Articles | Volume 17, issue 5
https://doi.org/10.5194/essd-17-1781-2025
© Author(s) 2025. 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-17-1781-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
06 May 2025
Data description paper |
| 06 May 2025
| 06 May 2025
The 20 m Africa rice distribution map of 2023
Jingling Jiang
Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
International Research Center of Big Data for Sustainable Development Goals, Beijing 100049, China
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
Hong Zhang
CORRESPONDING AUTHOR
International Research Center of Big Data for Sustainable Development Goals, Beijing 100049, China
Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
Ji Ge
Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
International Research Center of Big Data for Sustainable Development Goals, Beijing 100049, China
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
Lijun Zuo
Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
International Research Center of Big Data for Sustainable Development Goals, Beijing 100049, China
Mingyang Song
Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
International Research Center of Big Data for Sustainable Development Goals, Beijing 100049, China
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
Yinhaibin Ding
Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
International Research Center of Big Data for Sustainable Development Goals, Beijing 100049, China
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
Yazhe Xie
Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
International Research Center of Big Data for Sustainable Development Goals, Beijing 100049, China
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
Wenjiang Huang
Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
Related authors
Mingyang Song, Lu Xu, Ji Ge, Hong Zhang, Lijun Zuo, Jingling Jiang, Yinhaibin Ding, Yazhe Xie, and Fan Wu
Earth Syst. Sci. Data, 17, 661–683, https://doi.org/10.5194/essd-17-661-2025, https://doi.org/10.5194/essd-17-661-2025, 2025
Short summary
Short summary
We created a 10 m resolution rice distribution map for East Asia in 2023 (EARice10), achieving an overall accuracy (OA) of 90.48 % on validation samples. EARice10 shows strong consistency with statistical data (coefficient of determination, R2: 0.94–0.98) and existing datasets (R2: 0.79–0.98). It is the most up-to-date map, covering the four major rice-producing countries in East Asia at 10 m resolution.
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.
Mingyang Song, Lu Xu, Ji Ge, Hong Zhang, Lijun Zuo, Jingling Jiang, Yinhaibin Ding, Yazhe Xie, and Fan Wu
Earth Syst. Sci. Data, 17, 661–683, https://doi.org/10.5194/essd-17-661-2025, https://doi.org/10.5194/essd-17-661-2025, 2025
Short summary
Short summary
We created a 10 m resolution rice distribution map for East Asia in 2023 (EARice10), achieving an overall accuracy (OA) of 90.48 % on validation samples. EARice10 shows strong consistency with statistical data (coefficient of determination, R2: 0.94–0.98) and existing datasets (R2: 0.79–0.98). It is the most up-to-date map, covering the four major rice-producing countries in East Asia at 10 m resolution.
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.
Lei Chen, Liguo Zhang, Yixian Tang, and Hong Zhang
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-3, 53–59, https://doi.org/10.5194/isprs-annals-IV-3-53-2018, https://doi.org/10.5194/isprs-annals-IV-3-53-2018, 2018
Related subject area
Domain: ESSD – Land | Subject: Land Cover and Land Use
Statistical atlas of European agriculture: gridded data from the agricultural census 2020 and the spatial distribution of CAP contextual indicators
An annual cropland extent dataset for Africa at 30 m spatial resolution from 2000 to 2022
Global agricultural lands in the year 2015
The GIEMS-MethaneCentric database: a dynamic and comprehensive global product of methane-emitting aquatic areas
An annual 30 m cultivated-pasture dataset of the Tibetan Plateau from 1988 to 2021
GloUCP: a global 1 km spatially continuous urban canopy parameters for the WRF model
CCD-Rice: a long-term paddy rice distribution dataset in China at 30 m resolution
U-Surf: a global 1 km spatially continuous urban surface property dataset for kilometer-scale urban-resolving Earth system modeling
The Earth Topography 2022 (ETOPO 2022) global DEM dataset
A large-scale image-text dataset benchmark for farmland segmentation
Revised and updated geospatial monitoring of 21st century forest carbon fluxes
ChatEarthNet: a global-scale image–text dataset empowering vision–language geo-foundation models
GLC_FCS10: a global 10-m land-cover dataset with a fine classification system from Sentinel-1 and Sentinel-2 time-series data in Google Earth Engine
Aboveground biomass dataset from SMOS L-band vegetation optical depth and reference maps
GMIE: a global maximum irrigation extent and central pivot irrigation system dataset derived via irrigation performance during drought stress and deep learning methods
Annual vegetation maps in the Qinghai–Tibet Plateau (QTP) from 2000 to 2022 based on MODIS series satellite imagery
Time series of Landsat-based bimonthly and annual spectral indices for continental Europe for 2000–2022
EARice10: a 10 m resolution annual rice distribution map of East Asia for 2023
A Sentinel-2 machine learning dataset for tree species classification in Germany
Countrywide Digital Surface Models and Vegetation Height Models from Historical Aerial Images
High-resolution mapping of global winter-triticeae crops using a sample-free identification method
A flux tower site attribute dataset intended for land surface modeling
Large-scale forest stand height mapping in the northeastern U.S. and China using L-band spaceborne repeat-pass InSAR and GEDI LiDAR data
Advances in LUCAS Copernicus 2022: enhancing Earth observations with comprehensive in situ data on EU land cover and use
Global 30 m seamless data cube (2000–2022) of land surface reflectance generated from Landsat 5, 7, 8, and 9 and MODIS Terra constellations
Mapping rangeland health indicators in eastern Africa from 2000 to 2022
3D-GloBFP: the first global three-dimensional building footprint dataset
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
Multidecadal satellite-derived Portuguese Burn Severity Atlas (1984–2022)
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
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
Nicolas Lampach, Jon Olav Skøien, Helena Ramos, Julien Gaffuri, Renate Koeble, Linda See, and Marijn van der Velde
Earth Syst. Sci. Data, 17, 3893–3919, https://doi.org/10.5194/essd-17-3893-2025, https://doi.org/10.5194/essd-17-3893-2025, 2025
Short summary
Short summary
Eurostat and the Joint Research Centre developed a new methodology to make geospatial data from agricultural census available to users while ensuring that no confidential information from individuals is disclosed. The geospatial data presented in the article correspond to the contextual indicators of the monitoring framework of the Common Agricultural Policy. Our exploratory analysis reveals several interesting patterns which contribute to the broader debate on the future of European agriculture.
