Articles | Volume 15, issue 9
https://doi.org/10.5194/essd-15-4047-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-15-4047-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
13 Sep 2023
Data description paper |
| 13 Sep 2023
| 13 Sep 2023
ChinaWheatYield30m: a 30 m annual winter wheat yield dataset from 2016 to 2021 in China
Yu Zhao
Key Laboratory of Quantitative Remote Sensing in Agriculture of
Ministry of Agriculture and Rural Affairs, Information Technology Research
Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing
100097, China
College of agriculture, Shanxi Agricultural University, Taigu, Shanxi
030801, China
Shaoyu Han
Key Laboratory of Quantitative Remote Sensing in Agriculture of
Ministry of Agriculture and Rural Affairs, Information Technology Research
Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing
100097, China
Institute of Agricultural Economics and Information, Henan Academy of
Agricultural Sciences, Zhengzhou, Henan 450000, China
Jie Zheng
Key Laboratory of Quantitative Remote Sensing in Agriculture of
Ministry of Agriculture and Rural Affairs, Information Technology Research
Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing
100097, China
Hanyu Xue
Key Laboratory of Quantitative Remote Sensing in Agriculture of
Ministry of Agriculture and Rural Affairs, Information Technology Research
Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing
100097, China
Zhenhai Li
Key Laboratory of Quantitative Remote Sensing in Agriculture of
Ministry of Agriculture and Rural Affairs, Information Technology Research
Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing
100097, China
College of Geodesy and Geomatics, Shandong University of Science and
Technology, Qingdao 266590, China
Yang Meng
Key Laboratory of Quantitative Remote Sensing in Agriculture of
Ministry of Agriculture and Rural Affairs, Information Technology Research
Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing
100097, China
College of agriculture, Shanxi Agricultural University, Taigu, Shanxi
030801, China
Xuguang Li
Cultivated Land Monitoring and Protection Center of Hebei,
Shijiazhuang, 050056, China
Xiaodong Yang
Key Laboratory of Quantitative Remote Sensing in Agriculture of
Ministry of Agriculture and Rural Affairs, Information Technology Research
Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing
100097, China
Zhenhong Li
School of Geological Engineering and Geomatics, Chang'an University,
Xi'an 710054, China
Shuhong Cai
Cultivated Land Monitoring and Protection Center of Hebei,
Shijiazhuang, 050056, China
Guijun Yang
CORRESPONDING AUTHOR
Key Laboratory of Quantitative Remote Sensing in Agriculture of
Ministry of Agriculture and Rural Affairs, Information Technology Research
Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing
100097, China
School of Geological Engineering and Geomatics, Chang'an University,
Xi'an 710054, China
Related authors
No articles found.
C. Zhao, Z. Li, S. Zhang, G. Huang, C. Yang, and S. Duan
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-5-W1-2023, 59–64, https://doi.org/10.5194/isprs-archives-XLVIII-5-W1-2023-59-2023, https://doi.org/10.5194/isprs-archives-XLVIII-5-W1-2023-59-2023, 2023
Yanjie Wang, Qinhong Liao, Guijun Yang, Haikuan Feng, Xiaodong Yang, and Jibo Yue
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B7, 137–143, https://doi.org/10.5194/isprs-archives-XLI-B7-137-2016, https://doi.org/10.5194/isprs-archives-XLI-B7-137-2016, 2016
Related subject area
Domain: ESSD – Land | Subject: Land Cover and Land Use
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
Map of forest tree species for Poland based on Sentinel-2 data
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
Mapping sugarcane globally at 10 m resolution using GEDI and Sentinel-2
A 28 time-points cropland area change dataset in Northeast China from 1000 to 2020
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
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
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
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
Twenty-meter annual paddy rice area map for mainland Southeast Asia using Sentinel-1 synthetic-aperture-radar data
A 29-year time series of annual 300 m resolution plant-functional-type maps for climate models
Estimating local agricultural gross domestic product (AgGDP) across the world
Classification and mapping of European fuels using a hierarchical, multipurpose fuel classification system
Harmonising the land-use flux estimates of global models and national inventories for 2000–2020
Four-century history of land transformation by humans in the United States (1630–2020): annual and 1 km grid data for the HIStory of LAND changes (HISLAND-US)
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
Annual forest maps in the contiguous United States during 2015–2017 from analyses of PALSAR-2 and Landsat images
TreeSatAI Benchmark Archive: a multi-sensor, multi-label dataset for tree species classification in remote sensing
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.
