Articles | Volume 13, issue 10
https://doi.org/10.5194/essd-13-4799-2021
© Author(s) 2021. 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-13-4799-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
GCI30: a global dataset of 30 m cropping intensity using multisource remote sensing imagery
Miao Zhang
State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, PR China
Bingfang Wu
CORRESPONDING AUTHOR
State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, PR China
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, PR China
Hongwei Zeng
State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, PR China
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, PR China
Guojin He
State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, PR China
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, PR China
Chong Liu
CORRESPONDING AUTHOR
School of Geospatial Engineering and Science, Sun Yat-Sen University, Guangzhou, 510275, PR China
Shiqi Tao
Graduate School of Geography, Clark University, Worcester, MA 01610, USA
Department of Earth and Environment, Boston University, Boston, MA 02215, USA
Frederick S. Pardee Center for the Study of Longer-Range Future, Frederick S. Pardee School of Global Studies, Boston University, Boston, MA 02215, USA
Mohsen Nabil
State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, PR China
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, PR China
Division of Agriculture Applications, Soils, and Marine (AASMD), National Authority for Remote Sensing and Space Sciences (NARSS), Cairo, New Nozha, Alf Maskan, 1564, Egypt
Fuyou Tian
State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, PR China
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, PR China
José Bofana
State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, PR China
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, PR China
Center for Agricultural and Sustainable Development Research (CIADS), Faculty of Agricultural Sciences, Catholic University of Mozambique, Cuamba 3305, Mozambique
Awetahegn Niguse Beyene
State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, PR China
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, PR China
Tigray Agricultural Research Institute, P.O. Box 492, Mekelle 251, Ethiopia
Abdelrazek Elnashar
State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, PR China
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, PR China
Department of Natural Resources, Faculty of African Postgraduate Studies, Cairo University, Giza 12613, Egypt
Nana Yan
State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, PR China
Zhengdong Wang
State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, PR China
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, PR China
Yiliang Liu
National Remote Sensing Center of China, Beijing 100036, PR China
Related authors
No articles found.
Zolal Ayazpour, Shiqi Tao, Dan Li, Amy Jo Scarino, Ralph E. Kuehn, and Kang Sun
Atmos. Meas. Tech., 16, 563–580, https://doi.org/10.5194/amt-16-563-2023, https://doi.org/10.5194/amt-16-563-2023, 2023
Short summary
Short summary
Accurate knowledge of the planetary boundary layer height (PBLH) is essential to study air pollution. However, PBLH observations are sparse in space and time, and PBLHs used in atmospheric models are often inaccurate. Using PBLH observations from the Aircraft Meteorological DAta Relay (AMDAR), we present a machine learning framework to produce a spatially complete PBLH product over the contiguous US that shows a better agreement with reference PBLH observations than commonly used PBLH products.
Chong Liu, Xiaoqing Xu, Xuejie Feng, Xiao Cheng, Caixia Liu, and Huabing Huang
Earth Syst. Sci. Data, 15, 133–153, https://doi.org/10.5194/essd-15-133-2023, https://doi.org/10.5194/essd-15-133-2023, 2023
Short summary
Short summary
Rapid Arctic changes are increasingly influencing human society, both locally and globally. Land cover offers a basis for characterizing the terrestrial world, yet spatially detailed information on Arctic land cover is lacking. We employ multi-source data to develop a new land cover map for the circumpolar Arctic. Our product reveals regionally contrasting biome distributions not fully documented in existing studies and thus enhances our understanding of the Arctic’s terrestrial system.
Abdelrazek Elnashar, Linjiang Wang, Bingfang Wu, Weiwei Zhu, and Hongwei Zeng
Earth Syst. Sci. Data, 13, 447–480, https://doi.org/10.5194/essd-13-447-2021, https://doi.org/10.5194/essd-13-447-2021, 2021
Short summary
Short summary
Based on a site-pixel validation and comparison of different global evapotranspiration (ET) products, this paper aims to produce a synthesized ET which has a minimum level of uncertainty over as many conditions as possible from 1982 to 2019. Through a high-quality flux eddy covariance (EC) covering the globe, PML, SSEBop, MOD16A2105, and NTSG ET products were chosen to create the new dataset. It agreed well with flux EC ET and can be used without other datasets or further assessments.
W. Jiang, Y. Ni, Z. Pang, G. He, J. Fu, J. Lu, K. Yang, T. Long, and T. Lei
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-3-2020, 33–38, https://doi.org/10.5194/isprs-annals-V-3-2020-33-2020, https://doi.org/10.5194/isprs-annals-V-3-2020-33-2020, 2020
J. Y. Sun, G. Z. Wang, G. J. He, D. C. Pu, W. Jiang, T. T. Li, and X. F. Niu
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3-W10, 641–648, https://doi.org/10.5194/isprs-archives-XLII-3-W10-641-2020, https://doi.org/10.5194/isprs-archives-XLII-3-W10-641-2020, 2020
W. Jiang, G. He, T. Long, and Y. Ni
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3, 669–672, https://doi.org/10.5194/isprs-archives-XLII-3-669-2018, https://doi.org/10.5194/isprs-archives-XLII-3-669-2018, 2018
M. M. Wang, G. J. He, Z. M. Zhang, Z. J. Zhang, and X. G. Liu
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3, 1747–1750, https://doi.org/10.5194/isprs-archives-XLII-3-1747-2018, https://doi.org/10.5194/isprs-archives-XLII-3-1747-2018, 2018
Y. Ni, G. He, and W. Jiang
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W7, 1301–1305, https://doi.org/10.5194/isprs-archives-XLII-2-W7-1301-2017, https://doi.org/10.5194/isprs-archives-XLII-2-W7-1301-2017, 2017
W. Jiang, G. He, and Y. Ni
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W7, 1307–1312, https://doi.org/10.5194/isprs-archives-XLII-2-W7-1307-2017, https://doi.org/10.5194/isprs-archives-XLII-2-W7-1307-2017, 2017
Yan Zhao, Yongping Wei, Shoubo Li, and Bingfang Wu
Hydrol. Earth Syst. Sci., 20, 4469–4481, https://doi.org/10.5194/hess-20-4469-2016, https://doi.org/10.5194/hess-20-4469-2016, 2016
Short summary
Short summary
The paper finds that combined inflow from both current and previous years' discharge determines water availability in downstream regions. Temperature determines broad vegetation distribution while hydrological variables show significant effects only in near-river-channel regions. Agricultural development curtailed further vegetation recovery in the studied area. Enhancing current water allocation schemes and regulating regional agricultural activities are required for future restoration.
Weili Jiao, Tengfei Long, Saiguang Ling, and Guojin He
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B2, 305–312, https://doi.org/10.5194/isprs-archives-XLI-B2-305-2016, https://doi.org/10.5194/isprs-archives-XLI-B2-305-2016, 2016
Related subject area
Land Cover and Land Use
A 250 m annual alpine grassland AGB dataset over the Qinghai–Tibet Plateau (2000–2019) in China based on in situ measurements, UAV photos, and MODIS data
AsiaRiceYield4km: seasonal rice yield in Asia from 1995 to 2015
TreeSatAI Benchmark Archive: a multi-sensor, multi-label dataset for tree species classification in remote sensing
UGS-1m: fine-grained urban green space mapping of 31 major cities in China based on the deep learning framework
AI4Boundaries: an open AI-ready dataset to map field boundaries with Sentinel-2 and aerial photography
GWL_FCS30: a global 30 m wetland map with a fine classification system using multi-sourced and time-series remote sensing imagery in 2020
CALC-2020: a new baseline land cover map at 10 m resolution for the circumpolar Arctic
MDAS: a new multimodal benchmark dataset for remote sensing
Gridded pollen-based Holocene regional plant cover in temperate and northern subtropical China suitable for climate modelling
Location, biophysical and agronomic parameters for croplands in northern Ghana
Historical nitrogen fertilizer use in China from 1952 to 2018
SiDroForest: a comprehensive forest inventory of Siberian boreal forest investigations including drone-based point clouds, individually labeled trees, synthetically generated tree crowns, and Sentinel-2 labeled image patches
Estimating Local Agricultural GDP across the World
History of anthropogenic Nitrogen inputs (HaNi) to the terrestrial biosphere: a 5 arcmin resolution annual dataset from 1860 to 2019
LUCAS cover photos 2006–2018 over the EU: 874 646 spatially distributed geo-tagged close-up photos with land cover and plant species label
Gridded 5 arcmin datasets for simultaneously farm-size-specific and crop-specific harvested areas in 56 countries
A 29-year time series of annual 300-metre resolution plant functional type maps for climate models
Vectorized dataset of roadside noise barriers in China using street view imagery
A global map of local climate zones to support earth system modelling and urban-scale environmental science
Mapping land-use fluxes for 2001–2020 from global models to national inventories
Mapping 10 m global impervious surface area (GISA-10m) using multi-source geospatial data
Classification and mapping of European fuels using a hierarchical-multipurpose fuel classification system
Improving intelligent dasymetric mapping population density estimates at 30 m resolution for the conterminous United States by excluding uninhabited areas
Four-century history of land transformation by humans in the United States: 1630–2020
High-resolution map of sugarcane cultivation in Brazil using a phenology-based method
GISD30: global 30 m impervious-surface dynamic dataset from 1985 to 2020 using time-series Landsat imagery on the Google Earth Engine platform
High-resolution land use and land cover dataset for regional climate modelling: a plant functional type map for Europe 2015
A national extent map of cropland and grassland for Switzerland based on Sentinel-2 data
Implementation of the CCDC algorithm to produce the LCMAP Collection 1.0 annual land surface change product
Harmonized in situ datasets for agricultural land use mapping and monitoring in tropical countries
NESEA-Rice10: high-resolution annual paddy rice maps for Northeast and Southeast Asia from 2017 to 2019
Refined burned-area mapping protocol using Sentinel-2 data increases estimate of 2019 Indonesian burning
The dataset of walled cities and urban extent in late imperial China in the 15th–19th centuries
Land-use harmonization datasets for annual global carbon budgets
An update and beyond: key landscapes for conservation land cover and change monitoring, thematic and validation datasets for the African, Caribbean and Pacific regions
A historical reconstruction of cropland in China from 1900 to 2016
Dataset of 1 km cropland cover from 1690 to 1999 in Scandinavia
The RapeseedMap10 database: annual maps of rapeseed at a spatial resolution of 10 m based on multi-source data
GLC_FCS30: global land-cover product with fine classification system at 30 m using time-series Landsat imagery
A 30 m terrace mapping in China using Landsat 8 imagery and digital elevation model based on the Google Earth Engine
Mid-19th-century building structure locations in Galicia and Austrian Silesia under the Habsburg Monarchy
High-resolution global map of smallholder and industrial closed-canopy oil palm plantations
Fine-grained, spatiotemporal datasets measuring 200 years of land development in the United States
A 30 m resolution dataset of China's urban impervious surface area and green space, 2000–2018
A cultivated planet in 2010 – Part 2: The global gridded agricultural-production maps
Early-season mapping of winter wheat in China based on Landsat and Sentinel images
Key landscapes for conservation land cover and change monitoring, thematic and validation datasets for sub-Saharan Africa
Earth transformed: detailed mapping of global human modification from 1990 to 2017
A cultivated planet in 2010 – Part 1: The global synergy cropland map
Development of a global 30 m impervious surface map using multisource and multitemporal remote sensing datasets with the Google Earth Engine platform
Huifang Zhang, Zhonggang Tang, Binyao Wang, Hongcheng Kan, Yi Sun, Yu Qin, Baoping Meng, Meng Li, Jianjun Chen, Yanyan Lv, Jianguo Zhang, Shuli Niu, and Shuhua Yi
Earth Syst. Sci. Data, 15, 821–846, https://doi.org/10.5194/essd-15-821-2023, https://doi.org/10.5194/essd-15-821-2023, 2023
Short summary
Short summary
The accuracy of regional grassland aboveground biomass (AGB) is always limited by insufficient ground measurements and large spatial gaps with satellite pixels. This paper used more than 37 000 UAV images as bridges to successfully obtain AGB values matching MODIS pixels. The new AGB estimation model had good robustness, with an average R2 of 0.83 and RMSE of 34.13 g m2. Our new dataset provides important input parameters for understanding the Qinghai–Tibet Plateau during global climate change.
