Articles | Volume 14, issue 5
https://doi.org/10.5194/essd-14-2315-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-14-2315-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
13 May 2022
Data description paper |
| 13 May 2022
| 13 May 2022
A global long-term (1981–2019) daily land surface radiation budget product from AVHRR satellite data using a residual convolutional neural network
Jianglei Xu
School of Remote Sensing and Information Engineering, Wuhan
University, Wuhan 430079, China
Department of Geographical Sciences, University of Maryland, College
Park, MD 20742, USA
Bo Jiang
CORRESPONDING AUTHOR
Faculty of Geographical Science, Beijing Normal University, Beijing
100875, China
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Using data collected by the Hyperspectral Imager for the Coastal Ocean (HICO) between 2010–2014, hyperspectral reflectance of various floating matters in global oceans and lakes is derived for the spectral range of 400–800 nm. Such reflectance spectra are expected to provide spectral endmembers to differentiate and quantify the floating matters from existing multi-band satellite sensors and future hyperspectral satellite missions such as NASA’s PACE, SBG, and GLIMR missions.
Tobias K. D. Weber, Joachim Ingwersen, Petra Högy, Arne Poyda, Hans-Dieter Wizemann, Michael Scott Demyan, Kristina Bohm, Ravshan Eshonkulov, Sebastian Gayler, Pascal Kremer, Moritz Laub, Yvonne Funkiun Nkwain, Christian Troost, Irene Witte, Tim Reichenau, Thomas Berger, Georg Cadisch, Torsten Müller, Andreas Fangmeier, Volker Wulfmeyer, and Thilo Streck
Earth Syst. Sci. Data, 14, 1153–1181, https://doi.org/10.5194/essd-14-1153-2022, https://doi.org/10.5194/essd-14-1153-2022, 2022
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Presented are measurement results from six agricultural fields operated by local farmers in southwestern Germany over 9 years. Six eddy-covariance stations measuring water, energy, and carbon fluxes between the vegetated soil surface and the atmosphere provided the backbone of the measurement sites and were supplemented by extensive soil and vegetation state monitoring. The dataset is ideal for testing process models characterizing fluxes at the vegetated soil surface and in the atmosphere.
Runmei Ma, Jie Ban, Qing Wang, Yayi Zhang, Yang Yang, Shenshen Li, Wenjiao Shi, Zhen Zhou, Jiawei Zang, and Tiantian Li
Earth Syst. Sci. Data, 14, 943–954, https://doi.org/10.5194/essd-14-943-2022, https://doi.org/10.5194/essd-14-943-2022, 2022
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We constructed multi-variable random forest models based on 10-fold cross-validation and estimated daily PM2.5 and O3 concentration of China in 2005–2017 at a resolution of 1 km. The daily R2 values of PM2.5 and O3 were 0.85 and 0.77. The meteorological variables can significantly affect both PM2.5 and O3 modeling. During 2005–2017, PM2.5 exhibited an overall downward trend, while O3 experienced the opposite. The temporal trend of PM2.5 and O3 had spatial characteristics during the study period.
Guta Wakbulcho Abeshu, Hong-Yi Li, Zhenduo Zhu, Zeli Tan, and L. Ruby Leung
Earth Syst. Sci. Data, 14, 929–942, https://doi.org/10.5194/essd-14-929-2022, https://doi.org/10.5194/essd-14-929-2022, 2022
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Existing riverbed sediment particle size data are sparsely available at individual sites. We develop a continuous map of median riverbed sediment particle size over the contiguous US corresponding to millions of river segments based on the existing observations and machine learning methods. This map is useful for research in large-scale river sediment using model- and data-driven approaches, teaching environmental and earth system sciences, planning and managing floodplain zones, etc.
Anna M. Ukkola, Gab Abramowitz, and Martin G. De Kauwe
Earth Syst. Sci. Data, 14, 449–461, https://doi.org/10.5194/essd-14-449-2022, https://doi.org/10.5194/essd-14-449-2022, 2022
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Flux towers provide measurements of water, energy, and carbon fluxes. Flux tower data are invaluable in improving and evaluating land models but are not suited to modelling applications as published. Here we present flux tower data tailored for land modelling, encompassing 170 sites globally. Our dataset resolves several key limitations hindering the use of flux tower data in land modelling, including incomplete forcing variable, data format, and low data quality.
Hui Tao, Kaishan Song, Ge Liu, Qiang Wang, Zhidan Wen, Pierre-Andre Jacinthe, Xiaofeng Xu, Jia Du, Yingxin Shang, Sijia Li, Zongming Wang, Lili Lyu, Junbin Hou, Xiang Wang, Dong Liu, Kun Shi, Baohua Zhang, and Hongtao Duan
Earth Syst. Sci. Data, 14, 79–94, https://doi.org/10.5194/essd-14-79-2022, https://doi.org/10.5194/essd-14-79-2022, 2022
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During 1984–2018, lakes in the Tibetan-Qinghai Plateau had the clearest water (mean 3.32 ± 0.38 m), while those in the northeastern region had the lowest Secchi disk depth (SDD) (mean 0.60 ± 0.09 m). Among the 10 814 lakes with > 10 years of SDD results, 55.4 % and 3.5 % experienced significantly increasing and decreasing trends of SDD, respectively. With the exception of Inner Mongolia–Xinjiang, more than half of lakes in all the other regions exhibited a significant trend of increasing SDD.
Jiao Lu, Guojie Wang, Tiexi Chen, Shijie Li, Daniel Fiifi Tawia Hagan, Giri Kattel, Jian Peng, Tong Jiang, and Buda Su
Earth Syst. Sci. Data, 13, 5879–5898, https://doi.org/10.5194/essd-13-5879-2021, https://doi.org/10.5194/essd-13-5879-2021, 2021
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This study has combined three existing land evaporation (ET) products to obtain a single framework of a long-term (1980–2017) daily ET product at a spatial resolution of 0.25° to define the global proxy ET with lower uncertainties. The merged product is the best at capturing dynamics over different locations and times among all data sets. The merged product performed well over a range of vegetation cover scenarios and also captured the trend of land evaporation over different areas well.
Kytt MacManus, Deborah Balk, Hasim Engin, Gordon McGranahan, and Rya Inman
Earth Syst. Sci. Data, 13, 5747–5801, https://doi.org/10.5194/essd-13-5747-2021, https://doi.org/10.5194/essd-13-5747-2021, 2021
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New estimates of population and land area by settlement types within low-elevation coastal zones (LECZs) based on four sources of population data, four sources of settlement data and four sources of elevation data for the years 1990, 2000 and 2015. The paper describes the sensitivity of these estimates and discusses the fitness of use guiding user decisions. Data choices impact the number of people estimated within LECZs, but across all sources the LECZs are predominantly urban and growing.
Yanhua Xie, Holly K. Gibbs, and Tyler J. Lark
Earth Syst. Sci. Data, 13, 5689–5710, https://doi.org/10.5194/essd-13-5689-2021, https://doi.org/10.5194/essd-13-5689-2021, 2021
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We created 30 m resolution annual irrigation maps covering the conterminous US for the period of 1997–2017, together with derivative products and ground reference data. The products have several improvements over other data, including field-level details of change and frequency, an annual time step, a collection of ~ 10 000 ground reference locations for the eastern US, and improved mapping accuracy of over 90 %, especially in the east compared to others of 50 % to 80 %.
