Articles | Volume 15, issue 4
https://doi.org/10.5194/essd-15-1501-2023
© 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-15-1501-2023
© 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
| 
04 Apr 2023
Data description paper |
| 04 Apr 2023
| 04 Apr 2023
Twenty-meter annual paddy rice area map for mainland Southeast Asia using Sentinel-1 synthetic-aperture-radar data
Chunling Sun
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 100094, China
College of Resources and Environment, University of Chinese Academy of
Sciences, Beijing 100049, China
Hong Zhang
CORRESPONDING AUTHOR
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 100094, China
College of Resources and Environment, University of Chinese Academy of
Sciences, Beijing 100049, 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 100094, 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 100094, China
College of Resources and Environment, University of Chinese Academy of
Sciences, Beijing 100049, China
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 100094, 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
International Research Center of Big Data for Sustainable Development
Goals, Beijing 100094, China
Chao Wang
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 100094, China
College of Resources and Environment, University of Chinese Academy of
Sciences, Beijing 100049, China
Related authors
No articles found.
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.
Jingling Jiang, Hong Zhang, Ji Ge, Lijun Zuo, Lu Xu, Minyang Song, Yinhaibin Ding, Yazhe Xie, and Wenjiang Huang
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-402, https://doi.org/10.5194/essd-2024-402, 2024
Revised manuscript accepted for ESSD
Short summary
Short summary
This study employs temporal SAR data and optical imagery to conduct rice extraction experiments in 34 African countries with annual rice planting areas exceeding 5,000 hectares, achieving 20-meter resolution spatial distribution mapping of rice in Africa for 2023. The average classification accuracy 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.
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
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
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
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
Revised and updated geospatial monitoring of twenty-first century forest carbon fluxes
20 m Africa Rice Distribution Map of 2023
Time-series of Landsat-based bi-monthly and annual spectral indices for continental Europe for 2000–2022
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
The Earth Topography 2022 (ETOPO 2022) Global DEM dataset
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
Above ground biomass dataset from SMOS L band vegetation optical depth and reference maps
ChinaSoyArea10m: a dataset of soybean-planting areas with a spatial resolution of 10 m across China from 2017 to 2021
Annual vegetation maps in Qinghai-Tibet Plateau (QTP) from 2000 to 2022 based on MODIS series satellite imagery
Physical, social, and biological attributes for improved understanding and prediction of wildfires: FPA FOD-Attributes dataset
ChatEarthNet: A Global-Scale Image-Text Dataset Empowering Vision-Language Geo-Foundation Models
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
GMIE-100: a global maximum irrigation extent and irrigation type dataset derived through irrigation performance during drought stress and machine learning method
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
A new cropland area database by country circa 2020
FORMS: Forest Multiple Source height, wood volume, and biomass maps in France at 10 to 30 m resolution based on Sentinel-1, Sentinel-2, and Global Ecosystem Dynamics Investigation (GEDI) data with a deep learning approach
SinoLC-1: the first 1 m resolution national-scale land-cover map of China created with a deep learning framework and open-access data
HISDAC-ES: historical settlement data compilation for Spain (1900–2020)
LCM2021 – the UK Land Cover Map 2021
ChinaWheatYield30m: a 30 m annual winter wheat yield dataset from 2016 to 2021 in China
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.
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.
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.
David A. Gibbs, Melissa Rose, Giacomo Grassi, Joana Melo, Simone Rossi, Viola Heinrich, and Nancy L. Harris
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-397, https://doi.org/10.5194/essd-2024-397, 2024
Revised manuscript accepted for ESSD
Short summary
Short summary
Updated global maps of greenhouse gas 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 United States emits from fossil fuels. After reclassifying fluxes to countries’ reporting categories for national greenhouse gas inventories, we found that roughly two-thirds of the total net flux from forests is anthropogenic and one-third is non-anthropogenic.
Jingling Jiang, Hong Zhang, Ji Ge, Lijun Zuo, Lu Xu, Minyang Song, Yinhaibin Ding, Yazhe Xie, and Wenjiang Huang
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-402, https://doi.org/10.5194/essd-2024-402, 2024
Revised manuscript accepted for ESSD
Short summary
Short summary
This study employs temporal SAR data and optical imagery to conduct rice extraction experiments in 34 African countries with annual rice planting areas exceeding 5,000 hectares, achieving 20-meter resolution spatial distribution mapping of rice in Africa for 2023. The average classification accuracy 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.
Xuemeng Tian, Davide Consoli, Martijn Witjes, Florian Schneider, Leandro Parente, Murat Şahin, Yu-Feng Ho, Robert Minařík, and Tomislav Hengl
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-266, https://doi.org/10.5194/essd-2024-266, 2024
Revised manuscript accepted for ESSD
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 policymakers in managing environmental resources.