Huaqing Wu, Jing Zhang, Zhao Zhang, Jichong Han, Juan Cao, Liangliang Zhang, Yuchuan Luo, Qinghang Mei, Jialu Xu, and Fulu Tao
Earth Syst. Sci. Data, 15, 791–808, https://doi.org/10.5194/essd-15-791-2023, https://doi.org/10.5194/essd-15-791-2023, 2023
Short summary
Short summary
High-spatiotemporal-resolution rice yield datasets are limited over a large region. We proposed an explicit method to predict rice yield based on machine learning methods and generated a seasonal 4 km resolution rice yield dataset across Asia (AsiaRiceYield4km) for 1995–2015. The seasonal rice yield accuracy of AsiaRiceYield4km is high and much improved compared with previous datasets. AsiaRiceYield4km will fill the current data gap and better support agricultural monitoring systems.
Steve Ahlswede, Christian Schulz, Christiano Gava, Patrick Helber, Benjamin Bischke, Michael Förster, Florencia Arias, Jörn Hees, Begüm Demir, and Birgit Kleinschmit
Earth Syst. Sci. Data, 15, 681–695, https://doi.org/10.5194/essd-15-681-2023, https://doi.org/10.5194/essd-15-681-2023, 2023
Short summary
Short summary
Imagery from air and space is the primary source of large-scale forest mapping. Our study introduces a new dataset with over 50000 image patches prepared for deep learning tasks. We show how the information for 20 European tree species can be extracted from different remote sensing sensors. Our algorithms can detect single species with precision scores up to 88 %. With a pixel size of 20×20 cm, forestry administration can now derive large-scale tree species maps at a very high resolution.
Qian Shi, Mengxi Liu, Andrea Marinoni, and Xiaoping Liu
Earth Syst. Sci. Data, 15, 555–577, https://doi.org/10.5194/essd-15-555-2023, https://doi.org/10.5194/essd-15-555-2023, 2023
Short summary
Short summary
A large-scale and high-resolution urban green space (UGS) product with 1 m of 31 major cities in China (UGS-1m) is generated based on a deep learning framework to provide basic UGS information for relevant UGS research, such as distribution, area, and UGS rate. Moreover, an urban green space dataset (UGSet) with a total of 4454 samples of 512 × 512 in size are also supplied as the benchmark to support model training and algorithm comparison.
Raphaël d'Andrimont, Martin Claverie, Pieter Kempeneers, Davide Muraro, Momchil Yordanov, Devis Peressutti, Matej Batič, and François Waldner
Earth Syst. Sci. Data, 15, 317–329, https://doi.org/10.5194/essd-15-317-2023, https://doi.org/10.5194/essd-15-317-2023, 2023
Short summary
Short summary
AI4boundaries is an open AI-ready data set to map field boundaries with Sentinel-2 and aerial photography provided with harmonised labels covering seven countries and 2.5 M parcels in Europe.
Xiao Zhang, Liangyun Liu, Tingting Zhao, Xidong Chen, Shangrong Lin, Jinqing Wang, Jun Mi, and Wendi Liu
Earth Syst. Sci. Data, 15, 265–293, https://doi.org/10.5194/essd-15-265-2023, https://doi.org/10.5194/essd-15-265-2023, 2023
Short summary
Short summary
An accurate global 30 m wetland dataset that can simultaneously cover inland and coastal zones is lacking. This study proposes a novel method for wetland mapping and generates the first global 30 m wetland map with a fine classification system (GWL_FCS30), including five inland wetland sub-categories (permanent water, swamp, marsh, flooded flat and saline) and three coastal wetland sub-categories (mangrove, salt marsh and tidal flats).
Chong Liu, Xiaoqing Xu, Xuejie Feng, Xiao Cheng, Caixia Liu, and Huabing Huang
Earth Syst. Sci. Data, 15, 133–153, https://doi.org/10.5194/essd-15-133-2023, https://doi.org/10.5194/essd-15-133-2023, 2023
Short summary
Short summary
Rapid Arctic changes are increasingly influencing human society, both locally and globally. Land cover offers a basis for characterizing the terrestrial world, yet spatially detailed information on Arctic land cover is lacking. We employ multi-source data to develop a new land cover map for the circumpolar Arctic. Our product reveals regionally contrasting biome distributions not fully documented in existing studies and thus enhances our understanding of the Arctic’s terrestrial system.
Jingliang Hu, Rong Liu, Danfeng Hong, Andrés Camero, Jing Yao, Mathias Schneider, Franz Kurz, Karl Segl, and Xiao Xiang Zhu
Earth Syst. Sci. Data, 15, 113–131, https://doi.org/10.5194/essd-15-113-2023, https://doi.org/10.5194/essd-15-113-2023, 2023
Short summary
Short summary
Multimodal data fusion is an intuitive strategy to break the limitation of individual data in Earth observation. Here, we present a multimodal data set, named MDAS, consisting of synthetic aperture radar (SAR), multispectral, hyperspectral, digital surface model (DSM), and geographic information system (GIS) data for the city of Augsburg, Germany, along with baseline models for resolution enhancement, spectral unmixing, and land cover classification, three typical remote sensing applications.
Furong Li, Marie-José Gaillard, Xianyong Cao, Ulrike Herzschuh, Shinya Sugita, Jian Ni, Yan Zhao, Chengbang An, Xiaozhong Huang, Yu Li, Hongyan Liu, Aizhi Sun, and Yifeng Yao
Earth Syst. Sci. Data, 15, 95–112, https://doi.org/10.5194/essd-15-95-2023, https://doi.org/10.5194/essd-15-95-2023, 2023
Short summary
Short summary
The objective of this study is present the first gridded and temporally continuous quantitative plant-cover reconstruction for temperate and northern subtropical China over the last 12 millennia. The reconstructions are based on 94 pollen records and include estimates for 27 plant taxa, 10 plant functional types, and 3 land-cover types. The dataset is suitable for palaeoclimate modelling and the evaluation of simulated past vegetation cover and anthropogenic land-cover change from models.
Jose Luis Gómez-Dans, Philip Edward Lewis, Feng Yin, Kofi Asare, Patrick Lamptey, Kenneth Kobina Yedu Aidoo, Dilys Sefakor MacCarthy, Hongyuan Ma, Qingling Wu, Martin Addi, Stephen Aboagye-Ntow, Caroline Edinam Doe, Rahaman Alhassan, Isaac Kankam-Boadu, Jianxi Huang, and Xuecao Li
Earth Syst. Sci. Data, 14, 5387–5410, https://doi.org/10.5194/essd-14-5387-2022, https://doi.org/10.5194/essd-14-5387-2022, 2022
Short summary
Short summary
We provide a data set to support mapping croplands in smallholder landscapes in Ghana. The data set contains information on crop location on three agroecological zones for 2 years, temporal series of measurements of leaf area index and leaf chlorophyll concentration for maize canopies and yield. We demonstrate the use of these data to validate cropland masks, create a maize mask using satellite data and explore the relationship between satellite measurements and yield.