Holger Virro, Giuseppe Amatulli, Alexander Kmoch, Longzhu Shen, and Evelyn Uuemaa
Earth Syst. Sci. Data, 13, 5483–5507, https://doi.org/10.5194/essd-13-5483-2021, https://doi.org/10.5194/essd-13-5483-2021, 2021
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Water quality modeling is essential for understanding and mitigating water quality deterioration in river networks due to agricultural and industrial pollution. Improving the availability and usability of open data is vital to support global water quality modeling efforts. The GRQA extends the spatial and temporal coverage of previously available water quality data and provides a reproducible workflow for combining multi-source water quality datasets.
Bowen Cao, Le Yu, Xuecao Li, Min Chen, Xia Li, Pengyu Hao, and Peng Gong
Earth Syst. Sci. Data, 13, 5403–5421, https://doi.org/10.5194/essd-13-5403-2021, https://doi.org/10.5194/essd-13-5403-2021, 2021
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In the study, the first 1 km global cropland proportion dataset for 10 000 BCE–2100 CE was produced through the harmonization and downscaling framework. The mapping result coincides well with widely used datasets at present. With improved spatial resolution, our maps can better capture the cropland distribution details and spatial heterogeneity. The dataset will be valuable for long-term simulations and precise analyses. The framework can be extended to specific regions or other land use types.
Diyang Cui, Shunlin Liang, Dongdong Wang, and Zheng Liu
Earth Syst. Sci. Data, 13, 5087–5114, https://doi.org/10.5194/essd-13-5087-2021, https://doi.org/10.5194/essd-13-5087-2021, 2021
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Large portions of the Earth's surface are expected to experience changes in climatic conditions. The rearrangement of climate distributions can lead to serious impacts on ecological and social systems. Major climate zones are distributed in a predictable pattern and are largely defined following the Köppen climate classification. This creates an urgent need to compile a series of Köppen climate classification maps with finer spatial and temporal resolutions and improved accuracy.
Amanda R. Fay, Luke Gregor, Peter Landschützer, Galen A. McKinley, Nicolas Gruber, Marion Gehlen, Yosuke Iida, Goulven G. Laruelle, Christian Rödenbeck, Alizée Roobaert, and Jiye Zeng
Earth Syst. Sci. Data, 13, 4693–4710, https://doi.org/10.5194/essd-13-4693-2021, https://doi.org/10.5194/essd-13-4693-2021, 2021
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The movement of carbon dioxide from the atmosphere to the ocean is estimated using surface ocean carbon (pCO2) measurements and an equation including variables such as temperature and wind speed; the choices of these variables lead to uncertainties. We introduce the SeaFlux ensemble which provides carbon flux maps calculated in a consistent manner, thus reducing uncertainty by using common choices for wind speed and a set definition of "global" coverage.
Samuel J. Tomlinson, Edward J. Carnell, Anthony J. Dore, and Ulrike Dragosits
Earth Syst. Sci. Data, 13, 4677–4692, https://doi.org/10.5194/essd-13-4677-2021, https://doi.org/10.5194/essd-13-4677-2021, 2021
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Nitrogen (N) may impact the environment in many ways, and estimation of its deposition to the terrestrial surface is of interest. N deposition data have not been generated at a high resolution (1 km × 1 km) over a long time series in the UK before now. This study concludes that N deposition has reduced by ~ 40 % from 1990. The impact of these results allows analysis of environmental impacts at a high spatial and temporal resolution, using a consistent methodology and consistent set of input data.
Joaquín Muñoz-Sabater, Emanuel Dutra, Anna Agustí-Panareda, Clément Albergel, Gabriele Arduini, Gianpaolo Balsamo, Souhail Boussetta, Margarita Choulga, Shaun Harrigan, Hans Hersbach, Brecht Martens, Diego G. Miralles, María Piles, Nemesio J. Rodríguez-Fernández, Ervin Zsoter, Carlo Buontempo, and Jean-Noël Thépaut
Earth Syst. Sci. Data, 13, 4349–4383, https://doi.org/10.5194/essd-13-4349-2021, https://doi.org/10.5194/essd-13-4349-2021, 2021
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The creation of ERA5-Land responds to a growing number of applications requiring global land datasets at a resolution higher than traditionally reached. ERA5-Land provides operational, global, and hourly key variables of the water and energy cycles over land surfaces, at 9 km resolution, from 1981 until the present. This work provides evidence of an overall improvement of the water cycle compared to previous reanalyses, whereas the energy cycle variables perform as well as those of ERA5.
Yan Chen, Shunlin Liang, Han Ma, Bing Li, Tao He, and Qian Wang
Earth Syst. Sci. Data, 13, 4241–4261, https://doi.org/10.5194/essd-13-4241-2021, https://doi.org/10.5194/essd-13-4241-2021, 2021
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This study used remotely sensed and assimilated data to estimate all-sky land surface air temperature (Ta) using a machine learning method, and developed an all-sky 1 km daily mean land Ta product for 2003–2019 over mainland China. Validation results demonstrated that this dataset has achieved satisfactory accuracy and high spatial resolution simultaneously, which fills the current dataset gap in this field and plays an important role in studies of climate change and the hydrological cycle.
Guoqing Zhang, Youhua Ran, Wei Wan, Wei Luo, Wenfeng Chen, Fenglin Xu, and Xin Li
Earth Syst. Sci. Data, 13, 3951–3966, https://doi.org/10.5194/essd-13-3951-2021, https://doi.org/10.5194/essd-13-3951-2021, 2021
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Lakes can be effective indicators of climate change, especially over the Qinghai–Tibet Plateau. Here, we provide the most comprehensive lake mapping covering the past 100 years. The new features of this data set are (1) its temporal length, providing the longest period of lake observations from maps, (2) the data set provides a state-of-the-art lake inventory for the Landsat era (from the 1970s to 2020), and (3) it provides the densest lake observations for lakes with areas larger than 1 km2.
Jie Yang and Xin Huang
Earth Syst. Sci. Data, 13, 3907–3925, https://doi.org/10.5194/essd-13-3907-2021, https://doi.org/10.5194/essd-13-3907-2021, 2021
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We produce the 30 m annual China land cover dataset (CLCD), with an accuracy reaching 79.31 %. Trends and patterns of land cover changes during 1985 and 2019 were revealed, such as expansion of impervious surface (+148.71 %) and water (+18.39 %), decrease in cropland (−4.85 %) and increase in forest (+4.34 %). The CLCD generally reflected the rapid urbanization and a series of ecological projects in China and revealed the anthropogenic implications on LC under the condition of climate change.
Richard Porter-Smith, John McKinlay, Alexander D. Fraser, and Robert A. Massom
Earth Syst. Sci. Data, 13, 3103–3114, https://doi.org/10.5194/essd-13-3103-2021, https://doi.org/10.5194/essd-13-3103-2021, 2021
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This study quantifies the characteristic complexity
signaturesaround the Antarctic outer coastal margin, giving a multiscale estimate of the magnitude and direction of undulation or complexity at each point location along the entire coastline. It has numerous applications for both geophysical and biological studies and will contribute to Antarctic research requiring quantitative information about this important interface.
Gonçalo Vieira, Carla Mora, Pedro Pina, Ricardo Ramalho, and Rui Fernandes
Earth Syst. Sci. Data, 13, 3179–3201, https://doi.org/10.5194/essd-13-3179-2021, https://doi.org/10.5194/essd-13-3179-2021, 2021
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Fogo in Cabo Verde is one of the most active ocean island volcanoes on Earth, posing important hazards to local populations and at a regional level. The last eruption occurred from November 2014 to February 2015. A survey of the Chã das Caldeiras area was conducted using a fixed-wing unmanned aerial vehicle. A point cloud, digital surface model and orthomosaic with 10 and 25 cm resolutions are provided, together with the full aerial survey projects and datasets.