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.
Michael MacFerrin, Christopher Amante, Kelly Carignan, Matthew Love, and Elliot Lim
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-250, https://doi.org/10.5194/essd-2024-250, 2024
Revised manuscript accepted for ESSD
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 in resolution and accuracy from previous ETOPO releases, freely available in multiple data formats and resolutions for all uses (public or private), excepting navigation.
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.
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 Discuss., https://doi.org/10.5194/essd-2024-184, https://doi.org/10.5194/essd-2024-184, 2024
Revised manuscript accepted for ESSD
Short summary
Short summary
Above Ground Biomass (AGB) is a critical component of the Earth carbon cycle. The presented dataset aims to help monitoring 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.
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.
Guangsheng Zhou, Hongrui Ren, Lei Zhang, Xiaomin Lv, and Mengzi Zhou
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-193, https://doi.org/10.5194/essd-2024-193, 2024
Revised manuscript accepted for ESSD
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 through the MOD09A1 product. The overall accuracy of continuous annual QTP vegetation mapping reached 80.9%, with the reference annual 2020 reaching an accuracy of 86.5% and a Kappa coefficient of 0.85. The study supports the use of remote sensing data to mapping a long-time continuous annual vegetation.
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.
Zhenghang Yuan, Zhitong Xiong, Lichao Mou, and Xiao Xiang Zhu
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-140, https://doi.org/10.5194/essd-2024-140, 2024
Revised manuscript accepted for ESSD
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.
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).
Fuyou Tian, Bingfang Wu, Hongwei Zeng, Miao Zhang, Weiwei Zhu, Nana Yan, Yuming Lu, and Yifan Li
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2023-536, https://doi.org/10.5194/essd-2023-536, 2024
Revised manuscript accepted for ESSD
Short summary
Short summary
Our team has developed an irrigation map with 100 m resolution, which is more detailed than existing one. We used satellite images and focused on the crop status during the dry conditions. We found that 23.4 % of global cropland is irrigated, with the most extensive areas in India, China, the US, and Pakistan. We also explored the distribution of central pivot systems, which are commonly used in the US and Saudi Arabia. This new map can better support water management and food security globally.
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.
Francesco N. Tubiello, Giulia Conchedda, Leon Casse, Pengyu Hao, Giorgia De Santis, and Zhongxin Chen
Earth Syst. Sci. Data, 15, 4997–5015, https://doi.org/10.5194/essd-15-4997-2023, https://doi.org/10.5194/essd-15-4997-2023, 2023
Short summary
Short summary
We describe a new dataset of cropland area circa the year 2020, with global coverage and country detail. Data are generated from geospatial information on the agreement characteristics of six high-resolution cropland maps. By helping to highlight features of cropland characteristics and underlying causes for agreement across land cover products, the dataset can be used as a tool to help guide future mapping efforts towards improved agricultural monitoring.
Martin Schwartz, Philippe Ciais, Aurélien De Truchis, Jérôme Chave, Catherine Ottlé, Cedric Vega, Jean-Pierre Wigneron, Manuel Nicolas, Sami Jouaber, Siyu Liu, Martin Brandt, and Ibrahim Fayad
Earth Syst. Sci. Data, 15, 4927–4945, https://doi.org/10.5194/essd-15-4927-2023, https://doi.org/10.5194/essd-15-4927-2023, 2023
Short summary
Short summary
As forests play a key role in climate-related issues, their accurate monitoring is critical to reduce global carbon emissions effectively. Based on open-access remote-sensing sensors, and artificial intelligence methods, we created high-resolution tree height, wood volume, and biomass maps of metropolitan France that outperform previous products. This study, based on freely available data, provides essential information to support climate-efficient forest management policies at a low cost.
Zhuohong Li, Wei He, Mofan Cheng, Jingxin Hu, Guangyi Yang, and Hongyan Zhang
Earth Syst. Sci. Data, 15, 4749–4780, https://doi.org/10.5194/essd-15-4749-2023, https://doi.org/10.5194/essd-15-4749-2023, 2023
Short summary
Short summary
Nowadays, a very-high-resolution land-cover (LC) map with national coverage is still unavailable in China, hindering efficient resource allocation. To fill this gap, the first 1 m resolution LC map of China, SinoLC-1, was built. The results showed that SinoLC-1 had an overall accuracy of 73.61 % and conformed to the official survey reports. Comparison with other datasets suggests that SinoLC-1 can be a better support for downstream applications and provide more accurate LC information to users.
