Articles | Volume 14, issue 9
https://doi.org/10.5194/essd-14-4397-2022
© Author(s) 2022. 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-14-4397-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
27 Sep 2022
Data description paper |
| 27 Sep 2022
Gridded 5 arcmin datasets for simultaneously farm-size-specific and crop-specific harvested areas in 56 countries
Multidisciplinary Water Management group, Faculty of Engineering
Technology, University of Twente, Enschede, 7500AE, the Netherlands
Water Security group, International Institute for Applied Systems
Analysis (IIASA), Laxenburg, 2361, Austria
Bárbara Willaarts
Water Security group, International Institute for Applied Systems
Analysis (IIASA), Laxenburg, 2361, Austria
Diana Luna-Gonzalez
Water Security group, International Institute for Applied Systems
Analysis (IIASA), Laxenburg, 2361, Austria
Maarten S. Krol
Multidisciplinary Water Management group, Faculty of Engineering
Technology, University of Twente, Enschede, 7500AE, the Netherlands
Rick J. Hogeboom
Multidisciplinary Water Management group, Faculty of Engineering
Technology, University of Twente, Enschede, 7500AE, the Netherlands
Water Footprint Network, Enschede, 7522NB, the Netherlands
Related authors
No articles found.
Oleksandr Mialyk, Joep F. Schyns, Martijn J. Booij, and Rick J. Hogeboom
Hydrol. Earth Syst. Sci., 26, 923–940, https://doi.org/10.5194/hess-26-923-2022, https://doi.org/10.5194/hess-26-923-2022, 2022
Short summary
Short summary
As the global demand for crops is increasing, it is vital to understand spatial and temporal patterns of crop water footprints (WFs). Previous studies looked into spatial patterns but not into temporal ones. Here, we present a new process-based gridded crop model to simulate WFs and apply it for maize in 1986–2016. We show that despite the average unit WF reduction (−35 %), the global WF of maize production has increased (+50 %), which might harm ecosystems and human livelihoods in some regions.
Seyedabdolhossein Mehvar, Kathelijne Wijnberg, Bas Borsje, Norman Kerle, Jan Maarten Schraagen, Joanne Vinke-de Kruijf, Karst Geurs, Andreas Hartmann, Rick Hogeboom, and Suzanne Hulscher
Nat. Hazards Earth Syst. Sci., 21, 1383–1407, https://doi.org/10.5194/nhess-21-1383-2021, https://doi.org/10.5194/nhess-21-1383-2021, 2021
Short summary
Short summary
This review synthesizes and complements existing knowledge in designing resilient vital infrastructure systems (VIS). Results from a systematic literature review indicate that (i) VIS are still being built without taking resilience explicitly into account and (ii) measures to enhance the resilience of VIS have not been widely applied in practice. The main pressing topic to address is the integration of the combined social, ecological, and technical resilience of these systems.
Hatem Chouchane, Maarten S. Krol, and Arjen Y. Hoekstra
Hydrol. Earth Syst. Sci., 24, 3015–3031, https://doi.org/10.5194/hess-24-3015-2020, https://doi.org/10.5194/hess-24-3015-2020, 2020
Short summary
Short summary
Previous studies on water saving through food trade focussed either on comparing water productivities among countries or on analysing food trade in relation to national water endowments. Here, we consider, for the first time, both differences in water productivities and water endowments to analyse national comparative advantages. Our study reveals that blue water scarcity can be reduced to sustainable levels by changing cropping patterns while maintaining current levels of global production.
Sebastian Multsch, Maarten S. Krol, Markus Pahlow, André L. C. Assunção, Alberto G. O. P. Barretto, Quirijn de Jong van Lier, and Lutz Breuer
Hydrol. Earth Syst. Sci., 24, 307–324, https://doi.org/10.5194/hess-24-307-2020, https://doi.org/10.5194/hess-24-307-2020, 2020
Short summary
Short summary
Expanding irrigation in agriculture is one of Brazil's strategies to increase production. In this study the amount of water required to grow the main crops has been calculated and compared to the water that is available in rivers at least 95 % of the time. Future decisions regarding expanding irrigated cropping areas must, while intensifying production practices, consider the likely regional effects on water scarcity levels, in order to reach sustainable agricultural production.
