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
Lu Xu
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
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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
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Domain: ESSD – Land | Subject: Land Cover and Land Use
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Global urban fractional changes at a 1 km resolution throughout 2100 under eight scenarios of Shared Socioeconomic Pathways (SSPs) and Representative Concentration Pathways (RCPs)
China Building Rooftop Area: the first multi-annual (2016–2021) and high-resolution (2.5 m) building rooftop area dataset in China derived with super-resolution segmentation from Sentinel-2 imagery
High-resolution distribution maps of single-season rice in China from 2017 to 2022
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UGS-1m: fine-grained urban green space mapping of 31 major cities in China based on the deep learning framework
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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
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Feng Yang and Zhenzhong Zeng
Earth Syst. Sci. Data, 15, 4011–4021, https://doi.org/10.5194/essd-15-4011-2023, https://doi.org/10.5194/essd-15-4011-2023, 2023
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Adrià Descals, Serge Wich, Zoltan Szantoi, Matthew J. Struebig, Rona Dennis, Zoe Hatton, Thina Ariffin, Nabillah Unus, David L. A. Gaveau, and Erik Meijaard
Earth Syst. Sci. Data, 15, 3991–4010, https://doi.org/10.5194/essd-15-3991-2023, https://doi.org/10.5194/essd-15-3991-2023, 2023
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Zeping Liu, Hong Tang, Lin Feng, and Siqing Lyu
Earth Syst. Sci. Data, 15, 3547–3572, https://doi.org/10.5194/essd-15-3547-2023, https://doi.org/10.5194/essd-15-3547-2023, 2023
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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 Discuss., https://doi.org/10.5194/essd-2023-196, https://doi.org/10.5194/essd-2023-196, 2023
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Bingjie Li, Xiaocong Xu, Xiaoping Liu, Qian Shi, Haoming Zhuang, Yaotong Cai, and Da He
Earth Syst. Sci. Data, 15, 2347–2373, https://doi.org/10.5194/essd-15-2347-2023, https://doi.org/10.5194/essd-15-2347-2023, 2023
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A global land cover map with fine spatial resolution is important for climate and environmental studies, food security, or biodiversity conservation. In this study, we developed an improved global land cover map in 2015 with 30 m resolution (GLC-2015) by fusing the existing land cover products based on the Dempster–Shafer theory of evidence on the Google Earth Engine platform. The GLC-2015 performed well, with an OA of 79.5 % (83.6 %) assessed with the global point-based (patch-based) samples.
Richard A. Houghton and Andrea Castanho
Earth Syst. Sci. Data, 15, 2025–2054, https://doi.org/10.5194/essd-15-2025-2023, https://doi.org/10.5194/essd-15-2025-2023, 2023
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We update a previous analysis of carbon emissions (annual and national) from land use, land-use change, and forestry from 1850 to 2020. We use data from the latest (2020) Global Forest Resources Assessment, incorporate shifting cultivation, and include improvements to the bookkeeping model and recent estimates of emissions from peatlands. Net global emissions declined steadily over the decade from 2011 to 2020 (mean of 0.96 Pg C yr−1), falling below 1.0 Pg C yr−1 for the first time in 30 years.
Christopher G. Marston, Aneurin W. O'Neil, R. Daniel Morton, Claire M. Wood, and Clare S. Rowland
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2023-78, https://doi.org/10.5194/essd-2023-78, 2023
Revised manuscript accepted for ESSD
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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.
Charles H. Simpson, Oscar Brousse, Nahid Mohajeri, Michael Davies, and Clare Heaviside
Earth Syst. Sci. Data, 15, 1521–1541, https://doi.org/10.5194/essd-15-1521-2023, https://doi.org/10.5194/essd-15-1521-2023, 2023
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Adding plants to roofs of buildings can reduce indoor and outdoor temperatures and so can reduce urban overheating, which is expected to increase due to climate change and urban growth. To better understand the effect this has on the urban environment, we need data on how many buildings have green roofs already.
We used a computer vision model to find green roofs in aerial imagery in London, producing a dataset identifying what buildings have green roofs and improving on previous methods.
Kandice L. Harper, Céline Lamarche, Andrew Hartley, Philippe Peylin, Catherine Ottlé, Vladislav Bastrikov, Rodrigo San Martín, Sylvia I. Bohnenstengel, Grit Kirches, Martin Boettcher, Roman Shevchuk, Carsten Brockmann, and Pierre Defourny
Earth Syst. Sci. Data, 15, 1465–1499, https://doi.org/10.5194/essd-15-1465-2023, https://doi.org/10.5194/essd-15-1465-2023, 2023
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We built a spatially explicit annual plant-functional-type (PFT) dataset for 1992–2020 exhibiting intra-class spatial variability in PFT fractional cover at 300 m. For each year, 14 maps of percentage cover are produced: bare soil, water, permanent snow/ice, built, managed grasses, natural grasses, and trees and shrubs, each split into leaf type and seasonality. Model simulations indicate significant differences in simulated carbon, water, and energy fluxes in some regions using this new set.