Zhen Yu, Jing Liu, and Giri Kattel
Earth Syst. Sci. Data, 14, 5179–5194, https://doi.org/10.5194/essd-14-5179-2022, https://doi.org/10.5194/essd-14-5179-2022, 2022
Short summary
Short summary
We developed a 5 km annual nitrogen (N) fertilizer use dataset in China, covering the period from 1952 to 2018. We found that previous FAO-data-based N fertilizer products overestimated the N use in low, but underestimated in high, cropland coverage areas in China. The new dataset has improved the spatial distribution and corrected the existing biases, which is beneficial for biogeochemical cycle simulations in China, such as the assessment of greenhouse gas emissions and food production.
Femke van Geffen, Birgit Heim, Frederic Brieger, Rongwei Geng, Iuliia A. Shevtsova, Luise Schulte, Simone M. Stuenzi, Nadine Bernhardt, Elena I. Troeva, Luidmila A. Pestryakova, Evgenii S. Zakharov, Bringfried Pflug, Ulrike Herzschuh, and Stefan Kruse
Earth Syst. Sci. Data, 14, 4967–4994, https://doi.org/10.5194/essd-14-4967-2022, https://doi.org/10.5194/essd-14-4967-2022, 2022
Short summary
Short summary
SiDroForest is an attempt to remedy data scarcity regarding vegetation data in the circumpolar region, whilst providing adjusted and labeled data for machine learning and upscaling practices. SiDroForest contains four datasets that include SfM point clouds, individually labeled trees, synthetic tree crowns and labeled Sentinel-2 patches that provide insights into the vegetation composition and forest structure of two important vegetation transition zones in Siberia, Russia.
Yating Ru, Brian Blankespoor, Ulrike Wood-Sichra, Timothy S. Thomas, Liangzhi You, and Erwin Kalvelagen
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2022-336, https://doi.org/10.5194/essd-2022-336, 2022
Revised manuscript accepted for ESSD
Short summary
Short summary
Economic statistics are frequently produced at an administrative level that lacks detail to examine development patterns and the exposure to natural hazards. This paper disaggregates national and subnational administrative statistics of Agricultural GDP into a global dataset at the local level using satellite-derived indicators. The paper estimates the exposure of areas with at least one extreme drought to agricultural GDP is US$432 billion, where nearly 1.2 billion people live.
Hanqin Tian, Zihao Bian, Hao Shi, Xiaoyu Qin, Naiqing Pan, Chaoqun Lu, Shufen Pan, Francesco N. Tubiello, Jinfeng Chang, Giulia Conchedda, Junguo Liu, Nathaniel Mueller, Kazuya Nishina, Rongting Xu, Jia Yang, Liangzhi You, and Bowen Zhang
Earth Syst. Sci. Data, 14, 4551–4568, https://doi.org/10.5194/essd-14-4551-2022, https://doi.org/10.5194/essd-14-4551-2022, 2022
Short summary
Short summary
Nitrogen is one of the critical nutrients for growth. Evaluating the change in nitrogen inputs due to human activity is necessary for nutrient management and pollution control. In this study, we generated a historical dataset of nitrogen input to land at the global scale. This dataset consists of nitrogen fertilizer, manure, and atmospheric deposition inputs to cropland, pasture, and rangeland at high resolution from 1860 to 2019.
Raphaël d'Andrimont, Momchil Yordanov, Laura Martinez-Sanchez, Peter Haub, Oliver Buck, Carsten Haub, Beatrice Eiselt, and Marijn van der Velde
Earth Syst. Sci. Data, 14, 4463–4472, https://doi.org/10.5194/essd-14-4463-2022, https://doi.org/10.5194/essd-14-4463-2022, 2022
Short summary
Short summary
Between 2006 and 2018, 875 661 LUCAS cover (i.e. close-up) photos were taken over a systematic sample of the European Union. This geo-located photo dataset has been curated and is being made available along with the surveyed label data, including land cover and plant species.
Han Su, Bárbara Willaarts, Diana Luna-Gonzalez, Maarten S. Krol, and Rick J. Hogeboom
Earth Syst. Sci. Data, 14, 4397–4418, https://doi.org/10.5194/essd-14-4397-2022, https://doi.org/10.5194/essd-14-4397-2022, 2022
Short summary
Short summary
There are over 608 million farms around the world but they are not the same. We developed high spatial resolution maps showing where small and large farms were located and which crops were planted for 56 countries. We checked the reliability and have the confidence to use them for the country level and global studies. Our maps will help more studies to easily measure how agriculture policies, water availability, and climate change affect small and large farms.
Kandice L. Harper, Celine Lamarche, Andrew Hartley, Philippe Peylin, Catherine Ottlé, Vladislav Bastrikov, Rodrigo San Martín, Sylvia I. Bohnenstengel, Grit Kirches, Martin Boettcher, Roman Shevchuk, Carsten Brockmann, and Pierre Defourny
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2022-296, https://doi.org/10.5194/essd-2022-296, 2022
Revised manuscript accepted for ESSD
Short summary
Short summary
We built a spatially explicit annual PFT dataset for 1992–2020 exhibiting intraclass spatial variability in PFT fractional cover at 300 m. For each year, 14 maps of PFTs percentage cover are produced: bare soil, water, permanent snow/ice, built, managed grasses, natural grasses, and trees and shrubs each split into leaf type and seasonality. ORCHIDEE and JULES model simulations indicate significant differences in simulated carbon, water, and energy fluxes in some regions using this new PFT set.
Zhen Qian, Min Chen, Yue Yang, Teng Zhong, Fan Zhang, Rui Zhu, Kai Zhang, Zhixin Zhang, Zhuo Sun, Peilong Ma, Guonian Lü, Yu Ye, and Jinyue Yan
Earth Syst. Sci. Data, 14, 4057–4076, https://doi.org/10.5194/essd-14-4057-2022, https://doi.org/10.5194/essd-14-4057-2022, 2022
Short summary
Short summary
Roadside noise barriers (RNBs) are important urban infrastructures to ensure a city is liveable. This study provides the first reliable and nationwide vectorized RNB dataset with street view imagery in China. The generated RNB dataset is evaluated in terms of two aspects, i.e., the detection accuracy and the completeness and positional accuracy. The method is based on a developed geospatial artificial intelligence framework.
Matthias Demuzere, Jonas Kittner, Alberto Martilli, Gerald Mills, Christian Moede, Iain D. Stewart, Jasper van Vliet, and Benjamin Bechtel
Earth Syst. Sci. Data, 14, 3835–3873, https://doi.org/10.5194/essd-14-3835-2022, https://doi.org/10.5194/essd-14-3835-2022, 2022
Short summary
Short summary
Because urban areas are key contributors to climate change but are also susceptible to multiple hazards, one needs spatially detailed information on urban landscapes to support environmental services. This global local climate zone map describes this much-needed intra-urban heterogeneity across the whole surface of the earth in a universal language and can serve as a basic infrastructure to study e.g. environmental hazards, energy demand, and climate adaptation and mitigation solutions.
Giacomo Grassi, Clemens Schwingshackl, Thomas Gasser, Richard A. Houghton, Stephen Sitch, Josep G. Canadell, Alessandro Cescatti, Philippe Ciais, Sandro Federici, Pierre Friedlingstein, Werner A. Kurz, Maria J. Sanz Sanchez, Raúl Abad Viñas, Ramdane Alkama, Guido Ceccherini, Etsushi Kato, Daniel Kennedy, Jürgen Knauer, Anu Korosuo, Matthew J. McGrath, Julia Nabel, Benjamin Poulter, Simone Rossi, Anthony P. Walker, Wenping Yuan, Xu Yue, and Julia Pongratz
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2022-245, https://doi.org/10.5194/essd-2022-245, 2022
Revised manuscript accepted for ESSD
Short summary
Short summary
Striking differences exist in estimates of land-use CO2 fluxes between the national greenhouse gas inventories and the IPCC assessment reports. These differences hamper an accurate assessment of the collective progress under the Paris Agreement By implementing an approach that conceptually reconciles land-use CO2 flux from national inventories and the global models used by the IPCC, our study is an important step forward for increasing transparency and confidence in land-use CO2 flux estimates.
Xin Huang, Jie Yang, Wenrui Wang, and Zhengrong Liu
Earth Syst. Sci. Data, 14, 3649–3672, https://doi.org/10.5194/essd-14-3649-2022, https://doi.org/10.5194/essd-14-3649-2022, 2022
Short summary
Short summary
Using more than 2.7 million Sentinel images, we proposed a global ISA mapping method and produced the 10-m global ISA dataset (GISA-10m), with overall accuracy exceeding 86 %. The inter-comparison between different global ISA datasets showed the superiority of our results. The ISA distribution at urban and rural was discussed and compared. For the first time, courtesy of the high spatial resolution, the global road ISA was further identified, and its distribution was discussed.
Elena Aragoneses, Mariano García, Michele Salis, Luís M. Ribeiro, and Emilio Chuvieco
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2022-184, https://doi.org/10.5194/essd-2022-184, 2022
Revised manuscript accepted for ESSD
Short summary
Short summary
The authors present a new fuel classification system with a total of 85 fuels, useful for different spatial scales and purposes. Based on it, the authors developed a European fuel map (1 km resolution) using land cover datasets, biogeographic datasets, and bioclimatic modelling. The authors validated the map by comparing it to high-resolution data, obtaining high overall accuracy. Finally, the authors developed a crosswalk to standard fuel models as a first assignment of fuel parameters.
Jeremy Baynes, Anne Neale, and Torrin Hultgren
Earth Syst. Sci. Data, 14, 2833–2849, https://doi.org/10.5194/essd-14-2833-2022, https://doi.org/10.5194/essd-14-2833-2022, 2022
Short summary
Short summary
Census data are typically provided in irregularly shaped spatial units. To get a more refined estimate of population density, we downscaled population counts from United States (US) census blocks to a 30 m grid using intelligent dasymetric mapping. Furthermore, we improved our density estimates by using multiple spatial datasets to identify and mask uninhabited areas. Masking these uninhabited areas improved density estimates for every state in the conterminous US.
