Articles | Volume 15, issue 7
https://doi.org/10.5194/essd-15-2927-2023
© 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-15-2927-2023
© 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
| 
11 Jul 2023
Data description paper |
| 11 Jul 2023
| 11 Jul 2023
Mapping global non-floodplain wetlands
U.S. Environmental Protection Agency, Office of Research and
Development, Center for Environmental Measurement and Modeling, Athens,
Georgia, USA
Ellen D'Amico
CORRESPONDING AUTHOR
Pegasus Technical Service, Inc. c/o U.S. Environmental Protection
Agency, Office of Research and Development, Cincinnati, Ohio, USA
Jay R. Christensen
U.S. Environmental Protection Agency, Office of Research and
Development, Center for Environmental Measurement and Modeling, Cincinnati,
Ohio, USA
Heather E. Golden
U.S. Environmental Protection Agency, Office of Research and
Development, Center for Environmental Measurement and Modeling, Cincinnati,
Ohio, USA
Qiusheng Wu
Department of Geography & Sustainability, University of Tennessee,
Knoxville, Tennessee, USA
Adnan Rajib
Hydrology and Hydroinformatics Innovation Lab, Department of Civil
Engineering, University of Texas at Arlington, Arlington, Texas, USA
Related authors
Melanie K. Vanderhoof, Peter Nieuwlandt, Heather E. Golden, Charles R. Lane, Jay R. Christensen, William Keenan, and Wayana Dolan
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-298, https://doi.org/10.5194/hess-2024-298, 2024
Preprint under review for HESS
Short summary
Short summary
Streamflow signatures can help characterize a watershed’s response to meteorological conditions. We explored if surface water storage-related variables, which are typically excluded from streamflow signature analyses, may help explain spatial variability in streamflow signatures. We found that remotely sensed surface water storage extent and duration were correlated with and explained a portion of the variability in many of the hydrologic signatures across the 72 streamflow gages.
Melanie K. Vanderhoof, Peter Nieuwlandt, Heather E. Golden, Charles R. Lane, Jay R. Christensen, Will Keenan, and Wayana Dolan
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-119, https://doi.org/10.5194/hess-2024-119, 2024
Manuscript not accepted for further review
Short summary
Short summary
Streamflow signatures can help characterize a watershed’s response to rainfall and snowmelt events. We explored if surface water storage-related variables, which are typically excluded from streamflow signature analyses, may help explain the variability in streamflow signatures. We found that remotely sensed surface water storage watershed location and hydroperiod were correlated with or explained a portion of the variability in hydrologic signatures across 72 streamflow gages.
Michael L. Wine, Heather E. Golden, Jay R. Christensen, Charles R. Lane, and Oleg Makhnin
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2020-423, https://doi.org/10.5194/hess-2020-423, 2020
Preprint withdrawn
Short summary
Short summary
Mirroring a global water quality crisis, nitrogen concentrations in the Upper Mississippi River basin, USA regularly exceed levels considered safe for human consumption and aquatic exposure, particularly following fertilization in the spring. Counterfactual simulations suggest that restoring the historic distribution of wetlands would substantially reduce nitrogen concentrations, particularly when they are most elevated, in the spring. Results point to a tradeoff between food and water security.
Melanie K. Vanderhoof, Charles R. Lane, Michael G. McManus, Laurie C. Alexander, and Jay R. Christensen
Hydrol. Earth Syst. Sci., 22, 1851–1873, https://doi.org/10.5194/hess-22-1851-2018, https://doi.org/10.5194/hess-22-1851-2018, 2018
Short summary
Short summary
Effective monitoring and prediction of flood and drought events requires an improved understanding of surface water dynamics. We examined how the relationship between surface water extent, as mapped using Landsat imagery, and climate, is a function of landscape characteristics, using the Prairie Pothole Region and adjacent Northern Prairie in the United States as our study area. We found that at a landscape scale wetlands play a key role in informing how climate extremes influence surface water.
Qiusheng Wu and Charles R. Lane
Hydrol. Earth Syst. Sci., 21, 3579–3595, https://doi.org/10.5194/hess-21-3579-2017, https://doi.org/10.5194/hess-21-3579-2017, 2017
Short summary
Short summary
In this study, we developed new tools for identifying potential hydrologic connectivity between wetlands and stream networks, which can better inform regulatory decisions and enhance the ability to better manage wetlands under various planning scenarios. The resulting flow network delineated potential flow paths connecting wetland depressions to each other or to the river network on scales finer than those available through the National Hydrography Dataset.