Johannes H. Uhl, Dominic Royé, Keith Burghardt, José A. Aldrey Vázquez, Manuel Borobio Sanchiz, and Stefan Leyk
Earth Syst. Sci. Data, 15, 4713–4747, https://doi.org/10.5194/essd-15-4713-2023, https://doi.org/10.5194/essd-15-4713-2023, 2023
Short summary
Short summary
Historical, fine-grained geospatial datasets on built-up areas are rarely available, constraining studies of urbanization, settlement evolution, or the dynamics of human–environment interactions to recent decades. In order to provide such historical data, we used publicly available cadastral building data for Spain and created a series of gridded surfaces, measuring age, physical, and land-use-related features of the built environment in Spain and the evolution of settlements from 1900 to 2020.
Christopher G. Marston, Aneurin W. O'Neil, R. Daniel Morton, Claire M. Wood, and Clare S. Rowland
Earth Syst. Sci. Data, 15, 4631–4649, https://doi.org/10.5194/essd-15-4631-2023, https://doi.org/10.5194/essd-15-4631-2023, 2023
Short summary
Short summary
The UK Land Cover Map 2021 (LCM2021) is a UK-wide land cover data set, with 21- and 10-class versions. It is intended to support a broad range of UK environmental research, including ecological and hydrological research. LCM2021 was produced by classifying Sentinel-2 satellite imagery. LCM2021 is distributed as a suite of products to facilitate easy use for a range of applications. To support research at different spatial scales it includes 10 m, 25 m and 1 km resolution products.
Yu Zhao, Shaoyu Han, Jie Zheng, Hanyu Xue, Zhenhai Li, Yang Meng, Xuguang Li, Xiaodong Yang, Zhenhong Li, Shuhong Cai, and Guijun Yang
Earth Syst. Sci. Data, 15, 4047–4063, https://doi.org/10.5194/essd-15-4047-2023, https://doi.org/10.5194/essd-15-4047-2023, 2023
Short summary
Short summary
In the present study, we generated a 30 m Chinese winter wheat yield dataset from 2016 to 2021, called ChinaWheatYield30m. The dataset has high spatial resolution and great accuracy. It is the highest-resolution yield dataset known. Such a dataset will provide basic knowledge of detailed wheat yield distribution, which can be applied for many purposes including crop production modeling or regional climate evaluation.
Cited articles
Bridhikitti, A. and Overcamp, T. J.: Estimation of Southeast Asian rice
paddy areas with different ecosystems from moderate-resolution satellite
imagery, Agr. Ecosyst. Environ., 146, 113–120,
https://doi.org/10.1016/j.agee.2011.10.016, 2012.
Chang, L., Chen, Y.-T., Chan, Y.-L., and Wu, M.-C.: A Novel Feature for
Detection of Rice Field Distribution Using Time Series SAR Data, IGARSS 2020–2020 IEEE International Geoscience and Remote Sensing Symposium, 26 September–2 October 2020, Waikoloa, HI, USA, 4866–4869,
https://doi.org/10.1109/igarss39084.2020.9323278, 2020.
Chen, C. F., Son, N. T., and Chang, L. Y.: Monitoring of rice cropping
intensity in the upper Mekong Delta, Vietnam using time-series MODIS data,
Adv. Space Res., 49, 292–301, https://doi.org/10.1016/j.asr.2011.09.011, 2012.
Chen, C. F., Son, N. T., Chen, C. R., Chang, L. Y., and Chiang, S. H.: Rice
Crop Mapping Using Sentinel-1a Phenological Metrics, Int.
Arch. Photogramm., XLI-B8, 863–865, https://doi.org/10.5194/isprsarchives-XLI-B8-863-2016, 2016.
Clauss, K., Yan, H., and Kuenzer, C.: Mapping Paddy Rice in China in 2002,
2005, 2010 and 2014 with MODIS Time Series, Remote Sens.-Basel, 8, 434,
https://doi.org/10.3390/rs8050434, 2016.
Clauss, K., Ottinger, M., Leinenkugel, P., and Kuenzer, C.: Estimating rice
production in the Mekong Delta, Vietnam, utilizing time series of Sentinel-1
SAR data,
Int. J. Appl. Earth Obs., 73, 574–585, https://doi.org/10.1016/j.jag.2018.07.022, 2018.
Congalton, R. G.: A review of assessing the accuracy of classifications of
remotely sensed data, Remote Sens. Environ., 37, 35–46,
https://doi.org/10.1016/0034-4257(91)90048-B, 1991.