Zihang Lou, Dailiang Peng, Zhou Shi, Hongyan Wang, Ke Liu, Yaqiong Zhang, Xue Yan, Zhongxing Chen, Su Ye, Le Yu, Jinkang Hu, Yulong Lv, Hao Peng, Yizhou Zhang, and Bing Zhang
Earth Syst. Sci. Data, 17, 3777–3796, https://doi.org/10.5194/essd-17-3777-2025, https://doi.org/10.5194/essd-17-3777-2025, 2025
Short summary
Short summary
This study creates the first detailed annual maps of Africa's cropland extent from 2000 to 2022 in 30 m resolution to support global efforts against hunger and sustainable farming. Our findings show Africa's cropland grew by 8.5 % over 2 decades, while 11.5 % of cropland was abandoned by 2018, revealing hidden challenges in agricultural sustainability. These yearly field-sized maps help governments track where farming grows or shrinks, plan food supplies, and protect vital cropland.
Zia Mehrabi, Kaitai Tong, Julie Fortin, Radost Stanimirova, Mark Friedl, and Navin Ramankutty
Earth Syst. Sci. Data, 17, 3473–3496, https://doi.org/10.5194/essd-17-3473-2025, https://doi.org/10.5194/essd-17-3473-2025, 2025
Short summary
Short summary
We present a global geospatial database of cropland and pastures for the year 2015. We built these data by fusing satellite-based land cover data with agricultural census data using machine learning. This database is an update to an earlier version representing the year 2000. It can be used to study issues such as land use, food security, climate change, and biodiversity loss. We provide a reproducible code base that can be used to easily update the product for future years.
Juliette Bernard, Catherine Prigent, Carlos Jimenez, Etienne Fluet-Chouinard, Bernhard Lehner, Elodie Salmon, Philippe Ciais, Zhen Zhang, Shushi Peng, and Marielle Saunois
Earth Syst. Sci. Data, 17, 2985–3008, https://doi.org/10.5194/essd-17-2985-2025, https://doi.org/10.5194/essd-17-2985-2025, 2025
Short summary
Short summary
Wetlands are responsible for about a third of global emissions of methane, a potent greenhouse gas. We have developed the Global Inundation Extent from Multi-Satellites-MethaneCentric (GIEMS-MC) dataset to represent the dynamics of wetland extent on a global scale (0.25° × 0.25° resolution, monthly time step). This updated resource combines satellite data and existing wetland databases, covering 1992 to 2020. Consistent maps of other methane-emitting surface waters (lakes, rivers, reservoirs, rice paddies) are also provided.
Binghong Han, Jian Bi, Shengli Tao, Tong Yang, Yongli Tang, Mengshuai Ge, Hao Wang, Zhenong Jin, Jinwei Dong, Zhibiao Nan, and Jin-Sheng He
Earth Syst. Sci. Data, 17, 2933–2952, https://doi.org/10.5194/essd-17-2933-2025, https://doi.org/10.5194/essd-17-2933-2025, 2025
Short summary
Short summary
The Tibetan Plateau is an important pastoral area where cultivated pastures play an increasingly important role. However, little is known about the spatial distribution of the cultivated pastures due to the difficulty in distinguishing them from natural grasslands with remote sensing. For the first time, we have mapped the cultivated pastures on the plateau at a resolution of 30 m with decent accuracy. This dataset is valuable to scientists, policymakers, conservationists, and pastoralists.
Weilin Liao, Yanman Li, Xiaoping Liu, Yuhao Wang, Yangzi Che, Ledi Shao, Guangzhao Chen, Hua Yuan, Ning Zhang, and Fei Chen
Earth Syst. Sci. Data, 17, 2535–2551, https://doi.org/10.5194/essd-17-2535-2025, https://doi.org/10.5194/essd-17-2535-2025, 2025
Short summary
Short summary
The currently available urban canopy parameter (UCP) datasets are limited to just a few cities for urban climate simulations by the Weather Research and Forecasting (WRF) model. To address this gap, we develop a global 1 km spatially continuous UCP dataset (GloUCP) which provides superior spatial coverage and higher accuracy in capturing urban morphology across diverse regions. It has great potential to support further advancements in urban climate modeling and related applications.
Ruoque Shen, Qiongyan Peng, Xiangqian Li, Xiuzhi Chen, and Wenping Yuan
Earth Syst. Sci. Data, 17, 2193–2216, https://doi.org/10.5194/essd-17-2193-2025, https://doi.org/10.5194/essd-17-2193-2025, 2025
Short summary
Short summary
Rice is a vital staple crop that plays a crucial role in food security in China. However, long-term high-resolution rice distribution maps in China are lacking. This study developed a new rice-mapping method, mitigating the impact of cloud contamination and missing data in optical remote sensing observations on rice mapping. The resulting dataset, CCD-Rice (China Crop Dataset-Rice), achieved high accuracy and showed a strong correlation with statistical data.