Zhuohong Li, Wei He, Mofan Cheng, Jingxin Hu, Guangyi Yang, and Hongyan Zhang
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2023-87, https://doi.org/10.5194/essd-2023-87, 2023
Revised manuscript accepted for ESSD
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Nowadays, the 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-meter resolution LC map of China, SinoLC-1, was built. The results showed SinoLC-1 had an overall accuracy of 73.61 % and conformed closely to the official survey reports. The comparison with other datasets suggests SinoLC-1 can be a better support for downstream applications and provide more accurate info to users.
Yating Ru, Brian Blankespoor, Ulrike Wood-Sichra, Timothy S. Thomas, Liangzhi You, and Erwin Kalvelagen
Earth Syst. Sci. Data, 15, 1357–1387, https://doi.org/10.5194/essd-15-1357-2023, https://doi.org/10.5194/essd-15-1357-2023, 2023
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Economic statistics are frequently produced at an administrative level that lacks detail to examine development patterns and the exposure to natural hazards. This paper disaggregates national and subnational administrative statistics of agricultural GDP into a global dataset at the local level using satellite-derived indicators. As an illustration, the paper estimates that the exposure of areas with extreme drought to agricultural GDP is USD 432 billion, where nearly 1.2 billion people live.
Elena Aragoneses, Mariano García, Michele Salis, Luís M. Ribeiro, and Emilio Chuvieco
Earth Syst. Sci. Data, 15, 1287–1315, https://doi.org/10.5194/essd-15-1287-2023, https://doi.org/10.5194/essd-15-1287-2023, 2023
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We present a new hierarchical fuel classification system with a total of 85 fuels that is useful for preventing fire risk at different spatial scales. Based on this, we developed a European fuel map (1 km resolution) using land cover datasets, biogeographic datasets, and bioclimatic modelling. We validated the map by comparing it to high-resolution data, obtaining high overall accuracy. Finally, we developed a crosswalk for standard fuel models as a first assignment of fuel parameters.
Giacomo Grassi, Clemens Schwingshackl, Thomas Gasser, Richard A. Houghton, Stephen Sitch, Josep G. Canadell, Alessandro Cescatti, Philippe Ciais, Sandro Federici, Pierre Friedlingstein, Werner A. Kurz, Maria J. Sanz Sanchez, Raúl Abad Viñas, Ramdane Alkama, Selma Bultan, Guido Ceccherini, Stefanie Falk, Etsushi Kato, Daniel Kennedy, Jürgen Knauer, Anu Korosuo, Joana Melo, Matthew J. McGrath, Julia E. M. S. Nabel, Benjamin Poulter, Anna A. Romanovskaya, Simone Rossi, Hanqin Tian, Anthony P. Walker, Wenping Yuan, Xu Yue, and Julia Pongratz
Earth Syst. Sci. Data, 15, 1093–1114, https://doi.org/10.5194/essd-15-1093-2023, https://doi.org/10.5194/essd-15-1093-2023, 2023
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Striking differences exist in estimates of land-use CO2 fluxes between the national greenhouse gas inventories and the IPCC assessment reports. These differences hamper an accurate assessment of the collective progress under the Paris Agreement. By implementing an approach that conceptually reconciles land-use CO2 flux from national inventories and the global models used by the IPCC, our study is an important step forward for increasing confidence in land-use CO2 flux estimates.
Xiaoyong Li, Hanqin Tian, Chaoqun Lu, and Shufen Pan
Earth Syst. Sci. Data, 15, 1005–1035, https://doi.org/10.5194/essd-15-1005-2023, https://doi.org/10.5194/essd-15-1005-2023, 2023
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We reconstructed land use and land cover (LULC) history for the conterminous United States during 1630–2020 by integrating multi-source data. The results show the widespread expansion of cropland and urban land and the shrinking of natural vegetation in the past four centuries. Forest planting and regeneration accelerated forest recovery since the 1920s. The datasets can be used to assess the LULC impacts on the ecosystem's carbon, nitrogen, and water cycles.
Johannes H. Uhl, Dominic Royé, Keith Burghardt, José Antonio Aldrey Vázquez, Manuel Borobio Sanchiz, and Stefan Leyk
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2023-53, https://doi.org/10.5194/essd-2023-53, 2023
Revised manuscript accepted for ESSD
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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.