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.
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.
Stefania Di Tommaso, Sherrie Wang, Rob Strey, and David B. Lobell
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-121, https://doi.org/10.5194/essd-2024-121, 2024
Revised manuscript accepted for ESSD
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 10m 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 recall of our maps.
Ran Jia, Xiuqi Fang, Yundi Yang, Masayuki Yokozawa, and Yu Ye
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-94, https://doi.org/10.5194/essd-2024-94, 2024
Revised manuscript accepted for ESSD
Short summary
Short summary
One of the major manifestations of global change is the alteration of natural vegetation landscapes by human reclamation. Here we reconstruct a unified set of long-term time series cropland area change datasets with standardized criteria. The cropland in Northeast China exhibited phase changes of expansion-reduction-expansion over the past millennium. Compared to global historical LUCC datasets, our dataset has significant time resolution and reliability.
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).
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.
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
Preprint under review 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.
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.
Chunling Sun, Hong Zhang, Lu Xu, Ji Ge, Jingling Jiang, Lijun Zuo, and Chao Wang
Earth Syst. Sci. Data, 15, 1501–1520, https://doi.org/10.5194/essd-15-1501-2023, https://doi.org/10.5194/essd-15-1501-2023, 2023
Short summary
Short summary
Over 90 % of the world’s rice is produced in the Asia–Pacific region. In this study, a rice-mapping method based on Sentinel-1 data for mainland Southeast Asia is proposed. A combination of spatiotemporal features with strong generalization is selected and input into the U-Net model to obtain a 20 m resolution rice area map of mainland Southeast Asia in 2019. The accuracy of the proposed method is 92.20 %. The rice area map is concordant with statistics and other rice area maps.
Kandice L. Harper, Céline 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, 15, 1465–1499, https://doi.org/10.5194/essd-15-1465-2023, https://doi.org/10.5194/essd-15-1465-2023, 2023
Short summary
Short summary
We built a spatially explicit annual plant-functional-type (PFT) dataset for 1992–2020 exhibiting intra-class spatial variability in PFT fractional cover at 300 m. For each year, 14 maps of 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. Model simulations indicate significant differences in simulated carbon, water, and energy fluxes in some regions using this new set.
Yating Ru, Brian Blankespoor, Ulrike Wood-Sichra, Timothy S. Thomas, Liangzhi You, and Erwin Kalvelagen
Earth Syst. Sci. Data, 15, 1357–1387, https://doi.org/10.5194/essd-15-1357-2023, https://doi.org/10.5194/essd-15-1357-2023, 2023
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. As an illustration, the paper estimates that the exposure of areas with extreme drought to agricultural GDP is USD 432 billion, where nearly 1.2 billion people live.
Elena Aragoneses, Mariano García, Michele Salis, Luís M. Ribeiro, and Emilio Chuvieco
Earth Syst. Sci. Data, 15, 1287–1315, https://doi.org/10.5194/essd-15-1287-2023, https://doi.org/10.5194/essd-15-1287-2023, 2023
Short summary
Short summary
We present a new hierarchical fuel classification system with a total of 85 fuels that is useful for preventing fire risk at different spatial scales. Based on this, we developed a European fuel map (1 km resolution) using land cover datasets, biogeographic datasets, and bioclimatic modelling. We validated the map by comparing it to high-resolution data, obtaining high overall accuracy. Finally, we developed a crosswalk for standard fuel models as a first assignment of fuel parameters.
