Articles | Volume 14, issue 6
https://doi.org/10.5194/essd-14-2613-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-14-2613-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description article
| 
08 Jun 2022
Data description article |
| 08 Jun 2022
| 08 Jun 2022
A 1 km daily surface soil moisture dataset of enhanced coverage under all-weather conditions over China in 2003–2019
Peilin Song
Key Laboratory of Water Cycle and Related Land Surface Processes,
Institute of Geographic Sciences and Natural Resources Research, The Chinese
Academy of Sciences, Beijing 100101, China
State Key Laboratory of Remote Sensing Science, Aerospace
Information Research Institute, Chinese Academy of Sciences. Beijing 100101,
China
now at: School of Electronic Science and Engineering, Xi'an
Jiaotong University, Xi'an, 710049, China
Key Laboratory of Water Cycle and Related Land Surface Processes,
Institute of Geographic Sciences and Natural Resources Research, The Chinese
Academy of Sciences, Beijing 100101, China
State Key Laboratory of Severe Weather, Chinese Academy of
Meteorological Sciences, Beijing 100081, China
Jiancheng Shi
National Space Science Center, Chinese Academy of Sciences, Beijing
100190, China
Tianjie Zhao
State Key Laboratory of Remote Sensing Science, Aerospace
Information Research Institute, Chinese Academy of Sciences. Beijing 100101,
China
Bing Tong
State Key Laboratory of Severe Weather, Chinese Academy of
Meteorological Sciences, Beijing 100081, China
Related authors
No articles found.
Rongfang Yang, Deli Meng, Guanglei Zhang, Lihui Liu, Guocui Li, Zhen Zhang, and Jianping Guo
EGUsphere, https://doi.org/10.5194/egusphere-2026-4218, https://doi.org/10.5194/egusphere-2026-4218, 2026
This preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).
Short summary
Short summary
The air near the ground affects weather, pollution, and low-flying aircraft safety, but is hard to measure over hills. We flew a drone with weather sensors and compared it with a tall measurement tower. The drone accurately captured how wind, temperature, and humidity change with height. It shook most when strong shifting winds met weak stability, especially near cloud base. This helps make drone weather observations and low-altitude flight safer.
Weilong Deng, Jianping Guo, Ning Li, Zhen Zhang, Yifei Wang, and Yi Yang
EGUsphere, https://doi.org/10.5194/egusphere-2026-4109, https://doi.org/10.5194/egusphere-2026-4109, 2026
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
Short summary
Heavy rain far ahead of weather fronts is easily overlooked and hard to forecast. We used five summers of minute-level rain gauge data and wind-profiling radars over the eastern China plains to identify warm-sector heavy rainfall events. They are brief yet intense and locally supply up to half of all heavy rain. Before rain begins, low and strong wind jets appear more often and air 1–2 km above the ground rises faster. Both can be tracked in real time and may warn of coming downpours.
Zhenwu Xu, Yongqiang Zhang, Dongdong Kong, Ning Ma, and Xuanze Zhang
Earth Syst. Sci. Data, 18, 5663–5695, https://doi.org/10.5194/essd-18-5663-2026, https://doi.org/10.5194/essd-18-5663-2026, 2026
Short summary
Short summary
The Penman–Monteith–Leuning Version 2.2 dataset provides remote sensing-based coupled estimates of terrestrial evapotranspiration and gross primary production from 1982 to near present. Calibrated at 208 flux stations and validated against water balances in 152 river basins, this extended record reveals significant increases in global evapotranspiration, vegetation productivity, and water use efficiency, supporting diverse studies in hydrology, ecology, and Earth sciences.
Qi Zhang, Tianmeng Chen, Jianping Guo, and Xun Li
Atmos. Meas. Tech., 19, 4853–4873, https://doi.org/10.5194/amt-19-4853-2026, https://doi.org/10.5194/amt-19-4853-2026, 2026
Short summary
Short summary
Accurate monitoring of boundary-layer water vapor prior to nocturnal heavy precipitation remains challenging. This study extends a physically constrained retrieval framework by integrating Raman lidar observations to improve water vapor profile estimation. The method shows improved accuracy compared to reanalysis data and captures coherent pre-precipitation moisture evolution, demonstrating its potential for studying and monitoring severe weather processes.
Qi Zhang, Tianmeng Chen, and Jianping Guo
EGUsphere, https://doi.org/10.5194/egusphere-2026-3341, https://doi.org/10.5194/egusphere-2026-3341, 2026
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
This study presents an adaptive observation-weighting scheme for thermodynamic profile retrievals from ground-based microwave radiometers and Mie–Raman lidars. The method dynamically estimates observational contributions during the retrieval process, replacing the commonly used static weighting assumption. Evaluation using 107 heavy-precipitation cases shows improved retrieval accuracy, particularly for atmospheric moisture profiles.
Xiaozhong Cao, Qiyun Guo, Haowen Luo, Jincheng Wang, Rongkang Yang, Die Xiao, Yinfeng Liu, Zhongliang Sun, Shijun Liu, Sijie Chen, Anfan Huang, Guo Jianping, and Peng Zhang
Atmos. Meas. Tech., 19, 3231–3251, https://doi.org/10.5194/amt-19-3231-2026, https://doi.org/10.5194/amt-19-3231-2026, 2026
Short summary
Short summary
The Ascent-Drift-Descent Radiosonde System (ADDRS) represents an emerging cost-effective technology for upper-air measurements, Its 'Internet cloud + instrument terminal' design enables targeted observations for extreme weather research. Initial data application shows high potential for numerical weather prediction, The pilot application of ADDRS is list in the World Meteorological Organization Region II Coordination 2025-2027 demonstration project (No.RA II-18-I-DP-2).
Rumo Wang, Tianyi Fan, Zhanqing Li, and Jianping Guo
EGUsphere, https://doi.org/10.5194/egusphere-2026-1730, https://doi.org/10.5194/egusphere-2026-1730, 2026
Short summary
Short summary
This study investigated whether aerosols from anthropogenic emissions can make storm environment more favorable for producing tornados. We numerically modeled a real storm in the populated eastern China and found that aerosols can warm the low-level air and strengthen the updraft, allowing the storm to more effectively ingest environmental vorticity, and thus favoring the production of tornados. This suggests that air pollution may increase severe weather risk in densely populated regions.
Pierre Laluet, Chiara Corbari, Oscar Baez-Villanueva, Sophia Walther, Yongqiang Zhang, Joaquín Muñoz-Sabater, Clément Albergel, and Wouter Dorigo
Hydrol. Earth Syst. Sci., 30, 1779–1799, https://doi.org/10.5194/hess-30-1779-2026, https://doi.org/10.5194/hess-30-1779-2026, 2026
Short summary
Short summary
We examined how well global datasets estimate evapotranspiration, the water moving from land to the air, in irrigated areas. We compared six widely used products with irrigation maps, satellite estimates, and field measurements across different climate regions. Some datasets better capture irrigation effects, especially those using temperature and vegetation satellite data. This work helps guide dataset selection and supports better inclusion of irrigation in hydrological and climate models.
Yonglin Fang, Hancheng Hu, Xiangdong Zheng, Jianping Guo, Xingbing Zhao, Fang Ma, and Hao Wu
Atmos. Chem. Phys., 26, 4089–4104, https://doi.org/10.5194/acp-26-4089-2026, https://doi.org/10.5194/acp-26-4089-2026, 2026
Short summary
Short summary
This study shows how temperature inversions (warm air above cold) trap pollution in China. Using six years of national data, we find these "lids" frequently cause severe haze, especially in winter, by increasing pollution probability and intensity. Impacts differ by region and inversion type/strength. These findings help improve air quality forecasts and regional pollution control strategies.
