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.
Data description paper
| 
21 Oct 2021
Data description paper |
| 21 Oct 2021
| 21 Oct 2021
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
Fuyou Tian, Bingfang Wu, Hongwei Zeng, Miao Zhang, Weiwei Zhu, Nana Yan, Yuming Lu, and Yifan Li
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2023-536, https://doi.org/10.5194/essd-2023-536, 2024
Revised manuscript under review for ESSD
Short summary
Short summary
Our team has developed an irrigation map with 100 m resolution, which is more detailed than existing one. We used satellite images and focused on the crop status during the dry conditions. We found that 23.4 % of global cropland is irrigated, with the most extensive areas in India, China, the US, and Pakistan. We also explored the distribution of central pivot systems, which are commonly used in the US and Saudi Arabia. This new map can better support water management and food security globally.
Xingli Qin, Bingfang Wu, Hongwei Zeng, Miao Zhang, and Fuyou Tian
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2023-346, https://doi.org/10.5194/essd-2023-346, 2023
Preprint withdrawn
Short summary
Short summary
We developed the first time-series gridded dataset of maize, wheat, rice and soybean production from 2010–2020. It offers detailed spatiotemporal information of crop production, allowing for advanced monitoring of agricultural productivity and for research into the factors that drive production trends across regions and over time. This dataset can help assess food security, evaluate climate impacts, and inform policies for more sustainable, resilient crop production worldwide.
Yan Sun, Shaoyin Wang, Xiao Cheng, Teng Li, Chong Liu, Yufang Ye, and Xi Zhao
EGUsphere, https://doi.org/10.5194/egusphere-2024-1177, https://doi.org/10.5194/egusphere-2024-1177, 2024
Preprint archived
Short summary
Short summary
Arctic sea ice has rapidly declined due to global warming, leading to extreme weather events. Accurate ice monitoring is vital for understanding and forecasting these impacts. Combining SAR and AMSR2 data with machine learning is efficient but requires sufficient labels. We propose a framework integrating the U-Net model with the Multi-textRG algorithm to achieve ice-water classification at SAR-level resolution and to generate accurate labels for improved U-Net model training.
Sadia Bibi, Tingju Zhu, Ashraf Rateb, Bridget R. Scanlon, Muhammad Aqeel Kamran, Abdelrazek Elnashar, Ali Bennour, and Ci Li
Hydrol. Earth Syst. Sci., 28, 1725–1750, https://doi.org/10.5194/hess-28-1725-2024, https://doi.org/10.5194/hess-28-1725-2024, 2024
Short summary
Short summary
We assessed 13 global models using GRACE satellite data over 29 river basins. Simulated seasonal water storage cycles showed discrepancies compared to GRACE. The models overestimated seasonal amplitude in boreal basins and showed underestimation in tropical, arid, and temperate zones, with phase differences of 2–3 months compared to GRACE in cold basins and of 1 month in temperate, arid, and semi-arid basins. Seasonal amplitude and phase differences provide insights for model improvement.
Fuyou Tian, Bingfang Wu, Hongwei Zeng, Miao Zhang, Weiwei Zhu, Nana Yan, Yuming Lu, and Yifan Li
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2023-536, https://doi.org/10.5194/essd-2023-536, 2024
Revised manuscript under review for ESSD
Short summary
Short summary
Our team has developed an irrigation map with 100 m resolution, which is more detailed than existing one. We used satellite images and focused on the crop status during the dry conditions. We found that 23.4 % of global cropland is irrigated, with the most extensive areas in India, China, the US, and Pakistan. We also explored the distribution of central pivot systems, which are commonly used in the US and Saudi Arabia. This new map can better support water management and food security globally.
Xingli Qin, Bingfang Wu, Hongwei Zeng, Miao Zhang, and Fuyou Tian
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2023-346, https://doi.org/10.5194/essd-2023-346, 2023
Preprint withdrawn
Short summary
Short summary
We developed the first time-series gridded dataset of maize, wheat, rice and soybean production from 2010–2020. It offers detailed spatiotemporal information of crop production, allowing for advanced monitoring of agricultural productivity and for research into the factors that drive production trends across regions and over time. This dataset can help assess food security, evaluate climate impacts, and inform policies for more sustainable, resilient crop production worldwide.
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
Y. Zhao, B. F. Wu, and Y. Zeng
Biogeosciences, 10, 1219–1230, https://doi.org/10.5194/bg-10-1219-2013, https://doi.org/10.5194/bg-10-1219-2013, 2013
Related subject area
Land Cover and Land Use
Enhancing high-resolution forest stand mean height mapping in China through an individual tree-based approach with close-range lidar data
Annual high-resolution grazing-intensity maps on the Qinghai–Tibet Plateau from 1990 to 2020
Global mapping of oil palm planting year from 1990 to 2021
A 28-time-point cropland area change dataset in Northeast China from 1000 to 2020
Mapping sugarcane globally at 10 m resolution using Global Ecosystem Dynamics Investigation (GEDI) and Sentinel-2
Annual maps of forest and evergreen forest in the contiguous United States during 2015–2017 from analyses of PALSAR-2 and Landsat images
Monsoon Asia Rice Calendar (MARC): a gridded rice calendar in monsoon Asia based on Sentinel-1 and Sentinel-2 images
A 100 m gridded population dataset of China's seventh census using ensemble learning and big geospatial data
Annual time-series 1 km maps of crop area and types in the conterminous US (CropAT-US): cropping diversity changes during 1850–2021
Retrieval of dominant methane (CH4) emission sources, the first high-resolution (1–2 m) dataset of storage tanks of China in 2000–2021
A 10 m resolution land cover map of the Tibetan Plateau with detailed vegetation types
ChinaSoyArea10m: a dataset of soybean-planting areas with a spatial resolution of 10 m across China from 2017 to 2021
Physical, social, and biological attributes for improved understanding and prediction of wildfires: FPA FOD-Attributes dataset
3D-GloBFP: the first global three-dimensional building footprint dataset
Map of forest tree species for Poland based on Sentinel-2 data