Xiaoyong Li, Hanqin Tian, Shufen Pan, and Chaoqun Lu
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2022-135, https://doi.org/10.5194/essd-2022-135, 2022
Revised manuscript accepted for ESSD
Short summary
Short summary
We reconstructed land use and land cover history for the conterminous United States during 1630–2020 by integrating multi-source data. The results show the widespread expansion of cropland and urban land and the shrinking of natural vegetation in the past four centuries. Forest planting and regeneration accelerated forest recovery since the 1920s. The datasets can be used to assess the LULC impacts on the ecosystem's carbon, nitrogen, and water cycles.
Yi Zheng, Ana Cláudia dos Santos Luciano, Jie Dong, and Wenping Yuan
Earth Syst. Sci. Data, 14, 2065–2080, https://doi.org/10.5194/essd-14-2065-2022, https://doi.org/10.5194/essd-14-2065-2022, 2022
Short summary
Short summary
Brazil is the largest sugarcane producer. Sugarcane in Brazil can be harvested all year round. The flexible phenology makes it difficult to identify sugarcane in Brazil at a country scale. We developed a phenology-based method which can identify sugarcane with limited training data. The sugarcane maps for Brazil obtain high accuracy through comparison against field samples and statistical data. The maps can be used to monitor growing conditions and evaluate the feedback to climate of sugarcane.
Xiao Zhang, Liangyun Liu, Tingting Zhao, Yuan Gao, Xidong Chen, and Jun Mi
Earth Syst. Sci. Data, 14, 1831–1856, https://doi.org/10.5194/essd-14-1831-2022, https://doi.org/10.5194/essd-14-1831-2022, 2022
Short summary
Short summary
Accurately mapping impervious-surface dynamics has great scientific significance and application value for research on urban sustainable development, the assessment of anthropogenic carbon emissions and global ecological-environment modeling. In this study, a novel and accurate global 30 m impervious surface dynamic dataset (GISD30) for 1985 to 2020 was produced using the spectral-generalization method and time-series Landsat imagery on the Google Earth Engine cloud computing platform.
Vanessa Reinhart, Peter Hoffmann, Diana Rechid, Jürgen Böhner, and Benjamin Bechtel
Earth Syst. Sci. Data, 14, 1735–1794, https://doi.org/10.5194/essd-14-1735-2022, https://doi.org/10.5194/essd-14-1735-2022, 2022
Short summary
Short summary
The LANDMATE plant functional type (PFT) land cover dataset for Europe 2015 (Version 1.0) is a gridded, high-resolution dataset for use in regional climate models. LANDMATE PFT is prepared using the expertise of regional climate modellers all over Europe and is easily adjustable to fit into different climate model families. We provide comprehensive spatial quality information for LANDMATE PFT, which can be used to reduce uncertainty in regional climate model simulations.
Robert Pazúr, Nica Huber, Dominique Weber, Christian Ginzler, and Bronwyn Price
Earth Syst. Sci. Data, 14, 295–305, https://doi.org/10.5194/essd-14-295-2022, https://doi.org/10.5194/essd-14-295-2022, 2022
Short summary
Short summary
We mapped the distribution of cropland and permanent grassland across Switzerland, where the agricultural land is considerably spatially heterogeneous due to strong variability in topography and climate, thus presenting challenges to mapping. The resulting map has high accuracy in lowlands as well as in mountainous areas. Thus, we believe that the presented mapping approach and resulting map will provide a solid ground for further research in agricultural land cover and landscape structure.
George Z. Xian, Kelcy Smith, Danika Wellington, Josephine Horton, Qiang Zhou, Congcong Li, Roger Auch, Jesslyn F. Brown, Zhe Zhu, and Ryan R. Reker
Earth Syst. Sci. Data, 14, 143–162, https://doi.org/10.5194/essd-14-143-2022, https://doi.org/10.5194/essd-14-143-2022, 2022
Short summary
Short summary
Continuous change detection algorithms were implemented with time series satellite records to produce annual land surface change products for the conterminous United States. The land change products are in 30 m spatial resolution and represent land cover and change from 1985 to 2017 across the country. The LCMAP product suite provides useful information for land resource management and facilitates studies to improve the understanding of terrestrial ecosystems.
Audrey Jolivot, Valentine Lebourgeois, Louise Leroux, Mael Ameline, Valérie Andriamanga, Beatriz Bellón, Mathieu Castets, Arthur Crespin-Boucaud, Pierre Defourny, Santiana Diaz, Mohamadou Dieye, Stéphane Dupuy, Rodrigo Ferraz, Raffaele Gaetano, Marie Gely, Camille Jahel, Bertin Kabore, Camille Lelong, Guerric le Maire, Danny Lo Seen, Martha Muthoni, Babacar Ndao, Terry Newby, Cecília Lira Melo de Oliveira Santos, Eloise Rasoamalala, Margareth Simoes, Ibrahima Thiaw, Alice Timmermans, Annelise Tran, and Agnès Bégué
Earth Syst. Sci. Data, 13, 5951–5967, https://doi.org/10.5194/essd-13-5951-2021, https://doi.org/10.5194/essd-13-5951-2021, 2021
Short summary
Short summary
This paper presents nine standardized crop type reference datasets collected between 2013 and 2020 in seven tropical countries. It aims at participating in the difficult exercise of mapping agricultural land use through satellite image classification in those complex areas where few ground truth or census data are available. These quality-controlled datasets were collected in the framework of the international JECAM initiative and contain 27 074 polygons documented by detailed keywords.
Jichong Han, Zhao Zhang, Yuchuan Luo, Juan Cao, Liangliang Zhang, Fei Cheng, Huimin Zhuang, Jing Zhang, and Fulu Tao
Earth Syst. Sci. Data, 13, 5969–5986, https://doi.org/10.5194/essd-13-5969-2021, https://doi.org/10.5194/essd-13-5969-2021, 2021
Short summary
Short summary
The accurate planting area and spatial distribution information is the basis for ensuring food security at continental scales. We constructed a paddy rice map database in Southeast and Northeast Asia for 3 years (2017–2019) at a 10 m spatial resolution. There are fewer mixed pixels in our paddy rice map. The large-scale and high-resolution maps of paddy rice are useful for water resource management and yield monitoring.
David L. A. Gaveau, Adrià Descals, Mohammad A. Salim, Douglas Sheil, and Sean Sloan
Earth Syst. Sci. Data, 13, 5353–5368, https://doi.org/10.5194/essd-13-5353-2021, https://doi.org/10.5194/essd-13-5353-2021, 2021
Short summary
Short summary
Severe burning struck Indonesia in 2019. Drawing on new satellite imagery, we present and validate new 2019 burned-area estimates for Indonesia.
We show that > 3.11 million hectares (Mha) burned in 2019, double the official estimate from the Indonesian Ministry of Environment and Forestry. Our relatively more accurate estimates have important implications for carbon-emission calculations from forest and peatland fires in Indonesia.
Qiaofeng Xue, Xiaobin Jin, Yinong Cheng, Xuhong Yang, and Yinkang Zhou
Earth Syst. Sci. Data, 13, 5071–5085, https://doi.org/10.5194/essd-13-5071-2021, https://doi.org/10.5194/essd-13-5071-2021, 2021
Short summary
Short summary
We reconstructed the walled cities of China that extend from the 15th century to 19th century based on multiple historical documents. By restoring the extent of the city walls, it is helpful to explore the urban area in this period. The correlation and integration of the lifetime and the spatial data led to the creation of the China City Wall Areas Dataset (CCWAD). Based on the proximity to the time of most of the city walls, we produce the China Urban Extent Dataset (CUED) from CCWAD.
Louise Chini, George Hurtt, Ritvik Sahajpal, Steve Frolking, Kees Klein Goldewijk, Stephen Sitch, Raphael Ganzenmüller, Lei Ma, Lesley Ott, Julia Pongratz, and Benjamin Poulter
Earth Syst. Sci. Data, 13, 4175–4189, https://doi.org/10.5194/essd-13-4175-2021, https://doi.org/10.5194/essd-13-4175-2021, 2021
Short summary
Short summary
Carbon emissions from land-use change are a large and uncertain component of the global carbon cycle. The Land-Use Harmonization 2 (LUH2) dataset was developed as an input to carbon and climate simulations and has been updated annually for the Global Carbon Budget (GCB) assessments. Here we discuss the methodology for producing these annual LUH2 updates and describe the 2019 version which used new cropland and grazing land data inputs for the globally important region of Brazil.
Zoltan Szantoi, Andreas Brink, and Andrea Lupi
Earth Syst. Sci. Data, 13, 3767–3789, https://doi.org/10.5194/essd-13-3767-2021, https://doi.org/10.5194/essd-13-3767-2021, 2021
Short summary
Short summary
The ever-evolving landscapes in the African, Caribbean and Pacific regions should be monitored for land cover changes. The Global Land Monitoring Service of the Copernicus Programme, and in particular the Hot Spot Monitoring activity, developed a satellite-imagery-based workflow to monitor such areas. Here, we present a total of 852 025 km2 of areas mapped with up to 32 land cover classes. Thematic land cover and land cover change maps, as well as validation datasets, are presented.
Zhen Yu, Xiaobin Jin, Lijuan Miao, and Xuhong Yang
Earth Syst. Sci. Data, 13, 3203–3218, https://doi.org/10.5194/essd-13-3203-2021, https://doi.org/10.5194/essd-13-3203-2021, 2021
Short summary
Short summary
We reconstructed the annual, 5 km × 5 km resolution cropland percentage map that covers mainland China and spans from 1900 to 2016. Our results are advantageous, as they reconcile accuracy, temporal coverage, and spatial resolutions. We further examined the cropland shift pattern and its driving factors in China using the reconstructed maps. This work will greatly contribute to the field of global ecology and land surface modeling.
