Articles | Volume 15, issue 5
https://doi.org/10.5194/essd-15-2189-2023
© 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-15-2189-2023
© 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
| 
31 May 2023
Data description paper |
| 31 May 2023
| 31 May 2023
A global historical twice-daily (daytime and nighttime) land surface temperature dataset produced by Advanced Very High Resolution Radiometer observations from 1981 to 2021
Jia-Hao Li
State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
State Key Laboratory of Efficient Utilization of Arid and Semi-arid Arable Land in Northern China, Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China
Zhao-Liang Li
CORRESPONDING AUTHOR
State Key Laboratory of Efficient Utilization of Arid and Semi-arid Arable Land in Northern China, Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China
State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
Xiangyang Liu
State Key Laboratory of Efficient Utilization of Arid and Semi-arid Arable Land in Northern China, Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China
Si-Bo Duan
State Key Laboratory of Efficient Utilization of Arid and Semi-arid Arable Land in Northern China, Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China
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Bing Zhao, Kebiao Mao, Yulin Cai, Jiancheng Shi, Zhaoliang Li, Zhihao Qin, Xiangjin Meng, Xinyi Shen, and Zhonghua Guo
Earth Syst. Sci. Data, 12, 2555–2577, https://doi.org/10.5194/essd-12-2555-2020, https://doi.org/10.5194/essd-12-2555-2020, 2020
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Land surface temperature is a key variable for climate and ecological environment research. We reconstructed a land surface temperature dataset (2003–2017) to take advantage of the ground observation site through building a reconstruction model which overcomes the effects of cloud. The reconstructed dataset exhibited significant improvements and can be used for the spatiotemporal evaluation of land surface temperature and for high-temperature and drought-monitoring studies.
Related subject area
Domain: ESSD – Land | Subject: Geophysics and geodesy
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Comprehensive data set of in situ hydraulic stimulation experiments for geothermal purposes at the Äspö Hard Rock Laboratory (Sweden)
Synthetic ground motions in heterogeneous geologies: the HEMEW-3D dataset for scientific machine learning
An earthquake focal mechanism catalog for source and tectonic studies in Mexico from February 1928 to July 2022
Global physics-based database of injection-induced seismicity
The Weisweiler passive seismological network: optimised for state-of-the-art location and imaging methods
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In situ stress database of the greater Ruhr region (Germany) derived from hydrofracturing tests and borehole logs
The European Preinstrumental Earthquake Catalogue EPICA, the 1000–1899 catalogue for the European Seismic Hazard Model 2020
Rescue and quality control of historical geomagnetic measurement at Sheshan observatory, China
A newly integrated ground temperature dataset of permafrost along the China–Russia crude oil pipeline route in Northeast China
In situ observations of the Swiss periglacial environment using GNSS instruments
Permafrost changes in the northwestern Da Xing'anling Mountains, Northeast China, in the past decade
British Antarctic Survey's aerogeophysical data: releasing 25 years of airborne gravity, magnetic, and radar datasets over Antarctica
Hao Zhou, Lijun Zheng, Yaozong Li, Xiang Guo, Zebing Zhou, and Zhicai Luo
Earth Syst. Sci. Data, 16, 3261–3281, https://doi.org/10.5194/essd-16-3261-2024, https://doi.org/10.5194/essd-16-3261-2024, 2024
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The satellite gravimetry mission Gravity Recovery and Climate Experiment (GRACE) and its follower GRACE-FO play a vital role in monitoring mass transportation on Earth. Based on the latest observation data derived from GRACE and GRACE-FO and an updated data processing chain, a new monthly temporal gravity field series, HUST-Grace2024, was determined.
Francesca Pace, Andrea Vergnano, Alberto Godio, Gerardo Romano, Luigi Capozzoli, Ilaria Baneschi, Marco Doveri, and Alessandro Santilano
Earth Syst. Sci. Data, 16, 3171–3192, https://doi.org/10.5194/essd-16-3171-2024, https://doi.org/10.5194/essd-16-3171-2024, 2024
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Dino Bindi, Riccardo Zaccarelli, Angelo Strollo, Domenico Di Giacomo, Andres Heinloo, Peter Evans, Fabrice Cotton, and Frederik Tilmann
Earth Syst. Sci. Data, 16, 1733–1745, https://doi.org/10.5194/essd-16-1733-2024, https://doi.org/10.5194/essd-16-1733-2024, 2024
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The size of an earthquake is often described by a single number called the magnitude. Among the possible magnitude scales, the seismic moment (Mw) and the radiated energy (Me) scales are based on physical parameters describing the rupture process. Since these two magnitude scales provide complementary information that can be used for seismic hazard assessment and for seismic risk mitigation, we complement the Mw catalog disseminated by the GEOFON Data Centre with Me values.
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Earth Syst. Sci. Data, 16, 1589–1599, https://doi.org/10.5194/essd-16-1589-2024, https://doi.org/10.5194/essd-16-1589-2024, 2024
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This paper discusses strategies to improve the GRACE gravity field monthly solutions computed at the Astronomical Institute of the University of Bern. We updated the input observations and background models, as well as improving processing strategies in terms of instrument data screening and instrument parameterization.
