Articles | Volume 15, issue 1
https://doi.org/10.5194/essd-15-265-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-15-265-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
17 Jan 2023
Data description paper |
| 17 Jan 2023
| 17 Jan 2023
GWL_FCS30: a global 30 m wetland map with a fine classification system using multi-sourced and time-series remote sensing imagery in 2020
Xiao Zhang
International Research Center of Big Data for Sustainable Development
Goals, Beijing 100094, China
Key Laboratory of Digital Earth Science, Aerospace Information Research
Institute, Chinese Academy of Sciences, Beijing 100094, China
International Research Center of Big Data for Sustainable Development
Goals, Beijing 100094, China
Key Laboratory of Digital Earth Science, Aerospace Information Research
Institute, Chinese Academy of Sciences, Beijing 100094, China
School of Electronic, Electrical and Communication Engineering, University
of Chinese Academy of Sciences, Beijing 100049, China
Tingting Zhao
International Research Center of Big Data for Sustainable Development
Goals, Beijing 100094, China
College of Geomatics, Xi'an University of Science and Technology, Xi'an
710054, China
Xidong Chen
North China University of Water Resources and Electric Power, Zhengzhou
450046, China
Shangrong Lin
School of Atmospheric Sciences, Southern Marine Science and Engineering
Guangdong Laboratory (Zhuhai), Sun Yat-sen University, Zhuhai 519082,
Guangdong, China
Jinqing Wang
International Research Center of Big Data for Sustainable Development
Goals, Beijing 100094, China
Key Laboratory of Digital Earth Science, Aerospace Information Research
Institute, Chinese Academy of Sciences, Beijing 100094, China
School of Electronic, Electrical and Communication Engineering, University
of Chinese Academy of Sciences, Beijing 100049, China
Jun Mi
International Research Center of Big Data for Sustainable Development
Goals, Beijing 100094, China
Key Laboratory of Digital Earth Science, Aerospace Information Research
Institute, Chinese Academy of Sciences, Beijing 100094, China
School of Electronic, Electrical and Communication Engineering, University
of Chinese Academy of Sciences, Beijing 100049, China
Wendi Liu
International Research Center of Big Data for Sustainable Development
Goals, Beijing 100094, China
Key Laboratory of Digital Earth Science, Aerospace Information Research
Institute, Chinese Academy of Sciences, Beijing 100094, China
School of Electronic, Electrical and Communication Engineering, University
of Chinese Academy of Sciences, Beijing 100049, China
Related authors
Liangyun Liu and Xiao Zhang
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., X-2-2024, 137–143, https://doi.org/10.5194/isprs-annals-X-2-2024-137-2024, https://doi.org/10.5194/isprs-annals-X-2-2024-137-2024, 2024
Xiao Zhang, Tingting Zhao, Hong Xu, Wendi Liu, Jinqing Wang, Xidong Chen, and Liangyun Liu
Earth Syst. Sci. Data, 16, 1353–1381, https://doi.org/10.5194/essd-16-1353-2024, https://doi.org/10.5194/essd-16-1353-2024, 2024
Short summary
Short summary
This work describes GLC_FCS30D, the first global 30 m land-cover dynamics monitoring dataset, which contains 35 land-cover subcategories and covers the period of 1985–2022 in 26 time steps (its maps are updated every 5 years before 2000 and annually after 2000).
Xiaojin Qian, Liangyun Liu, Xidong Chen, Xiao Zhang, Siyuan Chen, and Qi Sun
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2022-277, https://doi.org/10.5194/essd-2022-277, 2022
Manuscript not accepted for further review
Short summary
Short summary
Leaf chlorophyll content (LCC) is an important plant physiological trait and a proxy for leaf photosynthetic capacity. We generated a global LCC dataset from ENVISAT MERIS and Sentinel-3 OLCI satellite data for the period 2003–2012 to 2018–2020 using a physically-based radiative transfer modeling approach. This new LCC dataset spanning nearly 20 years will provide a valuable opportunity for the monitoring of vegetation growth and terrestrial carbon cycle modeling on a global scale.
Xidong Chen, Liangyun Liu, Xiao Zhang, Junsheng Li, Shenglei Wang, Yuan Gao, and Jun Mi
Hydrol. Earth Syst. Sci., 26, 3517–3536, https://doi.org/10.5194/hess-26-3517-2022, https://doi.org/10.5194/hess-26-3517-2022, 2022
Short summary
Short summary
A 30 m LAke Water Secchi Depth (LAWSD30) dataset of China was first developed for 1985–2020, and national-scale water clarity estimations of lakes in China over the past 35 years were analyzed. Lake clarity in China exhibited a significant downward trend before the 21st century, but improved after 2000. The developed LAWSD30 dataset and the evaluation results can provide effective guidance for water preservation and restoration.
Xiao Zhang, Liangyun Liu, Tingting Zhao, Yuan Gao, Xidong Chen, and Jun Mi
Earth Syst. Sci. Data, 14, 1831–1856, https://doi.org/10.5194/essd-14-1831-2022, https://doi.org/10.5194/essd-14-1831-2022, 2022
Short summary
Short summary
Accurately mapping impervious-surface dynamics has great scientific significance and application value for research on urban sustainable development, the assessment of anthropogenic carbon emissions and global ecological-environment modeling. In this study, a novel and accurate global 30 m impervious surface dynamic dataset (GISD30) for 1985 to 2020 was produced using the spectral-generalization method and time-series Landsat imagery on the Google Earth Engine cloud computing platform.
Xiao Zhang, Liangyun Liu, Xidong Chen, Yuan Gao, Shuai Xie, and Jun Mi
Earth Syst. Sci. Data, 13, 2753–2776, https://doi.org/10.5194/essd-13-2753-2021, https://doi.org/10.5194/essd-13-2753-2021, 2021
Short summary
Short summary
Over past decades, a lot of global land-cover products have been released; however, these still lack a global land-cover map with a fine classification system and spatial resolution simultaneously. In this study, a novel global 30 m landcover classification with a fine classification system for the year 2015 (GLC_FCS30-2015) was produced by combining time series of Landsat imagery and high-quality training data from the GSPECLib on the Google Earth Engine computing platform.
Xiao Zhang, Liangyun Liu, Changshan Wu, Xidong Chen, Yuan Gao, Shuai Xie, and Bing Zhang
Earth Syst. Sci. Data, 12, 1625–1648, https://doi.org/10.5194/essd-12-1625-2020, https://doi.org/10.5194/essd-12-1625-2020, 2020
Short summary
Short summary
The amount of impervious surface is an important indicator in the monitoring of the intensity of human activity and environmental change. In this study, a global 30 m impervious surface map was developed by using multisource, multitemporal remote sensing data based on the Google Earth Engine platform. The accuracy assessment indicated that the generated map had more optimal measurement accuracy compared with other state-of-art impervious surface products.
Dianrun Zhao, Shanshan Du, Chu Zou, Longfei Tian, Meng Fan, Yulu Du, and Liangyun Liu
EGUsphere, https://doi.org/10.5194/egusphere-2024-3118, https://doi.org/10.5194/egusphere-2024-3118, 2024
This preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).
Short summary
Short summary
The TanSat-2 satellite is designed for global carbon monitoring. It provides high-resolution, dual-band observations of solar-induced chlorophyll fluorescence, a key indicator of plant photosynthesis. Through simulations, we optimized the satellite's data processing and found it can retrieve this fluorescence with great accuracy. These findings suggest that TanSat-2 will enhance global monitoring of carbon cycles and vegetation health, offering valuable insights for climate change research.
Chu Zou, Shanshan Du, Xinjie Liu, and Liangyun Liu
Earth Syst. Sci. Data, 16, 2789–2809, https://doi.org/10.5194/essd-16-2789-2024, https://doi.org/10.5194/essd-16-2789-2024, 2024
Short summary
Short summary
To obtain a temporally consistent satellite solar-induced chlorophyll fluorescence
(SIF) product (TCSIF), we corrected for time degradation of GOME-2A using a pseudo-invariant method. After the correction, the global SIF grew by 0.70 % per year from 2007 to 2021, and 62.91 % of vegetated regions underwent an increase in SIF. The dataset is a promising tool for monitoring global vegetation variation and will advance our understanding of vegetation's photosynthetic activities at a global scale.
(SIF) product (TCSIF), we corrected for time degradation of GOME-2A using a pseudo-invariant method. After the correction, the global SIF grew by 0.70 % per year from 2007 to 2021, and 62.91 % of vegetated regions underwent an increase in SIF. The dataset is a promising tool for monitoring global vegetation variation and will advance our understanding of vegetation's photosynthetic activities at a global scale.
