Articles | Volume 14, issue 3
https://doi.org/10.5194/essd-14-1377-2022
© 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-14-1377-2022
© 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
| 
30 Mar 2022
Data description paper |
| 30 Mar 2022
| 30 Mar 2022
TimeSpec4LULC: a global multispectral time series database for training LULC mapping models with machine learning
Rohaifa Khaldi
CORRESPONDING AUTHOR
Department of Computer Science and Artificial Intelligence, Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI), University of Granada, 18071, Granada, Spain
Department of Botany, Faculty of Science, University of Granada, 18071 Granada, Spain
ENSIAS, Mohammed V University, Rabat, 10170, Morocco
Domingo Alcaraz-Segura
CORRESPONDING AUTHOR
Department of Botany, Faculty of Science, University of Granada, 18071 Granada, Spain
Andalusian Center for the Assessment and Monitoring of Global Change (CAESCG), University of Almería, 04120, Almería, Spain
iEcolab, Inter-University Institute for Earth System Research, University of Granada, 18006 Granada, Spain
Emilio Guirado
Multidisciplinary Institute for Environment Studies Ramón Margalef, University of Alicante, San Vicente del Raspeig, 03690, Spain
Yassir Benhammou
Department of Computer Science and Artificial Intelligence, Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI), University of Granada, 18071, Granada, Spain
ENSA, Hassan I University, Berrechid, 218, Morocco
Abdellatif El Afia
ENSIAS, Mohammed V University, Rabat, 10170, Morocco
Francisco Herrera
Department of Computer Science and Artificial Intelligence, Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI), University of Granada, 18071, Granada, Spain
Siham Tabik
Department of Computer Science and Artificial Intelligence, Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI), University of Granada, 18071, Granada, Spain
Related authors
No articles found.
Beatriz P. Cazorla, Javier Cabello, Andrés Reyes, Emilio Guirado, Julio Peñas, Antonio J. Pérez-Luque, and Domingo Alcaraz-Segura
Earth Syst. Sci. Data, 15, 1871–1887, https://doi.org/10.5194/essd-15-1871-2023, https://doi.org/10.5194/essd-15-1871-2023, 2023
Short summary
Short summary
This dataset provides scientists, environmental managers, and the public in general with valuable information on the first characterization of ecosystem functional diversity based on primary production developed in the Sierra Nevada (Spain), a biodiversity hotspot in the Mediterranean basin and an exceptional natural laboratory for ecological research within the Long-Term Social-Ecological Research (LTSER) network.
Beatriz P. Cazorla, Javier Cabello, Andrés Reyes, Emilio Guirado, Julio Peñas, Antonio J. Pérez-Luque, and Domingo Alcaraz-Segura
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2019-198, https://doi.org/10.5194/essd-2019-198, 2020
Revised manuscript not accepted
Short summary
Short summary
Our dataset characterizes the spatial patterns and temporal dynamics of ecosystem functioning and functional diversity in Sierra Nevada (Spain) from time-series of satellite images of vegetation greenness from 2001 to 2018. The dataset brings to scientists, managers and citizens the first characterization of the functional diversity at ecosystem level developed in a Mediterranean biodiversity hotspot, useful to improve the understanding, monitoring and management of ecosystem processes.
Related subject area
Data, Algorithms, and Models
Improved maps of surface water bodies, large dams, reservoirs, and lakes in China
The Fengyun-3D (FY-3D) global active fire product: principle, methodology and validation
A high-resolution inland surface water body dataset for the tundra and boreal forests of North America
A Central Asia hydrologic monitoring dataset for food and water security applications in Afghanistan
HOTRUNZ: an open-access 1 km resolution monthly 1910–2019 time series of interpolated temperature and rainfall grids with associated uncertainty for New Zealand
A dataset of microphysical cloud parameters, retrieved from Fourier-transform infrared (FTIR) emission spectra measured in Arctic summer 2017
A global long-term (1981–2019) daily land surface radiation budget product from AVHRR satellite data using a residual convolutional neural network
First SMOS Sea Surface Salinity dedicated products over the Baltic Sea
HomogWS-se: a century-long homogenized dataset of near-surface wind speed observations since 1925 rescued in Sweden
Mapping long-term and high-resolution global gridded photosynthetically active radiation using the ISCCP H-series cloud product and reanalysis data
Description of the China global Merged Surface Temperature version 2.0
Hyperspectral reflectance spectra of floating matters derived from Hyperspectral Imager for the Coastal Ocean (HICO) observations
Multi-site, multi-crop measurements in the soil–vegetation–atmosphere continuum: a comprehensive dataset from two climatically contrasting regions in southwestern Germany for the period 2009–2018
Full-coverage 1 km daily ambient PM2.5 and O3 concentrations of China in 2005–2017 based on a multi-variable random forest model
Median bed-material sediment particle size across rivers in the contiguous US
A flux tower dataset tailored for land model evaluation