Zhe Tong, Boming Liu, Xin Ma, Jianping Guo, Haowei Zhang, Haoyu Dong, Ge Han, Yingying Ma, and Wei Gong
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2026-73, https://doi.org/10.5194/essd-2026-73, 2026
Preprint under review for ESSD
Short summary
Short summary
Near-surface wind is crucial for weather and wind energy studies. This work uses physically constrained machine learning combined with satellite wind observations to generate a wind speed profile dataset within the global atmospheric boundary layer. The dataset accurately depicts the spatial distribution and variation of global wind speeds with improved accuracy, finer vertical detail, and reduced data gaps, supporting boundary-layer meteorology, climate studies, and wind energy applications.
Hui Xu, Jianping Guo, Jianbo Deng, Rongfang Yang, Deli Meng, Zhen Zhang, Ning Li, Yuping Sun, Shuairu Jiang, Tianmeng Chen, Juan Chen, Liping Zeng, Yongshui Zhou, and Bing Tong
EGUsphere, https://doi.org/10.5194/egusphere-2026-1091, https://doi.org/10.5194/egusphere-2026-1091, 2026
Short summary
Short summary
Using a new national network of 80 cloud radars, we mapped how clouds are layered and how high they reach across China. Clouds vary strongly by region, season, and time of day, with deeper clouds in humid, unstable conditions and shallower clouds in stable or windy conditions. Also, land cover matters: forests tend to have lower cloud bases than barren land. These findings help improve weather and climate predictions.
Ning Li, Jianping Guo, Xiaoran Guo, Tianmeng Chen, Zhen Zhang, Na Tang, Yifei Wang, Honglong Yang, Yongguang Zheng, and Yongshui Zhou
Atmos. Chem. Phys., 26, 3339–3356, https://doi.org/10.5194/acp-26-3339-2026, https://doi.org/10.5194/acp-26-3339-2026, 2026
Short summary
Short summary
Nighttime rainfall often links to low-level jets (LLJs), but we lack clarity on nationwide LLJ features. We here used a nationwide radar wind profiler network to study LLJ changes 2 hours before rainfall, covering China’s 2023–2024 rainy seasons. 56% nighttime rainfall had LLJs. The LLJs-associated heavy rain needed a rapid adjustment of LLJs’ vertical structure, especially a significant intensification within 30 minutes preceding rain. This shows the importance of LLJ in nowcasting rainfall.
Xiaoran Guo, Jianping Guo, Ning Li, Zhen Zhang, Tianmeng Chen, Yu Shi, Pengzhan Yao, Shuairu Jiang, Lei Zhao, and Fei Hu
Atmos. Chem. Phys., 26, 2391–2409, https://doi.org/10.5194/acp-26-2391-2026, https://doi.org/10.5194/acp-26-2391-2026, 2026
Short summary
Short summary
Wind gusts threaten safety and infrastructure but are hard to predict. To address this gap, we studied an extreme wind gust event in Beijing on 30 May 2024. We used seven radar wind profilers to track how this gust developed. It formed when cold northeasterly air clashed with warm southerly winds as the storm moved downhill. Evaporation of rain cooled the air, boosting downward air movement and wind strength. The turbulence transferring energy from small to large eddies intensify winds.
Qi Zhang, Tianmeng Chen, Jianping Guo, Yu Wu, Bin Deng, and Junjie Yan
Geosci. Model Dev., 19, 505–522, https://doi.org/10.5194/gmd-19-505-2026, https://doi.org/10.5194/gmd-19-505-2026, 2026
Short summary
Short summary
We propose TCKF1D-Var, a thermodynamic-constrained variational framework for ground-based microwave radiometer retrievals. Using virtual potential temperature, a ratio-based cost function, and a microphysics closure, it reduces biases relative to ERA5 and 1D-Var, improves cloud liquid water representation, and enhances heavy rainfall precursors, extending lead times. This approach strengthens continuous profiling and supports high-impact weather nowcasting.
Defeng Feng, Tianjie Zhao, Jingyao Zheng, Yu Bai, Youhua Ran, Xiaokang Kou, Lingmei Jiang, Ziqian Zhang, Pei Yu, Jinbiao Zhu, Jie Pan, Jiancheng Shi, and Yuei-An Liou
Earth Syst. Sci. Data, 17, 6273–6293, https://doi.org/10.5194/essd-17-6273-2025, https://doi.org/10.5194/essd-17-6273-2025, 2025
Short summary
Short summary
This study introduces a downscaling approach integrating passive microwave and optical satellite data to generate a long-term (2002–2023), high-resolution (0.05°) global near-surface freeze-thaw (FT) state dataset with daily seamless continuity. The dataset attains 83.78 % overall accuracy, consistent with the microwave-based products but with finer spatial detail. This detailed FT record provides valuable information to enhance the understanding of global hydrological and ecological impacts.
Yutong Wang, Huazhe Shang, Chenqian Tang, Jian Xu, Tianyang Ji, Wenwu Wang, Lesi Wei, Yonghui Lei, Jiancheng Shi, and Husi Letu
Atmos. Chem. Phys., 25, 16167–16187, https://doi.org/10.5194/acp-25-16167-2025, https://doi.org/10.5194/acp-25-16167-2025, 2025
Short summary
Short summary
By analyzing global CloudSat data, we identified that most liquid cloud profiles have triangle-shaped or steadily decreasing structures, and we developed a new method using pattern recognition, fitting techniques, and machine learning to accurately estimate these profiles. This research advances our understanding of cloud life cycle and improves the ability to characterize cloud profiles, which is crucial for enhancing weather forecast and climate change research.
Deli Meng, Jianping Guo, Juan Chen, Xiaoran Guo, Ning Li, Yuping Sun, Zhen Zhang, Na Tang, Hui Xu, Tianmeng Chen, Rongfang Yang, and Jiajia Hua
Earth Syst. Sci. Data, 17, 4023–4037, https://doi.org/10.5194/essd-17-4023-2025, https://doi.org/10.5194/essd-17-4023-2025, 2025
Short summary
Short summary
This study provides a high-resolution dataset of low-level atmospheric turbulence across China, using radar and weather balloon observations. It reveals regional and seasonal variations in turbulence, with stronger activity in spring and summer. The dataset supports weather forecasting, aviation safety, and low-altitude flight planning, aiding China's growing low-altitude economy, and is accessible at https://doi.org/10.5281/zenodo.14959025.
Yongyong Zhang, Yongqiang Zhang, Xiaoyan Zhai, Jun Xia, Qiuhong Tang, Wei Wang, Jian Wu, Xiaoyu Niu, and Bing Han
Hydrol. Earth Syst. Sci., 29, 3257–3275, https://doi.org/10.5194/hess-29-3257-2025, https://doi.org/10.5194/hess-29-3257-2025, 2025
Short summary
Short summary
It is challenging to investigate flood variabilities and their formation mechanisms from massive event samples. This study explores spatiotemporal variabilities of 1446 flood events using hierarchical and partitional clustering methods. Control mechanisms of meteorological and physio-geographical factors are explored for individual flood event classes using constrained rank analysis. This gives insights into comprehensive changes in flood events and aids in flood prediction and control.