Clara Betancourt, Timo Stomberg, Ribana Roscher, Martin G. Schultz, and Scarlet Stadtler
Earth Syst. Sci. Data, 13, 3013–3033, https://doi.org/10.5194/essd-13-3013-2021, https://doi.org/10.5194/essd-13-3013-2021, 2021
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With the AQ-Bench dataset, we contribute to shared data usage and machine learning methods in the field of environmental science. The AQ-Bench dataset contains air quality data and metadata from more than 5500 air quality observation stations all over the world. The dataset offers a low-threshold entrance to machine learning on a real-world environmental dataset. AQ-Bench thus provides a blueprint for environmental benchmark datasets.
Christof Lorenz, Tanja C. Portele, Patrick Laux, and Harald Kunstmann
Earth Syst. Sci. Data, 13, 2701–2722, https://doi.org/10.5194/essd-13-2701-2021, https://doi.org/10.5194/essd-13-2701-2021, 2021
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Semi-arid regions depend on the freshwater resources from the rainy seasons as they are crucial for ensuring security for drinking water, food and electricity. Thus, forecasting the conditions for the next season is crucial for proactive water management. We hence present a seasonal forecast product for four semi-arid domains in Iran, Brazil, Sudan/Ethiopia and Ecuador/Peru. It provides a benchmark for seasonal forecasts and, finally, a crucial contribution for improved disaster preparedness.
Ana Maria Roxana Petrescu, Chunjing Qiu, Philippe Ciais, Rona L. Thompson, Philippe Peylin, Matthew J. McGrath, Efisio Solazzo, Greet Janssens-Maenhout, Francesco N. Tubiello, Peter Bergamaschi, Dominik Brunner, Glen P. Peters, Lena Höglund-Isaksson, Pierre Regnier, Ronny Lauerwald, David Bastviken, Aki Tsuruta, Wilfried Winiwarter, Prabir K. Patra, Matthias Kuhnert, Gabriel D. Oreggioni, Monica Crippa, Marielle Saunois, Lucia Perugini, Tiina Markkanen, Tuula Aalto, Christine D. Groot Zwaaftink, Hanqin Tian, Yuanzhi Yao, Chris Wilson, Giulia Conchedda, Dirk Günther, Adrian Leip, Pete Smith, Jean-Matthieu Haussaire, Antti Leppänen, Alistair J. Manning, Joe McNorton, Patrick Brockmann, and Albertus Johannes Dolman
Earth Syst. Sci. Data, 13, 2307–2362, https://doi.org/10.5194/essd-13-2307-2021, https://doi.org/10.5194/essd-13-2307-2021, 2021
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This study is topical and provides a state-of-the-art scientific overview of data availability from bottom-up and top-down CH4 and N2O emissions in the EU27 and UK. The data integrate recent emission inventories with process-based model data and regional/global inversions for the European domain, aiming at reconciling them with official country-level UNFCCC national GHG inventories in support to policy and to facilitate real-time verification procedures.
Ana Maria Roxana Petrescu, Matthew J. McGrath, Robbie M. Andrew, Philippe Peylin, Glen P. Peters, Philippe Ciais, Gregoire Broquet, Francesco N. Tubiello, Christoph Gerbig, Julia Pongratz, Greet Janssens-Maenhout, Giacomo Grassi, Gert-Jan Nabuurs, Pierre Regnier, Ronny Lauerwald, Matthias Kuhnert, Juraj Balkovič, Mart-Jan Schelhaas, Hugo A. C. Denier van der
Gon, Efisio Solazzo, Chunjing Qiu, Roberto Pilli, Igor B. Konovalov, Richard A. Houghton, Dirk Günther, Lucia Perugini, Monica Crippa, Raphael Ganzenmüller, Ingrid T. Luijkx, Pete Smith, Saqr Munassar, Rona L. Thompson, Giulia Conchedda, Guillaume Monteil, Marko Scholze, Ute Karstens, Patrick Brockmann, and Albertus Johannes Dolman
Earth Syst. Sci. Data, 13, 2363–2406, https://doi.org/10.5194/essd-13-2363-2021, https://doi.org/10.5194/essd-13-2363-2021, 2021
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This study is topical and provides a state-of-the-art scientific overview of data availability from bottom-up and top-down CO2 fossil emissions and CO2 land fluxes in the EU27+UK. The data integrate recent emission inventories with ecosystem data, land carbon models and regional/global inversions for the European domain, aiming at reconciling CO2 estimates with official country-level UNFCCC national GHG inventories in support to policy and facilitating real-time verification procedures.
Lilu Sun and Yunfei Fu
Earth Syst. Sci. Data, 13, 2293–2306, https://doi.org/10.5194/essd-13-2293-2021, https://doi.org/10.5194/essd-13-2293-2021, 2021
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Multi-source dataset use is hampered by use of different spatial and temporal resolutions. We merged Tropical Rainfall Measuring Mission precipitation radar and visible and infrared scanner measurements with ERA5 reanalysis. The statistical results indicate this process has no unacceptable influence on the original data. The merged dataset can help in studying characteristics of and changes in cloud and precipitation systems and provides an opportunity for data analysis and model simulations.
Yongyong Fu, Jinsong Deng, Hongquan Wang, Alexis Comber, Wu Yang, Wenqiang Wu, Shixue You, Yi Lin, and Ke Wang
Earth Syst. Sci. Data, 13, 1829–1842, https://doi.org/10.5194/essd-13-1829-2021, https://doi.org/10.5194/essd-13-1829-2021, 2021
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Marine aquaculture areas in a region up to 30 km from the coast in China were mapped for the first time. It was found to cover a total area of ~1100 km2, of which more than 85 % is marine plant culture areas, with 87 % found in four coastal provinces. The results confirm the applicability and effectiveness of deep learning when applied to GF-1 data at the national scale, identifying the detailed spatial distributions and supporting the sustainable management of coastal resources in China.
Sebastian Weinert, Kristian Bär, and Ingo Sass
Earth Syst. Sci. Data, 13, 1441–1459, https://doi.org/10.5194/essd-13-1441-2021, https://doi.org/10.5194/essd-13-1441-2021, 2021
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Physical rock properties are a key element for resource exploration, the interpretation of results from geophysical methods or the parameterization of physical or geological models. Despite the need for physical rock properties, data are still very scarce and often not available for the area of interest. The database presented aims to provide easy access to physical rock properties measured at 224 locations in Bavaria, Hessen, Rhineland-Palatinate and Thuringia (Germany).
Claire E. Simpson, Christopher D. Arp, Yongwei Sheng, Mark L. Carroll, Benjamin M. Jones, and Laurence C. Smith
Earth Syst. Sci. Data, 13, 1135–1150, https://doi.org/10.5194/essd-13-1135-2021, https://doi.org/10.5194/essd-13-1135-2021, 2021
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Sonar depth point measurements collected at 17 lakes on the Arctic Coastal Plain of Alaska are used to train and validate models to map lake bathymetry. These models predict depth from remotely sensed lake color and are able to explain 58.5–97.6 % of depth variability. To calculate water volumes, we integrate this modeled bathymetry with lake surface area. Knowledge of Alaskan lake bathymetries and volumes is crucial to better understanding water storage, energy balance, and ecological habitat.
Fei Feng and Kaicun Wang
Earth Syst. Sci. Data, 13, 907–922, https://doi.org/10.5194/essd-13-907-2021, https://doi.org/10.5194/essd-13-907-2021, 2021
Els Knaeps, Sindy Sterckx, Gert Strackx, Johan Mijnendonckx, Mehrdad Moshtaghi, Shungudzemwoyo P. Garaba, and Dieter Meire
Earth Syst. Sci. Data, 13, 713–730, https://doi.org/10.5194/essd-13-713-2021, https://doi.org/10.5194/essd-13-713-2021, 2021
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This paper describes a dataset consisting of 47 hyperspectral-reflectance measurements of plastic litter samples. The plastic litter samples include virgin and real samples. They were measured in dry conditions, and a selection of the samples were also measured in wet conditions and submerged in a water tank. The dataset can be used to better understand the effect of water absorption on the plastics and develop algorithms to detect and characterize marine plastics.