Yifan Cheng, Lei Zhao, TC Chakraborty, Keith Oleson, Matthias Demuzere, Xiaoping Liu, Yangzi Che, Weilin Liao, Yuyu Zhou, and Xinchang “Cathy” Li
Earth Syst. Sci. Data, 17, 2147–2174, https://doi.org/10.5194/essd-17-2147-2025, https://doi.org/10.5194/essd-17-2147-2025, 2025
Short summary
Short summary
The absence of globally consistent and spatially continuous urban surface input has long hindered large-scale high-resolution urban climate modeling. Using remote sensing, cloud computing, and machine learning, we developed U-Surf, a 1 km dataset providing key urban surface properties worldwide. U-Surf enhances urban representation across scales and supports kilometer-scale urban-resolving Earth system modeling unprecedentedly, with broader applications in urban studies and beyond.
Michael MacFerrin, Christopher Amante, Kelly Carignan, Matthew Love, and Elliot Lim
Earth Syst. Sci. Data, 17, 1835–1849, https://doi.org/10.5194/essd-17-1835-2025, https://doi.org/10.5194/essd-17-1835-2025, 2025
Short summary
Short summary
Here we present Earth TOPOgraphy (ETOPO) 2022, the latest iteration of NOAA's global seamless topographic–bathymetric dataset. ETOPO 2022 is a significant upgrade regarding resolution and accuracy from previous ETOPO releases and is freely available in multiple data formats and resolutions for all uses (public or private), excepting navigation.
Chao Tao, Dandan Zhong, Weiliang Mu, Zhuofei Du, and Haiyang Wu
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-184, https://doi.org/10.5194/essd-2025-184, 2025
Revised manuscript accepted for ESSD
Short summary
Short summary
We construct FarmSeg-VL, the first high-quality image-text dataset for farmland segmentation. It covers eight agricultural regions across four seasons in China, offering extensive spatiotemporal coverage and fine-grained annotations. This dataset fills the gap in remote sensing image-text datasets for farmland, alleviates the challenge of spatiotemporal heterogeneity in farmland segmentation, and provides valuable data to support large-scale farmland monitoring and mapping.
David A. Gibbs, Melissa Rose, Giacomo Grassi, Joana Melo, Simone Rossi, Viola Heinrich, and Nancy L. Harris
Earth Syst. Sci. Data, 17, 1217–1243, https://doi.org/10.5194/essd-17-1217-2025, https://doi.org/10.5194/essd-17-1217-2025, 2025
Short summary
Short summary
Updated global maps of greenhouse gas (GHG) emissions and sequestration by forests from 2001 onwards using satellite-derived data show that forests are strong net carbon sinks, capturing about as much CO2 each year on average as the USA emitted from fossil fuels in 2019. After reclassifying fluxes to countries’ reporting categories for national GHG inventories, we found that roughly two-thirds of the net CO2 flux from forests is anthropogenic and one-third is non-anthropogenic.
Zhenghang Yuan, Zhitong Xiong, Lichao Mou, and Xiao Xiang Zhu
Earth Syst. Sci. Data, 17, 1245–1263, https://doi.org/10.5194/essd-17-1245-2025, https://doi.org/10.5194/essd-17-1245-2025, 2025
Short summary
Short summary
ChatEarthNet is an image–text dataset that provides high-quality, detailed natural language descriptions for global-scale satellite data. It consists of 163 488 image-text pairs with captions generated by ChatGPT-3.5 and an additional 10 000 image-text pairs with captions generated by ChatGPT-4V(ision). This dataset has significant potential for training and evaluating vision–language geo-foundation models in remote sensing.
Xiao Zhang, Liangyun Liu, Tingting Zhao, Wenhan Zhang, Linlin Guan, Ming Bai, and Xidong Chen
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-73, https://doi.org/10.5194/essd-2025-73, 2025
Revised manuscript accepted for ESSD
Short summary
Short summary
This work describes a novel global 10 m land-cover dataset with fine classification system, which contains 30 land-cover subcategories and achieves the fulfilling performance over the globe.
Simon Boitard, Arnaud Mialon, Stéphane Mermoz, Nemesio J. Rodríguez-Fernández, Philippe Richaume, Julio César Salazar-Neira, Stéphane Tarot, and Yann H. Kerr
Earth Syst. Sci. Data, 17, 1101–1119, https://doi.org/10.5194/essd-17-1101-2025, https://doi.org/10.5194/essd-17-1101-2025, 2025
Short summary
Short summary
Aboveground biomass (AGB) is a critical component of the Earth's carbon cycle. The presented dataset aims to help monitor this essential climate variable with AGB time series from 2011 onward, derived with a carefully calibrated spatial relationship between the measurements of the Soil Moisture and Ocean Salinity (SMOS) mission and pre-existing AGB maps. The produced dataset has been extensively compared with other available AGB time series and can be used in AGB studies.
Fuyou Tian, Bingfang Wu, Hongwei Zeng, Miao Zhang, Weiwei Zhu, Nana Yan, Yuming Lu, and Yifan Li
Earth Syst. Sci. Data, 17, 855–880, https://doi.org/10.5194/essd-17-855-2025, https://doi.org/10.5194/essd-17-855-2025, 2025
Short summary
Short summary
Our study introduces GMIE, a high-resolution global map of irrigated cropland at 100 m resolution, covering 403.17 Mha and utilizing irrigation performance under drought stress. We found that 23.4 % of global cropland is irrigated, with the most extensive areas in India, China, the United States, and Pakistan. We identified the distribution of central pivot systems commonly used in the United States and Saudi Arabia. This new map can better support water management and food security globally.
Guangsheng Zhou, Hongrui Ren, Lei Zhang, Xiaomin Lv, and Mengzi Zhou
Earth Syst. Sci. Data, 17, 773–797, https://doi.org/10.5194/essd-17-773-2025, https://doi.org/10.5194/essd-17-773-2025, 2025
Short summary
Short summary
This study developed a new approach to long-time continuous annual vegetation mapping from remote sensing imagery, and mapped the vegetation of the Qinghai–Tibet Plateau (QTP) from 2000 to 2022 using the MOD09A1 product. The overall accuracy of continuous annual QTP vegetation mapping reached 83.3%, with the reference annual 2020 data reaching an accuracy of 83.3% and a kappa coefficient of 0.82. The study supports the use of remote sensing data to mapping long-time continuous annual vegetation.
