Articles | Volume 16, issue 3
https://doi.org/10.5194/essd-16-1333-2024
© Author(s) 2024. 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-16-1333-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
14 Mar 2024
Data description paper |
| 14 Mar 2024
| 14 Mar 2024
A global estimate of monthly vegetation and soil fractions from spatiotemporally adaptive spectral mixture analysis during 2001–2022
Qiangqiang Sun
College of Land Science and Technology, China Agricultural University, Beijing, 100193, China
Ping Zhang
National Geomatics Center of China, Ministry of Natural Resources, Beijing, 100830, China
Xin Jiao
College of Land Science and Technology, China Agricultural University, Beijing, 100193, China
Xin Lin
College of Land Science and Technology, China Agricultural University, Beijing, 100193, China
Wenkai Duan
China Agricultural University Library, China Agricultural University, Beijing, 100193, China
Su Ma
Institute of Environmental Information, Chinese Research Academy of Environmental Sciences, Beijing, 100012, China
Qidi Pan
College of Land Science and Technology, China Agricultural University, Beijing, 100193, China
Lu Chen
College of Land Science and Technology, China Agricultural University, Beijing, 100193, China
Yongxiang Zhang
College of Land Science and Technology, China Agricultural University, Beijing, 100193, China
Shucheng You
Land Satellite Remote Sensing Application Center, Ministry of Natural Resources, Beijing, China
Shunxi Liu
China Land Survey and Planning Institute, Ministry of Natural Resources, Beijing, China
Jinmin Hao
College of Land Science and Technology, China Agricultural University, Beijing, 100193, China
Hong Li
CORRESPONDING AUTHOR
Institute of plant nutrition and resources, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
College of Land Science and Technology, China Agricultural University, Beijing, 100193, China
Technology innovation Center of land engineering, Ministry of Natural Resources, Beijing, China
Related authors
No articles found.
J. Chen, P. Zhang, J. Zhang, and H. Wu
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., X-1-W1-2023, 387–395, https://doi.org/10.5194/isprs-annals-X-1-W1-2023-387-2023, https://doi.org/10.5194/isprs-annals-X-1-W1-2023-387-2023, 2023
Related subject area
Domain: ESSD – Land | Subject: Land Cover and Land Use
A 2020 forest age map for China with 30 m resolution
Country-level estimates of gross and net carbon fluxes from land use, land-use change and forestry
A global FAOSTAT reference database of cropland nutrient budgets and nutrient use efficiency (1961–2020): nitrogen, phosphorus and potassium
Annual maps of forest cover in the Brazilian Amazon from analyses of PALSAR and MODIS images
Global 500 m seamless dataset (2000–2022) of land surface reflectance generated from MODIS products
The first map of crop sequence types in Europe over 2012–2018
WorldCereal: a dynamic open-source system for global-scale, seasonal, and reproducible crop and irrigation mapping
A new cropland area database by country circa 2020
Harmonized European Union subnational crop statistics reveal climate impacts and crop cultivation shifts
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
The ABoVE L-band and P-band Airborne SAR Surveys
ChinaWheatYield30m: a 30 m annual winter wheat yield dataset from 2016 to 2021 in China
Refined fine-scale mapping of tree cover using time series of Planet-NICFI and Sentinel-1 imagery for Southeast Asia (2016–2021)
High-resolution global map of closed-canopy coconut palm
GLC_FCS30D: The first global 30-m land-cover dynamic monitoring product with a fine classification system from 1985 to 2022 using dense time-series Landsat imagery and continuous change-detection method
High-resolution land use and land cover dataset for regional climate modelling: historical and future changes in Europe
Global urban fractional changes at a 1 km resolution throughout 2100 under eight scenarios of Shared Socioeconomic Pathways (SSPs) and Representative Concentration Pathways (RCPs)
China Building Rooftop Area: the first multi-annual (2016–2021) and high-resolution (2.5 m) building rooftop area dataset in China derived with super-resolution segmentation from Sentinel-2 imagery
High-resolution distribution maps of single-season rice in China from 2017 to 2022
ChinaRiceCalendar-Seasonal Crop Calendars for Early, Middle, and Late Rice in China
Global 1km Land Surface Parameters for Kilometer-Scale Earth System Modeling
Mapping global non-floodplain wetlands
An improved global land cover mapping in 2015 with 30 m resolution (GLC-2015) based on a multisource product-fusion approach
Annual emissions of carbon from land use, land-use change, and forestry from 1850 to 2020
An open-source automatic survey of green roofs in London using segmentation of aerial imagery
Twenty-meter annual paddy rice area map for mainland Southeast Asia using Sentinel-1 synthetic-aperture-radar data
A 29-year time series of annual 300 m resolution plant-functional-type maps for climate models
Estimating local agricultural gross domestic product (AgGDP) across the world
Classification and mapping of European fuels using a hierarchical, multipurpose fuel classification system
Harmonising the land-use flux estimates of global models and national inventories for 2000–2020
Four-century history of land transformation by humans in the United States (1630–2020): annual and 1 km grid data for the HIStory of LAND changes (HISLAND-US)
A 250 m annual alpine grassland AGB dataset over the Qinghai–Tibet Plateau (2000–2019) in China based on in situ measurements, UAV photos, and MODIS data
AsiaRiceYield4km: seasonal rice yield in Asia from 1995 to 2015
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
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
Gridded 5 arcmin datasets for simultaneously farm-size-specific and crop-specific harvested areas in 56 countries
Vectorized dataset of roadside noise barriers in China using street view imagery
Kai Cheng, Yuling Chen, Tianyu Xiang, Haitao Yang, Weiyan Liu, Yu Ren, Hongcan Guan, Tianyu Hu, Qin Ma, and Qinghua Guo
Earth Syst. Sci. Data, 16, 803–819, https://doi.org/10.5194/essd-16-803-2024, https://doi.org/10.5194/essd-16-803-2024, 2024
Short summary
Short summary
To quantify forest carbon stock and its future potential accurately, we generated a 30 m resolution forest age map for China in 2020 using multisource remote sensing datasets based on machine learning and time series analysis approaches. Validation with independent field samples indicated that the mapped forest age had an R2 of 0.51--0.63. Nationally, the average forest age is 56.1 years (standard deviation of 32.7 years).
Wolfgang Alexander Obermeier, Clemens Schwingshackl, Ana Bastos, Giulia Conchedda, Thomas Gasser, Giacomo Grassi, Richard A. Houghton, Francesco Nicola Tubiello, Stephen Sitch, and Julia Pongratz
Earth Syst. Sci. Data, 16, 605–645, https://doi.org/10.5194/essd-16-605-2024, https://doi.org/10.5194/essd-16-605-2024, 2024
Short summary
Short summary
We provide and compare country-level estimates of land-use CO2 fluxes from a variety and large number of models, bottom-up estimates, and country reports for the period 1950–2021. Although net fluxes are small in many countries, they are often composed of large compensating emissions and removals. In many countries, the estimates agree well once their individual characteristics are accounted for, but in other countries, including some of the largest emitters, substantial uncertainties exist.
