Articles | Volume 13, issue 1
https://doi.org/10.5194/esd-13-219-2022
© 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/esd-13-219-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
31 Jan 2022
Research article |
| 31 Jan 2022
| 31 Jan 2022
Exploration of a novel geoengineering solution: lighting up tropical forests at night
Xueyuan Gao
Department of Geographical Sciences, University of Maryland, College Park, USA
Department of Geographical Sciences, University of Maryland, College Park, USA
Dongdong Wang
Department of Geographical Sciences, University of Maryland, College Park, USA
Faculty of Geographical Science, Beijing Normal University, Beijing, China
Bin He
College of Global Change and Earth System Science, Beijing Normal
University, Beijing, China
Aolin Jia
Department of Geographical Sciences, University of Maryland, College Park, USA
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Hui Liang, Shunlin Liang, Bo Jiang, Tao He, Feng Tian, Jianglei Xu, Wenyuan Li, Fengjiao Zhang, and Husheng Fang
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-136, https://doi.org/10.5194/essd-2025-136, 2025
Revised manuscript accepted for ESSD
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This paper describes 1 km daily mean land surface sensible heat flux (H) and land surface – air temperature difference (Tsa) datasets on the global scale during 2000–2020. The datasets were developed using a data-driven approach and rigorously validated against in situ observations and existing H and Tsa datasets, demonstrating both high accuracy and exceptional spatial resolution.
Yufang Zhang, Shunlin Liang, Han Ma, Tao He, Feng Tian, Guodong Zhang, and Jianglei Xu
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-553, https://doi.org/10.5194/essd-2024-553, 2025
Revised manuscript accepted for ESSD
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Soil moisture (SM) plays a vital role in climate, agriculture, and hydrology, yet reliable long-term seamless global datasets remain scarce. To fill this gap, we developed a four-decade seamless global daily 5 km SM product using multi-source datasets and deep learning techniques. This product has long-term coverage, spatial and temporal integrity, and high accuracy, making it a valuable tool for applications like SM trend analysis, drought monitoring, and assessing vegetation responses.
Shan Sang, Yan Li, Chengcheng Hou, Shuangshuang Zi, and Huiqing Lin
Hydrol. Earth Syst. Sci., 29, 67–84, https://doi.org/10.5194/hess-29-67-2025, https://doi.org/10.5194/hess-29-67-2025, 2025
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Green water exchanges between each province in China form an interconnected flow network and have substantial socioeconomic values. Green water flow and its teleconnected socioeconomic effects should be considered in water resource management in addition to blue water.
Bing Li, Shunlin Liang, Han Ma, Guanpeng Dong, Xiaobang Liu, Tao He, and Yufang Zhang
Earth Syst. Sci. Data, 16, 3795–3819, https://doi.org/10.5194/essd-16-3795-2024, https://doi.org/10.5194/essd-16-3795-2024, 2024
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This study describes 1 km all-weather instantaneous and daily mean land surface temperature (LST) datasets on the global scale during 2000–2020. It is the first attempt to synergistically estimate all-weather instantaneous and daily mean LST data on a long global-scale time series. The generated datasets were evaluated by the observations from in situ stations and other LST datasets, and the evaluation indicated that the dataset is sufficiently reliable.
Chengcheng Hou, Yan Li, Shan Sang, Xu Zhao, Yanxu Liu, Yinglu Liu, and Fang Zhao
Earth Syst. Sci. Data, 16, 2449–2464, https://doi.org/10.5194/essd-16-2449-2024, https://doi.org/10.5194/essd-16-2449-2024, 2024
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To fill the gap in the gridded industrial water withdrawal (IWW) data in China, we developed the China Industrial Water Withdrawal (CIWW) dataset, which provides monthly IWWs from 1965 to 2020 at a spatial resolution of 0.1°/0.25° and auxiliary data including subsectoral IWW and industrial output value in 2008. This dataset can help understand the human water use dynamics and support studies in hydrology, geography, sustainability sciences, and water resource management and allocation in China.