A Landsat-derived annual inland water clarity dataset of China between 1984 and 2018
A harmonized global land evaporation dataset from model-based products covering 1980–2017
Estimating population and urban areas at risk of coastal hazards, 1990–2015: how data choices matter
Landsat-based Irrigation Dataset (LANID): 30 m resolution maps of irrigation distribution, frequency, and change for the US, 1997–2017
GRQA: Global River Water Quality Archive
A 1 km global cropland dataset from 10 000 BCE to 2100 CE
A 1 km global dataset of historical (1979–2013) and future (2020–2100) Köppen–Geiger climate classification and bioclimatic variables
SeaFlux: harmonization of air–sea CO2 fluxes from surface pCO2 data products using a standardized approach
Nitrogen deposition in the UK at 1 km resolution from 1990 to 2017
ERA5-Land: a state-of-the-art global reanalysis dataset for land applications
An all-sky 1 km daily land surface air temperature product over mainland China for 2003–2019 from MODIS and ancillary data
100 years of lake evolution over the Qinghai–Tibet Plateau
The 30 m annual land cover dataset and its dynamics in China from 1990 to 2019
Coastal complexity of the Antarctic continent
UAV-based very high resolution point cloud, digital surface model and orthomosaic of the Chã das Caldeiras lava fields (Fogo, Cabo Verde)
AQ-Bench: a benchmark dataset for machine learning on global air quality metrics
Bias-corrected and spatially disaggregated seasonal forecasts: a long-term reference forecast product for the water sector in semi-arid regions
The consolidated European synthesis of CH4 and N2O emissions for the European Union and United Kingdom: 1990–2017
The consolidated European synthesis of CO2 emissions and removals for the European Union and United Kingdom: 1990–2018
A new merged dataset for analyzing clouds, precipitation and atmospheric parameters based on ERA5 reanalysis data and the measurements of the Tropical Rainfall Measuring Mission (TRMM) precipitation radar and visible and infrared scanner
A new satellite-derived dataset for marine aquaculture areas in China's coastal region
Database of petrophysical properties of the Mid-German Crystalline Rise
Landsat-derived bathymetry of lakes on the Arctic Coastal Plain of northern Alaska
Merging ground-based sunshine duration observations with satellite cloud and aerosol retrievals to produce high-resolution long-term surface solar radiation over China
Hyperspectral-reflectance dataset of dry, wet and submerged marine litter
A climate service for ecologists: sharing pre-processed EURO-CORDEX regional climate scenario data using the eLTER Information System
Crowdsourced air traffic data from the OpenSky Network 2019–2020
A restructured and updated global soil respiration database (SRDB-V5)
The Berkeley Earth Land/Ocean Temperature Record
Dielectric database of organic Arctic soils (DDOAS)
Global Carbon Budget 2020
A global long-term (1981–2000) land surface temperature product for NOAA AVHRR
A coastally improved global dataset of wet tropospheric corrections for satellite altimetry
Development of a standard database of reference sites for validating global burned area products
Xinxin Wang, Xiangming Xiao, Yuanwei Qin, Jinwei Dong, Jihua Wu, and Bo Li
Earth Syst. Sci. Data, 14, 3757–3771, https://doi.org/10.5194/essd-14-3757-2022, https://doi.org/10.5194/essd-14-3757-2022, 2022
Short summary
Short summary
We generated China’s surface water bodies, Large Dams, Reservoirs, and Lakes (China-LDRL) dataset by analyzing all available Landsat imagery in 2019 (19\,338 images) in Google Earth Engine. The dataset provides accurate information on the geographical locations and sizes of surface water bodies, large dams, reservoirs, and lakes in China. The China-LDRL dataset will contribute to the understanding of water security and water resources management in China.
Jie Chen, Qi Yao, Ziyue Chen, Manchun Li, Zhaozhan Hao, Cheng Liu, Wei Zheng, Miaoqing Xu, Xiao Chen, Jing Yang, Qiancheng Lv, and Bingbo Gao
Earth Syst. Sci. Data, 14, 3489–3508, https://doi.org/10.5194/essd-14-3489-2022, https://doi.org/10.5194/essd-14-3489-2022, 2022
Short summary
Short summary
The potential degradation of mainstream global fire products leads to large uncertainty in the effective monitoring of wildfires and their influence. To fill this gap, we produced a Fengyun-3D (FY-3D) global active fire product with a similar spatial and temporal resolution to MODIS fire products, aiming to serve as continuity and a replacement for MODIS fire products. The FY-3D fire product is an ideal tool for global fire monitoring and can be preferably employed for fire monitoring in China.
Yijie Sui, Min Feng, Chunling Wang, and Xin Li
Earth Syst. Sci. Data, 14, 3349–3363, https://doi.org/10.5194/essd-14-3349-2022, https://doi.org/10.5194/essd-14-3349-2022, 2022
Short summary
Short summary
High-latitude water bodies differ greatly in their morphological and topological characteristics related to their formation, type, and vulnerability. In this paper, we present a water body dataset for the North American high latitudes (WBD-NAHL). Nearly 6.5 million water bodies were identified, with approximately 6 million (~90 %) of them smaller than 0.1 km2.