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, Selma Bultan, Guido Ceccherini, Stefanie Falk, Etsushi Kato, Daniel Kennedy, Jürgen Knauer, Anu Korosuo, Joana Melo, Matthew J. McGrath, Julia E. M. S. Nabel, Benjamin Poulter, Anna A. Romanovskaya, Simone Rossi, Hanqin Tian, Anthony P. Walker, Wenping Yuan, Xu Yue, and Julia Pongratz
Earth Syst. Sci. Data, 15, 1093–1114, https://doi.org/10.5194/essd-15-1093-2023, https://doi.org/10.5194/essd-15-1093-2023, 2023
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 confidence in land-use CO2 flux estimates.
Xiaoyong Li, Hanqin Tian, Chaoqun Lu, and Shufen Pan
Earth Syst. Sci. Data, 15, 1005–1035, https://doi.org/10.5194/essd-15-1005-2023, https://doi.org/10.5194/essd-15-1005-2023, 2023
Short summary
Short summary
We reconstructed land use and land cover (LULC) 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.
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.
Jie Wang, Xiangming Xiao, Yuanwei Qin, Jinwei Dong, Geli Zhang, Xuebin Yang, Xiaocui Wu, Chandrashekhar Biradar, and Yang Hu
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2022-339, https://doi.org/10.5194/essd-2022-339, 2023
Revised manuscript accepted for ESSD
Short summary
Short summary
Existing satellite-based forest maps remain large uncertainties, due to different forest definitions and mapping algorithms. To effectively manage the forest resources, timely and accurate annual forest maps at a high spatial resolution are demanded. This study improved the forest maps by integrating the PALSAR-2 and Landsat images. The annual evergreen and non-evergreen forest type maps were also generated. This critical information supports the Global Forest Resources Assessment.
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.
Cited articles
Arslan, M., Guzel, M., Demirci, M., and Ozdemir, S.: SMOTE and Gaussian Noise
Based Sensor Data Augmentation, in: 2019 4th International Conference on
Computer Science and Engineering (UBMK), 11 September 2019, Samsun, Turkey, 458–462,
https://doi.org/10.1109/UBMK.2019.8907003, 2019.
Bailey-Serres, J., Parker, J. E., Ainsworth, E. A., Oldroyd, G. E. D.,
and Schroeder, J. I.: Genetic strategies for improving crop yields, Nature, 575,
109–118, https://doi.org/10.1038/s41586-019-1679-0, 2019.
Battude, M., Al Bitar, A., Morin, D., Cros, J., Huc, M., Marais Sicre, C.,
Dantec, V., and Demarez, V.: Estimating maize biomass and yield over large areas
using high spatial and temporal resolution sentinel-2 like remote sensing
data, Remote Sens. Environ., 184, 668–681,
https://doi.org/10.1016/j.rse.2016.07.030, 2016.
Breiman, L.: Random forests, Mach. Learn., 45, 5–32,
https://doi.org/10.1023/A:1010933404324, 2001.
Cabas, J., Weersink, A., and Olale, E.: Crop yield response to economic, site
and climatic variables, Clim. Change, 101, 599–616, https://doi.org/10.1007/s10584-009-9754-4, 2010.
Cai, Y., Guan, K., Lobell, D., Potgieter, A. B., Wang, S., Peng, J., Xu, T.,
Asseng, S., Zhang, Y., and You, L.: Integrating satellite and climate data
to predict wheat yield in Australia using machine learning approaches, Agric.
For. Meteorol., 274, 144–159, https://doi.org/10.1016/j.agrformet.2019.03.010,
2019.
Cao, J., Zhang, Z., Tao, F., Zhang, L., Luo, Y., Zhang, J., Han, J., and Xie,
J.: Integrating multi-Source data for rice yield prediction across China
using machine learning and deep learning approaches, Agric. For. Meteorol.,
297, 108275, https://doi.org/10.1016/j.agrformet.2020.108275, 2021.
Cheng, M., Jiao, X., Shi, L., Penueals, J., Kumar, L., Nie, C., Wu, T., Liu,
K., Wu, W., and Jin, X.: High-resolution crop yield and water productivity
dataset generated using random forest and remote sensing, Sci Data, 9, 641,
https://doi.org/10.1038/s41597-022-01761-0, 2022.