Crisóstomo de Castro Filho, H., Abílio de Carvalho Júnior, O.,
Ferreira de Carvalho, O. L., Pozzobon de Bem, P., dos Santos de Moura, R.,
Olino de Albuquerque, A., Rosa Silva, C., Guimarães Ferreira, P. H.,
Fontes Guimarães, R., and Trancoso Gomes, R. A.: Rice Crop Detection
Using LSTM, Bi-LSTM, and Machine Learning Models from Sentinel-1 Time
Series, Remote Sens.-Basel, 12, 2655, https://doi.org/10.3390/rs12162655, 2020.
Cué La Rosa, L. E., Queiroz Feitosa, R., Nigri Happ, P., Del'Arco
Sanches, I., and Ostwald Pedro da Costa, G. A.: Combining Deep Learning and
Prior Knowledge for Crop Mapping in Tropical Regions from Multitemporal SAR
Image Sequences, Remote Sens.-Basel, 11, 2029, https://doi.org/10.3390/rs11172029, 2019.
Desa, U.: Transforming our world: The 2030 agenda for sustainable
development, https://sustainabledevelopment.un.org/post2015/transformingourworld/publication (last access: 29 March 2023),
2016.
Dong, J., Xiao, X., Kou, W., Qin, Y., Zhang, G., Li, L., Jin, C., Zhou, Y.,
Wang, J., Biradar, C., Liu, J., and Moore, B.: Tracking the dynamics of
paddy rice planting area in 1986–2010 through time series Landsat images
and phenology-based algorithms, Remote Sens. Environ., 160, 99–113,
https://doi.org/10.1016/j.rse.2015.01.004, 2015.
Dong, J., Xiao, X., Menarguez, M. A., Zhang, G., Qin, Y., Thau, D., Biradar,
C., and Moore III, B.: Mapping paddy rice planting area in northeastern
Asia with Landsat 8 images, phenology-based algorithm and Google Earth
Engine, Remote Sens. Environ., 185, 142–154, https://doi.org/10.1016/j.rse.2016.02.016, 2016a.
Dong, J., Xiao, X., Zhang, G., Menarguez, M., Choi, C., Qin, Y., Luo, P.,
Zhang, Y., and Moore, B.: Northward expansion of paddy rice in northeastern
Asia during 2000–2014, Geophys. Res. Lett., 43, 3754–3761,
https://doi.org/10.1002/2016GL068191, 2016b.
Draper, N. R. and Smith, H.: Applied regression analysis, John Wiley &
Sons, https://doi.org/10.1002/bimj.19690110613, 1998.
FAO: World rice production (Crops > Items > Rice,
paddy): https://www.fao.org/faostat/en/#data/QCL (last
access: 7 November 2022), 2020.
FAOSTAT: Statistical Database of the Food and Agricultural Organization of
the United Nations, https://www.fao.org/statistics/en/ (last access: 29 March 2023), 2010.
Filipponi, F.: Sentinel-1 GRD Preprocessing Workflow, Proceedings, 18, 11, https://doi.org/10.3390/ECRS-3-06201, 2019.
Godfray, H. C., Beddington, J. R., Crute, I. R., Haddad, L., Lawrence, D.,
Muir, J. F., Pretty, J., Robinson, S., Thomas, S. M., and Toulmin, C.: Food
security: the challenge of feeding 9 billion people, Science, 327, 812–818,
https://doi.org/10.1126/science.1185383, 2010.
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.-Basel, 8, 19, https://doi.org/10.3390/rs8010019, 2016.
Gumma, M. K., Nelson, A., Thenkabail, P. S., and Singh, A. N.: Mapping rice
areas of South Asia using MODIS multitemporal data,
J. Appl. Remote Sens., 5, 053547, https://doi.org/10.1117/1.3619838, 2011a.
Gumma, M. K., Gauchan, D., Nelson, A., Pandey, S., and Rala, A.: Temporal
changes in rice-growing area and their impact on livelihood over a decade: A
case study of Nepal, Agr. Ecosyst. Environ., 142, 382–392,
https://doi.org/10.1016/j.agee.2011.06.010, 2011b.
Gumma, M. K., Thenkabail, P. S., Maunahan, A., Islam, S., and Nelson, A.:
Mapping seasonal rice cropland extent and area in the high cropping
intensity environment of Bangladesh using MODIS 500 m data for the year
2010, ISPRS J. Photogramm., 91, 98–113,
https://doi.org/10.1016/j.isprsjprs.2014.02.007, 2014.
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.
Han, J., Zhang, Z., Luo, Y., Cao, J., Zhang, L., Zhuang, H., Cheng, F.,
Zhang, J., and Tao, F.: Annual paddy rice planting area and cropping
intensity datasets and their dynamics in the Asian monsoon region from 2000
to 2020, Agr. Syst., 200, 103437, https://doi.org/10.1016/j.agsy.2022.103437, 2022.