Liangyun Liu and Xiao Zhang
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., X-2-2024, 137–143, https://doi.org/10.5194/isprs-annals-X-2-2024-137-2024, https://doi.org/10.5194/isprs-annals-X-2-2024-137-2024, 2024
Xiao Zhang, Tingting Zhao, Hong Xu, Wendi Liu, Jinqing Wang, Xidong Chen, and Liangyun Liu
Earth Syst. Sci. Data, 16, 1353–1381, https://doi.org/10.5194/essd-16-1353-2024, https://doi.org/10.5194/essd-16-1353-2024, 2024
Short summary
Short summary
This work describes GLC_FCS30D, the first global 30 m land-cover dynamics monitoring dataset, which contains 35 land-cover subcategories and covers the period of 1985–2022 in 26 time steps (its maps are updated every 5 years before 2000 and annually after 2000).
Xiaojin Qian, Liangyun Liu, Xidong Chen, Xiao Zhang, Siyuan Chen, and Qi Sun
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2022-277, https://doi.org/10.5194/essd-2022-277, 2022
Manuscript not accepted for further review
Short summary
Short summary
Leaf chlorophyll content (LCC) is an important plant physiological trait and a proxy for leaf photosynthetic capacity. We generated a global LCC dataset from ENVISAT MERIS and Sentinel-3 OLCI satellite data for the period 2003–2012 to 2018–2020 using a physically-based radiative transfer modeling approach. This new LCC dataset spanning nearly 20 years will provide a valuable opportunity for the monitoring of vegetation growth and terrestrial carbon cycle modeling on a global scale.
Xidong Chen, Liangyun Liu, Xiao Zhang, Junsheng Li, Shenglei Wang, Yuan Gao, and Jun Mi
Hydrol. Earth Syst. Sci., 26, 3517–3536, https://doi.org/10.5194/hess-26-3517-2022, https://doi.org/10.5194/hess-26-3517-2022, 2022
Short summary
Short summary
A 30 m LAke Water Secchi Depth (LAWSD30) dataset of China was first developed for 1985–2020, and national-scale water clarity estimations of lakes in China over the past 35 years were analyzed. Lake clarity in China exhibited a significant downward trend before the 21st century, but improved after 2000. The developed LAWSD30 dataset and the evaluation results can provide effective guidance for water preservation and restoration.
Xiao Zhang, Liangyun Liu, Tingting Zhao, Yuan Gao, Xidong Chen, and Jun Mi
Earth Syst. Sci. Data, 14, 1831–1856, https://doi.org/10.5194/essd-14-1831-2022, https://doi.org/10.5194/essd-14-1831-2022, 2022
Short summary
Short summary
Accurately mapping impervious-surface dynamics has great scientific significance and application value for research on urban sustainable development, the assessment of anthropogenic carbon emissions and global ecological-environment modeling. In this study, a novel and accurate global 30 m impervious surface dynamic dataset (GISD30) for 1985 to 2020 was produced using the spectral-generalization method and time-series Landsat imagery on the Google Earth Engine cloud computing platform.
Xiao Zhang, Liangyun Liu, Xidong Chen, Yuan Gao, Shuai Xie, and Jun Mi
Earth Syst. Sci. Data, 13, 2753–2776, https://doi.org/10.5194/essd-13-2753-2021, https://doi.org/10.5194/essd-13-2753-2021, 2021
Short summary
Short summary
Over past decades, a lot of global land-cover products have been released; however, these still lack a global land-cover map with a fine classification system and spatial resolution simultaneously. In this study, a novel global 30 m landcover classification with a fine classification system for the year 2015 (GLC_FCS30-2015) was produced by combining time series of Landsat imagery and high-quality training data from the GSPECLib on the Google Earth Engine computing platform.
Xiao Zhang, Liangyun Liu, Changshan Wu, Xidong Chen, Yuan Gao, Shuai Xie, and Bing Zhang
Earth Syst. Sci. Data, 12, 1625–1648, https://doi.org/10.5194/essd-12-1625-2020, https://doi.org/10.5194/essd-12-1625-2020, 2020
Short summary
Short summary
The amount of impervious surface is an important indicator in the monitoring of the intensity of human activity and environmental change. In this study, a global 30 m impervious surface map was developed by using multisource, multitemporal remote sensing data based on the Google Earth Engine platform. The accuracy assessment indicated that the generated map had more optimal measurement accuracy compared with other state-of-art impervious surface products.
Xiaojin Qian, Liangyun Liu, Holly Croft, and Jingming Chen
Biogeosciences Discuss., https://doi.org/10.5194/bg-2019-228, https://doi.org/10.5194/bg-2019-228, 2019
Preprint withdrawn
Short summary
Short summary
The leaf maximum carboxylation rate (Vcmax) is a key photosynthesis parameter. We attempt to investigate whether a universal and stable relationship exists between leaf Vcmax25 and chlorophyll content across different C3 plant types from a plant physiological perspective and verify it using field experiments. The results confirm that leaf chlorophyll can be a reliable proxy for estimating Vcmax25, providing an operational approach for the global mapping of Vcmax25 across different plant types.
Related subject area
Domain: ESSD – Land | Subject: Land Cover and Land Use
Monsoon Asia Rice Calendar (MARC): a gridded rice calendar in monsoon Asia based on Sentinel-1 and Sentinel-2 images
A 100 m gridded population dataset of China's seventh census using ensemble learning and big geospatial data
Annual time-series 1 km maps of crop area and types in the conterminous US (CropAT-US): cropping diversity changes during 1850–2021
Enhancing High-Resolution Forest Stand Mean Height Mapping in China through an Individual Tree-Based Approach with Close-Range LiDAR Data
Retrieval of dominant methane (CH4) emission sources, the first high-resolution (1–2 m) dataset of storage tanks of China in 2000–2021
A 10 m resolution land cover map of the Tibetan Plateau with detailed vegetation types
ChinaSoyArea10m: a dataset of soybean-planting areas with a spatial resolution of 10 m across China from 2017 to 2021
Physical, social, and biological attributes for improved understanding and prediction of wildfires: FPA FOD-Attributes dataset
3D-GloBFP: the first global three-dimensional building footprint dataset
Map of forest tree species for Poland based on Sentinel-2 data
Global 30-m seamless data cube (2000–2022) of land surface reflectance generated from Landsat-5,7,8,9 and MODIS Terra constellations
The ABoVE L-band and P-band airborne synthetic aperture radar surveys
Global mapping of oil palm planting year from 1990 to 2021
A 30 m annual cropland dataset of China from 1986 to 2021
Global 1 km land surface parameters for kilometer-scale Earth system modeling
Mapping sugarcane globally at 10 m resolution using GEDI and Sentinel-2
A 28 time-points cropland area change dataset in Northeast China from 1000 to 2020
ChinaRiceCalendar – seasonal crop calendars for early-, middle-, and late-season rice in China
Harmonized European Union subnational crop statistics can reveal climate impacts and crop cultivation shifts
GLC_FCS30D: the first global 30 m land-cover dynamics monitoring product with a fine classification system for the period from 1985 to 2022 generated using dense-time-series Landsat imagery and the continuous change-detection method
A global estimate of monthly vegetation and soil fractions from spatiotemporally adaptive spectral mixture analysis during 2001–2022
A 2020 forest age map for China with 30 m resolution
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
Annual high-resolution grazing intensity maps on the Qinghai-Tibet Plateau from 1990 to 2020
Mapping Rangeland Health Indicators in East Africa from 2000 to 2022
A new cropland area database by country circa 2020
FORMS: Forest Multiple Source height, wood volume, and biomass maps in France at 10 to 30 m resolution based on Sentinel-1, Sentinel-2, and Global Ecosystem Dynamics Investigation (GEDI) data with a deep learning approach
SinoLC-1: the first 1 m resolution national-scale land-cover map of China created with a deep learning framework and open-access data
HISDAC-ES: historical settlement data compilation for Spain (1900–2020)
LCM2021 – the UK Land Cover Map 2021
ChinaWheatYield30m: a 30 m annual winter wheat yield dataset from 2016 to 2021 in China
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
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
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
Xin Zhao, Kazuya Nishina, Haruka Izumisawa, Yuji Masutomi, Seima Osako, and Shuhei Yamamoto
Earth Syst. Sci. Data, 16, 3893–3911, https://doi.org/10.5194/essd-16-3893-2024, https://doi.org/10.5194/essd-16-3893-2024, 2024
Short summary
Short summary
Mapping a rice calendar in a spatially explicit manner with a consistent framework remains challenging at a global or continental scale. We successfully developed a new gridded rice calendar for monsoon Asia based on Sentinel-1 and Sentinel-2 images, which characterize transplanting and harvesting dates and the number of rice croppings in a comprehensive framework. Our rice calendar will be beneficial for rice management, production prediction, and the estimation of greenhouse gas emissions.