Global 30-m seamless data cube (2000–2022) of land surface reflectance generated from Landsat-5,7,8,9 and MODIS Terra constellations
The ABoVE L-band and P-band airborne synthetic aperture radar surveys
A 30 m annual cropland dataset of China from 1986 to 2021
Global 1 km land surface parameters for kilometer-scale Earth system modeling
A flux tower site attribute dataset intended for land surface modeling
ChinaRiceCalendar – seasonal crop calendars for early-, middle-, and late-season rice in China
Harmonized European Union subnational crop statistics can reveal climate impacts and crop cultivation shifts
GLC_FCS30D: the first global 30 m land-cover dynamics monitoring product with a fine classification system for the period from 1985 to 2022 generated using dense-time-series Landsat imagery and the continuous change-detection method
A global estimate of monthly vegetation and soil fractions from spatiotemporally adaptive spectral mixture analysis during 2001–2022
A 2020 forest age map for China with 30 m resolution
Country-level estimates of gross and net carbon fluxes from land use, land-use change and forestry
A global FAOSTAT reference database of cropland nutrient budgets and nutrient use efficiency (1961–2020): nitrogen, phosphorus and potassium
Advancements in LUCAS Copernicus 2022: Enhancing Earth Observation with Comprehensive In-Situ Data on EU Land Cover and Use
Annual maps of forest cover in the Brazilian Amazon from analyses of PALSAR and MODIS images
Global 500 m seamless dataset (2000–2022) of land surface reflectance generated from MODIS products
The first map of crop sequence types in Europe over 2012–2018
WorldCereal: a dynamic open-source system for global-scale, seasonal, and reproducible crop and irrigation mapping
High-resolution mapping of global winter-triticeae crops using a sample-free identification method
Mapping Rangeland Health Indicators in East Africa from 2000 to 2022
A new cropland area database by country circa 2020
FORMS: Forest Multiple Source height, wood volume, and biomass maps in France at 10 to 30 m resolution based on Sentinel-1, Sentinel-2, and Global Ecosystem Dynamics Investigation (GEDI) data with a deep learning approach
SinoLC-1: the first 1 m resolution national-scale land-cover map of China created with a deep learning framework and open-access data
HISDAC-ES: historical settlement data compilation for Spain (1900–2020)
LCM2021 – the UK Land Cover Map 2021
ChinaWheatYield30m: a 30 m annual winter wheat yield dataset from 2016 to 2021 in China
Refined fine-scale mapping of tree cover using time series of Planet-NICFI and Sentinel-1 imagery for Southeast Asia (2016–2021)
High-resolution global map of closed-canopy coconut palm
High-resolution land use and land cover dataset for regional climate modelling: historical and future changes in Europe
Global urban fractional changes at a 1 km resolution throughout 2100 under eight scenarios of Shared Socioeconomic Pathways (SSPs) and Representative Concentration Pathways (RCPs)
China Building Rooftop Area: the first multi-annual (2016–2021) and high-resolution (2.5 m) building rooftop area dataset in China derived with super-resolution segmentation from Sentinel-2 imagery
High-resolution distribution maps of single-season rice in China from 2017 to 2022
Mapping global non-floodplain wetlands
An improved global land cover mapping in 2015 with 30 m resolution (GLC-2015) based on a multisource product-fusion approach
Annual emissions of carbon from land use, land-use change, and forestry from 1850 to 2020
An open-source automatic survey of green roofs in London using segmentation of aerial imagery
Yuling Chen, Haitao Yang, Zekun Yang, Qiuli Yang, Weiyan Liu, Guoran Huang, Yu Ren, Kai Cheng, Tianyu Xiang, Mengxi Chen, Danyang Lin, Zhiyong Qi, Jiachen Xu, Yixuan Zhang, Guangcai Xu, and Qinghua Guo
Earth Syst. Sci. Data, 16, 5267–5285, https://doi.org/10.5194/essd-16-5267-2024, https://doi.org/10.5194/essd-16-5267-2024, 2024
Short summary
Short summary
The national-scale continuous maps of arithmetic mean height and weighted mean height across China address the challenges of accurately estimating forest stand mean height using a tree-based approach. These maps produced in this study provide critical datasets for forest sustainable management in China, including climate change mitigation (e.g., terrestrial carbon estimation), forest ecosystem assessment, and forest inventory practices.
Jia Zhou, Jin Niu, Ning Wu, and Tao Lu
Earth Syst. Sci. Data, 16, 5171–5189, https://doi.org/10.5194/essd-16-5171-2024, https://doi.org/10.5194/essd-16-5171-2024, 2024
Short summary
Short summary
The study provided an annual 100 m resolution glimpse into the grazing activities across the Qinghai–Tibet Plateau. The newly minted Gridded Dataset of Grazing Intensity (GDGI) not only boasts exceptional accuracy but also acts as a pivotal resource for further research and strategic planning, with the potential to shape sustainable grazing practices, guide informed environmental stewardship, and ensure the longevity of the region’s precious ecosystems.
Adrià Descals, David L. A. Gaveau, Serge Wich, Zoltan Szantoi, and Erik Meijaard
Earth Syst. Sci. Data, 16, 5111–5129, https://doi.org/10.5194/essd-16-5111-2024, https://doi.org/10.5194/essd-16-5111-2024, 2024
Short summary
Short summary
This study provides a 10 m global oil palm extent layer for 2021 and a 30 m oil palm planting-year layer from 1990 to 2021. The oil palm extent layer was produced using a convolutional neural network that identified industrial and smallholder plantations using Sentinel-1 data. The oil palm planting year was developed using a methodology specifically designed to detect the early stages of oil palm development in the Landsat time series.
Ran Jia, Xiuqi Fang, Yundi Yang, Masayuki Yokozawa, and Yu Ye
Earth Syst. Sci. Data, 16, 4971–4994, https://doi.org/10.5194/essd-16-4971-2024, https://doi.org/10.5194/essd-16-4971-2024, 2024
Short summary
Short summary
We reconstructed a cropland area change dataset in Northeast China over the past millennium by integrating multisource data with a unified standard using the historical and archaeological record, statistical yearbook, and national land survey. Cropland in Northeast China exhibited phases of expansion–reduction–expansion over the past millennium. This dataset can be used for improving the land use and land cover change (LUCC) dataset and assessing LUCC-induced carbon emission and climate change.