Xiaoran Guo, Jianping Guo, Deli Meng, Yuping Sun, Zhen Zhang, Hui Xu, Liping Zeng, Juan Chen, Ning Li, and Tianmeng Chen
Earth Syst. Sci. Data, 17, 3541–3552, https://doi.org/10.5194/essd-17-3541-2025, https://doi.org/10.5194/essd-17-3541-2025, 2025
Short summary
Short summary
Optimal atmospheric dynamic conditions are essential for convective storms. This study generates a dataset of high-resolution divergence and vorticity profiles using the measurements of a radar wind profiler mesonet in Beijing. The negative divergence and positive vorticity are present ahead of rainfall events. This suggests that this dataset can help improve our understanding of the pre-storm environment and has the potential to be applied in weather forecasting.
Juan Zhao, Jianping Guo, and Xiaohui Zheng
Geosci. Model Dev., 18, 4075–4101, https://doi.org/10.5194/gmd-18-4075-2025, https://doi.org/10.5194/gmd-18-4075-2025, 2025
Short summary
Short summary
A series of observing system simulation experiments are conducted to assess the impact of multiple radar wind profiler (RWP) networks on convective-scale numerical weather prediction. Results from three southwest-type heavy rainfall cases in the Beijing–Tianjin–Hebei region suggest the added forecast skill of ridge and foothill networks associated with the Taihang Mountains over the existing RWP network. This research provides valuable guidance for designing optimal RWP networks in the region.
Mingwei Li, Shouzhi Chen, Fanghua Hao, Nan Wang, Zhaofei Wu, Yue Xu, Jing Zhang, Yongqiang Zhang, and Yongshuo H. Fu
Hydrol. Earth Syst. Sci., 29, 2081–2095, https://doi.org/10.5194/hess-29-2081-2025, https://doi.org/10.5194/hess-29-2081-2025, 2025
Short summary
Short summary
Climate-driven shifts in vegetation phenology have a significant impact on hydrological processes. In this study, we integrated a process-based phenology module into the SWAT-Carbon model, which led to a substantial improvement in the simulation of vegetation dynamics and hydrological processes in the Jinsha River watershed. Our findings highlight the critical need to incorporate vegetation phenology into hydrological models to achieve a more accurate representation of ecohydrological processes.
Seoung Soo Lee, Chang Hoon Jung, Jinho Choi, Young Jun Yoon, Junshik Um, Youtong Zheng, Jianping Guo, Manguttathil G. Manoj, Sang-Keun Song, and Kyung-Ja Ha
Atmos. Chem. Phys., 25, 705–726, https://doi.org/10.5194/acp-25-705-2025, https://doi.org/10.5194/acp-25-705-2025, 2025
Short summary
Short summary
This study attempts to test a general factor that explains differences in the properties of different mixed-phase clouds using a modeling tool. Although this attempt is not to identify a factor that can perfectly explain and represent the properties of different mixed-phase clouds, we believe that this attempt acts as a valuable stepping stone towards a more complete, general way of using climate models to better predict climate change.
Zhiqi Xu, Jianping Guo, Guwei Zhang, Yuchen Ye, Haikun Zhao, and Haishan Chen
Earth Syst. Sci. Data, 16, 5753–5766, https://doi.org/10.5194/essd-16-5753-2024, https://doi.org/10.5194/essd-16-5753-2024, 2024
Short summary
Short summary
Tropical cyclones (TCs) are powerful weather systems that can cause extreme disasters. Here we generate a global long-term TC size and intensity reconstruction dataset, covering a time period from 1959 to 2022, with a 3 h temporal resolution, using machine learning models. These can be valuable for filling observational data gaps and advancing our understanding of TC climatology, thereby facilitating risk assessments and defenses against TC-related disasters.
Deli Meng, Jianping Guo, Xiaoran Guo, Yinjun Wang, Ning Li, Yuping Sun, Zhen Zhang, Na Tang, Haoran Li, Fan Zhang, Bing Tong, Hui Xu, and Tianmeng Chen
Atmos. Chem. Phys., 24, 8703–8720, https://doi.org/10.5194/acp-24-8703-2024, https://doi.org/10.5194/acp-24-8703-2024, 2024
Short summary
Short summary
The turbulence in the planetary boundary layer (PBL) over the Tibetan Plateau (TP) remains unclear. Here we elucidate the vertical profile of and temporal variation in the turbulence dissipation rate in the PBL over the TP based on a radar wind profiler (RWP) network. To the best of our knowledge, this is the first time that the turbulence profile over the whole TP has been revealed. Furthermore, the possible mechanisms of clouds acting on the PBL turbulence structure are investigated.
Xiaoran Guo, Jianping Guo, Tianmeng Chen, Ning Li, Fan Zhang, and Yuping Sun
Atmos. Chem. Phys., 24, 8067–8083, https://doi.org/10.5194/acp-24-8067-2024, https://doi.org/10.5194/acp-24-8067-2024, 2024
Short summary
Short summary
The prediction of downhill thunderstorms (DSs) remains elusive. We propose an objective method to identify DSs, based on which enhanced and dissipated DSs are discriminated. A radar wind profiler (RWP) mesonet is used to derive divergence and vertical velocity. The mid-troposphere divergence and prevailing westerlies enhance the intensity of DSs, whereas low-level divergence is observed when the DS dissipates. The findings highlight the key role that an RWP mesonet plays in the evolution of DSs.
Kaixu Bai, Ke Li, Liuqing Shao, Xinran Li, Chaoshun Liu, Zhengqiang Li, Mingliang Ma, Di Han, Yibing Sun, Zhe Zheng, Ruijie Li, Ni-Bin Chang, and Jianping Guo
Earth Syst. Sci. Data, 16, 2425–2448, https://doi.org/10.5194/essd-16-2425-2024, https://doi.org/10.5194/essd-16-2425-2024, 2024
Short summary
Short summary
A global gap-free high-resolution air pollutant dataset (LGHAP v2) was generated to provide spatially contiguous AOD and PM2.5 concentration maps with daily 1 km resolution from 2000 to 2021. This gap-free dataset has good data accuracies compared to ground-based AOD and PM2.5 concentration observations, which is a reliable database to advance aerosol-related studies and trigger multidisciplinary applications for environmental management, health risk assessment, and climate change analysis.
Boming Liu, Xin Ma, Jianping Guo, Renqiang Wen, Hui Li, Shikuan Jin, Yingying Ma, Xiaoran Guo, and Wei Gong
Atmos. Chem. Phys., 24, 4047–4063, https://doi.org/10.5194/acp-24-4047-2024, https://doi.org/10.5194/acp-24-4047-2024, 2024
Short summary
Short summary
Accurate wind profile estimation, especially for the lowest few hundred meters of the atmosphere, is of great significance for the weather, climate, and renewable energy sector. We propose a novel method that combines the power-law method with the random forest algorithm to extend wind profiles beyond the surface layer. Compared with the traditional algorithm, this method has better stability and spatial applicability and can be used to obtain the wind profiles on different land cover types.
Jianping Guo, Jian Zhang, Jia Shao, Tianmeng Chen, Kaixu Bai, Yuping Sun, Ning Li, Jingyan Wu, Rui Li, Jian Li, Qiyun Guo, Jason B. Cohen, Panmao Zhai, Xiaofeng Xu, and Fei Hu
Earth Syst. Sci. Data, 16, 1–14, https://doi.org/10.5194/essd-16-1-2024, https://doi.org/10.5194/essd-16-1-2024, 2024
Short summary
Short summary
A global continental merged high-resolution (PBLH) dataset with good accuracy compared to radiosonde is generated via machine learning algorithms, covering the period from 2011 to 2021 with 3-hour and 0.25º resolution in space and time. The machine learning model takes parameters derived from the ERA5 reanalysis and GLDAS product as input, with PBLH biases between radiosonde and ERA5 as the learning targets. The merged PBLH is the sum of the predicted PBLH bias and the PBLH from ERA5.
