Articles | Volume 15, issue 2
https://doi.org/10.5194/essd-15-869-2023
© Author(s) 2023. 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-15-869-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
16 Feb 2023
Data description paper |
| 16 Feb 2023
| 16 Feb 2023
Global hourly, 5 km, all-sky land surface temperature data from 2011 to 2021 based on integrating geostationary and polar-orbiting satellite data
Aolin Jia
Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA
Department of Geography, The University of Hong Kong, Hong Kong SAR 999077, China
Dongdong Wang
Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA
Lei Ma
Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA
Zhihao Wang
Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA
Shuo Xu
Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA
Related authors
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
Short summary
Short summary
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.
Xueyuan Gao, Shunlin Liang, Dongdong Wang, Yan Li, Bin He, and Aolin Jia
Earth Syst. Dynam., 13, 219–230, https://doi.org/10.5194/esd-13-219-2022, https://doi.org/10.5194/esd-13-219-2022, 2022
Short summary
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.
Aolin Jia, Shunlin Liang, Dongdong Wang, Bo Jiang, and Xiaotong Zhang
Atmos. Chem. Phys., 20, 881–899, https://doi.org/10.5194/acp-20-881-2020, https://doi.org/10.5194/acp-20-881-2020, 2020
Short summary
Short summary
The Tibetan Plateau (TP) plays a vital role in regional and global climate change due to its location and orography. After generating a long-term surface radiation (SR) dataset, we characterized the SR spatiotemporal variation along with temperature. Evidence from multiple data sources indicated that the TP dimming was primarily driven by increased aerosols from human activities, and the cooling effect of aerosol loading offsets TP surface warming, revealing the human impact on regional warming.
Bing Li, Shunlin Liang, Han Ma, Xiaobang Liu, Tao He, and Yufang Zhang
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-16, https://doi.org/10.5194/essd-2024-16, 2024
Preprint under review for ESSD
Short summary
Short summary
This study describes 1-km all-weather instantaneous, and daily mean LST datasets for global scale during 2000–2020. It is the first attempt to synergistically estimate all-weather instantaneous and daily mean LST data on a long time-series, global scale. The generated datasets were evaluated by the observations in-situ stations and other LST datasets, and the evaluation indicated the dataset is sufficiently reliable.
Pierre Friedlingstein, Michael O'Sullivan, Matthew W. Jones, Robbie M. Andrew, Dorothee C. E. Bakker, Judith Hauck, Peter Landschützer, Corinne Le Quéré, Ingrid T. Luijkx, Glen P. Peters, Wouter Peters, Julia Pongratz, Clemens Schwingshackl, Stephen Sitch, Josep G. Canadell, Philippe Ciais, Robert B. Jackson, Simone R. Alin, Peter Anthoni, Leticia Barbero, Nicholas R. Bates, Meike Becker, Nicolas Bellouin, Bertrand Decharme, Laurent Bopp, Ida Bagus Mandhara Brasika, Patricia Cadule, Matthew A. Chamberlain, Naveen Chandra, Thi-Tuyet-Trang Chau, Frédéric Chevallier, Louise P. Chini, Margot Cronin, Xinyu Dou, Kazutaka Enyo, Wiley Evans, Stefanie Falk, Richard A. Feely, Liang Feng, Daniel J. Ford, Thomas Gasser, Josefine Ghattas, Thanos Gkritzalis, Giacomo Grassi, Luke Gregor, Nicolas Gruber, Özgür Gürses, Ian Harris, Matthew Hefner, Jens Heinke, Richard A. Houghton, George C. Hurtt, Yosuke Iida, Tatiana Ilyina, Andrew R. Jacobson, Atul Jain, Tereza Jarníková, Annika Jersild, Fei Jiang, Zhe Jin, Fortunat Joos, Etsushi Kato, Ralph F. Keeling, Daniel Kennedy, Kees Klein Goldewijk, Jürgen Knauer, Jan Ivar Korsbakken, Arne Körtzinger, Xin Lan, Nathalie Lefèvre, Hongmei Li, Junjie Liu, Zhiqiang Liu, Lei Ma, Greg Marland, Nicolas Mayot, Patrick C. McGuire, Galen A. McKinley, Gesa Meyer, Eric J. Morgan, David R. Munro, Shin-Ichiro Nakaoka, Yosuke Niwa, Kevin M. O'Brien, Are Olsen, Abdirahman M. Omar, Tsuneo Ono, Melf Paulsen, Denis Pierrot, Katie Pocock, Benjamin Poulter, Carter M. Powis, Gregor Rehder, Laure Resplandy, Eddy Robertson, Christian Rödenbeck, Thais M. Rosan, Jörg Schwinger, Roland Séférian, T. Luke Smallman, Stephen M. Smith, Reinel Sospedra-Alfonso, Qing Sun, Adrienne J. Sutton, Colm Sweeney, Shintaro Takao, Pieter P. Tans, Hanqin Tian, Bronte Tilbrook, Hiroyuki Tsujino, Francesco Tubiello, Guido R. van der Werf, Erik van Ooijen, Rik Wanninkhof, Michio Watanabe, Cathy Wimart-Rousseau, Dongxu Yang, Xiaojuan Yang, Wenping Yuan, Xu Yue, Sönke Zaehle, Jiye Zeng, and Bo Zheng
Earth Syst. Sci. Data, 15, 5301–5369, https://doi.org/10.5194/essd-15-5301-2023, https://doi.org/10.5194/essd-15-5301-2023, 2023
Short summary
Short summary
The Global Carbon Budget 2023 describes the methodology, main results, and data sets used to quantify the anthropogenic emissions of carbon dioxide (CO2) and their partitioning among the atmosphere, land ecosystems, and the ocean over the historical period (1750–2023). These living datasets are updated every year to provide the highest transparency and traceability in the reporting of CO2, the key driver of climate change.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
Lei Ma, George Hurtt, Lesley Ott, Ritvik Sahajpal, Justin Fisk, Rachel Lamb, Hao Tang, Steve Flanagan, Louise Chini, Abhishek Chatterjee, and Joseph Sullivan
Geosci. Model Dev., 15, 1971–1994, https://doi.org/10.5194/gmd-15-1971-2022, https://doi.org/10.5194/gmd-15-1971-2022, 2022
Short summary
Short summary
We present a global version of the Ecosystem Demography (ED) model which can track vegetation 3-D structure and scale up ecological processes from individual vegetation to ecosystem scale. Model evaluation against multiple benchmarking datasets demonstrated the model’s capability to simulate global vegetation dynamics across a range of temporal and spatial scales. With this version, ED has the potential to be linked with remote sensing observations to address key scientific questions.
Xueyuan Gao, Shunlin Liang, Dongdong Wang, Yan Li, Bin He, and Aolin Jia
Earth Syst. Dynam., 13, 219–230, https://doi.org/10.5194/esd-13-219-2022, https://doi.org/10.5194/esd-13-219-2022, 2022
Short summary
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.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
Louise Chini, George Hurtt, Ritvik Sahajpal, Steve Frolking, Kees Klein Goldewijk, Stephen Sitch, Raphael Ganzenmüller, Lei Ma, Lesley Ott, Julia Pongratz, and Benjamin Poulter
Earth Syst. Sci. Data, 13, 4175–4189, https://doi.org/10.5194/essd-13-4175-2021, https://doi.org/10.5194/essd-13-4175-2021, 2021
Short summary
Short summary
Carbon emissions from land-use change are a large and uncertain component of the global carbon cycle. The Land-Use Harmonization 2 (LUH2) dataset was developed as an input to carbon and climate simulations and has been updated annually for the Global Carbon Budget (GCB) assessments. Here we discuss the methodology for producing these annual LUH2 updates and describe the 2019 version which used new cropland and grazing land data inputs for the globally important region of Brazil.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
George C. Hurtt, Louise Chini, Ritvik Sahajpal, Steve Frolking, Benjamin L. Bodirsky, Katherine Calvin, Jonathan C. Doelman, Justin Fisk, Shinichiro Fujimori, Kees Klein Goldewijk, Tomoko Hasegawa, Peter Havlik, Andreas Heinimann, Florian Humpenöder, Johan Jungclaus, Jed O. Kaplan, Jennifer Kennedy, Tamás Krisztin, David Lawrence, Peter Lawrence, Lei Ma, Ole Mertz, Julia Pongratz, Alexander Popp, Benjamin Poulter, Keywan Riahi, Elena Shevliakova, Elke Stehfest, Peter Thornton, Francesco N. Tubiello, Detlef P. van Vuuren, and Xin Zhang
Geosci. Model Dev., 13, 5425–5464, https://doi.org/10.5194/gmd-13-5425-2020, https://doi.org/10.5194/gmd-13-5425-2020, 2020
Short summary
Short summary
To estimate the effects of human land use activities on the carbon–climate system, a new set of global gridded land use forcing datasets was developed to link historical land use data to eight future scenarios in a standard format required by climate models. This new generation of land use harmonization (LUH2) includes updated inputs, higher spatial resolution, more detailed land use transitions, and the addition of important agricultural management layers; it will be used for CMIP6 simulations.
Lei Ma, George C. Hurtt, Louise P. Chini, Ritvik Sahajpal, Julia Pongratz, Steve Frolking, Elke Stehfest, Kees Klein Goldewijk, Donal O'Leary, and Jonathan C. Doelman
Geosci. Model Dev., 13, 3203–3220, https://doi.org/10.5194/gmd-13-3203-2020, https://doi.org/10.5194/gmd-13-3203-2020, 2020
Short summary
Short summary
Earth system models require information on historical land cover change. We present transition rules to generate land cover change from newly developed land use dataset (Land-use Harmonization, LUH2). The resulting forest cover, vegetation carbon, and emissions from land use and land cover change are simulated and evaluated against remote sensing data and other studies. The rules can guide the incorporation of land-cover information within earth system models for CMIP6.
Han Liu, Peng Gong, Jie Wang, Nicholas Clinton, Yuqi Bai, and Shunlin Liang
Earth Syst. Sci. Data, 12, 1217–1243, https://doi.org/10.5194/essd-12-1217-2020, https://doi.org/10.5194/essd-12-1217-2020, 2020
Short summary
Short summary
We built the first set of 5 km resolution CDRs to record the annual dynamics of global land cover (GLASS-GLC) from 1982 to 2015. The average overall accuracy is 82 %. By conducting long-term change analysis, significant land cover changes and spatiotemporal patterns at various scales were found, which can improve our understanding of global environmental change and help achieve sustainable development goals. This will be further applied in Earth system modeling to facilitate relevant studies.
Aolin Jia, Shunlin Liang, Dongdong Wang, Bo Jiang, and Xiaotong Zhang
Atmos. Chem. Phys., 20, 881–899, https://doi.org/10.5194/acp-20-881-2020, https://doi.org/10.5194/acp-20-881-2020, 2020
Short summary
Short summary
The Tibetan Plateau (TP) plays a vital role in regional and global climate change due to its location and orography. After generating a long-term surface radiation (SR) dataset, we characterized the SR spatiotemporal variation along with temperature. Evidence from multiple data sources indicated that the TP dimming was primarily driven by increased aerosols from human activities, and the cooling effect of aerosol loading offsets TP surface warming, revealing the human impact on regional warming.