Hoang-Phi, P., Nguyen-Kim, T., Nguyen-Van-Anh, V., Lam-Dao, N., Le-Van, T.,
and Pham-Duy, T.: Rice yield estimation in An Giang province, the Vietnamese
Mekong Delta using Sentinel-1 radar remote sensing data, IOP C.
Ser. Earth Env., 652, 012001,
https://doi.org/10.1088/1755-1315/652/1/012001, 2021.
Huang, X., Wang, J., Shang, J., Liao, C., and Liu, J.: Application of
polarization signature to land cover scattering mechanism analysis and
classification using multi-temporal C-band polarimetric RADARSAT-2 imagery,
Remote Sens. Environ., 193, 11–28, https://doi.org/10.1016/j.rse.2017.02.014, 2017.
Inoue, S., Ito, A., and Yonezawa, C.: Mapping Paddy fields in Japan by using
a Sentinel-1 SAR time series supplemented by Sentinel-2 images on Google
Earth Engine, Remote Sens.-Basel, 12, 1622, https://doi.org/10.3390/rs12101622, 2020.
Ioffe, S. and Szegedy, C.: Batch Normalization: Accelerating Deep Network
Training by Reducing Internal Covariate Shift, ArXiv [preprint], abs/1502.03167,
https://doi.org/10.48550/arXiv.1502.03167, 2015.
Jin, X., Kumar, L., Li, Z., Feng, H., Xu, X., Yang, G., and Wang, J.: A
review of data assimilation of remote sensing and crop models,
Eur. J. Agron., 92, 141–152, https://doi.org/10.1016/j.eja.2017.11.002, 2018.
Johnson, D. M. and Mueller, R.: The 2009 cropland data layer, Photogramm.
Eng. Rem. S., 76, 1201–1205, 2010.
Kang, J., Yang, X., Wang, Z., Huang, C., and Wang, J.: Collaborative
Extraction of Paddy Planting Areas with Multi-Source Information Based on
Google Earth Engine: A Case Study of Cambodia, Remote Sens.-Basel, 14, 1823,
https://doi.org/10.3390/rs14081823, 2022.
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,
2012.
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, H., Fu, D., Huang, C., Su, F., Liu, Q., Liu, G., and Wu, S.: An Approach
to High-Resolution Rice Paddy Mapping Using Time-Series Sentinel-1 SAR Data
in the Mun River Basin, Thailand, Remote Sens.-Basel, 12, 3959, https://doi.org/10.3390/rs12233959,
2020.
Lin, C., Zhong, L., Song, X.-P., Dong, J., Lobell, D. B., and Jin, Z.:
Early-and in-season crop type mapping without current-year ground truth:
Generating labels from historical information via a topology-based approach,
Remote Sens. Environ., 274, 112994,
https://doi.org/10.1016/j.rse.2022.112994, 2022.
Liu, C.-A., Chen, Z.-x., Shao, Y., Chen, J.-s., Hasi, T., and Pan, H.-z.:
Research advances of SAR remote sensing for agriculture applications: A
review, J. Integr. Agr., 18, 506–525,
https://doi.org/10.1016/s2095-3119(18)62016-7, 2019.
Liu, R., Zhang, G., Dong, J., Zhou, Y., You, N., He, Y., and Xiao, X.:
Evaluating Effects of Medium-Resolution Optical Data Availability on
Phenology-Based Rice Mapping in China, Remote Sens.-Basel, 14, 3134,
https://doi.org/10.3390/rs14133134, 2022.
Liu, Z., Hu, Q., Tan, J., and Zou, J.: Regional scale mapping of fractional
rice cropping change using a phenology-based temporal mixture analysis,
Int. J. Remote Sens., 40, 2703–2716,
https://doi.org/10.1080/01431161.2018.1530812, 2018.
Luo, Y., Zhang, Z., Li, Z., Chen, Y., Zhang, L., Cao, J., and Tao, F.:
Identifying the spatiotemporal changes of annual harvesting areas for three
staple crops in China by integrating multi-data sources,
Environ. Res. Lett., 15, 074003, https://doi.org/10.1088/1748-9326/ab80f0, 2020.
Manjunath, K., More, R. S., Jain, N., Panigrahy, S., and Parihar, J.:
Mapping of rice-cropping pattern and cultural type using remote-sensing and
ancillary data: A case study for South and Southeast Asian countries,
Int. J. Remote Sens., 36, 6008–6030,
https://doi.org/10.1080/01431161.2015.1110259, 2015.