Xueqiong Wei, Mats Widgren, Beibei Li, Yu Ye, Xiuqi Fang, Chengpeng Zhang, and Tiexi Chen
Earth Syst. Sci. Data, 13, 3035–3056, https://doi.org/10.5194/essd-13-3035-2021, https://doi.org/10.5194/essd-13-3035-2021, 2021
Short summary
Short summary
The cropland area of each administrative unit based on statistics in Scandinavia from 1690 to 1999 is allocated into 1 km grid cells. The cropland area increased from 1690 to 1950 and then decreasd in the following years, especially in southeastern Scandinavia. Comparing global datasets with this study, the spatial patterns show considerable differences. Our dataset is validated using satellite-based cropland cover data and results in previous studies.
Jichong Han, Zhao Zhang, Yuchuan Luo, Juan Cao, Liangliang Zhang, Jing Zhang, and Ziyue Li
Earth Syst. Sci. Data, 13, 2857–2874, https://doi.org/10.5194/essd-13-2857-2021, https://doi.org/10.5194/essd-13-2857-2021, 2021
Short summary
Short summary
Large-scale and high-resolution maps of rapeseed are important for ensuring global energy security. We generated a new database for the rapeseed planting area (2017–2019) at 10 m spatial resolution based on multiple data. Also, we analyzed the rapeseed rotation patterns in 25 representative areas from different countries. The derived rapeseed maps are useful for many purposes including crop growth monitoring and production and optimizing planting structure.
Xiao Zhang, Liangyun Liu, Xidong Chen, Yuan Gao, Shuai Xie, and Jun Mi
Earth Syst. Sci. Data, 13, 2753–2776, https://doi.org/10.5194/essd-13-2753-2021, https://doi.org/10.5194/essd-13-2753-2021, 2021
Short summary
Short summary
Over past decades, a lot of global land-cover products have been released; however, these still lack a global land-cover map with a fine classification system and spatial resolution simultaneously. In this study, a novel global 30 m landcover classification with a fine classification system for the year 2015 (GLC_FCS30-2015) was produced by combining time series of Landsat imagery and high-quality training data from the GSPECLib on the Google Earth Engine computing platform.
Bowen Cao, Le Yu, Victoria Naipal, Philippe Ciais, Wei Li, Yuanyuan Zhao, Wei Wei, Die Chen, Zhuang Liu, and Peng Gong
Earth Syst. Sci. Data, 13, 2437–2456, https://doi.org/10.5194/essd-13-2437-2021, https://doi.org/10.5194/essd-13-2437-2021, 2021
Short summary
Short summary
In this study, the first 30 m resolution terrace map of China was developed through supervised pixel-based classification using multisource, multi-temporal data based on the Google Earth Engine platform. The classification performed well with an overall accuracy of 94 %. The terrace mapping algorithm can be used to map large-scale terraces in other regions globally, and the terrace map will be valuable for studies on soil erosion, carbon cycle, and ecosystem service assessments.
Dominik Kaim, Marcin Szwagrzyk, Monika Dobosz, Mateusz Troll, and Krzysztof Ostafin
Earth Syst. Sci. Data, 13, 1693–1709, https://doi.org/10.5194/essd-13-1693-2021, https://doi.org/10.5194/essd-13-1693-2021, 2021
Short summary
Short summary
We present a dataset of mid-19th-century building structure locations in former Galicia and Austrian Silesia (parts of the Habsburg Monarchy), located in present-day Czechia, Poland, and Ukraine. It consists of two kinds of building structures: residential and farm-related buildings. The dataset may serve as an important input in studying long-term socio-economic processes and human–environmental interactions or as a valuable reference for continental settlement reconstructions.
Adrià Descals, Serge Wich, Erik Meijaard, David L. A. Gaveau, Stephen Peedell, and Zoltan Szantoi
Earth Syst. Sci. Data, 13, 1211–1231, https://doi.org/10.5194/essd-13-1211-2021, https://doi.org/10.5194/essd-13-1211-2021, 2021
Short summary
Short summary
Decision-making for sustainable vegetable oil production requires accurate global oil crop maps. We used high-resolution satellite data to train a deep learning model that accurately classified industrial and smallholder oil palm, the main oil-producing crop. Our results outperformed previous studies and proved the suitability of deep learning for land use mapping. The global oil palm area was 21±0.42 Mha for 2019; however, young and sparse plantations were not included in this estimate.
Johannes H. Uhl, Stefan Leyk, Caitlin M. McShane, Anna E. Braswell, Dylan S. Connor, and Deborah Balk
Earth Syst. Sci. Data, 13, 119–153, https://doi.org/10.5194/essd-13-119-2021, https://doi.org/10.5194/essd-13-119-2021, 2021
Short summary
Short summary
Fine-grained geospatial data on the spatial distribution of human settlements are scarce prior to the era of remote-sensing-based Earth observation. In this paper, we present datasets derived from a large, novel building stock database, enabling the spatially explicit analysis of 200 years of land development in the United States at an unprecedented spatial and temporal resolution. These datasets greatly facilitate long-term studies of socio-environmental systems in the conterminous USA.
Wenhui Kuang, Shu Zhang, Xiaoyong Li, and Dengsheng Lu
Earth Syst. Sci. Data, 13, 63–82, https://doi.org/10.5194/essd-13-63-2021, https://doi.org/10.5194/essd-13-63-2021, 2021
Short summary
Short summary
We propose a hierarchical principle for remotely sensed urban land use and land cover change for mapping intra-urban structure and component dynamics. China’s Land Use/cover Dataset (CLUD) is updated, delineating the imperviousness and green surface conditions in cities from 2000 to 2018. The newly developed datasets can be used to enhance our understanding of urbanization impacts on ecological and regional climatic conditions and on urban dwellers' environments.
Qiangyi Yu, Liangzhi You, Ulrike Wood-Sichra, Yating Ru, Alison K. B. Joglekar, Steffen Fritz, Wei Xiong, Miao Lu, Wenbin Wu, and Peng Yang
Earth Syst. Sci. Data, 12, 3545–3572, https://doi.org/10.5194/essd-12-3545-2020, https://doi.org/10.5194/essd-12-3545-2020, 2020
Short summary
Short summary
SPAM makes plausible estimates of crop distribution within disaggregated units. It moves the data from coarser units such as countries and provinces to finer units such as grid cells and creates a global gridscape at the confluence between earth and agricultural-production systems. It improves spatial understanding of crop production systems and allows policymakers to better target agricultural- and rural-development policies for increasing food security with minimal environmental impacts.
Jie Dong, Yangyang Fu, Jingjing Wang, Haifeng Tian, Shan Fu, Zheng Niu, Wei Han, Yi Zheng, Jianxi Huang, and Wenping Yuan
Earth Syst. Sci. Data, 12, 3081–3095, https://doi.org/10.5194/essd-12-3081-2020, https://doi.org/10.5194/essd-12-3081-2020, 2020
Short summary
Short summary
For the first time, we produced a 30 m winter wheat distribution map in China for 3 years during 2016–2018. Validated with 33 776 survey samples, the map had perfect performance with an overall accuracy of 89.88 %. Moreover, the method can identify planting areas of winter wheat 3 months prior to harvest; that is valuable information for production predictions and is urgently necessary for policymakers to reduce economic loss and assess food security.
Zoltan Szantoi, Andreas Brink, Andrea Lupi, Claudio Mammone, and Gabriel Jaffrain
Earth Syst. Sci. Data, 12, 3001–3019, https://doi.org/10.5194/essd-12-3001-2020, https://doi.org/10.5194/essd-12-3001-2020, 2020
Short summary
Short summary
Larger ecological zones and wildlife corridors in sub-Saharan Africa require monitoring, as social and economic demands put high pressure on them. Copernicus’ Hot-Spot Monitoring service developed a satellite-imagery-based monitoring workflow to map such areas. Here, we present a total of 560 442 km2 from which 153 665 km2 is mapped with eight land cover classes while 406 776 km2 is mapped with up to 32 classes. Besides presenting the thematic products, we also present our validation datasets.
David M. Theobald, Christina Kennedy, Bin Chen, James Oakleaf, Sharon Baruch-Mordo, and Joe Kiesecker
Earth Syst. Sci. Data, 12, 1953–1972, https://doi.org/10.5194/essd-12-1953-2020, https://doi.org/10.5194/essd-12-1953-2020, 2020
Short summary
Short summary
We developed a global, high-resolution dataset and quantified recent rates of land transformation and current patterns of human modification for 2017, globally. Briefly, we found that increased human activities and land use modification have caused 1.6 × 106 km2 of natural land to be lost between 1990 and 2015 and the rate of loss has increased over that time. While troubling, we believe these findings are invaluable to underpinning global and national discussions of conservation priorities.
Miao Lu, Wenbin Wu, Liangzhi You, Linda See, Steffen Fritz, Qiangyi Yu, Yanbing Wei, Di Chen, Peng Yang, and Bing Xue
Earth Syst. Sci. Data, 12, 1913–1928, https://doi.org/10.5194/essd-12-1913-2020, https://doi.org/10.5194/essd-12-1913-2020, 2020
Short summary
Short summary
Global cropland distribution is critical for agricultural monitoring and food security. We propose a new Self-adapting Statistics Allocation Model (SASAM) to develop the global map of cropland distribution. SASAM is based on the fusion of multiple existing cropland maps and multilevel statistics of cropland area, which is independent of training samples. The synergy map has higher accuracy than the input datasets and better consistency with the cropland statistics.