Cameron I. Ludemann, Nathan Wanner, Pauline Chivenge, Achim Dobermann, Rasmus Einarsson, Patricio Grassini, Armelle Gruere, Kevin Jackson, Luis Lassaletta, Federico Maggi, Griffiths Obli-Laryea, Martin K. van Ittersum, Srishti Vishwakarma, Xin Zhang, and Francesco N. Tubiello
Earth Syst. Sci. Data, 16, 525–541, https://doi.org/10.5194/essd-16-525-2024, https://doi.org/10.5194/essd-16-525-2024, 2024
Short summary
Short summary
Nutrient budgets help identify the excess or insufficient use of fertilizers and other nutrient sources in agriculture. They allow the calculation of indicators, such as the nutrient balance (surplus or deficit) and nutrient use efficiency, that help to monitor agricultural productivity and sustainability. This article describes a global cropland nutrient budget that provides data on 205 countries and territories from 1961 to 2020 (data available at https://www.fao.org/faostat/en/#data/ESB).
Yuanwei Qin, Xiangming Xiao, Hao Tang, Ralph Dubayah, Russell Doughty, Diyou Liu, Fang Liu, Yosio Shimabukuro, Egidio Arai, Xinxin Wang, and Berrien Moore III
Earth Syst. Sci. Data, 16, 321–336, https://doi.org/10.5194/essd-16-321-2024, https://doi.org/10.5194/essd-16-321-2024, 2024
Short summary
Short summary
Forest definition has two major biophysical parameters, i.e., canopy height and canopy coverage. However, few studies have assessed forest cover maps in terms of these two parameters at a large scale. Here, we assessed the annual forest cover maps in the Brazilian Amazon using 1.1 million footprints of canopy height and canopy coverage. Over 93 % of our forest cover maps are consistent with the FAO forest definition, showing the high accuracy of these forest cover maps in the Brazilian Amazon.
Xiangan Liang, Qiang Liu, Jie Wang, Shuang Chen, and Peng Gong
Earth Syst. Sci. Data, 16, 177–200, https://doi.org/10.5194/essd-16-177-2024, https://doi.org/10.5194/essd-16-177-2024, 2024
Short summary
Short summary
The state-of-the-art MODIS surface reflectance products suffer from temporal and spatial gaps, which make it difficult to characterize the continuous variation of the terrestrial surface. We proposed a framework for generating the first global 500 m daily seamless data cubes (SDC500), covering the period from 2000 to 2022. We believe that the SDC500 dataset can interest other researchers who study land cover mapping, quantitative remote sensing, and ecological science.
Rémy Ballot, Nicolas Guilpart, and Marie-Hélène Jeuffroy
Earth Syst. Sci. Data, 15, 5651–5666, https://doi.org/10.5194/essd-15-5651-2023, https://doi.org/10.5194/essd-15-5651-2023, 2023
Short summary
Short summary
Assessing the benefits of crop diversification – a key element of agroecological transition – on a large scale requires a description of current crop sequences as a baseline, which is lacking at the scale of Europe. To fill this gap, we used a dataset that provides temporally and spatially incomplete land cover information to create a map of dominant crop sequence types for Europe over 2012–2018. This map is a useful baseline for assessing the benefits of future crop diversification.
Kristof Van Tricht, Jeroen Degerickx, Sven Gilliams, Daniele Zanaga, Marjorie Battude, Alex Grosu, Joost Brombacher, Myroslava Lesiv, Juan Carlos Laso Bayas, Santosh Karanam, Steffen Fritz, Inbal Becker-Reshef, Belén Franch, Bertran Mollà-Bononad, Hendrik Boogaard, Arun Kumar Pratihast, Benjamin Koetz, and Zoltan Szantoi
Earth Syst. Sci. Data, 15, 5491–5515, https://doi.org/10.5194/essd-15-5491-2023, https://doi.org/10.5194/essd-15-5491-2023, 2023
Short summary
Short summary
WorldCereal is a global mapping system that addresses food security challenges. It provides seasonal updates on crop areas and irrigation practices, enabling informed decision-making for sustainable agriculture. Our global products offer insights into temporary crop extent, seasonal crop type maps, and seasonal irrigation patterns. WorldCereal is an open-source tool that utilizes space-based technologies, revolutionizing global agricultural mapping.
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.
Giulia Ronchetti, Luigi Nisini Scacchiafichi, Lorenzo Seguini, Iacopo Cerrani, and Marijn van der Velde
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2023-439, https://doi.org/10.5194/essd-2023-439, 2023
Revised manuscript accepted for ESSD
Short summary
Short summary
We present a dataset of European Union-wide harmonized subnational crop area, production, and yield statistics with information on data sources, processing steps, missing and calculated data, and quality checks. Statistical records (344282) collected from 1975–2020 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.
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.
Charles 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 Wilson, Paul Wilson, Stan D. Wullschleger, Yonghong Yi, Howard A. Zebker, Yu Zhang, Yuhuan Zhao, and Scott J. Goetz
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2021-172, https://doi.org/10.5194/essd-2021-172, 2023
Revised manuscript accepted for ESSD
Short summary
Short summary
NASA’s Arctic Boreal Vulnerability Experiment (ABoVE) conducted airborne synthetic aperture radar (SAR) surveys of over 4 million km2 in Alaska and northwestern Canada during 2017, 2018, and 2019. This paper summarizes those results and gives 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.
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.
Feng Yang and Zhenzhong Zeng
Earth Syst. Sci. Data, 15, 4011–4021, https://doi.org/10.5194/essd-15-4011-2023, https://doi.org/10.5194/essd-15-4011-2023, 2023
Short summary
Short summary
We generated a 4.77 m resolution annual tree cover map product for Southeast Asia (SEA) for 2016–2021 using Planet-NICFI and Sentinel-1 imagery. Maps were created with good accuracy and high consistency during 2016–2021. The baseline maps at 4.77 m can be converted to forest cover maps for SEA at various resolutions to meet different users’ needs. Our products can help resolve rounding errors in forest cover mapping by counting isolated trees and monitoring long, narrow forest cover removal.
Adrià Descals, Serge Wich, Zoltan Szantoi, Matthew J. Struebig, Rona Dennis, Zoe Hatton, Thina Ariffin, Nabillah Unus, David L. A. Gaveau, and Erik Meijaard
Earth Syst. Sci. Data, 15, 3991–4010, https://doi.org/10.5194/essd-15-3991-2023, https://doi.org/10.5194/essd-15-3991-2023, 2023
Short summary
Short summary
The spatial extent of coconut palm is understudied despite its increasing demand and associated impacts. We present the first global coconut palm layer at 20 m resolution. The layer was produced using deep learning and remotely sensed data. The global coconut area estimate is 12.31 Mha for dense coconut palm, but the estimate is 3 times larger when sparse coconut palm is considered. This means that coconut production can likely increase on the lands currently allocated to coconut palm.