Stefania Di Tommaso, Sherrie Wang, Rob Strey, and David B. Lobell
Earth Syst. Sci. Data, 16, 4931–4947, https://doi.org/10.5194/essd-16-4931-2024, https://doi.org/10.5194/essd-16-4931-2024, 2024
Short summary
Short summary
Sugarcane plays a vital role in food, biofuel, and farmer income globally, yet its cultivation faces numerous social and environmental challenges. Despite its significance, accurate mapping remains limited. Our study addresses this gap by introducing a novel 10 m global dataset of sugarcane maps spanning 2019–2022. Comparisons with field data, pre-existing maps, and official government statistics all indicate the high precision and high recall of our maps.
Jie Wang, Xiangming Xiao, Yuanwei Qin, Jinwei Dong, Geli Zhang, Xuebin Yang, Xiaocui Wu, Chandrashekhar Biradar, and Yang Hu
Earth Syst. Sci. Data, 16, 4619–4639, https://doi.org/10.5194/essd-16-4619-2024, https://doi.org/10.5194/essd-16-4619-2024, 2024
Short summary
Short summary
Existing satellite-based forest maps have large uncertainties due to different forest definitions and mapping algorithms. To effectively manage forest resources, timely and accurate annual forest maps at a high spatial resolution are needed. This study improved forest maps by integrating PALSAR-2 and Landsat images. Annual evergreen and non-evergreen forest-type maps were also generated. This critical information supports the Global Forest Resources Assessment.
Xin Zhao, Kazuya Nishina, Haruka Izumisawa, Yuji Masutomi, Seima Osako, and Shuhei Yamamoto
Earth Syst. Sci. Data, 16, 3893–3911, https://doi.org/10.5194/essd-16-3893-2024, https://doi.org/10.5194/essd-16-3893-2024, 2024
Short summary
Short summary
Mapping a rice calendar in a spatially explicit manner with a consistent framework remains challenging at a global or continental scale. We successfully developed a new gridded rice calendar for monsoon Asia based on Sentinel-1 and Sentinel-2 images, which characterize transplanting and harvesting dates and the number of rice croppings in a comprehensive framework. Our rice calendar will be beneficial for rice management, production prediction, and the estimation of greenhouse gas emissions.
Yuehong Chen, Congcong Xu, Yong Ge, Xiaoxiang Zhang, and Ya'nan Zhou
Earth Syst. Sci. Data, 16, 3705–3718, https://doi.org/10.5194/essd-16-3705-2024, https://doi.org/10.5194/essd-16-3705-2024, 2024
Short summary
Short summary
Population data is crucial for human–nature interactions. Gridded population data can address limitations of census data in irregular units. In China, rapid urbanization necessitates timely and accurate population grids. However, existing datasets for China are either outdated or lack recent census data. Hence, a novel approach was developed to disaggregate China’s seventh census data into 100 m population grids. The resulting dataset outperformed the existing LandScan and WorldPop datasets.
Shuchao Ye, Peiyu Cao, and Chaoqun Lu
Earth Syst. Sci. Data, 16, 3453–3470, https://doi.org/10.5194/essd-16-3453-2024, https://doi.org/10.5194/essd-16-3453-2024, 2024
Short summary
Short summary
We reconstructed annual cropland density and crop type maps, including nine major crop types (corn, soybean, winter wheat, spring wheat, durum wheat, cotton, sorghum, barley, and rice), from 1850 to 2021 at 1 km × 1 km resolution. We found that the US total crop acreage has increased by 118 × 106 ha (118 Mha), mainly driven by corn (30 Mha) and soybean (35 Mha). Additionally, the US cropping diversity experienced an increase in the 1850s–1960s, followed by a decline over the past 6 decades.
Fang Chen, Lei Wang, Yu Wang, Haiying Zhang, Ning Wang, Pengfei Ma, and Bo Yu
Earth Syst. Sci. Data, 16, 3369–3382, https://doi.org/10.5194/essd-16-3369-2024, https://doi.org/10.5194/essd-16-3369-2024, 2024
Short summary
Short summary
Storage tanks are responsible for approximately 25 % of CH4 emissions in the atmosphere, exacerbating climate warming. Currently there is no publicly accessible storage tank inventory. We generated the first high-spatial-resolution (1–2 m) storage tank dataset (STD) over 92 typical cities in China in 2021, totaling 14 461 storage tanks with the construction year from 2000–2021. It shows significant agreement with CH4 emission spatially and temporally, promoting the CH4 control strategy proposal.
Xingyi Huang, Yuwei Yin, Luwei Feng, Xiaoye Tong, Xiaoxin Zhang, Jiangrong Li, and Feng Tian
Earth Syst. Sci. Data, 16, 3307–3332, https://doi.org/10.5194/essd-16-3307-2024, https://doi.org/10.5194/essd-16-3307-2024, 2024
Short summary
Short summary
The Tibetan Plateau, with its diverse vegetation ranging from forests to alpine grasslands, plays a key role in understanding climate change impacts. Existing maps lack detail or miss unique ecosystems. Our research, using advanced satellite technology and machine learning, produced the map TP_LC10-2022. Comparisons with other maps revealed TP_LC10-2022's excellence in capturing local variations. Our map is significant for in-depth ecological studies.
Qinghang Mei, Zhao Zhang, Jichong Han, Jie Song, Jinwei Dong, Huaqing Wu, Jialu Xu, and Fulu Tao
Earth Syst. Sci. Data, 16, 3213–3231, https://doi.org/10.5194/essd-16-3213-2024, https://doi.org/10.5194/essd-16-3213-2024, 2024
Short summary
Short summary
In order to make up for the lack of long-term soybean planting area maps in China, we firstly generated a dataset of soybean planting area with a spatial resolution of 10 m for major producing areas in China from 2017 to 2021 (ChinaSoyArea10m). Compared with existing datasets, ChinaSoyArea10m has higher consistency with census data and further improvement in spatial details. The dataset can provide reliable support for subsequent studies on yield monitoring and food security.