Xiao Zhang, Liangyun Liu, Changshan Wu, Xidong Chen, Yuan Gao, Shuai Xie, and Bing Zhang
Earth Syst. Sci. Data, 12, 1625–1648, https://doi.org/10.5194/essd-12-1625-2020, https://doi.org/10.5194/essd-12-1625-2020, 2020
Short summary
Short summary
The amount of impervious surface is an important indicator in the monitoring of the intensity of human activity and environmental change. In this study, a global 30 m impervious surface map was developed by using multisource, multitemporal remote sensing data based on the Google Earth Engine platform. The accuracy assessment indicated that the generated map had more optimal measurement accuracy compared with other state-of-art impervious surface products.
Cited articles
Amani, M., Ghorbanian, A., Ahmadi, S. A., Kakooei, M., Moghimi, A.,
Mirmazloumi, S. M., Moghaddam, S. H. A., Mahdavi, S., Ghahremanloo, M., and
Parsian, S.: Google earth engine cloud computing platform for remote sensing
big data applications: A comprehensive review, IEEE J. Sel.
Top. Appl., 13, 5326–5350,
https://doi.org/10.1109/JSTARS.2020.3021052, 2020.
Beck, H. E., Zimmermann, N. E., McVicar, T. R., Vergopolan, N., Berg, A.,
and Wood, E.: Present and future Köppen-Geiger climate classification
maps at 1-km resolution, Scientific Data, 5, 180214, https://doi.org/10.1038/sdata.2018.214, 2018.
Becker, M. and Johnson, D. E.: Cropping intensity effects on upland rice
yield and sustainability in West Africa, Nutr. Cycl. Agroecosys.,
59, 107–117, https://doi.org/10.1023/A:1017551529813, 2001.
Bolton, D. K., Gray, J. M., Melaas, E. K., Moon, M., Eklundh, L., and
Friedl, M. A.: Continental-scale land surface phenology from harmonized
Landsat 8 and Sentinel-2 imagery, Remote Sens. Environ., 240,
111685, https://doi.org/10.1016/j.rse.2020.111685, 2020.
Challinor, A. J., Parkes, B., and Ramirez-Villegas, J.: Crop yield response
to climate change varies with cropping intensity, Glob. Change Biol., 21,
1679–1688, https://doi.org/10.1111/gcb.12808, 2015.
Chastain, R., Housman, I., Goldstein, J., Finco, M., and Tenneson, K.:
Empirical cross sensor comparison of Sentinel-2A and 2B MSI, Landsat-8 OLI,
and Landsat-7 ETM+ top of atmosphere spectral characteristics over the
conterminous United States, Remote Sens. Environ., 221, 274–285,
https://doi.org/10.1016/j.rse.2018.11.012, 2019.
Chiew, F., Prosser, I., and Post, D.: On climate variability and climate
change and impact on water resources, in: MODSIM 2011, 12–16 December 2011, Perth, Australia, Modelling and Simulation Society of Australia and New Zealand, 3553–3559, available at: http://hdl.handle.net/102.100.100/102035?index=1 (last access: 6 November 2020), 2011.
Claverie, M., Ju, J., Masek, J. G., Dungan, J. L., Vermote, E. F., Roger,
J.-C., Skakun, S. V., and Justice, C.: The Harmonized Landsat and Sentinel-2
surface reflectance data set, Remote Sens. Environ., 219, 145–161,
https://doi.org/10.1016/j.rse.2018.09.002, 2018.
Defourny, P., Bontemps, S., Bellemans, N., Cara, C., Dedieu, G., Guzzonato,
E., Hagolle, O., Inglada, J., Nicola, L., and Rabaute, T.: Near real-time
agriculture monitoring at national scale at parcel resolution: Performance
assessment of the Sen2-Agri automated system in various cropping systems
around the world, Remote Sens. Environ., 221, 551–568, https://doi.org/10.1016/j.rse.2018.11.007, 2019.
Didan, K. and Barreto, A.: NASA MEaSUREs vegetation index and phenology
(VIP) vegetation indices monthly global 0.05 Deg CMG, NASA EOSDIS Land
Process, DAAC [data set], https://doi.org/10.5067/MEaSUREs/VIP/VIP15.004, 2016.
Ding, M., Chen, Q., Xiao, X., Xin, L., Zhang, G., and Li, L.: Variation in
cropping intensity in northern China from 1982 to 2012 based on GIMMS-NDVI
data, Sustainability, 8, 1123, https://doi.org/10.3390/su8111123, 2016.
Ding, M., Guan, Q., Li, L., Zhang, H., Liu, C., and Zhang, L.:
Phenology-based rice paddy mapping using multi-source satellite imagery and
a fusion algorithm applied to the Poyang Lake Plain, Southern China, Remote
Sens., 12, 1022, https://doi.org/10.3390/rs12061022, 2020.
Dong, J. and Xiao, X.: Evolution of regional to global paddy rice mapping
methods: A review, ISPRS J. Photogramm., 119,
214–227, https://doi.org/10.1016/j.isprsjprs.2016.05.010, 2016.
Dong, J., Xiao, X., Kou, W., Qin, Y., Zhang, G., Li, L., Jin, C., Zhou, Y.,
Wang, J., and Biradar, C.: Tracking the dynamics of paddy rice planting area
in 1986–2010 through time series Landsat images and phenology-based
algorithms, Remote Sens. Environ., 160, 99–113, https://doi.org/10.1016/j.rse.2015.01.004, 2015.
Dong, J., Xiao, X., Menarguez, M. A., Zhang, G., Qin, Y., Thau, D., Biradar,
C., and Moore III, B.: Mapping paddy rice planting area in northeastern Asia
with Landsat 8 images, phenology-based algorithm and Google Earth Engine,
Remote Sens. Environ., 185, 142–154, https://doi.org/10.1016/j.rse.2016.02.016, 2016.
Eilers, P. H.: A perfect smoother, Anal. Chem., 75, 3631–3636,
2003.
Estel, S., Kuemmerle, T., Levers, C., Baumann, M., and Hostert, P.: Mapping
cropland-use intensity across Europe using MODIS NDVI time series,
Environ. Res. Lett., 11, 024015, https://doi.org/10.1088/1748-9326/11/2/024015, 2016.
FAO, IFAD, UNICEF, WFP, and WHO: The State of Food Security and Nutrition in
the World 2020. Transforming food systems for affordable healthy diets, FAO,
Rome, Italy, https://doi.org/10.4060/ca9692en, 2020.
FAOSTAT: FAOSTAT database, Food and Agriculture Organization of the United Nations (FAO), Rome, Italy [data set], available at: https://www.fao.org/faostat/en/#data, last access: 4 September 2019.
Fritz, S., McCallum, I., Schill, C., Perger, C., See, L., Schepaschenko, D., Van der Velde, M., Kraxner, F., and Obersteiner, M.: Geo-Wiki: An online platform for improving global land cover, Environ. Model Softw., 31, 110–123, https://doi.org/10.1016/j.envsoft.2011.11.015, 2012.
Fritz, S., See, L., McCallum, I., You, L., Bun, A., Moltchanova, E.,
Duerauer, M., Albrecht, F., Schill, C., Perger, C., Havlik, P., Mosnier, A.,
Thornton, P., Wood-Sichra, U., Herrero, M., Becker-Reshef, I., Justice, C.,
Hansen, M., Gong, P., Abdel Aziz, S., Cipriani, A., Cumani, R., Cecchi, G.,
Conchedda, G., Ferreira, S., Gomez, A., Haffani, M., Kayitakire, F.,
Malanding, J., Mueller, R., Newby, T., Nonguierma, A., Olusegun, A., Ortner,
S., Rajak, D. R., Rocha, J., Schepaschenko, D., Schepaschenko, M., Terekhov,
A., Tiangwa, A., Vancutsem, C., Vintrou, E., Wenbin, W., van der Velde, M.,
Dunwoody, A., Kraxner, F., and Obersteiner, M.: Mapping global cropland and
field size, Glob. Change Biol., 21, 1980–1992, https://doi.org/10.1111/gcb.12838, 2015.
Galdo, V., Lopez-Acevedo, G., and Rama, M.: Conflict and the Composition of
Economic Activity in Afghanistan, World Bank Policy Research Working Paper, The World Bank, Washington, D.C., USA, No. 9188, available at: https://ssrn.com/abstract=3556240 (last access: 24 February 2021), 2020.
Gommes, R., Wu, B., Li, Z., and Zeng, H.: Design and characterization of
spatial units for monitoring global impacts of environmental factors on
major crops and food security, Food and Energy Security, 5, 40–55,
https://doi.org/10.1002/fes3.73, 2016.
Gommes, R., Wu, B., Zhang, N., Feng, X., Zeng, H., Li, Z., and Chen, B.:
CropWatch agroclimatic indicators (CWAIs) for weather impact assessment on
global agriculture, Int. J. Biometeorol., 61, 199–215,
https://doi.org/10.1007/s00484-016-1199-7, 2017.
Gong, P., Wang, J., Yu, L., Zhao, Y., Zhao, Y., Liang, L., Niu, Z., Huang,
X., Fu, H., Liu, S., Li, C., Li, X., Fu, W., Liu, C., Xu, Y., Wang, X.,
Cheng, Q., Hu, L., Yao, W., Zhang, H., Zhu, P., Zhao, Z., Zhang, H., Zheng,
Y., Ji, L., Zhang, Y., Chen, H., Yan, A., Guo, J., Yu, L., Wang, L., Liu,
X., Shi, T., Zhu, M., Chen, Y., Yang, G., Tang, P., Xu, B., Giri, C.,
Clinton, N., Zhu, Z., Chen, J., and Chen, J.: 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,
https://doi.org/10.1080/01431161.2012.748992, 2013.
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.