Xiao Zhang, Tingting Zhao, Hong Xu, Wendi Liu, Jinqing Wang, Xidong Chen, and Liangyun Liu
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2023-320, https://doi.org/10.5194/essd-2023-320, 2023
Revised manuscript accepted for ESSD
Short summary
Short summary
GLC_FCS30D is described as the first global 30-m land-cover dynamic monitoring dataset, containing 35 land-cover subcategories and covering the period of 1985–2022 with 26 time-steps (maps updated every five years before 2000 and annually after 2000).
Peter Hoffmann, Vanessa Reinhart, Diana Rechid, Nathalie de Noblet-Ducoudré, Edouard L. Davin, Christina Asmus, Benjamin Bechtel, Jürgen Böhner, Eleni Katragkou, and Sebastiaan Luyssaert
Earth Syst. Sci. Data, 15, 3819–3852, https://doi.org/10.5194/essd-15-3819-2023, https://doi.org/10.5194/essd-15-3819-2023, 2023
Short summary
Short summary
This paper introduces the new high-resolution land use and land cover change dataset LUCAS LUC for Europe (version 1.1), tailored for use in regional climate models. Historical and projected future land use change information from the Land-Use Harmonization 2 (LUH2) dataset is translated into annual plant functional type changes from 1950 to 2015 and 2016 to 2100, respectively, by employing a newly developed land use translator.
Wanru He, Xuecao Li, Yuyu Zhou, Zitong Shi, Guojiang Yu, Tengyun Hu, Yixuan Wang, Jianxi Huang, Tiecheng Bai, Zhongchang Sun, Xiaoping Liu, and Peng Gong
Earth Syst. Sci. Data, 15, 3623–3639, https://doi.org/10.5194/essd-15-3623-2023, https://doi.org/10.5194/essd-15-3623-2023, 2023
Short summary
Short summary
Most existing global urban products with future projections were developed in urban and non-urban categories, which ignores the gradual change of urban development at the local scale. Using annual global urban extent data from 1985 to 2015, we forecasted global urban fractional changes under eight scenarios throughout 2100. The developed dataset can provide spatially explicit information on urban fractions at 1 km resolution, which helps support various urban studies (e.g., urban heat island).
Zeping Liu, Hong Tang, Lin Feng, and Siqing Lyu
Earth Syst. Sci. Data, 15, 3547–3572, https://doi.org/10.5194/essd-15-3547-2023, https://doi.org/10.5194/essd-15-3547-2023, 2023
Short summary
Short summary
Large-scale maps of building rooftop area (BRA) are crucial for addressing policy decisions and sustainable development. In this paper, we propose a deep-learning method for high-resolution BRA mapping (2.5 m) from Sentinel-2 imagery (10 m). The resulting China building rooftop area dataset (CBRA) is the first multi-annual (2016–2021) and high-resolution (2.5 m) BRA dataset in China. Cross-comparisons show that the CBRA achieves the best performance in capturing the spatiotemporal information.
Ruoque Shen, Baihong Pan, Qiongyan Peng, Jie Dong, Xuebing Chen, Xi Zhang, Tao Ye, Jianxi Huang, and Wenping Yuan
Earth Syst. Sci. Data, 15, 3203–3222, https://doi.org/10.5194/essd-15-3203-2023, https://doi.org/10.5194/essd-15-3203-2023, 2023
Short summary
Short summary
Paddy rice is the second-largest grain crop in China and plays an important role in ensuring global food security. This study developed a new rice-mapping method and produced distribution maps of single-season rice in 21 provincial administrative regions of China from 2017 to 2022 at a 10 or 20 m resolution. The accuracy was examined using 108 195 survey samples and county-level statistical data, and we found that the distribution maps have good accuracy.
Hui Li, Xiaobo Wang, Shaoqiang Wang, Yuanyuan Liu, Zhenhai Liu, Shiliang Chen, Qinyi Wang, Tongtong Zhu, Lunche Wang, and Lizhe Wang
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2023-125, https://doi.org/10.5194/essd-2023-125, 2023
Revised manuscript accepted for ESSD
Short summary
Short summary
As one of the major rice producing countries, China has obvious regional differences in rice types and planting patternsIn this study, the 2003–2020 Chinese seasonal rice calendar extracted from remote sensing data and other data is used to prove that the planting period of rice in China is significantly delayed and the maturity period is advanced. The results will provide data support for rice management and related industry development under future climate scenarios in China.
Lingcheng Li, Gautam Bisht, Dalei Hao, and Lai-Yung Ruby Leung
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2023-242, https://doi.org/10.5194/essd-2023-242, 2023
Revised manuscript accepted for ESSD
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.
Charles R. Lane, Ellen D'Amico, Jay R. Christensen, Heather E. Golden, Qiusheng Wu, and Adnan Rajib
Earth Syst. Sci. Data, 15, 2927–2955, https://doi.org/10.5194/essd-15-2927-2023, https://doi.org/10.5194/essd-15-2927-2023, 2023
Short summary
Short summary
Non-floodplain wetlands (NFWs) – wetlands located outside floodplains – confer watershed-scale resilience to hydrological, biogeochemical, and biotic disturbances. Although they are frequently unmapped, we identified ~ 33 million NFWs covering > 16 × 10 km2 across the globe. NFWs constitute the majority of the world's wetlands (53 %). Despite their small size (median 0.039 km2), these imperiled systems have an outsized impact on watershed functions and sustainability and require protection.
Bingjie Li, Xiaocong Xu, Xiaoping Liu, Qian Shi, Haoming Zhuang, Yaotong Cai, and Da He
Earth Syst. Sci. Data, 15, 2347–2373, https://doi.org/10.5194/essd-15-2347-2023, https://doi.org/10.5194/essd-15-2347-2023, 2023
Short summary
Short summary
A global land cover map with fine spatial resolution is important for climate and environmental studies, food security, or biodiversity conservation. In this study, we developed an improved global land cover map in 2015 with 30 m resolution (GLC-2015) by fusing the existing land cover products based on the Dempster–Shafer theory of evidence on the Google Earth Engine platform. The GLC-2015 performed well, with an OA of 79.5 % (83.6 %) assessed with the global point-based (patch-based) samples.