Amy McNally, Jossy Jacob, Kristi Arsenault, Kimberly Slinski, Daniel P. Sarmiento, Andrew Hoell, Shahriar Pervez, James Rowland, Mike Budde, Sujay Kumar, Christa Peters-Lidard, and James P. Verdin
Earth Syst. Sci. Data, 14, 3115–3135, https://doi.org/10.5194/essd-14-3115-2022, https://doi.org/10.5194/essd-14-3115-2022, 2022
Short summary
Short summary
The Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (FLDAS) global and Central Asia data streams described here generate routine estimates of snow, soil moisture, runoff, and other variables useful for tracking water availability. These data are hosted by NASA and USGS data portals for public use.
Thomas R. Etherington, George L. W. Perry, and Janet M. Wilmshurst
Earth Syst. Sci. Data, 14, 2817–2832, https://doi.org/10.5194/essd-14-2817-2022, https://doi.org/10.5194/essd-14-2817-2022, 2022
Short summary
Short summary
Long time series of temperature and rainfall grids are fundamental to understanding how these variables affects environmental or ecological patterns and processes. We present a History of Open Temperature and Rainfall with Uncertainty in New Zealand (HOTRUNZ) that is an open-access dataset that provides monthly 1 km resolution grids of rainfall and mean, minimum, and maximum daily temperatures with associated uncertainties for New Zealand from 1910 to 2019.
Philipp Richter, Mathias Palm, Christine Weinzierl, Hannes Griesche, Penny M. Rowe, and Justus Notholt
Earth Syst. Sci. Data, 14, 2767–2784, https://doi.org/10.5194/essd-14-2767-2022, https://doi.org/10.5194/essd-14-2767-2022, 2022
Short summary
Short summary
We present a dataset of cloud optical depths, effective radii and water paths from optically thin clouds observed in the Arctic around Svalbard. The data have been retrieved from infrared spectral radiance measured using a Fourier-transform infrared (FTIR) spectrometer. Besides a description of the measurements and retrieval technique, the data are put into context with results of corresponding measurements from microwave radiometer, lidar and cloud radar.
Jianglei Xu, Shunlin Liang, and Bo Jiang
Earth Syst. Sci. Data, 14, 2315–2341, https://doi.org/10.5194/essd-14-2315-2022, https://doi.org/10.5194/essd-14-2315-2022, 2022
Short summary
Short summary
Land surface all-wave net radiation (Rn) is a key parameter in many land processes. Current products have drawbacks of coarse resolutions, large uncertainty, and short time spans. A deep learning method was used to obtain global surface Rn. A long-term Rn product was generated from 1981 to 2019 using AVHRR data. The product has the highest accuracy and a reasonable spatiotemporal variation compared to three other products. Our product will play an important role in long-term climate change.
Verónica González-Gambau, Estrella Olmedo, Antonio Turiel, Cristina González-Haro, Aina García-Espriu, Justino Martínez, Pekka Alenius, Laura Tuomi, Rafael Catany, Manuel Arias, Carolina Gabarró, Nina Hoareau, Marta Umbert, Roberto Sabia, and Diego Fernández
Earth Syst. Sci. Data, 14, 2343–2368, https://doi.org/10.5194/essd-14-2343-2022, https://doi.org/10.5194/essd-14-2343-2022, 2022
Short summary
Short summary
We present the first Soil Moisture and Ocean Salinity Sea Surface Salinity (SSS) dedicated products over the Baltic Sea (ESA Baltic+ Salinity Dynamics). The Baltic+ L3 product covers 9 days in a 0.25° grid. The Baltic+ L4 is derived by merging L3 SSS with sea surface temperature information, giving a daily product in a 0.05° grid. The accuracy of L3 is 0.7–0.8 and 0.4 psu for the L4. Baltic+ products have shown to be useful, covering spatiotemporal data gaps and for validating numerical models.
Chunlüe Zhou, Cesar Azorin-Molina, Erik Engström, Lorenzo Minola, Lennart Wern, Sverker Hellström, Jessika Lönn, and Deliang Chen
Earth Syst. Sci. Data, 14, 2167–2177, https://doi.org/10.5194/essd-14-2167-2022, https://doi.org/10.5194/essd-14-2167-2022, 2022
Short summary
Short summary
To fill the key gap of short availability and inhomogeneity of wind speed (WS) in Sweden, we rescued the early paper records of WS since 1925 and built the first 10-member centennial homogenized WS dataset (HomogWS-se) for community use. An initial WS stilling and recovery before the 1990s was observed, and a strong link with North Atlantic Oscillation was found. HomogWS-se improves our knowledge of uncertainty and causes of historical WS changes.
Wenjun Tang, Jun Qin, Kun Yang, Yaozhi Jiang, and Weihao Pan
Earth Syst. Sci. Data, 14, 2007–2019, https://doi.org/10.5194/essd-14-2007-2022, https://doi.org/10.5194/essd-14-2007-2022, 2022
Short summary
Short summary
Photosynthetically active radiation (PAR) is a fundamental physiological variable for research in the ecological, agricultural, and global change fields. In this study, we produced a 35-year high-resolution global gridded PAR dataset. Compared with the well-known global satellite-based PAR product of the Earth's Radiant Energy System (CERES), our PAR product was found to be a more accurate dataset with higher resolution.