Abebe D. Chukalla, Maarten S. Krol, and Arjen Y. Hoekstra
Hydrol. Earth Syst. Sci., 21, 3507–3524, https://doi.org/10.5194/hess-21-3507-2017, https://doi.org/10.5194/hess-21-3507-2017, 2017
Short summary
Short summary
In the current study, we have developed a method to obtain marginal cost curves (MCCs) for WF reduction in crop production. The method is innovative by employing a model that combines soil water balance accounting and a crop growth model and assessing costs and WF reduction for all combinations of irrigation techniques, irrigation strategies and mulching practices. While this approach has been used in the field of constructing MCCs for carbon footprint reduction, this has never been done before.
Related subject area
Domain: ESSD – Land | Subject: Land Cover and Land Use
An open-source automatic survey of green roofs in London using segmentation of aerial imagery
Twenty-meter annual paddy rice area map for mainland Southeast Asia using Sentinel-1 synthetic-aperture-radar data
A 29-year time series of annual 300 m resolution plant-functional-type maps for climate models
Estimating local agricultural gross domestic product (AgGDP) across the world
Classification and mapping of European fuels using a hierarchical, multipurpose fuel classification system
Harmonising the land-use flux estimates of global models and national inventories for 2000–2020
Four-century history of land transformation by humans in the United States (1630–2020): annual and 1 km grid data for the HIStory of LAND changes (HISLAND-US)
A 250 m annual alpine grassland AGB dataset over the Qinghai–Tibet Plateau (2000–2019) in China based on in situ measurements, UAV photos, and MODIS data
AsiaRiceYield4km: seasonal rice yield in Asia from 1995 to 2015
TreeSatAI Benchmark Archive: a multi-sensor, multi-label dataset for tree species classification in remote sensing
UGS-1m: fine-grained urban green space mapping of 31 major cities in China based on the deep learning framework
AI4Boundaries: an open AI-ready dataset to map field boundaries with Sentinel-2 and aerial photography
GWL_FCS30: a global 30 m wetland map with a fine classification system using multi-sourced and time-series remote sensing imagery in 2020
CALC-2020: a new baseline land cover map at 10 m resolution for the circumpolar Arctic
MDAS: a new multimodal benchmark dataset for remote sensing
Gridded pollen-based Holocene regional plant cover in temperate and northern subtropical China suitable for climate modelling
Location, biophysical and agronomic parameters for croplands in northern Ghana
Historical nitrogen fertilizer use in China from 1952 to 2018
SiDroForest: a comprehensive forest inventory of Siberian boreal forest investigations including drone-based point clouds, individually labeled trees, synthetically generated tree crowns, and Sentinel-2 labeled image patches
Annual emissions of carbon from land use, land-use change, and forestry 1850–2020
History of anthropogenic Nitrogen inputs (HaNi) to the terrestrial biosphere: a 5 arcmin resolution annual dataset from 1860 to 2019
LUCAS cover photos 2006–2018 over the EU: 874 646 spatially distributed geo-tagged close-up photos with land cover and plant species label
Vectorized dataset of roadside noise barriers in China using street view imagery
A global map of local climate zones to support earth system modelling and urban-scale environmental science
Mapping 10 m global impervious surface area (GISA-10m) using multi-source geospatial data
Charles H. Simpson, Oscar Brousse, Nahid Mohajeri, Michael Davies, and Clare Heaviside
Earth Syst. Sci. Data, 15, 1521–1541, https://doi.org/10.5194/essd-15-1521-2023, https://doi.org/10.5194/essd-15-1521-2023, 2023
Short summary
Short summary
Adding plants to roofs of buildings can reduce indoor and outdoor temperatures and so can reduce urban overheating, which is expected to increase due to climate change and urban growth. To better understand the effect this has on the urban environment, we need data on how many buildings have green roofs already.
We used a computer vision model to find green roofs in aerial imagery in London, producing a dataset identifying what buildings have green roofs and improving on previous methods.
Chunling Sun, Hong Zhang, Lu Xu, Ji Ge, Jingling Jiang, Lijun Zuo, and Chao Wang
Earth Syst. Sci. Data, 15, 1501–1520, https://doi.org/10.5194/essd-15-1501-2023, https://doi.org/10.5194/essd-15-1501-2023, 2023
Short summary
Short summary
Over 90 % of the world’s rice is produced in the Asia–Pacific region. In this study, a rice-mapping method based on Sentinel-1 data for mainland Southeast Asia is proposed. A combination of spatiotemporal features with strong generalization is selected and input into the U-Net model to obtain a 20 m resolution rice area map of mainland Southeast Asia in 2019. The accuracy of the proposed method is 92.20 %. The rice area map is concordant with statistics and other rice area maps.