Mansaray, L. R., Kabba, V. T. S., Zhang, L., and Bebeley, H. A.: Optimal
multi-temporal Sentinel-1A SAR imagery for paddy rice field discrimination;
a recommendation for operational mapping initiatives,
Remote Sensing Applications: Society and Environment, 22, 100533, https://doi.org/10.1016/j.rsase.2021.100533,
2021.
McHugh, M. L.: Interrater reliability: the kappa statistic,
Biochem. Medica, 22, 276–282, https://doi.org/10.11613/BM.2012.031,
2012.
Mosleh, M. K., Hassan, Q. K., and Chowdhury, E. H.: Application of remote
sensors in mapping rice area and forecasting its production: a review,
Sensors-Basel, 15, 769–791, https://doi.org/10.3390/s150100769, 2015.
Ndikumana, E., Ho Tong Minh, D., Baghdadi, N., Courault, D., and Hossard,
L.: Deep Recurrent Neural Network for Agricultural Classification using
multitemporal SAR Sentinel-1 for Camargue, France, Remote Sens.-Basel, 10,
1217, https://doi.org/10.3390/rs10081217, 2018.
Nelson, A. and Gumma, M. K.: A map of lowland rice extent in the major rice
growing countries of Asia, IRRI [data set], http://irri.org/our-work/research/policy-and-markets/mapping.37 (last access: 11 October 2022), 2015.
Nelson, A., Setiyono, T., Rala, A., Quicho, E., Raviz, J., Abonete, P.,
Maunahan, A., Garcia, C., Bhatti, H., Villano, L., Thongbai, P., Holecz, F.,
Barbieri, M., Collivignarelli, F., Gatti, L., Quilang, E., Mabalay, M.,
Mabalot, P., Barroga, M., Bacong, A., Detoito, N., Berja, G., Varquez, F.,
Wahyunto, Kuntjoro, D., Murdiyati, S., Pazhanivelan, S., Kannan, P., Mary,
P., Subramanian, E., Rakwatin, P., Intrman, A., Setapayak, T., Lertna, S.,
Minh, V., Tuan, V., Duong, T., Quyen, N., Van Kham, D., Hin, S., Veasna, T.,
Yadav, M., Chin, C., and Ninh, N.: Towards an Operational SAR-Based Rice
Monitoring System in Asia: Examples from 13 Demonstration Sites across Asia
in the RIICE Project, Remote Sens.-Basel, 6, 10773–10812, https://doi.org/10.3390/rs61110773,
2014.
Nguyen, D. B. and Wagner, W.: European Rice Cropland Mapping with Sentinel-1
Data: The Mediterranean Region Case Study, Water, 9, 392, https://doi.org/10.3390/w9060392, 2017.
Ni, R., Tian, J., Li, X., Yin, D., Li, J., Gong, H., Zhang, J., Zhu, L., and
Wu, D.: An enhanced pixel-based phenological feature for accurate paddy rice
mapping with Sentinel-2 imagery in Google Earth Engine, ISPRS J.
Photogramm., 178, 282–296,
https://doi.org/10.1016/j.isprsjprs.2021.06.018, 2021.
Orynbaikyzy, A., Gessner, U., and Conrad, C.: Crop type classification using
a combination of optical and radar remote sensing data: a review,
Int. J. Remote Sens., 40, 6553–6595,
https://doi.org/10.1080/01431161.2019.1569791, 2019.
Pan, B., Zheng, Y., Shen, R., Ye, T., Zhao, W., Dong, J., Ma, H., and Yuan,
W.: High Resolution Distribution Dataset of Double-Season Paddy Rice in
China, Remote Sens.-Basel, 13, 4609, https://doi.org/10.3390/rs13224609, 2021.
Phan, D. C., Trung, T. H., Truong, V. T., Sasagawa, T., Vu, T. P. T., Bui,
D. T., Hayashi, M., Tadono, T., and Nasahara, K. N.: First comprehensive
quantification of annual land use/cover from 1990 to 2020 across mainland
Vietnam, Sci. Rep.-UK, 11, 9979, https://doi.org/10.1038/s41598-021-89034-5, 2021.
Qiu, B., Hu, X., Chen, C., Tang, Z., Yang, P., Zhu, X., Yan, C., and Jian,
Z.: Maps of cropping patterns in China during 2015–2021, Sci. Data, 9, 479,
https://doi.org/10.1038/s41597-022-01589-8, 2022.
Ronneberger, O., Fischer, P., and Brox, T.: U-Net: Convolutional Networks
for Biomedical Image Segmentation, ArXiv [preprint], abs/1505.04597,
https://doi.org/10.48550/arXiv.1505.04597, 2015.