Hui Xu, Jianping Guo, Bing Tong, Jinqiang Zhang, Tianmeng Chen, Xiaoran Guo, Jian Zhang, and Wenqing Chen
Atmos. Chem. Phys., 23, 15011–15038, https://doi.org/10.5194/acp-23-15011-2023, https://doi.org/10.5194/acp-23-15011-2023, 2023
Short summary
Short summary
The radiative effect of cloud remains one of the largest uncertain factors in climate change, largely due to the lack of cloud vertical structure (CVS) observations. The study presents the first near-global CVS climatology using high-vertical-resolution soundings. Single-layer cloud mainly occurs over arid regions. As the number of cloud layers increases, clouds tend to have lower bases and thinner layer thicknesses. The occurrence frequency of cloud exhibits a pronounced seasonal diurnal cycle.
Boming Liu, Xin Ma, Jianping Guo, Hui Li, Shikuan Jin, Yingying Ma, and Wei Gong
Atmos. Chem. Phys., 23, 3181–3193, https://doi.org/10.5194/acp-23-3181-2023, https://doi.org/10.5194/acp-23-3181-2023, 2023
Short summary
Short summary
Wind energy is one of the most essential clean and renewable forms of energy in today’s world. However, the traditional power law method generally estimates the hub-height wind speed by assuming a constant exponent between surface and hub-height wind speeds. This inevitably leads to significant uncertainties in estimating the wind speed profile. To minimize the uncertainties, we here use a machine learning algorithm known as random forest to estimate the wind speed at hub height.
Seoung Soo Lee, Junshik Um, Won Jun Choi, Kyung-Ja Ha, Chang Hoon Jung, Jianping Guo, and Youtong Zheng
Atmos. Chem. Phys., 23, 273–286, https://doi.org/10.5194/acp-23-273-2023, https://doi.org/10.5194/acp-23-273-2023, 2023
Short summary
Short summary
This paper elaborates on process-level mechanisms regarding how the interception of radiation by aerosols interacts with the surface heat fluxes and atmospheric instability in warm cumulus clouds. This paper elucidates how these mechanisms vary with the location or altitude of an aerosol layer. This elucidation indicates that the location of aerosol layers should be taken into account for parameterizations of aerosol–cloud interactions.
Shaoyang He, Yongqiang Zhang, Ning Ma, Jing Tian, Dongdong Kong, and Changming Liu
Earth Syst. Sci. Data, 14, 5463–5488, https://doi.org/10.5194/essd-14-5463-2022, https://doi.org/10.5194/essd-14-5463-2022, 2022
Short summary
Short summary
This study developed a daily, 500 m evapotranspiration and gross primary production product (PML-V2(China)) using a locally calibrated water–carbon coupled model, PML-V2, which was well calibrated against observations at 26 flux sites across nine land cover types. PML-V2 (China) performs satisfactorily in the plot- and basin-scale evaluations compared with other mainstream products. It improved intra-annual ET and GPP dynamics, particularly in the cropland ecosystem.
Seoung Soo Lee, Jinho Choi, Goun Kim, Kyung-Ja Ha, Kyong-Hwan Seo, Chang Hoon Jung, Junshik Um, Youtong Zheng, Jianping Guo, Sang-Keun Song, Yun Gon Lee, and Nobuyuki Utsumi
Atmos. Chem. Phys., 22, 9059–9081, https://doi.org/10.5194/acp-22-9059-2022, https://doi.org/10.5194/acp-22-9059-2022, 2022
Short summary
Short summary
This study investigates how aerosols affect clouds and precipitation and how the aerosol effects vary with varying types of clouds that are characterized by cloud depth in two metropolitan areas in East Asia. As cloud depth increases, the enhancement of precipitation amount transitions to no changes in precipitation amount with increasing aerosol concentrations. This indicates that cloud depth needs to be considered for a comprehensive understanding of aerosol-cloud interactions.
Ming Li, Husi Letu, Yiran Peng, Hiroshi Ishimoto, Yanluan Lin, Takashi Y. Nakajima, Anthony J. Baran, Zengyuan Guo, Yonghui Lei, and Jiancheng Shi
Atmos. Chem. Phys., 22, 4809–4825, https://doi.org/10.5194/acp-22-4809-2022, https://doi.org/10.5194/acp-22-4809-2022, 2022
Short summary
Short summary
To build on the previous investigations of the Voronoi model in the remote sensing retrievals of ice cloud products, this paper developed an ice cloud parameterization scheme based on the single-scattering properties of the Voronoi model and evaluate it through simulations with the Community Integrated Earth System Model (CIESM). Compared with four representative ice cloud schemes, results show that the Voronoi model has good capabilities of ice cloud modeling in the climate model.
Kaixu Bai, Ke Li, Mingliang Ma, Kaitao Li, Zhengqiang Li, Jianping Guo, Ni-Bin Chang, Zhuo Tan, and Di Han
Earth Syst. Sci. Data, 14, 907–927, https://doi.org/10.5194/essd-14-907-2022, https://doi.org/10.5194/essd-14-907-2022, 2022
Short summary
Short summary
The Long-term Gap-free High-resolution Air Pollutant concentration dataset, providing gap-free aerosol optical depth (AOD) and PM2.5 and PM10 concentration with a daily 1 km resolution for 2000–2020 in China, is generated and made publicly available. This is the first long-term gap-free high-resolution aerosol dataset in China and has great potential to trigger multidisciplinary applications in Earth observations, climate change, public health, ecosystem assessment, and environment management.
Linye Song, Shangfeng Chen, Wen Chen, Jianping Guo, Conglan Cheng, and Yong Wang
Atmos. Chem. Phys., 22, 1669–1688, https://doi.org/10.5194/acp-22-1669-2022, https://doi.org/10.5194/acp-22-1669-2022, 2022
Short summary
Short summary
This study shows that in most years when haze pollution (HP) over the North China Plain (NCP) is more (less) serious in winter, air conditions in the following spring are also worse (better) than normal. Conversely, there are some years when HP in the following spring is opposed to that in winter. It is found that North Atlantic sea surface temperature (SST) anomalies play important roles in HP evolution over the NCP. Thus North Atlantic SST is an important preceding signal for NCP HP evolution.
Boming Liu, Jianping Guo, Wei Gong, Yong Zhang, Lijuan Shi, Yingying Ma, Jian Li, Xiaoran Guo, Ad Stoffelen, Gerrit de Leeuw, and Xiaofeng Xu
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2022-26, https://doi.org/10.5194/amt-2022-26, 2022
Publication in AMT not foreseen
Short summary
Short summary
Aeolus is the first satellite mission to directly observe wind profile information on a global scale. However, Aeolus wind products over China were thus far not evaluated by in-situ comparison. This work is the comparison of wind speed on a large scale between the Aeolus, ERA5 and RS , shedding important light on the data application of Aeolus wind products.
Jianping Guo, Jian Zhang, Kun Yang, Hong Liao, Shaodong Zhang, Kaiming Huang, Yanmin Lv, Jia Shao, Tao Yu, Bing Tong, Jian Li, Tianning Su, Steve H. L. Yim, Ad Stoffelen, Panmao Zhai, and Xiaofeng Xu
Atmos. Chem. Phys., 21, 17079–17097, https://doi.org/10.5194/acp-21-17079-2021, https://doi.org/10.5194/acp-21-17079-2021, 2021
Short summary
Short summary
The planetary boundary layer (PBL) is the lowest part of the troposphere, and boundary layer height (BLH) is the depth of the PBL and is of critical importance to the dispersion of air pollution. The study presents the first near-global BLH climatology by using high-resolution (5-10 m) radiosonde measurements. The variations in BLH exhibit large spatial and temporal dependence, with a peak at 17:00 local solar time. The most promising reanalysis product is ERA-5 in terms of modeling BLH.