Yavar Pourmohamad, John T. Abatzoglou, Erin J. Belval, Erica Fleishman, Karen Short, Matthew C. Reeves, Nicholas Nauslar, Philip E. Higuera, Eric Henderson, Sawyer Ball, Amir AghaKouchak, Jeffrey P. Prestemon, Julia Olszewski, and Mojtaba Sadegh
Earth Syst. Sci. Data, 16, 3045–3060, https://doi.org/10.5194/essd-16-3045-2024, https://doi.org/10.5194/essd-16-3045-2024, 2024
Short summary
Short summary
The FPA FOD-Attributes dataset provides > 300 biological, physical, social, and administrative attributes associated with > 2.3×106 wildfire incidents across the US from 1992 to 2020. The dataset can be used to (1) answer numerous questions about the covariates associated with human- and lightning-caused wildfires and (2) support descriptive, diagnostic, predictive, and prescriptive wildfire analytics, including the development of machine learning models.
Yangzi Che, Xuecao Li, Xiaoping Liu, Yuhao Wang, Weilin Liao, Xianwei Zheng, Xucai Zhang, Xiaocong Xu, Qian Shi, Jiajun Zhu, Hua Yuan, and Yongjiu Dai
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-217, https://doi.org/10.5194/essd-2024-217, 2024
Revised manuscript accepted for ESSD
Short summary
Short summary
Given the limited coverage or spatial resolution of existing datasets, we develop the first global building height map (3D-GloBFP) at the building footprint scale using Earth observation datasets and advanced machine learning techniques. Our map reveals the complex 3-D morphology of the world's building heights at a finer scale and provides reliable results (i.e., R2: 0.66–0.96, RMSEs: 1.9 m–14.6 m) over global regions 3D-GloBFP has great potential to support both macro- and micro-urban analysis
Ewa Grabska-Szwagrzyk, Dirk Tiede, Martin Sudmanns, and Jacek Kozak
Earth Syst. Sci. Data, 16, 2877–2891, https://doi.org/10.5194/essd-16-2877-2024, https://doi.org/10.5194/essd-16-2877-2024, 2024
Short summary
Short summary
We accurately mapped 16 dominant tree species and genera in Poland using Sentinel-2 observations from short periods in spring, summer, and autumn (2018–2021). The classification achieved more than 80% accuracy in country-wide forest species mapping, with variation based on species, region, and observation frequency. Freely accessible resources, including the forest tree species map and training and test data, can be found at https://doi.org/10.5281/zenodo.10180469.
Shuang Chen, Jie Wang, Qiang Liu, Xiangan Liang, Rui Liu, Peng Qin, Jincheng Yuan, Junbo Wei, Shuai Yuan, Huabing Huang, and Peng Gong
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-178, https://doi.org/10.5194/essd-2024-178, 2024
Revised manuscript accepted for ESSD
Short summary
Short summary
The inconsistent coverage of Landsat data due to its long revisit intervals and frequent cloud cover poses challenges to large-scale land monitoring. We developed a global, 30-m, 23-year (2000–2022), and daily Seamless Data Cube (SDC) of surface reflectance based on Landsat 5,7,8,9 and MODIS products. The SDC exhibits enhanced capabilities for monitoring land cover changes and robust consistency in both spatial and temporal dimensions, which are important for global environmental monitoring.
Charles E. Miller, Peter C. Griffith, Elizabeth Hoy, Naiara S. Pinto, Yunling Lou, Scott Hensley, Bruce D. Chapman, Jennifer Baltzer, Kazem Bakian-Dogaheh, W. Robert Bolton, Laura Bourgeau-Chavez, Richard H. Chen, Byung-Hun Choe, Leah K. Clayton, Thomas A. Douglas, Nancy French, Jean E. Holloway, Gang Hong, Lingcao Huang, Go Iwahana, Liza Jenkins, John S. Kimball, Tatiana Loboda, Michelle Mack, Philip Marsh, Roger J. Michaelides, Mahta Moghaddam, Andrew Parsekian, Kevin Schaefer, Paul R. Siqueira, Debjani Singh, Alireza Tabatabaeenejad, Merritt Turetsky, Ridha Touzi, Elizabeth Wig, Cathy J. Wilson, Paul Wilson, Stan D. Wullschleger, Yonghong Yi, Howard A. Zebker, Yu Zhang, Yuhuan Zhao, and Scott J. Goetz
Earth Syst. Sci. Data, 16, 2605–2624, https://doi.org/10.5194/essd-16-2605-2024, https://doi.org/10.5194/essd-16-2605-2024, 2024
Short summary
Short summary
NASA’s Arctic Boreal Vulnerability Experiment (ABoVE) conducted airborne synthetic aperture radar (SAR) surveys of over 120 000 km2 in Alaska and northwestern Canada during 2017, 2018, 2019, and 2022. This paper summarizes those results and provides links to details on ~ 80 individual flight lines. This paper is presented as a guide to enable interested readers to fully explore the ABoVE L- and P-band SAR data.
Ying Tu, Shengbiao Wu, Bin Chen, Qihao Weng, Yuqi Bai, Jun Yang, Le Yu, and Bing Xu
Earth Syst. Sci. Data, 16, 2297–2316, https://doi.org/10.5194/essd-16-2297-2024, https://doi.org/10.5194/essd-16-2297-2024, 2024
Short summary
Short summary
We developed the first 30 m annual cropland dataset of China (CACD) for 1986–2021. The overall accuracy of CACD reached up to 0.93±0.01 and was superior to other products. Our fine-resolution cropland maps offer valuable information for diverse applications and decision-making processes in the future.
Lingcheng Li, Gautam Bisht, Dalei Hao, and L. Ruby Leung
Earth Syst. Sci. Data, 16, 2007–2032, https://doi.org/10.5194/essd-16-2007-2024, https://doi.org/10.5194/essd-16-2007-2024, 2024
Short summary
Short summary
This study fills a gap to meet the emerging needs of kilometer-scale Earth system modeling by developing global 1 km land surface parameters for land use, vegetation, soil, and topography. Our demonstration simulations highlight the substantial impacts of these parameters on spatial variability and information loss in water and energy simulations. Using advanced explainable machine learning methods, we identified influential factors driving spatial variability and information loss.