X. Xie, S. Meng, S. Liang, and Y. Yao
Hydrol. Earth Syst. Sci., 18, 3923–3936, https://doi.org/10.5194/hess-18-3923-2014, https://doi.org/10.5194/hess-18-3923-2014, 2014
Q. Shi and S. Liang
Atmos. Chem. Phys., 14, 5659–5677, https://doi.org/10.5194/acp-14-5659-2014, https://doi.org/10.5194/acp-14-5659-2014, 2014
N. F. Liu, Q. Liu, L. Z. Wang, S. L. Liang, J. G. Wen, Y. Qu, and S. H. Liu
Hydrol. Earth Syst. Sci., 17, 2121–2129, https://doi.org/10.5194/hess-17-2121-2013, https://doi.org/10.5194/hess-17-2121-2013, 2013
T. R. Xu, S. M. Liu, Z. W. Xu, S. Liang, and L. Xu
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hessd-10-3927-2013, https://doi.org/10.5194/hessd-10-3927-2013, 2013
Preprint withdrawn
D. C. Morton, G. J. Collatz, D. Wang, J. T. Randerson, L. Giglio, and Y. Chen
Biogeosciences, 10, 247–260, https://doi.org/10.5194/bg-10-247-2013, https://doi.org/10.5194/bg-10-247-2013, 2013
Related subject area
Domain: ESSD – Land | Subject: Hydrology
FOCA: a new quality-controlled database of floods and catchment descriptors in Italy
Dams in the Mekong: a comprehensive database, spatiotemporal distribution, and hydropower potentials
A global dataset of the shape of drainage systems
An extensive spatiotemporal water quality dataset covering four decades (1980–2022) in China
Flood simulation with the RiverCure approach: the open dataset of the 2016 Águeda flood event
GloLakes: water storage dynamics for 27 000 lakes globally from 1984 to present derived from satellite altimetry and optical imaging
AltiMaP: altimetry mapping procedure for hydrography data
CAMELS-CH: hydro-meteorological time series and landscape attributes for 331 catchments in hydrologic Switzerland
The use of GRDC gauging stations for calibrating large-scale hydrological models
A long-term dataset of simulated epilimnion and hypolimnion temperatures in 401 French lakes (1959–2020)
GTWS-MLrec: global terrestrial water storage reconstruction by machine learning from 1940 to present
A hydrogeomorphic dataset for characterizing catchment hydrological behavior across the Tibetan Plateau
A global 5 km monthly potential evapotranspiration dataset (1982–2015) estimated by the Shuttleworth–Wallace model
A gridded dataset of consumptive water footprints, evaporation, transpiration, and associated benchmarks related to crop production in China during 2000–2018
Simbi: historical hydro-meteorological time series and signatures for 24 catchments in Haiti
Hydro-PE: gridded datasets of historical and future Penman–Monteith potential evaporation for the United Kingdom
A global streamflow indices time series dataset for large-sample hydrological analyses on streamflow regime (until 2022)
Soil water retention and hydraulic conductivity measured in a wide saturation range
A high-frequency, long-term data set of hydrology and sediment yield: the alpine badland catchments of Draix-Bléone Observatory
Geospatial dataset for hydrologic analyses in India (GHI): a quality-controlled dataset on river gauges, catchment boundaries and hydrometeorological time series
Lake-TopoCat: a global lake drainage topology and catchment database
Three years of soil moisture observations by a dense cosmic-ray neutron sensing cluster at an agricultural research site in north-east Germany
CAMELE: Collocation-Analyzed Multi-source Ensembled Land Evapotranspiration Data
A long-term monthly surface water storage dataset for the Congo basin from 1992 to 2015
A global database of historic glacier lake outburst floods
Past and future discharge and stream temperature at high spatial resolution in a large European basin (Loire basin, France)
A Synthesis of Global Streamflow characteristics, Hydrometeorology, and catchment Attributes (GSHA) for Large Sample River-Centric Studies
Res-CN (Reservoir dataset in China): hydrometeorological time series and landscape attributes across 3254 Chinese reservoirs
An ensemble of 48 physically perturbed model estimates of the 1∕8° terrestrial water budget over the conterminous United States, 1980–2015
The UKSCAPE-G2G river flow and soil moisture datasets: Grid-to-Grid model estimates for the UK for historical and potential future climates
The enhanced future Flows and Groundwater dataset: development and evaluation of nationally consistent hydrological projections based on UKCP18
RC4USCoast: a river chemistry dataset for regional ocean model applications in the US East Coast, Gulf of Mexico, and US West Coast
Generation of global 1 km daily soil moisture product from 2000 to 2020 using ensemble learning
Panta Rhei benchmark dataset: socio-hydrological data of paired events of floods and droughts
Twelve years of profile soil moisture and temperature measurements in Twente, the Netherlands
Shallow-groundwater-level time series and a groundwater chemistry survey from a boreal headwater catchment, Krycklan, Sweden
Weekly high-resolution multi-spectral and thermal uncrewed-aerial-system mapping of an alpine catchment during summer snowmelt, Niwot Ridge, Colorado
Nunataryuk field campaigns: understanding the origin and fate of terrestrial organic matter in the coastal waters of the Mackenzie Delta region
Integrated ecohydrological hydrometric and stable water isotope data of a drought-sensitive mixed land use lowland catchment
Regional data sets of high-resolution (1 and 6 km) irrigation estimates from space
Lake surface temperature retrieved from Landsat satellite series (1984 to 2021) for the North Slave Region
Flood detection using Gravity Recovery and Climate Experiment (GRACE) terrestrial water storage and extreme precipitation data
The pan-Arctic catchment database (ARCADE)
Multi-hazard susceptibility mapping of cryospheric hazards in a high-Arctic environment: Svalbard Archipelago
High-resolution water level and storage variation datasets for 338 reservoirs in China during 2010–2021
WaterBench-Iowa: a large-scale benchmark dataset for data-driven streamflow forecasting
A dataset of 10-year regional-scale soil moisture and soil temperature measurements at multiple depths on the Tibetan Plateau
OpenMRG: Open data from Microwave links, Radar, and Gauges for rainfall quantification in Gothenburg, Sweden
A 1 km daily soil moisture dataset over China using in situ measurement and machine learning
Downscaled hyper-resolution (400 m) gridded datasets of daily precipitation and temperature (2008–2019) for the East–Taylor subbasin (western United States)
Pierluigi Claps, Giulia Evangelista, Daniele Ganora, Paola Mazzoglio, and Irene Monforte
Earth Syst. Sci. Data, 16, 1503–1522, https://doi.org/10.5194/essd-16-1503-2024, https://doi.org/10.5194/essd-16-1503-2024, 2024
Short summary
Short summary
FOCA (Italian FlOod and Catchment Atlas) is the first systematic collection of data on Italian river catchments. It comprises geomorphological, soil, land cover, NDVI, climatological and extreme rainfall catchment attributes. FOCA also contains 631 peak and daily discharge time series covering the 1911–2016 period. Using this first nationwide data collection, a wide range of applications, in particular flood studies, can be undertaken within the Italian territory.
Wei Jing Ang, Edward Park, Yadu Pokhrel, Dung Duc Tran, and Ho Huu Loc
Earth Syst. Sci. Data, 16, 1209–1228, https://doi.org/10.5194/essd-16-1209-2024, https://doi.org/10.5194/essd-16-1209-2024, 2024
Short summary
Short summary
Dams have burgeoned in the Mekong, but information on dams is scattered and inconsistent. Up-to-date evaluation of dams is unavailable, and basin-wide hydropower potential has yet to be systematically assessed. We present a comprehensive database of 1055 dams, a spatiotemporal analysis of the dams, and a total hydropower potential of 1 334 683 MW. Considering projected dam development and hydropower potential, the vulnerability and the need for better dam management may be highest in Laos.
Chuanqi He, Ci-Jian Yang, Jens M. Turowski, Richard F. Ott, Jean Braun, Hui Tang, Shadi Ghantous, Xiaoping Yuan, and Gaia Stucky de Quay
Earth Syst. Sci. Data, 16, 1151–1166, https://doi.org/10.5194/essd-16-1151-2024, https://doi.org/10.5194/essd-16-1151-2024, 2024
Short summary
Short summary
The shape of drainage basins and rivers holds significant implications for landscape evolution processes and dynamics. We used a global 90 m resolution topography to obtain ~0.7 million drainage basins with sizes over 50 km2. Our dataset contains the spatial distribution of drainage systems and their morphological parameters, supporting fields such as geomorphology, climatology, biology, ecology, hydrology, and natural hazards.
Jingyu Lin, Peng Wang, Jinzhu Wang, Youping Zhou, Xudong Zhou, Pan Yang, Hao Zhang, Yanpeng Cai, and Zhifeng Yang
Earth Syst. Sci. Data, 16, 1137–1149, https://doi.org/10.5194/essd-16-1137-2024, https://doi.org/10.5194/essd-16-1137-2024, 2024
Short summary
Short summary
Our paper provides a repository comprising over 330 000 observations encompassing daily, weekly, and monthly records of surface water quality spanning the period 1980–2022. It included 18 distinct indicators, meticulously gathered at 2384 monitoring sites, ranging from inland locations to coastal and oceanic areas. This dataset will be very useful for researchers and decision-makers in the fields of hydrology, ecological studies, climate change, policy development, and oceanography.
Ana M. Ricardo, Rui M. L. Ferreira, Alberto Rodrigues da Silva, Jacinto Estima, Jorge Marques, Ivo Gamito, and Alexandre Serra
Earth Syst. Sci. Data, 16, 375–385, https://doi.org/10.5194/essd-16-375-2024, https://doi.org/10.5194/essd-16-375-2024, 2024
Short summary
Short summary
Floods are among the most common natural disasters responsible for severe damages and human losses. Agueda.2016Flood, a synthesis of locally sensed data and numerically produced data, allows complete characterization of the flood event that occurred in February 2016 in the Portuguese Águeda River. The dataset was managed through the RiverCure Portal, a collaborative web platform connected to a validated shallow-water model.
Jiawei Hou, Albert I. J. M. Van Dijk, Luigi J. Renzullo, and Pablo R. Larraondo
Earth Syst. Sci. Data, 16, 201–218, https://doi.org/10.5194/essd-16-201-2024, https://doi.org/10.5194/essd-16-201-2024, 2024
Short summary
Short summary
The GloLakes dataset provides historical and near-real-time time series of relative (i.e. storage change) and absolute (i.e. total stored volume) storage for more than 27 000 lakes worldwide using multiple sources of satellite data, including laser and radar altimetry and optical remote sensing. These data can help us understand the influence of climate variability and anthropogenic activities on water availability and system ecology over the last 4 decades.
Menaka Revel, Xudong Zhou, Prakat Modi, Jean-François Cretaux, Stephane Calmant, and Dai Yamazaki
Earth Syst. Sci. Data, 16, 75–88, https://doi.org/10.5194/essd-16-75-2024, https://doi.org/10.5194/essd-16-75-2024, 2024
Short summary
Short summary
As satellite technology advances, there is an incredible amount of remotely sensed data for observing terrestrial water. Satellite altimetry observations of water heights can be utilized to calibrate and validate large-scale hydrodynamic models. However, because large-scale models are discontinuous, comparing satellite altimetry to predicted water surface elevation is difficult. We developed a satellite altimetry mapping procedure for high-resolution river network data.
Marvin Höge, Martina Kauzlaric, Rosi Siber, Ursula Schönenberger, Pascal Horton, Jan Schwanbeck, Marius Günter Floriancic, Daniel Viviroli, Sibylle Wilhelm, Anna E. Sikorska-Senoner, Nans Addor, Manuela Brunner, Sandra Pool, Massimiliano Zappa, and Fabrizio Fenicia
Earth Syst. Sci. Data, 15, 5755–5784, https://doi.org/10.5194/essd-15-5755-2023, https://doi.org/10.5194/essd-15-5755-2023, 2023
Short summary
Short summary
CAMELS-CH is an open large-sample hydro-meteorological data set that covers 331 catchments in hydrologic Switzerland from 1 January 1981 to 31 December 2020. It comprises (a) daily data of river discharge and water level as well as meteorologic variables like precipitation and temperature; (b) yearly glacier and land cover data; (c) static attributes of, e.g, topography or human impact; and (d) catchment delineations. CAMELS-CH enables water and climate research and modeling at catchment level.