Seoung Soo Lee, Kyung-Ja Ha, Manguttathil Gopalakrishnan Manoj, Mohammad Kamruzzaman, Hyungjun Kim, Nobuyuki Utsumi, Youtong Zheng, Byung-Gon Kim, Chang Hoon Jung, Junshik Um, Jianping Guo, Kyoung Ock Choi, and Go-Un Kim
Atmos. Chem. Phys., 21, 16843–16868, https://doi.org/10.5194/acp-21-16843-2021, https://doi.org/10.5194/acp-21-16843-2021, 2021
Short summary
Short summary
Using a modeling framework, a midlatitude stratocumulus cloud system is simulated. It is found that cloud mass in the system becomes very low due to interactions between ice and liquid particles compared to that in the absence of ice particles. It is also found that interactions between cloud mass and aerosols lead to a reduction in cloud mass in the system, and this is contrary to an aerosol-induced increase in cloud mass in the absence of ice particles.
Ifeanyichukwu C. Nduka, Chi-Yung Tam, Jianping Guo, and Steve Hung Lam Yim
Atmos. Chem. Phys., 21, 13443–13454, https://doi.org/10.5194/acp-21-13443-2021, https://doi.org/10.5194/acp-21-13443-2021, 2021
Short summary
Short summary
This study analyzed the nature, mechanisms and drivers for hot-and-polluted episodes (HPEs) in the Pearl River Delta, China. A total of eight HPEs were identified and can be grouped into three clusters of HPEs that were respectively driven (1) by weak subsidence and convection induced by approaching tropical cyclones, (2) by calm conditions with low wind speed in the lower atmosphere and (3) by the combination of both aforementioned conditions.
Cited articles
Albergel, C., de Rosnay, P., Gruhier, C., Munoz-Sabater, J., Hasenauer, S., Isaksen, L., Kerr, Y., and Wagner, W.: Evaluation of remotely sensed and modelled
soil moisture products using global ground-based in situ observations,
Remote Sens. Environ., 118, 215–226, https://doi.org/10.1016/j.rse.2011.11.017, 2012.
Brunsdon, C., Fotheringham, A. S., and Charlton, M. E.: Geographically
weighted regression: A method for exploring spatial nonstationarity, Geogr.
Anal., 28, 281–298, 1996.
Busch, F. A., Niemann, J. D., and Coleman, M.: Evaluation of an empirical
orthogonal function-based method to downscale soil moisture patterns based
on topographical attributes, Hydrol. Process., 26, 2696–2709, 2012.
Carlson, T. N., Gillies, R. R., and Perry, E. M.: A method to make use of
thermal infrared temperature and NDVI measurements to infer surface soil
water content and fractional vegetation cover, Remote Sens. Rev., 9,
161–173, 1994.
Champagne, C., McNairn, H., and Berg, A. A.: Monitoring agricultural soil
moisture extremes in Canada using passive microwave remote sensing, Remote
Sens. Environ., 115, 2434–2444, 2011.
Chauhan, N. S., Miller, S., and Ardanuy, P.: Spaceborne soil moisture
estimation at high resolution: a microwave-optical/IR synergistic approach,
Int. J. Remote Sens., 24, 4599–4622, https://doi.org/10.1080/0143116031000156837, 2003.
Chen, Y., Yuan, H., Yang, Y., and Sun, R.: Sub-daily soil moisture estimate
using dynamic Bayesian model averaging, J. Hydrol., 590, 125445, https://doi.org/10.1016/j.jhydrol.2020.125445, 2020.
Choi, M. and Hur, Y.: A microwave-optical/infrared disaggregation for
improving spatial representation of soil moisture using AMSR-E and MODIS
products, Remote Sens. Environ., 124, 259–269, https://doi.org/10.1016/j.rse.2012.05.009, 2012.
Das, N., Entekhabi, D., Dunbar, R. S., Kim, S., Yueh, S., Colliander, A., O'Neill, P. E., Jackson, T., Jagdhuber, T., Chen, F., Crow, W. T., Walke, J., Berg, A., Bosch, D., Caldwell, T., and Cosh, M.: SMAP/Sentinel-1 L2 Radiometer/Radar 30-Second Scene 3 km
EASE-Grid Soil Moisture, Version 3, NASA National Snow and Ice Data Center Distributed Active Archive Center [data set], https://doi.org/10.5067/ASB0EQO2LYJV, 2020.
Das, N. N., Entekhabi, D., Dunbar, R. S., Chaubell, M. J., Colliander, A., Yueh, S., Jagdhuber, T., Chen, F., Crow, W., O'Neill, P. E., Walker, J. P., Berg, A., Bosch, D. D., Caldwell, T., Cosh, M. H., Collins, C. H., Lopez-Baeza, E., and Thibeault, M.: The SMAP and Copernicus Sentinel 1A/B
microwave active-passive high resolution surface soil moisture product,
Remote Sens. Environ., 233, 111380, https://doi.org/10.1016/j.rse.2019.111380, 2019.
den Besten, N., Steele-Dunne, S., de Jeu, R., and van der Zaag, P.: Towards
Monitoring Waterlogging with Remote Sensing for Sustainable Irrigated
Agriculture, Remote Sens., 13, 2929, https://doi.org/10.3390/rs13152929, 2021.
Dorigo, W., Himmelbauer, I., Aberer, D., Schremmer, L., Petrakovic, I., Zappa, L., Preimesberger, W., Xaver, A., Annor, F., Ardö, J., Baldocchi, D., Bitelli, M., Blöschl, G., Bogena, H., Brocca, L., Calvet, J.-C., Camarero, J. J., Capello, G., Choi, M., Cosh, M. C., van de Giesen, N., Hajdu, I., Ikonen, J., Jensen, K. H., Kanniah, K. D., de Kat, I., Kirchengast, G., Kumar Rai, P., Kyrouac, J., Larson, K., Liu, S., Loew, A., Moghaddam, M., Martínez Fernández, J., Mattar Bader, C., Morbidelli, R., Musial, J. P., Osenga, E., Palecki, M. A., Pellarin, T., Petropoulos, G. P., Pfeil, I., Powers, J., Robock, A., Rüdiger, C., Rummel, U., Strobel, M., Su, Z., Sullivan, R., Tagesson, T., Varlagin, A., Vreugdenhil, M., Walker, J., Wen, J., Wenger, F., Wigneron, J. P., Woods, M., Yang, K., Zeng, Y., Zhang, X., Zreda, M., Dietrich, S., Gruber, A., van Oevelen, P., Wagner, W., Scipal, K., Drusch, M., and Sabia, R.: The International Soil Moisture Network: serving Earth system science for over a decade, Hydrol. Earth Syst. Sci., 25, 5749–5804, https://doi.org/10.5194/hess-25-5749-2021, 2021.
Dowling, T. P. F., Song, P., Jong, M. C. D., Merbold, L., Wooster, M. J.,
Huang, J., and Zhang, Y.: An Improved Cloud Gap-Filling Method for Longwave
Infrared Land Surface Temperatures through Introducing Passive Microwave
Techniques, Remote Sens., 13, 3522, https://doi.org/10.3390/rs13173522, 2021.
Du, J. Y., Kimball, J. S., and Jones, L. A.: Passive microwave remote
sensing of soil moisture based on dynamic vegetation scattering properties
for AMSR-E, IEEE T. Geosci. Remote, 54, 597–608, 2016.