Kandice L. Harper, Céline Lamarche, Andrew Hartley, Philippe Peylin, Catherine Ottlé, Vladislav Bastrikov, Rodrigo San Martín, Sylvia I. Bohnenstengel, Grit Kirches, Martin Boettcher, Roman Shevchuk, Carsten Brockmann, and Pierre Defourny
Earth Syst. Sci. Data, 15, 1465–1499, https://doi.org/10.5194/essd-15-1465-2023, https://doi.org/10.5194/essd-15-1465-2023, 2023
Short summary
Short summary
We built a spatially explicit annual plant-functional-type (PFT) dataset for 1992–2020 exhibiting intra-class spatial variability in PFT fractional cover at 300 m. For each year, 14 maps of percentage cover are produced: bare soil, water, permanent snow/ice, built, managed grasses, natural grasses, and trees and shrubs, each split into leaf type and seasonality. Model simulations indicate significant differences in simulated carbon, water, and energy fluxes in some regions using this new set.
Yating Ru, Brian Blankespoor, Ulrike Wood-Sichra, Timothy S. Thomas, Liangzhi You, and Erwin Kalvelagen
Earth Syst. Sci. Data, 15, 1357–1387, https://doi.org/10.5194/essd-15-1357-2023, https://doi.org/10.5194/essd-15-1357-2023, 2023
Short summary
Short summary
Economic statistics are frequently produced at an administrative level that lacks detail to examine development patterns and the exposure to natural hazards. This paper disaggregates national and subnational administrative statistics of agricultural GDP into a global dataset at the local level using satellite-derived indicators. As an illustration, the paper estimates that the exposure of areas with extreme drought to agricultural GDP is USD 432 billion, where nearly 1.2 billion people live.
Elena Aragoneses, Mariano García, Michele Salis, Luís M. Ribeiro, and Emilio Chuvieco
Earth Syst. Sci. Data, 15, 1287–1315, https://doi.org/10.5194/essd-15-1287-2023, https://doi.org/10.5194/essd-15-1287-2023, 2023
Short summary
Short summary
We present a new hierarchical fuel classification system with a total of 85 fuels that is useful for preventing fire risk at different spatial scales. Based on this, we developed a European fuel map (1 km resolution) using land cover datasets, biogeographic datasets, and bioclimatic modelling. We validated the map by comparing it to high-resolution data, obtaining high overall accuracy. Finally, we developed a crosswalk for standard fuel models as a first assignment of fuel parameters.
Giacomo Grassi, Clemens Schwingshackl, Thomas Gasser, Richard A. Houghton, Stephen Sitch, Josep G. Canadell, Alessandro Cescatti, Philippe Ciais, Sandro Federici, Pierre Friedlingstein, Werner A. Kurz, Maria J. Sanz Sanchez, Raúl Abad Viñas, Ramdane Alkama, Selma Bultan, Guido Ceccherini, Stefanie Falk, Etsushi Kato, Daniel Kennedy, Jürgen Knauer, Anu Korosuo, Joana Melo, Matthew J. McGrath, Julia E. M. S. Nabel, Benjamin Poulter, Anna A. Romanovskaya, Simone Rossi, Hanqin Tian, Anthony P. Walker, Wenping Yuan, Xu Yue, and Julia Pongratz
Earth Syst. Sci. Data, 15, 1093–1114, https://doi.org/10.5194/essd-15-1093-2023, https://doi.org/10.5194/essd-15-1093-2023, 2023
Short summary
Short summary
Striking differences exist in estimates of land-use CO2 fluxes between the national greenhouse gas inventories and the IPCC assessment reports. These differences hamper an accurate assessment of the collective progress under the Paris Agreement. By implementing an approach that conceptually reconciles land-use CO2 flux from national inventories and the global models used by the IPCC, our study is an important step forward for increasing confidence in land-use CO2 flux estimates.