Xuemeng Tian, Davide Consoli, Martijn Witjes, Florian Schneider, Leandro Parente, Murat Şahin, Yu-Feng Ho, Robert Minařík, and Tomislav Hengl
Earth Syst. Sci. Data, 17, 741–772, https://doi.org/10.5194/essd-17-741-2025, https://doi.org/10.5194/essd-17-741-2025, 2025
Short summary
Short summary
Our study introduces a Landsat-based data cube simplifying access to detailed environmental data across Europe from 2000 to 2022, covering vegetation, water, soil, and crops. Our experiments demonstrate its effectiveness in developing environmental models and maps. Tailored feature selection is crucial for its effective use in environmental modeling. It aims to support comprehensive environmental monitoring and analysis, helping researchers and policy-makers in managing environmental resources.
Mingyang Song, Lu Xu, Ji Ge, Hong Zhang, Lijun Zuo, Jingling Jiang, Yinhaibin Ding, Yazhe Xie, and Fan Wu
Earth Syst. Sci. Data, 17, 661–683, https://doi.org/10.5194/essd-17-661-2025, https://doi.org/10.5194/essd-17-661-2025, 2025
Short summary
Short summary
We created a 10 m resolution rice distribution map for East Asia in 2023 (EARice10), achieving an overall accuracy (OA) of 90.48 % on validation samples. EARice10 shows strong consistency with statistical data (coefficient of determination, R2: 0.94–0.98) and existing datasets (R2: 0.79–0.98). It is the most up-to-date map, covering the four major rice-producing countries in East Asia at 10 m resolution.
Maximilian Freudenberg, Sebastian Schnell, and Paul Magdon
Earth Syst. Sci. Data, 17, 351–367, https://doi.org/10.5194/essd-17-351-2025, https://doi.org/10.5194/essd-17-351-2025, 2025
Short summary
Short summary
Classifying tree species in satellite images is an important task for environmental monitoring and forest management. Here we present a dataset containing Sentinel-2 satellite pixel time series of individual trees intended for training machine learning models. The dataset was created by merging information from the German National Forest Inventory in 2012 with satellite data. It sparsely covers the whole of Germany for the years 2015 to 2022 and comprises 48 species and 3 species groups.
Mauro Marty, Livia Piermattei, Lars T. Waser, and Christian Ginzler
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-428, https://doi.org/10.5194/essd-2024-428, 2025
Revised manuscript accepted for ESSD
Short summary
Short summary
Millions of aerial photographs represent an enormous, resource for geoscientists. In this study, we used freely available historical stereo images covering Switzerland, allowing us to derive four countrywide DSMs at a 1 m spatial resolution across four epochs. Our DSMs achieved sub-metric accuracy compared to reference data and high image matching completeness, demonstrating the feasibility of capturing continuous surface change at a high spatial resolution over different land cover classes.
Yangyang Fu, Xiuzhi Chen, Chaoqing Song, Xiaojuan Huang, Jie Dong, Qiongyan Peng, and Wenping Yuan
Earth Syst. Sci. Data, 17, 95–115, https://doi.org/10.5194/essd-17-95-2025, https://doi.org/10.5194/essd-17-95-2025, 2025
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 66 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.
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, 17, 117–134, https://doi.org/10.5194/essd-17-117-2025, https://doi.org/10.5194/essd-17-117-2025, 2025
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.
Yanghai Yu, Yang Lei, Paul Siqueira, Xiaotong Liu, Denuo Gu, Anmin Fu, Yong Pang, Wenli Huang, and Jiancheng Shi
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-596, https://doi.org/10.5194/essd-2024-596, 2025
Revised manuscript accepted for ESSD
Short summary
Short summary
This paper presents a global-to-local method to improve forest height estimates by fusing InSAR and GEDI data. The large-scale ability was tested on open-access ALOS-1 data, where a two-fold solution is used to address temporal gap between GEDI and ALOS data. Produced products of 30 m gridded forest height mosaics for the northeastern U.S. and China show improved accuracy at 3–4 m/ha and 20 % enhancement over interpolated GEDI maps. The prototype is promising to fuse GEDI and future NISAR data.
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, 16, 5723–5735, https://doi.org/10.5194/essd-16-5723-2024, https://doi.org/10.5194/essd-16-5723-2024, 2024
Short summary
Short summary
The Land Use/Cover Area frame Survey (LUCAS) Copernicus 2022 is a large and systematic in situ field survey of 137 966 polygons over the European Union in 2022. The data contain 82 land cover classes and 40 land use classes.
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, 16, 5449–5475, https://doi.org/10.5194/essd-16-5449-2024, https://doi.org/10.5194/essd-16-5449-2024, 2024
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) daily seamless data cube (SDC) of surface reflectance based on Landsat 5, 7, 8, and 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.
Gerardo E. Soto, Steven W. Wilcox, Patrick E. Clark, Francesco P. Fava, Nathaniel D. Jensen, Njoki Kahiu, Chuan Liao, Benjamin Porter, Ying Sun, and Christopher B. Barrett
Earth Syst. Sci. Data, 16, 5375–5404, https://doi.org/10.5194/essd-16-5375-2024, https://doi.org/10.5194/essd-16-5375-2024, 2024
Short summary
Short summary
This paper uses machine learning and linear unmixing to produce rangeland health indicators: Landsat time series of land cover classes and vegetation fractional cover of photosynthetic vegetation, non-photosynthetic vegetation, and bare ground in arid and semi-arid Kenya, Ethiopia, and Somalia. This represents the first multi-decadal Landsat-resolution dataset specifically designed for mapping and monitoring rangeland health in the arid and semi-arid rangelands of this portion of eastern Africa.