Wenbin Sun, Yang Yang, Liya Chao, Wenjie Dong, Boyin Huang, Phil Jones, and Qingxiang Li
Earth Syst. Sci. Data, 14, 1677–1693, https://doi.org/10.5194/essd-14-1677-2022, https://doi.org/10.5194/essd-14-1677-2022, 2022
Short summary
Short summary
The new China global Merged Surface Temperature CMST 2.0 is the updated version of CMST-Interim used in the IPCC's AR6. The updated dataset is described in this study, containing three versions: CMST2.0 – Nrec, CMST2.0 – Imax, and CMST2.0 – Imin. The reconstructed datasets significantly improve data coverage, especially in the high latitudes in the Northern Hemisphere, thus increasing the long-term trends at global, hemispheric, and regional scales since 1850.
Chuanmin Hu
Earth Syst. Sci. Data, 14, 1183–1192, https://doi.org/10.5194/essd-14-1183-2022, https://doi.org/10.5194/essd-14-1183-2022, 2022
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
Large portions of the Earth's surface are expected to experience changes in climatic conditions. The rearrangement of climate distributions can lead to serious impacts on ecological and social systems. Major climate zones are distributed in a predictable pattern and are largely defined following the Köppen climate classification. This creates an urgent need to compile a series of Köppen climate classification maps with finer spatial and temporal resolutions and improved accuracy.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
This study used remotely sensed and assimilated data to estimate all-sky land surface air temperature (Ta) using a machine learning method, and developed an all-sky 1 km daily mean land Ta product for 2003–2019 over mainland China. Validation results demonstrated that this dataset has achieved satisfactory accuracy and high spatial resolution simultaneously, which fills the current dataset gap in this field and plays an important role in studies of climate change and the hydrological cycle.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
Land surface temperature is an important parameter in the research of climate change and many land surface processes. This article describes the development and testing of an algorithm for generating a consistent global long-term land surface temperature product from 20 years of NOAA AVHRR radiance data. The preliminary validation results indicate good accuracy of this new long-term product, which has been designed to simplify applications and support the scientific research community.
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
Short summary
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
Alexakis, D. D., Grillakis, M. G., Koutroulis, A. G., Agapiou, A., Themistocleous, K., Tsanis, I. K., Michaelides, S., Pashiardis, S., Demetriou, C., Aristeidou, K., Retalis, A., Tymvios, F., and Hadjimitsis, D. G.: GIS and remote sensing techniques for the assessment of land use change impact on flood hydrology: the case study of Yialias basin in Cyprus, Nat. Hazards Earth Syst. Sci., 14, 413–426, https://doi.org/10.5194/nhess-14-413-2014, 2014. a
Aqu: MYD09A1,
https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MYD09A1?hl=en,
last access: 10 January 2022. a
Arino, O., Bicheron, P., Achard, F., Latham, J., Witt, R., and Weber, J.-L.:
The most detailed portrait of Earth, Eur. Space Agency, 136, 25–31, 2008. a
Bartholome, E. and Belward, A. S.: GLC2000: a new approach to global land cover
mapping from Earth observation data, Int. J. Remote Sens.,
26, 1959–1977, 2005. a
Bojinski, S., Verstraete, M., Peterson, T. C., Richter, C., Simmons, A., and
Zemp, M.: The concept of essential climate variables in support of climate
research, applications, and policy, B. Am. Meteorol.
Soc., 95, 1431–1443, 2014. a
Buchhorn, M., Lesiv, M., Tsendbazar, N.-E., Herold, M., Bertels, L., and Smets,
B.: Copernicus global land cover layers – collection 2, Remote Sens., 12,
1044, https://doi.org/10.3390/rs12061044, 2020. a
Cao, W., Wang, D., Li, J., Zhou, H., Li, L., and Li, Y.: Brits: Bidirectional
recurrent imputation for time series, arXiv preprint arXiv:1805.10572, 2018. a
CGL: CGLS-LC100,
https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_Landcover_100m_Proba-V-C3_Global?hl=en##description,
last access: 10 January 2022. a
de la Cruz, M., Quintana-Ascencio, P. F., Cayuela, L., Espinosa, C. I., and
Escudero, A.: Comment on “The extent of forest in dryland biomes”,
Science, 358, 2017. a
Di Gregorio, A.: Land cover classification system: classification concepts and
user manual: LCCS, vol. 2, Food & Agriculture Org., ISBN 92-5-105327-8, 2005. a
Duveiller, G., Caporaso, L., Abad-Viñas, R., Perugini, L., Grassi, G.,
Arneth, A., and Cescatti, A.: Local biophysical effects of land use and land
cover change: towards an assessment tool for policy makers, Land Use Policy,
91, 104382, https://doi.org/10.1016/j.landusepol.2019.104382, 2020. a
Feddema, J. J., Oleson, K. W., Bonan, G. B., Mearns, L. O., Buja, L. E., Meehl,
G. A., and Washington, W. M.: The importance of land-cover change in
simulating future climates, Science, 310, 1674–1678, 2005. a
Feng, M. and Bai, Y.: A global land cover map produced through integrating
multi-source datasets, Big Earth Data, 3, 191–219, 2019. a
Friedl, M. and Sulla-Menashe, D.: MCD12Q1 MODIS/Terra+Aqua Land Cover Type Yearly L3
Global 500 m SIN Grid V006, NASA EOSDIS Land Processes DAAC [data set], doi10.5067/MODIS/MCD12Q1.006, 2019. a
Fritz, S., You, L., Bun, A., See, L., McCallum, I., Schill, C., Perger, C.,
Liu, J., Hansen, M., and Obersteiner, M.: Cropland for sub-Saharan Africa: A
synergistic approach using five land cover data sets, Geophys. Res.