Yuehong Chen, Congcong Xu, Yong Ge, Xiaoxiang Zhang, and Ya'nan Zhou
Earth Syst. Sci. Data, 16, 3705–3718, https://doi.org/10.5194/essd-16-3705-2024, https://doi.org/10.5194/essd-16-3705-2024, 2024
Short summary
Short summary
Population data is crucial for human–nature interactions. Gridded population data can address limitations of census data in irregular units. In China, rapid urbanization necessitates timely and accurate population grids. However, existing datasets for China are either outdated or lack recent census data. Hence, a novel approach was developed to disaggregate China’s seventh census data into 100 m population grids. The resulting dataset outperformed the existing LandScan and WorldPop datasets.
Shuchao Ye, Peiyu Cao, and Chaoqun Lu
Earth Syst. Sci. Data, 16, 3453–3470, https://doi.org/10.5194/essd-16-3453-2024, https://doi.org/10.5194/essd-16-3453-2024, 2024
Short summary
Short summary
We reconstructed annual cropland density and crop type maps, including nine major crop types (corn, soybean, winter wheat, spring wheat, durum wheat, cotton, sorghum, barley, and rice), from 1850 to 2021 at 1 km × 1 km resolution. We found that the US total crop acreage has increased by 118 × 106 ha (118 Mha), mainly driven by corn (30 Mha) and soybean (35 Mha). Additionally, the US cropping diversity experienced an increase in the 1850s–1960s, followed by a decline over the past 6 decades.
Yuling Chen, Haitao Yang, Zekun Yang, Qiuli Yang, Weiyan Liu, Guoran Huang, Yu Ren, Kai Cheng, Tianyu Xiang, Mengxi Chen, Danyang Lin, Zhiyong Qi, Jiachen Xu, Yixuan Zhang, Guangcai Xu, and Qinghua Guo
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-274, https://doi.org/10.5194/essd-2024-274, 2024
Revised manuscript accepted for ESSD
Short summary
Short summary
The national scale, continuous maps of arithmetic mean height and weighted mean height across China address the challenges of accurately estimating forest stand mean height using a tree-based approach. These maps produced in this study provide critical datasets for forest sustainable management in China, including climate change mitigation (e.g., terrestrial carbon estimation), forest ecosystem assessment, and forest inventory practices.
Fang Chen, Lei Wang, Yu Wang, Haiying Zhang, Ning Wang, Pengfei Ma, and Bo Yu
Earth Syst. Sci. Data, 16, 3369–3382, https://doi.org/10.5194/essd-16-3369-2024, https://doi.org/10.5194/essd-16-3369-2024, 2024
Short summary
Short summary
Storage tanks are responsible for approximately 25 % of CH4 emissions in the atmosphere, exacerbating climate warming. Currently there is no publicly accessible storage tank inventory. We generated the first high-spatial-resolution (1–2 m) storage tank dataset (STD) over 92 typical cities in China in 2021, totaling 14 461 storage tanks with the construction year from 2000–2021. It shows significant agreement with CH4 emission spatially and temporally, promoting the CH4 control strategy proposal.
Xingyi Huang, Yuwei Yin, Luwei Feng, Xiaoye Tong, Xiaoxin Zhang, Jiangrong Li, and Feng Tian
Earth Syst. Sci. Data, 16, 3307–3332, https://doi.org/10.5194/essd-16-3307-2024, https://doi.org/10.5194/essd-16-3307-2024, 2024
Short summary
Short summary
The Tibetan Plateau, with its diverse vegetation ranging from forests to alpine grasslands, plays a key role in understanding climate change impacts. Existing maps lack detail or miss unique ecosystems. Our research, using advanced satellite technology and machine learning, produced the map TP_LC10-2022. Comparisons with other maps revealed TP_LC10-2022's excellence in capturing local variations. Our map is significant for in-depth ecological studies.
Qinghang Mei, Zhao Zhang, Jichong Han, Jie Song, Jinwei Dong, Huaqing Wu, Jialu Xu, and Fulu Tao
Earth Syst. Sci. Data, 16, 3213–3231, https://doi.org/10.5194/essd-16-3213-2024, https://doi.org/10.5194/essd-16-3213-2024, 2024
Short summary
Short summary
In order to make up for the lack of long-term soybean planting area maps in China, we firstly generated a dataset of soybean planting area with a spatial resolution of 10 m for major producing areas in China from 2017 to 2021 (ChinaSoyArea10m). Compared with existing datasets, ChinaSoyArea10m has higher consistency with census data and further improvement in spatial details. The dataset can provide reliable support for subsequent studies on yield monitoring and food security.
Yavar Pourmohamad, John T. Abatzoglou, Erin J. Belval, Erica Fleishman, Karen Short, Matthew C. Reeves, Nicholas Nauslar, Philip E. Higuera, Eric Henderson, Sawyer Ball, Amir AghaKouchak, Jeffrey P. Prestemon, Julia Olszewski, and Mojtaba Sadegh
Earth Syst. Sci. Data, 16, 3045–3060, https://doi.org/10.5194/essd-16-3045-2024, https://doi.org/10.5194/essd-16-3045-2024, 2024
Short summary
Short summary
The FPA FOD-Attributes dataset provides > 300 biological, physical, social, and administrative attributes associated with > 2.3×106 wildfire incidents across the US from 1992 to 2020. The dataset can be used to (1) answer numerous questions about the covariates associated with human- and lightning-caused wildfires and (2) support descriptive, diagnostic, predictive, and prescriptive wildfire analytics, including the development of machine learning models.
Yangzi Che, Xuecao Li, Xiaoping Liu, Yuhao Wang, Weilin Liao, Xianwei Zheng, Xucai Zhang, Xiaocong Xu, Qian Shi, Jiajun Zhu, Hua Yuan, and Yongjiu Dai
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-217, https://doi.org/10.5194/essd-2024-217, 2024
Revised manuscript accepted for ESSD
Short summary
Short summary
Given the limited coverage or spatial resolution of existing datasets, we develop the first global building height map (3D-GloBFP) at the building footprint scale using Earth observation datasets and advanced machine learning techniques. Our map reveals the complex 3-D morphology of the world's building heights at a finer scale and provides reliable results (i.e., R2: 0.66–0.96, RMSEs: 1.9 m–14.6 m) over global regions 3D-GloBFP has great potential to support both macro- and micro-urban analysis
Ewa Grabska-Szwagrzyk, Dirk Tiede, Martin Sudmanns, and Jacek Kozak
Earth Syst. Sci. Data, 16, 2877–2891, https://doi.org/10.5194/essd-16-2877-2024, https://doi.org/10.5194/essd-16-2877-2024, 2024
Short summary
Short summary
We accurately mapped 16 dominant tree species and genera in Poland using Sentinel-2 observations from short periods in spring, summer, and autumn (2018–2021). The classification achieved more than 80% accuracy in country-wide forest species mapping, with variation based on species, region, and observation frequency. Freely accessible resources, including the forest tree species map and training and test data, can be found at https://doi.org/10.5281/zenodo.10180469.
Shuang Chen, Jie Wang, Qiang Liu, Xiangan Liang, Rui Liu, Peng Qin, Jincheng Yuan, Junbo Wei, Shuai Yuan, Huabing Huang, and Peng Gong
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-178, https://doi.org/10.5194/essd-2024-178, 2024
Revised manuscript accepted for ESSD
Short summary
Short summary
The inconsistent coverage of Landsat data due to its long revisit intervals and frequent cloud cover poses challenges to large-scale land monitoring. We developed a global, 30-m, 23-year (2000–2022), and daily Seamless Data Cube (SDC) of surface reflectance based on Landsat 5,7,8,9 and MODIS products. The SDC exhibits enhanced capabilities for monitoring land cover changes and robust consistency in both spatial and temporal dimensions, which are important for global environmental monitoring.