Huifang Zhang, Zhonggang Tang, Binyao Wang, Hongcheng Kan, Yi Sun, Yu Qin, Baoping Meng, Meng Li, Jianjun Chen, Yanyan Lv, Jianguo Zhang, Shuli Niu, and Shuhua Yi
Earth Syst. Sci. Data, 15, 821–846, https://doi.org/10.5194/essd-15-821-2023, https://doi.org/10.5194/essd-15-821-2023, 2023
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The accuracy of regional grassland aboveground biomass (AGB) is always limited by insufficient ground measurements and large spatial gaps with satellite pixels. This paper used more than 37 000 UAV images as bridges to successfully obtain AGB values matching MODIS pixels. The new AGB estimation model had good robustness, with an average R2 of 0.83 and RMSE of 34.13 g m2. Our new dataset provides important input parameters for understanding the Qinghai–Tibet Plateau during global climate change.
Huaqing Wu, Jing Zhang, Zhao Zhang, Jichong Han, Juan Cao, Liangliang Zhang, Yuchuan Luo, Qinghang Mei, Jialu Xu, and Fulu Tao
Earth Syst. Sci. Data, 15, 791–808, https://doi.org/10.5194/essd-15-791-2023, https://doi.org/10.5194/essd-15-791-2023, 2023
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High-spatiotemporal-resolution rice yield datasets are limited over a large region. We proposed an explicit method to predict rice yield based on machine learning methods and generated a seasonal 4 km resolution rice yield dataset across Asia (AsiaRiceYield4km) for 1995–2015. The seasonal rice yield accuracy of AsiaRiceYield4km is high and much improved compared with previous datasets. AsiaRiceYield4km will fill the current data gap and better support agricultural monitoring systems.
Steve Ahlswede, Christian Schulz, Christiano Gava, Patrick Helber, Benjamin Bischke, Michael Förster, Florencia Arias, Jörn Hees, Begüm Demir, and Birgit Kleinschmit
Earth Syst. Sci. Data, 15, 681–695, https://doi.org/10.5194/essd-15-681-2023, https://doi.org/10.5194/essd-15-681-2023, 2023
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Imagery from air and space is the primary source of large-scale forest mapping. Our study introduces a new dataset with over 50000 image patches prepared for deep learning tasks. We show how the information for 20 European tree species can be extracted from different remote sensing sensors. Our algorithms can detect single species with precision scores up to 88 %. With a pixel size of 20×20 cm, forestry administration can now derive large-scale tree species maps at a very high resolution.
Qian Shi, Mengxi Liu, Andrea Marinoni, and Xiaoping Liu
Earth Syst. Sci. Data, 15, 555–577, https://doi.org/10.5194/essd-15-555-2023, https://doi.org/10.5194/essd-15-555-2023, 2023
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A large-scale and high-resolution urban green space (UGS) product with 1 m of 31 major cities in China (UGS-1m) is generated based on a deep learning framework to provide basic UGS information for relevant UGS research, such as distribution, area, and UGS rate. Moreover, an urban green space dataset (UGSet) with a total of 4454 samples of 512 × 512 in size are also supplied as the benchmark to support model training and algorithm comparison.
Raphaël d'Andrimont, Martin Claverie, Pieter Kempeneers, Davide Muraro, Momchil Yordanov, Devis Peressutti, Matej Batič, and François Waldner
Earth Syst. Sci. Data, 15, 317–329, https://doi.org/10.5194/essd-15-317-2023, https://doi.org/10.5194/essd-15-317-2023, 2023
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AI4boundaries is an open AI-ready data set to map field boundaries with Sentinel-2 and aerial photography provided with harmonised labels covering seven countries and 2.5 M parcels in Europe.
Xiao Zhang, Liangyun Liu, Tingting Zhao, Xidong Chen, Shangrong Lin, Jinqing Wang, Jun Mi, and Wendi Liu
Earth Syst. Sci. Data, 15, 265–293, https://doi.org/10.5194/essd-15-265-2023, https://doi.org/10.5194/essd-15-265-2023, 2023
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An accurate global 30 m wetland dataset that can simultaneously cover inland and coastal zones is lacking. This study proposes a novel method for wetland mapping and generates the first global 30 m wetland map with a fine classification system (GWL_FCS30), including five inland wetland sub-categories (permanent water, swamp, marsh, flooded flat and saline) and three coastal wetland sub-categories (mangrove, salt marsh and tidal flats).