Susannah Rennie, Klaus Goergen, Christoph Wohner, Sander Apweiler, Johannes Peterseil, and John Watkins
Earth Syst. Sci. Data, 13, 631–644, https://doi.org/10.5194/essd-13-631-2021, https://doi.org/10.5194/essd-13-631-2021, 2021
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This paper describes a pan-European climate service data product intended for ecological researchers. Access to regional climate scenario data will save ecologists time, and, for many, it will allow them to work with data resources that they will not previously have used due to a lack of knowledge and skills to access them. Providing easy access to climate scenario data in this way enhances long-term ecological research, for example in general regional climate change or impact assessments.
Martin Strohmeier, Xavier Olive, Jannis Lübbe, Matthias Schäfer, and Vincent Lenders
Earth Syst. Sci. Data, 13, 357–366, https://doi.org/10.5194/essd-13-357-2021, https://doi.org/10.5194/essd-13-357-2021, 2021
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Flight data have been used widely for research by academic researchers and (supra)national institutions. Example domains range from epidemiology (e.g. examining the spread of COVID-19 via air travel) to economics (e.g. use as proxy for immediate forecasting of the state of a country's economy) and Earth sciences (climatology in particular). Until now, accurate flight data have been available only in small pieces from closed, proprietary sources. This work changes this with a crowdsourced effort.
Jinshi Jian, Rodrigo Vargas, Kristina Anderson-Teixeira, Emma Stell, Valentine Herrmann, Mercedes Horn, Nazar Kholod, Jason Manzon, Rebecca Marchesi, Darlin Paredes, and Ben Bond-Lamberty
Earth Syst. Sci. Data, 13, 255–267, https://doi.org/10.5194/essd-13-255-2021, https://doi.org/10.5194/essd-13-255-2021, 2021
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Field soil-to-atmosphere CO2 flux (soil respiration, Rs) observations were compiled into a global database (SRDB) a decade ago. Here, we restructured and updated the database to the fifth version, SRDB-V5, with data published through 2017 included. SRDB-V5 aims to be a data framework for the scientific community to share seasonal to annual field Rs measurements, and it provides opportunities for the scientific community to better understand the spatial and temporal variability of Rs.
Robert A. Rohde and Zeke Hausfather
Earth Syst. Sci. Data, 12, 3469–3479, https://doi.org/10.5194/essd-12-3469-2020, https://doi.org/10.5194/essd-12-3469-2020, 2020
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A global land and ocean temperature record was created by combining the Berkeley Earth monthly land temperature field with a newly interpolated version of the HadSST3 ocean dataset. The resulting dataset covers the period from 1850 to present.
This paper describes the methods used to create that combination and compares the results to other estimates of global temperature and the associated recent climate change, giving similar results.
Igor Savin, Valery Mironov, Konstantin Muzalevskiy, Sergey Fomin, Andrey Karavayskiy, Zdenek Ruzicka, and Yuriy Lukin
Earth Syst. Sci. Data, 12, 3481–3487, https://doi.org/10.5194/essd-12-3481-2020, https://doi.org/10.5194/essd-12-3481-2020, 2020
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This article presents a dielectric database of organic Arctic soils. This database was created based on dielectric measurements of seven samples of organic soils collected in various parts of the Arctic tundra. The created database can serve not only as a source of experimental data for the development of new soil dielectric models for the Arctic tundra but also as a source of training data for artificial intelligence satellite algorithms of soil moisture retrievals based on neural networks.
Pierre Friedlingstein, Michael O'Sullivan, Matthew W. Jones, Robbie M. Andrew, Judith Hauck, Are Olsen, Glen P. Peters, Wouter Peters, Julia Pongratz, Stephen Sitch, Corinne Le Quéré, Josep G. Canadell, Philippe Ciais, Robert B. Jackson, Simone Alin, Luiz E. O. C. Aragão, Almut Arneth, Vivek Arora, Nicholas R. Bates, Meike Becker, Alice Benoit-Cattin, Henry C. Bittig, Laurent Bopp, Selma Bultan, Naveen Chandra, Frédéric Chevallier, Louise P. Chini, Wiley Evans, Liesbeth Florentie, Piers M. Forster, Thomas Gasser, Marion Gehlen, Dennis Gilfillan, Thanos Gkritzalis, Luke Gregor, Nicolas Gruber, Ian Harris, Kerstin Hartung, Vanessa Haverd, Richard A. Houghton, Tatiana Ilyina, Atul K. Jain, Emilie Joetzjer, Koji Kadono, Etsushi Kato, Vassilis Kitidis, Jan Ivar Korsbakken, Peter Landschützer, Nathalie Lefèvre, Andrew Lenton, Sebastian Lienert, Zhu Liu, Danica Lombardozzi, Gregg Marland, Nicolas Metzl, David R. Munro, Julia E. M. S. Nabel, Shin-Ichiro Nakaoka, Yosuke Niwa, Kevin O'Brien, Tsuneo Ono, Paul I. Palmer, Denis Pierrot, Benjamin Poulter, Laure Resplandy, Eddy Robertson, Christian Rödenbeck, Jörg Schwinger, Roland Séférian, Ingunn Skjelvan, Adam J. P. Smith, Adrienne J. Sutton, Toste Tanhua, Pieter P. Tans, Hanqin Tian, Bronte Tilbrook, Guido van der Werf, Nicolas Vuichard, Anthony P. Walker, Rik Wanninkhof, Andrew J. Watson, David Willis, Andrew J. Wiltshire, Wenping Yuan, Xu Yue, and Sönke Zaehle
Earth Syst. Sci. Data, 12, 3269–3340, https://doi.org/10.5194/essd-12-3269-2020, https://doi.org/10.5194/essd-12-3269-2020, 2020
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The Global Carbon Budget 2020 describes the data sets and methodology used to quantify the emissions of carbon dioxide and their partitioning among the atmosphere, land, and ocean. These living data are updated every year to provide the highest transparency and traceability in the reporting of CO2, the key driver of climate change.
Jin Ma, Ji Zhou, Frank-Michael Göttsche, Shunlin Liang, Shaofei Wang, and Mingsong Li
Earth Syst. Sci. Data, 12, 3247–3268, https://doi.org/10.5194/essd-12-3247-2020, https://doi.org/10.5194/essd-12-3247-2020, 2020
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Land surface temperature is an important parameter in the research of climate change and many land surface processes. This article describes the development and testing of an algorithm for generating a consistent global long-term land surface temperature product from 20 years of NOAA AVHRR radiance data. The preliminary validation results indicate good accuracy of this new long-term product, which has been designed to simplify applications and support the scientific research community.
Clara Lázaro, Maria Joana Fernandes, Telmo Vieira, and Eliana Vieira
Earth Syst. Sci. Data, 12, 3205–3228, https://doi.org/10.5194/essd-12-3205-2020, https://doi.org/10.5194/essd-12-3205-2020, 2020
Short summary
Short summary
In satellite altimetry (SA), the wet tropospheric correction (WTC) accounts for the path delay induced mainly by atmospheric water vapour. In coastal regions, the accuracy of the WTC determined by the on-board radiometer deteriorates. The GPD+ methodology, developed by the University of Porto in the remit of ESA-funded projects, computes improved WTCs for SA. Global enhanced products are generated for all past and operational altimetric missions, forming a relevant dataset for coastal altimetry.