Athina Peidou, Donald F. Argus, Felix W. Landerer, David N. Wiese, and Matthias Ellmer
Earth Syst. Sci. Data, 16, 1317–1332, https://doi.org/10.5194/essd-16-1317-2024, https://doi.org/10.5194/essd-16-1317-2024, 2024
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Lavinia Tunini, Andrea Magrin, Giuliana Rossi, and David Zuliani
Earth Syst. Sci. Data, 16, 1083–1106, https://doi.org/10.5194/essd-16-1083-2024, https://doi.org/10.5194/essd-16-1083-2024, 2024
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This study presents 20-year time series of more than 350 GNSS stations located in NE Italy and surroundings, together with the outgoing velocities. An overview of the input data, station information, data processing and solution quality is provided. The documented dataset constitutes a crucial and complete source of information about the deformation of an active but slowly converging margin over the last 2 decades, also contributing to the regional seismic hazard assessment of NE Italy.
Wenbin Tang, Ji Zhou, Jin Ma, Ziwei Wang, Lirong Ding, Xiaodong Zhang, and Xu Zhang
Earth Syst. Sci. Data, 16, 387–419, https://doi.org/10.5194/essd-16-387-2024, https://doi.org/10.5194/essd-16-387-2024, 2024
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Arno Zang, Peter Niemz, Sebastian von Specht, Günter Zimmermann, Claus Milkereit, Katrin Plenkers, and Gerd Klee
Earth Syst. Sci. Data, 16, 295–310, https://doi.org/10.5194/essd-16-295-2024, https://doi.org/10.5194/essd-16-295-2024, 2024
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Fanny Lehmann, Filippo Gatti, Michaël Bertin, and Didier Clouteau
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2023-470, https://doi.org/10.5194/essd-2023-470, 2024
Revised manuscript accepted for ESSD
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Numerical simulations are a promising approach to characterize the intensity of ground motion in the presence of geological uncertainties. However, the computational cost of three-dimensional simulations can limit their usability. We present the first database of seismic-induced ground motion generated by an earthquake simulator for a collection of 30,000 heterogeneous geologies. The HEMEW-3D dataset can be helpful for geophysicists, seismologists, and machine learning scientists, among others.
Quetzalcoatl Rodríguez-Pérez and F. Ramón Zúñiga
Earth Syst. Sci. Data, 15, 4781–4801, https://doi.org/10.5194/essd-15-4781-2023, https://doi.org/10.5194/essd-15-4781-2023, 2023
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We present a comprehensive catalog of focal mechanisms for earthquakes in Mexico and neighboring areas spanning February 1928 to July 2022. The catalog comprises a wide range of earthquake magnitudes and depths and includes data from diverse geological environments. We collected and revised focal mechanism data from various sources and methods. The catalog is a valuable resource for future studies on earthquake source mechanisms, tectonics, and seismic hazard in the region.
Iman R. Kivi, Auregan Boyet, Haiqing Wu, Linus Walter, Sara Hanson-Hedgecock, Francesco Parisio, and Victor Vilarrasa
Earth Syst. Sci. Data, 15, 3163–3182, https://doi.org/10.5194/essd-15-3163-2023, https://doi.org/10.5194/essd-15-3163-2023, 2023
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Passive seismic analyses are a key technology for geothermal projects. The Lower Rhine Embayment, at the western border of North Rhine-Westphalia in Germany, is a geologically complex region with high potential for geothermal exploitation. Here, we report on a passive seismic dataset recorded with 48 seismic stations and a total extent of 20 km. We demonstrate that the network design allows for the application of state-of-the-art seismological methods.
Konstantinos Michailos, György Hetényi, Matteo Scarponi, Josip Stipčević, Irene Bianchi, Luciana Bonatto, Wojciech Czuba, Massimo Di Bona, Aladino Govoni, Katrin Hannemann, Tomasz Janik, Dániel Kalmár, Rainer Kind, Frederik Link, Francesco Pio Lucente, Stephen Monna, Caterina Montuori, Stefan Mroczek, Anne Paul, Claudia Piromallo, Jaroslava Plomerová, Julia Rewers, Simone Salimbeni, Frederik Tilmann, Piotr Środa, Jérôme Vergne, and the AlpArray-PACASE Working Group
Earth Syst. Sci. Data, 15, 2117–2138, https://doi.org/10.5194/essd-15-2117-2023, https://doi.org/10.5194/essd-15-2117-2023, 2023
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We examine the spatial variability of the crustal thickness beneath the broader European Alpine region by using teleseismic earthquake information (receiver functions) on a large amount of seismic waveform data. We compile a new Moho depth map of the broader European Alps and make our results freely available. We anticipate that our results can potentially provide helpful hints for interdisciplinary imaging and numerical modeling studies.
Muhammad Rizwan Asif, Nikolaj Foged, Thue Bording, Jakob Juul Larsen, and Anders Vest Christiansen
Earth Syst. Sci. Data, 15, 1389–1401, https://doi.org/10.5194/essd-15-1389-2023, https://doi.org/10.5194/essd-15-1389-2023, 2023
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To apply a deep learning (DL) algorithm to electromagnetic (EM) methods, subsurface resistivity models and/or the corresponding EM responses are often required. To date, there are no standardized EM datasets, which hinders the progress and evolution of DL methods due to data inconsistency. Therefore, we present a large-scale physics-driven model database of geologically plausible and EM-resolvable subsurface models to incorporate consistency and reliability into DL applications for EM methods.