Richard A. Houghton and Andrea Castanho
Earth Syst. Sci. Data, 15, 2025–2054, https://doi.org/10.5194/essd-15-2025-2023, https://doi.org/10.5194/essd-15-2025-2023, 2023
Short summary
Short summary
We update a previous analysis of carbon emissions (annual and national) from land use, land-use change, and forestry from 1850 to 2020. We use data from the latest (2020) Global Forest Resources Assessment, incorporate shifting cultivation, and include improvements to the bookkeeping model and recent estimates of emissions from peatlands. Net global emissions declined steadily over the decade from 2011 to 2020 (mean of 0.96 Pg C yr−1), falling below 1.0 Pg C yr−1 for the first time in 30 years.
Charles H. Simpson, Oscar Brousse, Nahid Mohajeri, Michael Davies, and Clare Heaviside
Earth Syst. Sci. Data, 15, 1521–1541, https://doi.org/10.5194/essd-15-1521-2023, https://doi.org/10.5194/essd-15-1521-2023, 2023
Short summary
Short summary
Adding plants to roofs of buildings can reduce indoor and outdoor temperatures and so can reduce urban overheating, which is expected to increase due to climate change and urban growth. To better understand the effect this has on the urban environment, we need data on how many buildings have green roofs already.
We used a computer vision model to find green roofs in aerial imagery in London, producing a dataset identifying what buildings have green roofs and improving on previous methods.
Chunling Sun, Hong Zhang, Lu Xu, Ji Ge, Jingling Jiang, Lijun Zuo, and Chao Wang
Earth Syst. Sci. Data, 15, 1501–1520, https://doi.org/10.5194/essd-15-1501-2023, https://doi.org/10.5194/essd-15-1501-2023, 2023
Short summary
Short summary
Over 90 % of the world’s rice is produced in the Asia–Pacific region. In this study, a rice-mapping method based on Sentinel-1 data for mainland Southeast Asia is proposed. A combination of spatiotemporal features with strong generalization is selected and input into the U-Net model to obtain a 20 m resolution rice area map of mainland Southeast Asia in 2019. The accuracy of the proposed method is 92.20 %. The rice area map is concordant with statistics and other rice area maps.
Kandice L. Harper, Céline Lamarche, Andrew Hartley, Philippe Peylin, Catherine Ottlé, Vladislav Bastrikov, Rodrigo San Martín, Sylvia I. Bohnenstengel, Grit Kirches, Martin Boettcher, Roman Shevchuk, Carsten Brockmann, and Pierre Defourny
Earth Syst. Sci. Data, 15, 1465–1499, https://doi.org/10.5194/essd-15-1465-2023, https://doi.org/10.5194/essd-15-1465-2023, 2023
Short summary
Short summary
We built a spatially explicit annual plant-functional-type (PFT) dataset for 1992–2020 exhibiting intra-class spatial variability in PFT fractional cover at 300 m. For each year, 14 maps of percentage cover are produced: bare soil, water, permanent snow/ice, built, managed grasses, natural grasses, and trees and shrubs, each split into leaf type and seasonality. Model simulations indicate significant differences in simulated carbon, water, and energy fluxes in some regions using this new set.
Yating Ru, Brian Blankespoor, Ulrike Wood-Sichra, Timothy S. Thomas, Liangzhi You, and Erwin Kalvelagen
Earth Syst. Sci. Data, 15, 1357–1387, https://doi.org/10.5194/essd-15-1357-2023, https://doi.org/10.5194/essd-15-1357-2023, 2023
Short summary
Short summary
Economic statistics are frequently produced at an administrative level that lacks detail to examine development patterns and the exposure to natural hazards. This paper disaggregates national and subnational administrative statistics of agricultural GDP into a global dataset at the local level using satellite-derived indicators. As an illustration, the paper estimates that the exposure of areas with extreme drought to agricultural GDP is USD 432 billion, where nearly 1.2 billion people live.
Elena Aragoneses, Mariano García, Michele Salis, Luís M. Ribeiro, and Emilio Chuvieco
Earth Syst. Sci. Data, 15, 1287–1315, https://doi.org/10.5194/essd-15-1287-2023, https://doi.org/10.5194/essd-15-1287-2023, 2023
Short summary
Short summary
We present a new hierarchical fuel classification system with a total of 85 fuels that is useful for preventing fire risk at different spatial scales. Based on this, we developed a European fuel map (1 km resolution) using land cover datasets, biogeographic datasets, and bioclimatic modelling. We validated the map by comparing it to high-resolution data, obtaining high overall accuracy. Finally, we developed a crosswalk for standard fuel models as a first assignment of fuel parameters.
Giacomo Grassi, Clemens Schwingshackl, Thomas Gasser, Richard A. Houghton, Stephen Sitch, Josep G. Canadell, Alessandro Cescatti, Philippe Ciais, Sandro Federici, Pierre Friedlingstein, Werner A. Kurz, Maria J. Sanz Sanchez, Raúl Abad Viñas, Ramdane Alkama, Selma Bultan, Guido Ceccherini, Stefanie Falk, Etsushi Kato, Daniel Kennedy, Jürgen Knauer, Anu Korosuo, Joana Melo, Matthew J. McGrath, Julia E. M. S. Nabel, Benjamin Poulter, Anna A. Romanovskaya, Simone Rossi, Hanqin Tian, Anthony P. Walker, Wenping Yuan, Xu Yue, and Julia Pongratz
Earth Syst. Sci. Data, 15, 1093–1114, https://doi.org/10.5194/essd-15-1093-2023, https://doi.org/10.5194/essd-15-1093-2023, 2023
Short summary
Short summary
Striking differences exist in estimates of land-use CO2 fluxes between the national greenhouse gas inventories and the IPCC assessment reports. These differences hamper an accurate assessment of the collective progress under the Paris Agreement. By implementing an approach that conceptually reconciles land-use CO2 flux from national inventories and the global models used by the IPCC, our study is an important step forward for increasing confidence in land-use CO2 flux estimates.
Xiaoyong Li, Hanqin Tian, Chaoqun Lu, and Shufen Pan
Earth Syst. Sci. Data, 15, 1005–1035, https://doi.org/10.5194/essd-15-1005-2023, https://doi.org/10.5194/essd-15-1005-2023, 2023
Short summary
Short summary
We reconstructed land use and land cover (LULC) history for the conterminous United States during 1630–2020 by integrating multi-source data. The results show the widespread expansion of cropland and urban land and the shrinking of natural vegetation in the past four centuries. Forest planting and regeneration accelerated forest recovery since the 1920s. The datasets can be used to assess the LULC impacts on the ecosystem's carbon, nitrogen, and water cycles.