Melanie K. Vanderhoof, Peter Nieuwlandt, Heather E. Golden, Charles R. Lane, Jay R. Christensen, William Keenan, and Wayana Dolan
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-298, https://doi.org/10.5194/hess-2024-298, 2024
Preprint under review for HESS
Short summary
Short summary
Streamflow signatures can help characterize a watershed’s response to meteorological conditions. We explored if surface water storage-related variables, which are typically excluded from streamflow signature analyses, may help explain spatial variability in streamflow signatures. We found that remotely sensed surface water storage extent and duration were correlated with and explained a portion of the variability in many of the hydrologic signatures across the 72 streamflow gages.
Melanie K. Vanderhoof, Peter Nieuwlandt, Heather E. Golden, Charles R. Lane, Jay R. Christensen, Will Keenan, and Wayana Dolan
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-119, https://doi.org/10.5194/hess-2024-119, 2024
Manuscript not accepted for further review
Short summary
Short summary
Streamflow signatures can help characterize a watershed’s response to rainfall and snowmelt events. We explored if surface water storage-related variables, which are typically excluded from streamflow signature analyses, may help explain the variability in streamflow signatures. We found that remotely sensed surface water storage watershed location and hydroperiod were correlated with or explained a portion of the variability in hydrologic signatures across 72 streamflow gages.
Michael L. Wine, Heather E. Golden, Jay R. Christensen, Charles R. Lane, and Oleg Makhnin
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2020-423, https://doi.org/10.5194/hess-2020-423, 2020
Preprint withdrawn
Short summary
Short summary
Mirroring a global water quality crisis, nitrogen concentrations in the Upper Mississippi River basin, USA regularly exceed levels considered safe for human consumption and aquatic exposure, particularly following fertilization in the spring. Counterfactual simulations suggest that restoring the historic distribution of wetlands would substantially reduce nitrogen concentrations, particularly when they are most elevated, in the spring. Results point to a tradeoff between food and water security.
Melanie K. Vanderhoof, Jay R. Christensen, and Laurie C. Alexander
Hydrol. Earth Syst. Sci., 23, 4269–4292, https://doi.org/10.5194/hess-23-4269-2019, https://doi.org/10.5194/hess-23-4269-2019, 2019
Short summary
Short summary
We evaluated trends (1984–2016) in riparian wetness across the Upper Missouri River headwaters basin during peak irrigation months (June, July and August). We found that 8 of the 19 riparian reaches across the basin showed a significant drying trend from 1984 to 2016. The temporal drying trends persisted after removing variability attributable to climate. Instead, the drying trends co-occurred with a shift towards center-pivot irrigation across the basin.
Xuecao Li, Yuyu Zhou, Lin Meng, Ghassem R. Asrar, Chaoqun Lu, and Qiusheng Wu
Earth Syst. Sci. Data, 11, 881–894, https://doi.org/10.5194/essd-11-881-2019, https://doi.org/10.5194/essd-11-881-2019, 2019
Short summary
Short summary
We generated a long-term (1985–2015) and medium-resolution (30 m) product of phenology indicators in urban domains in the conterminous US using Landsat satellite observations. The derived phenology indicators agree well with in situ observations and provide more spatial details in complex urban areas compared to the existing coarse resolution phenology products (e.g., MODIS). The published data are of great use for urban phenology studies (e.g., pollen-induced respiratory allergies).
Melanie K. Vanderhoof, Charles R. Lane, Michael G. McManus, Laurie C. Alexander, and Jay R. Christensen
Hydrol. Earth Syst. Sci., 22, 1851–1873, https://doi.org/10.5194/hess-22-1851-2018, https://doi.org/10.5194/hess-22-1851-2018, 2018
Short summary
Short summary
Effective monitoring and prediction of flood and drought events requires an improved understanding of surface water dynamics. We examined how the relationship between surface water extent, as mapped using Landsat imagery, and climate, is a function of landscape characteristics, using the Prairie Pothole Region and adjacent Northern Prairie in the United States as our study area. We found that at a landscape scale wetlands play a key role in informing how climate extremes influence surface water.