Charles E. Miller, Peter C. Griffith, Elizabeth Hoy, Naiara S. Pinto, Yunling Lou, Scott Hensley, Bruce D. Chapman, Jennifer Baltzer, Kazem Bakian-Dogaheh, W. Robert Bolton, Laura Bourgeau-Chavez, Richard H. Chen, Byung-Hun Choe, Leah K. Clayton, Thomas A. Douglas, Nancy French, Jean E. Holloway, Gang Hong, Lingcao Huang, Go Iwahana, Liza Jenkins, John S. Kimball, Tatiana Loboda, Michelle Mack, Philip Marsh, Roger J. Michaelides, Mahta Moghaddam, Andrew Parsekian, Kevin Schaefer, Paul R. Siqueira, Debjani Singh, Alireza Tabatabaeenejad, Merritt Turetsky, Ridha Touzi, Elizabeth Wig, Cathy J. Wilson, Paul Wilson, Stan D. Wullschleger, Yonghong Yi, Howard A. Zebker, Yu Zhang, Yuhuan Zhao, and Scott J. Goetz
Earth Syst. Sci. Data, 16, 2605–2624, https://doi.org/10.5194/essd-16-2605-2024, https://doi.org/10.5194/essd-16-2605-2024, 2024
Short summary
Short summary
NASA’s Arctic Boreal Vulnerability Experiment (ABoVE) conducted airborne synthetic aperture radar (SAR) surveys of over 120 000 km2 in Alaska and northwestern Canada during 2017, 2018, 2019, and 2022. This paper summarizes those results and provides links to details on ~ 80 individual flight lines. This paper is presented as a guide to enable interested readers to fully explore the ABoVE L- and P-band SAR data.
Adrià Descals, David L. A. Gaveau, Serge Wich, Zoltan Szantoi, and Erik Meijaard
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-157, https://doi.org/10.5194/essd-2024-157, 2024
Revised manuscript accepted for ESSD
Short summary
Short summary
This study provides a 10-m global oil palm extent layer for 2021 and a 30-m oil palm planting year layer from 1990 to 2021. The oil palm extent layer was produced using a convolutional neural network that identified industrial and smallholder plantations using Sentinel-1 data. The oil palm planting year was developed using a methodology specifically designed to detect the early stages of oil palm development in the Landsat time series.
Ying Tu, Shengbiao Wu, Bin Chen, Qihao Weng, Yuqi Bai, Jun Yang, Le Yu, and Bing Xu
Earth Syst. Sci. Data, 16, 2297–2316, https://doi.org/10.5194/essd-16-2297-2024, https://doi.org/10.5194/essd-16-2297-2024, 2024
Short summary
Short summary
We developed the first 30 m annual cropland dataset of China (CACD) for 1986–2021. The overall accuracy of CACD reached up to 0.93±0.01 and was superior to other products. Our fine-resolution cropland maps offer valuable information for diverse applications and decision-making processes in the future.
Lingcheng Li, Gautam Bisht, Dalei Hao, and L. Ruby Leung
Earth Syst. Sci. Data, 16, 2007–2032, https://doi.org/10.5194/essd-16-2007-2024, https://doi.org/10.5194/essd-16-2007-2024, 2024
Short summary
Short summary
This study fills a gap to meet the emerging needs of kilometer-scale Earth system modeling by developing global 1 km land surface parameters for land use, vegetation, soil, and topography. Our demonstration simulations highlight the substantial impacts of these parameters on spatial variability and information loss in water and energy simulations. Using advanced explainable machine learning methods, we identified influential factors driving spatial variability and information loss.
Stefania Di Tommaso, Sherrie Wang, Rob Strey, and David B. Lobell
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-121, https://doi.org/10.5194/essd-2024-121, 2024
Revised manuscript accepted for ESSD
Short summary
Short summary
Sugarcane plays a vital role in food, biofuel, and farmer income globally, yet its cultivation faces numerous social and environmental challenges. Despite its significance, accurate mapping remains limited. Our study addresses this gap by introducing a novel 10m global dataset of sugarcane maps spanning 2019–2022. Comparisons with field data, pre-existing maps, and official government statistics all indicate the high precision and recall of our maps.
Ran Jia, Xiuqi Fang, Yundi Yang, Masayuki Yokozawa, and Yu Ye
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-94, https://doi.org/10.5194/essd-2024-94, 2024
Revised manuscript accepted for ESSD
Short summary
Short summary
One of the major manifestations of global change is the alteration of natural vegetation landscapes by human reclamation. Here we reconstruct a unified set of long-term time series cropland area change datasets with standardized criteria. The cropland in Northeast China exhibited phase changes of expansion-reduction-expansion over the past millennium. Compared to global historical LUCC datasets, our dataset has significant time resolution and reliability.
Hui Li, Xiaobo Wang, Shaoqiang Wang, Jinyuan Liu, Yuanyuan Liu, Zhenhai Liu, Shiliang Chen, Qinyi Wang, Tongtong Zhu, Lunche Wang, and Lizhe Wang
Earth Syst. Sci. Data, 16, 1689–1701, https://doi.org/10.5194/essd-16-1689-2024, https://doi.org/10.5194/essd-16-1689-2024, 2024
Short summary
Short summary
Utilizing satellite remote sensing data, we established a multi-season rice calendar dataset named ChinaRiceCalendar. It exhibits strong alignment with field observations collected by agricultural meteorological stations across China. ChinaRiceCalendar stands as a reliable dataset for investigating and optimizing the spatiotemporal dynamics of rice phenology in China, particularly in the context of climate and land use changes.
Giulia Ronchetti, Luigi Nisini Scacchiafichi, Lorenzo Seguini, Iacopo Cerrani, and Marijn van der Velde
Earth Syst. Sci. Data, 16, 1623–1649, https://doi.org/10.5194/essd-16-1623-2024, https://doi.org/10.5194/essd-16-1623-2024, 2024
Short summary
Short summary
We present a dataset of EU-wide harmonized subnational crop area, production, and yield statistics with information on data sources, processing steps, missing and derived data, and quality checks. Statistical records (344 282) collected from 1975 to 2020 for soft and durum wheat, winter and spring barley, grain maize, sunflower, and sugar beet were aligned with the EUROSTAT crop legend and the 2016 territorial classification for 961 regions. Time series have a median length of 21 years.
Xiao Zhang, Tingting Zhao, Hong Xu, Wendi Liu, Jinqing Wang, Xidong Chen, and Liangyun Liu
Earth Syst. Sci. Data, 16, 1353–1381, https://doi.org/10.5194/essd-16-1353-2024, https://doi.org/10.5194/essd-16-1353-2024, 2024
Short summary
Short summary
This work describes GLC_FCS30D, the first global 30 m land-cover dynamics monitoring dataset, which contains 35 land-cover subcategories and covers the period of 1985–2022 in 26 time steps (its maps are updated every 5 years before 2000 and annually after 2000).
Qiangqiang Sun, Ping Zhang, Xin Jiao, Xin Lin, Wenkai Duan, Su Ma, Qidi Pan, Lu Chen, Yongxiang Zhang, Shucheng You, Shunxi Liu, Jinmin Hao, Hong Li, and Danfeng Sun
Earth Syst. Sci. Data, 16, 1333–1351, https://doi.org/10.5194/essd-16-1333-2024, https://doi.org/10.5194/essd-16-1333-2024, 2024
Short summary
Short summary
To provide multifaceted changes under climate change and anthropogenic impacts, we estimated monthly vegetation and soil fractions in 2001–2022, providing an accurate estimate of surface heterogeneous composition, better than vegetation index and vegetation continuous-field products. We find a greening trend on Earth except for the tropics. A combination of interactive changes in vegetation and soil can be adopted as a valuable measurement of climate change and anthropogenic impacts.
Kai Cheng, Yuling Chen, Tianyu Xiang, Haitao Yang, Weiyan Liu, Yu Ren, Hongcan Guan, Tianyu Hu, Qin Ma, and Qinghua Guo
Earth Syst. Sci. Data, 16, 803–819, https://doi.org/10.5194/essd-16-803-2024, https://doi.org/10.5194/essd-16-803-2024, 2024
Short summary
Short summary
To quantify forest carbon stock and its future potential accurately, we generated a 30 m resolution forest age map for China in 2020 using multisource remote sensing datasets based on machine learning and time series analysis approaches. Validation with independent field samples indicated that the mapped forest age had an R2 of 0.51--0.63. Nationally, the average forest age is 56.1 years (standard deviation of 32.7 years).
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.
Jia Zhou, Jin Niu, Ning Wu, and Tao Lu
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2023-403, https://doi.org/10.5194/essd-2023-403, 2023
Revised manuscript accepted for ESSD
Short summary
Short summary
The study provided an annual 100-meter resolution glimpse into the grazing activities across the Qinghai-Tibet Plateau. The newly minted Gridded Dataset of Grazing Intensity (GDGI) not only boasts exceptional accuracy but also acts as a pivotal resource for further research and strategic planning, with the potential to shape sustainable grazing practices, guide informed environmental stewardship, and ensure the longevity of the region’s precious ecosystems.