Gray, J., Friedl, M., Frolking, S., Ramankutty, N., Nelson, A., and Gumma,
M. K.: Mapping Asian cropping intensity with MODIS, IEEE J. Sel.
Top. Appl., 7, 3373–3379, https://doi.org/10.1109/JSTARS.2014.2344630, 2014.
Gray, J., Sulla-Menashe, D., and Friedl, M. A.: User guide to collection 6
modis land cover dynamics (mcd12q2) product, NASA EOSDIS Land Processes
DAAC, Missoula, MT, USA, 2019.
Guo, H.: Big Earth data in support of the sustainable development goals
(2019), Science Press and EDP Sciences, Beijing, China, 2021.
Guo, H., Bao, A., Liu, T., Ndayisaba, F., Jiang, L., Kurban, A., and De
Maeyer, P.: Spatial and temporal characteristics of droughts in Central Asia
during 1966–2015, Sci. Total Environ., 624, 1523–1538,
https://doi.org/10.1016/j.scitotenv.2017.12.120, 2018.
Hao, L., Sun, G., Liu, Y., Wan, J., Qin, M., Qian, H., Liu, C., Zheng, J., John, R., Fan, P., and Chen, J.: Urbanization dramatically altered the water balances of a paddy field-dominated basin in southern China, Hydrol. Earth Syst. Sci., 19, 3319–3331, https://doi.org/10.5194/hess-19-3319-2015, 2015.
Hao, P.-Y., Tang, H.-J., Chen, Z.-X., Le, Y. U., and Wu, M.-Q.: High
resolution crop intensity mapping using harmonized Landsat-8 and Sentinel-2
data, J. Integr. Agr., 18, 2883–2897, https://doi.org/10.1016/S2095-3119(19)62599-2, 2019.
Hinz, R., Sulser, T. B., Hüfner, R., Mason-D'Croz, D., Dunston, S.,
Nautiyal, S., Ringler, C., Schüngel, J., Tikhile, P., and Wimmer, F.:
Agricultural development and land use change in India: A scenario analysis
of trade-offs between UN Sustainable Development Goals (SDGs), Earth's
Future, 8, e2019EF001287, https://doi.org/10.1029/2019EF001287,
2020.
Iizumi, T. and Ramankutty, N.: How do weather and climate influence cropping
area and intensity?, Global Food Security, 4, 46–50, https://doi.org/10.1016/j.gfs.2014.11.003, 2015.
Iqbal, M. W., Donjadee, S., Kwanyuen, B., and Liu, S.-y.: Farmers'
perceptions of and adaptations to drought in Herat Province, Afghanistan,
J. Mt. Sci., 15, 1741–1756, https://doi.org/10.1007/s11629-017-4750-z, 2018.
Jain, M., Mondal, P., DeFries, R. S., Small, C., and Galford, G. L.: Mapping
cropping intensity of smallholder farms: A comparison of methods using
multiple sensors, Remote Sens. Environ., 134, 210–223, https://doi.org/10.1016/j.rse.2013.02.029, 2013.
Jankowski, K., Neill, C., Davidson, E. A., Macedo, M. N., Costa, C.,
Galford, G. L., Santos, L. M., Lefebvre, P., Nunes, D., and Cerri, C. E. P.:
Deep soils modify environmental consequences of increased nitrogen
fertilizer use in intensifying Amazon agriculture, Sci. Rep., 8,
13478, https://doi.org/10.1038/s41598-018-31175-1, 2018.
King, A. D., Pitman, A. J., Henley, B. J., Ukkola, A. M., and Brown, J. R.:
The role of climate variability in Australian drought, Nat. Clim.
Change, 10, 177–179, https://doi.org/10.1038/s41558-020-0718-z,
2020.
Kong, D., Zhang, Y., Gu, X., and Wang, D.: A robust method for
reconstructing global MODIS EVI time series on the Google Earth Engine,
ISPRS J. Photogramm., 155, 13–24, https://doi.org/10.1016/j.isprsjprs.2019.06.014, 2019.
Kontgis, C., Schneider, A., and Ozdogan, M.: Mapping rice paddy extent and
intensification in the Vietnamese Mekong River Delta with dense time stacks
of Landsat data, Remote Sens. Environ., 169, 255–269, https://doi.org/10.1016/j.rse.2015.08.004, 2015.
Köppen, W., Volken, E., and Brönnimann, S.: The thermal zones of the
earth according to the duration of hot, moderate and cold periods and to the
impact of heat on the organic world, Meteorol. Z., 20,
351–360, https://doi.org/10.1127/0941-2948/2011/105, 2011.
Kotsuki, S. and Tanaka, K.: SACRA – a method for the estimation of global high-resolution crop calendars from a satellite-sensed NDVI, Hydrol. Earth Syst. Sci., 19, 4441–4461, https://doi.org/10.5194/hess-19-4441-2015, 2015.
Lal, R.: Soil carbon dynamics in cropland and rangeland, Environ.
Poll., 116, 353–362, https://doi.org/10.1016/S0269-7491(01)00211-1, 2002.
Lewis, A., Oliver, S., Lymburner, L., Evans, B., Wyborn, L., Mueller, N.,
Raevksi, G., Hooke, J., Woodcock, R., and Sixsmith, J.: The Australian
geoscience data cube – foundations and lessons learned, Remote Sens. Environ., 202, 276–292, https://doi.org/10.1016/j.rse.2017.03.015, 2017.
Li, L., Friedl, M. A., Xin, Q., Gray, J., Pan, Y., and Frolking, S.: Mapping
crop cycles in China using MODIS-EVI time series, Remote Sens., 6,
2473–2493, https://doi.org/10.3390/rs6032473, 2014.
Liu, C., Zhang, Q., Tao, S., Qi, J., Ding, M., Guan, Q., Wu, B., Zhang, M.,
Nabil, M., and Tian, F.: A new framework to map fine resolution cropping
intensity across the globe: Algorithm, validation, and implication, Remote Sens. Environ., 251, 112095, https://doi.org/10.1016/j.rse.2020.112095, 2020.
Liu, H., Gong, P., Wang, J., Clinton, N., Bai, Y., and Liang, S.: Annual dynamics of global land cover and its long-term changes from 1982 to 2015, Earth Syst. Sci. Data, 12, 1217–1243, https://doi.org/10.5194/essd-12-1217-2020, 2020.
Liu, L., Xiao, X., Qin, Y., Wang, J., Xu, X., Hu, Y., and Qiao, Z.: Mapping
cropping intensity in China using time series Landsat and Sentinel-2 images
and Google Earth Engine, Remote Sens. Environ., 239, 111624,
https://doi.org/10.1016/j.rse.2019.111624, 2020.
Lowder, S. K., Skoet, J., and Raney, T.: The number, size, and distribution
of farms, smallholder farms, and family farms worldwide, World Dev.,
87, 16–29, https://doi.org/10.1016/j.worlddev.2015.10.041,
2016.
Mason-D'Croz, D., Sulser, T. B., Wiebe, K., Rosegrant, M. W., Lowder, S. K.,
Nin-Pratt, A., Willenbockel, D., Robinson, S., Zhu, T., and Cenacchi, N.:
Agricultural investments and hunger in Africa modeling potential
contributions to SDG2–Zero Hunger, World Dev., 116, 38–53,
https://doi.org/10.1016/j.worlddev.2018.12.006, 2019.
Morton, D. C., DeFries, R. S., Shimabukuro, Y. E., Anderson, L. O., Arai,
E., del Bon Espirito-Santo, F., Freitas, R., and Morisette, J.: Cropland
expansion changes deforestation dynamics in the southern Brazilian Amazon,
P. Natl. Acad. Sci. USA, 103, 14637–14641,
https://doi.org/10.1073/pnas.0606377103, 2006.
Nabil, M., Zhang, M., Bofana, J., Wu, B., Stein, A., Dong, T., Zeng, H., and
Shang, J.: Assessing factors impacting the spatial discrepancy of remote
sensing based cropland products: A case study in Africa, Int.
J. Appl. Earth Obs., 85, 102010,
https://doi.org/10.1016/j.jag.2019.102010, 2020.
Oliver, M. A. and Webster, R.: Kriging: a method of interpolation for
geographical information systems, Int. J. Geogr.
Inf. Syst., 4, 313–332, https://doi.org/10.1080/02693799008941549, 1990.
Pielke Sr., R. A., Adegoke, J. O., Chase, T. N., Marshall, C. H., Matsui, T.,
and Niyogi, D.: A new paradigm for assessing the role of agriculture in the
climate system and in climate change, Agr. Forest Meteorol.,
142, 234–254, https://doi.org/10.1016/j.agrformet.2006.06.012,
2007.
Qiu, S., Zhu, Z., and He, B.: Fmask 4.0: Improved cloud and cloud shadow
detection in Landsats 4–8 and Sentinel-2 imagery, Remote Sens. Environ., 231, 111205, https://doi.org/10.1016/j.rse.2019.05.024, 2019.
Ray, D. K. and Foley, J. A.: Increasing global crop harvest frequency:
recent trends and future directions, Environ. Res. Lett., 8,
044041, https://doi.org/10.1088/1748-9326/8/4/044041, 2013.
Richardson, A. D., Hufkens, K., Milliman, T., and Frolking, S.:
Intercomparison of phenological transition dates derived from the PhenoCam
Dataset V1. 0 and MODIS satellite remote sensing, Sci. Rep., 8,
5679, https://doi.org/10.1038/s41598-018-23804-6, 2018a.
Richardson, A. D., Hufkens, K., Milliman, T., Aubrecht, D. M., Chen, M.,
Gray, J. M., Johnston, M. R., Keenan, T. F., Klosterman, S. T., and Kosmala,
M.: Tracking vegetation phenology across diverse North American biomes using
PhenoCam imagery, Scientific Data, 5, 180028, https://doi.org/10.1038/sdata.2018.28, 2018b.