Qiusheng Wu and Charles R. Lane
Hydrol. Earth Syst. Sci., 21, 3579–3595, https://doi.org/10.5194/hess-21-3579-2017, https://doi.org/10.5194/hess-21-3579-2017, 2017
Short summary
Short summary
In this study, we developed new tools for identifying potential hydrologic connectivity between wetlands and stream networks, which can better inform regulatory decisions and enhance the ability to better manage wetlands under various planning scenarios. The resulting flow network delineated potential flow paths connecting wetland depressions to each other or to the river network on scales finer than those available through the National Hydrography Dataset.
Related subject area
Domain: ESSD – Land | Subject: Land Cover and Land Use
Global 30 m seamless data cube (2000–2022) of land surface reflectance generated from Landsat 5, 7, 8, and 9 and MODIS Terra constellations
Mapping rangeland health indicators in eastern Africa from 2000 to 2022
3D-GloBFP: the first global three-dimensional building footprint dataset
Enhancing high-resolution forest stand mean height mapping in China through an individual tree-based approach with close-range lidar data
Annual high-resolution grazing-intensity maps on the Qinghai–Tibet Plateau from 1990 to 2020
Global mapping of oil palm planting year from 1990 to 2021
A 28-time-point cropland area change dataset in Northeast China from 1000 to 2020
Mapping sugarcane globally at 10 m resolution using Global Ecosystem Dynamics Investigation (GEDI) and Sentinel-2
Annual maps of forest and evergreen forest in the contiguous United States during 2015–2017 from analyses of PALSAR-2 and Landsat images
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
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
A Sentinel-2 Machine Learning Dataset for Tree Species Classification in Germany
Physical, social, and biological attributes for improved understanding and prediction of wildfires: FPA FOD-Attributes dataset
Map of forest tree species for Poland based on Sentinel-2 data
The ABoVE L-band and P-band airborne synthetic aperture radar surveys
A 30 m annual cropland dataset of China from 1986 to 2021
Global 1 km land surface parameters for kilometer-scale Earth system modeling
A flux tower site attribute dataset intended for land surface modeling
ChinaRiceCalendar – seasonal crop calendars for early-, middle-, and late-season rice in China
Harmonized European Union subnational crop statistics can reveal climate impacts and crop cultivation shifts
GLC_FCS30D: the first global 30 m land-cover dynamics monitoring product with a fine classification system for the period from 1985 to 2022 generated using dense-time-series Landsat imagery and the continuous change-detection method
A global estimate of monthly vegetation and soil fractions from spatiotemporally adaptive spectral mixture analysis during 2001–2022
A 2020 forest age map for China with 30 m resolution
GMIE-100: a global maximum irrigation extent and irrigation type dataset derived through irrigation performance during drought stress and machine learning method
Country-level estimates of gross and net carbon fluxes from land use, land-use change and forestry
A global FAOSTAT reference database of cropland nutrient budgets and nutrient use efficiency (1961–2020): nitrogen, phosphorus and potassium
Advancements in LUCAS Copernicus 2022: Enhancing Earth Observation with Comprehensive In-Situ Data on EU Land Cover and Use
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
High-resolution mapping of global winter-triticeae crops using a sample-free identification method
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
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
Shuang Chen, Jie Wang, Qiang Liu, Xiangan Liang, Rui Liu, Peng Qin, Jincheng Yuan, Junbo Wei, Shuai Yuan, Huabing Huang, and Peng Gong
Earth Syst. Sci. Data, 16, 5449–5475, https://doi.org/10.5194/essd-16-5449-2024, https://doi.org/10.5194/essd-16-5449-2024, 2024
Short summary
Short summary
The inconsistent coverage of Landsat data due to its long revisit intervals and frequent cloud cover poses challenges to large-scale land monitoring. We developed a global 30 m 23-year (2000–2022) daily seamless data cube (SDC) of surface reflectance based on Landsat 5, 7, 8, and 9 and MODIS products. The SDC exhibits enhanced capabilities for monitoring land cover changes and robust consistency in both spatial and temporal dimensions, which are important for global environmental monitoring.
Gerardo E. Soto, Steven W. Wilcox, Patrick E. Clark, Francesco P. Fava, Nathaniel D. Jensen, Njoki Kahiu, Chuan Liao, Benjamin Porter, Ying Sun, and Christopher B. Barrett
Earth Syst. Sci. Data, 16, 5375–5404, https://doi.org/10.5194/essd-16-5375-2024, https://doi.org/10.5194/essd-16-5375-2024, 2024
Short summary
Short summary
This paper uses machine learning and linear unmixing to produce rangeland health indicators: Landsat time series of land cover classes and vegetation fractional cover of photosynthetic vegetation, non-photosynthetic vegetation, and bare ground in arid and semi-arid Kenya, Ethiopia, and Somalia. This represents the first multi-decadal Landsat-resolution dataset specifically designed for mapping and monitoring rangeland health in the arid and semi-arid rangelands of this portion of eastern Africa.