Dong, J., Fu, Y., Wang, J., Tian, H., Fu, S., Niu, Z., Han, W., Zheng, Y., Huang, J., and Yuan, W.: Early-season mapping of winter wheat in China based on Landsat and Sentinel images, Earth Syst. Sci. Data, 12, 3081–3095, https://doi.org/10.5194/essd-12-3081-2020, 2020a.
Dong, J., Lu, H., Wang, Y., Ye, T., and Yuan, W.: Estimating winter wheat yield
based on a light use efficiency model and wheat variety data, ISPRS J.
Photogramm. Remote, 160, 18–32, https://doi.org/10.1016/j.isprsjprs.2019.12.005, 2020b.
Duchemin, B., Maisongrande, P., Boulet, G., and Benhadj, I.: A simple algorithm
for yield estimates: Evaluation for semi-arid irrigated winter wheat
monitored with green leaf area index, Environ. Model. Softw., 23, 876–892,
https://doi.org/10.1016/j.envsoft.2007.10.003, 2008.
Ebrahimy, H., Wang, Y., and Zhang, Z.: Utilization of synthetic minority
oversampling technique for improving potato yield prediction using remote
sensing data and machine learning algorithms with small sample size of yield
data, ISPRS J. Photogramm. Remote, 201, 12–25,
https://doi.org/10.1016/j.isprsjprs.2023.05.015, 2023.
Erenstein, O., Jaleta, M., Mottaleb, K. A., Sonder, K., Donovan, J., and
Braun, H.-J.: Global trends in wheat production, consumption and trade, in:
Wheat improvement: food security in a changing climate, Springer
International Publishing, Cham, 47–66, 2022.
FAO, IFAD, UNICEF, WFP, and WHO.: The State of Food Security and Nutrition
in the World. ransforming food systems for affordable healthy diets, FAO,
Rome, Italy, https://doi.org/10.4060/ca9692en, 2020.
FAOSTAT: Food and Agriculture Organization of the United Nations, FAO
Statistical Databases, http://www.fao.org/faostat/en/ (last
access: 17 February 2020), 2018.
Feng, P., Wang, B., Liu, D.L., Waters, C., Xiao, D., Shi, L., and Yu, Q.:
Dynamic wheat yield forecasts are improved by a hybrid approach using a
biophysical model and machine learning technique, Agric. For. Meteorol.,
285–286, 107922, https://doi.org/10.1016/j.agrformet.2020.107922, 2020.
Fushiki, T.: Estimation of prediction error by using K-fold
cross-validation, Sta. Comput., 21, 137–146, https://doi.org/10.1007/s11222-009-9153-8, 2011.
Grogan, D., Frolking, S., Wisser, D., Prusevich, A., and Glidden, S.: Global
gridded crop harvested area, production, yield, and monthly physical area
data circa 2015, Sci. Data, 9, 15,
https://doi.org/10.1038/s41597-021-01115-2, 2022.
Hu, S., Shi, L., Zha, Y., Williams, M., and Lin, L.: Simultaneous
state-parameter estimation supports the evaluation of data assimilation
performance and measurement design for soil-water atmosphere-plant system,
J. Hydrol., 555, 812–831, https://doi.org/10.1016/j.jhydrol.2017.10.061, 2017.
Huang, J., Tian, L., Liang, S., Ma, H., Becker-Reshef, I., Huang, Y., Su,
W., Zhang, X., Zhu, D., and Wu, W.: Improving winter wheat yield estimation by
assimilation of the leaf area index from Landsat TM and MODIS data into the
WOFOST model, Agric. For. Meteorol., 204, 106–121, https://doi.org/10.1016/j.agrformet.2015.02.001, 2015.
Huang, J., Ma, H., Sedano, F., Lewis, P., Liang, S., Wu, Q., Su, W., Zhang,
X., and Zhu, D.: Evaluation of regional estimates of winter wheat yield by
assimilating three remotely sensed reflectance datasets into the coupled
WOFOST–PROSAIL model, Eur. J. Agron., 102, 1–13,
https://doi.org/10.1016/j.eja.2018.10.008, 2019.