Duan, S. and Li, Z.: Spatial Downscaling of MODIS Land Surface Temperatures
Using Geographically Weighted Regression: Case Study in Northern China, IEEE
T. Geosci. Remote, 54, 6458–6469, https://doi.org/10.1109/TGRS.2016.2585198, 2016.
Entekhabi, D., Reichle, R. H., Koster, R. D., and Crow, W. T.: Performance
Metrics for Soil Moisture Retrievals and Application Requirements, J.
Hydrometeorol., 11, 832–840, https://doi.org/10.1175/2010jhm1223.1, 2010a.
Entekhabi, D., Njoku, E. G., O'Neill, P. E., Kellogg, K. H., Crow, W. T., Edelstein, W. N., Entin, J. K., Goodman, S. D., Jackson, T. J., Johnson, J., Kimball, J., Piepmeier, J. R., Koster, R. D., Martin, N., McDonald, K. C., Moghaddam, M., Moran, S., Reichle, R., Shi, J. C., Spencer, M. W., Thurman, S. W., Tsang, L., and Van Zyl, J.: The Soil Moisture Active Passive (SMAP)
Mission, Proc. IEEE, 98, 704–716, https://doi.org/10.1109/JPROC.2010.2043918, 2010b.
Entekhabi, D., Das, N., Kim, S., Jagdhuber, T., Piles, M., Yueh, S., Colliander, A., Lopez-baeza, E., and Martínez-Fernández, J.: High-Resolution Enhanced Product based on SMAP Active-Passive Approach and Sentinel 1A Radar Data, AGU Fall Meeting Abstracts, San Francisco, USA, https://ui.adsabs.harvard.edu/abs/2016AGUFM.H24C..08E/abstract (last access: 2 April 2021), 2016.
Fang, B. and Lakshmi, V.: Passive Microwave Soil Moisture Downscaling Using
Vegetation and Surface Temperatures, Vadose Zone J., 12, 1712–1717, 2013.
Fang, B., Lakshmi, V., Bindlish, R., Jackson, T. J., Cosh, M., and Basara,
J.: Passive Microwave Soil Moisture Downscaling Using Vegetation Index and
Skin Surface Temperature, Vadose Zone J., 12, 1712–1717, 2013.
Fang, B., Lakshmi, V., Bindlish, R., and Jackson, T.: AMSR2 Soil Moisture
Downscaling Using Temperature and Vegetation Data, Remote Sens., 10, 1575, https://doi.org/10.3390/rs10101575, 2018.
Friedl, M. and Sulla-Menashe, D.: MCD12Q1 MODIS/Terra+Aqua Land Cover Type Yearly L3 Global 500 m SIN Grid V006, NASA EOSDIS Land Processes DAAC [data set], https://doi.org/10.5067/MODIS/MCD12Q1.006, 2019.
Fujii, H., Koike, T., and Imaoka, K.: Improvement of the AMSR-E Algorithm
for Soil Moisture Estimation by Introducing a Fractional Vegetation Coverage
Dataset Derived from MODIS Data, Journal of the Remote Sensing Society of
Japan, 29, 282–292, 2009.
Im, J., Park, S., Rhee, J., Baik, J., and Choi, M.: Downscaling of AMSR-E
soil moisture with MODIS products using machine learning approaches, Environ.
Earth Sci., 75, 1–19, https://doi.org/10.1007/s12665-016-5917-6,
2016.
Ines, A. V. M., Das, N. N., Hansen, J. W., and Njoku, E. G.: Assimilation of
remotely sensed soil moisture and vegetation with a crop simulation model
for maize yield prediction, Remote Sens. Environ., 138, 149–164,
https://doi.org/10.1016/j.rse.2013.07.018, 2013.
Jiménez, C., Prigent, C., Ermida, S. L., and Moncet, J. L.: Inversion of
AMSR-E observations for land surface temperature estimation: 1. Methodology
and evaluation with station temperature, J. Geophys. Res.-Atmos., 122, 3330–3347, https://doi.org/10.1002/2016jd026144, 2017.
Jing, Z. and Zhang, X.: A soil moisture assimilation scheme using
satellite-retrieved skin temperature in meso-scale weather forecast model,
Atmos. Res., 95, 333–352, 2010.
Jones, L. A., Kimball, J. S., Podest, E., McDonald, K. C., Chan, S. K., and
Njoku, E. G.: A method for deriving land surface moisture, vegetation
optical depth, and open water fraction from AMSR-E, IEEE IGARSS 2009, Cape
Town, South Africa, 2009, III-916-III-919, https://doi.org/10.1109/IGARSS.2009.5417921,
Jung, M., Reichstein, M., Ciais, P., Seneviratne, S. I., Sheffield, J., Goulden, M. L., Bonan, G., Cescatti, A., Chen, J., de Jeu, R., Dolman, A. J., Eugster, W., Gerten, D., Gianelle, D., Gobron, N., Heinke, J., Kimball, J., Law, B. E., Montagnani, L., Mu, Q., Mueller, B., Oleson, K., Papale, D., Richardson, A. D., Roupsard, O., Running, S., Tomelleri, E., Viovy, N., Weber, U., Williams, C., Wood, E., Zaehle, S., and Zhang, K.: Recent decline in the global land
evapotranspiration trend due to limited moisture supply, Nature, 467,
951–954, https://doi.org/10.1038/nature09396, 2010.
Kim, J. and Hogue, T. S.: Improving spatial soil moisture representation
through integration of AMSR-E and MODIS products, IEEE T. Geosci. Remote, 50, 446–460, https://doi.org/10.1109/TGRS.2011.2161318,
2012.
Koike, T., Nakamura, Y., Kaihotsu, I., Davva, G., Matsuura, N., Tamagawa,
K., and Fujii, H.: Development of an Advanced Microwave Scanning Radiometer
(AMSR-E) algorithm of soil moisture and vegetation water content (written in
Japanese), Annual Journal of Hydraulic Engineering, 48, 217–222 2004.
Komatsu, T. S.: Toward a Robust Phenomenological Expression of Evaporation
Efficiency for Unsaturated Soil Surfaces, J. Appl. Meteorol.,
42, 1330–1334, https://doi.org/10.1175/1520-0450(2003)042<1330:Tarpeo>2.0.Co;2, 2003.
Kong, D., Zhang, Y., Gu, X., and Wang, D.: A robust method for
reconstructing global MODIS EVI time series on the Google Earth Engine,
Isprs J. Photogramm., 155, 13–24, 2019.
Koster, R. D., Mahanama, S., Livneh, B., Lettenmaier, D. P., and Reichle, R.
H.: Skill in streamflow forecasts derived from large-scale estimates of soil
moisture and snow, Nat. Geosci., 3, 613–616, 2010.
Liu, S., Li, X., Che, T., Xu, Z., Zhang, Y., and Tan, J.: Qilian Mountains integrated observatory network: Dataset of the Heihe River Basin integrated observatory network (eddy covariance system of Yakou station, 2018), National Tibetan Plateau Data Center [data set], https://doi.org/10.11888/Meteoro.tpdc.270781, 2019.
Liu, S., Xiao, Q., Xu, Z., and Bai, J.: Multi-scale surface flux and meteorological elements observation dataset in the Hai River Basin (Huailai station-eddy covariance system-40m tower, 2018). National Tibetan Plateau Data Center [data set], https://doi.org/10.11888/Meteoro.tpdc.271094, 2021.
Ma, Y.: A long-term dataset of integrated land-atmosphere interaction observations on the Tibetan Plateau (2005–2016), National Tibetan Plateau Data Center [data set], https://doi.org/10.11888/Meteoro.tpdc.270910, 2020.