Peter Burek and Mikhail Smilovic
Earth Syst. Sci. Data, 15, 5617–5629, https://doi.org/10.5194/essd-15-5617-2023, https://doi.org/10.5194/essd-15-5617-2023, 2023
Short summary
Short summary
We address an annoying problem every grid-based hydrological model must solve to compare simulated and observed river discharge. First, station locations do not fit the high-resolution river network. We update the database with stations based on a new high-resolution network. Second, station locations do not work with a coarser grid-based network. We use a new basin shape similarity concept for station locations on a coarser grid, reducing the error of assigning stations to the wrong basin.
Najwa Sharaf, Jordi Prats, Nathalie Reynaud, Thierry Tormos, Rosalie Bruel, Tiphaine Peroux, and Pierre-Alain Danis
Earth Syst. Sci. Data, 15, 5631–5650, https://doi.org/10.5194/essd-15-5631-2023, https://doi.org/10.5194/essd-15-5631-2023, 2023
Short summary
Short summary
We present a regional long-term (1959–2020) dataset (LakeTSim) of daily epilimnion and hypolimnion water temperature simulations in 401 French lakes. Overall, less uncertainty is associated with the epilimnion compared to the hypolimnion. LakeTSim is valuable for providing new insights into lake water temperature for assessing the impact of climate change, which is often hindered by the lack of observations, and for decision-making by stakeholders.
Jiabo Yin, Louise J. Slater, Abdou Khouakhi, Le Yu, Pan Liu, Fupeng Li, Yadu Pokhrel, and Pierre Gentine
Earth Syst. Sci. Data, 15, 5597–5615, https://doi.org/10.5194/essd-15-5597-2023, https://doi.org/10.5194/essd-15-5597-2023, 2023
Short summary
Short summary
This study presents long-term (i.e., 1940–2022) and high-resolution (i.e., 0.25°) monthly time series of TWS anomalies over the global land surface. The reconstruction is achieved by using a set of machine learning models with a large number of predictors, including climatic and hydrological variables, land use/land cover data, and vegetation indicators (e.g., leaf area index). Our proposed GTWS-MLrec performs overall as well as, or is more reliable than, previous TWS datasets.
Yuhan Guo, Hongxing Zheng, Yuting Yang, Yanfang Sang, and Congcong Wen
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2023-422, https://doi.org/10.5194/essd-2023-422, 2023
Revised manuscript accepted for ESSD
Short summary
Short summary
Our research has provided an inaugural version of the hydrogeomorphic dataset for catchments over the Tibetan Plateau. We first provide the WFIUH for each HydroBASINs catchment, which can be used to investigate the spatial heterogeneity of hydrological behavior across the Tibetan Plateau. It is expected to facilitate hydrological modeling across the Tibetan Plateau.
Shanlei Sun, Zaoying Bi, Jingfeng Xiao, Yi Liu, Ge Sun, Weimin Ju, Chunwei Liu, Mengyuan Mu, Jinjian Li, Yang Zhou, Xiaoyuan Li, Yibo Liu, and Haishan Chen
Earth Syst. Sci. Data, 15, 4849–4876, https://doi.org/10.5194/essd-15-4849-2023, https://doi.org/10.5194/essd-15-4849-2023, 2023
Short summary
Short summary
Based on various existing datasets, we comprehensively considered spatiotemporal differences in land surfaces and CO2 effects on plant stomatal resistance to parameterize the Shuttleworth–Wallace model, and we generated a global 5 km ensemble mean monthly potential evapotranspiration (PET) dataset (including potential transpiration PT and soil evaporation PE) during 1982–2015. The new dataset may be used by academic communities and various agencies to conduct various studies.
Wei Wang, La Zhuo, Xiangxiang Ji, Zhiwei Yue, Zhibin Li, Meng Li, Huimin Zhang, Rong Gao, Chenjian Yan, Ping Zhang, and Pute Wu
Earth Syst. Sci. Data, 15, 4803–4827, https://doi.org/10.5194/essd-15-4803-2023, https://doi.org/10.5194/essd-15-4803-2023, 2023
Short summary
Short summary
The consumptive water footprint of crop production (WFCP) measures blue and green evapotranspiration of either irrigated or rainfed crops in time and space. A gridded monthly WFCP dataset for China is established. There are four improvements from existing datasets: (i) distinguishing water supply modes and irrigation techniques, (ii) distinguishing evaporation and transpiration, (iii) consisting of both total and unit WFCP, and (iv) providing benchmarks for unit WFCP by climatic zones.
Ralph Bathelemy, Pierre Brigode, Vazken Andréassian, Charles Perrin, Vincent Moron, Cédric Gaucherel, Emmanuel Tric, and Dominique Boisson
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2023-259, https://doi.org/10.5194/essd-2023-259, 2023
Preprint under review for ESSD
Short summary
Short summary
The aim of this work is to provide the first hydro-climatic database for Haiti, a Caribbean country particularly vulnerable to meteorological and hydrological hazards. The resulting database, named SIMBI, provides hydro-climatic time series for around 150 stations and 24 catchment areas.
Emma L. Robinson, Matthew J. Brown, Alison L. Kay, Rosanna A. Lane, Rhian Chapman, Victoria A. Bell, and Eleanor M. Blyth
Earth Syst. Sci. Data, 15, 4433–4461, https://doi.org/10.5194/essd-15-4433-2023, https://doi.org/10.5194/essd-15-4433-2023, 2023
Short summary
Short summary
This work presents two new Penman–Monteith potential evaporation datasets for the UK, calculated with the same methodology applied to historical climate data (Hydro-PE HadUK-Grid) and an ensemble of future climate projections (Hydro-PE UKCP18 RCM). Both include an optional correction for evaporation of rain that lands on the surface of vegetation. The historical data are consistent with existing PE datasets, and the future projections include effects of rising atmospheric CO2 on vegetation.
Xinyu Chen, Liguang Jiang, Yuning Luo, and Junguo Liu
Earth Syst. Sci. Data, 15, 4463–4479, https://doi.org/10.5194/essd-15-4463-2023, https://doi.org/10.5194/essd-15-4463-2023, 2023
Short summary
Short summary
River flow is experiencing changes under the impacts of climate change and human activities. For example, flood events are occurring more often and are more destructive in many places worldwide. To deal with such issues, hydrologists endeavor to understand the features of extreme events as well as other hydrological changes. One key approach is analyzing flow characteristics, represented by hydrological indices. Building such a comprehensive global large-sample dataset is essential.
Tobias L. Hohenbrink, Conrad Jackisch, Wolfgang Durner, Kai Germer, Sascha C. Iden, Janis Kreiselmeier, Frederic Leuther, Johanna C. Metzger, Mahyar Naseri, and Andre Peters
Earth Syst. Sci. Data, 15, 4417–4432, https://doi.org/10.5194/essd-15-4417-2023, https://doi.org/10.5194/essd-15-4417-2023, 2023
Short summary
Short summary
The article describes a collection of 572 data sets of soil water retention and unsaturated hydraulic conductivity data measured with state-of-the-art laboratory methods. Furthermore, the data collection contains basic soil properties such as soil texture and organic carbon content. We expect that the data will be useful for various important purposes, for example, the development of soil hydraulic property models and related pedotransfer functions.
Sebastien Klotz, Caroline Le Bouteiller, Nicolle Mathys, Firmin Fontaine, Xavier Ravanat, Jean-Emmanuel Olivier, Frédéric Liébault, Hugo Jantzi, Patrick Coulmeau, Didier Richard, Jean-Pierre Cambon, and Maurice Meunier
Earth Syst. Sci. Data, 15, 4371–4388, https://doi.org/10.5194/essd-15-4371-2023, https://doi.org/10.5194/essd-15-4371-2023, 2023
Short summary
Short summary
Mountain badlands are places of intense erosion. They deliver large amounts of sediment to river systems, with consequences for hydropower sustainability, habitat quality and biodiversity, and flood hazard and river management. Draix-Bleone Observatory was created in 1983 to understand and quantify sediment delivery from such badland areas. Our paper describes how water and sediment fluxes have been monitored for almost 40 years in the small mountain catchments of this observatory.
Gopi Goteti
Earth Syst. Sci. Data, 15, 4389–4415, https://doi.org/10.5194/essd-15-4389-2023, https://doi.org/10.5194/essd-15-4389-2023, 2023
Short summary
Short summary
Data on river gauging stations, river basin boundaries and river flow paths are critical for hydrological analyses, but existing data for India's river basins have limited availability and reliability. This work fills the gap by building a new dataset. Data for 645 stations in 15 basins of India were compiled and checked against global data sources; data were supplemented with additional information where needed. This dataset will serve as a reliable building block in hydrological analyses.
Md Safat Sikder, Jida Wang, George H. Allen, Yongwei Sheng, Dai Yamazaki, Chunqiao Song, Meng Ding, Jean-François Crétaux, and Tamlin M. Pavelsky
Earth Syst. Sci. Data, 15, 3483–3511, https://doi.org/10.5194/essd-15-3483-2023, https://doi.org/10.5194/essd-15-3483-2023, 2023
Short summary
Short summary
We introduce Lake-TopoCat to reveal detailed lake hydrography information. It contains the location of lake outlets, the boundary of lake catchments, and a wide suite of attributes that depict detailed lake drainage relationships. It was constructed using lake boundaries from a global lake dataset, with the help of high-resolution hydrography data. This database may facilitate a variety of applications including water quality, agriculture and fisheries, and integrated lake–river modeling.
Maik Heistermann, Till Francke, Lena Scheiffele, Katya Dimitrova Petrova, Christian Budach, Martin Schrön, Benjamin Trost, Daniel Rasche, Andreas Güntner, Veronika Döpper, Michael Förster, Markus Köhli, Lisa Angermann, Nikolaos Antonoglou, Manuela Zude-Sasse, and Sascha E. Oswald
Earth Syst. Sci. Data, 15, 3243–3262, https://doi.org/10.5194/essd-15-3243-2023, https://doi.org/10.5194/essd-15-3243-2023, 2023
Short summary
Short summary
Cosmic-ray neutron sensing (CRNS) allows for the non-invasive estimation of root-zone soil water content (SWC). The signal observed by a single CRNS sensor is influenced by the SWC in a radius of around 150 m (the footprint). Here, we have put together a cluster of eight CRNS sensors with overlapping footprints at an agricultural research site in north-east Germany. That way, we hope to represent spatial SWC heterogeneity instead of retrieving just one average SWC estimate from a single sensor.
Changming Li, Ziwei Liu, Wencong Yang, Zhuoyi Tu, Juntai Han, Sien Li, and Hanbo Yang
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2023-226, https://doi.org/10.5194/essd-2023-226, 2023
Revised manuscript accepted for ESSD
Short summary
Short summary
Using a collocation-based approach, we developed a reliable global land evapotranspiration product (CAMELE) by merging multi-source datasets. The CAMELE product outperformed individual input datasets and showed satisfactory performance compared to reference data. It also demonstrated superiority for different plant functional types. Our study provides a promising solution for data fusion. The CAMELE dataset allows for detailed research and a better understanding of land-atmosphere interactions.
Benjamin M. Kitambo, Fabrice Papa, Adrien Paris, Raphael M. Tshimanga, Frederic Frappart, Stephane Calmant, Omid Elmi, Ayan Santos Fleischmann, Melanie Becker, Mohammad J. Tourian, Rômulo A. Jucá Oliveira, and Sly Wongchuig
Earth Syst. Sci. Data, 15, 2957–2982, https://doi.org/10.5194/essd-15-2957-2023, https://doi.org/10.5194/essd-15-2957-2023, 2023
Short summary
Short summary
The surface water storage (SWS) in the Congo River basin (CB) remains unknown. In this study, the multi-satellite and hypsometric curve approaches are used to estimate SWS in the CB over 1992–2015. The results provide monthly SWS characterized by strong variability with an annual mean amplitude of ~101 ± 23 km3. The evaluation of SWS against independent datasets performed well. This SWS dataset contributes to the better understanding of the Congo basin’s surface hydrology using remote sensing.