Yangzi Che, Xuecao Li, Xiaoping Liu, Yuhao Wang, Weilin Liao, Xianwei Zheng, Xucai Zhang, Xiaocong Xu, Qian Shi, Jiajun Zhu, Honghui Zhang, Hua Yuan, and Yongjiu Dai
Earth Syst. Sci. Data, 16, 5357–5374, https://doi.org/10.5194/essd-16-5357-2024, https://doi.org/10.5194/essd-16-5357-2024, 2024
Short summary
Short summary
Most existing building height products are limited with respect to either spatial resolution or coverage, not to mention the spatial heterogeneity introduced by global building forms. Using Earth Observation (EO) datasets for 2020, we developed a global height dataset at the individual building scale. The dataset provides spatially explicit information on 3D building morphology, supporting both macro- and microanalysis of urban areas.
Yuling Chen, Haitao Yang, Zekun Yang, Qiuli Yang, Weiyan Liu, Guoran Huang, Yu Ren, Kai Cheng, Tianyu Xiang, Mengxi Chen, Danyang Lin, Zhiyong Qi, Jiachen Xu, Yixuan Zhang, Guangcai Xu, and Qinghua Guo
Earth Syst. Sci. Data, 16, 5267–5285, https://doi.org/10.5194/essd-16-5267-2024, https://doi.org/10.5194/essd-16-5267-2024, 2024
Short summary
Short summary
The national-scale continuous maps of arithmetic mean height and weighted mean height across China address the challenges of accurately estimating forest stand mean height using a tree-based approach. These maps produced in this study provide critical datasets for forest sustainable management in China, including climate change mitigation (e.g., terrestrial carbon estimation), forest ecosystem assessment, and forest inventory practices.
Jia Zhou, Jin Niu, Ning Wu, and Tao Lu
Earth Syst. Sci. Data, 16, 5171–5189, https://doi.org/10.5194/essd-16-5171-2024, https://doi.org/10.5194/essd-16-5171-2024, 2024
Short summary
Short summary
The study provided an annual 100 m resolution glimpse into the grazing activities across the Qinghai–Tibet Plateau. The newly minted Gridded Dataset of Grazing Intensity (GDGI) not only boasts exceptional accuracy but also acts as a pivotal resource for further research and strategic planning, with the potential to shape sustainable grazing practices, guide informed environmental stewardship, and ensure the longevity of the region’s precious ecosystems.
Adrià Descals, David L. A. Gaveau, Serge Wich, Zoltan Szantoi, and Erik Meijaard
Earth Syst. Sci. Data, 16, 5111–5129, https://doi.org/10.5194/essd-16-5111-2024, https://doi.org/10.5194/essd-16-5111-2024, 2024
Short summary
Short summary
This study provides a 10 m global oil palm extent layer for 2021 and a 30 m oil palm planting-year layer from 1990 to 2021. The oil palm extent layer was produced using a convolutional neural network that identified industrial and smallholder plantations using Sentinel-1 data. The oil palm planting year was developed using a methodology specifically designed to detect the early stages of oil palm development in the Landsat time series.
Ran Jia, Xiuqi Fang, Yundi Yang, Masayuki Yokozawa, and Yu Ye
Earth Syst. Sci. Data, 16, 4971–4994, https://doi.org/10.5194/essd-16-4971-2024, https://doi.org/10.5194/essd-16-4971-2024, 2024
Short summary
Short summary
We reconstructed a cropland area change dataset in Northeast China over the past millennium by integrating multisource data with a unified standard using the historical and archaeological record, statistical yearbook, and national land survey. Cropland in Northeast China exhibited phases of expansion–reduction–expansion over the past millennium. This dataset can be used for improving the land use and land cover change (LUCC) dataset and assessing LUCC-induced carbon emission and climate change.
Stefania Di Tommaso, Sherrie Wang, Rob Strey, and David B. Lobell
Earth Syst. Sci. Data, 16, 4931–4947, https://doi.org/10.5194/essd-16-4931-2024, https://doi.org/10.5194/essd-16-4931-2024, 2024
Short summary
Short summary
Sugarcane plays a vital role in food, biofuel, and farmer income globally, yet its cultivation faces numerous social and environmental challenges. Despite its significance, accurate mapping remains limited. Our study addresses this gap by introducing a novel 10 m global dataset of sugarcane maps spanning 2019–2022. Comparisons with field data, pre-existing maps, and official government statistics all indicate the high precision and high recall of our maps.