Lett., 38, 1–6, https://doi.org/10.1029/2010GL046213, 2011. a
Gao, Y., Liu, L., Zhang, X., Chen, X., Mi, J., and Xie, S.: Consistency
Analysis and Accuracy Assessment of Three Global 30-m Land-Cover Products
over the European Union using the LUCAS Dataset, Remote Sens., 12, 3479, https://doi.org/10.3390/rs12213479,
2020. a, b
García-Gil, D., Luengo, J., García, S., and Herrera, F.: Enabling
smart data: noise filtering in big data classification, Information Sciences,
479, 135–152, 2019. a
GAU: FAO-GAUL Level0,
https://developers.google.com/earth-engine/datasets/catalog/FAO_GAUL_2015_level0,
last access: 10 January 2022a. a
GAU: FAO-GAUL Level1,
https://developers.google.com/earth-engine/datasets/catalog/FAO_GAUL_2015_level1,
last access: 10 January 2022b. a
Gengler, S. and Bogaert, P.: Combining land cover products using a minimum
divergence and a Bayesian data fusion approach, Int. J.
Geogr. Inf. Sci., 32, 806–826, 2018. a
GFC: GFCC,
https://developers.google.com/earth-engine/datasets/catalog/NASA_MEASURES_GFCC_TC_v3?hl=en,
last access: 10 January 2022a. a
GFC: GFCH,
https://developers.google.com/earth-engine/datasets/catalog/NASA_JPL_global_forest_canopy_height_2005,
last access: 10 January 2022b. a
GFS: GFSAD 1000,
https://developers.google.com/earth-engine/datasets/catalog/USGS_GFSAD1000_V1?hl=en,
last access: 10 January 2022. a
Glo: GlobCover,
https://developers.google.com/earth-engine/datasets/catalog/ESA_GLOBCOVER_L4_200901_200912_V2_3?hl=en,
last access: 10 January 2022. a
Gómez, C., White, J. C., and Wulder, M. A.: Optical remotely sensed time
series data for land cover classification: A review, ISPRS J. Photogramm., 116, 55–72, 2016. a
Gong, P., Wang, J., Yu, L., Zhao, Y., Zhao, Y., Liang, L., Niu, Z., Huang, X.,
Fu, H., Liu, S., et al.: Finer resolution observation and monitoring of
global land cover: First mapping results with Landsat TM and ETM+ data,
Int. J. Remote Sens., 34, 2607–2654, 2013. a
Gong, P., Li, X., Wang, J., Bai, Y., Chen, B., Hu, T., Liu, X., Xu, B., Yang,
J., Zhang, W., et al.: Annual maps of global artificial impervious area
(GAIA) between 1985 and 2018, Remote Sens. Environ., 236, 111510, https://doi.org/10.1016/j.rse.2019.111510,
2020. a
Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., and Moore,
R.: Google Earth Engine: Planetary-scale geospatial analysis for everyone,
Remote Sens. Environ., 202, 18–27, https://doi.org/10.1016/j.rse.2017.06.031, 2017. a
Guirado, E., Tabik, S., Alcaraz-Segura, D., Cabello, J., and Herrera, F.:
Deep-learning versus OBIA for scattered shrub detection with Google earth
imagery: Ziziphus Lotus as case study, Remote Sensing, 9, 1220, https://doi.org/10.3390/rs9121220, 2017. a
Guirado, E., Blanco-Sacristán, J., Rodríguez-Caballero, E., Tabik, S.,
Alcaraz-Segura, D., Martínez-Valderrama, J., and Cabello, J.: Mask R-CNN
and OBIA Fusion Improves the Segmentation of Scattered Vegetation in Very
High-Resolution Optical Sensors, Sensors, 21, 320, https://doi.org/10.3390/s21010320, 2021. a
Han: Hansen,
https://developers.google.com/earth-engine/datasets/catalog/UMD_hansen_global_forest_change_2019_v1_7,
last access: 10 January 2022. a
Hansen, M. C., Potapov, P. V., Moore, R., Hancher, M., Turubanova, S. A., Tyukavina, A., Thau, D., Stehman,
S. V., Goetz, S. J., Loveland, T. R., Kommareddy, A., Egorov, A., Chini, L., Justice, C. O., and
Townshend, J. R. G.: High-Resolution Global Maps of 21st-Century Forest Cover Change, Science, 342, 850–853, https://doi.org/10.1126/science.1244693, 2013. a
Hoskins, A. J., Bush, A., Gilmore, J., Harwood, T., Hudson, L. N., Ware, C.,
Williams, K. J., and Ferrier, S.: Downscaling land-use data to provide global
estimates of five land-use classes, Ecol. Evol., 6,
3040–3055, 2016. a
Hubert-Moy, L., Thibault, J., Fabre, E., Rozo, C., Arvor, D., Corpetti, T., and
Rapinel, S.: Time-series spectral dataset for croplands in France