Yangzi Che, Xuecao Li, Xiaoping Liu, Yuhao Wang, Weilin Liao, Xianwei Zheng, Xucai Zhang, Xiaocong Xu, Qian Shi, Jiajun Zhu, Honghui Zhang, Hua Yuan, and Yongjiu Dai
Earth Syst. Sci. Data, 16, 5357–5374, https://doi.org/10.5194/essd-16-5357-2024, https://doi.org/10.5194/essd-16-5357-2024, 2024
Short summary
Short summary
Most existing building height products are limited with respect to either spatial resolution or coverage, not to mention the spatial heterogeneity introduced by global building forms. Using Earth Observation (EO) datasets for 2020, we developed a global height dataset at the individual building scale. The dataset provides spatially explicit information on 3D building morphology, supporting both macro- and microanalysis of urban areas.
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.
Dina Jahanianfard, Joana Parente, Oscar Gonzalez-Pelayo, and Akli Benali
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-305, https://doi.org/10.5194/essd-2024-305, 2024
Revised manuscript accepted for ESSD
Short summary
Short summary
This data description paper provides details on the development of the first Portuguese burn severity atlas in Portugal from 1984 to 2022 derived from satellite imagery via Google Earth Engine platform. Moreover, a semi-automated code was also developed, which can be used to create burn severity atlas of any other region in the world. The maps of this atlas can be used not only in fields related to fire ecology and management, but also within research areas related to air, water, and soil.
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.
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).
Cited articles
Achanta, R. and Susstrunk, S.: Superpixels and polygons using simple non-iterative clustering, in: Proceedings of the IEEE conference on computer vision and pattern recognition, 21–26 July 2017, Honolulu, HI, USA, 4651–4660, https://doi.org/10.1109/cvpr.2017.520, 2017.
AfricaRice – Africa Rice Center: Transformation of Rice-based Agri-food Systems for Food and Nutrition Security in Africa: 2030 rice research and innovation strategy for Africa, Abidjan, Côte d'Ivoire, vi+58 pp., https://www.africarice.org/strategy (last access: 9 April 2024), 2023.
Ajala, A. and Gana, A.: Analysis of challenges facing rice processing in Nigeria, J. Food Process., 2015, 893673, https://doi.org/10.1155/2015/893673, 2015.
Arouna, A., Fatognon, I. A., Saito, K., and Futakuchi, K.: Moving toward rice self-sufficiency in sub-Saharan Africa by 2030: Lessons learned from 10 years of the Coalition for African Rice Development, World Dev. Perspect., 21, 100291, https://doi.org/10.1016/j.wdp.2021.100291, 2021.
Avcı, C., Budak, M., Yağmur, N., and Balçık, F.: Comparison between random forest and support vector machine algorithms for LULC classification, Int. J. Eng. Geosci., 8, 1–10, https://doi.org/10.26833/ijeg.987605, 2023.
Balasubramanian, V., Sie, M., Hijmans, R. J., and Otsuka, K.: Increasing Rice Production in Sub-Saharan Africa: Challenges and Opportunities, in: Advances in Agronomy, edited by: Sparks, D. L., Academic Press, 55–133, https://doi.org/10.1016/S0065-2113(06)94002-4, 2007.
Barro, M., Kassankogno, A. I., Wonni, I., Sereme, D., Somda, I., Kabore, H. K., Bena, G., Brugidou, C., Tharreau, D., and Tollenaere, C.: Spatiotemporal Survey of Multiple Rice Diseases in Irrigated Areas Compared to Rainfed Lowlands in the Western Burkina Faso, Plant Dis., 105, 3889–3899, https://doi.org/10.1094/PDIS-03-21-0579-RE, 2021.
Bhogapurapu, N., Dey, S., Mandal, D., Bhattacharya, A., Karthikeyan, L., McNairn, H., and Rao, Y.: Soil moisture retrieval over croplands using dual-pol L-band GRD SAR data, Remote Sens. Environ., 271, 112900, https://doi.org/10.1016/j.rse.2022.112900, 2022.
CARD – Coalition for African Rice Development: Countries, https://riceforafrica.net/ (last access: 1 February 2024), 2020/2021.
CARD – Coalition for African Rice Development: Countries, https://riceforafrica.net/ (last access: 1 February 2024), 2022.
Chang, J. G., Shoshany, M., and Oh, Y.: Polarimetric radar vegetation index for biomass estimation in desert fringe ecosystems, IEEE T. Geosci. Remote, 56, 7102–7108, https://doi.org/10.1109/tgrs.2018.2848285, 2018.
Chhabra, A., Rüdiger, C., Yebra, M., Jagdhuber, T., and Hilton, J.: RADAR-Vegetation Structural Perpendicular Index (R-VSPI) for the Quantification of Wildfire Impact and Post-Fire Vegetation Recovery, Remote Sens., 14, 3132, https://doi.org/10.3390/rs14133132, 2022.
de Lima, I. P., Jorge, R. G., and de Lima, J. L. P.: Remote sensing monitoring of rice fields: Towards assessing water saving irrigation management practices, Front. Remote Sens., 2, 762093, https://doi.org/10.3389/frsen.2021.762093, 2021.
Diuk-Wasser, M. A., Toure, M. B., Dolo, G., Bagayoko, M., Sogoba, N., Sissoko, I., Traoré, S. F., and Taylor, C. E.: Effect of rice cultivation patterns on malaria vector abundance in rice-growing villages in Mali, Am. J. Trop. Med. Hyg., 76, 869, https://doi.org/10.4269/ajtmh.2007.76.869, 2007.
FAO: Crops and livestock products, https://www.fao.org/faostat/en/#data/QCL (last access: 1 February 2024), 2022.
FAO: World Food and Agriculture – Statistical Yearbook 2023, https://doi.org/10.4060/cc8166en, 2023.