Xiaoyong Li, Hanqin Tian, Chaoqun Lu, and Shufen Pan
Earth Syst. Sci. Data, 15, 1005–1035, https://doi.org/10.5194/essd-15-1005-2023, https://doi.org/10.5194/essd-15-1005-2023, 2023
Short summary
Short summary
We reconstructed land use and land cover (LULC) history for the conterminous United States during 1630–2020 by integrating multi-source data. The results show the widespread expansion of cropland and urban land and the shrinking of natural vegetation in the past four centuries. Forest planting and regeneration accelerated forest recovery since the 1920s. The datasets can be used to assess the LULC impacts on the ecosystem's carbon, nitrogen, and water cycles.
Huifang Zhang, Zhonggang Tang, Binyao Wang, Hongcheng Kan, Yi Sun, Yu Qin, Baoping Meng, Meng Li, Jianjun Chen, Yanyan Lv, Jianguo Zhang, Shuli Niu, and Shuhua Yi
Earth Syst. Sci. Data, 15, 821–846, https://doi.org/10.5194/essd-15-821-2023, https://doi.org/10.5194/essd-15-821-2023, 2023
Short summary
Short summary
The accuracy of regional grassland aboveground biomass (AGB) is always limited by insufficient ground measurements and large spatial gaps with satellite pixels. This paper used more than 37 000 UAV images as bridges to successfully obtain AGB values matching MODIS pixels. The new AGB estimation model had good robustness, with an average R2 of 0.83 and RMSE of 34.13 g m2. Our new dataset provides important input parameters for understanding the Qinghai–Tibet Plateau during global climate change.
Huaqing Wu, Jing Zhang, Zhao Zhang, Jichong Han, Juan Cao, Liangliang Zhang, Yuchuan Luo, Qinghang Mei, Jialu Xu, and Fulu Tao
Earth Syst. Sci. Data, 15, 791–808, https://doi.org/10.5194/essd-15-791-2023, https://doi.org/10.5194/essd-15-791-2023, 2023
Short summary
Short summary
High-spatiotemporal-resolution rice yield datasets are limited over a large region. We proposed an explicit method to predict rice yield based on machine learning methods and generated a seasonal 4 km resolution rice yield dataset across Asia (AsiaRiceYield4km) for 1995–2015. The seasonal rice yield accuracy of AsiaRiceYield4km is high and much improved compared with previous datasets. AsiaRiceYield4km will fill the current data gap and better support agricultural monitoring systems.
Steve Ahlswede, Christian Schulz, Christiano Gava, Patrick Helber, Benjamin Bischke, Michael Förster, Florencia Arias, Jörn Hees, Begüm Demir, and Birgit Kleinschmit
Earth Syst. Sci. Data, 15, 681–695, https://doi.org/10.5194/essd-15-681-2023, https://doi.org/10.5194/essd-15-681-2023, 2023
Short summary
Short summary
Imagery from air and space is the primary source of large-scale forest mapping. Our study introduces a new dataset with over 50000 image patches prepared for deep learning tasks. We show how the information for 20 European tree species can be extracted from different remote sensing sensors. Our algorithms can detect single species with precision scores up to 88 %. With a pixel size of 20×20 cm, forestry administration can now derive large-scale tree species maps at a very high resolution.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
Rapid Arctic changes are increasingly influencing human society, both locally and globally. Land cover offers a basis for characterizing the terrestrial world, yet spatially detailed information on Arctic land cover is lacking. We employ multi-source data to develop a new land cover map for the circumpolar Arctic. Our product reveals regionally contrasting biome distributions not fully documented in existing studies and thus enhances our understanding of the Arctic’s terrestrial system.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
Richard A. Houghton and Andrea Castanho
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2022-351, https://doi.org/10.5194/essd-2022-351, 2022
Revised manuscript accepted for ESSD
Short summary
Short summary
The paper updates a previous analysis of carbon emissions (annual and national) from land use, land-use change, and forestry, 1850–2020. It used data from the latest (2020) Forest Resource Assessment from the U.N., incorporated shifting cultivation, and included improvements to the bookkeeping model and recent estimates of emissions from peatlands. Net global emissions declined steadily over the decade 2011–2020 (average 0.96 PgC yr-1), falling below 1.0 PgC yr-1 for the first time in 30 years.