IFPRI: June 2021, International Food Policy Research Institute (IFPRI), Washington, DC, https://doi.org/10.2499/9780896294165, 2021.
Iizumi, T. and Sakai, T.: The global dataset of historical yields for major
crops 1981–2016, Sci Data, 7, 97, https://doi.org/10.1038/s41597-020-0433-7,
2020.
Ines, A. V. M., Das, N. N., Hansen, J. W., and Njoku, E. G.: Assimilation of remotely
sensed soil moisture and vegetation with a crop simulation model for maize
yield prediction, Remote Sens. Environ., 138, 149–164, https://doi.org/10.1016/j.rse.2013.07.018, 2013.
Ji, Z., Pan, Y., Zhu, X., Zhang, D., and Wang, J.: A generalized model to
predict large-scale crop yields integrating satellite-based vegetation index
time series and phenology metrics, Ecol. Indic., 137, 108759,
https://doi.org/10.1016/j.ecolind.2022.108759, 2022.
Jiang, H., Hu, H., Zhong, R., Xu, J., Xu, J., Huang, J., Wang, S., Ying, Y.,
and Lin, T.: A deep learning approach to conflating heterogeneous geospatial
data for corn yield estimation: A case study of the US Corn Belt at the
county level, Glob. Change Biol., 26, 1754–1766, https://doi.org/10.1111/gcb.14885, 2019.
Jiang, Z., Huete, A. R., Didan, K., and Miura, T.: Development of a two-band
enhanced vegetation index without a blue band, Remote Sens. Environ., 112,
3833–3845, https://doi.org/10.1016/j.rse.2008.06.006, 2008.
Jiang, H., Hu, H., Zhong, R., Xu, J., Xu, J., Huang, J., Wang, S., Ying, Y., and Lin, T.: A deep learning approach to conflating heterogeneous geospatial data for corn yield estimation: A case study of the US Corn Belt at the county level, Global Change Biol., 26, 1754–1766, https://doi.org/10.1111/gcb.14885, 2020.
Jones, J. W., Hoogenboom, G., Porter, C. H., Boote, K. J., Batchelor, W. D.,
Hunt, L. A., Wilkens, P. W., Singh, U., Gijsman, A. J., and Ritchie, J. T.: The DSSAT
cropping system model, Eur. J. Agron., 18, 235–265, https://doi.org/10.1016/S1161-0301(02)00107-7, 2003.
Kang, Y. and Ozdogan, M.: Field-level crop yield mapping with Landsat using a
hierarchical data assimilation approach, Remote Sens. Environ., 228, 144–163,
https://doi.org/10.1016/j.rse.2019.04.005, 2019.
Keating, B. A., Carberry, P. S., Hammer, G. L., Probert, M. E., Robertson, M. J.,
Holzworth, D., Huth, N. I., Hargreaves, J. N. G., Meinke, H., and Hochman, Z.: An
overview of APSIM, a model designed for farming systems simulation, Eur. J.
Agron., 18, 267–288, https://doi.org/10.1016/S1161-0301(02)00108-9, 2003.
Lee, B. H., Kenkel, P., and Brorsen, B. W.: Pre-harvest forecasting of county
wheat yield and wheat quality using weather information, Agric. For. Meteorol.,
168, 26–35, https://doi.org/10.1016/j.agrformet.2012.08.010,
2013.
Li, L., Wang, B., Feng, P., Wang, H., He, Q., Wang, Y., Liu, D. L., Li, Y.,
He, J., and Feng, H.: Crop yield forecasting and associated optimum lead time
analysis based on multi-source environmental data across China, Agric. For. Meteorol., 308–309, 108558, https://doi.org/10.1016/j.agrformet.2021.108558,
2021.
Li, Z., Taylor, J., Yang, H., Casa, R., Jin, X., Li, Z., Song, X., and Yang, G.:
A hierarchical interannual wheat yield and grain protein prediction model
using spectral vegetative indices and meteorological data, Field Crop Res.,
248, 107711, https://doi.org/10.1016/j.fcr.2019.107711, 2020.