Jie Wang, Xiangming Xiao, Yuanwei Qin, Jinwei Dong, Geli Zhang, Xuebin Yang, Xiaocui Wu, Chandrashekhar Biradar, and Yang Hu
Earth Syst. Sci. Data, 16, 4619–4639, https://doi.org/10.5194/essd-16-4619-2024, https://doi.org/10.5194/essd-16-4619-2024, 2024
Short summary
Short summary
Existing satellite-based forest maps have large uncertainties due to different forest definitions and mapping algorithms. To effectively manage forest resources, timely and accurate annual forest maps at a high spatial resolution are needed. This study improved forest maps by integrating PALSAR-2 and Landsat images. Annual evergreen and non-evergreen forest-type maps were also generated. This critical information supports the Global Forest Resources Assessment.
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.
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.
Maximilian Freudenberg, Sebastian Schnell, and Paul Magdon
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-206, https://doi.org/10.5194/essd-2024-206, 2024
Revised manuscript accepted for ESSD
Short summary
Short summary
Classifying tree species in satellite images is an important task for environmental monitoring and forest management. Here we present a dataset containing Sentinel-2 satellite pixel time series of individual trees, intended for training machine learning models. The dataset was created by merging information from the German national forest inventory in 2012 with satellite data. It sparsely covers entire Germany for the years 2015 to 2022 and comprises 51 species and species groups.
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.
Ewa Grabska-Szwagrzyk, Dirk Tiede, Martin Sudmanns, and Jacek Kozak
Earth Syst. Sci. Data, 16, 2877–2891, https://doi.org/10.5194/essd-16-2877-2024, https://doi.org/10.5194/essd-16-2877-2024, 2024
Short summary
Short summary
We accurately mapped 16 dominant tree species and genera in Poland using Sentinel-2 observations from short periods in spring, summer, and autumn (2018–2021). The classification achieved more than 80% accuracy in country-wide forest species mapping, with variation based on species, region, and observation frequency. Freely accessible resources, including the forest tree species map and training and test data, can be found at https://doi.org/10.5281/zenodo.10180469.
Charles E. Miller, Peter C. Griffith, Elizabeth Hoy, Naiara S. Pinto, Yunling Lou, Scott Hensley, Bruce D. Chapman, Jennifer Baltzer, Kazem Bakian-Dogaheh, W. Robert Bolton, Laura Bourgeau-Chavez, Richard H. Chen, Byung-Hun Choe, Leah K. Clayton, Thomas A. Douglas, Nancy French, Jean E. Holloway, Gang Hong, Lingcao Huang, Go Iwahana, Liza Jenkins, John S. Kimball, Tatiana Loboda, Michelle Mack, Philip Marsh, Roger J. Michaelides, Mahta Moghaddam, Andrew Parsekian, Kevin Schaefer, Paul R. Siqueira, Debjani Singh, Alireza Tabatabaeenejad, Merritt Turetsky, Ridha Touzi, Elizabeth Wig, Cathy J. Wilson, Paul Wilson, Stan D. Wullschleger, Yonghong Yi, Howard A. Zebker, Yu Zhang, Yuhuan Zhao, and Scott J. Goetz
Earth Syst. Sci. Data, 16, 2605–2624, https://doi.org/10.5194/essd-16-2605-2024, https://doi.org/10.5194/essd-16-2605-2024, 2024
Short summary
Short summary
NASA’s Arctic Boreal Vulnerability Experiment (ABoVE) conducted airborne synthetic aperture radar (SAR) surveys of over 120 000 km2 in Alaska and northwestern Canada during 2017, 2018, 2019, and 2022. This paper summarizes those results and provides links to details on ~ 80 individual flight lines. This paper is presented as a guide to enable interested readers to fully explore the ABoVE L- and P-band SAR data.
Ying Tu, Shengbiao Wu, Bin Chen, Qihao Weng, Yuqi Bai, Jun Yang, Le Yu, and Bing Xu
Earth Syst. Sci. Data, 16, 2297–2316, https://doi.org/10.5194/essd-16-2297-2024, https://doi.org/10.5194/essd-16-2297-2024, 2024
Short summary
Short summary
We developed the first 30 m annual cropland dataset of China (CACD) for 1986–2021. The overall accuracy of CACD reached up to 0.93±0.01 and was superior to other products. Our fine-resolution cropland maps offer valuable information for diverse applications and decision-making processes in the future.