Rivera, J. A., Otta, S., Lauro, C., and Zazulie, N.: A decade of
hydrological drought in Central-Western Argentina, Frontiers in Water, 3,
640544, https://doi.org/10.3389/frwa.2021.640544, 2021.
Rousta, I., Olafsson, H., Moniruzzaman, M., Zhang, H., Liou, Y.-A., Mushore,
T. D., and Gupta, A.: Impacts of drought on vegetation assessed by
vegetation indices and meteorological factors in Afghanistan, Remote
Sens., 12, 2433, https://doi.org/10.3390/rs12152433, 2020.
Seyednasrollah, B., Young, A. M., Hufkens, K., Milliman, T., Friedl, M. A.,
Frolking, S., and Richardson, A. D.: Tracking vegetation phenology across
diverse biomes using Version 2.0 of the PhenoCam Dataset, Scientific Data,
6, 222, https://doi.org/10.1038/s41597-019-0229-9, 2019.
Sherrod, L. A., Peterson, G. A., Westfall, D. G., and Ahuja, L. R.: Cropping
intensity enhances soil organic carbon and nitrogen in a no-till
agroecosystem, Soil Sci. Soc. Am. J., 67, 1533–1543,
https://doi.org/10.2136/sssaj2003.1533, 2003.
Siebert, S., Portmann, F. T., and Döll, P.: Global patterns of cropland
use intensity, Remote Sens., 2, 1625–1643, https://doi.org/10.3390/rs2071625, 2010.
Singha, M., Dong, J., Zhang, G., and Xiao, X.: High resolution paddy rice
maps in cloud-prone Bangladesh and Northeast India using Sentinel-1 data,
Scientific Data, 6, 26, https://doi.org/10.1038/s41597-019-0036-3, 2019.
Song, X.-P., Potapov, P. V., Krylov, A., King, L., Di Bella, C. M., Hudson,
A., Khan, A., Adusei, B., Stehman, S. V., and Hansen, M. C.: National-scale
soybean mapping and area estimation in the United States using medium
resolution satellite imagery and field survey, Remote Sens.
Environ., 190, 383–395, https://doi.org/10.1016/j.rse.2017.01.008, 2017.
Tamiminia, H., Salehi, B., Mahdianpari, M., Quackenbush, L., Adeli, S., and
Brisco, B.: Google Earth Engine for geo-big data applications: A
meta-analysis and systematic review, ISPRS J. Photogramm., 164, 152–170, https://doi.org/10.1016/j.isprsjprs.2020.04.001, 2020.
Tilman, D., Balzer, C., Hill, J., and Befort, B. L.: Global food demand and
the sustainable intensification of agriculture, P. Natl.
Acad. Sci. USA, 108, 20260–20264, https://doi.org/10.1073/pnas.1116437108, 2011.
UN: Transforming our world: the 2030 Agenda for Sustainable Development, UN
General Assembly, United Nations, New York, NY, USA, 2015.
Waha, K., Dietrich, J. P., Portmann, F. T., Siebert, S., Thornton, P. K.,
Bondeau, A., and Herrero, M.: Multiple cropping systems of the world and the
potential for increasing cropping intensity, Global Environ. Change,
64, 102131, https://doi.org/10.1016/j.gloenvcha.2020.102131,
2020.
Waldner, F., De Abelleyra, D., Verón, S. R., Zhang, M., Wu, B.,
Plotnikov, D., Bartalev, S., Lavreniuk, M., Skakun, S., and Kussul, N.:
Towards a set of agrosystem-specific cropland mapping methods to address the
global cropland diversity, Int. J. Remote Sens., 37,
3196–3231, https://doi.org/10.1080/01431161.2016.1194545, 2016.
Whitcraft, A. K., Vermote, E. F., Becker-Reshef, I., and Justice, C. O.:
Cloud cover throughout the agricultural growing season: Impacts on passive
optical earth observations, Remote Sens. Environ., 156, 438–447,
https://doi.org/10.1016/j.rse.2014.10.009, 2015.
Whitcraft, A. K., Becker-Reshef, I., Justice, C. O., Gifford, L., Kavvada,
A., and Jarvis, I.: No pixel left behind: Toward integrating Earth
Observations for agriculture into the United Nations Sustainable Development
Goals framework, Remote Sens. Environ., 235, 111470, https://doi.org/10.1016/j.rse.2019.111470, 2019.
Wu, B., Ahmed, S., and He, C.: Shared Agronomic Information Community for
the Belt and Road Initiative, Bulletin of Chinese Academy of Sciences, 32,
34–41, 2017.
Wu, W., Yu, Q., You, L., Chen, K., Tang, H., and Liu, J.: Global cropping
intensity gaps: Increasing food production without cropland expansion, Land
Use Policy, 76, 515–525, https://doi.org/10.1016/j.landusepol.2018.02.032, 2018.
Xiao, X., Boles, S., Liu, J., Zhuang, D., Frolking, S., Li, C., Salas, W.,
and Moore Iii, B.: Mapping paddy rice agriculture in southern China using
multi-temporal MODIS images, Remote Sens. Environ., 95, 480–492,
https://doi.org/10.1016/j.rse.2004.12.009, 2005.
Xie, Y., Lark, T. J., Brown, J. F., and Gibbs, H. K.: Mapping irrigated
cropland extent across the conterminous United States at 30 m resolution
using a semi-automatic training approach on Google Earth Engine, ISPRS
J. Photogramm., 155, 136–149, https://doi.org/10.1016/j.isprsjprs.2019.07.005, 2019.
Xiong, J., Thenkabail, P. S., Tilton, J. C., Gumma, M. K., Teluguntla, P.,
Oliphant, A., Congalton, R. G., Yadav, K., and Gorelick, N.: Nominal 30 m
cropland extent map of continental Africa by integrating pixel-based and
object-based algorithms using Sentinel-2 and Landsat-8 data on Google Earth
Engine, Remote Sens., 9, 1065, https://doi.org/10.3390/rs9101065, 2017.
Yan, H., Xiao, X., Huang, H., Liu, J., Chen, J., and Bai, X.: Multiple
cropping intensity in China derived from agro-meteorological observations
and MODIS data, Chinese Geogr. Sci., 24, 205–219, https://doi.org/10.1007/s11769-013-0637-2, 2014.
Yan, H., Liu, F., Qin, Y., Doughty, R., and Xiao, X.: Tracking the
spatio-temporal change of cropping intensity in China during 2000–2015,
Environ. Res. Lett., 14, 035008, https://doi.org/10.1088/1748-9326/aaf9c7, 2019.
Zeng, Z., Estes, L., Ziegler, A. D., Chen, A., Searchinger, T., Hua, F.,
Guan, K., Jintrawet, A., and Wood, E. F.: Highland cropland expansion and
forest loss in Southeast Asia in the twenty-first century, Nat.
Geosci., 11, 556–562, https://doi.org/10.1038/s41561-018-0166-9, 2018.
Zhang, M. and Liu, C.: The script of core GCI30 algorithm on Google Earth Engine, Google Earth Engine (GEE) [code], available at: https://code.earthengine.google.com/64f569c03f8fd633a896a3ec6f56b89a, last access: 29 September 2021.
Zhang, M., Wu, B., Meng, J., Dong, T., and You, X.: Fallow land mapping for
better crop monitoring in Huang-Huai-Hai Plain using HJ-1 CCD data, IOP Conf. Ser.: Earth Environ. Sci., 17, 012048,
https://doi.org/10.1088/1755-1315/17/1/012048, 2014a.
Zhang, M., Wu, B., Yu, M., Zou, W., and Zheng, Y.: Crop condition assessment
with adjusted NDVI using the uncropped arable land ratio, Remote Sens., 6,
5774–5794, https://doi.org/10.3390/rs6065774, 2014b.
Zhang, M., Wu, B., Zeng, H., He, G., Liu, C., Nabil, M., Tian, F., Bofana,
J., Wang, Z., and Yan, N.: GCI30: Global Cropping Intensity at 30 m
resolution (2), V2, Harvard Dataverse [data set], https://doi.org/10.7910/DVN/86M4PO,
2020.
Zhang, Y., Kong, D., Gan, R., Chiew, F. H., McVicar, T. R., Zhang, Q., and
Yang, Y.: Coupled estimation of 500 m and 8-day resolution global
evapotranspiration and gross primary production in 2002–2017, Remote Sens. Environ., 222, 165–182, https://doi.org/10.1016/j.rse.2018.12.031, 2019.
Zhu, Z. and Woodcock, C. E.: Object-based cloud and cloud shadow detection
in Landsat imagery, Remote Sens. Environ., 118, 83–94, https://doi.org/10.1016/j.rse.2011.10.028, 2012.
Zohaib, M. and Choi, M.: Satellite-based global-scale irrigation water use
and its contemporary trends, Sci. Total Environ., 714, 136719,
https://doi.org/10.1016/j.scitotenv.2020.136719, 2020.
Download
The requested paper has a corresponding corrigendum published. Please read the corrigendum first before downloading the article.
- Article
(6873 KB) - Full-text XML
- Corrigendum
-
Supplement
(567 KB) - BibTeX
- EndNote
Short summary
Cropping intensity (CI) is essential for agricultural land use management, but fine-resolution global CI is not available. We used multiple satellite data on Google Earth Engine to develop a first 30 m resolution global CI (GCI30). GCI30 performed well, with an overall accuracy of 92 %. GCI30 not only exhibited high agreement with existing CI products but also provided many spatial details. GCI30 can facilitate research on sustained cropland intensification to improve food production.
Cropping intensity (CI) is essential for agricultural land use management, but fine-resolution...