Magí Franquesa, Melanie K. Vanderhoof, Dimitris Stavrakoudis, Ioannis Z. Gitas, Ekhi Roteta, Marc Padilla, and Emilio Chuvieco
Earth Syst. Sci. Data, 12, 3229–3246, https://doi.org/10.5194/essd-12-3229-2020, https://doi.org/10.5194/essd-12-3229-2020, 2020
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Short summary
The article presents a database of reference sites for the validation of burned area products. We have compiled 2661 reference files from different international projects. The paper describes the methods used to generate and standardize the data. The Burned Area Reference Data (BARD) is publicly available and will facilitate the arduous task of validating burned area algorithms.
Cited articles
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, 2341–2358, 2000.
Ball, J. E., Anderson, D. T., and Chan, C. S.: Comprehensive survey of deep
learning in remote sensing: theories, tools, and challenges for the
community, J. Appl. Remote Sens., 11, 042609, https://doi.org/10.1117/1.JRS.11.042609, 2017.
Barker, H. W. and Li, Z.: Interpreting shortwave albedo-transmittance plots:
True or apparent anomalous absorption?, Geophys. Res. Lett., 24,
2023–2026, 1997.
Betts, A. K., Zhao, M., Dirmeyer, P., and Beljaars, A.: Comparison of ERA40
and NCEP/DOE near-surface data sets with other ISLSCP-II data sets, J.
Geophys. Res.-Atmos., 111, D22S04, https://doi.org/10.1029/2006JD007174, 2006.
Bisht, G. and Bras, R. L.: Estimation of net radiation from the MODIS data
under all sky conditions: Southern Great Plains case study, Remote Sens.
Environ., 114, 1522–1534, 2010.
Bisht, G., Venturini, V., Islam, S., and Jiang, L.: Estimation of the net
radiation using MODIS (Moderate Resolution Imaging Spectroradiometer) data
for clear sky days, Remote Sens. Environ., 97, 52–67, 2005.
Box, J. E. and Rinke, A.: Evaluation of Greenland ice sheet surface climate
in the HIRHAM regional climate model using automatic weather station data,
J. Climate, 16, 1302–1319, 2003.
Chen, F., Crow, W. T., Colliander, A., Cosh, M. H., Jackson, T. J.,
Bindlish, R., Reichle, R. H., Chan, S. K., Bosch, D. D., Starks, P. J.,
Goodrich, D. C., and Seyfried, M. S.: Application of Triple Collocation in
Ground-Based Validation of Soil Moisture Active/Passive (SMAP) Level 2 Data
Products, IEEE J. Sel. Top. Appl., 10, 489–502, https://doi.org/10.1109/JSTARS.2016.2569998, 2017.
Chen, J., He, T., Jiang, B., and Liang, S.: Estimation of all-sky all-wave
daily net radiation at high latitudes from MODIS data, Remote Sens.
Environ., 245, 111842, https://doi.org/10.1016/j.rse.2020.111842, 2020.
Cheng, J. and Liang, S.: Global estimates for high-spatial-resolution
clear-sky land surface upwelling longwave radiation from MODIS data, IEEE
T. Geosci. Remote, 54, 4115–4129, 2016.
Cheng, J., Liang, S., Wang, W., and Guo, Y.: An efficient hybrid method for
estimating clear-sky surface downward longwave radiation from MODIS data,
J. Geophys. Res.-Atmos., 122, 2616–2630, https://doi.org/10.1002/2016JD026250, 2017.
Crow, W. T., Berg, A. A., Cosh, M. H., Loew, A., Mohanty, B. P., Panciera,
R., de Rosnay, P., Ryu, D., and Walker, J. P.: Upscaling sparse ground-based
soil moisture observations for the validation of coarse-resolution satellite
soil moisture products, Rev. Geophys., 50, RG2002, https://doi.org/10.1029/2011RG000372, 2012.
Delgado-Bonal, A., Marshak, A., Yang, Y., and Holdaway, D.: Analyzing
changes in the complexity of climate in the last four decades using MERRA-2
radiation data, Sci. Rep., 10, 922, https://doi.org/10.1038/s41598-020-57917-8,
2020.
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, https://doi.org/10.1175/jtech-d-12-00136.1, 2013.
Doelling, D. R., Sun, M., Nguyen, L. T., 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.
Draper, C., Reichle, R., de Jeu, R., Naeimi, V., Parinussa, R., and Wagner,
W.: Estimating root mean square errors in remotely sensed soil moisture over
continental scale domains, Remote Sens. Environ., 137, 288–298,
2013.
Fillmore, D., Rutan, D., Kato, S., Rose, F., and Caldwell, T.: Evaluation of aerosol optical depths and clear-sky radiative fluxes of the CERES Edition 4.1 SYN1deg data product, Atmos. Chem. Phys. Discuss. [preprint], https://doi.org/10.5194/acp-2021-283, in review, 2021.
Gelaro, R., McCarty, W., Suárez, M. J., Todling, R., Molod, A., Takacs,
L., Randles, C. A., Darmenov, A., Bosilovich, M. G., and Reichle, R.: The
modern-era retrospective analysis for research and applications, version 2
(MERRA-2), J. Climate, 30, 5419–5454, 2017a.
Gelaro, R., McCarty, W., Suárez, M. J., Todling, R., Molod, A., Takacs,
L., Randles, C. A., Darmenov, A., Bosilovich, M. G., Reichle, R., Wargan,
K., Coy, L., Cullather, R., Draper, C., Akella, S., Buchard, V., Conaty, A.,
da Silva, A. M., Gu, W., Kim, G.-K., Koster, R., Lucchesi, R., Merkova, D.,
Nielsen, J. E., Partyka, G., Pawson, S., Putman, W., Rienecker, M.,
Schubert, S. D., Sienkiewicz, M., and Zhao, B.: The Modern-Era Retrospective
Analysis for Research and Applications, Version 2 (MERRA-2), J.
Climate, 30, 5419–5454, https://doi.org/10.1175/jcli-d-16-0758.1, 2017b.
Géron, A.: Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow: Concepts, tools, and techniques to build intelligent systems, O'Reilly Media, Inc., Sebastopol, CA, USA, 2019.
Gruber, A., Su, C.-H., Zwieback, S., Crow, W., Dorigo, W., and Wagner, W.:
Recent advances in (soil moisture) triple collocation analysis,
Int. J. Appl. Earth Obs., 45,
200–211, 2016.
Guo, X., Yao, Y., Zhang, Y., Lin, Y., and Bei, X.: Discrepancies in the
Simulated Global Terrestrial Latent Heat Flux from GLASS and MERRA-2 Surface
Net Radiation Products, Remote Sensing, 12, 2763, https://doi.org/10.3390/rs12172763, 2020.
Hakuba, M. Z., Folini, D., Sanchez-Lorenzo, A., and Wild, M.: Spatial
representativeness of ground-based solar radiation measurements, J.
Geophys. Res.-Atmos., 118, 8585–8597, 2013.
Hao, D., Wen, J., Xiao, Q., Wu, S., Lin, X., Dou, B., You, D., and Tang, Y.:
Simulation and analysis of the topographic effects on snow-free albedo over
rugged terrain, Remote Sensing, 10, 278, https://doi.org/10.1016/j.rse.2019.111320, 2018.
Hao, D., Asrar, G., Zeng, Y., Zhu, Q., Wen, J., Xiao, Q., and Chen, M.:
Estimating hourly land surface downward shortwave and photosynthetically
active radiation from DSCOVR/EPIC observations, Remote Sens.
Environ., 232, 111320, https://doi.org/10.3390/rs10020278, 2019.