Médéric Gravelle, Guy Wöppelmann, Kevin Gobron, Zuheir Altamimi, Mikaël Guichard, Thomas Herring, and Paul Rebischung
Earth Syst. Sci. Data, 15, 497–509, https://doi.org/10.5194/essd-15-497-2023, https://doi.org/10.5194/essd-15-497-2023, 2023
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We produced a reanalysis of GNSS data near tide gauges worldwide within the International GNSS Service. It implements advances in data modelling and corrections, extending the record length by about 7 years. A 28 % reduction in station velocity uncertainties is achieved over the previous solution. These estimates of vertical land motion at the coast supplement data from satellite altimetry or tide gauges for an improved understanding of sea level changes and their impacts along coastal areas.
Michal Kruszewski, Gerd Klee, Thomas Niederhuber, and Oliver Heidbach
Earth Syst. Sci. Data, 14, 5367–5385, https://doi.org/10.5194/essd-14-5367-2022, https://doi.org/10.5194/essd-14-5367-2022, 2022
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The authors assemble an in situ stress magnitude and orientation database based on 429 hydrofracturing tests that were carried out in six coal mines and two coal bed methane boreholes between 1986 and 1995 within the greater Ruhr region (Germany). Our study summarises the results of the extensive in situ stress test campaign and assigns quality to each data record using the established quality ranking schemes of the World Stress Map project.
Andrea Rovida, Andrea Antonucci, and Mario Locati
Earth Syst. Sci. Data, 14, 5213–5231, https://doi.org/10.5194/essd-14-5213-2022, https://doi.org/10.5194/essd-14-5213-2022, 2022
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EPICA is the 1000–1899 catalogue compiled for the European Seismic Hazard Model 2020 and contains 5703 earthquakes with Mw ≥ 4.0. It relies on the data of the European Archive of Historical Earthquake Data (AHEAD), both macroseismic intensities from historical seismological studies and parameters from regional catalogues. For each earthquake, the most representative datasets were selected and processed in order to derive harmonised parameters, both from intensity data and parametric catalogues.
Suqin Zhang, Changhua Fu, Jianjun Wang, Guohao Zhu, Chuanhua Chen, Shaopeng He, Pengkun Guo, and Guoping Chang
Earth Syst. Sci. Data, 14, 5195–5212, https://doi.org/10.5194/essd-14-5195-2022, https://doi.org/10.5194/essd-14-5195-2022, 2022
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The Sheshan observatory has nearly 150 years of observation history, and its observation data have important scientific value. However, with time, these precious historical data face the risk of damage and loss. We have carried out a series of rescues on the historical data of the Sheshan observatory. New historical datasets were released, including the quality-controlled absolute hourly mean values of three components (D, H, and Z) from 1933 to 2019.
Guoyu Li, Wei Ma, Fei Wang, Huijun Jin, Alexander Fedorov, Dun Chen, Gang Wu, Yapeng Cao, Yu Zhou, Yanhu Mu, Yuncheng Mao, Jun Zhang, Kai Gao, Xiaoying Jin, Ruixia He, Xinyu Li, and Yan Li
Earth Syst. Sci. Data, 14, 5093–5110, https://doi.org/10.5194/essd-14-5093-2022, https://doi.org/10.5194/essd-14-5093-2022, 2022
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A permafrost monitoring network was established along the China–Russia crude oil pipeline (CRCOP) route at the eastern flank of the northern Da Xing'anling Mountains in Northeast China. The resulting datasets fill the gaps in the spatial coverage of mid-latitude mountain permafrost databases. Results show that permafrost warming has been extensively observed along the CRCOP route, and local disturbances triggered by the CRCOPs have resulted in significant permafrost thawing.
Alessandro Cicoira, Samuel Weber, Andreas Biri, Ben Buchli, Reynald Delaloye, Reto Da Forno, Isabelle Gärtner-Roer, Stephan Gruber, Tonio Gsell, Andreas Hasler, Roman Lim, Philippe Limpach, Raphael Mayoraz, Matthias Meyer, Jeannette Noetzli, Marcia Phillips, Eric Pointner, Hugo Raetzo, Cristian Scapozza, Tazio Strozzi, Lothar Thiele, Andreas Vieli, Daniel Vonder Mühll, Vanessa Wirz, and Jan Beutel
Earth Syst. Sci. Data, 14, 5061–5091, https://doi.org/10.5194/essd-14-5061-2022, https://doi.org/10.5194/essd-14-5061-2022, 2022
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This paper documents a monitoring network of 54 positions, located on different periglacial landforms in the Swiss Alps: rock glaciers, landslides, and steep rock walls. The data serve basic research but also decision-making and mitigation of natural hazards. It is the largest dataset of its kind, comprising over 209 000 daily positions and additional weather data.
Xiaoli Chang, Huijun Jin, Ruixia He, Yanlin Zhang, Xiaoying Li, Xiaoying Jin, and Guoyu Li
Earth Syst. Sci. Data, 14, 3947–3959, https://doi.org/10.5194/essd-14-3947-2022, https://doi.org/10.5194/essd-14-3947-2022, 2022
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Based on 10-year observations of ground temperatures in seven deep boreholes in Gen’he, Mangui, and Yituli’he, a wide range of mean annual ground temperatures at the depth of 20 m (−2.83 to −0.49 ℃) and that of annual maximum thawing depth (about 1.1 to 7.0 m) have been revealed. This study demonstrates that most trajectories of permafrost changes in Northeast China are ground warming and permafrost degradation, except that the shallow permafrost is cooling in Yituli’he.