Xinyan Liu, Tao He, Shunlin Liang, Ruibo Li, Xiongxin Xiao, Rui Ma, and Yichuan Ma
Earth Syst. Sci. Data, 15, 3641–3671, https://doi.org/10.5194/essd-15-3641-2023, https://doi.org/10.5194/essd-15-3641-2023, 2023
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We proposed a data fusion strategy that combines the complementary features of multiple-satellite cloud fraction (CF) datasets and generated a continuous monthly 1° daytime cloud fraction product covering the entire Arctic during the sunlit months in 2000–2020. This study has positive significance for reducing the uncertainties for the assessment of surface radiation fluxes and improving the accuracy of research related to climate change and energy budgets, both regionally and globally.
Yufang Zhang, Shunlin Liang, Han Ma, Tao He, Qian Wang, Bing Li, Jianglei Xu, Guodong Zhang, Xiaobang Liu, and Changhao Xiong
Earth Syst. Sci. Data, 15, 2055–2079, https://doi.org/10.5194/essd-15-2055-2023, https://doi.org/10.5194/essd-15-2055-2023, 2023
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Soil moisture observations are important for a range of earth system applications. This study generated a long-term (2000–2020) global seamless soil moisture product with both high spatial and temporal resolutions (1 km, daily) using an XGBoost model and multisource datasets. Evaluation of this product against dense in situ soil moisture datasets and microwave soil moisture products showed that this product has reliable accuracy and more complete spatial coverage.
Ruohan Li, Dongdong Wang, Weile Wang, and Ramakrishna Nemani
Earth Syst. Sci. Data, 15, 1419–1436, https://doi.org/10.5194/essd-15-1419-2023, https://doi.org/10.5194/essd-15-1419-2023, 2023
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There has been an increasing need for high-spatiotemporal-resolution surface downward shortwave radiation (DSR) and photosynthetically active radiation (PAR) data for ecological, hydrological, carbon, and solar photovoltaic research. This study produced a new 1 km hourly product of land surface DSR and PAR from the enhanced GeoNEX new-generation geostationary data. Our validation indicated that the GeoNEX DSR and PAR product has a higher accuracy than other existing products.
Aolin Jia, Shunlin Liang, Dongdong Wang, Lei Ma, Zhihao Wang, and Shuo Xu
Earth Syst. Sci. Data, 15, 869–895, https://doi.org/10.5194/essd-15-869-2023, https://doi.org/10.5194/essd-15-869-2023, 2023
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Satellites are now producing multiple global land surface temperature (LST) products; however, they suffer from data gaps caused by cloud cover, seriously restricting the applications, and few products provide gap-free global hourly LST. We produced global hourly, 5 km, all-sky LST data from 2011 to 2021 using geostationary and polar-orbiting satellite data. Based on the assessment, it has high accuracy and can be used to estimate evapotranspiration, drought, etc.
Han Ma, Shunlin Liang, Changhao Xiong, Qian Wang, Aolin Jia, and Bing Li
Earth Syst. Sci. Data, 14, 5333–5347, https://doi.org/10.5194/essd-14-5333-2022, https://doi.org/10.5194/essd-14-5333-2022, 2022
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The fraction of absorbed photosynthetically active radiation (FAPAR) is one of the essential climate variables. This study generated a global land surface FAPAR product with a 250 m resolution based on a deep learning model that takes advantage of the existing FAPAR products and MODIS time series of observation information. Direct validation and intercomparison revealed that our product better meets user requirements and has a greater spatiotemporal continuity than other existing products.
Rui Ma, Jingfeng Xiao, Shunlin Liang, Han Ma, Tao He, Da Guo, Xiaobang Liu, and Haibo Lu
Geosci. Model Dev., 15, 6637–6657, https://doi.org/10.5194/gmd-15-6637-2022, https://doi.org/10.5194/gmd-15-6637-2022, 2022
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Parameter optimization can improve the accuracy of modeled carbon fluxes. Few studies conducted pixel-level parameterization because it requires a high computational cost. Our paper used high-quality spatial products to optimize parameters at the pixel level, and also used the machine learning method to improve the speed of optimization. The results showed that there was significant spatial variability of parameters and we also improved the spatial pattern of carbon fluxes.