Gerardo E. Soto, Steven Wilcox, Patrick E. Clark, Francesco P. Fava, Nathan M. Jensen, Njoki Kahiu, Chuan Liao, Benjamin Porter, Ying Sun, and Christopher Barrett
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2023-217, https://doi.org/10.5194/essd-2023-217, 2023
Revised manuscript accepted for ESSD
Short summary
Short summary
Using machine learning classification and linear unmixing, this paper produced Landsat-based time series of land cover classes and vegetation fractional cover of photosynthetic vegetation, non-photosynthetic vegetation, and bare ground. This dataset represents a first multi-decadal high-resolution dataset specifically designed for mapping and monitoring rangelands health in East Africa including Kenya, Ethiopia, and Somalia, which are dominated by arid and semi-arid extensive rangeland systems.
Francesco N. Tubiello, Giulia Conchedda, Leon Casse, Pengyu Hao, Giorgia De Santis, and Zhongxin Chen
Earth Syst. Sci. Data, 15, 4997–5015, https://doi.org/10.5194/essd-15-4997-2023, https://doi.org/10.5194/essd-15-4997-2023, 2023
Short summary
Short summary
We describe a new dataset of cropland area circa the year 2020, with global coverage and country detail. Data are generated from geospatial information on the agreement characteristics of six high-resolution cropland maps. By helping to highlight features of cropland characteristics and underlying causes for agreement across land cover products, the dataset can be used as a tool to help guide future mapping efforts towards improved agricultural monitoring.
Martin Schwartz, Philippe Ciais, Aurélien De Truchis, Jérôme Chave, Catherine Ottlé, Cedric Vega, Jean-Pierre Wigneron, Manuel Nicolas, Sami Jouaber, Siyu Liu, Martin Brandt, and Ibrahim Fayad
Earth Syst. Sci. Data, 15, 4927–4945, https://doi.org/10.5194/essd-15-4927-2023, https://doi.org/10.5194/essd-15-4927-2023, 2023
Short summary
Short summary
As forests play a key role in climate-related issues, their accurate monitoring is critical to reduce global carbon emissions effectively. Based on open-access remote-sensing sensors, and artificial intelligence methods, we created high-resolution tree height, wood volume, and biomass maps of metropolitan France that outperform previous products. This study, based on freely available data, provides essential information to support climate-efficient forest management policies at a low cost.
Zhuohong Li, Wei He, Mofan Cheng, Jingxin Hu, Guangyi Yang, and Hongyan Zhang
Earth Syst. Sci. Data, 15, 4749–4780, https://doi.org/10.5194/essd-15-4749-2023, https://doi.org/10.5194/essd-15-4749-2023, 2023
Short summary
Short summary
Nowadays, a very-high-resolution land-cover (LC) map with national coverage is still unavailable in China, hindering efficient resource allocation. To fill this gap, the first 1 m resolution LC map of China, SinoLC-1, was built. The results showed that SinoLC-1 had an overall accuracy of 73.61 % and conformed to the official survey reports. Comparison with other datasets suggests that SinoLC-1 can be a better support for downstream applications and provide more accurate LC information to users.
Johannes H. Uhl, Dominic Royé, Keith Burghardt, José A. Aldrey Vázquez, Manuel Borobio Sanchiz, and Stefan Leyk
Earth Syst. Sci. Data, 15, 4713–4747, https://doi.org/10.5194/essd-15-4713-2023, https://doi.org/10.5194/essd-15-4713-2023, 2023
Short summary
Short summary
Historical, fine-grained geospatial datasets on built-up areas are rarely available, constraining studies of urbanization, settlement evolution, or the dynamics of human–environment interactions to recent decades. In order to provide such historical data, we used publicly available cadastral building data for Spain and created a series of gridded surfaces, measuring age, physical, and land-use-related features of the built environment in Spain and the evolution of settlements from 1900 to 2020.
Christopher G. Marston, Aneurin W. O'Neil, R. Daniel Morton, Claire M. Wood, and Clare S. Rowland
Earth Syst. Sci. Data, 15, 4631–4649, https://doi.org/10.5194/essd-15-4631-2023, https://doi.org/10.5194/essd-15-4631-2023, 2023
Short summary
Short summary
The UK Land Cover Map 2021 (LCM2021) is a UK-wide land cover data set, with 21- and 10-class versions. It is intended to support a broad range of UK environmental research, including ecological and hydrological research. LCM2021 was produced by classifying Sentinel-2 satellite imagery. LCM2021 is distributed as a suite of products to facilitate easy use for a range of applications. To support research at different spatial scales it includes 10 m, 25 m and 1 km resolution products.
Yu Zhao, Shaoyu Han, Jie Zheng, Hanyu Xue, Zhenhai Li, Yang Meng, Xuguang Li, Xiaodong Yang, Zhenhong Li, Shuhong Cai, and Guijun Yang
Earth Syst. Sci. Data, 15, 4047–4063, https://doi.org/10.5194/essd-15-4047-2023, https://doi.org/10.5194/essd-15-4047-2023, 2023
Short summary
Short summary
In the present study, we generated a 30 m Chinese winter wheat yield dataset from 2016 to 2021, called ChinaWheatYield30m. The dataset has high spatial resolution and great accuracy. It is the highest-resolution yield dataset known. Such a dataset will provide basic knowledge of detailed wheat yield distribution, which can be applied for many purposes including crop production modeling or regional climate evaluation.
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.
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.
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.
Cited articles
Amani, M., Mahdavi, S., Afshar, M., Brisco, B., Huang, W., Mohammad Javad
Mirzadeh, S., White, L., Banks, S., Montgomery, J., and Hopkinson, C.:
Canadian Wetland Inventory using Google Earth Engine: The First Map and
Preliminary Results, Remote Sens.-Basel, 11, 842,
https://doi.org/10.3390/rs11070842, 2019.
Azzari, G. and Lobell, D. B.: Landsat-based classification in the cloud: An
opportunity for a paradigm shift in land cover monitoring,
Remote Sens. Environ., 202, 64–74, https://doi.org/10.1016/j.rse.2017.05.025, 2017.
Büttner, G.: CORINE land cover and land cover change products, in: Land
use and land cover mapping in Europe, Springer, https://doi.org/10.1007/978-94-007-7969-3_5, 2014.
Belgiu, M. and Drăguţh, L.: Random forest in remote sensing: A review
of applications and future directions,
ISPRS J. Photogramm., 114, 24–31, https://doi.org/10.1016/j.isprsjprs.2016.01.011,
2016.
Breiman, L.: Random Forests, Mach. Learn., 45, 5–32,
https://doi.org/10.1023/a:1010933404324, 2001.
Brown, C. F., Brumby, S. P., Guzder-Williams, B., Birch, T., Hyde, S. B.,
Mazzariello, J., Czerwinski, W., Pasquarella, V. J., Haertel, R.,
Ilyushchenko, S., Schwehr, K., Weisse, M., Stolle, F., Hanson, C., Guinan,
O., Moore, R., and Tait, A. M.: Dynamic World, Near real-time global 10 m
land use land cover mapping, Scientific Data, 9, 1–7,
https://doi.org/10.1038/s41597-022-01307-4, 2022.
Bunting, P., Rosenqvist, A., Lucas, R., Rebelo, L.-M., Hilarides, L.,
Thomas, N., Hardy, A., Itoh, T., Shimada, M., and Finlayson, C.: The Global
Mangrove Watch–A New 2010 Global Baseline of Mangrove Extent, Remote Sens.-Basel, 10, 1669, https://doi.org/10.3390/rs10101669, 2018.
Bunting, P., Rosenqvist, A., Hilarides, L., Lucas, R. M., Thomas, N.,
Tadono, T., Worthington, T. A., Spalding, M., Murray, N. J., and Rebelo,
L.-M.: Global Mangrove Extent Change 1996–2020: Global Mangrove Watch
Version 3.0, Remote Sens.-Basel, 14, 3657, https://doi.org/10.3390/rs14153657,
2022.
Bwangoy, J.-R. B., Hansen, M. C., Roy, D. P., Grandi, G. D., and Justice, C.
O.: Wetland mapping in the Congo Basin using optical and radar remotely
sensed data and derived topographical indices, Remote Sens. Environ., 114, 73–86, https://doi.org/10.1016/j.rse.2009.08.004, 2010.
Cao, W., Zhou, Y., Li, R., and Li, X.: Mapping changes in coastlines and
tidal flats in developing islands using the full time series of Landsat
images, Remote Sens. Environ., 239, 111665,
https://doi.org/10.1016/j.rse.2020.111665, 2020.
Chen, B., Chen, L., Huang, B., Michishita, R., and Xu, B.: Dynamic
monitoring of the Poyang Lake wetland by integrating Landsat and MODIS
observations, ISPRS J. Photogramm., 139,
75–87, https://doi.org/10.1016/j.isprsjprs.2018.02.021, 2018.