Chong Liu, Xiaoqing Xu, Xuejie Feng, Xiao Cheng, Caixia Liu, and Huabing Huang
Earth Syst. Sci. Data, 15, 133–153, https://doi.org/10.5194/essd-15-133-2023, https://doi.org/10.5194/essd-15-133-2023, 2023
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Rapid Arctic changes are increasingly influencing human society, both locally and globally. Land cover offers a basis for characterizing the terrestrial world, yet spatially detailed information on Arctic land cover is lacking. We employ multi-source data to develop a new land cover map for the circumpolar Arctic. Our product reveals regionally contrasting biome distributions not fully documented in existing studies and thus enhances our understanding of the Arctic’s terrestrial system.
Jingliang Hu, Rong Liu, Danfeng Hong, Andrés Camero, Jing Yao, Mathias Schneider, Franz Kurz, Karl Segl, and Xiao Xiang Zhu
Earth Syst. Sci. Data, 15, 113–131, https://doi.org/10.5194/essd-15-113-2023, https://doi.org/10.5194/essd-15-113-2023, 2023
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Multimodal data fusion is an intuitive strategy to break the limitation of individual data in Earth observation. Here, we present a multimodal data set, named MDAS, consisting of synthetic aperture radar (SAR), multispectral, hyperspectral, digital surface model (DSM), and geographic information system (GIS) data for the city of Augsburg, Germany, along with baseline models for resolution enhancement, spectral unmixing, and land cover classification, three typical remote sensing applications.
Furong Li, Marie-José Gaillard, Xianyong Cao, Ulrike Herzschuh, Shinya Sugita, Jian Ni, Yan Zhao, Chengbang An, Xiaozhong Huang, Yu Li, Hongyan Liu, Aizhi Sun, and Yifeng Yao
Earth Syst. Sci. Data, 15, 95–112, https://doi.org/10.5194/essd-15-95-2023, https://doi.org/10.5194/essd-15-95-2023, 2023
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The objective of this study is present the first gridded and temporally continuous quantitative plant-cover reconstruction for temperate and northern subtropical China over the last 12 millennia. The reconstructions are based on 94 pollen records and include estimates for 27 plant taxa, 10 plant functional types, and 3 land-cover types. The dataset is suitable for palaeoclimate modelling and the evaluation of simulated past vegetation cover and anthropogenic land-cover change from models.
Jose Luis Gómez-Dans, Philip Edward Lewis, Feng Yin, Kofi Asare, Patrick Lamptey, Kenneth Kobina Yedu Aidoo, Dilys Sefakor MacCarthy, Hongyuan Ma, Qingling Wu, Martin Addi, Stephen Aboagye-Ntow, Caroline Edinam Doe, Rahaman Alhassan, Isaac Kankam-Boadu, Jianxi Huang, and Xuecao Li
Earth Syst. Sci. Data, 14, 5387–5410, https://doi.org/10.5194/essd-14-5387-2022, https://doi.org/10.5194/essd-14-5387-2022, 2022
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We provide a data set to support mapping croplands in smallholder landscapes in Ghana. The data set contains information on crop location on three agroecological zones for 2 years, temporal series of measurements of leaf area index and leaf chlorophyll concentration for maize canopies and yield. We demonstrate the use of these data to validate cropland masks, create a maize mask using satellite data and explore the relationship between satellite measurements and yield.
Zhen Yu, Jing Liu, and Giri Kattel
Earth Syst. Sci. Data, 14, 5179–5194, https://doi.org/10.5194/essd-14-5179-2022, https://doi.org/10.5194/essd-14-5179-2022, 2022
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We developed a 5 km annual nitrogen (N) fertilizer use dataset in China, covering the period from 1952 to 2018. We found that previous FAO-data-based N fertilizer products overestimated the N use in low, but underestimated in high, cropland coverage areas in China. The new dataset has improved the spatial distribution and corrected the existing biases, which is beneficial for biogeochemical cycle simulations in China, such as the assessment of greenhouse gas emissions and food production.
Femke van Geffen, Birgit Heim, Frederic Brieger, Rongwei Geng, Iuliia A. Shevtsova, Luise Schulte, Simone M. Stuenzi, Nadine Bernhardt, Elena I. Troeva, Luidmila A. Pestryakova, Evgenii S. Zakharov, Bringfried Pflug, Ulrike Herzschuh, and Stefan Kruse
Earth Syst. Sci. Data, 14, 4967–4994, https://doi.org/10.5194/essd-14-4967-2022, https://doi.org/10.5194/essd-14-4967-2022, 2022
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SiDroForest is an attempt to remedy data scarcity regarding vegetation data in the circumpolar region, whilst providing adjusted and labeled data for machine learning and upscaling practices. SiDroForest contains four datasets that include SfM point clouds, individually labeled trees, synthetic tree crowns and labeled Sentinel-2 patches that provide insights into the vegetation composition and forest structure of two important vegetation transition zones in Siberia, Russia.