Jianglei Xu, Shunlin Liang, and Bo Jiang
Earth Syst. Sci. Data, 14, 2315–2341, https://doi.org/10.5194/essd-14-2315-2022, https://doi.org/10.5194/essd-14-2315-2022, 2022
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Land surface all-wave net radiation (Rn) is a key parameter in many land processes. Current products have drawbacks of coarse resolutions, large uncertainty, and short time spans. A deep learning method was used to obtain global surface Rn. A long-term Rn product was generated from 1981 to 2019 using AVHRR data. The product has the highest accuracy and a reasonable spatiotemporal variation compared to three other products. Our product will play an important role in long-term climate change.
Xiaona Chen, Shunlin Liang, Lian He, Yaping Yang, and Cong Yin
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2021-279, https://doi.org/10.5194/essd-2021-279, 2021
Preprint withdrawn
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The present study developed a 39 year consistent 8-day 0.05 degree gap-free SCE dataset over the NH for the period 1981–2019 as part of the Global LAnd Surface Satellite dataset (GLASS) product suite based on the NOAA AVHRR-SR CDR and several contributory datasets. Compared with published SCE datasets, GLASS SCE has several advantages in snow cover studies, including long time series, finer spatial resolution (especially for years before 2000), and complete spatial coverage.
Diyang Cui, Shunlin Liang, Dongdong Wang, and Zheng Liu
Earth Syst. Sci. Data, 13, 5087–5114, https://doi.org/10.5194/essd-13-5087-2021, https://doi.org/10.5194/essd-13-5087-2021, 2021
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Large portions of the Earth's surface are expected to experience changes in climatic conditions. The rearrangement of climate distributions can lead to serious impacts on ecological and social systems. Major climate zones are distributed in a predictable pattern and are largely defined following the Köppen climate classification. This creates an urgent need to compile a series of Köppen climate classification maps with finer spatial and temporal resolutions and improved accuracy.
Yan Chen, Shunlin Liang, Han Ma, Bing Li, Tao He, and Qian Wang
Earth Syst. Sci. Data, 13, 4241–4261, https://doi.org/10.5194/essd-13-4241-2021, https://doi.org/10.5194/essd-13-4241-2021, 2021
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This study used remotely sensed and assimilated data to estimate all-sky land surface air temperature (Ta) using a machine learning method, and developed an all-sky 1 km daily mean land Ta product for 2003–2019 over mainland China. Validation results demonstrated that this dataset has achieved satisfactory accuracy and high spatial resolution simultaneously, which fills the current dataset gap in this field and plays an important role in studies of climate change and the hydrological cycle.
Yongkang Xue, Tandong Yao, Aaron A. Boone, Ismaila Diallo, Ye Liu, Xubin Zeng, William K. M. Lau, Shiori Sugimoto, Qi Tang, Xiaoduo Pan, Peter J. van Oevelen, Daniel Klocke, Myung-Seo Koo, Tomonori Sato, Zhaohui Lin, Yuhei Takaya, Constantin Ardilouze, Stefano Materia, Subodh K. Saha, Retish Senan, Tetsu Nakamura, Hailan Wang, Jing Yang, Hongliang Zhang, Mei Zhao, Xin-Zhong Liang, J. David Neelin, Frederic Vitart, Xin Li, Ping Zhao, Chunxiang Shi, Weidong Guo, Jianping Tang, Miao Yu, Yun Qian, Samuel S. P. Shen, Yang Zhang, Kun Yang, Ruby Leung, Yuan Qiu, Daniele Peano, Xin Qi, Yanling Zhan, Michael A. Brunke, Sin Chan Chou, Michael Ek, Tianyi Fan, Hong Guan, Hai Lin, Shunlin Liang, Helin Wei, Shaocheng Xie, Haoran Xu, Weiping Li, Xueli Shi, Paulo Nobre, Yan Pan, Yi Qin, Jeff Dozier, Craig R. Ferguson, Gianpaolo Balsamo, Qing Bao, Jinming Feng, Jinkyu Hong, Songyou Hong, Huilin Huang, Duoying Ji, Zhenming Ji, Shichang Kang, Yanluan Lin, Weiguang Liu, Ryan Muncaster, Patricia de Rosnay, Hiroshi G. Takahashi, Guiling Wang, Shuyu Wang, Weicai Wang, Xu Zhou, and Yuejian Zhu
Geosci. Model Dev., 14, 4465–4494, https://doi.org/10.5194/gmd-14-4465-2021, https://doi.org/10.5194/gmd-14-4465-2021, 2021
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The subseasonal prediction of extreme hydroclimate events such as droughts/floods has remained stubbornly low for years. This paper presents a new international initiative which, for the first time, introduces spring land surface temperature anomalies over high mountains to improve precipitation prediction through remote effects of land–atmosphere interactions. More than 40 institutions worldwide are participating in this effort. The experimental protocol and preliminary results are presented.