Malbéteau, Y., Merlin, O., Molero, B., Rüdiger, C., and Bacon, S.:
DisPATCh as a tool to evaluate coarse-scale remotely sensed soil moisture
using localized in situ measurements: Application to SMOS and AMSR-E data in
Southeastern Australia, Int. J. Appl. Earth Obs., 45, 221–234, https://doi.org/10.1016/j.jag.2015.10.002, 2016.
Meesters, A. G. C. A., De Jeu, R. A. M., and Owe, M.: Analytical derivation
of the vegetation optical depth from the microwave polarization difference
index, IEEE Geosci. Remote Sens. Lett., 2, 121–123, 2005.
Mendoza, P. A., Mizukami, N., Ikeda, K., Clark, M. P., Gutmann, E. D., Arnold, J. R., Brekke, L. D., and Rajagopalan, B.: Effects of different regional climate
model resolution and forcing scales on projected hydrologic changes, J.
Hydrol., 541, 1003–1019, https://doi.org/10.1016/j.jhydrol.2016.08.010, 2016.
Meng, X., Mao, K., Meng, F., Shi, J., Zeng, J., Shen, X., Cui, Y., Jiang, L., and Guo, Z.: A fine-resolution soil moisture dataset for China in 2002–2018, Earth Syst. Sci. Data, 13, 3239–3261, https://doi.org/10.5194/essd-13-3239-2021, 2021.
Merlin, O., Chehbouni, A. G., Kerr, Y. H., Njoku, E. G., and Entekhabi, D.:
A combined modeling and multipectral/multiresolution remote sensing approach
for disaggregation of surface soil moisture: Application to SMOS
configuration, IEEE T. Geosci. Remote, 43, 2036–2050, https://doi.org/10.1109/TGRS.2005.853192, 2005.
Merlin, O., Walker, J. P., Chehbouni, A., and Kerr, Y.: Towards
deterministic downscaling of SMOS soil moisture using MODIS derived soil
evaporative efficiency, Remote Sens. Environ., 112, 3935–3946, https://doi.org/10.1016/j.rse.2008.06.012, 2008.
Merlin, O., Al Bitar, A., Walker, J. P., and Kerr, Y.: An improved algorithm
for disaggregating microwave-derived soil moisture based on red,
near-infrared and thermal-infrared data, Remote Sens. Environ., 114,
2305–2316, https://doi.org/10.1016/j.rse.2010.05.007, 2010.
Merlin, O., Escorihuela, M. J., Mayoral, M. A., Hagolle, O., Al Bitar, A.,
and Kerr, Y.: Self-calibrated evaporation-based disaggregation of SMOS soil
moisture: An evaluation study at 3 km and 100 m resolution in Catalunya,
Spain, Remote Sens. Environ., 130, 25–38, https://doi.org/10.1016/j.rse.2012.11.008, 2013.
Merlin, O., Malbeteau, Y., Notfi, Y., Bacon, S., Er-Raki, S., Khabba, S.,
and Jarlan, L.: Performance Metrics for Soil Moisture Downscaling Methods:
Application to DISPATCH Data in Central Morocco, Remote Sens., 7, 3783–3807,
https://doi.org/10.3390/rs70403783, 2015.
Molero, B., Merlin, O., Malbéteau, Y., Al Bitar, A., Cabot, F., Stefan, V., Kerr, Y., Bacon, S., Cosh, M. H., Bindlish, R., and Jackson, T. J.: SMOS disaggregated soil moisture product at 1 km
resolution: Processor overview and first validation results, Remote Sens.
Environ., 180, 361–376, https://doi.org/10.1016/j.rse.2016.02.045, 2016.
Montaldo, N., Albertson, J. D., Mancini, M., and Kiely, G.: Robust
simulation of root zone soil moisture with assimilation of surface soil
moisture data, Water Resour. Res., 37, 2889–2900, https://doi.org/10.1029/2000WR000209, 2001.
O'Neill, P. E., Bindlish, R., Chan, S., Chaubell, J., Colliander, A., Njoku,
E., and Jackson, T.: SMAP Algorithm Theoretical Basis Document: Level 2 &
3 Soil Moisture (Passive) Data Products, Revision G., Jet Propulsion
Laboratory, Pasadena, CA, https://nsidc.org/sites/nsidc.org/files/technical-references/L2_SM_P_ATBD_rev_G_final_Oct2021.pdf (last access: 5 May 2021), 2021a.
O'Neill, P. E., Chan, S., Njoku, E. G., Jackson, T., Bindlish, R., and Chaubell, J.: SMAP L3 Radiometer Global Daily 36 km EASE-Grid Soil Moisture, Version 8, Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center [data set], https://doi.org/10.5067/OMHVSRGFX38O, 2021b.
Owe, M., de Jeu, R., and Walker, J.: A methodology for surface soil moisture
and vegetation optical depth retrieval using the microwave polarization
difference index, IEEE T. Geosci. Remote, 39, 1643–1654, 2001.
Pan, H., Chen, Z., Wit, A. D., and Ren, J.: Joint Assimilation of Leaf Area Index and Soil Moisture from Sentinel-1 and Sentinel-2 Data into the WOFOST Model for Winter Wheat Yield Estimation, Sensors, 19, 3161, https://doi.org/10.3390/s19143161, 2019.
Peng, J., Loew, A., Zhang, S. Q., Wang, J., and Niesel, J.: Spatial
downscaling of satellite soil moisture data using a vegetation temperature
condition index, IEEE T. Geosci. Remote, 54, 558–566, https://doi.org/10.1109/TGRS.2015.2462074, 2016.
Piles, M., Entekhabi, D., and Camps, A.: A Change Detection Algorithm for
Retrieving High-Resolution Soil Moisture From SMAP Radar and Radiometer
Observations, IEEE T. Geosci. Remote, 47, 4125–4131,
https://doi.org/10.1109/TGRS.2009.2022088, 2009.
Sabaghy, S., Walker, J. P., Renzullo, L. J., Akbar, R., Chan, S., Chaubell, J., Das, N., Dunbar, R. S., Entekhabi, D., Gevaert, A., Jackson, T. J., Loew, A., Merlin, O., Moghaddam, M., Peng, J., Peng, J., Piepmeier, J., Rüdiger, C., Stefan, V., Wu, X., Ye, N., and Yueh, S.: Comprehensive analysis of alternative downscaled soil
moisture products, Remote Sens. Environ., 239, 111586, https://doi.org/10.1016/j.rse.2019.111586, 2020.
Sanchez-Ruiz, S., Piles, M., Sanchez, N., Martinez-Fernandez, J.,
Vall-Ilossera, M., and Camps, A.: Combining SMOS with visible and
near/shortwave/thermal infrared satellite data for high resolution soil
moisture estimates, J. Hydrol., 516, 273–283, https://doi.org/10.1016/j.jhydrol.2013.12.047,
2014.
Scaini, A., Sanchez, N., Vicente-Serrano, S. M., and Martinez-Fernandez, J.:
SMOS-derived soil moisture anomalies and drought indices: a comparative
analysis using in situ measurements, Hydrol. Process., 29, 373–383,
https://doi.org/10.1002/hyp.10150, 2015.
Song, P. and Zhang, Y.: An improved non-linear inter-calibration method on
different radiometers for enhancing coverage of daily LST estimates in low
latitudes, Remote Sens. Environ., 264, 112626, https://doi.org/10.1016/j.rse.2021.112626, 2021a.