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
Short summary
Short summary
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
Short summary
Short summary
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.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Bosc, P.-M., Berdegué, J., Goïta, M., van der Ploeg, J. D., Sekine,
K., and Zhang, L.: Investing in smallholder agriculture for food security,
HLPE, https://www.fao.org/3/i2953e/i2953e.pdf (last access: 22 September 2022), 2013.
Descals, A., Wich, S., Meijaard, E., Gaveau, D. L. A., Peedell, S., and Szantoi, Z.: High-resolution global map of smallholder and industrial closed-canopy oil palm plantations, Earth Syst. Sci. Data, 13, 1211–1231, https://doi.org/10.5194/essd-13-1211-2021, 2021.
EUROSTAT: EUROSTAT, Agriculture, forestry and fisheries, Africulture, Farm
structure, https://ec.europa.eu/eurostat/data/database (last access: 22 September 2022), 2021.
FAO: World programme for the census of agriculture 2020, Volume 1,
Programme, concepts and definitions, FAO UN, Rome, Italy, https://www.fao.org/3/i4913e/i4913e.pdf (last access: 22 September 2022), 2015.
FAO: Small family farms data portrait. Basic information document.
Methodology and data description, FAO UN, Rome, Italy, https://www.ipcinfo.org/fileadmin/user_upload/smallholders_dataportrait/docs/Smallholders_Data_Portrait_-_Methodology_and_Data_Description.pdf (last access: 22 September 2022), 2017.
FAO: Methodology for computing and monitoring the Sustainable Development
Goal indicators 2.3.1 and 2.3.2, FAO UN, Rome, Italy, https://www.fao.org/3/ca3043en/ca3043en.pdf (last access: 22 September 2022), 2019.
FAO: RuLIS Codebook, Rural Livelihoods Information System, FAO UN, Rome,
Italy, https://www.fao.org/3/ca9548en/ca9548en.pdf (last access: 22 September 2022), 2020.
FAO: RuLIS – Rural Livelihoods Information System, FAO UN, Rome, Italy, https://www.fao.org/in-action/rural-livelihoods-dataset-rulis/en/ (last access: 22 September 2022),
2021.
FAO: FAOSTAT, FAO UN, Rome, Italy, https://www.fao.org/faostat/en/#home, last access: 22 September 2022.
FAO and IIASA: Global Agro Ecological Zones version 4 (GAEZ v4), FAO UN,
Rome, Italy, https://gaez.fao.org/ (last access: 22 September 2022), 2021.
Fischer, G., Nachtergaele, F. O., van Velthuizen, H., Chiozza, F.,
Francheschini, G., Henry, M., Muchoney, D., and Tramberend, S.: Global
Agro-ecological Zones (GAEZ v4)-Model Documentation, FAO Rome, Italy, https://doi.org/10.4060/cb4744en, 2021.
Fritz, S., See, L., McCallum, I., You, L., Bun, A., Moltchanova, E.,
Duerauer, M., Albrecht, F., Schill, C., Perger, C., Havlik, P., Mosnier, A.,
Thornton, P., Wood-Sichra, U., Herrero, M., Becker-Reshef, I., Justice, C.,
Hansen, M., Gong, P., Abdel Aziz, S., Cipriani, A., Cumani, R., Cecchi, G.,
Conchedda, G., Ferreira, S., Gomez, A., Haffani, M., Kayitakire, F.,
Malanding, J., Mueller, R., Newby, T., Nonguierma, A., Olusegun, A., Ortner,
S., Rajak, D. R., Rocha, J., Schepaschenko, D., Schepaschenko, M., Terekhov,
A., Tiangwa, A., Vancutsem, C., Vintrou, E., Wenbin, W., van der Velde, M.,
Dunwoody, A., Kraxner, F., and Obersteiner, M.: Mapping global cropland and
field size, Glob. Chang. Biol., 21, 1980–1992, https://doi.org/10.1111/gcb.12838, 2015.
Giller, K. E., Delaune, T., Silva, J. V., Descheemaeker, K., van de Ven, G.,
Schut, A. G., van Wijk, M., Hammond, J., Hochman, Z., and Taulya, G.: The
future of farming: Who will produce our food?, Food Security, 13, 1073–1099,
2021.
Gollin, D.: Farm size and productivity: Lessons from recent literature,
1–35, International Fund for Agricultural Development (IFAD),
https://www.ifad.org/documents/38714170/40974017/Research+Series+34.pdf/64a10247-6fdd-e397-b75b-3d45767d956c (last access: 22 September 2022), 2019.