Natalie Lützow, Georg Veh, and Oliver Korup
Earth Syst. Sci. Data, 15, 2983–3000, https://doi.org/10.5194/essd-15-2983-2023, https://doi.org/10.5194/essd-15-2983-2023, 2023
Short summary
Short summary
Glacier lake outburst floods (GLOFs) are a prominent natural hazard, and climate change may change their magnitude, frequency, and impacts. A global, literature-based GLOF inventory is introduced, entailing 3151 reported GLOFs. The reporting density varies temporally and regionally, with most cases occurring in NW North America. Since 1900, the number of yearly documented GLOFs has increased 6-fold. However, many GLOFs have incomplete records, and we call for a systematic reporting protocol.
Hanieh Seyedhashemi, Florentina Moatar, Jean-Philippe Vidal, and Dominique Thiéry
Earth Syst. Sci. Data, 15, 2827–2839, https://doi.org/10.5194/essd-15-2827-2023, https://doi.org/10.5194/essd-15-2827-2023, 2023
Short summary
Short summary
This paper presents a past and future dataset of daily time series of discharge and stream temperature for 52 278 reaches over the Loire River basin (100 000 km2) in France, using thermal and hydrological models. Past data are provided over 1963–2019. Future data are available over the 1976–2100 period under different future climate change models (warm and wet, intermediate, and hot and dry) and scenarios (optimistic, intermediate, and pessimistic).
Ziyun Yin, Peirong Lin, Ryan Riggs, George H. Allen, Xiangyong Lei, Ziyan Zheng, and Siyu Cai
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2023-256, https://doi.org/10.5194/essd-2023-256, 2023
Revised manuscript accepted for ESSD
Short summary
Short summary
Large-sample hydrology (LSH) datasets have been the backbone of the hydrological model parameter estimation as well as data-driven machine learning models for hydrological processes. Existing LSH datasets are still insufficient in terms of sample coverage, uncertainty estimates, and dynamic descriptions of human activities, which are all crucial to hydrological understanding and modeling, as highlighted by a recent LSH review article. Therefore, we contribute GHSA to address these limitations.
Youjiang Shen, Karina Nielsen, Menaka Revel, Dedi Liu, and Dai Yamazaki
Earth Syst. Sci. Data, 15, 2781–2808, https://doi.org/10.5194/essd-15-2781-2023, https://doi.org/10.5194/essd-15-2781-2023, 2023
Short summary
Short summary
Res-CN fills a gap in a comprehensive and extensive dataset of reservoir-catchment characteristics for 3254 Chinese reservoirs with 512 catchment-level attributes and significantly enhanced spatial and temporal coverage (e.g., 67 % increase in water level and 225 % in storage anomaly) of time series of reservoir water level (data available for 20 % of 3254 reservoirs), water area (99 %), storage anomaly (92 %), and evaporation (98 %), supporting a wide range of applications and disciplines.
Hui Zheng, Wenli Fei, Zong-Liang Yang, Jiangfeng Wei, Long Zhao, Lingcheng Li, and Shu Wang
Earth Syst. Sci. Data, 15, 2755–2780, https://doi.org/10.5194/essd-15-2755-2023, https://doi.org/10.5194/essd-15-2755-2023, 2023
Short summary
Short summary
An ensemble of evapotranspiration, runoff, and water storage is estimated here using the Noah-MP land surface model by perturbing model parameterization schemes. The data could be beneficial for monitoring and understanding the variability of water resources. Model developers could also gain insights by intercomparing the ensemble members.
Alison L. Kay, Victoria A. Bell, Helen N. Davies, Rosanna A. Lane, and Alison C. Rudd
Earth Syst. Sci. Data, 15, 2533–2546, https://doi.org/10.5194/essd-15-2533-2023, https://doi.org/10.5194/essd-15-2533-2023, 2023
Short summary
Short summary
Climate change will affect the water cycle, including river flows and soil moisture. We have used both observational data (1980–2011) and the latest UK climate projections (1980–2080) to drive a national-scale grid-based hydrological model. The data, covering Great Britain and Northern Ireland, suggest potential future decreases in summer flows, low flows, and summer/autumn soil moisture, and possible future increases in winter and high flows. Society must plan how to adapt to such impacts.
Jamie Hannaford, Jonathan D. Mackay, Matthew Ascott, Victoria A. Bell, Thomas Chitson, Steven Cole, Christian Counsell, Mason Durant, Christopher R. Jackson, Alison L. Kay, Rosanna A. Lane, Majdi Mansour, Robert Moore, Simon Parry, Alison C. Rudd, Michael Simpson, Katie Facer-Childs, Stephen Turner, John R. Wallbank, Steven Wells, and Amy Wilcox
Earth Syst. Sci. Data, 15, 2391–2415, https://doi.org/10.5194/essd-15-2391-2023, https://doi.org/10.5194/essd-15-2391-2023, 2023
Short summary
Short summary
The eFLaG dataset is a nationally consistent set of projections of future climate change impacts on hydrology. eFLaG uses the latest available UK climate projections (UKCP18) run through a series of computer simulation models which enable us to produce future projections of river flows, groundwater levels and groundwater recharge. These simulations are designed for use by water resource planners and managers but could also be used for a wide range of other purposes.
Fabian A. Gomez, Sang-Ki Lee, Charles A. Stock, Andrew C. Ross, Laure Resplandy, Samantha A. Siedlecki, Filippos Tagklis, and Joseph E. Salisbury
Earth Syst. Sci. Data, 15, 2223–2234, https://doi.org/10.5194/essd-15-2223-2023, https://doi.org/10.5194/essd-15-2223-2023, 2023
Short summary
Short summary
We present a river chemistry and discharge dataset for 140 rivers in the United States, which integrates information from the Water Quality Database of the US Geological Survey (USGS), the USGS’s Surface-Water Monthly Statistics for the Nation, and the U.S. Army Corps of Engineers. This dataset includes dissolved inorganic carbon and alkalinity, two key properties to characterize the carbonate system, as well as nutrient concentrations, such as nitrate, phosphate, and silica.
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
Short summary
Short summary
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.
Heidi Kreibich, Kai Schröter, Giuliano Di Baldassarre, Anne F. Van Loon, Maurizio Mazzoleni, Guta Wakbulcho Abeshu, Svetlana Agafonova, Amir AghaKouchak, Hafzullah Aksoy, Camila Alvarez-Garreton, Blanca Aznar, Laila Balkhi, Marlies H. Barendrecht, Sylvain Biancamaria, Liduin Bos-Burgering, Chris Bradley, Yus Budiyono, Wouter Buytaert, Lucinda Capewell, Hayley Carlson, Yonca Cavus, Anaïs Couasnon, Gemma Coxon, Ioannis Daliakopoulos, Marleen C. de Ruiter, Claire Delus, Mathilde Erfurt, Giuseppe Esposito, Didier François, Frédéric Frappart, Jim Freer, Natalia Frolova, Animesh K. Gain, Manolis Grillakis, Jordi Oriol Grima, Diego A. Guzmán, Laurie S. Huning, Monica Ionita, Maxim Kharlamov, Dao Nguyen Khoi, Natalie Kieboom, Maria Kireeva, Aristeidis Koutroulis, Waldo Lavado-Casimiro, Hong-Yi Li, Maria Carmen LLasat, David Macdonald, Johanna Mård, Hannah Mathew-Richards, Andrew McKenzie, Alfonso Mejia, Eduardo Mario Mendiondo, Marjolein Mens, Shifteh Mobini, Guilherme Samprogna Mohor, Viorica Nagavciuc, Thanh Ngo-Duc, Huynh Thi Thao Nguyen, Pham Thi Thao Nhi, Olga Petrucci, Nguyen Hong Quan, Pere Quintana-Seguí, Saman Razavi, Elena Ridolfi, Jannik Riegel, Md Shibly Sadik, Nivedita Sairam, Elisa Savelli, Alexey Sazonov, Sanjib Sharma, Johanna Sörensen, Felipe Augusto Arguello Souza, Kerstin Stahl, Max Steinhausen, Michael Stoelzle, Wiwiana Szalińska, Qiuhong Tang, Fuqiang Tian, Tamara Tokarczyk, Carolina Tovar, Thi Van Thu Tran, Marjolein H. J. van Huijgevoort, Michelle T. H. van Vliet, Sergiy Vorogushyn, Thorsten Wagener, Yueling Wang, Doris E. Wendt, Elliot Wickham, Long Yang, Mauricio Zambrano-Bigiarini, and Philip J. Ward
Earth Syst. Sci. Data, 15, 2009–2023, https://doi.org/10.5194/essd-15-2009-2023, https://doi.org/10.5194/essd-15-2009-2023, 2023
Short summary
Short summary
As the adverse impacts of hydrological extremes increase in many regions of the world, a better understanding of the drivers of changes in risk and impacts is essential for effective flood and drought risk management. We present a dataset containing data of paired events, i.e. two floods or two droughts that occurred in the same area. The dataset enables comparative analyses and allows detailed context-specific assessments. Additionally, it supports the testing of socio-hydrological models.
Rogier van der Velde, Harm-Jan F. Benninga, Bas Retsios, Paul C. Vermunt, and M. Suhyb Salama
Earth Syst. Sci. Data, 15, 1889–1910, https://doi.org/10.5194/essd-15-1889-2023, https://doi.org/10.5194/essd-15-1889-2023, 2023
Short summary
Short summary
From 2009, a network of 20 profile soil moisture and temperature monitoring stations has been operational in the Twente region, east of the Netherlands. In addition, field campaigns have been conducted covering four growing seasons during which soil moisture was measured near 12 monitoring stations. We describe the monitoring network and field campaigns, and we provide an overview of open third-party datasets that may support the use of the Twente datasets.
Jana Erdbrügger, Ilja van Meerveld, Jan Seibert, and Kevin Bishop
Earth Syst. Sci. Data, 15, 1779–1800, https://doi.org/10.5194/essd-15-1779-2023, https://doi.org/10.5194/essd-15-1779-2023, 2023
Short summary
Short summary
Groundwater can respond quickly to precipitation and is the main source of streamflow in most catchments in humid, temperate climates. To better understand shallow groundwater dynamics, we installed a network of groundwater wells in two boreal headwater catchments in Sweden. We recorded groundwater levels in 75 wells for 2 years and sampled the water and analyzed its chemical composition in one summer. This paper describes these datasets.
Oliver Wigmore and Noah P. Molotch
Earth Syst. Sci. Data, 15, 1733–1747, https://doi.org/10.5194/essd-15-1733-2023, https://doi.org/10.5194/essd-15-1733-2023, 2023
Short summary
Short summary
We flew a custom-built drone fitted with visible, near-infrared and thermal cameras every week over a summer season at Niwot Ridge in Colorado's alpine tundra. We processed these images into seamless orthomosaics that record changes in snow cover, vegetation health and the movement of water over the land surface. These novel datasets provide a unique centimetre resolution snapshot of ecohydrologic processes, connectivity and spatial and temporal heterogeneity in the alpine zone.