Li, Z., Zhao, Y., Taylor, J., Gaulton, R., Jin, X., Song, X., Li, Z., Meng, Y., Chen, P., Feng, H., and Wang, C.: Comparison and transferability of thermal, temporal and phenological-based in-season predictions of above-ground biomass in wheat crops from proximal crop reflectance data, Remote Sens. Environ., 273, 112967, https://doi.org/10.1016/j.rse.2022.112967, 2022.
Lorenz, E. and Haman, K.: The essence of chaos, Pure Appl. Geophys.,
147, 598–599, https://doi.org/10.2307/1347942, 1996.
Luo, Y., Zhang, Z., Chen, Y., Li, Z., and Tao, F.: ChinaCropPhen1km: a high-resolution crop phenological dataset for three staple crops in China during 2000–2015 based on leaf area index (LAI) products, Earth Syst. Sci. Data, 12, 197–214, https://doi.org/10.5194/essd-12-197-2020, 2020.
Luo, Y., Zhang, Z., Cao, J., Zhang, L., Zhang, J., Han, J., Zhuang, H., Cheng, F., Xu, J., and Tao, F.: GlobalWheatYield4km: a global wheat yield dataset at 4-km resolution during 1982–2020 based on deep learning approaches, Earth Syst. Sci. Data Discuss. [preprint], https://doi.org/10.5194/essd-2022-297, 2022.
Magney, T. S., Eitel, J. U. H., Huggins, D. R., and Vierling, L. A.: Proximal
NDVI derived phenology improves in-season predictions of wheat quantity and
quality, Agric. For. Meteorol., 217, 46–60,
https://doi.org/10.1016/j.agrformet.2015.11.009, 2016.
Monfreda, C., Ramankutty, N., and Foley, J. A.: Farming the planet: 2.
Geographic distribution of crop areas, yields, physiological types, and net
primary production in the year 2000, Glob. Biogeochem. Cy., 22, GB1022,
https://doi.org/10.1029/2007GB002947, 2008.
Moschini, G. and Hennessy, D. A.: Uncertainty, risk aversion, and risk
management for agricultural producers, Handb. Agric. Econ., 1, 87–153,
https://doi.org/10.1016/S1574-0072(01)10005-8, 2001.
Muñoz-Sabater, J.: ERA5-Land monthly averaged data from 1981 to present,
Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.68d2bb30, 2019.
National Bureau of Statistics of China: National statistical yearbook, China
Statistics Press, http://www.stats.gov.cn/tjsj/ndsj/2021/indexch.htm (last
access: 8 August 2022), 2021.
Paudel, D., Boogaard, H., Wit, A., Janssen, S., Osinga, S., Pylianidis, C.,
and Athanasiadis, I.: Machine learning for large-scale crop yield forecasting,
Agric. Syst., 187, 103016,
https://doi.org/10.1016/j.agsy.2020.103016, 2021.
Rondeaux, G., Steven, M., and Baret, F.: Optimization of soil-adjusted
vegetation indices, Remote Sens. Environ., 55, 95–107, https://doi.org/10.1016/0034-4257(95)00186-7, 1996.
Rouse, J., Haas, R., Schell, J., and Deering, D.: Monitoring Vegetation Systems
in the Great Plains with ERTS, NASA Special Publication, Washington, D.C.,
USA, 1, 48–62, 1974.
Saltelli, A., Tarantola, S., and Chan, P. S.: A quantitative model-independent
method for global sensitivity analysis of model output, Technometrics,
41, 39–56, https://doi.org/10.2307/1270993, 1999
Sims, D. A., Rahman, A. F., Cordova, V. D., El-Masri, B. Z., Baldocchi, D. D.,
Bolstad, P. V., Flanagan, L. B., Goldstein, A. H., Hollinger, D. Y., and Misson, L.:
A new model of gross primary productivity for North American ecosystems
based solely on the enhanced vegetation index and land surface temperature
from MODIS, Remote Sens. Environ., 112, 1633–1646,
https://doi.org/10.1016/j.rse.2007.08.004, 2008.