Lingcheng Li, Gautam Bisht, Dalei Hao, and L. Ruby Leung
Earth Syst. Sci. Data, 16, 2007–2032, https://doi.org/10.5194/essd-16-2007-2024, https://doi.org/10.5194/essd-16-2007-2024, 2024
Short summary
Short summary
This study fills a gap to meet the emerging needs of kilometer-scale Earth system modeling by developing global 1 km land surface parameters for land use, vegetation, soil, and topography. Our demonstration simulations highlight the substantial impacts of these parameters on spatial variability and information loss in water and energy simulations. Using advanced explainable machine learning methods, we identified influential factors driving spatial variability and information loss.
Jiahao Shi, Hua Yuan, Wanyi Lin, Wenzong Dong, Hongbin Liang, Zhuo Liu, Jianxin Zeng, Haolin Zhang, Nan Wei, Zhongwang Wei, Shupeng Zhang, Shaofeng Liu, Xingjie Lu, and Yongjiu Dai
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-77, https://doi.org/10.5194/essd-2024-77, 2024
Revised manuscript accepted for ESSD
Short summary
Short summary
Flux tower data are widely recognized as benchmarking data for land surface models, but insufficient emphasis on and deficiency in site attribute data limits their true value. We collect site-observed vegetation, soil, and topography data from various sources. The final dataset encompasses 90 sites globally with relatively complete site attribute data and high-quality flux validation data. This work has provided more reliable site attribute data, benefiting land surface model development.
Hui Li, Xiaobo Wang, Shaoqiang Wang, Jinyuan Liu, Yuanyuan Liu, Zhenhai Liu, Shiliang Chen, Qinyi Wang, Tongtong Zhu, Lunche Wang, and Lizhe Wang
Earth Syst. Sci. Data, 16, 1689–1701, https://doi.org/10.5194/essd-16-1689-2024, https://doi.org/10.5194/essd-16-1689-2024, 2024
Short summary
Short summary
Utilizing satellite remote sensing data, we established a multi-season rice calendar dataset named ChinaRiceCalendar. It exhibits strong alignment with field observations collected by agricultural meteorological stations across China. ChinaRiceCalendar stands as a reliable dataset for investigating and optimizing the spatiotemporal dynamics of rice phenology in China, particularly in the context of climate and land use changes.
Giulia Ronchetti, Luigi Nisini Scacchiafichi, Lorenzo Seguini, Iacopo Cerrani, and Marijn van der Velde
Earth Syst. Sci. Data, 16, 1623–1649, https://doi.org/10.5194/essd-16-1623-2024, https://doi.org/10.5194/essd-16-1623-2024, 2024
Short summary
Short summary
We present a dataset of EU-wide harmonized subnational crop area, production, and yield statistics with information on data sources, processing steps, missing and derived data, and quality checks. Statistical records (344 282) collected from 1975 to 2020 for soft and durum wheat, winter and spring barley, grain maize, sunflower, and sugar beet were aligned with the EUROSTAT crop legend and the 2016 territorial classification for 961 regions. Time series have a median length of 21 years.
Xiao Zhang, Tingting Zhao, Hong Xu, Wendi Liu, Jinqing Wang, Xidong Chen, and Liangyun Liu
Earth Syst. Sci. Data, 16, 1353–1381, https://doi.org/10.5194/essd-16-1353-2024, https://doi.org/10.5194/essd-16-1353-2024, 2024
Short summary
Short summary
This work describes GLC_FCS30D, the first global 30 m land-cover dynamics monitoring dataset, which contains 35 land-cover subcategories and covers the period of 1985–2022 in 26 time steps (its maps are updated every 5 years before 2000 and annually after 2000).
Qiangqiang Sun, Ping Zhang, Xin Jiao, Xin Lin, Wenkai Duan, Su Ma, Qidi Pan, Lu Chen, Yongxiang Zhang, Shucheng You, Shunxi Liu, Jinmin Hao, Hong Li, and Danfeng Sun
Earth Syst. Sci. Data, 16, 1333–1351, https://doi.org/10.5194/essd-16-1333-2024, https://doi.org/10.5194/essd-16-1333-2024, 2024
Short summary
Short summary
To provide multifaceted changes under climate change and anthropogenic impacts, we estimated monthly vegetation and soil fractions in 2001–2022, providing an accurate estimate of surface heterogeneous composition, better than vegetation index and vegetation continuous-field products. We find a greening trend on Earth except for the tropics. A combination of interactive changes in vegetation and soil can be adopted as a valuable measurement of climate change and anthropogenic impacts.