Martine Lizotte, Bennet Juhls, Atsushi Matsuoka, Philippe Massicotte, Gaëlle Mével, David Obie James Anikina, Sofia Antonova, Guislain Bécu, Marine Béguin, Simon Bélanger, Thomas Bossé-Demers, Lisa Bröder, Flavienne Bruyant, Gwénaëlle Chaillou, Jérôme Comte, Raoul-Marie Couture, Emmanuel Devred, Gabrièle Deslongchamps, Thibaud Dezutter, Miles Dillon, David Doxaran, Aude Flamand, Frank Fell, Joannie Ferland, Marie-Hélène Forget, Michael Fritz, Thomas J. Gordon, Caroline Guilmette, Andrea Hilborn, Rachel Hussherr, Charlotte Irish, Fabien Joux, Lauren Kipp, Audrey Laberge-Carignan, Hugues Lantuit, Edouard Leymarie, Antonio Mannino, Juliette Maury, Paul Overduin, Laurent Oziel, Colin Stedmon, Crystal Thomas, Lucas Tisserand, Jean-Éric Tremblay, Jorien Vonk, Dustin Whalen, and Marcel Babin
Earth Syst. Sci. Data, 15, 1617–1653, https://doi.org/10.5194/essd-15-1617-2023, https://doi.org/10.5194/essd-15-1617-2023, 2023
Short summary
Short summary
Permafrost thaw in the Mackenzie Delta region results in the release of organic matter into the coastal marine environment. What happens to this carbon-rich organic matter as it transits along the fresh to salty aquatic environments is still underdocumented. Four expeditions were conducted from April to September 2019 in the coastal area of the Beaufort Sea to study the fate of organic matter. This paper describes a rich set of data characterizing the composition and sources of organic matter.
Doerthe Tetzlaff, Aaron Smith, Lukas Kleine, Hauke Daempfling, Jonas Freymueller, and Chris Soulsby
Earth Syst. Sci. Data, 15, 1543–1554, https://doi.org/10.5194/essd-15-1543-2023, https://doi.org/10.5194/essd-15-1543-2023, 2023
Short summary
Short summary
We present a comprehensive set of ecohydrological hydrometric and stable water isotope data of 2 years of data. The data set is unique as the different compartments of the landscape were sampled and the effects of a prolonged drought (2018–2020) captured by a marked negative rainfall anomaly (the most severe regional drought of the 21st century). Thus, the data allow the drought effects on water storage, flux and age dynamics, and persistence of lowland landscapes to be investigated.
Jacopo Dari, Luca Brocca, Sara Modanesi, Christian Massari, Angelica Tarpanelli, Silvia Barbetta, Raphael Quast, Mariette Vreugdenhil, Vahid Freeman, Anaïs Barella-Ortiz, Pere Quintana-Seguí, David Bretreger, and Espen Volden
Earth Syst. Sci. Data, 15, 1555–1575, https://doi.org/10.5194/essd-15-1555-2023, https://doi.org/10.5194/essd-15-1555-2023, 2023
Short summary
Short summary
Irrigation is the main source of global freshwater consumption. Despite this, a detailed knowledge of irrigation dynamics (i.e., timing, extent of irrigated areas, and amounts of water used) are generally lacking worldwide. Satellites represent a useful tool to fill this knowledge gap and monitor irrigation water from space. In this study, three regional-scale and high-resolution (1 and 6 km) products of irrigation amounts estimated by inverting the satellite soil moisture signals are presented.
Gifty Attiah, Homa Kheyrollah Pour, and K. Andrea Scott
Earth Syst. Sci. Data, 15, 1329–1355, https://doi.org/10.5194/essd-15-1329-2023, https://doi.org/10.5194/essd-15-1329-2023, 2023
Short summary
Short summary
Lake surface temperature (LST) is a significant indicator of climate change and influences local weather and climate. This study developed a LST dataset retrieved from Landsat archives for 535 lakes across the North Slave Region, NWT, Canada. The data consist of individual NetCDF files for all observed days for each lake. The North Slave LST dataset will provide communities, scientists, and stakeholders with the changing spatiotemporal trends of LST for the past 38 years (1984–2021).
Jianxin Zhang, Kai Liu, and Ming Wang
Earth Syst. Sci. Data, 15, 521–540, https://doi.org/10.5194/essd-15-521-2023, https://doi.org/10.5194/essd-15-521-2023, 2023
Short summary
Short summary
This study successfully extracted global flood days based on gravity satellite and precipitation data between 60° S and 60° N from 1 April 2002 to 31 August 2016. Our flood days data performed well compared with current available observations. This provides an important data foundation for analyzing the spatiotemporal distribution of large-scale floods and exploring the impact of ocean–atmosphere oscillations on floods in different regions.
Niek Jesse Speetjens, Gustaf Hugelius, Thomas Gumbricht, Hugues Lantuit, Wouter R. Berghuijs, Philip A. Pika, Amanda Poste, and Jorien E. Vonk
Earth Syst. Sci. Data, 15, 541–554, https://doi.org/10.5194/essd-15-541-2023, https://doi.org/10.5194/essd-15-541-2023, 2023
Short summary
Short summary
The Arctic is rapidly changing. Outside the Arctic, large databases changed how researchers look at river systems and land-to-ocean processes. We present the first integrated pan-ARctic CAtchments summary DatabasE (ARCADE) (> 40 000 river catchments draining into the Arctic Ocean). It incorporates information about the drainage area with 103 geospatial, environmental, climatic, and physiographic properties and covers small watersheds , which are especially subject to change, at a high resolution
Ionut Cristi Nicu, Letizia Elia, Lena Rubensdotter, Hakan Tanyaş, and Luigi Lombardo
Earth Syst. Sci. Data, 15, 447–464, https://doi.org/10.5194/essd-15-447-2023, https://doi.org/10.5194/essd-15-447-2023, 2023
Short summary
Short summary
Thaw slumps and thermo-erosion gullies are cryospheric hazards that are widely encountered in Nordenskiöld Land, the largest and most compact ice-free area of the Svalbard Archipelago. By statistically analysing the landscape characteristics of locations where these processes occurred, we can estimate where they may occur in the future. We mapped 562 thaw slumps and 908 thermo-erosion gullies and used them to create the first multi-hazard susceptibility map in a high-Arctic environment.
Youjiang Shen, Dedi Liu, Liguang Jiang, Karina Nielsen, Jiabo Yin, Jun Liu, and Peter Bauer-Gottwein
Earth Syst. Sci. Data, 14, 5671–5694, https://doi.org/10.5194/essd-14-5671-2022, https://doi.org/10.5194/essd-14-5671-2022, 2022
Short summary
Short summary
A data gap of 338 Chinese reservoirs with their surface water area (SWA), water surface elevation (WSE), and reservoir water storage change (RWSC) during 2010–2021. Validation against the in situ observations of 93 reservoirs indicates the relatively high accuracy and reliability of the datasets. The unique and novel remotely sensed dataset would benefit studies involving many aspects (e.g., hydrological models, water resources related studies, and more).
Ibrahim Demir, Zhongrun Xiang, Bekir Demiray, and Muhammed Sit
Earth Syst. Sci. Data, 14, 5605–5616, https://doi.org/10.5194/essd-14-5605-2022, https://doi.org/10.5194/essd-14-5605-2022, 2022
Short summary
Short summary
We provide a large benchmark dataset, WaterBench-Iowa, with valuable features for hydrological modeling. This dataset is designed to support cutting-edge deep learning studies for a more accurate streamflow forecast model. We also propose a modeling task for comparative model studies and provide sample models with codes and results as the benchmark for reference. This makes up for the lack of benchmarks in earth science research.
Pei Zhang, Donghai Zheng, Rogier van der Velde, Jun Wen, Yaoming Ma, Yijian Zeng, Xin Wang, Zuoliang Wang, Jiali Chen, and Zhongbo Su
Earth Syst. Sci. Data, 14, 5513–5542, https://doi.org/10.5194/essd-14-5513-2022, https://doi.org/10.5194/essd-14-5513-2022, 2022
Short summary
Short summary
Soil moisture and soil temperature (SMST) are important state variables for quantifying the heat–water exchange between land and atmosphere. Yet, long-term, regional-scale in situ SMST measurements at multiple depths are scarce on the Tibetan Plateau (TP). The presented dataset would be valuable for the evaluation and improvement of long-term satellite- and model-based SMST products on the TP, enhancing the understanding of TP hydrometeorological processes and their response to climate change.
Jafet C. M. Andersson, Jonas Olsson, Remco (C. Z.) van de Beek, and Jonas Hansryd
Earth Syst. Sci. Data, 14, 5411–5426, https://doi.org/10.5194/essd-14-5411-2022, https://doi.org/10.5194/essd-14-5411-2022, 2022
Short summary
Short summary
This article presents data from three types of sensors for rain measurement, i.e. commercial microwave links (CMLs), gauges, and weather radar. Access to CML data is typically restricted, which limits research and applications. We openly share a large CML database (364 CMLs at 10 s resolution with true coordinates), along with 11 gauges and one radar composite. This opens up new opportunities to study CMLs, to benchmark algorithms, and to investigate how multiple sensors can best be combined.
Qingliang Li, Gaosong Shi, Wei Shangguan, Vahid Nourani, Jianduo Li, Lu Li, Feini Huang, Ye Zhang, Chunyan Wang, Dagang Wang, Jianxiu Qiu, Xingjie Lu, and Yongjiu Dai
Earth Syst. Sci. Data, 14, 5267–5286, https://doi.org/10.5194/essd-14-5267-2022, https://doi.org/10.5194/essd-14-5267-2022, 2022
Short summary
Short summary
SMCI1.0 is a 1 km resolution dataset of daily soil moisture over China for 2000–2020 derived through machine learning trained with in situ measurements of 1789 stations, meteorological forcings, and land surface variables. It contains 10 soil layers with 10 cm intervals up to 100 cm deep. Evaluated by in situ data, the error (ubRMSE) ranges from 0.045 to 0.051, and the correlation (R) range is 0.866-0.893. Compared with ERA5-Land, SMAP-L4, and SoMo.ml, SIMI1.0 has higher accuracy and resolution.
Utkarsh Mital, Dipankar Dwivedi, James B. Brown, and Carl I. Steefel
Earth Syst. Sci. Data, 14, 4949–4966, https://doi.org/10.5194/essd-14-4949-2022, https://doi.org/10.5194/essd-14-4949-2022, 2022
Short summary
Short summary
We present a new dataset that estimates small-scale variations in precipitation and temperature in mountainous terrain. The dataset is generated using a new machine learning framework that extracts relationships between climate and topography from existing coarse-scale datasets. The generated dataset is shown to capture small-scale variations more reliably than existing datasets and constitutes a valuable resource to model the water cycle in the mountains of Colorado, western United States.
Cited articles
Anderson, M. C., Kustas, W. P., Norman, J. M., Hain, C. R., Mecikalski, J. R., Schultz, L., González-Dugo, M. P., Cammalleri, C., d'Urso, G., Pimstein, A., and Gao, F.: Mapping daily evapotranspiration at field to continental scales using geostationary and polar orbiting satellite imagery, Hydrol. Earth Syst. Sci., 15, 223–239, https://doi.org/10.5194/hess-15-223-2011, 2011.
Anderson, M. C., Zolin, C. A., Sentelhas, P. C., Hain, C. R., Semmens, K.,
Yilmaz, M. T., Gao, F., Otkin, J. A., and Tetrault, R.: The Evaporative
Stress Index as an indicator of agricultural drought in Brazil: An
assessment based on crop yield impacts, Remote Sens. Environ., 174,
82–99, 2016.
André, C., Ottlé, C., Royer, A., and Maignan, F.: Land surface
temperature retrieval over circumpolar Arctic using SSM/I–SSMIS and MODIS
data, Remote Sens. Environ., 162, 1–10, 2015.
Augustine, J. A., DeLuisi, J. J., and Long, C. N.: SURFRAD – A national
surface radiation budget network for atmospheric research, B.
Am. Meteorol. Soc., 81, 2341–2357, https://doi.org/10.1175/1520-0477(2000)081<2341:Sansrb>2.3.Co;2, 2000.