He, K., Zhang, X., Ren, S., and Sun, J.: Deep Residual Learning for Image
Recognition, 2016 IEEE Conference on Computer Vision and Pattern Recognition
(CVPR), 770–778, https://doi.org/10.1109/CVPR.2016.90, 2016.
He, T., Liang, S., Wang, D., Shi, Q., and Goulden, M. L.: Estimation of
high-resolution land surface net shortwave radiation from AVIRIS data:
Algorithm development and preliminary results, Remote Sens.
Environ., 167, 20–30, 2015.
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A.,
Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., and Schepers, D.:
The ERA5 global reanalysis, Q. J. Roy. Meteor.
Soc., 146, 1999–2049, 2020.
Huang, G., Ma, M., Liang, S., Liu, S., and Li, X.: A LUT-based approach to
estimate surface solar irradiance by combining MODIS and MTSAT data, J.
Geophys. Res-Atmos., 116, 6255–6265, https://doi.org/10.1029/2011JD016120, 2011.
Huang, G., Li, X., Huang, C., Liu, S., Ma, Y., and Chen, H.:
Representativeness errors of point-scale ground-based solar radiation
measurements in the validation of remote sensing products, Remote Sens.
Environ., 181, 198–206, 2016.
Huang, G., Li, X., Lu, N., Wang, X., and He, T.: A General Parameterization
Scheme for the Estimation of Incident Photosynthetically Active Radiation
Under Cloudy Skies, IEEE T. Geosci. Remote, 116, D22201, https://doi.org/10.1029/2011JD016120,
2020.
Jia, A., Jiang, B., Liang, S., Zhang, X., and Ma, H.: Validation and
spatiotemporal analysis of CERES surface net radiation product, Remote
Sensing, 8, 90, https://doi.org/10.3390/rs8020090, 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.
Jiang, B., Zhang, Y., Liang, S., Zhang, X., and Xiao, Z.: Surface Daytime
Net Radiation Estimation Using Artificial Neural Networks, Remote Sensing,
6, 11031–11050, 2014.
Jiang, B., Zhang, Y., Liang, S., Wohlfahrt, G., Arain, A., Cescatti, A.,
Georgiadis, T., Jia, K., Kiely, G., Lund, M., Montagnani, L., Magliulo, V.,
Ortiz, P. S., Oechel, W., Vaccari, F. P., Yao, Y., and Zhang, X.: Empirical
estimation of daytime net radiation from shortwave radiation and ancillary
information, Agr. Forest Meteorol., 211–212, 23–36, 2015.
Jiang, B., Liang, S., Ma, H., Zhang, X., Xiao, Z., Zhao, X., Jia, K., Yao,
Y., and Jia, A.: GLASS daytime all-wave net radiation product: Algorithm
development and preliminary validation, Remote Sensing, 8, 222, https://doi.org/10.3390/rs8030222, 2016.
Jiang, B., Liang, S., Jia, A., Xu, J., Zhang, X., Xiao, Z., Zhao, X., Jia,
K., and Yao, Y.: Validation of the Surface Daytime Net Radiation Product
From Version 4.0 GLASS Product Suite, IEEE Geosci. Remote Sens.
Lett., 16, 509–513, 2018.
Jiang, H., Lu, N., Qin, J., and Yao, L.: Surface global and diffuse solar radiation over China acquired from geostationary Multi-functional Transport Satellite data, Earth Syst. Sci. Data Discuss. [preprint], https://doi.org/10.5194/essd-2019-209, 2019a.
Jiang, H., Lu, N., Qin, J., Tang, W., and Yao, L.: A deep learning algorithm
to estimate hourly global solar radiation from geostationary satellite data,
Renew. Sust.e Energ. Rev., 114, 109327, https://doi.org/10.1016/j.rser.2019.109327, 2019b.
Jiang, H., Lu, N., Qin, J., and Yao, L.: Hourly 5-km surface total and
diffuse solar radiation in China, 2007–2018, Sci. Data, 7, 311, https://doi.org/10.1038/s41597-020-00654-4, 2020a.
Jiang, H., Lu, N., Huang, G., Yao, L., Qin, J., and Liu, H.: Spatial scale
effects on retrieval accuracy of surface solar radiation using satellite
data, Appl. Energ., 270, 115178, https://doi.org/10.1016/j.apenergy.2020.115178, 2020b.
Jiao, Z., Yan, G., Zhao, J., Wang, T., and Chen, L.: Estimation of surface
upward longwave radiation from MODIS and VIIRS clear-sky data in the Tibetan
Plateau, Remote Sens. Environ., 162, 221–237, 2015.
Jung, M., Koirala, S., Weber, U., Ichii, K., Gans, F., Camps-Valls, G.,
Papale, D., Schwalm, C., Tramontana, G., and Reichstein, M.: The FLUXCOM
ensemble of global land-atmosphere energy fluxes, Sci. Data, 6, 1–14,
2019a.
Jung, M., Koirala, S., Weber, U., Ichii, K., Gans, F., Camps-Valls, G.,
Papale, D., Schwalm, C., Tramontana, G., and Reichstein, M.: The FLUXCOM
ensemble of global land-atmosphere energy fluxes, Sci. Data, 6, 74, https://doi.org/10.1038/s41597-019-0076-8, 2019b.
Kanamitsu, M., Ebisuzaki, W., Woollen, J., Yang, S.-K., Hnilo, J., Fiorino,
M., and Potter, G.: Ncep–doe amip-ii reanalysis (r-2), B.
Am. Meteorol. Soc., 83, 1631–1644, 2002.
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.
Kobayashi, S., Ota, Y., Harada, Y., Ebita, A., Moriya, M., Onoda, H., Onogi,
K., Kamahori, H., Kobayashi, C., and Endo, H.: The JRA-55 reanalysis:
General specifications and basic characteristics, J.
Meteorol. Soc. Jpn. Ser. II, 93, 5–48, 2015.
Letu, H., Shi, J., Li, M., Wang, T., Shang, H., Lei, Y., Ji, D., Wen, J., Yang, K., and Chen, L.: A review of the estimation of downward surface shortwave radiation based on satellite data: Methods, progress and problems, Science China Earth Sciences, 63, 774–789, https://doi.org/10.1007/s11430-019-9589-0, 2020.
Li, T. and Cheng, X.: Estimating daily full-coverage surface ozone
concentration using satellite observations and a spatiotemporally embedded
deep learning approach, Int. J. Appl. Earth Obs., 101, 102356, https://doi.org/10.1016/j.jag.2021.102356, 2021.
Li, T., Wang, Y., and Yuan, Q.: Remote Sensing Estimation of Regional NO2
via Space-Time Neural Networks, Remote Sensing, 12, 2514, https://doi.org/10.3390/rs12162514, 2020a.
Li, T., Shen, H., Yuan, Q., and Zhang, L.: Geographically and temporally
weighted neural networks for satellite-based mapping of ground-level PM2.5,
ISPRS J. Photogramm, 167, 178–188, 2020b.
Liang, S., Wang, K., Zhang, X., and Wild, M.: Review on estimation of land
surface radiation and energy budgets from ground measurement, remote sensing
and model simulations, IEEE J. Sel. Top. Appl., 3, 225–240, 2010.
Liang, S., Wang, D., He, T., and Yu, Y.: Remote sensing of earth's energy
budget: synthesis and review, Int. J. Digit. Earth, 12,
737–780, 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, 2020.
Liang, S., Cheng, J., Jia, K., Jiang, B., Liu, Q., Xiao, Z., Yao, Y., Yuan, W., Zhang, X., Zhao, X., and Zhou, J.: 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, 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 Sensing, 11, 2843, https://doi.org/10.3390/rs11232843, 2019.