Grafton, R. Q., Williams, J., Perry, C. J., Molle, F., Ringler, C., Steduto,
P., Udall, B., Wheeler, S. A., Wang, Y., Garrick, D., and Allen, R. G.: The
paradox of irrigation efficiency, Science, 361, 748–750, 2018.
Gurobi Optimization, LLC: Gurobi Optimizer Reference Manual, https://www.gurobi.com/wp-content/plugins/hd_documentations/documentation/9.0/refman.pdf (last access: 22 September 2022), 2021.
Herrero, M., Thornton, P. K., Power, B., Bogard, J. R., Remans, R., Fritz,
S., Gerber, J. S., Nelson, G., See, L., Waha, K., Watson, R. A., West, P.
C., Samberg, L. H., van de Steeg, J., Stephenson, E., van Wijk, M., and
Havlík, P.: Farming and the geography of nutrient production for human
use: a transdisciplinary analysis, The Lancet Planetary Health, 1, e33–e42,
https://doi.org/10.1016/s2542-5196(17)30007-4, 2017.
Iizumi, T., Hosokawa, N., and Wagai, R.: Soil carbon-food synergy: sizable
contributions of small-scale farmers, CABI Agriculture and Bioscience, 2,
43, https://doi.org/10.1186/s43170-021-00063-6, 2021.
Kavats, O., Khramov, D., Sergieieva, K., and Vasyliev, V.: Monitoring of
sugarcane harvest in Brazil based on optical and SAR data, Remote Sensing,
12, 1–26, https://doi.org/10.3390/rs12244080, 2020.
Khalil, C. A., Conforti, P., Ergin, I., and Gennari, P.: Defining small
scale food producers to monitor target 2.3 of the 2030 Agenda for
Sustainable Development, FAO, Rome, https://www.fao.org/3/i6858e/i6858e.pdf (last access: 22 September 2022), 2017.
Kim, K.-H., Doi, Y., Ramankutty, N., and Iizumi, T.: A review of global
gridded cropping system data products, Environ. Res. Lett., 16, 093005,
https://doi.org/10.1088/1748-9326/ac20f4, 2021.
Latham, J., Cumani, R., Rosati, I., and Bloise, M.: Global land cover share
(GLC-SHARE) database beta-release version 1.0-2014, FAO, Rome, Italy, https://www.fao.org/uploads/media/glc-share-doc.pdf (last access: 22 September 2022), 2014.
Lesiv, M., Laso Bayas, J. C., See, L., Duerauer, M., Dahlia, D., Durando,
N., Hazarika, R., Kumar Sahariah, P., Vakolyuk, M., Blyshchyk, V., Bilous,
A., Perez-Hoyos, A., Gengler, S., Prestele, R., Bilous, S., Akhtar, I. U.
H., Singha, K., Choudhury, S. B., Chetri, T., Malek, Z., Bungnamei, K.,
Saikia, A., Sahariah, D., Narzary, W., Danylo, O., Sturn, T., Karner, M.,
McCallum, I., Schepaschenko, D., Moltchanova, E., Fraisl, D., Moorthy, I.,
and Fritz, S.: Estimating the global distribution of field size using
crowdsourcing, Glob. Chang. Biol., 25, 174–186, https://doi.org/10.1111/gcb.14492, 2019.
Lowder, S. K., Skoet, J., and Raney, T.: The Number, Size, and Distribution
of Farms, Smallholder Farms, and Family Farms Worldwide, World Development,
87, 16–29, https://doi.org/10.1016/j.worlddev.2015.10.041, 2016.
Lowder, S. K., Sánchez, M. V., and Bertini, R.: Which farms feed the
world and has farmland become more concentrated?, World Development, 142, 105455,
https://doi.org/10.1016/j.worlddev.2021.105455, 2021.
Lu, M., Wu, W., You, L., See, L., Fritz, S., Yu, Q., Wei, Y., Chen, D., Yang, P., and Xue, B.: A cultivated planet in 2010 – Part 1: The global synergy cropland map, Earth Syst. Sci. Data, 12, 1913–1928, https://doi.org/10.5194/essd-12-1913-2020, 2020.