(2006–2017), Data in Brief, 27, 104810,
https://doi.org/10.1016/j.dib.2019.104810, 2019. a
JRC: JRC Global Surface Water,
https://developers.google.com/earth-engine/datasets/catalog/JRC_GSW1_2_GlobalSurfaceWater,
last access: 10 January 2022a. a
JRC: JRC Yearly Water,
https://developers.google.com/earth-engine/datasets/catalog/JRC_GSW1_2_YearlyHistory,
last access: 10 January 2022b. a
Jung, M., Henkel, K., Herold, M., and Churkina, G.: Exploiting synergies of
global land cover products for carbon cycle modeling, Remote Sens. Environ., 101, 534–553, 2006. a
Kennedy, C. M., Oakleaf, J. R., Theobald, D. M., Baruch-Mordo, S., and
Kiesecker, J.: Managing the middle: A shift in conservation priorities based
on the global human modification gradient, Global Change Biol., 25,
811–826, 2019. a
Kerr, J. T. and Ostrovsky, M.: From space to species: ecological applications
for remote sensing, Trends Ecol. Evol., 18, 299–305, 2003. a
Khaldi, R., Alcaraz-Segura, D., Guirado, E., Benhammou, Y., and Tabik, S.:
TimeSpec4LULC: A Smart-Global Dataset of Multi- Spectral Time Series of
MODIS Terra-Aqua from 2000 to 2021 for Training Machine Learning models to
perform LULC Mapping, https://doi.org/10.5281/zenodo.5913554, 2022. a, b, c
Kong, F., Li, X., Wang, H., Xie, D., Li, X., and Bai, Y.: Land cover
classification based on fused data from GF-1 and MODIS NDVI time series,
Remote Sensing, 8, 741, https://doi.org/10.3390/rs8090741, 2016. a, b
Lambin, E. F. and Geist, H. J. (Eds.): Land-use and land-cover change: local processes and global
impacts, Springer Science & Business Media, ISBN-10 3-540-32201-9, 2008. a
Loveland, T. R., Reed, B. C., Brown, J. F., Ohlen, D. O., Zhu, Z., Yang, L.,
and Merchant, J. W.: Development of a global land cover characteristics
database and IGBP DISCover from 1 km AVHRR data, Int. J. Remote Sens., 21, 1303–1330, 2000. a
Luengo, J., García-Gil, D., Ramírez-Gallego, S., García, S., and
Herrera, F.: Big data preprocessing – Enabling Smart Data, Springer, Cham, ISBN 978-3-030-39105-8,
2020. a
Luo, Y., Zhang, Y., Cai, X., and Yuan, X.: E2gan: End-to-end generative
adversarial network for multivariate time series imputation, in: AAAI Press,
3094–3100, ISBN
9780999241141, 2019. a
Luoto, M., Virkkala, R., and Heikkinen, R. K.: The role of land cover in
bioclimatic models depends on spatial resolution, Global Ecol.
Biogeogr., 16, 34–42, 2007. a
MCD: MCD12Q1.006,
https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MCD12Q1?hl=en,
last access: 10 January 2022. a
Menke, S., Holway, D., Fisher, R., and Jetz, W.: Characterizing and predicting
species distributions across environments and scales: Argentine ant
occurrences in the eye of the beholder, Global Ecol. Biogeogr., 18,
50–63, 2009. a
Meyer, W. B. and Turner II, B. L.: Changes in land use and land
cover: a global perspective, vol. 4, Cambridge University Press, ISBN 0-521-47085-4, 1994. a
Moser, S. C.: A partial instructional module on global and regional land
use/cover change: assessing the data and searching for general relationships,
GeoJournal, 39, 241–283, 1996. a
Muchoney, D., Strahler, A., Hodges, J., and LoGastro, J.: Terrestrial Ecosystem
Parameteilzation: Tools for Validating Global Land-Gover Data,
Photogramm. Eng., 65, 1061–1067, 1999. a
Oki, T., Blyth, E. M., Berbery, E. H., and Alcaraz-Segura, D.: Land use and
land cover changes and their impacts on hydroclimate, ecosystems and society,
in: Climate science for serving society, edited by: Asrar, G. R. and Hurrell, J. W., Springer, 185–203, ISBN 978-94-007-6692-1, 2013. a
PAL: PALSAR,
https://developers.google.com/earth-engine/datasets/catalog/JAXA_ALOS_PALSAR_YEARLY_FNF?hl=en,
last access: 10 January 2022. a
Patel, S. K., Verma, P., and Singh, G. S.: Agricultural growth and land use
land cover change in peri-urban India, Environ. Monit.