Shew, A. M. and Ghosh, A.: Identifying Dry-Season Rice-Planting Patterns in
Bangladesh Using the Landsat Archive, Remote Sens.-Basel, 11, 1235,
https://doi.org/10.3390/rs11101235, 2019.
Singha, M., Dong, J., Zhang, G., and Xiao, X.: High resolution paddy rice
maps in cloud-prone Bangladesh and Northeast India using Sentinel-1 data,
Sci. Data, 6, 26, https://doi.org/10.1038/s41597-019-0036-3, 2019.
Soh, N. C., Shah, R. M., Giap, S. G. E., Setiawan, B. I., and Minasny, B.:
High-Resolution Mapping of Paddy Rice Extent and Growth Stages across
Peninsular Malaysia Using a Fusion of Sentinel-1 and 2 Time Series Data in
Google Earth Engine, Remote Sens.-Basel, 14, 1875, https://doi.org/10.3390/rs14081875, 2022.
Sun, C., Zhang, H., Xu, L., Wang, C., and Li, L.: Rice Mapping Using a
BiLSTM-Attention Model from Multitemporal Sentinel-1 Data, Agriculture, 11,
977, https://doi.org/10.3390/agriculture11100977, 2021.
Sun, C., Zhang, H., Ge, J., Wang, C., Li, L., and Xu, L.: Rice Mapping in a
Subtropical Hilly Region Based on Sentinel-1 Time Series Feature Analysis
and the Dual Branch BiLSTM Model, Remote Sens.-Basel, 14, 3213, https://doi.org/10.3390/rs14133213,
2022a.
Sun, C., Zhang, H., Xu, L., Ge, J., Jiang, J., Zuo, L., and Wang, C.: 20 m
Annual Paddy Rice Map for Mainland Southeast Asia Using Sentinel-1 SAR Data
(1), Zenodo [data set], https://doi.org/10.5281/zenodo.7315076, 2022b.
Sun, H.-S., Huang, J.-F., Huete, A. R., Peng, D.-L., and Zhang, F.: Mapping
paddy rice with multi-date moderate-resolution imaging spectroradiometer
(MODIS) data in China, J. Zhejiang Univ.-Sc. A, 10,
1509–1522, https://doi.org/10.1631/jzus.A0820536, 2009.
Thenkabail, P. S., Biradar, C. M., Noojipady, P., Dheeravath, V., Li, Y.,
Velpuri, M., Gumma, M., Gangalakunta, O. R. P., Turral, H., and Cai, X.:
Global irrigated area map (GIAM), derived from remote sensing, for the end
of the last millennium, Int. J. Remote Sens., 30,
3679–3733, https://doi.org/10.1080/01431160802698919, 2009.
Torbick, N., Chowdhury, D., Salas, W., and Qi, J.: Monitoring Rice
Agriculture across Myanmar Using Time Series Sentinel-1 Assisted by
Landsat-8 and PALSAR-2, Remote Sens.-Basel, 9, 119, https://doi.org/10.3390/rs9020119, 2017.
Torres, R., Snoeij, P., Geudtner, D., Bibby, D., Davidson, M., Attema, E.,
Potin, P., Rommen, B., Floury, N., and Brown, M.: GMES Sentinel-1 mission,
Remote Sens. Environ., 120, 9–24, https://doi.org/10.1016/j.rse.2011.05.028, 2012.
Tsokas, A., Rysz, M., Pardalos, P. M., and Dipple, K.: SAR data applications
in earth observation: An overview, Expert Syst. Appl., 205,
117342, https://doi.org/10.1016/j.eswa.2022.117342, 2022.
Vapnik, V. N.: An overview of statistical learning theory,
IEEE T Neural Networ., 10, 988–999, https://doi.org/10.1109/72.788640, 1999.
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.-Basel, 14, 759,
https://doi.org/10.3390/rs14030759, 2022.
Wei, P., Chai, D., Lin, T., Tang, C., Du, M., and Huang, J.: Large-scale
rice mapping under different years based on time-series Sentinel-1 images
using deep semantic segmentation model, ISPRS J. Photogramm., 174, 198–214, https://doi.org/10.1016/j.isprsjprs.2021.02.011, 2021.
Wei, S., Zhang, H., Wang, C., Wang, Y., and Xu, L.: Multi-Temporal SAR Data
Large-Scale Crop Mapping Based on U-Net Model, Remote Sens.-Basel, 11,
68, https://doi.org/10.3390/rs11010068, 2019.
Weiss, M., Jacob, F., and Duveiller, G.: Remote sensing for agricultural
applications: A meta-review, Remote Sens. Environ., 236,
111402, https://doi.org/10.1016/j.rse.2019.111402, 2020.