Loeb, N. G., Doelling, D. R., Wang, H., Su, W., Nguyen, C., Corbett, J. G.,
Liang, L., 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, 2018.
Lu, N., Qin, J., Yang, K., and Sun, J.: A simple and efficient algorithm to
estimate daily global solar radiation from geostationary satellite data,
Energy, 36, 3179–3188, 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.
McColl, K. A., Vogelzang, J., Konings, A. G., Entekhabi, D., Piles, M., and
Stoffelen, A.: Extended triple collocation: Estimating errors and
correlation coefficients with respect to an unknown target, Geophys.
Res. Lett., 41, 6229–6236, 2014.
Ohmura, A., Dutton, E. G., Forgan, B., Fröhlich, C., Gilgen, H., Hegner,
H., Heimo, A., König-Langlo, G., McArthur, B., and Müller, G.:
Baseline Surface Radiation Network (BSRN/WCRP): New precision radiometry for
climate research, B. Am. Meteorol. Soc., 79,
2115–2136, 1998.
Pedelty, J., Devadiga, S., Masuoka, E., Brown, M., Pinzon, J., Tucker, C.,
Vermote, E., Prince, S., Nagol, J., Justice, C., Roy, D., Junchang, J.,
Schaaf, C., Jicheng, L., Privette, J., and Pinheiro, A.: Generating a
Long-term Land Data Record from the AVHRR and MODIS Instruments, 2007 IEEE
International Geoscience and Remote Sensing Symposium, 23–28 July 2007,
1021–1025, https://doi.org/10.1109/IGARSS.2007.4422974, 2007.
Philipona, R., Fröhlich, C., Dehne, K., DeLuisi, J., Augustine, J.,
Dutton, E., Nelson, D., Forgan, B., Novotny, P., and Hickey, J.: The
Baseline Surface Radiation Network pyrgeometer round-robin calibration
experiment, J. Atmos. Ocean. Tech., 15, 687–696,
1998.
Pinker, R. and Laszlo, I.: Modeling surface solar irradiance for satellite
applications on a global scale, J. Appl. Meteorol.
Clim., 31, 194–211, 1992.
Qin, J., Yang, K., Liang, S., and Tang, W.: Estimation of Daily Mean
Photosynthetically Active Radiation under All-Sky Conditions Based on
Relative Sunshine Data, J. Appl. Meteorol. Clim.gy, 51,
150–160, 2012.
Reichstein, M., Camps-Valls, G., Stevens, B., Jung, M., Denzler, J., and
Carvalhais, N.: Deep learning and process understanding for data-driven
Earth system science, Nature, 566, 195–204, 2019.
Román, M. O., Schaaf, C. B., Woodcock, C. E., Strahler, A. H., Yang, X.,
Braswell, R. H., Curtis, P. S., Davis, K. J., Dragoni, D., and Goulden, M.
L.: The MODIS (Collection V005) BRDF/albedo product: Assessment of spatial
representativeness over forested landscapes, Remote Sens. Environ.,
113, 2476–2498, 2009.
Romanuke, V.: Appropriate Number and Allocation of RELUS in Convolutional Neural Networks. Research Bulletin of the National Technical University of Ukraine Kyiv Politechnic Institute, 69–78, https://doi.org/10.20535/1810-0546.2017.1.88156, 2017.
Rossow, W. and Zhang, Y. C.: Calculation of surface and top of atmosphere
radiative fluxes from physical quantities based on ISCCP data sets: 2.
Validation and first results, J. Geophys. Res.-Atmos.,
100, 1167–1197, 1995.
Saha, S., Moorthi, S., Pan, H.-L., Wu, X., Wang, J., Nadiga, S., Tripp, P.,
Kistler, R., Woollen, J., and Behringer, D.: The NCEP climate forecast
system reanalysis, B. Am. Meteor. Soc., 91,
1015–1058, 2010.
Sellers, P., Dickinson, R., Randall, D., Betts, A., Hall, F., Berry, J.,
Collatz, G., Denning, A., Mooney, H., and Nobre, C.: Modeling the exchanges
of energy, water, and carbon between continents and the atmosphere, Science,
275, 502–509, 1997.
Shupe, M. D., Walden, V. P., Eloranta, E., Uttal, T., Campbell, J. R.,
Starkweather, S. M., and Shiobara, M.: Clouds at Arctic atmospheric
observatories. Part I: Occurrence and macrophysical properties, J.
Appl. Meteorol. Clim., 50, 626–644, 2011.
Song, Z., Liang, S., Wang, D., Zhou, Y., and Jia, A.: Long-term record of
top-of-atmosphere albedo over land generated from AVHRR data, Remote Sens.
Environ., 211, 71–88, 2018.
Stackhouse Jr., P., Gupta, S., Cox, S., Chiacchio, M., and Mikovitz, J.: The WCRP/GEWEX surface radiation budget project release 2: An assessment of surface fluxes at 1 degree resolution, in: IRS 2000: Current problems in atmospheric radiation, edited by: Smith, W. L. and Timofeyev, Y. M., International Radiation Symposium, St. Petersburg, Russia (2000), 24–29, 2000.
Steffen, K. and Box, J.: Surface climatology of the Greenland ice sheet:
Greenland Climate Network 1995–1999, J. Geophys. Res.-Atmos., 106, 33951–33964, 2001.
Stoffelen, A.: Toward the true near-surface wind speed: Error modeling and
calibration using triple collocation, J. Geophys. Res.-Oceans, 103, 7755–7766, 1998.
Stokes, G. M. and Schwartz, S. E.: The Atmospheric Radiation Measurement
(ARM) Program: Programmatic background and design of the cloud and radiation
test bed, B. Am. Meteor. Soc., 75, 1201–1222,
1994.
Tang, W., Yang, K., Qin, J., and Min, M.: Development of a 50-year daily
surface solar radiation dataset over China, Science China Earth Sciences,
56, 1555–1565, 2013.
Tang, W., Qin, J., Yang, K., Niu, X., Min, M., and Liang, S.: An efficient
algorithm for calculating photosynthetically active radiation with MODIS
products, Remote Sens. Enviro., 194, 146–154, 2017.
Tang, W., Yang, K., Qin, J., Li, X., and Niu, X.: A 16-year dataset (2000–2015) of high-resolution (3 h, 10 km) global surface solar radiation, Earth Syst. Sci. Data, 11, 1905–1915, https://doi.org/10.5194/essd-11-1905-2019, 2019.
Tang, W.-J., Yang, K., Qin, J., Cheng, C. C. K., and He, J.: Solar radiation trend across China in recent decades: a revisit with quality-controlled data, Atmos. Chem. Phys., 11, 393–406, https://doi.org/10.5194/acp-11-393-2011, 2011.
Verma, M., Fisher, J. B., Mallick, K., Ryu, Y., Kobayashi, H., Guillaume,
A., Moore, G., Ramakrishnan, L., Hendrix, V., and Wolf, S.: Global surface
net-radiation at 5 km from MODIS Terra, Remote Sensing, 8, 739, https://doi.org/10.3390/rs8090739, 2016.
Vermote, E. and Kaufman, Y.: Absolute calibration of AVHRR visible and
near-infrared channels using ocean and cloud views, Int. J.
Remote Sens., 16, 2317–2340, 1995.
Wang, B., Yuan, Q., Yang, Q., Zhu, L., Li, T., and Zhang, L.: Estimate
hourly PM2.5 concentrations from Himawari-8 TOA reflectance directly using
Geo-intelligent long short-term memory network, Environ. Pollut.,
271, 116327, https://doi.org/10.1016/j.envpol.2020.116327, 2020.
Wang, D. and Liang, S.: Estimating top-of-atmosphere daily reflected
shortwave radiation flux over land from MODIS data, IEEE T.