Bhardwaj, A., Singh, S., Sam, L., Bhardwaj, A., Martín-Torres, F. J.,
Singh, A., and Kumar, R.: MODIS-based estimates of strong snow surface
temperature anomaly related to high altitude earthquakes of 2015, Remote Sens. Environ., 188, 1–8, 2017.
Boukabara, S.-A., Garrett, K., Chen, W., Iturbide-Sanchez, F., Grassotti,
C., Kongoli, C., Chen, R., Liu, Q., Yan, B., and Weng, F.: MiRS: An
all-weather 1DVAR satellite data assimilation and retrieval system, IEEE
T. Geosci. Remote, 49, 3249–3272, 2011.
Chang, Y., Xiao, J., Li, X., Frolking, S., Zhou, D., Schneider, A., Weng,
Q., Yu, P., Wang, X., and Li, X.: Exploring diurnal cycles of surface urban
heat island intensity in Boston with land surface temperature data derived
from GOES-R geostationary satellites, Sci. Total Environ., 763, 144224, https://doi.org/10.1016/j.scitotenv.2020.144224, 2021.
Chen, X., Su, Z., Ma, Y., Cleverly, J., and Liddell, M.: An accurate
estimate of monthly mean land surface temperatures from MODIS clear-sky
retrievals, J. Hydrometeorol., 18, 2827–2847, 2017.
Coccia, G., Siemann, A. L., Pan, M., and Wood, E. F.: Creating consistent
datasets by combining remotely-sensed data and land surface model estimates
through Bayesian uncertainty post-processing: The case of Land Surface
Temperature from HIRS, Remote Sens. Environ., 170, 290–305, https://doi.org/10.1016/j.rse.2015.09.010, 2015.
Doelling, D. R., Loeb, N. G., Keyes, D. F., Nordeen, M. L., Morstad, D.,
Nguyen, C., Wielicki, B. A., Young, D. F., and Sun, M.: Geostationary
enhanced temporal interpolation for CERES flux products, J.
Atmos. Ocean. Tech., 30, 1072–1090, 2013.
Doelling, D. R., Sun, M., Nordeen, M. L., Haney, C. O., Keyes, D. F., and
Mlynczak, P. E.: Advances in geostationary-derived longwave fluxes for the
CERES synoptic (SYN1deg) product, J. Atmos. Ocean.
Tech., 33, 503–521, 2016.
Driemel, A., Augustine, J., Behrens, K., Colle, S., Cox, C., Cuevas-Agulló, E., Denn, F. M., Duprat, T., Fukuda, M., Grobe, H., Haeffelin, M., Hodges, G., Hyett, N., Ijima, O., Kallis, A., Knap, W., Kustov, V., Long, C. N., Longenecker, D., Lupi, A., Maturilli, M., Mimouni, M., Ntsangwane, L., Ogihara, H., Olano, X., Olefs, M., Omori, M., Passamani, L., Pereira, E. B., Schmithüsen, H., Schumacher, S., Sieger, R., Tamlyn, J., Vogt, R., Vuilleumier, L., Xia, X., Ohmura, A., and König-Langlo, G.: Baseline Surface Radiation Network (BSRN): structure and data description (1992–2017), Earth Syst. Sci. Data, 10, 1491–1501, https://doi.org/10.5194/essd-10-1491-2018, 2018.
Duan, S.-B., Li, Z.-L., Wang, N., Wu, H., and Tang, B.-H.: Evaluation of six
land-surface diurnal temperature cycle models using clear-sky in situ and
satellite data, Remote Sens. Environ., 124, 15–25, 2012.
Duan, S. B., Li, Z. L., and Leng, P.: A framework for the retrieval of
all-weather land surface temperature at a high spatial resolution from
polar-orbiting thermal infrared and passive microwave data, Remote Sens. Environ., 195, 107–117, https://doi.org/10.1016/j.rse.2017.04.008, 2017.
Dumitrescu, A., Brabec, M., and Cheval, S.: Statistical Gap-Filling of
SEVIRI Land Surface Temperature, Remote Sens., 12, 1423,
https://doi.org/10.3390/rs12091423, 2020.
Ermida, S. L., Trigo, I. F., DaCamara, C. C., Jimenez, C., and Prigent, C.:
Quantifying the clear-sky bias of satellite land surface temperature using
microwave-based estimates, J. Geophys. Res.-Atmos.,
124, 844–857, 2019.
Fensholt, R., Anyamba, A., Huber, S., Proud, S. R., Tucker, C. J., Small,
J., Pak, E., Rasmussen, M. O., Sandholt, I., and Shisanya, C.: Analysing the
advantages of high temporal resolution geostationary MSG SEVIRI data
compared to Polar Operational Environmental Satellite data for land surface
monitoring in Africa, Int. J. Appl. Earth Obs., 13, 721–729, 2011.
Freitas, S. C., Trigo, I. F., Bioucas-Dias, J. M., and Gottsche, F.-M.:
Quantifying the uncertainty of land surface temperature retrievals from
SEVIRI/Meteosat, IEEE T. Geosci. Remote, 48,
523–534, 2009.
Freitas, S. C., Trigo, I. F., Macedo, J., Barroso, C., Silva, R., and
Perdigão, R.: Land surface temperature from multiple geostationary
satellites, Int. J. Remote Sens., 34, 3051–3068, 2013.
Fu, Q., Liou, K., Cribb, M., Charlock, T., and Grossman, A.: Multiple
scattering parameterization in thermal infrared radiative transfer, J. Atmos. Sci., 54, 2799–2812, 1997.
Good, E. J.: An in situ-based analysis of the relationship between land
surface “skin” and screen-level air temperatures, J. Geophys. Res.-Atmos., 121, 8801–8819, 2016.
Good, E. J., Ghent, D. J., Bulgin, C. E., and Remedios, J. J.: A
spatiotemporal analysis of the relationship between near-surface air
temperature and satellite land surface temperatures using 17 years of data
from the ATSR series, J. Geophys. Res.-Atmos., 122,
9185–9210, 2017.
Guillevic, P. C., Bork-Unkelbach, A., Göttsche, F. M., Hulley, G.,
Gastellu-Etchegorry, J.-P., Olesen, F. S., and Privette, J. L.: Directional
viewing effects on satellite land surface temperature products over sparse
vegetation canopies – A multisensor analysis, IEEE Geosci. Remote
Sens. Lett., 10, 1464–1468, 2013.
Hachem, S., Duguay, C. R., and Allard, M.: Comparison of MODIS-derived land surface temperatures with ground surface and air temperature measurements in continuous permafrost terrain, The Cryosphere, 6, 51–69, https://doi.org/10.5194/tc-6-51-2012, 2012.
Hansen, J., Sato, M., and Ruedy, R.: Long-term changes of the diurnal
temperature cycle: implications about mechanisms of global climate change,
Atmos. Res., 37, 175–209, 1995.
Hernandez-Barrera, S., Rodriguez-Puebla, C., and Challinor, A.: Effects of
diurnal temperature range and drought on wheat yield in Spain, Theor. Appl. Climatol., 129, 503–519, 2017.
Hong, F., Zhan, W., Göttsche, F.-M., Lai, J., Liu, Z., Hu, L., Fu, P.,
Huang, F., Li, J., and Li, H.: A simple yet robust framework to estimate
accurate daily mean land surface temperature from thermal observations of
tandem polar orbiters, Remote Sens. Environ., 264, 112612, https://doi.org/10.1016/j.rse.2021.112612, 2021.
Hong, F., Zhan, W., Göttsche, F.-M., Liu, Z., Dong, P., Fu, H., Huang, F., and Zhang, X.: A global dataset of spatiotemporally seamless daily mean land surface temperatures: generation, validation, and analysis, Earth Syst. Sci. Data, 14, 3091–3113, https://doi.org/10.5194/essd-14-3091-2022, 2022.
Hrisko, J., Ramamurthy, P., Yu, Y., Yu, P., and Melecio-Vázquez, D.:
Urban air temperature model using GOES-16 LST and a diurnal regressive
neural network algorithm, Remote Sens. Environ., 237, 111495, https://doi.org/10.1016/j.rse.2019.111495, 2020.
Hu, L., Sun, Y., Collins, G., and Fu, P.: Improved estimates of monthly land
surface temperature from MODIS using a diurnal temperature cycle (DTC)
model, ISPRS J. Photogramm. Remote, 168, 131–140,
2020.
Hulley, G.: MYD21 MODIS/Aqua land surface Temperature/3-Band emissivity
5-Min L2 1km V006, NASA EOSDIS L. Process. DAAC, 10, https://doi.org/10.5067/MODIS/MYD21.006, 2015.
Hulley, G. C. and Hook, S. J.: Intercomparison of versions 4, 4.1 and 5 of
the MODIS Land Surface Temperature and Emissivity products and validation
with laboratory measurements of sand samples from the Namib desert, Namibia,
Remote Sens. Environ., 113, 1313–1318, 2009.
Hulley, G., Malakar, N., and Freepartner, R.: Moderate Resolution Imaging
Spectroradiometer (MODIS) land surface temperature and emissivity product
(MxD21) algorithm theoretical basis document collection-6, JPL Publication,
12–17, 2016.
Jia, A., Liang, S., Jiang, B., Zhang, X., and Wang, G.: Comprehensive
assessment of global surface net radiation products and uncertainty
analysis, J. Geophys. Res.-Atmos., 123, 1970–1989,
2018.
Jia, A., Liang, S., Wang, D., Jiang, B., and Zhang, X.: Air pollution slows down surface warming over the Tibetan Plateau, Atmos. Chem. Phys., 20, 881–899, https://doi.org/10.5194/acp-20-881-2020, 2020.
Jia, A., Ma, H., Liang, S., and Wang, D.: Cloudy-sky land surface
temperature from VIIRS and MODIS satellite data using a surface energy
balance-based method, Remote Sens. Environ., 263, 112566, https://doi.org/10.1016/j.rse.2021.112566, 2021.
Jia, A., Liang, S., and Wang, D.: Generating a 2-km, all-sky, hourly land
surface temperature product from Advanced Baseline Imager data, Remote Sens. Environ., 278, 113105, https://doi.org/10.1016/j.rse.2022.113105, 2022a.
Jia, A., Liang, S., Wang, D., Ma, L., Wang, Z., and Xu, S.: Global Hourly,
5-km, All-sky Land Surface Temperature (GHA-LST) (V01), Zenodo [data set],
https://doi.org/10.5281/zenodo.7487284, 2022b.
Jia, A., Liang, S., Wang, D., Ma, L., Wang, Z., and Xu, S.: Global Hourly,
5-km, All-sky Land Surface Temperature (GHA-LST) (V01), http://glass.umd.edu/allsky_LST/GHA-LST (last access: 10 February 2023), 2022c.
Jia, A., Wang, D., Liang, S., Peng, J., and Yu, Y.: Global daily actual and
snow-free blue-sky land surface albedo climatology from 20-year MODIS
products, J. Geophys. Res.-Atmos., 127, e2021JD035987, https://doi.org/10.1029/2021JD035987,
2022d.
Jiang, Y., Fu, P., and Weng, Q.: Downscaling GOES land surface temperature
for assessing heat wave health risks, IEEE Geosci. Remote Sens.
Lett., 12, 1605–1609, 2015.
Jin, M.: Interpolation of surface radiative temperature measured from polar
orbiting satellites to a diurnal cycle: 2. Cloudy-pixel treatment, J. Geophys. Res.-Atmos., 105, 4061–4076, 2000.
Jin, M.: Analysis of land skin temperature using AVHRR observations,
B. Am. Meteorol. Soc., 85, 587–600, 2004.
Jin, M. and Dickinson, R. E.: A generalized algorithm for retrieving cloudy
sky skin temperature from satellite thermal infrared radiances, J. Geophys. Res.-Atmos., 105, 27037–27047, 2000.