Alice C. Frémand, Julien A. Bodart, Tom A. Jordan, Fausto Ferraccioli, Carl Robinson, Hugh F. J. Corr, Helen J. Peat, Robert G. Bingham, and David G. Vaughan
Earth Syst. Sci. Data, 14, 3379–3410, https://doi.org/10.5194/essd-14-3379-2022, https://doi.org/10.5194/essd-14-3379-2022, 2022
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This paper presents the release of large swaths of airborne geophysical data (including gravity, magnetics, and radar) acquired between 1994 and 2020 over Antarctica by the British Antarctic Survey. These include a total of 64 datasets from 24 different surveys, amounting to >30 % of coverage over the Antarctic Ice Sheet. This paper discusses how these data were acquired and processed and presents the methods used to standardize and publish the data in an interactive and reproducible manner.
Cited articles
Augustine, J. A., DeLuisi, J. J., and Long, C. N.: SURFRAD–A national surface radiation budget network for atmospheric research. B. Am. Metrorol. Soc.,
81, 2341–2358, https://doi.org/10.1175/1520-0477(2000)081<2341:SANSRB>2.3.CO;2, 2000.
Bai, L., Long, D., and Yan, L.: Estimation of Surface Soil Moisture With
Downscaled Land Surface Temperatures Using a Data Fusion Approach for
Heterogeneous Agricultural Land, Water Resour. Res., 55, 1105–1128,
https://doi.org/10.1029/2018WR024162, 2019.
Becker, F. and Li, Z.-L.: Towards a local split window method over land
surfaces, Int. J. Remote Sens, 11, 369–393,
https://doi.org/10.1080/01431169008955028, 1990.
Bright, R. M., Davin, E., O'Halloran, T., Pongratz, J., Zhao, K., and
Cescatti, A.: Local temperature response to land cover and management change
driven by non-radiative processes, Nat. Clim. Change, 7, 296–302,
https://doi.org/10.1038/nclimate3250, 2017.
Chedin, A., Scott, N. A., Wahiche, C., and Moulinier, P.: The Improved
Initialization Inversion Method: A High Resolution Physical Method for
Temperature Retrievals from Satellites of the TIROS-N Series, J. Clim. Appl.
Meteorol., 24, 128–143, https://doi.org/10.1175/1520-0450(1985)024<0128:TIIIMA>2.0.CO;2, 1985.
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,
https://doi.org/10.1175/JHM-D-17-0009.1,2017.
Cracknell, A.: The Advanced Very High Resolution Radiometer, CRC Press, Taylor and Francis, London, UK, ISBN 0748402098, 1997.
Devasthale, A., Raspaud, M., Schlundt, C., Hanschmann, T., Finkensieper, S., Dybbroe, A., Hörnquist, S., Håkansson, N., Stengel, M., and Karlsson, K.: PyGAC: an open-source,community-driven Python interface to preprocess more than 30-year AVHRR Global Area Coverage (GAC) data, GSICS Quarterly, 11, 3–5, https://doi.org/10.7289/V5R78CFR, 2017.
Duan, S.-B., Li, Z.-L., Li, H., Göttsche, F.-M., Wu, H., Zhao, W., Leng,
P., Zhang, X., and Coll, C.: Validation of Collection 6 MODIS land surface
temperature product using in situ measurements, Remote Sens. Environ. 225,
16–29, https://doi.org/10.1016/j.rse.2019.02.020, 2019.
Duveiller, G., Hooker, J., and Cescatti, A.: The mark of vegetation change on
Earth's surface energy balance, Nat. Commun., 9, 679,
https://doi.org/10.1038/s41467-017-02810-8, 2018.
El Saleous, N. Z., Vermote, E. F., Justice, C. O., Townshend, J. R. G.,
Tucker, C. J., and Goward, S. N.: Improvements in the global biospheric
record from the Advanced Very High Resolution Radiometer (AVHRR), Int. J.
Remote Sens, 21, 1251–1277, https://doi.org/10.1080/014311600210164, 2000.
Frey, C. M., Kuenzer, C., and Dech, S.: Quantitative comparison of the
operational NOAA-AVHRR LST product of DLR and the MODIS LST product V005,
Int. J. Remote Sens., 33, 7165–7183,
https://doi.org/10.1080/01431161.2012.699693, 2012.
Frey, C. M., Kuenzer, C., and Dech, S.: Assessment of Mono- and Split-Window
Approaches for Time Series Processing of LST from AVHRR – A TIMELINE Round
Robin, Remote Sens., 9, 72, https://doi.org/10.3390/rs9010072, 2017.
Gillespie, A., Rokugawa, S., Matsunaga, T., Cothern, J. S., Hook, S., and
Kahle, A. B.: A temperature and emissivity separation algorithm for Advanced
Spaceborne Thermal Emission and Reflection Radiometer (ASTER) images, IEEE
T. Geosci. Remote, 36, 1113–1126,
https://doi.org/10.1109/36.700995, 1998.
