle Lagrangian Integrated Trajectory (HYSPLIT) model and Potential Source Contribution Function (PSCF). Additionally, DAOD variations over different land cover types and the impacts of meteorological driving factors were discussed with geographic detectors. Results indicate that: (1) The multi–year average of DAOD in China from 2007 to 2021 was 0.076. A mutation in annual average DAOD occurred between 2010 and 2011, with an insignificant increasing trend between 2007–2010, a downward trend between 2011–2021 and a significant downward trend between 2014–2017. (2) The Taklamakan Desert exhibited the highest DAOD for the entire China during spring throughout the years, with DAOD values ranging from 0.4 to 0.6 and UVAI values exceeding 2.0. The highest frequency of dust occurrence in the northwest and northern regions is at an altitude of 2–4 km in spring and summer, and of 0–2 km in autumn and winter, while it is at an altitude of 4–6 km for the Qinghai–Tibet region. (3) The dust transport routes for the provincial capital cities can be primarily divided into: western, northwestern, northern, southwestern, and local. Northwest cities are notably affected by dust from surrounding deserts. Dust originating from the Qaidam Basin and the Hexi Corridor can be carried further downwind to inland cities, such as Xining and Lanzhou. A dust backflow was found in Beijing and Tianjin. Moreover, the discussion revealed that barren and cropland had the highest DAOD and additionally precipitation, evaporation, and soil moisture were identified as the strongest driving factors affecting dust aerosol variations. The combination effect of precipitation and temperature had the highest explanatory power, ranging from 0.72–0.84, followed by 0.75–0.81 for precipitation and U10m wind speed and 0.67–0.75 for temperature and evaporation. [ABSTRACT FROM AUTHOR]

On 16 April 2022, China successfully launched the world's first spaceborne high-spectral-resolution lidar (HSRL), which is called the Aerosol and Carbon Detection Lidar (ACDL), on board the Atmospheric Environment Monitoring Satellite known as Daqi-1 (DQ-1). The ACDL is expected to precisely detect the three-dimensional distribution of aerosol and cloud globally with high spatial–temporal resolutions. To assess the performance of the newly launched satellite lidar, the ACDL-retrieved observations were compared with ground-based lidar measurements of atmospheric aerosol and cloud over northwest China from May to July 2022 using the Belt and Road lidar network (BR-lidarnet) initiated by Lanzhou University in China and the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) lidar observations. A total of six cases in the daytime and nighttime, including clear days, dust events, and cloudy conditions, were selected for further analysis. Moreover, profiles of the total attenuated backscatter coefficient (TABC) and the volume depolarization ratio (VDR) at 532 nm measured by the ACDL, the CALIPSO lidar, and ground-based lidar are compared in detail. Comparison is made between the 532 nm extinction coefficient and lidar ratio obtained from ACDL HSRL retrieval and the Raman retrieval results obtained from BR-lidarnet. The achieved results revealed that the ACDL observations were in good agreement with the ground-based lidar measurements during dust events with a relative deviation of about - 10.5 ± 25.4 % for the TABC and - 6.0 ± 38.5 % for the VDR. Additionally, the heights of the cloud top and bottom from these two measurements were well matched and comparable. Compared with the observation of CALIPSO, the ACDL also shows high consistency. This study proves that the ACDL provides reliable observations of aerosol and cloud in the presence of various climatic conditions, which helps to further evaluate the impacts of aerosol on climate and the environment, as well as on the ecosystem in the future. [ABSTRACT FROM AUTHOR]