Hanqin Tian, Zihao Bian, Hao Shi, Xiaoyu Qin, Naiqing Pan, Chaoqun Lu, Shufen Pan, Francesco N. Tubiello, Jinfeng Chang, Giulia Conchedda, Junguo Liu, Nathaniel Mueller, Kazuya Nishina, Rongting Xu, Jia Yang, Liangzhi You, and Bowen Zhang
Earth Syst. Sci. Data, 14, 4551–4568, https://doi.org/10.5194/essd-14-4551-2022, https://doi.org/10.5194/essd-14-4551-2022, 2022
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Nitrogen is one of the critical nutrients for growth. Evaluating the change in nitrogen inputs due to human activity is necessary for nutrient management and pollution control. In this study, we generated a historical dataset of nitrogen input to land at the global scale. This dataset consists of nitrogen fertilizer, manure, and atmospheric deposition inputs to cropland, pasture, and rangeland at high resolution from 1860 to 2019.
Raphaël d'Andrimont, Momchil Yordanov, Laura Martinez-Sanchez, Peter Haub, Oliver Buck, Carsten Haub, Beatrice Eiselt, and Marijn van der Velde
Earth Syst. Sci. Data, 14, 4463–4472, https://doi.org/10.5194/essd-14-4463-2022, https://doi.org/10.5194/essd-14-4463-2022, 2022
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Between 2006 and 2018, 875 661 LUCAS cover (i.e. close-up) photos were taken over a systematic sample of the European Union. This geo-located photo dataset has been curated and is being made available along with the surveyed label data, including land cover and plant species.
Han Su, Bárbara Willaarts, Diana Luna-Gonzalez, Maarten S. Krol, and Rick J. Hogeboom
Earth Syst. Sci. Data, 14, 4397–4418, https://doi.org/10.5194/essd-14-4397-2022, https://doi.org/10.5194/essd-14-4397-2022, 2022
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There are over 608 million farms around the world but they are not the same. We developed high spatial resolution maps showing where small and large farms were located and which crops were planted for 56 countries. We checked the reliability and have the confidence to use them for the country level and global studies. Our maps will help more studies to easily measure how agriculture policies, water availability, and climate change affect small and large farms.
Zhen Qian, Min Chen, Yue Yang, Teng Zhong, Fan Zhang, Rui Zhu, Kai Zhang, Zhixin Zhang, Zhuo Sun, Peilong Ma, Guonian Lü, Yu Ye, and Jinyue Yan
Earth Syst. Sci. Data, 14, 4057–4076, https://doi.org/10.5194/essd-14-4057-2022, https://doi.org/10.5194/essd-14-4057-2022, 2022
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Roadside noise barriers (RNBs) are important urban infrastructures to ensure a city is liveable. This study provides the first reliable and nationwide vectorized RNB dataset with street view imagery in China. The generated RNB dataset is evaluated in terms of two aspects, i.e., the detection accuracy and the completeness and positional accuracy. The method is based on a developed geospatial artificial intelligence framework.
Matthias Demuzere, Jonas Kittner, Alberto Martilli, Gerald Mills, Christian Moede, Iain D. Stewart, Jasper van Vliet, and Benjamin Bechtel
Earth Syst. Sci. Data, 14, 3835–3873, https://doi.org/10.5194/essd-14-3835-2022, https://doi.org/10.5194/essd-14-3835-2022, 2022
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Because urban areas are key contributors to climate change but are also susceptible to multiple hazards, one needs spatially detailed information on urban landscapes to support environmental services. This global local climate zone map describes this much-needed intra-urban heterogeneity across the whole surface of the earth in a universal language and can serve as a basic infrastructure to study e.g. environmental hazards, energy demand, and climate adaptation and mitigation solutions.
Xin Huang, Jie Yang, Wenrui Wang, and Zhengrong Liu
Earth Syst. Sci. Data, 14, 3649–3672, https://doi.org/10.5194/essd-14-3649-2022, https://doi.org/10.5194/essd-14-3649-2022, 2022
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Using more than 2.7 million Sentinel images, we proposed a global ISA mapping method and produced the 10-m global ISA dataset (GISA-10m), with overall accuracy exceeding 86 %. The inter-comparison between different global ISA datasets showed the superiority of our results. The ISA distribution at urban and rural was discussed and compared. For the first time, courtesy of the high spatial resolution, the global road ISA was further identified, and its distribution was discussed.
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...
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