Göttsche, F.-M., Olesen, F.-S., Trigo, I., Bork-Unkelbach, A., and
Martin, M.: Long Term Validation of Land Surface Temperature Retrieved from
MSG/SEVIRI with Continuous in-Situ Measurements in Africa, Remote Sens., 8,
410, https://doi.org/10.3390/rs8050410, 2016.
Guillevic, P. C., Privette, J. L., Coudert, B., Palecki, M. A., Demarty, J.,
Ottlé, C., and Augustine, J. A.: Land Surface Temperature product
validation using NOAA's surface climate observation networks – Scaling
methodology for the Visible Infrared Imager Radiometer Suite (VIIRS), Remote
Sens. Environ., 124, 282–298, https://doi.org/10.1016/j.rse.2012.05.004, 2012.
Guillevic, P. C., Biard, J. C., Hulley, G. C., Privette, J. L., Hook, S. J.,
Olioso, A., Göttsche, F. M., Radocinski, R., Román, M. O., Yu, Y.
Y., and Csiszar, I.: Validation of Land Surface Temperature products derived
from the Visible Infrared Imaging Radiometer Suite (VIIRS) using
ground-based and heritage satellite measurements, Remote Sens. Environ.,
154, 19–37, https://doi.org/10.1016/j.rse.2014.08.013, 2014.
Hansen, J., Ruedy, R., Sato, M., and Lo, K.: Global surface temperature
change, Rev. Geophys., 48, RG4004, https://doi.org/10.1029/2010RG000345, 2010.
Holzwarth, S., Asam, S., Bachmann, M., Böttcher, M., Dietz, A., Eisfelder, C., Hirner, A., Hofmann, M., Kirches, G., and Krause, D.: Mapping of geophysical land, ocean and atmosphere products over Europe from 40 years of AVHRR data – the TIMELINE project, EARSeL Symposium “European Remote Sensing-New Solutions for Science and Practice”, Warsaw, Poland, 7–10 June 2021, 1–12, 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.
Hulley, G. and Hook, S.: VIIRS/NPP Land Surface Temperature Daily L3 Global
1km SIN Grid Day V001, NASA EOSDIS Land Processes DAAC [data set],
https://doi.org/10.5067/VIIRS/VNP21A1D.001, 2018a.
Hulley, G. and Hook, S.: VIIRS/NPP Land Surface Temperature Daily L3 Global
1km SIN Grid Night V001, NASA EOSDIS Land Processes DAAC [data set],
https://doi.org/10.5067/VIIRS/VNP21A1N.001, 2018b.
Hulley, G. C., Hook, S. J., Abbott, E., Malakar, N., Islam, T., and Abrams,
M.: The ASTER Global Emissivity Dataset (ASTER GED): Mapping Earth's
emissivity at 100 meter spatial scale, Geophys. Res. Lett., 42, 7966–7976,
https://doi.org/10.1002/2015gl065564, 2015.
Hulley, G., Islam, T., Freepartner, R., and Malakar, N.: Visible Infrared Imaging Radiometer Suite (VIIRS) Land Surface Temperature and Emissivity Product Collection 1 Algorithm Theoretical Basis Document, Jet Propulsion Laboratory, National Aeronautics and Space Administration, Pasadena, CA, USA, 2016.
IPCC: Climate Change 2014: Synthesis Report, Contribution of Working Groups
I, II and III to the Fifth Assessment Report of the Intergovernmental Panel
on Climate Change, Geneva, Switzerland, https://www.ipcc.ch/site/assets/uploads/2018/02/SYR_AR5_FINAL_full.pdf (last access: 13 May 2023), 2014.
Jin, M.: Analysis of Land Skin Temperature using AVHRR Observations, B. Am.
Metrorol. Soc., 85, 587–600, https://doi.org/10.1175/BAMS-85-4-587, 2004.
Jin, M. and Dickinson, R. E.: New observational evidence for global warming
from satellite, Geophys. Res. Lett., 29, 1400,
https://doi.org/10.1029/2001GL013833, 2002.
Karlsson, K.-G., Anttila, K., Trentmann, J., Stengel, M., Fokke Meirink, J., Devasthale, A., Hanschmann, T., Kothe, S., Jääskeläinen, E., Sedlar, J., Benas, N., van Zadelhoff, G.-J., Schlundt, C., Stein, D., Finkensieper, S., Håkansson, N., and Hollmann, R.: CLARA-A2: the second edition of the CM SAF cloud and radiation data record from 34 years of global AVHRR data, Atmos. Chem. Phys., 17, 5809–5828, https://doi.org/10.5194/acp-17-5809-2017, 2017.
Kerr, Y. H., Guillou, C., Lagouarde, J. P., Nerry, F., and Ottlé, C.: World land surface temperature atlas 1992–1993: LST processor: Algorithm theoretical basis document, European Space Agency, http://data.ceda.ac.uk/badc/CDs/wlsta/doc/atbd/ATBDindex.htm (last access: 13 May 2023), 1998.
Khorchani, M., Martin-Hernandez, N., Vicente-Serrano, S. M., Azorin-Molina,
C., Garcia, M., Domínguez-Duran, M. A., Reig, F., Peña-Gallardo,
M., and Domínguez-Castro, F.: Average annual and seasonal land surface
temperature, Spanish Peninsular, J. Maps, 14, 465–475,
https://doi.org/10.1080/17445647.2018.1500316, 2018.