Chen, G., Jin, R., Ye, Z., Li, Q., Gu, J., Luo, M., Luo, Y., Christakos, G.,
Morris, J., He, J., Li, D., Wang, H., Song, L., Wang, Q., and Wu, J.:
Spatiotemporal Mapping of Salt Marshes in the Intertidal Zone of China
during 1985–2019, Journal of Remote Sensing, 2022, 1–15,
https://doi.org/10.34133/2022/9793626, 2022.
Chen, J., Chen, J., Liao, A., Cao, X., Chen, L., Chen, X., He, C., Han, G.,
Peng, S., Lu, M., Zhang, W., Tong, X., and Mills, J.: Global land cover
mapping at 30m resolution: A POK-based operational approach, ISPRS J. Photogramm., 103, 7–27,
https://doi.org/10.1016/j.isprsjprs.2014.09.002, 2015.
Davidson, N. C.: How much wetland has the world lost? Long-term and recent
trends in global wetland area, Mar. Freshwater Res., 65, 934–941,
https://doi.org/10.1071/mf14173, 2014.
Defourny, P., Kirches, G., Brockmann, C., Boettcher, M., Peters, M.,
Bontemps, S., Lamarche, C., Schlerf, M., and Santoro, M.: Land Cover CCI: Product
User Guide Version 2,
https://www.esa-landcover-cci.org/?q=webfm_send/84 (last
access: 22 November 2022), 2018.
DeVries, B., Huang, C., Armston, J., Huang, W., Jones, J. W., and Lang, M.
W.: Rapid and robust monitoring of flood events using Sentinel-1 and Landsat
data on the Google Earth Engine, Remote Sens. Environ., 240, 111664,
https://doi.org/10.1016/j.rse.2020.111664, 2020.
Dixon, M. J. R., Loh, J., Davidson, N. C., Beltrame, C., Freeman, R., and
Walpole, M.: Tracking global change in ecosystem area: The Wetland Extent
Trends index, Biol. Conserv., 193, 27–35,
https://doi.org/10.1016/j.biocon.2015.10.023, 2016.
Gómez, C., White, J. C., and Wulder, M. A.: Optical remotely sensed time
series data for land cover classification: A review, ISPRS J. Photogramm., 116, 55–72,
https://doi.org/10.1016/j.isprsjprs.2016.03.008, 2016.
Gage, E., Cooper, D. J., and Lichvar, R.: Comparison of USACE three-factor
wetland delineations to national wetland inventory maps, Wetlands, 40,
1097–1105, https://doi.org/10.1007/s13157-019-01234-y, 2020.
Gardner, R. C. and Davidson, N. C.: The ramsar convention, in: Wetlands,
Springer, https://doi.org/10.1007/978-94-007-0551-7_11, 2011.
Giri, C., Ochieng, E., Tieszen, L. L., Zhu, Z., Singh, A., Loveland, T.,
Masek, J., and Duke, N.: Status and distribution of mangrove forests of the
world using earth observation satellite data,
Global Ecol. Biogeogr., 20, 154–159, https://doi.org/10.1111/j.1466-8238.2010.00584.x,
2011.
Gislason, P. O., Benediktsson, J. A., and Sveinsson, J. R.: Random Forests
for land cover classification, Pattern Recogn. Lett., 27, 294–300,
https://doi.org/10.1016/j.patrec.2005.08.011, 2006.
Gong, P., Wang, J., Yu, L., Zhao, Y., Zhao, Y., Liang, L., Niu, Z., Huang,
X., Fu, H., Liu, S., Li, C., Li, X., Fu, W., Liu, C., Xu, Y., Wang, X.,
Cheng, Q., Hu, L., Yao, W., Zhang, H., Zhu, P., Zhao, Z., Zhang, H., Zheng,
Y., Ji, L., Zhang, Y., Chen, H., Yan, A., Guo, J., Yu, L., Wang, L., Liu,
X., Shi, T., Zhu, M., Chen, Y., Yang, G., Tang, P., Xu, B., Giri, C.,
Clinton, N., Zhu, Z., Chen, J., and Chen, J.: Finer resolution observation
and monitoring of global land cover: first mapping results with Landsat TM
and ETM+ data, Int. J. Remote Sens., 34, 2607–2654,
https://doi.org/10.1080/01431161.2012.748992, 2013.
Gong, P., Liu, H., Zhang, M., Li, C., Wang, J., Huang, H., Clinton, N., Ji,
L., Li, W., Bai, Y., Chen, B., Xu, B., Zhu, Z., Yuan, C., Ping Suen, H.,
Guo, J., Xu, N., Li, W., Zhao, Y., Yang, J., Yu, C., Wang, X., Fu, H., Yu,
L., Dronova, I., Hui, F., Cheng, X., Shi, X., Xiao, F., Liu, Q., and Song,
L.: Stable classification with limited sample: transferring a 30 m
resolution sample set collected in 2015 to mapping 10 m resolution global
land cover in 2017, Sci. Bull., 64, 370–373,
https://doi.org/10.1016/j.scib.2019.03.002, 2019.
Gumbricht, T.: Hybrid mapping of pantropical wetlands from optical satellite
images, hydrology, and geomorphology, Remote Sensing of Wetlands,
CRC Press, 435–454, https://doi.org/10.1201/b18210, 2015.
Gumbricht, T., Roman-Cuesta, R. M., Verchot, L.,
Herold, M., Wittmann, F., Householder, E., Herold, N., and Murdiyarso, D.:
An expert system model for mapping tropical wetlands and peatlands reveals
South America as the largest contributor, Glob. Change Biol., 23,
3581–3599, https://doi.org/10.1111/gcb.13689, 2017.
Guo, M., Li, J., Sheng, C., Xu, J., and Wu, L.: A Review of Wetland Remote Sensing, Sensors, 17, 777, https://doi.org/10.3390/s17040777, 2017.
Hamilton, S. E. and Casey, D.: Creation of a high spatio-temporal resolution
global database of continuous mangrove forest cover for the 21st century
(CGMFC-21), Global Ecol. Biogeogr., 25, 729–738,
https://doi.org/10.1111/geb.12449, 2016.
Hansen, M. C., Potapov, P. V., Moore, R., Hancher, M., Turubanova, S. A.,
Tyukavina, A., Thau, D., Stehman, S. V., Goetz, S. J., Loveland, T. R.,
Kommareddy, A., Egorov, A., Chini, L., Justice, C. O., and Townshend, J. R.:
High-resolution global maps of 21st-century forest cover change, Science,
342, 850–853, https://doi.org/10.1126/science.1244693, 2013.
Hansen, M. C., Egorov, A., Potapov, P. V., Stehman, S. V., Tyukavina, A.,
Turubanova, S. A., Roy, D. P., Goetz, S. J., Loveland, T. R., Ju, J.,
Kommareddy, A., Kovalskyy, V., Forsyth, C., and Bents, T.: Monitoring
conterminous United States (CONUS) land cover change with Web-Enabled
Landsat Data (WELD), Remote Sens. Environ., 140, 466–484,
https://doi.org/10.1016/j.rse.2013.08.014, 2014.
Homer, C., Dewitz, J., Jin, S., Xian, G., Costello, C., Danielson, P., Gass,
L., Funk, M., Wickham, J., Stehman, S., Auch, R., and Riitters, K.:
Conterminous United States land cover change patterns 2001–2016 from the
2016 National Land Cover Database, ISPRS J. Photogramm., 162, 184–199,
https://doi.org/10.1016/j.isprsjprs.2020.02.019, 2020.
Hu, S., Niu, Z., and Chen, Y.: Global Wetland Datasets: a Review, Wetlands,
37, 807–817, https://doi.org/10.1007/s13157-017-0927-z, 2017a.
Hu, S., Niu, Z., Chen, Y., Li, L., and Zhang, H.: Global wetlands: Potential
distribution, wetland loss, and status, Sci. Total Environ., 586, 319–327,
https://doi.org/10.1016/j.scitotenv.2017.02.001, 2017b.
Huang, X., Li, J., Yang, J., Zhang, Z., Li, D., and Liu, X.: 30 m global
impervious surface area dynamics and urban expansion pattern observed by
Landsat satellites: From 1972 to 2019, Science China Earth Sciences,
64, 1922–1933, https://doi.org/10.1007/s11430-020-9797-9, 2021.
Jia, M., Mao, D., Wang, Z., Ren, C., Zhu, Q., Li, X., and Zhang, Y.:
Tracking long-term floodplain wetland changes: A case study in the China
side of the Amur River Basin,
Int. J. Appl. Earth Observ., 92, 102185,
https://doi.org/10.1016/j.jag.2020.102185, 2020.
Jia, M., Wang, Z., Mao, D., Ren, C., Wang, C., and Wang, Y.: Rapid, robust,
and automated mapping of tidal flats in China using time series Sentinel-2
images and Google Earth Engine, Remote Sens. Environ., 255, 112285,
https://doi.org/10.1016/j.rse.2021.112285, 2021.