Diyang Cui, Shunlin Liang, Dongdong Wang, and Zheng Liu
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2021-53, https://doi.org/10.5194/essd-2021-53, 2021
Preprint withdrawn
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The Köppen-Geiger climate classification has been widely applied in climate change and ecology studies to characterize climatic conditions. We present a new 1-km global dataset of Köppen-Geiger climate classification and bioclimatic variables for historical and future climates. The new climate maps offer higher classification accuracy, correspond well with distributions of vegetation and topographic features, and demonstrate the ability to identify recent and future changes in climate zones.
Xiongxin Xiao, Shunlin Liang, Tao He, Daiqiang Wu, Congyuan Pei, and Jianya Gong
The Cryosphere, 15, 835–861, https://doi.org/10.5194/tc-15-835-2021, https://doi.org/10.5194/tc-15-835-2021, 2021
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Daily time series and full space-covered sub-pixel snow cover area data are urgently needed for climate and reanalysis studies. Due to the fact that observations from optical satellite sensors are affected by clouds, this study attempts to capture dynamic characteristics of snow cover at a fine spatiotemporal resolution (daily; 6.25 km) accurately by using passive microwave data. We demonstrate the potential to use the passive microwave and the MODIS data to map the fractional snow cover area.
Jin Ma, Ji Zhou, Frank-Michael Göttsche, Shunlin Liang, Shaofei Wang, and Mingsong Li
Earth Syst. Sci. Data, 12, 3247–3268, https://doi.org/10.5194/essd-12-3247-2020, https://doi.org/10.5194/essd-12-3247-2020, 2020
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Land surface temperature is an important parameter in the research of climate change and many land surface processes. This article describes the development and testing of an algorithm for generating a consistent global long-term land surface temperature product from 20 years of NOAA AVHRR radiance data. The preliminary validation results indicate good accuracy of this new long-term product, which has been designed to simplify applications and support the scientific research community.
Yi Zheng, Ruoque Shen, Yawen Wang, Xiangqian Li, Shuguang Liu, Shunlin Liang, Jing M. Chen, Weimin Ju, Li Zhang, and Wenping Yuan
Earth Syst. Sci. Data, 12, 2725–2746, https://doi.org/10.5194/essd-12-2725-2020, https://doi.org/10.5194/essd-12-2725-2020, 2020
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Accurately reproducing the interannual variations in vegetation gross primary production (GPP) is a major challenge. A global GPP dataset was generated by integrating the regulations of several major environmental variables with long-term changes. The dataset can effectively reproduce the spatial, seasonal, and particularly interannual variations in global GPP. Our study will contribute to accurate carbon flux estimates at long timescales.
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Short summary
Numerical experiments with a coupled Earth system model show that large-scale nighttime artificial lighting in tropical forests will significantly increase carbon sink, local temperature, and precipitation, and it requires less energy than direct air carbon capture for capturing 1 t of carbon, suggesting that it could be a powerful climate mitigation option. Side effects include CO2 outgassing after the termination of the nighttime lighting and impacts on local wildlife.
Numerical experiments with a coupled Earth system model show that large-scale nighttime...
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