Kidwell, K. B.: NOAA Polar Orbiter Data (TIROS-N, NOAA-6, NOAA-7, NOAA-8, NOAA-9, NOAA-10, NOAA-11, NOAA-12, NOAA-13, and NOAA-14) Users Guide: 1998 Version, National Oceanic and Atmospheric Administration, National Environmental Satellite, Data, and Information Service, National Climatic Data Center, Satellite Data Services Division, https://www1.ncdc.noaa.gov/pub/data/satellite/publications/podguides/TIROS-N thru N-14/pdf/NCDCPD-ch1.pdf (last access: 13 May 2023), 1998.
Li, J. H., Liu, X., Li, Z. L., and Duan, S. B.: A global historical
twice-daily (daytime and nighttime) land surface temperature dataset
produced by AVHRR observations from 1981 to 2021 (1981–2000), Zenodo [data set],
https://doi.org/10.5281/zenodo.7113080, 2022a.
Li, J. H., Liu, X., Li, Z. L., and Duan, S. B.: A global historical
twice-daily (daytime and nighttime) land surface temperature dataset
produced by AVHRR observations from 1981 to 2021 (2001–2005), Zenodo [data set],
https://doi.org/10.5281/zenodo.7134158, 2022b.
Li, J. H., Liu, X., Li, Z. L., and Duan, S. B.: A global historical
twice-daily (daytime and nighttime) land surface temperature dataset
produced by AVHRR observations from 1981 to 2021 (2006–2021), Zenodo [data set],
https://doi.org/10.5281/zenodo.7813607, 2023.
Li, Y., Zhao, M., Motesharrei, S., Mu, Q., Kalnay, E., and Li, S.: Local
cooling and warming effects of forests based on satellite observations, Nat.
Commun., 6, 6603, https://doi.org/10.1038/ncomms7603, 2015.
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, M., 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, 2023.
Liu, H., Gong, P., Wang, J., Clinton, N., Bai, Y., and Liang, S.: Annual dynamics of global land cover and its long-term changes from 1982 to 2015, Earth Syst. Sci. Data, 12, 1217–1243, https://doi.org/10.5194/essd-12-1217-2020, 2020.
Liu, X., Tang, B. H., and Li, Z. L.: A Refined Generalized Split-Window Algorithm for Retrieving Long-Term Global Land Surface Temperature from Series NOAA-AVHRR Data, in: Proceedings of the International Geoscience and Remote Sensing Symposium, IGARSS 2018, Valencia, Spain, 22–27 July 2018, 2551–2554, https://doi.org/10.1109/IGARSS.2018.8518648, 2018.
Liu, X., Tang, B.-H., Yan, G., Li, Z.-L., and Liang, S.: Retrieval of Global
Orbit Drift Corrected Land Surface Temperature from Long-term AVHRR Data,
Remote Sens., 11, 2843, https://doi.org/10.3390/rs11232843, 2019.
Liu, X., Li, Z.-L., Li, J.-H., Leng, P., Liu, M., and Gao. M.: Temporal
upscaling of MODIS 1-km instantaneous land surface temperature to monthly
mean value: Method evaluation and product generation, IEEE T. Geosci.
Remote, 61, 5001214, https://doi.org/10.1109/TGRS.2023.3247428, 2023.
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.
Malakar, N. K., Hulley, G. C., Hook, S. J., Laraby, K., Cook, M., and
Schott, J. R.: An operational land surface temperature product for Landsat
thermal data: Methodology and validation, IEEE T. Geosci. Remote,
56, 5717–5735, https://doi.org/10.1109/TGRS.2018.2824828, 2018.
Martin, M., Ghent, D., Pires, A., Göttsche, F.-M., Cermak, J., and
Remedios, J.: Comprehensive in situ validation of five satellite land
surface temperature data sets over multiple stations and years, Remote
Sens., 11, 479, https://doi.org/10.3390/rs11050479, 2019.
Meng, X., Li, H., Du, Y., Cao, B., Liu, Q., Sun, L., and Zhu, J.: Estimating
land surface emissivity from ASTER GED products, J. Remote Sens., 4619,
382–396, https://doi.org/10.11834/jrs.20165230, 2016.
Ouaidrari, H., Goward, S. N., Czajkowski, K. P., Sobrino, J. A., and
Vermote, E.: Land surface temperature estimation from AVHRR thermal infrared
measurements: An assessment for the AVHRR Land Pathfinder II data set,
Remote Sens. Environ., 81, 114–128,
https://doi.org/10.1016/S0034-4257(01)00338-8, 2002.
Ouyang, X., Chen, D., Duan, S.-B., Lei, Y., Dou, Y., and Hu, G.: Validation
and Analysis of Long-Term AATSR Land Surface Temperature Product in the
Heihe River Basin, China, Remote Sens., 9, 152,
https://doi.org/10.3390/rs9020152, 2017.
Pareeth, S., Delucchi, L., Metz, M., Rocchini, D., Devasthale, A., Raspaud,
M., Adrian, R., Salmaso, N., and Neteler, M.: New automated method to
develop geometrically corrected time series of brightness temperatures from
historical AVHRR LAC data, Remote Sens., 8, 169,
https://doi.org/10.3390/rs8030169, 2016.