Assess., 191, 1–17, 2019. a
Pérez-Hoyos, A., García-Haro, F. J., and San-Miguel-Ayanz, J.: A
methodology to generate a synergetic land-cover map by fusion of different
land-cover products, Int. J. Appl. Earth Obs., 19, 72–87, 2012. a
Pettorelli, N., Wegmann, M., Skidmore, A., Mücher, S., Dawson, T.
P., Fernandez, M., Lucas, R., Schaepman, M. E., Wang, T., O'Connor, B., Jongman, R.
H. G., Kempeneers, P., Sonnenschein, R., Leidner, A. K., Böhm, M., He, K. S.,
Nagendra, H., Dubois, G., Fatoyinbo, T., Hansen, M. C., Paganini, M., de Klerk, H. M., Asner,
G. P., Kerr, J. T., Estes, A. B., Schmeller, D. S., Heiden, U., Rocchini, D., Pereira, H.
M., Turak, E., Fernandez, N., Lausch, A., Cho, M. A., Alcaraz-Segura, D., McGeoch,
M. A., Turner, W., Mueller, A., St-Louis, V., Penner, J.,
Vihervaara, P., Belward, A., Reyers, B., and Geller, G. N.:
Framing the concept of satellite remote sensing essential biodiversity
variables: challenges and future directions, Remote Sens. Ecol.
Conserv., 2, 122–131, 2016. a
Pfeifer, M., Disney, M., Quaife, T., and Marchant, R.: Terrestrial ecosystems
from space: a review of earth observation products for macroecology
applications, Global Ecol. Biogeogr., 21, 603–624, 2012. a
Polykretis, C., Grillakis, M. G., and Alexakis, D. D.: Exploring the impact of
various spectral indices on land cover change detection using change vector
analysis: A case study of Crete Island, Greece, Remote Sensing, 12, 319, https://doi.org/10.3390/rs12020319,
2020. a, b, c
Rußwurm, M., Pelletier, C., Zollner, M., Lefèvre, S., and Körner,
M.: BreizhCrops: A Time Series Dataset for Crop Type Mapping, arXiv preprint
arXiv:1905.11893, 2019. a
Safonova, A., Guirado, E., Maglinets, Y., Alcaraz-Segura, D., and Tabik, S.:
Olive Tree Biovolume from UAV Multi-Resolution Image Segmentation with Mask
R-CNN, Sensors, 21, 1617, https://doi.org/10.3390/s21051617, 2021. a
Sexton, J. O., Song, X.-P., Feng, M., Noojipady, P., Anand, A., Huang, C., Kim,
D.-H., Collins, K. M., Channan, S., DiMiceli, C.,
Townshend, J. R.: Global, 30-m
resolution continuous fields of tree cover: Landsat-based rescaling of MODIS
vegetation continuous fields with lidar-based estimates of error,
Int. J. Dig. Earth, 6, 427–448, 2013. a
Shimada, M., Itoh, T., Motooka, T., Watanabe, M., Shiraishi, T., Thapa, R., and
Lucas, R.: New global forest/non-forest maps from ALOS PALSAR data
(2007–2010), Remote Sens. Environ., 155, 13–31, 2014. a
Simard, M., Pinto, N., Fisher, J. B., and Baccini, A.: Mapping forest canopy
height globally with spaceborne lidar, J. Geophys. Res.-Biogeo., 116, 1–12, 2011. a
Steffen, W., Richardson, K., Rockström, J., Cornell, S. E., Fetzer, I.,
Bennett, E. M., Biggs, R., Carpenter, S. R., De Vries, W., De Wit, C. A., Folke, C., Gerten, D., Heinke, J., Mace, G. M., Persson, L. M., Ramanathan,
V., Reyers, B., and Sörlin, S.: Planetary boundaries: Guiding human development on a changing planet,
Science, 347, 1–11, 2015. a
Teluguntla, P., Thenkabail, P. S., Xiong, J., Gumma, M. K., Giri, C., Milesi, C.,
Ozdogan, M., Congalton, R., Tilton, J., Sankey, T. T., Massey, R., Phalke, A., and Yadav, K.:
Global Cropland Area Database (GCAD) derived from Remote Sensing in Support of Food Security
in the Twenty-first Century: Current Achievements and Future Possibilities, in: Land Resources
Monitoring, Modeling, and Mapping with Remote Sensing (Remote Sensing Handbook). Taylor & Francis,
Boca Raton, Florida, 01–45, 2015. a
Ter: MOD09A1,
https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MOD09A1?hl=en,
last access: 10 January 2022. a
TiS: TiSeLaC,
https://sites.google.com/site/dinoienco/tiselac-time-series-land-cover-classification-challenge?authuser=0,
last access: 10 January 2022. a
Townshend, J., Justice, C., Li, W., Gurney, C., and McManus, J.: Global land
cover classification by remote sensing: present capabilities and future
possibilities, Remote Sens. Environ., 35, 243–255, 1991. a
Tsendbazar, N., De Bruin, S., and Herold, M.: Assessing global land cover