FEWSNET: Livelihoods Zone Map and Descriptions for South Sudan, https://pdf.usaid.gov/pdf_docs/PA00TGSD.pdf (last access: 4 February 2024), 2018.
Field, C. B. and Barros, V. R.: Climate change 2014 – Impacts, adaptation and vulnerability: Regional aspects, Cambridge University Press, 1199–1206, https://doi.org/10.1017/CBO9781107415386, 2014.
Frolking, S., Wisser, D., Grogan, D., Proussevitch, A., and Glidden, S.: GAEZ+_2015 Crop Harvest Area (V4), Harvard Dataverse [data set], https://doi.org/10.7910/DVN/KAGRFI, 2020.
Ginting, F. I., Rudiyanto, R., Fatchurahman, Mohd Shah, R., Che Soh, N., Giap, S. G. E., Fiantis, D., Setiawan, B. I., Schiller, S., Davitt, A., and Minasny, B.: SEA-Rice-Ci10: High-resolution Mapping of Rice Cropping Intensity and Harvested Area Across Southeast Asia using the Integration of Sentinel-1 and Sentinel-2 Data, Earth Syst. Sci. Data Discuss. [preprint], https://doi.org/10.5194/essd-2024-90, 2024.
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.
Guan, X., Huang, C., Liu, G., Meng, X., and Liu, Q.: Mapping Rice Cropping Systems in Vietnam Using an NDVI-Based Time-Series Similarity Measurement Based on DTW Distance, Remote Sens., 8, 19, https://doi.org/10.3390/rs8010019, 2016.
Guo, Y., Jia, X., Paull, D., and Benediktsson, J. A.: Nomination-favoured opinion pool for optical-SAR-synergistic rice mapping in face of weakened flooding signals, ISPRS J. Photogram. Remote Sens., 155, 187–205, https://doi.org/10.1016/j.isprsjprs.2019.07.008, 2019.
Han, J., Zhang, Z., Luo, Y., Cao, J., Zhang, L., Cheng, F., Zhuang, H., Zhang, J., and Tao, F.: NESEA-Rice10: high-resolution annual paddy rice maps for Northeast and Southeast Asia from 2017 to 2019, Earth Syst. Sci. Data, 13, 5969–5986, https://doi.org/10.5194/essd-13-5969-2021, 2021.
Hussain, S., Huang, J., Huang, J., Ahmad, S., Nanda, S., Anwar, S., Shakoor, A., Zhu, C., Zhu, L., and Cao, X.: Rice production under climate change: adaptations and mitigating strategies, Environment, climate, plant and vegetation growth, 659–686, Springer Nature, https://doi.org/10.1007/978-3-030-49732-3_26, 2020.
International Food Policy Research: Spatially-Disaggregated Crop Production Statistics Data in Africa South of the Sahara for 2017 (V3), Harvard Dataverse [data set], https://doi.org/10.7910/DVN/FSSKBW, 2020.
Jiang, J., Zhang, H., Ge, J., Zuo, L., Xu, L., Song, M., Ding, Y., Xie, Y., and Huang, W.: 20 m Africa rice distribution map in 2023 [dataset], Zenodo [data set], https://doi.org/10.5281/zenodo.13729353, 2024.
Kajisa, K. and Vu, T. T.: The importance of farm management training for the African rice Green Revolution: Experimental evidence from rainfed lowland areas in Mozambique, Food Policy, 114, 102401, https://doi.org/10.1016/j.foodpol.2022.102401, 2023.
Kuenzer, C. and Knauer, K.: Remote sensing of rice crop areas, Int. J. Remote Sens., 34, 2101–2139, https://doi.org/10.1080/01431161.2012.738946, 2013.
Kwesiga, J., Grotelüschen, K., Neuhoff, D., Senthilkumar, K., Döring, T. F., and Becker, M.: Site and management effects on grain yield and yield variability of rainfed lowland rice in the Kilombero Floodplain of Tanzania, Agronomy, 9, 632, https://doi.org/10.3390/agronomy9100632, 2019.
Laborte, A. G., Gutierrez, M. A., Balanza, J. G., Saito, K., Zwart, S. J., Boschetti, M., Murty, M. V. R., Villano, L., Aunario, J. K., Reinke, R., Koo, J., Hijmans, R. J., and Nelson, A.: RiceAtlas, a spatial database of global rice calendars and production, Sci. Data, 4, 170074, https://doi.org/10.1038/sdata.2017.74, 2017.
Li, J. and Wang, S.: Using SAR-Derived Vegetation Descriptors in a Water Cloud Model to Improve Soil Moisture Retrieval, Remote Sens., 10, 1370, https://doi.org/10.3390/rs10091370, 2018.
Liang, S., Li, Y., Zheng, Z., Cui, C., and Zhao, J.: Development Suggestions and Cultivation Performance of Chinese Hybrid Rice Varieties in Chad, Hubei Agricult. Sci., 56, 1422–1426, https://doi.org/10.14088/j.cnki.issn0439-8114.2017.08.006, 2017.
Liu, X., Zhai, H., Shen, Y., Lou, B., Jiang, C., Li, T., Hussain, S. B., and Shen, G.: Large-scale crop mapping from multisource remote sensing images in google earth engine, IEEE J. Select. Top. Appl.Earth Obs. Remote Sens., 13, 414–427, https://doi.org/10.1109/jstars.2019.2963539, 2020.
Liu, X., Zheng, J., Yu, L., Hao, P., Chen, B., Xin, Q., Fu, H., and Gong, P.: Annual dynamic dataset of global cropping intensity from 2001 to 2019, Sci. Data, 8, 283, https://doi.org/10.1038/s41597-021-01065-9, 2021.