Phan, T. N. and Kappas, M.: Application of MODIS land surface temperature
data: a systematic literature review and analysis, J. Appl. Remote Sens.,
12, 041501, https://doi.org/10.1117/1.jrs.12.041501, 2018.
Pinheiro, A. C. T., Mahoney, R., Privette, J. L., and Tucker, C. J.:
Development of a daily long term record of NOAA-14 AVHRR land surface
temperature over Africa, Remote Sens. Environ., 103, 153–164,
https://doi.org/10.1016/j.rse.2006.03.009, 2006.
Prata, A. J.: Land surface temperature measurement from space: AATSR algorithm theoretical basis document, in: Contract Report to ESA, CSIRO
Atmospheric Research, Aspendale, Victoria, Australia, 34 pp., 2002.
Qin, Z., Karnieli, A., and Berliner, P.: A mono-window algorithm for
retrieving land surface temperature from Landsat TM data and its application
to the Israel-Egypt border region, Int. J. Remote Sens, 22, 3719–3746,
https://doi.org/10.1080/01431160010006971, 2001.
Reiners, P., Asam, S., Frey, C., Holzwarth, S., Bachmann, M., Sobrino, J.,
Göttsche, F., Bendix, J., and Kuenzer, C.: Validation of AVHRR Land
Surface Temperature with MODIS and In Situ LST – A TIMELINE Thematic
Processor, Remote Sens., 13, 3473, https://doi.org/10.3390/rs13173473, 2021.
Ren, H., Yan, G., Chen, L., and Li, Z.: Angular effect of MODIS emissivity
products and its application to the split-window algorithm, ISPRS J.
Photogramm., 66, 498–507, https://doi.org/10.1016/j.isprsjprs.2011.02.008,
2011.
Sánchez, N., González-Zamora, Á., Martínez-Fernández,
J., Piles, M., and Pablos, M.: Integrated remote sensing approach to global
agricultural drought monitoring, Agr. Forest Meteorol., 259, 141–153,
https://doi.org/10.1016/j.agrformet.2018.04.022, 2018.
Si, M., Li, Z. L., Nerry, F., Tang, B. H., Leng, P., Wu, H., Zhang, X., and
Shang, G.: Spatiotemporal pattern and long-term trend of global surface
urban heat islands characterized by dynamic urban-extent method and MODIS
data, ISPRS J. Photogramm., 183, 321–335,
https://doi.org/10.1016/j.isprsjprs.2021.11.017, 2022.
Sims, D. A., Rahman, A. F., Cordova, V. D., El-Masri, B. Z., Baldocchi, D.
D., Bolstad, P. V., Flanagan, L. B., Goldstein, A. H., Hollinger, D. Y.,
Misson, L., Monson, R. K., Oechel, W. C., Schmid, H. P., Wofsy, S. C., and
Xu, L.: A new model of gross primary productivity for North American
ecosystems based solely on the enhanced vegetation index and land surface
temperature from MODIS, Remote Sens. Environ., 112, 1633–1646,
https://doi.org/10.1016/j.rse.2007.08.004, 2008.
Sobrino, J. A., Raissouni, N., and Li, Z. L.: A comparative study of land
surface emissivity retrieval from NOAA data, Remote Sens. Environ., 75,
256–266, https://doi.org/10.1016/S0034-4257(00)00171-1, 2001.
Sobrino, J. A., Jiménez-Muñoz, J. C., Sòria, G., Romaguera, M.,
Guanter, L., Moreno, J., Plaza, A., and Martínez, P.: Land surface
emissivity retrieval from different VNIR and TIR sensors, IEEE T.
Geosci. Remote, 46, 316–327,
https://doi.org/10.1109/TGRS.2007.904834, 2008.
Song, P., Zhang, Y., Guo, J., Shi, J., Zhao, T., and Tong, B.: A 1 km daily surface soil moisture dataset of enhanced coverage under all-weather conditions over China in 2003–2019, Earth Syst. Sci. Data, 14, 2613–2637, https://doi.org/10.5194/essd-14-2613-2022, 2022.
Stowe, L. L., Davis, P. A., and McClain, E. P.: Scientific basis and initial
evaluation of the CLAVR-1 global clear/cloud classification algorithm for
the Advanced Very High Resolution Radiometer, J. Atmos. Ocean. Tech., 16,
656–681, https://doi.org/10.1175/1520-0426(1999)016<0656:SBAIEO>2.0.CO;2, 1999.
Sulla-Menashe, D. and Friedl, M. A.: User guide to collection 6 MODIS land
cover (MCD12Q1 and MCD12C1) product,
https://lpdaac.usgs.gov/documents/101/MCD12_User_Guide_V6.pdf (last access: 5 April 2023), 2018.
Tang, B.-H.: Nonlinear split-window algorithms for estimating land and sea
surface temperatures from simulated Chinese gaofen-5 satellite data, IEEE T.
Geosci. Remote, 56, 6280–6289, https://doi.org/10.1109/tgrs.2018.2833859,
2018.
Tang, B., Bi, Y., Li, Z. L., and Xia, J.: Generalized split-window algorithm
for estimate of land surface temperature from Chinese geostationary FengYun
meteorological satellite (FY-2C) data, Sensors, 8, 933–951,
https://doi.org/10.3390/s8020933, 2008.