Mehrabi, Z., McDowell, M. J., Ricciardi, V., Levers, C., Martinez, J. D.,
Mehrabi, N., Wittman, H., Ramankutty, N., and Jarvis, A.: The global divide
in data-driven farming, Nature Sustainability, 4, 154-160,
10.1038/s41893-020-00631-0, 2020.
Mekonnen, M. M. and Hoekstra, A. Y.: Four billion people facing severe water
scarcity, Sci. Adv., 2, e1500323, https://doi.org/10.1126/sciadv.1500323, 2016.
Meyfroidt, P.: Mapping farm size globally: benchmarking the smallholders
debate, Environ. Res. Lett., 12, 031002, https://doi.org/10.1088/1748-9326/aa5ef6, 2017.
Muyanga, M. and Jayne, T. S.: Revisiting the Farm Size-Productivity
Relationship Based on a Relatively Wide Range of Farm Sizes: Evidence from
Kenya, 101, 1140–1163, https://doi.org/10.1093/ajae/aaz003, 2019.
Noack, F., Larsen, A., Kamp, J., and Levers, C.: A bird's eye view of farm
size and biodiversity: The ecological legacy of the iron curtain, Am.
J. Agr. Econ., 104, 1460–1484, https://doi.org/10.1111/ajae.12274, 2021.
Ren, C., Liu, S., van Grinsven, H., Reis, S., Jin, S., Liu, H., and Gu, B.:
The impact of farm size on agricultural sustainability, J. Clean.
Prod., 220, 357–367, https://doi.org/10.1016/j.jclepro.2019.02.151, 2019.
Ricciardi, V., Ramankutty, N., Mehrabi, Z., Jarvis, L., and Chookolingo, B.:
How much of the world's food do smallholders produce?, Global Food Security,
17, 64–72, 2018a.
Ricciardi, V., Ramankutty, N., Mehrabi, Z., Jarvis, L., and Chookolingo, B.:
An open-access dataset of crop production by farm size from agricultural
censuses and surveys, Data Brief, 19, 1970–1988, https://doi.org/10.1016/j.dib.2018.06.057,
2018b.
Ricciardi, V., Wane, A., Sidhu, B. S., Godde, C., Solomon, D., McCullough,
E., Diekmann, F., Porciello, J., Jain, M., and Randall, N.: A scoping review
of research funding for small-scale farmers in water scarce regions, Nature
Sustainability, 3, 836–844, 2020.
Ricciardi, V., Mehrabi, Z., Wittman, H., James, D., and Ramankutty, N.:
Higher yields and more biodiversity on smaller farms, Nature Sustainability,
4, 651–657, https://doi.org/10.1038/s41893-021-00699-2, 2021.
Riesgo, L., Louhichi, K., Gomez y Paloma, S., Hazell, P., Ricker-Gilbert,
J., Wiggins, S., Sahn, D. E., and Mishra, A. K.: Food and nutrition security
and role of smallholder farms: challenges and opportunities, Institute for
Prospective Technological Studies; Information for Meeting Africa's
Agricultural Transformation and Food Security Goals (IMAAFS), https://doi.org/10.2791/653314, 2016.
Rudra, A.: Farm size and yield per acre, Economic Political Weekly,
1041–1044, 1968.
Samberg, L. H., Gerber, J. S., Ramankutty, N., Herrero, M., and West, P. C.:
Subnational distribution of average farm size and smallholder contributions
to global food production, Environ. Res. Lett., 11, 124010,
https://doi.org/10.1088/1748-9326/11/12/124010, 2016.
Savastano, S. and Scandizzo, P.: Farm Size and Productivity: A
“Direct-Inverse-Direct” Relationship, World Bank, https://doi.org/10.1596/1813-9450-8127, 2017.
Su, H., Willaarts, B., Luna Gonzalez, D., S. Krol, M., and Hogeboom, J. R.:
Gridded 5-arcmin, simultaneously farm-size- and crop-specific harvested area
for 56 countries, Zenodo [code and data], https://doi.org/10.5281/zenodo.6976249, 2022.
UNSD: SDG Indicators, United Nations Statistics Division,
https://unstats.un.org/sdgs/indicators/indicators-list/ (last access: 4 July 2020), 2022.
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.
Short summary
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.
There are over 608 million farms around the world but they are not the same. We developed high...