Huifang Zhang, Zhonggang Tang, Binyao Wang, Hongcheng Kan, Yi Sun, Yu Qin, Baoping Meng, Meng Li, Jianjun Chen, Yanyan Lv, Jianguo Zhang, Shuli Niu, and Shuhua Yi
Earth Syst. Sci. Data, 15, 821–846, https://doi.org/10.5194/essd-15-821-2023, https://doi.org/10.5194/essd-15-821-2023, 2023
Short summary
Short summary
The accuracy of regional grassland aboveground biomass (AGB) is always limited by insufficient ground measurements and large spatial gaps with satellite pixels. This paper used more than 37 000 UAV images as bridges to successfully obtain AGB values matching MODIS pixels. The new AGB estimation model had good robustness, with an average R2 of 0.83 and RMSE of 34.13 g m2. Our new dataset provides important input parameters for understanding the Qinghai–Tibet Plateau during global climate change.
Huaqing Wu, Jing Zhang, Zhao Zhang, Jichong Han, Juan Cao, Liangliang Zhang, Yuchuan Luo, Qinghang Mei, Jialu Xu, and Fulu Tao
Earth Syst. Sci. Data, 15, 791–808, https://doi.org/10.5194/essd-15-791-2023, https://doi.org/10.5194/essd-15-791-2023, 2023
Short summary
Short summary
High-spatiotemporal-resolution rice yield datasets are limited over a large region. We proposed an explicit method to predict rice yield based on machine learning methods and generated a seasonal 4 km resolution rice yield dataset across Asia (AsiaRiceYield4km) for 1995–2015. The seasonal rice yield accuracy of AsiaRiceYield4km is high and much improved compared with previous datasets. AsiaRiceYield4km will fill the current data gap and better support agricultural monitoring systems.
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.
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.
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
Short summary
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.
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.
Cited articles
Alkama, R. and Cescatti, A.: Biophysical climate impacts of recent changes in global forest cover, Science, 351, 600–604, https://doi.org/10.1126/science.aac8083, 2016.
Baret, F., Weiss, M., Lacaze, R., Camacho, F., Makhmara, H., Pacholczyk, P., and Smets, B.: GEOV1: LAI, FAPAR Essential Climate Variables and FCover global times series capitalizing over existing products, Part1: Principles of development and production, Remote Sens. Environ., 137, 299–309, https://doi.org/10.1016/j.rse.2013.02.030, 2013.
Baumann, M., Levers, C., Macchi, L., Bluhm, H., Waske, B., Gasparri, N. I., and Kuemmerle, T.: Mapping continuous fields of tree and shrub cover across the Gran Chaco using Landsat 8 and Sentinel-1 data, Remote Sens. Environ., 216, 201–211, https://doi.org/10.1016/j.rse.2018.06.044, 2018.
Beck, H. E., Zimmermann, N. E., Mcvicar, T. R., Vergopolan, N., and Wood, E. F.: Present and future kppen-geiger climate classification maps at 1-km resolution, Sci. Data, 5, 180214, https://doi.org/10.1038/sdata.2018.214, 2018.
Boardman, J. W.: Automating spectral unmixing of aviris data using convex geometry concepts, in: Jpl Airborne Geoscience Workshop, 11–14, 1993.
Bradley, J. V.: Distribution-Free Statistical Test, Englewood Cliffs, Prentice-Hall, ISBN 0132162598, 9780132162593, 1968.
Caswell, T. A., Sales de Andrade, E., Lee, A., Droettboom, M., Hoffmann, T., Klymak, J., Hunter, J., Firing, E., Stansby, D., Varoquaux, N., Nielsen, J. H., Gustafsson, O., Sunden, K., Root, B., May, R., hannah, Elson, P., Seppänen, J. K., Lee, J.-J., and Silvester, S.: matplotlib/matplotlib: REL: v3.7.5 (v3.7.5), Zenodo [code], https://doi.org/10.5281/zenodo.10669804, 2024.
Chasek, P., Mariam, A. S., Orr, B. J., Luise, A., Ratsimba, H. R., and Safriel, U.: Land degradation neutrality: the science-policy interface from the UNCCD to national implementation, Environ. Sci. Pol., 92, 182–190, https://doi.org/10.1016/j.envsci.2018.11.017, 2018.
Chen, C., Park, T., Wang, X., Piao, S., Xu, B., Chaturvedi, R. K., Fuchs, R., Brovkin, V., Ciais, P., and Fensholt, R.: China and India lead in greening of the world through land-use management, Nat. Sustain., 2, 122–129, https://doi.org/10.1038/s41893-019-0220-7, 2019.
Daldegan, G. A., Roberts, D. A., and Ribeiro, F.: Spectral mixture analysis in Google Earth Engine to model and delineate fire scars over a large extent and a long time-series in a rainforest-savanna transition zone, Remote Sens. Environ., 232, 111340, https://doi.org/10.1016/j.rse.2019.111340, 2019.
DiMiceli, C., Carroll, M., Sohlberg, R., Kim, D., Kelly, M., and Townshend, J.: MOD44B MODIS/Terra Vegetation Continuous Fields Yearly L3 Global 250 m SIN Grid V006, NASA EOSDIS Land Processes DAAC [data set], https://doi.org/10.5067/MODIS/MOD44B.006, 2015.
Elmore, A. J., Mustard, J. F., Manning, S. J., and Lobell, D. B.: Quantifying vegetation change in semiarid environments: Precision and accuracy of spectral mixture analysis and the normalized difference vegetation index, Remote Sens. Environ., 73, 87–102, https://doi.org/10.1016/S0034-4257(00)00100-0, 2000.
Franke, J., Roberts, D. A., Halligan, K., and Menz, G.: Hierarchical Multiple Endmember Spectral Mixture Analysis (MESMA) of hyperspectral imagery for urban environments, Remote Sens. Environ., 113, 1712–1723, https://doi.org/10.1016/j.rse.2009.03.018, 2009.
Friedl, M. and Sulla-Menashe, D.: MCD12Q1 MODIS/Terra+Aqua Land Cover Type Yearly L3 Global 500m SIN Grid V006, NASA EOSDIS Land Processes DAAC [data set], https://doi.org/10.5067/MODIS/MCD12Q1.006, 2015.
Fritz, S., See, L., Perger, C., McCallum, I., Schill, C., Schepaschenko, D., Duerauer, M., Karner, M., Dresel, C.,r Laso-Bayas, J. C., Lesiv, M., Moorthy, I., Salk, C. F., Danylo, O., Sturn, T., Albrecht, F., You, L., Kraxner F., and Obersteiner, M.: A global dataset of crowdsourced land cover and land use reference data, Sci. Data, 4, 1–8, https://doi.org/10.1038/sdata.2017.75, 2017.
Guerschman, J. P., Hill, M. J., Renzullo, L. J., Barrett, D. J., Marks, A. S., and Botha, E. J.: Estimating fractional cover of photosynthetic vegetation, non-photosynthetic vegetation and bare soil in the Australian tropical savanna region upscaling the EO-1 Hyperion and MODIS sensors, Remote Sens. Environ., 113, 928–945, https://doi.org/10.1016/j.rse.2009.01.006, 2009.