Trigo, I. F., Monteiro, I. T., Olesen, F., and Kabsch, E.: An assessment of
remotely sensed land surface temperature, J. Geophys. Res-Atmos., 113, D17108,
https://doi.org/10.1029/2008jd010035, 2008.
Trigo, I. F., Dacamara, C. C., Viterbo, P., Roujean, J. L., Olesen, F.,
Barroso, C., Camacho-de-Coca, F., Carrer, D., Freitas, S. C.,
García-Haro, J., Geiger, B., Gellens-Meulenberghs, F., Ghilain, N.,
Meliá, J., Pessanha, L., Siljamo, N., and Arboleda, A.: The satellite
application facility for land surface analysis, Int. J. Remote Sens, 32,
2725–2744, https://doi.org/10.1080/01431161003743199, 2011.
Wan, Z.: MODIS land surface temperature products users’ guide, Santa Barbara, Institute for Computational Earth System Science, University of California, https://icess.eri.ucsb.edu/modis/LstUsrGuide/MODIS_LST_products_Users_guide.pdf (last access: 13 May 2023), 2006.
Wan, Z.: New refinements and validation of the collection-6 MODIS
land-surface temperature/emissivity product, Remote Sens. Environ., 140,
36–45, https://doi.org/10.1016/j.rse.2013.08.027, 2014.
Wan, Z. and Dozier, J.: A generalized split-window algorithm for retrieving
land-surface temperature from space, IEEE T. Geosci. Remote, 34,
892–905, https://doi.org/10.1109/36.508406, 1996.
Wan, Z. and Li, Z. L.: A physics-based algorithm for retrieving
land-surface emissivity and temperature from EOS/MODIS data, IEEE T.
Geosci. Remote, 35, 980–996, https://doi.org/10.1109/36.602541, 1997.
Wang, K. and Liang, S.: Evaluation of ASTER and MODIS land surface
temperature and emissivity products using long-term surface longwave
radiation observations at SURFRAD sites, Remote Sens. Environ., 113,
1556–1565, https://doi.org/10.1016/j.rse.2009.03.009, 2009.
Wang, W., Liang, S., and Meyers, T. P.: Validating MODIS land surface
temperature products using long-term nighttime ground measurements, Remote
Sens. Environ., 112, 623–635, https://doi.org/10.1016/j.rse.2007.05.024,
2008.
Weng, Q., Lu, D., and Schubring, J.: Estimation of land surface temperature
vegetation abundance relationship for urban heat island studies, Remote
Sens. Environ., 89, 467–483, https://doi.org/10.1016/j.rse.2003.11.005,
2004.
Wu, X., Naegeli, K., and Wunderle, S.: Geometric accuracy assessment of coarse-resolution satellite datasets: a study based on AVHRR GAC data at the sub-pixel level, Earth Syst. Sci. Data, 12, 539–553, https://doi.org/10.5194/essd-12-539-2020, 2020.
Wu, X., Naegeli, K., Premier, V., Marin, C., Ma, D., Wang, J., and Wunderle, S.: Evaluation of snow extent time series derived from Advanced Very High Resolution Radiometer global area coverage data (1982–2018) in the Hindu Kush Himalayas, The Cryosphere, 15, 4261–4279, https://doi.org/10.5194/tc-15-4261-2021, 2021.
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., 178, 51–67,
https://doi.org/10.1016/j.isprsjprs.2021.05.017, 2021.
Yamamoto, Y., Ishikawa, H., Oku, Y., and Hu, Z.: An algorithm for land
surface temperature retrieval using three thermal infrared bands of
Himawari-8, J. Meteorol. Soc. Jpn., 96B, 59–76,
https://doi.org/10.2151/jmsj.2018-005, 2018.
Yang, J., Zhang, Z., Wei, C., Lu, F., and Guo, Q.: Introducing the New
Generation of Chinese Geostationary Weather Satellites, Fengyun-4, B. Am.
Metrorol. Soc., 98, 1637–1658, https://doi.org/10.1175/bams-d-16-0065.1,
2017.
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, https://doi.org/10.1109/TGRS.2008.2006180, 2008.
Zhao, W., Wen, F., Wang, Q., Sanchez, N., and Piles, M.: Seamless
downscaling of the ESA CCI soil moisture data at the daily scale with MODIS
land products, J. Hydrol., 603, 126930,
https://doi.org/10.1016/j.jhydrol.2021.126930, 2021.
Zhang, L., Jiao, W., Zhang, H., Huang, C., and Tong, Q.: Studying drought
phenomena in the Continental United States in 2011 and 2012 using various
drought indices, Remote Sens. Environ., 190, 96–106,
https://doi.org/10.1016/j.rse.2016.12.010, 2017.
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.
Short summary
The Advanced Very High Resolution Radiometer (AVHRR) is the only sensor that has the advantages of frequent revisits (twice per day), relatively high spatial resolution (4 km at the nadir), global coverage, and easy access prior to 2000. This study developed a global historical twice-daily LST product for 1981–2021 based on AVHRR GAC data. The product is suitable for detecting and analyzing climate changes over the past 4 decades.
The Advanced Very High Resolution Radiometer (AVHRR) is the only sensor that has the advantages...
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