Jin, H., Stehman, S. V., and Mountrakis, G.: Assessing the impact of
training sample selection on accuracy of an urban classification: a case
study in Denver, Colorado, Int. J. Remote Sens., 35,
2067–2081, https://doi.org/10.1080/01431161.2014.885152, 2014.
Khandelwal, A., Karpatne, A., Ravirathinam, P., Ghosh, R., Wei, Z., Dugan,
H. A., Hanson, P. C., and Kumar, V.: ReaLSAT, a global dataset of reservoir
and lake surface area variations, Scientific Data, 9, 1–12,
https://doi.org/10.1038/s41597-022-01449-5, 2022.
LaRocque, A., Phiri, C., Leblon, B., Pirotti, F., Connor, K., and Hanson,
A.: Wetland Mapping with Landsat 8 OLI, Sentinel-1, ALOS-1 PALSAR, and LiDAR
Data in Southern New Brunswick, Canada, Remote Sens.-Basel, 12, 2095,
https://doi.org/10.3390/rs12132095, 2020.
Lehner, B. and Döll, P.: Development and validation of a global database
of lakes, reservoirs and wetlands, J. Hydrol., 296, 1–22,
https://doi.org/10.1016/j.jhydrol.2004.03.028, 2004.
Li, Z., Chen, H., White, J. C., Wulder, M. A., and Hermosilla, T.:
Discriminating treed and non-treed wetlands in boreal ecosystems using time
series Sentinel-1 data, Int. J. Appl. Earth Observ., 85, 102007, https://doi.org/10.1016/j.jag.2019.102007,
2020.
Liu, L., Zhang, X., Gao, Y., Chen, X., Shuai, X., and Mi, J.:
Finer-Resolution Mapping of Global Land Cover: Recent Developments,
Consistency Analysis, and Prospects, Journal of Remote Sensing, 2021, 1-38,
https://doi.org/10.34133/2021/5289697, 2021.
Liu, L., Zhang, X., and Zhao, T.: GWL_FCS30: global 30 m wetland map with fine classification system using multi-sourced and time-series remote sensing imagery in 2020, Zenodo [data set], https://doi.org/10.5281/zenodo.7340516, 2022.
Lu, Y. and Wang, L.: How to automate timely large-scale mangrove mapping
with remote sensing, Remote Sens. Environ., 264, 112584,
https://doi.org/10.1016/j.rse.2021.112584, 2021.
Ludwig, C., Walli, A., Schleicher, C., Weichselbaum, J., and Riffler, M.: A
highly automated algorithm for wetland detection using multi-temporal
optical satellite data, Remote Sens. Environ., 224, 333–351,
https://doi.org/10.1016/j.rse.2019.01.017, 2019.
Mahdianpari, M., Salehi, B., Mohammadimanesh, F., Homayouni, S., and Gill,
E.: The First Wetland Inventory Map of Newfoundland at a Spatial Resolution
of 10 m Using Sentinel-1 and Sentinel-2 Data on the Google Earth Engine
Cloud Computing Platform, Remote Sens.-Basel, 11, 43,
https://doi.org/10.3390/rs11010043, 2018.
Mahdianpari, M., Jafarzadeh, H., Granger, J. E., Mohammadimanesh, F.,
Brisco, B., Salehi, B., Homayouni, S., and Weng, Q.: A large-scale change
monitoring of wetlands using time series Landsat imagery on Google Earth
Engine: a case study in Newfoundland, GISci. Remote Sens., 57,
1102–1124, https://doi.org/10.1080/15481603.2020.1846948, 2020.
Mao, D., Wang, Z., Du, B., Li, L., Tian, Y., Jia, M., Zeng, Y., Song, K.,
Jiang, M., and Wang, Y.: National wetland mapping in China: A new product
resulting from object-based and hierarchical classification of Landsat 8 OLI
images, ISPRS J. Photogramm., 164, 11–25,
https://doi.org/10.1016/j.isprsjprs.2020.03.020, 2020.
Mao, D., Wang, Z., Wang, Y., Choi, C. Y., Jia, M., Jackson, M. V., and
Fuller, R. A.: Remote Observations in China's Ramsar Sites: Wetland
Dynamics, Anthropogenic Threats, and Implications for Sustainable
Development Goals, Journal of Remote Sensing, 2021, 1–13,
https://doi.org/10.34133/2021/9849343, 2021.
Matthews, E. and Fung, I.: Methane emission from natural wetlands: Global
distribution, area, and environmental characteristics of sources,
Global Biogeochem. Cy., 1, 61–86, https://doi.org/10.1029/GB001i001p00061,
1987.
McCarthy, M. J., Radabaugh, K. R., Moyer, R. P., and Muller-Karger, F. E.:
Enabling efficient, large-scale high-spatial resolution wetland mapping
using satellites, Remote Sens. Environ., 208, 189–201,
https://doi.org/10.1016/j.rse.2018.02.021, 2018.
McOwen, C. J., Weatherdon, L. V., Bochove, J. V., Sullivan, E., Blyth, S.,
Zockler, C., Stanwell-Smith, D., Kingston, N., Martin, C. S., Spalding, M.,
and Fletcher, S.: A global map of saltmarshes, Biodivers. Data J.,
5, e11764, https://doi.org/10.3897/BDJ.5.e11764, 2017.
Murray, N. J., Phinn, S. R., DeWitt, M., Ferrari, R., Johnston, R., Lyons,
M. B., Clinton, N., Thau, D., and Fuller, R. A.: The global distribution and
trajectory of tidal flats, Nature, 565, 222–225,
https://doi.org/10.1038/s41586-018-0805-8, 2019.
Murray, N. J., Worthington, T. A., Bunting, P., Duce, S., Hagger, V.,
Lovelock, C. E., Lucas, R., Saunders, M. I., Sheaves, M., and Spalding, M.:
High-resolution mapping of losses and gains of Earth's tidal wetlands,
Science, 376, 744–749, https://doi.org/10.1126/science.abm9583, 2022.
Olofsson, P., Foody, G. M., Herold, M., Stehman, S. V., Woodcock, C. E., and
Wulder, M. A.: Good practices for estimating area and assessing accuracy of
land change, Remote Sens. Environ., 148, 42–57,
https://doi.org/10.1016/j.rse.2014.02.015, 2014.
Pekel, J. F., Cottam, A., Gorelick, N., and Belward, A. S.: High-resolution
mapping of global surface water and its long-term changes, Nature, 540,
418–422, https://doi.org/10.1038/nature20584, 2016.
Radoux, J., Lamarche, C., Van Bogaert, E., Bontemps, S., Brockmann, C., and
Defourny, P.: Automated Training Sample Extraction for Global Land Cover
Mapping, Remote Sens.-Basel, 6, 3965–3987, https://doi.org/10.3390/rs6053965,
2014.
Richardson, D. C., Holgerson, M. A., Farragher, M. J., Hoffman, K. K., King,
K. B. S., Alfonso, M. B., Andersen, M. R., Cheruveil, K. S., Coleman, K. A.,
Farruggia, M. J., Fernandez, R. L., Hondula, K. L., Lopez Moreira Mazacotte,
G. A., Paul, K., Peierls, B. L., Rabaey, J. S., Sadro, S., Sanchez, M. L.,
Smyth, R. L., and Sweetman, J. N.: A functional definition to distinguish
ponds from lakes and wetlands, Sci. Rep.-UK, 12, 10472,
https://doi.org/10.1038/s41598-022-14569-0, 2022.
Sexton, J., Feng, M., Channan, S., Song, X., Kim, D., Noojipady, P., Song,
D., Huanga, C., Annand, A., and Collins, K.: Earth Science Data Records of
Global Forest Cover and Change, User guide, 38,
https://lpdaac.usgs.gov/documents/1370/GFCC_ATBD.pdf (last
access: 22 November 2022), 2016.
Sexton, J. O., Song, X.-P., Feng, M., Noojipady, P., Anand, A., Huang, C.,
Kim, D.-H., Collins, K. M., Channan, S., DiMiceli, C., and Townshend, J. R.:
Global, 30 m resolution continuous fields of tree cover: Landsat-based
rescaling of MODIS vegetation continuous fields with lidar-based estimates
of error, Int. J. Digit. Earth, 6, 427–448,
https://doi.org/10.1080/17538947.2013.786146, 2013.
Slagter, B., Tsendbazar, N.-E., Vollrath, A., and Reiche, J.: Mapping
wetland characteristics using temporally dense Sentinel-1 and Sentinel-2
data: A case study in the St. Lucia wetlands, South Africa, Int. J. Appl. Earth Observ., 86, 102009,
https://doi.org/10.1016/j.jag.2019.102009, 2020.