Xiao, X., Boles, S., Liu, J., Zhuang, D., Frolking, S., Li, C., Salas, W.,
and Moore III, B.: Mapping paddy rice agriculture in southern China using
multi-temporal MODIS images, Remote Sens. Environ., 95, 480–492,
https://doi.org/10.1016/j.rse.2004.12.009, 2005.
Xiao, X., Boles, S., Frolking, S., Li, C., Babu, J. Y., Salas, W., and Moore
III, B.: Mapping paddy rice agriculture in South and Southeast Asia using
multi-temporal MODIS images, Remote Sens. Environ., 100, 95–113,
https://doi.org/10.1016/j.rse.2005.10.004, 2006.
Xin, F., Xiao, X., Dong, J., Zhang, G., Zhang, Y., Wu, X., Li, X., Zou, Z.,
Ma, J., Du, G., Doughty, R. B., Zhao, B., and Li, B.: Large increases of
paddy rice area, gross primary production, and grain production in Northeast
China during 2000–2017, Sci. Total Environ., 711, 135183,
https://doi.org/10.1016/j.scitotenv.2019.135183, 2020.
Xu, L., Zhang, H., Wang, C., Wei, S., Zhang, B., Wu, F., and Tang, Y.: Paddy
Rice Mapping in Thailand Using Time-Series Sentinel-1 Data and Deep Learning
Model, Remote Sens.-Basel, 13, 3994, https://doi.org/10.3390/rs13193994, 2021.
Yang, L., Wang, L., Huang, J., Mansaray, L. R., and Mijiti, R.: Monitoring
policy-driven crop area adjustments in northeast China using Landsat-8
imagery, Int. J. Appl. Earth Obs., 82, 101892, https://doi.org/10.1016/j.jag.2019.06.002, 2019.
Yang, L., Huang, R., Huang, J., Lin, T., Wang, L., Mijiti, R., Wei, P.,
Tang, C., Shao, J., Li, Q., and Du, X.: Semantic Segmentation Based on
Temporal Features: Learning of Temporal-Spatial Information From Time-Series
SAR Images for Paddy Rice Mapping, IEEE T. Geosci.
Remote, 60, 4403216, https://doi.org/10.1109/tgrs.2021.3099522, 2021.
You, N. and Dong, J.: Examining earliest identifiable timing of crops using
all available Sentinel 1/2 imagery and Google Earth Engine, ISPRS J.
Photogramm., 161, 109–123,
https://doi.org/10.1016/j.isprsjprs.2020.01.001, 2020.
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. B., 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., Stuart, A. M., Laborte, A. G., Rattalino Edreira, J. I.,
Dobermann, A., Kien, L. V. N., Thúy, L. T., Paothong, K., Traesang, P.,
Tint, K. M., San, S. S., Villafuerte, M. Q., Quicho, E. D., Pame, A. R. P.,
Then, R., Flor, R. J., Thon, N., Agus, F., Agustiani, N., Deng, N., Li, T.,
and Grassini, P.: Southeast Asia must narrow down the yield gap to continue
to be a major rice bowl, Nature Food, 3, 217–226,
https://doi.org/10.1038/s43016-022-00477-z, 2022.
Zanaga, D., Van De Kerchove, R., De Keersmaecker, W., Souverijns, N.,
Brockmann, C., Quast, R., Wevers, J., Grosu, A., Paccini, A., and Vergnaud,
S.: ESA WorldCover 10 m 2020 v100, Zenodo,
https://doi.org/10.5281/zenodo.5571936, 2021.
Zhang, G., Xiao, X., Biradar, C. M., Dong, J., Qin, Y., Menarguez, M. A.,
Zhou, Y., Zhang, Y., Jin, C., Wang, J., Doughty, R. B., Ding, M., and Moore,
B., 3rd: Spatiotemporal patterns of paddy rice croplands in China and India
from 2000 to 2015, Sci. Total Environ., 579, 82–92,
https://doi.org/10.1016/j.scitotenv.2016.10.223, 2017.
Zhang, X., Wu, B., Ponce-Campos, G., Zhang, M., Chang, S., and Tian, F.:
Mapping up-to-Date Paddy Rice Extent at 10 M Resolution in China through the
Integration of Optical and Synthetic Aperture Radar Images, Remote Sens.-Basel,
10, 1200, https://doi.org/10.3390/rs10081200, 2018.
Zhao, R., Li, Y., and Ma, M.: Mapping Paddy Rice with Satellite Remote
Sensing: A Review, Sustainability, 13, 503, https://doi.org/10.3390/su13020503, 2021.
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.
Over 90 % of the world’s rice is produced in the Asia–Pacific region. In this study, a...
Altmetrics
Final-revised paper
Preprint