A magnitude 6.8 strike-slip earthquake (EQ) struck Luding, Sichuan Province, China, on 5 September 2022, resulting in significant damage to nearby Ganzi Prefecture and the city of Ya'an. In this research, the near-surface atmospheric electric field (AEF) recorded at four sites 15 d before the Luding EQ was analyzed and differentiated, and multisource auxiliary data including precipitation, cloud base height, and low cloud cover were used at the same time. Nine possible seismic AEF anomalies at four sites were obtained preliminarily. Accordingly, microwave brightness temperature (MBT) data, which are very sensitive to the surface dielectrics and are closely related to the air ionization, together with surface soil moisture, lithology, and a 3D-simulated crustal stress field, were jointly analyzed to confirm the seismic relations of the obtained negative AEF anomalies. The geophysical environment for crustal high-stress concentration, positive charge carrier transfer, and surface accumulation was demonstrated to exist and to meet the conditions necessary to generate local negative AEF anomalies. Furthermore, to deal with the spatial disparities in sites and regions with potential atmospheric ionization, near-surface wind field data were employed to scrutinize the reliability of the AEF anomalies by comprehensively analyzing the spatial relationships among surface charges accumulation areas, wind direction and speed, and the AEF sites. Finally, four negative AEF anomalies were deemed to be closely related to the Luding EQ, and the remaining five possible anomalies were ruled out. A possible mechanism of negative AEF anomalies before the Luding EQ is proposed: positive charge carriers were generated from the underground high-stress concentration areas and then transferred to and accumulated on the ground surface to ionize the surface air, thus disturbing the AEF above the ground. This study presents a method for identifying and analyzing seismic AEF anomalies and is also beneficial for the examination of the pre-earthquake coupling process between the coversphere and the atmosphere. [ABSTRACT FROM AUTHOR]

The total amount of sugarcane planting in China ranks third in the world, and Guangxi is the largest sugarcane planting base in China. In order to help the Guangxi government and industry better understand the annual planting distribution and scale of sugarcane in the whole region, it is convenient to optimize the planting structure and improve the efficiency of resource utilization, so as to ensure the stable development of Guangxi sugar industry. Based on the satellite-based hyperspectral image data of ' Zhuhai No.1 ', this paper carries out sugarcane recognition and area extraction, which fills the application gap of satellite-based hyperspectral image data in extracting sugarcane area in China. A globally optimized extended random walk model was used to identify and extract sugarcane by combining the spatial-spectral information of hyperspectral imagery. Firstly, the Initial probability estimation is performed using the LOR classifier, and then use the data after MNF dimensionality reduction to construct a weighted graph, and finally the probability optimization and recognition are carried out based on the extended random walk model. The results show that the spatial-spectral joint method based on extended random walk can effectively identify sugarcane, and the sugarcane recognition accuracy of 2021 year image can reach 91.4%, the misrecognition rate is 2.8%; the sugarcane recognition accuracy of 2020 year image can reach 90.5 %, the misrecognition rate is 3.1%. [ABSTRACT FROM AUTHOR]

Sugarcane is an annual or perennial persistent rooted tropical and subtropical herb that grows in tropical and subtropical regions. As China's production ranks among the world's leading, sugarcane industry is an important part of agricultural economy in China. As the largest sugarcane production center in China, Guangxi is one of the most suitable areas for sugarcane cultivation in China and even in the world. Sugarcane industry, as an agricultural advantageous industry in Guangxi, not only has a significant image to the national economy of the region, but also is closely related to the issue of security of national sugar supply. Continuous cropping of sugarcane is very common in Guangxi, which is very helpful for the concentration selection of sugarcane samples. The wide application of satellite remote sensing monitoring technology has become an indispensable means of natural resources monitoring. Using optical satellite remote sensing image to identify and extract sugarcane planting areas is of great significance to quickly and conveniently grasp the information of sugarcane distribution and yield. In this paper, the precision of sugarcane extraction from GF1 and GF2 satellite images is analyzed by using deeplab V3 + model, the effect of optical remote sensing images with different resolution on sugarcane extraction accuracy was studied to provide better data support for dynamic monitoring of sugarcane planting. [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]

The Wei River catchment in the southern part of the Chinese Loess Plateau (CLP) is one of the centers of the agricultural revolution in China. The area has experienced intense land use changes since ∼6000 BCE, which makes it an ideal place to study the response of fluvial systems to past anthropogenic land cover change (ALCC). We apply a numerical landscape evolution model that combines the Landlab landscape evolution model with an evapotranspiration model to investigate the direct and indirect effects of ALCC on hydrological and morphological processes in the Wei River catchment since the mid-Holocene. The results show that ALCC has not only led to changes in discharge and sediment load in the catchment but also affected their sensitivity to climate change. When the proportion of agricultural land area exceeded 50 % (around 1000 BCE), the sensitivity of discharge and sediment yield to climate change increased abruptly indicating a regime change in the fluvial catchment. This was associated with a large sediment pulse in the lower reaches. The model simulation results also show a link between human settlement, ALCC and floodplain development: changes in agricultural land use led to downstream sediment accumulation and floodplain development, which in turn resulted in further spatial expansion of agriculture and human settlement. [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]