Guerschman, J. P., Scarth, P. F., McVicar, T. R., Renzullo, L. J., Malthus, T. J., Stewart, J. B., and Trevithick, R.: Assessing the effects of site heterogeneity and soil properties when unmixing photosynthetic vegetation, non-photosynthetic vegetation and bare soil fractions from Landsat and MODIS data, Remote Sens. Environ., 161, 12–26, https://doi.org/10.1016/j.rse.2015.01.021, 2015.
Heinz, D. C. and Chan, C. I.: Fully constrained least squares linear spectral mixture analysis method for material quantification in hyperspectral imagery, IEEE T. Geosci. Remote, 39, 529–545, https://doi.org/10.1109/36.911111, 2002.
Hirsch, R. M., Slack, J. R., and Smith, R. A.: Techniques of trend analysis for monthly water quality data, Water Resour. Res., 18, 107–121, https://doi.org/10.1029/WR018i001p00107, 1982.
Hugonnet, R., Mcnabb, R., Berthier, E., Menounos, B., and Kb, A.: Accelerated global glacier mass loss in the early twenty-first century, Nature, 592, 726–731, https://doi.org/10.1038/s41586-021-03436-z, 2021.
IPCC: Climate Change 2013: The Physical Science Basis, Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, edited by: Stocker, T. F., Qin, D., Plattner, G.-K., Tignor, M., Allen, S. K., Boschung, J., Nauels, A., Xia, Y., Bex, V., and Midgley, P. M., Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 1535 pp., https://doi.org/10.1017/9781009157896, 2013.
Jia, K., Liang, S., Liu, S., Li, Y., Xiao, Z., Yao, Y., Jiang, B., Zhao, X., Wang, X., and Xu, S.: Global land surface fractional vegetation cover estimation using general regression neural networks from MODIS surface reflectance, IEEE T. Geosci. Remote, 53, 4787–4796, https://doi.org/10.1109/TGRS.2015.2409563, 2015.
Kendall, M. G.: Rank correlation methods, London, Charles Griffin, ISBN-10 0195205723, ISBN-13 978-0195205725, 1975.
Kuang, W., Du, G., Lu, D., Dou, Y., and Miao, C.: Global observation of urban expansion and land-cover dynamics using satellite big-data, Sci. Bull., 66, 297–300, https://doi.org/10.1016/j.scib.2020.10.022, 2020.
Kuang, W., Zhang, S., Li, X., and Lu, D.: A 30 m resolution dataset of China's urban impervious surface area and green space, 2000–2018, Earth Syst. Sci. Data, 13, 63–82, https://doi.org/10.5194/essd-13-63-2021, 2021.
Lawrence, D. and Vandecar, K.: Effects of tropical deforestation on climate and agriculture, Nat. Clim. Change, 5, 27–36, https://doi.org/10.1038/nclimate2430, 2015.
Lewińska, K. E., Hostert, P., Buchner, J., Bleyhl, B., and Radeloff, V. C.: Short-term vegetation loss versus decadal degradation of grasslands in the Caucasus based on Cumulative Endmember Fractions, Remote Sens. Environ., 248, 111969, https://doi.org/10.1016/j.rse.2020.111969, 2020.
Liu, H., Gong, P., Wang, J., Clinton, N., Bai, Y., and Liang, S.: Annual dynamics of global land cover and its long-term changes from 1982 to 2015, Earth Syst. Sci. Data, 12, 1217–1243, https://doi.org/10.5194/essd-12-1217-2020, 2020.
Liu, X., Huang, Y., Xu, X., Li, X., and Zeng, Z.: High-spatiotemporal-resolution mapping of global urban change from 1985 to 2015, Nat. Sustain., 3, 564–570, https://doi.org/10.1038/s41893-020-0521-x, 2020.
James, G., Witten, D., Hastie, T., and Tibshirani, R.: An introduction to statistical learning, New York, Springer, https://doi.org/10.1007/978-1-0716-1418-1, 2013.
Mann, H. B.: Nonparametric tests against trend, Econometrica, 13, 245–259, https://doi.org/10.2307/1907187, 1945.
Meyer, T. and Okin, G. S.: Evaluation of spectral unmixing techniques using MODIS in a structurally complex savanna environment for retrieval of green vegetation, nonphotosynthetic vegetation, and soil fractional cover, Remote Sens. Environ., 161, 122–130, https://doi.org/10.1016/j.rse.2015.02.013, 2015.
Mu, X. H., Liu, Q. H., Ruan, G. Y., Zhao, J., Zhong, B., Wu, S. L., and Peng, J. J.: A 1 km/5 day Fractional Vegetation Cover Dataset Over China-ASEAN (2013), J. Glob. Change Data Dis., 1, 45–51, https://doi.org/10.3974/geodp.2017.01.07, 2017.
Okin, G. S.: Relative spectral mixture analysis – A multitemporal index of total vegetation cover, Remote Sens. Environ., 106, 467–479, https://doi.org/10.1016/j.rse.2006.09.018, 2007.
Okin, G. S., Clarke, K. D., and Lewis, M. M.: Comparison of methods for estimation of absolute vegetation and soil fractional cover using MODIS normalized BRDF-adjusted reflectance data, Remote Sens. Environ., 130, 266–279, https://doi.org/10.1016/j.rse.2012.11.021, 2013.
Olofsson, P., Stehman, S. V., Woodcock, C. E., Sulla-Menashe, D., Sibley, A. M., Newell, J. D., Friedl, M. A., and Herold, M.: A global land-cover validation data set, part I: Fundamental design principles, Int. J. Remote Sens., 33, 5768–5788, https://doi.org/10.1080/01431161.2012.674230, 2012.
Pengra, B., Long, J., Dahal, D., Stehman, S. V., and Loveland, T. R.: A global reference database from very high resolution commercial satellite data and methodology for application to Landsat derived 30 m continuous field tree cover data, Remote Sens. Environ., 165, 234–248, https://doi.org/10.1016/j.rse.2015.01.018, 2015.
Piao, S., Friedlingstein, P., Ciais, P., Zhou, L., and Chen, A.: Effect of climate and CO2 changes on the greening of the Northern Hemisphere over the past two decades, Geophys. Res. Lett., 33, L23402, https://doi.org/10.1029/2006GL028205, 2006.
Qin, Y., Xiao, X., Dong, J., Zhang, Y., Wu, X., Shimabukuro, Y., Arai, E., Biradar, C., Wang, J., and Zou, Z.: Improved estimates of forest cover and loss in the Brazilian Amazon in 2000–2017, Nat. Sustain., 2, 764–772, https://doi.org/10.1038/s41893-019-0336-9, 2019.