Jin, M. and Treadon, R.: Correcting the orbit drift effect on AVHRR land
surface skin temperature measurements, Int. J. Remote
Sens., 24, 4543–4558, 2003.
King, M. D., Platnick, S., Menzel, W. P., Ackerman, S. A., and Hubanks, P.
A.: Spatial and temporal distribution of clouds observed by MODIS onboard
the Terra and Aqua satellites, IEEE T. Geosci. Remote, 51, 3826–3852, 2013.
Kuang, X. and Jiao, J. J.: Review on climate change on the Tibetan Plateau
during the last half century, J. Geophys. Res.-Atmos., 121, 3979–4007, https://doi.org/10.1002/2015JD024728, 2016.
Lenssen, N. J., Schmidt, G. A., Hansen, J. E., Menne, M. J., Persin, A.,
Ruedy, R., and Zyss, D.: Improvements in the GISTEMP uncertainty model,
J. Geophys. Res.-Atmos., 124, 6307–6326, 2019.
Li, B., Liang, S., Liu, X., Ma, H., Chen, Y., Liang, T., and He, T.:
Estimation of all-sky 1 km land surface temperature over the conterminous
United States, Remote Sens. Environ., 266, 112707, https://doi.org/10.1016/j.rse.2021.112707, 2021.
Li, H., Sun, D., Yu, Y., Wang, H., Liu, Y., Liu, Q., Du, Y., Wang, H., and
Cao, B.: Evaluation of the VIIRS and MODIS LST products in an arid area of
Northwest China, Remote Sens. Environ., 142, 111–121, 2014.
Li, H., Li, R., Yang, Y., Cao, B., Bian, Z., Hu, T., Du, Y., Sun, L., and
Liu, Q.: Temperature-based and radiance-based validation of the collection 6
MYD11 and MYD21 land surface temperature products over barren surfaces in
northwestern China, IEEE T. Geosci. Remote, 59,
1794–1807, 2020.
Li, R., Wang, D., Liang, S., Jia, A., and Wang, Z.: Estimating global
downward shortwave radiation from VIIRS data using a transfer-learning
neural network, Remote Sens. Environ., 274, 112999, https://doi.org/10.1016/j.rse.2022.112999, 2022.
Li, X., Cheng, G., Liu, S., Xiao, Q., Ma, M., Jin, R., Che, T., Liu, Q.,
Wang, W., and Qi, Y.: Heihe watershed allied telemetry experimental research
(HiWATER): Scientific objectives and experimental design, B. Am. Meteorol. Soc., 94, 1145–1160, 2013.
Li, Z.-L., Tang, B.-H., Wu, H., Ren, H., Yan, G., Wan, Z., Trigo, I. F., and
Sobrino, J. A.: Satellite-derived land surface temperature: Current status
and perspectives, Remote Sens. Environ., 131, 14–37, https://doi.org/10.1016/j.rse.2012.12.008, 2013.
Li, Z.-L., Wu, H., Duan, S.-B., Zhao, W., Ren, H., Liu, X., Leng, P.,
Tang, R., Ye, X., Zhu, J., Sun, Y., Si, S., Liu, M., Li, J., Zhang,
X., Shang, G., Tang, B.-H., Yan, G., and Zhou, C.: Satellite Remote
Sensing of Global Land Surface Temperature: Definition, Methods, Products,
and Applications, Rev. Geophys., 61, e2022RG000777, https://doi.org/10.1029/2022RG000777, 2022.
Liang, S., Wang, D., He, T., and Yu, Y.: Remote sensing of earth's energy
budget: synthesis and review, Int. J. Digit. Earth, 12, 1–44, https://doi.org/10.1080/17538947.2019.1597189, 2019.
Liang, S., Cheng, J., Jia, K., Jiang, B., Liu, Q., Xiao, Z., Yao, Y., Yuan,
W., Zhang, X., and Zhao, X.: The global land surface satellite (GLASS)
product suite, B. Am. Meteorol. Soc., 102,
E323–E337, https://doi.org/10.1175/BAMS-D-18-0341.1, 2021.
Liu, Y., Hiyama, T., and Yamaguchi, Y.: Scaling of land surface temperature
using satellite data: A case examination on ASTER and MODIS products over a
heterogeneous terrain area, Remote Sens. Environ., 105, 115–128,
2006.
Liu, Z., Zhan, W., Lai, J., Bechtel, B., Lee, X., Hong, F., Li, L., Huang,
F., and Li, J.: Taxonomy of seasonal and diurnal clear-sky climatology of
surface urban heat island dynamics across global cities, ISPRS J. Photogramm. Remote, 187, 14–33, 2022.
Liu, Z. H., Wu, P. H., Duan, S. B., Zhan, W. F., Ma, X. S., and Wu, Y. L.:
Spatiotemporal Reconstruction of Land Surface Temperature Derived From
FengYun Geostationary Satellite Data, IEEE J. Sel. Top.
Appl. Earth Obs., 10, 4531–4543, https://doi.org/10.1109/Jstars.2017.2716376, 2017.
Loeb, N. G., Doelling, D. R., Wang, H. L., Su, W. Y., Nguyen, C., Corbett,
J. G., Liang, L. S., Mitrescu, C., Rose, F. G., and Kato, S.: Clouds and the
Earth's Radiant Energy System (CERES) Energy Balanced and Filled (EBAF)
Top-of-Atmosphere (TOA) Edition-4.0 Data Product, J. Climate, 31,
895–918, https://doi.org/10.1175/Jcli-D-17-0208.1, 2018.
Long, D., Yan, L., Bai, L., Zhang, C., Li, X., Lei, H., Yang, H., Tian, F.,
Zeng, C., and Meng, X.: Generation of MODIS-like land surface temperatures
under all-weather conditions based on a data fusion approach, Remote Sens. Environ., 246, 111863, https://doi.org/10.1016/j.rse.2020.111863, 2020.
Lu, L., Venus, V., Skidmore, A., Wang, T., and Luo, G.: Estimating
land-surface temperature under clouds using MSG/SEVIRI observations,
Int. J. Appl. Earth Obs., 13,
265-276, 2011.
Ma, J., Zhou, J., Göttsche, F.-M., Liang, S., Wang, S., and Li, M.: A global long-term (1981–2000) land surface temperature product for NOAA AVHRR, Earth Syst. Sci. Data, 12, 3247–3268, https://doi.org/10.5194/essd-12-3247-2020, 2020.
Ma, J., Zhou, J., Liu, S., Göttsche, F.-M., Zhang, X., Wang, S., and Li,
M.: Continuous evaluation of the spatial representativeness of land surface
temperature validation sites, Remote Sens. Environ., 265, 112669, https://doi.org/10.1016/j.rse.2021.112669,
2021.
Ma, J., Shen, H., Wu, P., Wu, J., Gao, M., and Meng, C.: Generating gapless
land surface temperature with a high spatio-temporal resolution by fusing
multi-source satellite-observed and model-simulated data, Remote Sens. Environ., 278, 113083, https://doi.org/10.1016/j.rse.2022.113083, 2022.
Martins, J., Trigo, I. F., Ghilain, N., Jimenez, C., Göttsche, F.-M.,
Ermida, S. L., Olesen, F.-S., Gellens-Meulenberghs, F., and Arboleda, A.: An
All-Weather Land Surface Temperature Product Based on MSG/SEVIRI
Observations, Remote Sensing, 11, 3044, https://doi.org/10.3390/rs11243044, 2019.
Marullo, S., Santoleri, R., Ciani, D., Le Borgne, P., Pere, S., Pinardi, N.,
Tonani, M., and Nardone, G.: Combining model and geostationary satellite
data to reconstruct hourly SST field over the Mediterranean Sea, Remote Sens. Environ., 146, 11–23, https://doi.org/10.1016/j.rse.2013.11.001, 2014.
Melvin, A. M., Larsen, P., Boehlert, B., Neumann, J. E., Chinowsky, P.,
Espinet, X., Martinich, J., Baumann, M. S., Rennels, L., and Bothner, A.:
Climate change damages to Alaska public infrastructure and the economics of
proactive adaptation, P. Natl. Acad. Sci. USA, 114,
E122–E131, 2017.
Meng, X., Lyu, S., Zhang, T., Zhao, L., Li, Z., Han, B., Li, S., Ma, D.,
Chen, H., Ao, Y., Luo, S., Shen, Y., Guo, J., and Wen, L.: Simulated cold
bias being improved by using MODIS time-varying albedo in the Tibetan
Plateau in WRF model, Environ, Res, Lett,, 13, 044028, https://doi.org/10.1088/1748-9326/aab44a, 2018.
Metz, M., Andreo, V., and Neteler, M.: A new fully gap-free time series of
land surface temperature from MODIS LST data, Remote Sens., 9, 1333, https://doi.org/10.3390/rs9121333, 2017.
Metz, M., Rocchini, D., and Neteler, M.: Surface temperatures at the
continental scale: tracking changes with remote sensing at unprecedented
detail, Remote Sens., 6, 3822–3840, 2014.
Minnis, P., Sun-Mack, S., Chen, Y., Chang, F.-L., Yost, C. R., Smith, W. L.,
Heck, P. W., Arduini, R. F., Bedka, S. T., and Yi, Y.: CERES MODIS cloud
product retrievals for edition 4 – Part I: Algorithm changes, IEEE
T. Geosci. Remote, 59, 2744–2780, 2020.
Molod, A., Takacs, L., Suarez, M., and Bacmeister, J.: Development of the GEOS-5 atmospheric general circulation model: evolution from MERRA to MERRA2, Geosci. Model Dev., 8, 1339–1356, https://doi.org/10.5194/gmd-8-1339-2015, 2015.
Mudele, O., Bayer, F. M., Zanandrez, L. F., Eiras, A. E., and Gamba, P.:
Modeling the Temporal Population Distribution of Ae. aegypti Mosquito Using
Big Earth Observation Data, Ieee Access, 8, 14182–14194, 2020.
Muñoz-Sabater, J., Dutra, E., Agustí-Panareda, A., Albergel, C., Arduini, G., Balsamo, G., Boussetta, S., Choulga, M., Harrigan, S., Hersbach, H., Martens, B., Miralles, D. G., Piles, M., Rodríguez-Fernández, N. J., Zsoter, E., Buontempo, C., and Thépaut, J.-N.: ERA5-Land: a state-of-the-art global reanalysis dataset for land applications, Earth Syst. Sci. Data, 13, 4349–4383, https://doi.org/10.5194/essd-13-4349-2021, 2021.
Mutiibwa, D., Strachan, S., and Albright, T.: Land surface temperature and
surface air temperature in complex terrain, IEEE J. Sel. Top. Appl. Earth Obs., 8, 4762–4774, 2015.
Nogueira, M., Boussetta, S., Balsamo, G., Albergel, C., Trigo, I. F.,
Johannsen, F., Miralles, D. G., and Dutra, E.: Upgrading Land-Cover and
Vegetation Seasonality in the ECMWF Coupled System: Verification With
FLUXNET Sites, METEOSAT Satellite Land Surface Temperatures, and ERA5
Atmospheric Reanalysis, J. Geophys. Res.-Atmos., 126,
e2020JD034163, https://doi.org/10.1029/2020JD034163, 2021.
Novick, K. A., Biederman, J., Desai, A., Litvak, M., Moore, D. J., Scott,
R., and Torn, M.: The AmeriFlux network: A coalition of the willing,
Agric. Forest Meteorol., 249, 444–456, 2018.
Orth, R., Dutra, E., Trigo, I. F., and Balsamo, G.: Advancing land surface model development with satellite-based Earth observations, Hydrol. Earth Syst. Sci., 21, 2483–2495, https://doi.org/10.5194/hess-21-2483-2017, 2017.