Jiahao Shi, Hua Yuan, Wanyi Lin, Wenzong Dong, Hongbin Liang, Zhuo Liu, Jianxin Zeng, Haolin Zhang, Nan Wei, Zhongwang Wei, Shupeng Zhang, Shaofeng Liu, Xingjie Lu, and Yongjiu Dai
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-77, https://doi.org/10.5194/essd-2024-77, 2024
Revised manuscript accepted for ESSD
Short summary
Short summary
Flux tower data are widely recognized as benchmarking data for land surface models, but insufficient emphasis on and deficiency in site attribute data limits their true value. We collect site-observed vegetation, soil, and topography data from various sources. The final dataset encompasses 90 sites globally with relatively complete site attribute data and high-quality flux validation data. This work has provided more reliable site attribute data, benefiting land surface model development.
Hui Li, Xiaobo Wang, Shaoqiang Wang, Jinyuan Liu, Yuanyuan Liu, Zhenhai Liu, Shiliang Chen, Qinyi Wang, Tongtong Zhu, Lunche Wang, and Lizhe Wang
Earth Syst. Sci. Data, 16, 1689–1701, https://doi.org/10.5194/essd-16-1689-2024, https://doi.org/10.5194/essd-16-1689-2024, 2024
Short summary
Short summary
Utilizing satellite remote sensing data, we established a multi-season rice calendar dataset named ChinaRiceCalendar. It exhibits strong alignment with field observations collected by agricultural meteorological stations across China. ChinaRiceCalendar stands as a reliable dataset for investigating and optimizing the spatiotemporal dynamics of rice phenology in China, particularly in the context of climate and land use changes.
Giulia Ronchetti, Luigi Nisini Scacchiafichi, Lorenzo Seguini, Iacopo Cerrani, and Marijn van der Velde
Earth Syst. Sci. Data, 16, 1623–1649, https://doi.org/10.5194/essd-16-1623-2024, https://doi.org/10.5194/essd-16-1623-2024, 2024
Short summary
Short summary
We present a dataset of EU-wide harmonized subnational crop area, production, and yield statistics with information on data sources, processing steps, missing and derived data, and quality checks. Statistical records (344 282) collected from 1975 to 2020 for soft and durum wheat, winter and spring barley, grain maize, sunflower, and sugar beet were aligned with the EUROSTAT crop legend and the 2016 territorial classification for 961 regions. Time series have a median length of 21 years.
Xiao Zhang, Tingting Zhao, Hong Xu, Wendi Liu, Jinqing Wang, Xidong Chen, and Liangyun Liu
Earth Syst. Sci. Data, 16, 1353–1381, https://doi.org/10.5194/essd-16-1353-2024, https://doi.org/10.5194/essd-16-1353-2024, 2024
Short summary
Short summary
This work describes GLC_FCS30D, the first global 30 m land-cover dynamics monitoring dataset, which contains 35 land-cover subcategories and covers the period of 1985–2022 in 26 time steps (its maps are updated every 5 years before 2000 and annually after 2000).
Qiangqiang Sun, Ping Zhang, Xin Jiao, Xin Lin, Wenkai Duan, Su Ma, Qidi Pan, Lu Chen, Yongxiang Zhang, Shucheng You, Shunxi Liu, Jinmin Hao, Hong Li, and Danfeng Sun
Earth Syst. Sci. Data, 16, 1333–1351, https://doi.org/10.5194/essd-16-1333-2024, https://doi.org/10.5194/essd-16-1333-2024, 2024
Short summary
Short summary
To provide multifaceted changes under climate change and anthropogenic impacts, we estimated monthly vegetation and soil fractions in 2001–2022, providing an accurate estimate of surface heterogeneous composition, better than vegetation index and vegetation continuous-field products. We find a greening trend on Earth except for the tropics. A combination of interactive changes in vegetation and soil can be adopted as a valuable measurement of climate change and anthropogenic impacts.
Kai Cheng, Yuling Chen, Tianyu Xiang, Haitao Yang, Weiyan Liu, Yu Ren, Hongcan Guan, Tianyu Hu, Qin Ma, and Qinghua Guo
Earth Syst. Sci. Data, 16, 803–819, https://doi.org/10.5194/essd-16-803-2024, https://doi.org/10.5194/essd-16-803-2024, 2024
Short summary
Short summary
To quantify forest carbon stock and its future potential accurately, we generated a 30 m resolution forest age map for China in 2020 using multisource remote sensing datasets based on machine learning and time series analysis approaches. Validation with independent field samples indicated that the mapped forest age had an R2 of 0.51--0.63. Nationally, the average forest age is 56.1 years (standard deviation of 32.7 years).
Wolfgang Alexander Obermeier, Clemens Schwingshackl, Ana Bastos, Giulia Conchedda, Thomas Gasser, Giacomo Grassi, Richard A. Houghton, Francesco Nicola Tubiello, Stephen Sitch, and Julia Pongratz
Earth Syst. Sci. Data, 16, 605–645, https://doi.org/10.5194/essd-16-605-2024, https://doi.org/10.5194/essd-16-605-2024, 2024
Short summary
Short summary
We provide and compare country-level estimates of land-use CO2 fluxes from a variety and large number of models, bottom-up estimates, and country reports for the period 1950–2021. Although net fluxes are small in many countries, they are often composed of large compensating emissions and removals. In many countries, the estimates agree well once their individual characteristics are accounted for, but in other countries, including some of the largest emitters, substantial uncertainties exist.