Loko, Y. L. E., Gbemavo, C. D. S. J., Djedatin, G., Ewedje, E.-E., Orobiyi, A., Toffa, J., Tchakpa, C., Sedah, P., and Sabot, F.: Characterization of rice farming systems, production constraints and determinants of adoption of improved varieties by smallholder farmers of the Republic of Benin, Sci. Rep., 12, 3959, https://doi.org/10.1038/s41598-022-07946-2, 2022.
Luo, K., Lu, L., Xie, Y., Chen, F., Yin, F., and Li, Q.: Crop type mapping in the central part of the North China Plain using Sentinel-2 time series and machine learning, Comput. Elect. Agricult., 205, 107577, https://doi.org/10.1016/j.compag.2022.107577, 2023.
Mathieu, R.: Mapping of Rice Areas in Egypt using SAR Imagery, Zenodo [report], https://doi.org/10.5281/zenodo.7539805, 2022.
Menge, D. M., Musila, R. N., Kagito, S., Bii, L., Gichuki, J., Gichuhi, E., Kundu, C. A., Murori, R., Ismail, A., and Panchbhai, A.: Using principal component analysis to assess soil chemical properties in the mwea irrigation Scheme, Kenya: Implications for rice agronomic management, Int. J. Plant Soil Sci., 36, 106–126, https://doi.org/10.9734/ijpss/2024/v36i54508, 2024.
Mwakyusa, L., Dixit, S., Herzog, M., Heredia, M. C., Madege, R. R., and Kilasi, N. L.: Flood-tolerant rice for enhanced production and livelihood of smallholder farmers of Africa, Front. Sustain. Food Syst., 7, 1244460, https://doi.org/10.3389/fsufs.2023.1244460, 2023.
Ogisi, O. D. and Begho, T.: Adoption of climate-smart agricultural practices in sub-Saharan Africa: A review of the progress, barriers, gender differences and recommendations, Farm. Syst., 1, 100019, https://doi.org/10.1016/j.farsys.2023.100019, 2023.
Panda, D. and Barik, J.: Flooding tolerance in rice: Focus on mechanisms and approaches, Rice Sci., 28, 43–57, https://doi.org/10.1016/j.rsci.2020.11.006, 2021.
Qiu, B., Li, W., Tang, Z., Chen, C., and Qi, W.: Mapping paddy rice areas based on vegetation phenology and surface moisture conditions, Ecol. Indic., 56, 79–86, https://doi.org/10.1016/j.ecolind.2015.03.039, 2015.
Saad El Imanni, H., El Harti, A., Hssaisoune, M., Velastegui-Montoya, A., Elbouzidi, A., Addi, M., El Iysaouy, L., and El Hachimi, J.: Rapid and automated approach for early crop mapping using Sentinel-1 and Sentinel-2 on Google earth engine; a case of a highly heterogeneous and fragmented agricultural region, J. Imag., 8, 316, https://doi.org/10.3390/jimaging8120316, 2022.
Seck, P. A., Diagne, A., Mohanty, S., and Wopereis, M. C.: Crops that feed the world 7: Rice, Food Secur., 4, 7–24, https://doi.org/10.1007/s12571-012-0168-1, 2012.
Shen, R., Pan, B., Peng, Q., Dong, J., Chen, X., Zhang, X., Ye, T., Huang, J., and Yuan, W.: High-resolution distribution maps of single-season rice in China from 2017 to 2022, Earth Syst. Sci. Data, 15, 3203–3222, https://doi.org/10.5194/essd-15-3203-2023, 2023.
Singh, R. K., Rizvi, J., Behera, M. D., and Biradar, C.: Automated crop type mapping using time-weighted dynamic time warping – A basis to derive inputs for enhanced food and Nutritional Security, Curr. Res. Environ. Sustain., 3, 100032, https://doi.org/10.1016/j.crsust.2021.100032, 2021.
Sun, C., Zhang, H., Xu, L., Ge, J., Jiang, J., Zuo, L., and Wang, C.: Twenty-meter annual paddy rice area map for mainland Southeast Asia using Sentinel-1 synthetic-aperture-radar data, Earth Syst. Sci. Data, 15, 1501–1520, https://doi.org/10.5194/essd-15-1501-2023, 2023.
Sun, C., Zhang, H., Xu, L., Ge, J., Jiang, J., Song, M., and Wang, C.: Rice yield prediction using radar vegetation indices from Sentinel-1 data and multiscale one-dimensional convolutional long-and short-term memory network model, J. Appl. Remote Sens., 18, 024505, https://doi.org/10.1117/1.jrs.18.024505, 2024.
Tang, F. H. M., Nguyen, T. H., Conchedda, G., Casse, L., Tubiello, F. N., and Maggi, F.: CROPGRIDS: A global geo-referenced dataset of 173 crops circa 2020, Earth Syst. Sci. Data Discuss. [preprint], https://doi.org/10.5194/essd-2023-130, 2023.
Tassi, A. and Vizzari, M.: Object-oriented lulc classification in google earth engine combining snic, glcm, and machine learning algorithms, Remote Sens., 12, 3776, https://doi.org/10.3390/rs12223776, 2020.
Tian, G., Li, H., Feng, X., Jiang, Q., Li, N., Guo, Z., Zhao, J., and Yang, H.: An automatic method for rice mapping in Taishan, China using Sentinel-1A Time-series images, Remote Sens. Lett., 15, 99–109, https://doi.org/10.1080/2150704x.2024.2302946, 2024.
United Nations: Standard country or area codes for statistical use (M49)/Geographic Regions, https://unstats.un.org/unsd/methodology/m49 (last access: 6 April 2024), 2013.
USDA: Foreign Agricultural Service, https://ipad.fas.usda.gov/countrysummary (last access: 1 February 2024), 2023.
Voahanyinirina, R. and Elie, R.: Effects of planting location and storage time on lipids and fatty acids contents of some Madagascan rice varieties, Afr. J. Agricult. Res., 2, 349–355, 2007.