reference datasets for different user communities, ISPRS J. Photogramm., 103, 93–114, 2015a. a
Tsi: Tsinghua FROM-GLC,
https://developers.google.com/earth-engine/datasets/catalog/Tsinghua_FROM-GLC_GAIA_v10?hl=en,
last access: 10 January 2022. a
Van Etten, A., Hogan, D., Martinez-Manso, J., Shermeyer, J., Weir, N., and
Lewis, R.: The Multi-Temporal Urban Development SpaceNet Dataset, arXiv
preprint arXiv:2102.04420, 2021. a
Vancutsem, C., Marinho, E., Kayitakire, F., See, L., and Fritz, S.: Harmonizing
and combining existing land cover/land use datasets for cropland area
monitoring at the African continental scale, Remote Sens., 5, 19–41, 2013. a
Verburg, P. H., Van De Steeg, J., Veldkamp, A., and Willemen, L.: From land
cover change to land function dynamics: a major challenge to improve land
characterization, J. Environ. Manage., 90, 1327–1335, 2009. a
Virnodkar, S. S., Pachghare, V. K., Patil, V., and Jha, S. K.: CaneSat dataset
to leverage convolutional neural networks for sugarcane classification from
Sentinel-2, Journal of King Saud University-Computer and Information
Sciences, 2020. a
Xiong, J., Thenkabail, P. S., Gumma, M. K., Teluguntla, P., Poehnelt, J.,
Congalton, R. G., Yadav, K., and Thau, D.: Automated cropland mapping of
continental Africa using Google Earth Engine cloud computing, ISPRS J. Photogramm., 126, 225–244, 2017. a
Xu, P., Herold, M., Tsendbazar, N.-E., and Clevers, J. G.: Towards a
comprehensive and consistent global aquatic land cover characterization
framework addressing multiple user needs, Remote Sens. Environ., 250,
112034, https://doi.org/10.1016/j.rse.2020.112034, 2020. a
Yirsaw, E., Wu, W., Shi, X., Temesgen, H., and Bekele, B.: Land use/land cover
change modeling and the prediction of subsequent changes in ecosystem service
values in a coastal area of China, the Su-Xi-Chang Region, Sustainability, 9,
1204, https://doi.org/10.3390/su9071204, 2017. a
Zhang, C., Pan, X., Li, H., Gardiner, A., Sargent, I., Hare, J., and Atkinson,
P. M.: A hybrid MLP-CNN classifier for very fine resolution remotely sensed
image classification, ISPRS J. Photogramm.,
140, 133–144, 2018a. a
Zhang, C., Ye, Y., Fang, X., Li, H., and Wei, X.: Synergistic Modern Global 1
Km Cropland Dataset Derived from Multi-Sets of Land Cover Products, Remote
Sens., 11, 2250, https://doi.org/10.3390/rs11192250, 2019. a
Zhang, Q., Yuan, Q., Zeng, C., Li, X., and Wei, Y.: Missing data reconstruction
in remote sensing image with a unified spatial–temporal–spectral deep
convolutional neural network, IEEE T. Geosci. Remote, 56, 4274–4288, 2018b. a
Zhang, W., Yang, G., Lin, Y., Ji, C., and Gupta, M. M.: On definition of deep
learning, in: 2018 World automation congress (WAC), IEEE, 1–5, https://doi.org/10.23919/WAC.2018.8430387,
2018c. a
Zhao, W. and Du, S.: Learning multiscale and deep representations for
classifying remotely sensed imagery, ISPRS J. Photogramm., 113, 155–165, 2016. a
Zhao, W., Guo, Z., Yue, J., Zhang, X., and Luo, L.: On combining multiscale
deep learning features for the classification of hyperspectral remote sensing
imagery, Int. J. Remote Sens., 36, 3368–3379, 2015. a
Zimmer-Gembeck, M. J. and Helfand, M.: Ten years of longitudinal research on US
adolescent sexual behavior: Developmental correlates of sexual intercourse,
and the importance of age, gender and ethnic background, Dev.
Rev. 28, 153–224, 2008. a
Short summary
This dataset with millions of 22-year time series for seven spectral bands was built by merging Terra and Aqua satellite data and annotated for 29 LULC classes by spatial–temporal agreement across 15 global LULC products. The mean F1 score was 96 % at the coarsest classification level and 87 % at the finest one. The dataset is born to develop and evaluate machine learning models to perform global LULC mapping given the disagreement between current global LULC products.
This dataset with millions of 22-year time series for seven spectral bands was built by merging...
Altmetrics
Final-revised paper
Preprint