Spalding, M.: World atlas of mangroves, Routledge, A collaborative project
of ITTO, ISME, FAO, UNEP-WCMC, UNESCO-MAB, UNU-INWEH and TNC, London (UK),
Earthscan, London, https://doi.org/10.4324/9781849776608, 2010.
Tachikawa, T., Hato, M., Kaku, M., and Iwasaki, A.: Characteristics of ASTER
GDEM Version 2, Geoscience and Remote Sensing Symposium (IGARSS), 24–29 July 2011, Vancouver, 12477285, 3657–3660,
https://doi.org/10.1109/IGARSS.2011.6050017, 2011a.
Tachikawa, T., Kaku, M., Iwasaki, A., Gesch, D. B., Oimoen, M. J., Zhang,
Z., Danielson, J., Krieger, T., Curtis, B., and Haase, J.: ASTER Global
Digital Elevation Model Version 2 – Summary of validation results, Kim
Fakultas Sastra Dan Budaya,
https://doi.org/10.1093/oxfordjournals.pubmed.a024792, 2011b.
Thomas, N., Lucas, R., Bunting, P., Hardy, A., Rosenqvist, A., and Simard,
M.: Distribution and drivers of global mangrove forest change, 1996–2010,
PloS One, 12, e0179302, https://doi.org/10.1371/journal.pone.0179302, 2017.
Tootchi, A., Jost, A., and Ducharne, A.: Multi-source global wetland maps combining surface water imagery and groundwater constraints, Earth Syst. Sci. Data, 11, 189–220, https://doi.org/10.5194/essd-11-189-2019, 2019.
Torres, R., Snoeij, P., Geudtner, D., Bibby, D., Davidson, M., Attema, E.,
Potin, P., Rommen, B., Floury, N., Brown, M., Traver, I. N., Deghaye, P.,
Duesmann, B., Rosich, B., Miranda, N., Bruno, C., L'Abbate, M., Croci, R.,
Pietropaolo, A., Huchler, M., and Rostan, F.: GMES Sentinel-1 mission,
Remote Sens. Environ., 120, 9–24,
https://doi.org/10.1016/j.rse.2011.05.028, 2012.
Townshend, J. R., Masek, J. G., Huang, C., Vermote, E. F., Gao, F., Channan,
S., Sexton, J. O., Feng, M., Narasimhan, R., Kim, D., Song, K., Song, D.,
Song, X.-P., Noojipady, P., Tan, B., Hansen, M. C., Li, M., and Wolfe, R.
E.: Global characterization and monitoring of forest cover using Landsat
data: opportunities and challenges, Int. J. Digit. Earth,
5, 373–397, https://doi.org/10.1080/17538947.2012.713190, 2012.
Veci, L., Prats-Iraola, P., Scheiber, R., Collard, F., Fomferra, N., and
Engdahl, M.: The sentinel-1 toolbox,
https://sentinels.copernicus.eu/web/sentinel/toolboxes/sentinel-1 (last
access: 25 May 2022), 2014.
Vermote, E., Justice, C., Claverie, M., and Franch, B.: Preliminary analysis
of the performance of the Landsat 8/OLI land surface reflectance product,
Remote Sens. Environ., 185, 46–56,
https://doi.org/10.1016/j.rse.2016.04.008, 2016.
Wang, X., Xiao, X., Zou, Z., Hou, L., Qin, Y., Dong, J., Doughty, R. B.,
Chen, B., Zhang, X., Chen, Y., Ma, J., Zhao, B., and Li, B.: Mapping coastal
wetlands of China using time series Landsat images in 2018 and Google Earth
Engine, ISPRS J. Photogramm., 163, 312–326,
https://doi.org/10.1016/j.isprsjprs.2020.03.014, 2020.
Wang, X., Xiao, X., Xu, X., Zou, Z., Chen, B., Qin, Y., Zhang, X., Dong, J.,
Liu, D., Pan, L., and Li, B.: Rebound in China's coastal wetlands following
conservation and restoration, Nature Sustainability, 4, 1076–1083,
https://doi.org/10.1038/s41893-021-00793-5, 2021.
Wilen, B. O. and Bates, M.: The US fish and wildlife service's national
wetlands inventory project, in: Classification and inventory of the world's
wetlands, Springer, https://doi.org/10.1007/BF00045197, 1995.
Worthington, T. A., Zu Ermgassen, P. S., Friess, D. A., Krauss, K. W.,
Lovelock, C. E., Thorley, J., Tingey, R., Woodroffe, C. D., Bunting, P., and
Cormier, N.: A global biophysical typology of mangroves and its relevance
for ecosystem structure and deforestation, Sci. Rep.-UK, 10, 1–11,
https://doi.org/10.1038/s41598-020-71194-5, 2020.
Xi, Y., Peng, S., Ciais, P., and Chen, Y.: Future impacts of climate change
on inland Ramsar wetlands, Nat. Clim. Change, 11, 45–51,
https://doi.org/10.1038/s41558-020-00942-2, 2020.
Zanaga, D., Van De Kerchove, R., De Keersmaecker, W., Souverijns, N.,
Brockmann, C., Quast, R., Wevers, J., Grosu, A., Paccini, A., and Vergnaud,
S.: ESA WorldCover 10 m 2020 v100, Zenodo,
https://doi.org/10.5281/zenodo.5571936, 2021.
Zhang, H., Wang, T., Liu, M., Jia, M., Lin, H., Chu, L. M., and Devlin, A.:
Potential of Combining Optical and Dual Polarimetric SAR Data for Improving
Mangrove Species Discrimination Using Rotation Forest, Remote Sens.-Basel, 10,
467, https://doi.org/10.3390/rs10030467, 2018.
Zhang, H. K. and Roy, D. P.: Using the 500 m MODIS land cover product to
derive a consistent continental scale 30 m Landsat land cover
classification, Remote Sens. Environ., 197, 15–34,
https://doi.org/10.1016/j.rse.2017.05.024, 2017.
Zhang, X., Liu, L., Wu, C., Chen, X., Gao, Y., Xie, S., and Zhang, B.: Development of a global 30 m impervious surface map using multisource and multitemporal remote sensing datasets with the Google Earth Engine platform, Earth Syst. Sci. Data, 12, 1625–1648, https://doi.org/10.5194/essd-12-1625-2020, 2020.
Zhang, X., Liu, L., Chen, X., Gao, Y., and Jiang, M.: Automatically
Monitoring Impervious Surfaces Using Spectral Generalization and Time Series
Landsat Imagery from 1985 to 2020 in the Yangtze River Delta,
Journal of Remote Sensing, 2021, 1–16, https://doi.org/10.34133/2021/9873816, 2021a.
Zhang, X., Liu, L., Chen, X., Gao, Y., Xie, S., and Mi, J.: GLC_FCS30: global land-cover product with fine classification system at 30 m using time-series Landsat imagery, Earth Syst. Sci. Data, 13, 2753–2776, https://doi.org/10.5194/essd-13-2753-2021, 2021b.
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, Z., Xu, N., Li, Y., and Li, Y.: Sub-continental-scale mapping of
tidal wetland composition for East Asia: A novel algorithm integrating
satellite tide-level and phenological features, Remote Sens. Environ., 269, 112799, https://doi.org/10.1016/j.rse.2021.112799, 2022.
Zhu, P. and Gong, P.: Suitability mapping of global wetland areas and
validation with remotely sensed data, Science China Earth Sciences, 57,
2283–2292, https://doi.org/10.1007/s11430-014-4925-1, 2014.
Zhu, Z. and Woodcock, C. E.: Object-based cloud and cloud shadow detection
in Landsat imagery, Remote Sens. Environ., 118, 83–94,
https://doi.org/10.1016/j.rse.2011.10.028, 2012.
Zhu, Z., Wang, S. X., and Woodcock, C. E.: Improvement and expansion of the
Fmask algorithm: cloud, cloud shadow, and snow detection for Landsats 4-7,
8, and Sentinel 2 images, Remote Sens. Environ., 159, 269–277,
https://doi.org/10.1016/j.rse.2014.12.014, 2015.
Zhu, Z., Gallant, A. L., Woodcock, C. E., Pengra, B., Olofsson, P.,
Loveland, T. R., Jin, S., Dahal, D., Yang, L., and Auch, R. F.: Optimizing
selection of training and auxiliary data for operational land cover
classification for the LCMAP initiative, ISPRS J. Photogramm., 122, 206–221,
https://doi.org/10.1016/j.isprsjprs.2016.11.004, 2016.
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).
An accurate global 30 m wetland dataset that can simultaneously cover inland and coastal zones...
Altmetrics
Final-revised paper
Preprint