Osborn, T. J. and Jones, P. D.: The CRUTEM4 land-surface air temperature data set: construction, previous versions and dissemination via Google Earth, Earth Syst. Sci. Data, 6, 61–68, https://doi.org/10.5194/essd-6-61-2014, 2014.
Ouyang, B., Che, T., DAI, L.-y., and WANG, Z.-y.: Estimating Mean Daily
Surface Temperature over the Tibetan Plateau Based on MODIS LST Products, J. Glaciol. Geocryol., 2, 296–303, 2012.
Peng, S.-S., Piao, S., Zeng, Z., Ciais, P., Zhou, L., Li, L. Z., Myneni, R.
B., Yin, Y., and Zeng, H.: Afforestation in China cools local land surface
temperature, P. Natl. Acad. Sci. USA, 111,
2915–2919, 2014.
Piao, S., Tan, J., Chen, A., Fu, Y. H., Ciais, P., Liu, Q., Janssens, I. A.,
Vicca, S., Zeng, Z., and Jeong, S.-J.: Leaf onset in the northern hemisphere
triggered by daytime temperature, Nat. Commun., 6, 1–8, 2015.
Piles, M., Petropoulos, G. P., Sánchez, N., González-Zamora, Á.,
and Ireland, G.: Towards improved spatio-temporal resolution soil moisture
retrievals from the synergy of SMOS and MSG SEVIRI spaceborne observations,
Remote Sens. Environ., 180, 403–417, 2016.
Pinker, R. T., Ma, Y., Chen, W., Hulley, G., Borbas, E., Islam, T., Hain,
C., Cawse-Nicholson, K., Hook, S., and Basara, J.: Towards a unified and
coherent land surface temperature earth system data record from
geostationary satellites, Remote Sens., 11, 1399, https://doi.org/10.3390/rs11121399, 2019.
Quintano, C., Fernández-Manso, A., Calvo, L., Marcos, E., and Valbuena,
L.: Land surface temperature as potential indicator of burn severity in
forest Mediterranean ecosystems, Int. J. Appl. Earth
Obs., 36, 1–12, 2015.
Rains, D., Trigo, I., Dutra, E., Ermida, S., Ghent, D., Hulsman, P., Gómez-Dans, J., and Miralles, D. G.: High-resolution all-sky land surface temperature and net radiation over Europe, Earth Syst. Sci. Data Discuss. [preprint], https://doi.org/10.5194/essd-2022-302, in review, 2022.
Remer, L. A., Tanre, D., Kaufman, Y. J., Levy, R., and Mattoo, S.: Algorithm
for Remote Sensing of Tropospheric Aerosol from MODIS: Collection 5–Product
ID: MOD04/MYD04, NASA, http://MODIS-atmos.gsfc.nasa.gov/_docs/MOD04-MYD04_ATBD_C005.pdf (last access: 10 February 2023), 2006.
Rodell, M., Houser, P., Jambor, U., Gottschalck, J., Mitchell, K., Meng,
C.-J., Arsenault, K., Cosgrove, B., Radakovich, J., and Bosilovich, M.: The
global land data assimilation system, B. Am. Meteorol. Soc., 85, 381–394, 2004.
Rohde, R., Muller, R., Jacobsen, R., Perlmutter, S., Rosenfeld, A., Wurtele,
J., Curry, J., Wickhams, C., and Mosher, S.: Berkeley Earth Temperature
Averaging Process, Geoinfor. Geostat.-An Overview, 1, 20–100, 2013.
Rutan, D. A., Kato, S., Doelling, D. R., Rose, F. G., Nguyen, L. T.,
Caldwell, T. E., and Loeb, N. G.: CERES synoptic product: Methodology and
validation of surface radiant flux, J. Atmos. Ocean.
Tech., 32, 1121–1143, 2015.
Shiff, S., Helman, D., and Lensky, I. M.: Worldwide continuous gap-filled
MODIS land surface temperature dataset, Sci. Data, 8, 1–10, 2021.
Sohrabinia, M., Zawar-Reza, P., and Rack, W.: Spatio-temporal analysis of
the relationship between LST from MODIS and air temperature in New Zealand,
Theor. Appl. Climatol., 119, 567–583, 2015.
Stisen, S., Sandholt, I., Nørgaard, A., Fensholt, R., and Jensen, K. H.:
Combining the triangle method with thermal inertia to estimate regional
evapotranspiration – Applied to MSG-SEVIRI data in the Senegal River basin,
Remote Sens. Environ., 112, 1242–1255, 2008.
Sun, D., Pinker, R. T., and Kafatos, M.: Diurnal temperature range over the
United States: A satellite view, Geophys. Res. Lett., 33, 5, https://doi.org/10.1029/2005GL024780, 2006.
Wan, Z.: New refinements and validation of the MODIS land-surface
temperature/emissivity products, Remote Sens. Environ., 112, 59–74,
2008.
Wang, K. and Dickinson, R. E.: Global atmospheric downward longwave
radiation at the surface from ground-based observations, satellite
retrievals, and reanalyses, Rev. Geophys., 51, 150–185, 2013.
Wang, T., Shi, J., Ma, Y., Letu, H., and Li, X.: All-sky longwave downward
radiation from satellite measurements: General parameterizations based on
LST, column water vapor and cloud top temperature, ISPRS J. Photogramm. Remote, 161, 52–60, https://doi.org/10.1016/j.isprsjprs.2020.01.011, 2020.
Wang, Y.-R., Hessen, D. O., Samset, B. H., and Stordal, F.: Evaluating
global and regional land warming trends in the past decades with both MODIS
and ERA5-Land land surface temperature data, Remote Sens. Environ.,
280, 113181, https://doi.org/10.1016/j.rse.2022.113181, 2022.
Wu, P., Su, Y., Duan, S.-b., Li, X., Yang, H., Zeng, C., Ma, X., Wu, Y., and
Shen, H.: A two-step deep learning framework for mapping gapless all-weather
land surface temperature using thermal infrared and passive microwave data,
Remote Sens. Environ., 277, 113070, https://doi.org/10.1016/j.rse.2022.113070, 2022.
Wu, P. H., Yin, Z. X., Yang, H., Wu, Y. L., and Ma, X. S.: Reconstructing
Geostationary Satellite Land Surface Temperature Imagery Based on a
Multiscale Feature Connected Convolutional Neural Network, Remote Sens.,
11, 300,
https://doi.org/10.3390/rs11030300, 2019.
Xing, Z., Li, Z.-L., Duan, S.-B., Liu, X., Zheng, X., Leng, P., Gao, M.,
Zhang, X., and Shang, G.: Estimation of daily mean land surface temperature
at global scale using pairs of daytime and nighttime MODIS instantaneous
observations, ISPRS J. Photogramm. Remote, 178,
51–67, 2021.
Xu, S. and Cheng, J.: A new land surface temperature fusion strategy based
on cumulative distribution function matching and multiresolution Kalman
filtering, Remote Sens. Environ., 254, 112256, https://doi.org/10.1016/j.rse.2020.112256, 2021.
Yan, Y., Mao, K., Shi, J., Piao, S., Shen, X., Dozier, J., Liu, Y., Ren,
H.-l., and Bao, Q.: Driving forces of land surface temperature anomalous
changes in North America in 2002–2018, Sci. Rep.-UK, 10, 1–13, 2020.
Yao, R., Wang, L., Wang, S., Wang, L., Wei, J., Li, J., and Yu, D.: A
detailed comparison of MYD11 and MYD21 land surface temperature products in
mainland China, Int. J. Digit. Earth, 13, 1391–1407, 2020.
Yu, P., Zhao, T., Shi, J., Ran, Y., Jia, L., Ji, D., and Xue, H.: Global
spatiotemporally continuous MODIS land surface temperature dataset, Sci.
Data, 9, 1–15, 2022.
Yu, Y., Tarpley, D., Privette, J. L., Goldberg, M. D., Raja, M. R. V.,
Vinnikov, K. Y., and Xu, H.: Developing algorithm for operational GOES-R
land surface temperature product, IEEE T. Geosci. Remote, 47, 936–951, 2008.
Zhan, W., Chen, Y., Zhou, J., Wang, J., Liu, W., Voogt, J., Zhu, X., Quan,
J., and Li, J.: Disaggregation of remotely sensed land surface temperature:
Literature survey, taxonomy, issues, and caveats, Remote Sens. Environ., 131, 119–139, 2013.
Zhang, Q., Wang, N., Cheng, J., and Xu, S.: A stepwise downscaling method
for generating high-resolution land surface temperature from AMSR-E data,
IEEE J. Sel. Top. Appl. Earth Ob., 13, 5669–5681, 2020.
Zhang, T., Zhou, Y., Zhu, Z., Li, X., and Asrar, G. R.: A global seamless 1 km resolution daily land surface temperature dataset (2003–2020), Earth Syst. Sci. Data, 14, 651–664, https://doi.org/10.5194/essd-14-651-2022, 2022.
Zhang, X., Liang, S., Wild, M., and Jiang, B.: Analysis of surface incident
shortwave radiation from four satellite products, Remote Sens. Environ., 165, 186–202, 2015.
Zhang, X., Zhou, J., Gottsche, F.-M., Zhan, W., Liu, S., and Cao, R.: A
Method Based on Temporal Component Decomposition for Estimating 1-km
All-Weather Land Surface Temperature by Merging Satellite Thermal Infrared
and Passive Microwave Observations, IEEE T. Geosci. Remote, 57, 4670–4691, https://doi.org/10.1109/tgrs.2019.2892417, 2019.
Zhang, X., Zhou, J., Liang, S., and Wang, D.: A practical reanalysis data
and thermal infrared remote sensing data merging (RTM) method for
reconstruction of a 1-km all-weather land surface temperature, Remote Sens. Environ., 260, 112437, https://doi.org/10.1016/j.rse.2021.112437, 2021.
Zhao, B., Mao, K., Cai, Y., Shi, J., Li, Z., Qin, Z., Meng, X., Shen, X., and Guo, Z.: A combined Terra and Aqua MODIS land surface temperature and meteorological station data product for China from 2003 to 2017, Earth Syst. Sci. Data, 12, 2555–2577, https://doi.org/10.5194/essd-12-2555-2020, 2020.
Zhao, P., Xu, X., Chen, F., Guo, X., Zheng, X., Liu, L., Hong, Y., Li, Y.,
La, Z., and Peng, H.: The third atmospheric scientific experiment for
understanding the earth–atmosphere coupled system over the Tibetan Plateau
and its effects, B. Am. Meteorol. Soc., 99,
757–776, 2018.
Zhao, W., Wu, H., Yin, G., and Duan, S.-B.: Normalization of the temporal
effect on the MODIS land surface temperature product using random forest
regression, ISPRS J. Photogramm. Remote, 152,
109–118, 2019.
Zhou, L., Tian, Y., Baidya Roy, S., Thorncroft, C., Bosart, L. F., and Hu,
Y.: Impacts of wind farms on land surface temperature, Nat. Clim.
Change, 2, 539–543, 2012.
Zou, D., Zhao, L., Sheng, Y., Chen, J., Hu, G., Wu, T., Wu, J., Xie, C., Wu, X., Pang, Q., Wang, W., Du, E., Li, W., Liu, G., Li, J., Qin, Y., Qiao, Y., Wang, Z., Shi, J., and Cheng, G.: A new map of permafrost distribution on the Tibetan Plateau, The Cryosphere, 11, 2527–2542, https://doi.org/10.5194/tc-11-2527-2017, 2017.
Short summary
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.
Satellites are now producing multiple global land surface temperature (LST) products; however,...
Altmetrics
Final-revised paper
Preprint