Geosci. Remote, 55, 4022–4031, 2017.
Wang, D., Liang, S., He, T., and Shi, Q.: Estimation of daily surface
shortwave net radiation from the combined MODIS data, IEEE T.
Geosci. Remote, 53, 5519–5529, 2015a.
Wang, D., Liang, S., He, T., and Shi, Q.: Estimating clear-sky all-wave net
radiation from combined visible and shortwave infrared (VSWIR) and thermal
infrared (TIR) remote sensing data, Remote Sens. Environ., 167,
31–39, 2015b.
Wang, D., Liang, S., Zhang, Y., Gao, X., and Jia, A.: A New Set of MODIS
Land Products (MCD18): Downward Shortwave Radiation and Photosynthetically
Active Radiation, Remote Sensing, 12, 168, https://doi.org/10.3390/rs12010168, 2020.
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, K. and Liang, S.: Estimation of daytime net radiation from shortwave
radiation measurements and meteorological observations, J. Appl.
Meteorol. Clim., 48, 634–643, 2009.
Wang, Y., Jiang, B., Liang, S., Wang, D., He, T., Wang, Q., Zhao, X., and
Xu, J.: Surface Shortwave net radiation estimation from Landsat TM/ETM+
data using four machine learning algorithms, Remote Sensing, 11, 2847, https://doi.org/10.3390/rs11232847, 2019.
Wei, Y., Zhang, X., Hou, N., Zhang, W., Jia, K., and Yao, Y.: Estimation of
surface downward shortwave radiation over China from AVHRR data based on
four machine learning methods, Sol. Energy, 177, 32–46, 2019.
Wielicki, B. A., Barkstrom, B. R., Harrison, E. F., Lee, R. B., III, Smith,
G. L., and Cooper, J. E.: Clouds and the Earth's Radiant Energy System
(CERES): An Earth Observing System Experiment, B. Am.
Meteorol. Soc., 77, 853-868, https://doi.org/10.1175/1520-0477(1996)077<0853:catere>2.0.co;2, 1996.
Wilson, K., Goldstein, A., Falge, E., Aubinet, M., Baldocchi, D., Berbigier,
P., Bernhofer, C., Ceulemans, R., Dolman, H., and Field, C.: Energy balance
closure at FLUXNET sites, Agr. Forest Meteorol., 113, 223–243,
2002.
Wu, H., Yang, Q., Liu, J., and Wang, G.: A spatiotemporal deep fusion model
for merging satellite and gauge precipitation in China, J.
Hydrol., 584, 124664, https://doi.org/10.1016/j.jhydrol.2020.124664, 2020.
Wu, X., Xiao, Q., Wen, J., and You, D.: Direct comparison and triple
collocation: Which is more reliable in the validation of coarse-scale
satellite surface albedo products, J. Geophys. Res.-Atmos., 124, 5198–5213, 2019.
Wyser, K., O'Hirok, W., Gautier, C., and Jones, C.: Remote sensing of
surface solar irradiance with corrections for 3-D cloud effects, Remote
Sens. Environ., 80, 272–284, https://doi.org/10.1016/S0034-4257(01)00309-1, 2002.
Xi, B., Qiu, S., Dong, X., Smith Jr, W. L., Minnis, P., and Sun-Mack, S.:
Evaluation of CERES-MODIS Ed4 Cloud Macrophysical Properties using 11-yr ARM
Ground-based Observations over the Arctic, AGUFM, 2018, A11I–2348, 2018.
Xiao, Z., Liang, S., Tian, X., Jia, K., Yao, Y., and Jiang, B.:
Reconstruction of long-term temporally continuous NDVI and surface
reflectance from AVHRR data, IEEE J. Sel. Top. Appl., 10, 5551–5568, 2017.
Xu, J., Liang, S., and Jiang, B.: Daily surface all-wave net radiation over
global land (1981–2019) from AVHRR data, Zenodo [data set],
https://doi.org/10.5281/zenodo.5546316, 2021.
Xu, J., Jiang, B., Liang, S., Li, X., Wang, Y., Peng, J., Chen, H., Liang,
H., and Li, S.: Generating a High-Resolution Time-Series Ocean Surface Net
Radiation Product by Downscaling J-OFURO3, IEEE T. Geosci.
Remote, 59, 2794–2809, https://doi.org/10.1109/TGRS.2020.3021585, 2020.
Yamamoto, S., Saigusa, N., Gamo, M., Fujinuma, Y., Inoue, G., and Hirano,
T.: Findings through the AsiaFlux network and a view toward the future,
J. Geograph. Sci., 15, 142–148, 2005.
Yang, D.: Quantifying the spatial scale mismatch between satellite-derived
solar irradiance and in situ measurements: A case study using CERES synoptic
surface shortwave flux and the Oklahoma Mesonet, J. Renew.
Sustain. Energ., 12, 056104, https://doi.org/10.1063/5.0025771, 2020.
Yang, F. and Cheng, J.: A framework for estimating cloudy sky surface
downward longwave radiation from the derived active and passive cloud
property parameters, Remote Sens. Environ., 248, 111972, https://doi.org/10.1016/j.rse.2020.11197, 2020.
Yang, L., Zhang, X., Liang, S., Yao, Y., Jia, K., and Jia, A.: Estimating
surface downward shortwave radiation over China based on the gradient
boosting decision tree method, Remote Sens., 10, 185, https://doi.org/10.3390/rs10020185, 2018.
Yin, Z., Wu, P., Foody, G. M., Wu, Y., Liu, Z., Du, Y., and Ling, F.:
Spatiotemporal Fusion of Land Surface Temperature Based on a Convolutional
Neural Network, IEEE T. Geosci. Remote, 59, 1808–1822, https://doi.org/10.1109/TGRS.2020.2999943, 2020.
Yuan, Q., Xu, H., Li, T., Shen, H., and Zhang, L.: Estimating surface soil
moisture from satellite observations using a generalized regression neural
network trained on sparse ground-based measurements in the continental US,
J. Hydrol., 580, 124351, https://doi.org/10.1016/j.jhydrol.2019.124351, 2020a.
Yuan, Q., Shen, H., Li, T., Li, Z., Li, S., Jiang, Y., Xu, H., Tan, W.,
Yang, Q., and Wang, J.: Deep learning in environmental remote sensing:
Achievements and challenges, Remote Sens. Environ., 241, 111716, https://doi.org/10.1016/j.rse.2020.111716,
2020b.
Zhang, X., Liang, S., Wild, M., and Jiang, B.: Analysis of surface incident
shortwave radiation from four satellite products, Remote Sensing of
Environment, 165, 186–202, 2015.
Zhang, X., Lu, N., Jiang, H., and Yao, L.: Evaluation of Reanalysis Surface
Incident Solar Radiation Data in China, Sci. Rep., 10, 3494, https://doi.org/10.1038/s41598-020-60460-1, 2020.
Zhang, X., Liang, S., Wang, G., Yao, Y., Jiang, B., and Cheng, J.:
Evaluation of the reanalysis surface incident shortwave radiation products
from NCEP, ECMWF, GSFC, and JMA using satellite and surface observations,
Remote Sens., 8, 225, https://doi.org/10.3390/rs8030225, 2016.
Zhang, Y., Rossow, W. B., Lacis, A. A., Oinas, V., and Mishchenko, M. I.:
Calculation of radiative fluxes from the surface to top of atmosphere based
on ISCCP and other global data sets: Refinements of the radiative transfer
model and the input data, J. Geophys. Res.-Atmos., 109, D19105, https://doi.org/10.1029/2003JD004457,
2004.
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.
Land surface all-wave net radiation (Rn) is a key parameter in many land processes. Current...
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