Qin, Y., Xiao, X., Wigneron, J., Ciais, P., Brandt, M., Fan, L., Li, X., Crowell, S., Wu, X., and Doughty, R.: Carbon loss from forest degradation exceeds that from deforestation in the Brazilian Amazon, Nat. Clim. Change, 11, 442–448, https://doi.org/10.1038/s41558-021-01026-5, 2021.
Rashed, T., Weeks, J. R., Roberts, D., Rogan, J., and Powell, R.: Measuring the physical composition of urban morphology using multiple endmember spectral mixture models, Photogramm. Eng. Rem. S., 69, 1011–1020, https://doi.org/10.14358/PERS.69.9.1011, 2003.
Réjou-Méchain, M., Mortier, F., Bastin, J., Cornu, G., Barbier, N., Bayol, N., Bénédet, F., Bry, X., Dauby, G., and Deblauwe, V.: Unveiling African rainforest composition and vulnerability to global change, Nature, 593, 90–94, https://doi.org/10.1038/s41586-021-03483-6, 2021.
Roberts, D. A., Smith, M. O., and Adams, J. B.: Green vegetation, nonphotosynthetic vegetation, and soils in AVIRIS data, Remote Sens. Environ., 44, 255–269, https://doi.org/10.1016/0034-4257(93)90020-X, 1993.
Roberts, D. A., Gardner, M., Church, R., Ustin, S., Scheer, G., and Green, R. O.: Mapping Chaparral in the Santa Monica Mountains Using Multiple Endmember Spectral Mixture Models, Remote Sens. Environ., 65, 267–279, https://doi.org/10.1016/S0034-4257(98)00037-6, 1998.
Rogan, J., Franklin, J., and Roberts, D. A.: A comparison of methods for monitoring multitemporal vegetation change using Thematic Mapper imagery, Remote Sens. Environ., 80, 143–156, https://doi.org/10.1016/S0034-4257(01)00296-6, 2002.
Schaaf, C. and Wang, Z.: MCD43A4 MODIS/Terra+Aqua BRDF/Albedo Nadir BRDF Adjusted Ref Daily L3 Global – 500m V006, NASA EOSDIS Land Processes DAAC [data set], https://doi.org/10.5067/MODIS/MCD43A4.006, 2015.
Sen, P. K.: Estimates of the Regression Coefficient Based on Kendall's Tau, Publ. Am. Stat. Assoc., 63, 1379–1389, https://doi.org/10.1080/01621459.1968.10480934, 1968.
Small, C.: The Landsat ETM+ spectral mixing space, Remote Sens. Environ., 93, 1–17, https://doi.org/10.1016/j.rse.2004.06.007, 2004.
Small, C. and Milesi, C.: Multi-scale standardized spectral mixture models, Remote Sens. Environ., 136, 442–454, https://doi.org/10.1016/j.rse.2013.05.024, 2013.
Smith, A. M. S., Drake, N. A., Woosterj, M. J., Hudak, A. T., Holden, Z. A., and Gibbons, C. J.: Production of Landsat ETM+ reference imagery of burned areas within Southern African savannahs: comparison of methods and application to MODIS, Int. J. Remote Sens., 28, 2753–2775, https://doi.org/10.1080/01431160600954704, 2007.
Smith, M. O., Ustin, S. L., Adams, J. B., and Gillespie, A. R.: Vegetation in deserts: I. A regional measure of abundance from multispectral images, Remote Sens. Environ., 31, 1–26, https://doi.org/10.1016/0034-4257(90)90074-V, 1990.
Smith, W. K., Dannenberg, M. P., Yan, D., Herrmann, S., Barnes, M. L., Barron-Gafford, G. A., Biederman, J. A, Ferrenberg, S., Fox, A. M., Hudson, A., Knowles, J. F., MacBean, N., Moore, D. J. P., Nagler, P. L., Reed, S. C., Rutherford, W. A., Scott, R. L., Wang, X., and Yang, J.: Remote sensing of dryland ecosystem structure and function: Progress, challenges, and opportunities, Remote Sens. Environ., 233, 111401, https://doi.org/10.1016/j.rse.2019.111401, 2019.
Soheb, M., Ramanathan, A., Bhardwaj, A., Coleman, M., Rea, B. R., Spagnolo, M., Singh, S., and Sam, L.: Multitemporal glacier inventory revealing four decades of glacier changes in the Ladakh region, Earth Syst. Sci. Data, 14, 4171–4185, https://doi.org/10.5194/essd-14-4171-2022, 2022.
Song, X., Hansen, M. C., Stehman, S. V., Potapov, P. V., Tyukavina, A., Vermote, E. F., and Townshend, J. R.: Global land change from 1982 to 2016, Nature, 560, 639–643, https://doi.org/10.1038/s41586-018-0411-9, 2018.
Sonnentag, O., Chen, J. M., Roberts, D. A., Talbot, J., Halligan, K. Q., and Govind, A.: Mapping tree and shrub leaf area indices in an ombrotrophic peatland through multiple endmember spectral unmixing, Remote Sens. Environ., 109, 342–360, https://doi.org/10.1016/j.rse.2007.01.010, 2007.
Sousa, D. and Small, C.: Spectral mixture analysis as a unified framework for the remote sensing of evapotranspiration, Remote Sens., 10, 1961, https://doi.org /10.3390/rs10121961, 2018.
Sousa, D. and Small, C.: Globally standardized MODIS spectral mixture models, Remote Sens. Lett., 10, 1018–1027, https://doi.org/10.1080/2150704X.2019.1634299, 2019.
Stehman, S. V., Olofsson, P., Woodcock, C. E., Herold, M., and Friedl, M. A.: A global land-cover validation data set, II: Augmenting a stratified sampling design to estimate accuracy by region and land-cover class, Int. J. Remote Sens., 33, 6975–6993, https://doi.org/10.1080/01431161.2012.695092, 2012.
Sun, D.: Detection of dryland degradation using Landsat spectral unmixing remote sensing with syndrome concept in Minqin County, China, Int. J. Appl. Earth Obs., 41, 34–45, https://doi.org/10.1016/j.jag.2015.04.015, 2015.
Sun, D. and Liu, N.: Coupling spectral unmixing and multiseasonal remote sensing for temperate dryland land-use/land-cover mapping in minqin county, china, Int. J. Remote Sens., 36, 3636–3658, https://doi.org/10.1080/01431161.2015.1047046, 2015.
Sun, D., Zhang, P., Sun, Q., and Jiang, W.: A dryland cover state mapping using catastrophe model in a spectral endmember space of OLI: a case study in Minqin, China, Int. J. Remote Sens., 40, 5673–5694, https://doi.org/10.1080/01431161.2019.1580795, 2019.
Sun, Q.: GEE_mesma, Zenodo [code], https://doi.org/10.5281/zenodo.10796386, 2024.