Cameron I. Ludemann, Nathan Wanner, Pauline Chivenge, Achim Dobermann, Rasmus Einarsson, Patricio Grassini, Armelle Gruere, Kevin Jackson, Luis Lassaletta, Federico Maggi, Griffiths Obli-Laryea, Martin K. van Ittersum, Srishti Vishwakarma, Xin Zhang, and Francesco N. Tubiello
Earth Syst. Sci. Data, 16, 525–541, https://doi.org/10.5194/essd-16-525-2024, https://doi.org/10.5194/essd-16-525-2024, 2024
Short summary
Short summary
Nutrient budgets help identify the excess or insufficient use of fertilizers and other nutrient sources in agriculture. They allow the calculation of indicators, such as the nutrient balance (surplus or deficit) and nutrient use efficiency, that help to monitor agricultural productivity and sustainability. This article describes a global cropland nutrient budget that provides data on 205 countries and territories from 1961 to 2020 (data available at https://www.fao.org/faostat/en/#data/ESB).
Raphaël d'Andrimont, Momchil Yordanov, Fernando Sedano, Astrid Verhegghen, Peter Strobl, Savvas Zachariadis, Flavia Camilleri, Alessandra Palmieri, Beatrice Eiselt, Jose Miguel Rubio Iglesias, and Marijn van der Velde
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2023-494, https://doi.org/10.5194/essd-2023-494, 2024
Revised manuscript accepted for ESSD
Short summary
Short summary
LUCAS 2022 Copernicus is a large an systematic in-situ field survey of 137,966 polygons over the EU in 2022. The data holds 82 land cover classes and 40 land use classes.
Yuanwei Qin, Xiangming Xiao, Hao Tang, Ralph Dubayah, Russell Doughty, Diyou Liu, Fang Liu, Yosio Shimabukuro, Egidio Arai, Xinxin Wang, and Berrien Moore III
Earth Syst. Sci. Data, 16, 321–336, https://doi.org/10.5194/essd-16-321-2024, https://doi.org/10.5194/essd-16-321-2024, 2024
Short summary
Short summary
Forest definition has two major biophysical parameters, i.e., canopy height and canopy coverage. However, few studies have assessed forest cover maps in terms of these two parameters at a large scale. Here, we assessed the annual forest cover maps in the Brazilian Amazon using 1.1 million footprints of canopy height and canopy coverage. Over 93 % of our forest cover maps are consistent with the FAO forest definition, showing the high accuracy of these forest cover maps in the Brazilian Amazon.
Xiangan Liang, Qiang Liu, Jie Wang, Shuang Chen, and Peng Gong
Earth Syst. Sci. Data, 16, 177–200, https://doi.org/10.5194/essd-16-177-2024, https://doi.org/10.5194/essd-16-177-2024, 2024
Short summary
Short summary
The state-of-the-art MODIS surface reflectance products suffer from temporal and spatial gaps, which make it difficult to characterize the continuous variation of the terrestrial surface. We proposed a framework for generating the first global 500 m daily seamless data cubes (SDC500), covering the period from 2000 to 2022. We believe that the SDC500 dataset can interest other researchers who study land cover mapping, quantitative remote sensing, and ecological science.
Rémy Ballot, Nicolas Guilpart, and Marie-Hélène Jeuffroy
Earth Syst. Sci. Data, 15, 5651–5666, https://doi.org/10.5194/essd-15-5651-2023, https://doi.org/10.5194/essd-15-5651-2023, 2023
Short summary
Short summary
Assessing the benefits of crop diversification – a key element of agroecological transition – on a large scale requires a description of current crop sequences as a baseline, which is lacking at the scale of Europe. To fill this gap, we used a dataset that provides temporally and spatially incomplete land cover information to create a map of dominant crop sequence types for Europe over 2012–2018. This map is a useful baseline for assessing the benefits of future crop diversification.
Kristof Van Tricht, Jeroen Degerickx, Sven Gilliams, Daniele Zanaga, Marjorie Battude, Alex Grosu, Joost Brombacher, Myroslava Lesiv, Juan Carlos Laso Bayas, Santosh Karanam, Steffen Fritz, Inbal Becker-Reshef, Belén Franch, Bertran Mollà-Bononad, Hendrik Boogaard, Arun Kumar Pratihast, Benjamin Koetz, and Zoltan Szantoi
Earth Syst. Sci. Data, 15, 5491–5515, https://doi.org/10.5194/essd-15-5491-2023, https://doi.org/10.5194/essd-15-5491-2023, 2023
Short summary
Short summary
WorldCereal is a global mapping system that addresses food security challenges. It provides seasonal updates on crop areas and irrigation practices, enabling informed decision-making for sustainable agriculture. Our global products offer insights into temporary crop extent, seasonal crop type maps, and seasonal irrigation patterns. WorldCereal is an open-source tool that utilizes space-based technologies, revolutionizing global agricultural mapping.
Yangyang Fu, Xiuzhi Chen, Chaoqing Song, Xiaojuan Huang, Jie Dong, Qiongyan Peng, and Wenping Yuan
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2023-432, https://doi.org/10.5194/essd-2023-432, 2023
Revised manuscript accepted for ESSD
Short summary
Short summary
This study proposed the Winter-Triticeae Crops Index (WTCI)，which had great performance and stable spatiotemporal transferability in identifying winter-triticeae crops in 65 countries worldwide, with an overall accuracy of 87.7 %. The first global 30 m resolution distribution maps of winter-triticeae crops from 2017 to 2022 were further produced based on the WTCI method. The product can serve as an important basis for agricultural applications.
Gerardo E. Soto, Steven Wilcox, Patrick E. Clark, Francesco P. Fava, Nathan M. Jensen, Njoki Kahiu, Chuan Liao, Benjamin Porter, Ying Sun, and Christopher Barrett
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2023-217, https://doi.org/10.5194/essd-2023-217, 2023
Revised manuscript accepted for ESSD
Short summary
Short summary
Using machine learning classification and linear unmixing, this paper produced Landsat-based time series of land cover classes and vegetation fractional cover of photosynthetic vegetation, non-photosynthetic vegetation, and bare ground. This dataset represents a first multi-decadal high-resolution dataset specifically designed for mapping and monitoring rangelands health in East Africa including Kenya, Ethiopia, and Somalia, which are dominated by arid and semi-arid extensive rangeland systems.