Sivakumar, J., Ramamurthy, K., Radhakrishnan, M., and Won, D.: Synthetic
sampling from small datasets: a modified mega-trend diffusion approach using
k-nearest neighbors, Knowl.-Based Syst., 236, 107687,
https://doi.org/10.1016/j.knosys.2021.107687, 2022.
Tian, H., Wang, P., Tansey, K., Zhang, J., Zhang, S., and Li, H.: An LSTM neural
network for improving wheat yield estimates by integrating remote sensing
data and meteorological data in the Guanzhong Plain, PR China, Agric. For. Meteorol., 310, 108629, https://doi.org/10.1016/j.agrformet.2021.108629,
2021.
Wang, F., Yi, Q., Hu, J., Xie, L., Yao, X., Xu, T., and Zheng, J.: Combining
spectral and textural information in UAV hyperspectral images to estimate
rice grain yield, J. Appl. Earth Obs., 102, 102397,
https://doi.org/10.1016/j.jag.2021.102397, 2021.
Wang, X., Huang, J., Feng, Q., and Yin, D.: Winter Wheat Yield Prediction at
County Level and Uncertainty Analysis in Main Wheat-Producing Regions of
China with Deep Learning Approaches, Remote Sens., 12, 1744, https://doi.org/10.3390/rs12111744, 2020.
Wei, S., Li, X., Lu, Z., Zhang, H., Ye, X., Zhou, Y., Li, J., Yan, Y., Pei,
H., Duan, F., Wang, D., Chen, S., Wang, P., Zhang, C., Shang, L., Zhou, Y.,
Pan, P., Zhao, M, Huang, J., Bock, R., Qian, Q., and Zhou, W.: A transcriptional
regulator that boosts grain yields and shortens the growth duration of rice,
Science, 377, eabi8455, https://doi.org/10.1126/science.abi8455,
2022.
Xu, X., Teng, C., Zhao, Y., Du, Y., Zhao, C., Yang, G., Jin, X., Song, X.,
Gu, X., Casa, R., Chen, L., and Li, Z.: Prediction of wheat grain protein by
coupling multisource remote sensing imagery and ECMWF data, Remote Sens.,
12, 1349, https://doi.org/10.3390/rs12081349, 2020.
You, L. Z., Wood, S., Wood-Sichra, U., and Wu, W. B.: Generating global crop
distribution maps: From census to grid, AgrSyst, 127, 53–60,
https://doi.org/10.1016/j.agsy.2014.01.002, 2014.
Zhao, Y., Han, S., Meng, Y., Feng, H., Li, Z., Chen, J., Song, X., Zhu, Y.,
and Yang, G.: Transfer-Learning-Based Approach for Yield Prediction of Winter
Wheat from Planet Data and SAFY Model, Remote Sens., 14, 5474,
https://doi.org/10.3390/rs14215474, 2022a.
Zhao, Y., Han, S., Zheng, J., Xue, H., Li Z., Meng, Y., Li, X., Yang, X.,
Li, Z., Cai, S., and Yang, G.: ChinaWheatYield30m: A 30-m annual winter wheat
yield dataset from 2016 to 2021 in China, Zenodo [data set],
https://doi.org/10.5281/zenodo.7360753, 2022b.
Zhao, Y., Meng, Y., Feng, H., Han, S., Yang, G., and Li, Z.: Should phenological
information be applied to predict agronomic traits across growth stages of
winter wheat?, Crop J., 10, 1346–1352,
https://doi.org/10.1016/j.cj.2022.08.003, 2022c.
Zhang, Y., Hui, J., Qin, Q., Sun, Y., Zhang, T., Sun, H., and Li, M.:
Transfer-learning-based approach for leaf chlorophyll content estimation of
winter wheat from hyperspectral data, Remote Sens. Environ., 267, 112724,
https://doi.org/10.1016/j.rse.2021.112724, 2021.
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.
In the present study, we generated a 30 m Chinese winter wheat yield dataset from 2016 to 2021,...
Altmetrics
Final-revised paper
Preprint