Kai Cheng, Yuling Chen, Tianyu Xiang, Haitao Yang, Weiyan Liu, Yu Ren, Hongcan Guan, Tianyu Hu, Qin Ma, and Qinghua Guo
Earth Syst. Sci. Data, 16, 803–819, https://doi.org/10.5194/essd-16-803-2024, https://doi.org/10.5194/essd-16-803-2024, 2024
Short summary
Short summary
To quantify forest carbon stock and its future potential accurately, we generated a 30 m resolution forest age map for China in 2020 using multisource remote sensing datasets based on machine learning and time series analysis approaches. Validation with independent field samples indicated that the mapped forest age had an R2 of 0.51--0.63. Nationally, the average forest age is 56.1 years (standard deviation of 32.7 years).
Fuyou Tian, Bingfang Wu, Hongwei Zeng, Miao Zhang, Weiwei Zhu, Nana Yan, Yuming Lu, and Yifan Li
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2023-536, https://doi.org/10.5194/essd-2023-536, 2024
Revised manuscript accepted for ESSD
Short summary
Short summary
Our team has developed an irrigation map with 100 m resolution, which is more detailed than existing one. We used satellite images and focused on the crop status during the dry conditions. We found that 23.4 % of global cropland is irrigated, with the most extensive areas in India, China, the US, and Pakistan. We also explored the distribution of central pivot systems, which are commonly used in the US and Saudi Arabia. This new map can better support water management and food security globally.
Wolfgang Alexander Obermeier, Clemens Schwingshackl, Ana Bastos, Giulia Conchedda, Thomas Gasser, Giacomo Grassi, Richard A. Houghton, Francesco Nicola Tubiello, Stephen Sitch, and Julia Pongratz
Earth Syst. Sci. Data, 16, 605–645, https://doi.org/10.5194/essd-16-605-2024, https://doi.org/10.5194/essd-16-605-2024, 2024
Short summary
Short summary
We provide and compare country-level estimates of land-use CO2 fluxes from a variety and large number of models, bottom-up estimates, and country reports for the period 1950–2021. Although net fluxes are small in many countries, they are often composed of large compensating emissions and removals. In many countries, the estimates agree well once their individual characteristics are accounted for, but in other countries, including some of the largest emitters, substantial uncertainties exist.
Cameron I. Ludemann, Nathan Wanner, Pauline Chivenge, Achim Dobermann, Rasmus Einarsson, Patricio Grassini, Armelle Gruere, Kevin Jackson, Luis Lassaletta, Federico Maggi, Griffiths Obli-Laryea, Martin K. van Ittersum, Srishti Vishwakarma, Xin Zhang, and Francesco N. Tubiello
Earth Syst. Sci. Data, 16, 525–541, https://doi.org/10.5194/essd-16-525-2024, https://doi.org/10.5194/essd-16-525-2024, 2024
Short summary
Short summary
Nutrient budgets help identify the excess or insufficient use of fertilizers and other nutrient sources in agriculture. They allow the calculation of indicators, such as the nutrient balance (surplus or deficit) and nutrient use efficiency, that help to monitor agricultural productivity and sustainability. This article describes a global cropland nutrient budget that provides data on 205 countries and territories from 1961 to 2020 (data available at https://www.fao.org/faostat/en/#data/ESB).
Raphaël d'Andrimont, Momchil Yordanov, Fernando Sedano, Astrid Verhegghen, Peter Strobl, Savvas Zachariadis, Flavia Camilleri, Alessandra Palmieri, Beatrice Eiselt, Jose Miguel Rubio Iglesias, and Marijn van der Velde
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2023-494, https://doi.org/10.5194/essd-2023-494, 2024
Revised manuscript accepted for ESSD
Short summary
Short summary
LUCAS 2022 Copernicus is a large an systematic in-situ field survey of 137,966 polygons over the EU in 2022. The data holds 82 land cover classes and 40 land use classes.
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.