Francesco N. Tubiello, Giulia Conchedda, Leon Casse, Pengyu Hao, Giorgia De Santis, and Zhongxin Chen
Earth Syst. Sci. Data, 15, 4997–5015, https://doi.org/10.5194/essd-15-4997-2023, https://doi.org/10.5194/essd-15-4997-2023, 2023
Short summary
Short summary
We describe a new dataset of cropland area circa the year 2020, with global coverage and country detail. Data are generated from geospatial information on the agreement characteristics of six high-resolution cropland maps. By helping to highlight features of cropland characteristics and underlying causes for agreement across land cover products, the dataset can be used as a tool to help guide future mapping efforts towards improved agricultural monitoring.
Martin Schwartz, Philippe Ciais, Aurélien De Truchis, Jérôme Chave, Catherine Ottlé, Cedric Vega, Jean-Pierre Wigneron, Manuel Nicolas, Sami Jouaber, Siyu Liu, Martin Brandt, and Ibrahim Fayad
Earth Syst. Sci. Data, 15, 4927–4945, https://doi.org/10.5194/essd-15-4927-2023, https://doi.org/10.5194/essd-15-4927-2023, 2023
Short summary
Short summary
As forests play a key role in climate-related issues, their accurate monitoring is critical to reduce global carbon emissions effectively. Based on open-access remote-sensing sensors, and artificial intelligence methods, we created high-resolution tree height, wood volume, and biomass maps of metropolitan France that outperform previous products. This study, based on freely available data, provides essential information to support climate-efficient forest management policies at a low cost.
Zhuohong Li, Wei He, Mofan Cheng, Jingxin Hu, Guangyi Yang, and Hongyan Zhang
Earth Syst. Sci. Data, 15, 4749–4780, https://doi.org/10.5194/essd-15-4749-2023, https://doi.org/10.5194/essd-15-4749-2023, 2023
Short summary
Short summary
Nowadays, a very-high-resolution land-cover (LC) map with national coverage is still unavailable in China, hindering efficient resource allocation. To fill this gap, the first 1 m resolution LC map of China, SinoLC-1, was built. The results showed that SinoLC-1 had an overall accuracy of 73.61 % and conformed to the official survey reports. Comparison with other datasets suggests that SinoLC-1 can be a better support for downstream applications and provide more accurate LC information to users.
Johannes H. Uhl, Dominic Royé, Keith Burghardt, José A. Aldrey Vázquez, Manuel Borobio Sanchiz, and Stefan Leyk
Earth Syst. Sci. Data, 15, 4713–4747, https://doi.org/10.5194/essd-15-4713-2023, https://doi.org/10.5194/essd-15-4713-2023, 2023
Short summary
Short summary
Historical, fine-grained geospatial datasets on built-up areas are rarely available, constraining studies of urbanization, settlement evolution, or the dynamics of human–environment interactions to recent decades. In order to provide such historical data, we used publicly available cadastral building data for Spain and created a series of gridded surfaces, measuring age, physical, and land-use-related features of the built environment in Spain and the evolution of settlements from 1900 to 2020.
Christopher G. Marston, Aneurin W. O'Neil, R. Daniel Morton, Claire M. Wood, and Clare S. Rowland
Earth Syst. Sci. Data, 15, 4631–4649, https://doi.org/10.5194/essd-15-4631-2023, https://doi.org/10.5194/essd-15-4631-2023, 2023
Short summary
Short summary
The UK Land Cover Map 2021 (LCM2021) is a UK-wide land cover data set, with 21- and 10-class versions. It is intended to support a broad range of UK environmental research, including ecological and hydrological research. LCM2021 was produced by classifying Sentinel-2 satellite imagery. LCM2021 is distributed as a suite of products to facilitate easy use for a range of applications. To support research at different spatial scales it includes 10 m, 25 m and 1 km resolution products.
Yu Zhao, Shaoyu Han, Jie Zheng, Hanyu Xue, Zhenhai Li, Yang Meng, Xuguang Li, Xiaodong Yang, Zhenhong Li, Shuhong Cai, and Guijun Yang
Earth Syst. Sci. Data, 15, 4047–4063, https://doi.org/10.5194/essd-15-4047-2023, https://doi.org/10.5194/essd-15-4047-2023, 2023
Short summary
Short summary
In the present study, we generated a 30 m Chinese winter wheat yield dataset from 2016 to 2021, called ChinaWheatYield30m. The dataset has high spatial resolution and great accuracy. It is the highest-resolution yield dataset known. Such a dataset will provide basic knowledge of detailed wheat yield distribution, which can be applied for many purposes including crop production modeling or regional climate evaluation.
Feng Yang and Zhenzhong Zeng
Earth Syst. Sci. Data, 15, 4011–4021, https://doi.org/10.5194/essd-15-4011-2023, https://doi.org/10.5194/essd-15-4011-2023, 2023
Short summary
Short summary
We generated a 4.77 m resolution annual tree cover map product for Southeast Asia (SEA) for 2016–2021 using Planet-NICFI and Sentinel-1 imagery. Maps were created with good accuracy and high consistency during 2016–2021. The baseline maps at 4.77 m can be converted to forest cover maps for SEA at various resolutions to meet different users’ needs. Our products can help resolve rounding errors in forest cover mapping by counting isolated trees and monitoring long, narrow forest cover removal.
Adrià Descals, Serge Wich, Zoltan Szantoi, Matthew J. Struebig, Rona Dennis, Zoe Hatton, Thina Ariffin, Nabillah Unus, David L. A. Gaveau, and Erik Meijaard
Earth Syst. Sci. Data, 15, 3991–4010, https://doi.org/10.5194/essd-15-3991-2023, https://doi.org/10.5194/essd-15-3991-2023, 2023
Short summary
Short summary
The spatial extent of coconut palm is understudied despite its increasing demand and associated impacts. We present the first global coconut palm layer at 20 m resolution. The layer was produced using deep learning and remotely sensed data. The global coconut area estimate is 12.31 Mha for dense coconut palm, but the estimate is 3 times larger when sparse coconut palm is considered. This means that coconut production can likely increase on the lands currently allocated to coconut palm.