Waleed, M., Mubeen, M., Ahmad, A., Habib-ur-Rahman, M., Amin, A., Farid, H. U., Hussain, S., Ali, M., Qaisrani, S. A., and Nasim, W.: Evaluating the efficiency of coarser to finer resolution multispectral satellites in mapping paddy rice fields using GEE implementation, Sci. Rep., 12, 13210, https://doi.org/10.1038/s41598-022-17454-y, 2022.
Wang, G., Meng, D., Chen, R., Yang, G., Wang, L., Jin, H., Ge, X., and Feng, H.: Automatic Rice Early-Season Mapping Based on Simple Non-Iterative Clustering and Multi-Source Remote Sensing Images, Remote Sens., 16, 277, https://doi.org/10.3390/rs16020277, 2024.
Wang, Y., Zang, S., and Tian, Y.: Mapping paddy rice with the random forest algorithm using MODIS and SMAP time series, Chaos Solit. Fract., 140, 110116, https://doi.org/10.1016/j.chaos.2020.110116, 2020.
Wei, J., Cui, Y., Luo, W., and Luo, Y.: Mapping paddy rice distribution and cropping intensity in China from 2014 to 2019 with landsat images, effective flood signals, and google earth engine, Remote Sens., 14, 759, https://doi.org/10.3390/rs14030759, 2022.
Wu, H., Zhang, J., Zhang, Z., Han, J., Cao, J., Zhang, L., Luo, Y., Mei, Q., Xu, J., and Tao, F.: AsiaRiceYield4km: seasonal rice yield in Asia from 1995 to 2015, Earth Syst. Sci. Data, 15, 791–808, https://doi.org/10.5194/essd-15-791-2023, 2023.
Yamamoto, Y., Tsujimoto, Y., Fujihara, Y., Sakagami, J.-i., Ochi, S., and Fosu, M.: Assessing the probability of land submergence for lowland rice cultivation in Africa using satellite imagery and geospatial data, Environ. Dev. Sustain., 14, 955–971, https://doi.org/10.1007/s10668-012-9363-7, 2012.
You, N., Dong, J., Huang, J., Du, G., Zhang, G., He, Y., Yang, T., Di, Y., and Xiao, X.: The 10-m crop type maps in Northeast China during 2017–2019, Sci. Data, 8, 41, https://doi.org/10.1038/s41597-021-00827-9, 2021.
Yu, Q., You, L., Wood-Sichra, U., Ru, Y., Joglekar, A. K., Fritz, S., Xiong, W., Lu, M., Wu, W., and Yang, P.: A cultivated planet in 2010 – Part 2: The global gridded agricultural-production maps, Earth Syst. Sci. Data, 12, 3545–3572, https://doi.org/10.5194/essd-12-3545-2020, 2020.
Yuan, S., Saito, K., van Oort, P. A. J., van Ittersum, M. K., Peng, S., and Grassini, P.: Intensifying rice production to reduce imports and land conversion in Africa, Nat. Commun., 15, 835, https://doi.org/10.1038/s41467-024-44950-8, 2024.
Zhan, P., Zhu, W., and Li, N.: An automated rice mapping method based on flooding signals in synthetic aperture radar time series, Remote Sens. Environ., 252, 112112, https://doi.org/10.1016/j.rse.2020.112112, 2021.
Zhang, B., Liu, X., Liu, M., and Meng, Y.: Detection of Rice Phenological Variations under Heavy Metal Stress by Means of Blended Landsat and MODIS Image Time Series, Remote Sens., 11, 13, https://doi.org/10.3390/rs11010013, 2019.
Zhang, M., Wu, B., Zeng, H., He, G., Liu, C., Tao, S., Zhang, Q., Nabil, M., Tian, F., Bofana, J., Beyene, A. N., Elnashar, A., Yan, N., Wang, Z., and Liu, Y.: GCI30: a global dataset of 30 m cropping intensity using multisource remote sensing imagery, Earth Syst. Sci. Data, 13, 4799–4817, https://doi.org/10.5194/essd-13-4799-2021, 2021.
Zhang, W., Liu, H., Wu, W., Zhan, L., and Wei, J.: Mapping rice paddy based on machine learning with Sentinel-2 multi-temporal data: Model comparison and transferability, Remote Sens., 12, 1620, https://doi.org/10.3390/rs12101620, 2020.
Zhang, X., Shen, R., Zhu, X., Pan, B., Fu, Y., Zheng, Y., Chen, X., Peng, Q., and Yuan, W.: Sample-free automated mapping of double-season rice in China using Sentinel-1 SAR imagery, Front. Environ. Sci., 11, 1207882, https://doi.org/10.3389/fenvs.2023.1207882, 2023.
Zhu, A.-X., Zhao, F.-H., Pan, H.-B., and Liu, J.-Z.: Mapping rice paddy distribution using remote sensing by coupling deep learning with phenological characteristics, Remote Sens., 13, 1360, https://doi.org/10.3390/rs13071360, 2021.
Zoungrana, L. E., Barbouchi, M., Toukabri, W., Babasy, M. O., Khatra, N. B., Annabi, M., and Bahri, H.: Sentinel SAR-optical fusion for improving in-season wheat crop mapping at a large scale using machine learning and the Google Earth engine platform, Appl. Geomat., 16, 147–160, https://doi.org/10.1007/s12518-023-00545-4, 2024.
Short summary
This study uses temporal synthetic aperture radar (SAR) data and optical imagery to conduct rice-mapping experiments in 34 African countries with rice-planting areas exceeding 5000 ha in 2022, achieving a 20 m resolution spatial distribution mapping for 2023. The average classification accuracy based on the validation set exceeded 85 %, and the R2; values for linear fitting with existing statistical data all surpassed 0.9, demonstrating the effectiveness of the proposed mapping method.
This study uses temporal synthetic aperture radar (SAR) data and optical imagery to conduct...
Altmetrics
Final-revised paper
Preprint