Song, P. and Zhang, Y.: Daily all weather surface soil moisture data set
with 1 km resolution in China (2003–2019), National Tibetan Plateau Data
Center [data set], https://doi.org/10.11888/Hydro.tpdc.271762, 2021b.
Song, P., Mansaray, L. R., Huang, J., and Huang, W.: Mapping paddy rice
agriculture over China using AMSR-E time series data, Isprs J. Photogramm.,
144, 469–482, https://doi.org/10.1016/j.isprsjprs.2018.08.015, 2018.
Song, P., Huang, J., and Mansaray, L. R.: An improved surface soil moisture
downscaling approach over cloudy areas based on geographically weighted
regression, Agr. Forest Meteorol., 275, 146–158,
https://doi.org/10.1016/j.agrformet.2019.05.022, 2019a.
Song, P., Huang, J., Mansaray, L. R., Wen, H., Wu, H., Liu, Z., and Wang, X.: An Improved Soil Moisture Retrieval Algorithm Based on the Land Parameter Retrieval Model for Water-
Land Mixed Pixels Using AMSR-E Data, IEEE T. Geosci. Remote, 57, 7643–7657, https://doi.org/10.1109/TGRS.2019.2915346,
2019b.
Song, P., Zhang, Y., and Tian, J.: Improving Surface Soil Moisture Estimates
in Humid Regions by an Enhanced Remote Sensing Technique, Geophys. Res. Lett.,
48, e2020GL091459, https://doi.org/10.1029/2020GL091459, 2021.
Stefan, V. G., Merlin, O., Escorihuela, M.-J., Molero, B., and Er-Raki, S.:
Temporal Calibration of an Evaporation-Based Spatial Disaggregation Method
of SMOS Soil Moisture Data, Remote Sens., 12, 1671, https://doi.org/10.3390/rs12101671, 2020.
Sui, D. Z.: Tobler's First Law of Geography: A Big Idea for a Small World?,
Ann. Assoc. Am. Geogr., 94, 269–277, https://doi.org/10.1111/j.1467-8306.2004.09402003.x, 2004.
Ulaby, F. T. and Wilson, E. A.: Microwave Attenuation Properties of
Vegetation Canopies, IEEE T. Geosci. Remote, GE-23, 746–753,
https://doi.org/10.1109/TGRS.1985.289393, 1985.
Vergopolan, N., Xiong, S., Estes, L., Wanders, N., Chaney, N. W., Wood, E. F., Konar, M., Caylor, K., Beck, H. E., Gatti, N., Evans, T., and Sheffield, J.: Field-scale soil moisture bridges the spatial-scale gap between drought monitoring and agricultural yields, Hydrol. Earth Syst. Sci., 25, 1827–1847, https://doi.org/10.5194/hess-25-1827-2021, 2021.
Verstraeten, W. W., Veroustraete, F., van der Sande, C. J., Grootaers, I.,
and Feyen, J.: Soil moisture retrieval using thermal inertia, determined
with visible and thermal spaceborne data, validated for European forests,
Remote Sens. Environ., 101, 299–314, 2006.
Walker, J. P. and Houser, P. R.: A methodology for initializing soil moisture in a global climate model: Assimilation of near-surface soil moisture observations, J. Geophys. Res.-Atmos., 106, 11761–11774, https://doi.org/10.1029/2001jd900149, 2001.
Wang, K. and Liang, S.: Evaluation of ASTER and MODIS land surface
temperature and emissivity products using long-term surface longwave
radiation observations at SURFRAD sites, Remote Sens. Environ., 113,
1556–1565, https://doi.org/10.1016/j.rse.2009.03.009, 2009.
Wang, L. and Qu, J. J.: NMDI: A normalized multi-band drought index for
monitoring soil and vegetation moisture with satellite remote sensing,
Geophys. Res. Lett., 34, L20405, https://doi.org/10.1029/2007GL031021, 2007.
Wei, Z., Meng, Y., Zhang, W., Peng, J., and Meng, L.: Downscaling SMAP soil
moisture estimation with gradient boosting decision tree regression over the
Tibetan Plateau, Remote Sens. Environ., 225, 30–44, 2019.
Wu, D., Liang, H., Cao, T., Yang, D., Zhou, W., and Wu, X.: Construction of
operation monitoring system of automatic soil moisture observation network
in China, Meteorol. Sci. Technol., 42, 278–282, 2014
Yang, G., Sun, W. W., Shen, H. F., Meng, X. C., and Li, J. L.: An Integrated
Method for Reconstructing Daily MODIS Land Surface Temperature Data, IEEE J.
Sel. Top. Appl. Earth Observ. Remote Sens., 12, 1026–1040, 2019.
Yao, P., Lu, H., Shi, J., Zhao, T., Yang, K., Cosh, M. H., Gianotti, D. J. S., and Entekhabi, D.: A long term global daily soil moisture dataset derived from AMSR-E and
AMSR2 (2002–2019), Sci. Data, 8, 143, https://doi.org/10.1038/s41597-021-00925-8,
2021.
Zeng, Y., Feng, Z., and Xiang, N.: Assessment of soil moisture using Landsat
ETM+ temperature/vegetation index in semiarid environment, IEEE
International Geoscience & Remote Sensing Symposium, Piscataway NJ, 4306,
4306–4309, https://doi.org/10.1109/IGARSS.2004.1370089, 2004.
Zhang, J., Zhou, Z., Yao, F., Yang, L., and Hao, C.: Validating the Modified
Perpendicular Drought Index in the North China Region Using In Situ Soil
Moisture Measurement, IEEE Geosci. Remote Sens. Lett., 12,
542–546, 2014.
Zhang, Y., Kong, D., Gan, R., Chiew, F. H. S., Mcvicar, T. R., Zhang, Q.,
and Yang, Y.: Coupled estimation of 500 m and 8-day resolution global
evapotranspiration and gross primary production in 2002–2017, Remote Sens.
Environ., 222, 165–182, 2019.
Zhang, Y. Q., Chiew, F. H. S., Liu, C. M., Tang, Q. H., Xia, J., Tian, J., Kong, D. D., and Li, C. C.: Can Remotely Sensed Actual Evapotranspiration Facilitate
Hydrological Prediction in Ungauged Regions Without Runoff Calibration?,
Water Resour. Res., 56, 2020.
Zheng, J. Y., Lu, H. S., Crow, W. T., Zhao, T. J., Merlin, O., Rodriguez-Fernandez, N., Shi, J. C., Zhu, Y. H., Su, J. B., Chuen, S. A. K., Wang, X. Y., and Gou, Q. Q.: Soil moisture downscaling using
multiple modes of the DISPATCH algorithm in a semi-humid/humid region, Int. J.
Appl. Earth Obs., 104, 102530, https://doi.org/10.1016/j.jag.2021.102530, 2021.
Zhou, S., Williams, A. P., Lintner, B., Berg, A. M., and Gentine, P.: Soil
moisture–atmosphere feedbacks mitigate declining water availability in
drylands, Nat. Clim. Change, 11, 38–44, 2021.
Zhu, Z. and Shi, C.: Simulation and Evaluation of CLDAS and GLDAS Soil
Moisture Data in China (written in Chinese), Sci. Technol.
Eng., 32, 138–144, 2014.
Short summary
Soil moisture information is crucial for understanding the earth surface, but currently available satellite-based soil moisture datasets are imperfect either in their spatiotemporal resolutions or in ensuring image completeness from cloudy weather. In this study, therefore, we developed one soil moisture data product over China that has tackled most of the above problems. This data product has the potential to promote the investigation of earth hydrology and be extended to the global scale.
Soil moisture information is crucial for understanding the earth surface, but currently...
Altmetrics
Final-revised paper
Preprint