Peter Hoffmann, Vanessa Reinhart, Diana Rechid, Nathalie de Noblet-Ducoudré, Edouard L. Davin, Christina Asmus, Benjamin Bechtel, Jürgen Böhner, Eleni Katragkou, and Sebastiaan Luyssaert
Earth Syst. Sci. Data, 15, 3819–3852, https://doi.org/10.5194/essd-15-3819-2023, https://doi.org/10.5194/essd-15-3819-2023, 2023
Short summary
Short summary
This paper introduces the new high-resolution land use and land cover change dataset LUCAS LUC for Europe (version 1.1), tailored for use in regional climate models. Historical and projected future land use change information from the Land-Use Harmonization 2 (LUH2) dataset is translated into annual plant functional type changes from 1950 to 2015 and 2016 to 2100, respectively, by employing a newly developed land use translator.
Wanru He, Xuecao Li, Yuyu Zhou, Zitong Shi, Guojiang Yu, Tengyun Hu, Yixuan Wang, Jianxi Huang, Tiecheng Bai, Zhongchang Sun, Xiaoping Liu, and Peng Gong
Earth Syst. Sci. Data, 15, 3623–3639, https://doi.org/10.5194/essd-15-3623-2023, https://doi.org/10.5194/essd-15-3623-2023, 2023
Short summary
Short summary
Most existing global urban products with future projections were developed in urban and non-urban categories, which ignores the gradual change of urban development at the local scale. Using annual global urban extent data from 1985 to 2015, we forecasted global urban fractional changes under eight scenarios throughout 2100. The developed dataset can provide spatially explicit information on urban fractions at 1 km resolution, which helps support various urban studies (e.g., urban heat island).
Zeping Liu, Hong Tang, Lin Feng, and Siqing Lyu
Earth Syst. Sci. Data, 15, 3547–3572, https://doi.org/10.5194/essd-15-3547-2023, https://doi.org/10.5194/essd-15-3547-2023, 2023
Short summary
Short summary
Large-scale maps of building rooftop area (BRA) are crucial for addressing policy decisions and sustainable development. In this paper, we propose a deep-learning method for high-resolution BRA mapping (2.5 m) from Sentinel-2 imagery (10 m). The resulting China building rooftop area dataset (CBRA) is the first multi-annual (2016–2021) and high-resolution (2.5 m) BRA dataset in China. Cross-comparisons show that the CBRA achieves the best performance in capturing the spatiotemporal information.
Ruoque Shen, Baihong Pan, Qiongyan Peng, Jie Dong, Xuebing Chen, Xi Zhang, Tao Ye, Jianxi Huang, and Wenping Yuan
Earth Syst. Sci. Data, 15, 3203–3222, https://doi.org/10.5194/essd-15-3203-2023, https://doi.org/10.5194/essd-15-3203-2023, 2023
Short summary
Short summary
Paddy rice is the second-largest grain crop in China and plays an important role in ensuring global food security. This study developed a new rice-mapping method and produced distribution maps of single-season rice in 21 provincial administrative regions of China from 2017 to 2022 at a 10 or 20 m resolution. The accuracy was examined using 108 195 survey samples and county-level statistical data, and we found that the distribution maps have good accuracy.
Charles R. Lane, Ellen D'Amico, Jay R. Christensen, Heather E. Golden, Qiusheng Wu, and Adnan Rajib
Earth Syst. Sci. Data, 15, 2927–2955, https://doi.org/10.5194/essd-15-2927-2023, https://doi.org/10.5194/essd-15-2927-2023, 2023
Short summary
Short summary
Non-floodplain wetlands (NFWs) – wetlands located outside floodplains – confer watershed-scale resilience to hydrological, biogeochemical, and biotic disturbances. Although they are frequently unmapped, we identified ~ 33 million NFWs covering > 16 × 10 km2 across the globe. NFWs constitute the majority of the world's wetlands (53 %). Despite their small size (median 0.039 km2), these imperiled systems have an outsized impact on watershed functions and sustainability and require protection.
Bingjie Li, Xiaocong Xu, Xiaoping Liu, Qian Shi, Haoming Zhuang, Yaotong Cai, and Da He
Earth Syst. Sci. Data, 15, 2347–2373, https://doi.org/10.5194/essd-15-2347-2023, https://doi.org/10.5194/essd-15-2347-2023, 2023
Short summary
Short summary
A global land cover map with fine spatial resolution is important for climate and environmental studies, food security, or biodiversity conservation. In this study, we developed an improved global land cover map in 2015 with 30 m resolution (GLC-2015) by fusing the existing land cover products based on the Dempster–Shafer theory of evidence on the Google Earth Engine platform. The GLC-2015 performed well, with an OA of 79.5 % (83.6 %) assessed with the global point-based (patch-based) samples.
Richard A. Houghton and Andrea Castanho
Earth Syst. Sci. Data, 15, 2025–2054, https://doi.org/10.5194/essd-15-2025-2023, https://doi.org/10.5194/essd-15-2025-2023, 2023
Short summary
Short summary
We update a previous analysis of carbon emissions (annual and national) from land use, land-use change, and forestry from 1850 to 2020. We use data from the latest (2020) Global Forest Resources Assessment, incorporate shifting cultivation, and include improvements to the bookkeeping model and recent estimates of emissions from peatlands. Net global emissions declined steadily over the decade from 2011 to 2020 (mean of 0.96 Pg C yr−1), falling below 1.0 Pg C yr−1 for the first time in 30 years.
Charles H. Simpson, Oscar Brousse, Nahid Mohajeri, Michael Davies, and Clare Heaviside
Earth Syst. Sci. Data, 15, 1521–1541, https://doi.org/10.5194/essd-15-1521-2023, https://doi.org/10.5194/essd-15-1521-2023, 2023
Short summary
Short summary
Adding plants to roofs of buildings can reduce indoor and outdoor temperatures and so can reduce urban overheating, which is expected to increase due to climate change and urban growth. To better understand the effect this has on the urban environment, we need data on how many buildings have green roofs already.
We used a computer vision model to find green roofs in aerial imagery in London, producing a dataset identifying what buildings have green roofs and improving on previous methods.
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...
Altmetrics
Final-revised paper
Preprint