Sun, Q. and Sun, D: A global estimate of monthly vegetation and soil fractions from spatio-temporally adaptive spectral mixture analysis during 2001–2022, Science Data Bank [data set], https://doi.org/10.57760/sciencedb.13287, 2023.
Sun, Q., Zhang, P., Sun, D., Liu, A., and Dai, J.: Desert vegetation-habitat complexes mapping using Gaofen-1 WFV (wide field of view) time series images in Minqin County, China, Int J. Appl Earth Obs, 73, 522–534, https://doi.org/10.1016/j.jag.2018.07.021, 2018.
Sun, Q., Zhang, P., Wei, H., Liu, A., You, S., and Sun, D.: Improved mapping and understanding of desert vegetation-habitat complexes from intraannual series of spectral endmember space using cross-wavelet transform and logistic regression. Remote Sens. Environ., 236, 111516. https://doi.org/10.1016/j.rse.2019.111516, 2020.
Sun, Q., Zhang, P., Jiao, X., Han, W., Sun, Y., and Sun, D.: Identifying and understanding alternative states of dryland landscape: a hierarchical analysis of time series of fractional vegetation-soil nexuses in China's Hexi Corridor, Landscape Urban Plan., 215, 104225, https://doi.org/10.1016/j.landurbplan.2021.104225, 2021.
Sun, Q., Zhang, P., Jiao, X., Lun, F., Dong, S., Lin, X., Li, X., and Sun, D.: A Remotely Sensed Framework for Spatially-Detailed Dryland Soil Organic Matter Mapping: Coupled Cross-Wavelet Transform with Fractional Vegetation and Soil-Related Endmember Time Series, Remote Sens., 14, 1701, https://doi.org/10.3390/rs14071701, 2022.
Suess, S., van der Linden, S., Okujeni, A., Griffiths, P., Leitão, P. J., Schwieder, M., and Hostert, P.: Characterizing 32 years of shrub cover dynamics in southern Portugal using annual Landsat composites and machine learning regression modeling, Remote Sens. Environ., 219, 353–364, https://doi.org/10.1016/j.rse.2018.10.004, 2018.
Tong, X., Brandt, M., Yue, Y., Horion, S., Wang, K., De Keersmaecker, W., Tian, F., Schurgers, G., Xiao, X., Luo, Y., Chen, C., Myneni, R., Shi, Z., Chen, H., Fensholt, R. 2018. Increased vegetation growth and carbon stock in China karst via ecological engineering, Nat. Sustain., 1, 44–50, https://doi.org/10.1038/s41893-017-0004-x, 2021.
Wang, Q., Ding, X., Tong, X., and Atkinson, P. M.: Spatio-temporal spectral unmixing of time-series images, Remote Sens. Environ., 259, 112407, https://doi.org/10.1016/j.rse.2021.112407, 2021.
Wu, C. and Murray, A. T.: Estimating impervious surface distribution by spectral mixture analysis, Remote Sens. Environ., 84, 493–505, https://doi.org/10.1016/S0034-4257(02)00136-0, 2003.
Yan, K., Gao, S., Chi, H., Qi, J., Song, W., Tong, Y., Yan, G.: Evaluation of the vegetation-index-based dimidiate pixel model for fractional vegetation cover estimation, IEEE T. Geosci. Remote, 60, 1–14, https://doi.org/10.1109/TGRS.2020.3048493, 2021.
Yu, Z., Jin, X., Miao, L., and Yang, X.: A historical reconstruction of cropland in China from 1900 to 2016, Earth Syst. Sci. Data, 13, 3203–3218, https://doi.org/10.5194/essd-13-3203-2021, 2021.
Zemp, M., Huss, M., Thibert, E., Eckert, N., Mcnabb, R., Huber, J., Barandun, M., Machguth, H., Nussbaumer, S. U., and Gärtner-Roer, I.: Global glacier mass changes and their contributions to sea-level rise from 1961 to 2016, Nature, 568, 382–386, https://doi.org/10.1038/s41586-019-1071-0, 2019.
Zeng, Y., Hao, D., Park, T., Zhu, P., Huete, A., Myneni, R., and Chen, M.: Structural complexity biases vegetation greenness measures, Nat. Ecol. Evol., 7, 1790–1798, https://doi.org/10.1038/s41559-023-02187-6, 2023.
Zhang, C., Ma, L., Chen, J., Rao, Y., and Chen, X.: Assessing the impact of endmember variability on linear spectral mixture analysis (LSMA): a theoretical and simulation analysis. Remote Sens. Environ., 235, 111471, https://doi.org/10.1016/j.rse.2019.111471, 2019.
Zhang, X., Liu, L., Zhao, T., Gao, Y., Chen, X., and Mi, J.: GISD30: global 30 m impervious-surface dynamic dataset from 1985 to 2020 using time-series Landsat imagery on the Google Earth Engine platform, Earth Syst. Sci. Data, 14, 1831–1856, https://doi.org/10.5194/essd-14-1831-2022, 2022.
Zhang, Y., Foody, G. M., Ling, F., Li, X., Ge, Y., Du, Y., and Atkinson, P. M.: Spatial-temporal fraction map fusion with multi-scale remotely sensed images, Remote Sens. Environ., 213, 162–181, https://doi.org/10.1016/j.rse.2018.05.010, 2018.
Zhao, J., Li, J., Liu, Q., Xu, B., Mu, X., and Dong, Y.: Generation of a 16 m/10-day fractional vegetation cover product over China based on Chinese GaoFen-1 observations: method and validation, Int. J. Digit. Earth, 16, 4229–4246, https://doi.org/10.1080/17538947.2023.2264815, 2023.
Zhao, M., Cheng, C., Zhou, Y., Li, X., Shen, S., and Song, C.: A global dataset of annual urban extents (1992–2020) from harmonized nighttime lights, Earth Syst. Sci. Data, 14, 517–534, https://doi.org/10.5194/essd-14-517-2022, 2022.
Zhen, Z., Estes, L., Ziegler, A. D., Chen, A., Searchinger, T., Hua, F., Guan, K., Jintrawet, A., and Wood, E. F.: Highland cropland expansion and forest loss in southeast asia in the twenty-first century, Nat. Geosci., 11, 556–562, https://doi.org/10.1038/s41561-018-0166-9, 2018.
Zhu, Z., Piao, S., Myneni, R. B., Huang, M., Zeng, Z., Canadell, J. G., Ciais, P., Sitch, S., Friedlingstein, P., and Arneth, A.: Greening of the Earth and its drivers, Nat. Clim. Change, 6, 791–795, https://doi.org/10.1038/nclimate3004, 2016.
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.
To provide multifaceted changes under climate change and anthropogenic impacts, we estimated...
Altmetrics
Final-revised paper
Preprint