Yangyang Fu, Xiuzhi Chen, Chaoqing Song, Xiaojuan Huang, Jie Dong, Qiongyan Peng, and Wenping Yuan
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2023-432, https://doi.org/10.5194/essd-2023-432, 2023
Revised manuscript accepted for ESSD
Short summary
Short summary
This study proposed the Winter-Triticeae Crops Index (WTCI)，which had great performance and stable spatiotemporal transferability in identifying winter-triticeae crops in 65 countries worldwide, with an overall accuracy of 87.7 %. The first global 30 m resolution distribution maps of winter-triticeae crops from 2017 to 2022 were further produced based on the WTCI method. The product can serve as an important basis for agricultural applications.
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.
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.
Cited articles
Adame, M. F., Arthington, A. H., Waltham, N., Hasan, S., Selles, A., and
Ronan, M.: Managing threats and restoring wetlands within catchments of the
Great Barrier Reef, Australia, Aquat. Conserv., 29, 829–839, https://doi.org/10.1002/aqc.3096,
2019.
Alfieri, L., Salamon, P., Bianchi, A., Neal, J., Bates, P., and Feyen, L.:
Advances in pan-European flood hazard mapping, Hydrol. Process., 28,
4067–4077, https://doi.org/10.1002/hyp.9947, 2014.
Ameli, A. A. and Creed, I. F.: Does Wetland Location Matter When Managing
Wetlands for Watershed-Scale Flood and Drought Resilience?, J.
Ame. Water Resour. Assoc., 55, 529–542, https://doi.org/10.1111/1752-1688.12737, 2019.
Aronica, G., Bates, P. D., and Horritt, M. S.: Assessing the uncertainty in
distributed model predictions using observed binary pattern information
within GLUE, Hydrol. Process., 16, 2001–2016, https://doi.org/10.1002/hyp.398, 2002.
Badiou, P., Page, B., and Akinremi, W.: Phosphorus Retention in Intact and
Drained Prairie Wetland Basins: Implications for Nutrient Export, J.
Environ. Qual., 47, 902–913, https://doi.org/10.2134/jeq2017.08.0336, 2018.
Bam, E. K. P., Ireson, A. M., van der Kamp, G., and Hendry, J. M.: Ephemeral
Ponds: Are They the Dominant Source of Depression-Focused Groundwater
Recharge?, Water Resour. Res., 56, e2019WR026640, https://doi.org/10.1029/2019WR026640, 2020.
Bates, P. D. and De Roo, A. P. J.: A simple raster-based model for flood
inundation simulation, J. Hydrol., 236, 54–77,
https://doi.org/10.1016/S0022-1694(00)00278-X, 2000.
Beck, H. E., Zimmermann, N. E., McVicar, T. R., Vergopolan, N., Berg, A.,
and Wood, E. F.: Present and future Köppen-Geiger climate classification
maps at 1-km resolution, Sci. Data, 5, 180214, https://doi.org/10.1038/sdata.2018.214,
2018.
Berhane, T., Lane, C., Wu, Q., Autrey, B., Anenkhonov, O., Chepinoga, V.,
and Liu, H.: Decision-Tree, Rule-Based, and Random Forest Classification of
High-Resolution Multispectral Imagery for Wetland Mapping and Inventory,
Remote Sensing, 10, 580, https://doi.org/10.3390/rs10040580, 2018.
Biggs, J., von Fumetti, S., and Kelly-Quinn, M.: The importance of small
waterbodies for biodiversity and ecosystem services: implications for policy
makers, Hydrobiologia, 793, 3–39, https://doi.org/10.1007/s10750-016-3007-0, 2017.
Blanchette, M., Rousseau, A. N., Savary, S., and Foulon, É.: Are spatial
distribution and aggregation of wetlands reliable indicators of stream flow
mitigation?, J. Hydrol., 608, 127646,
https://doi.org/10.1016/j.jhydrol.2022.127646, 2022.
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, Sci. Data, 9, 251, https://doi.org/10.1038/s41597-022-01307-4, 2022.
Buttle, J. M.: Mediating stream baseflow response to climate change: The
role of basin storage, Hydrol. Process., 32, 363–378, https://doi.org/10.1002/hyp.11418, 2018.
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 30 m resolution: A POK-based operational approach, ISPRS J.
Photogramm., 103, 7–27,
https://doi.org/10.1016/j.isprsjprs.2014.09.002, 2015.
Chen, W., Thorslund, J., Nover, D. M., Rains, M. C., Li, X., Xu, B., He, B.,
Su, H., Yen, H., Liu, L., Yuan, H., Jarsjö, J., and Viers, J. H.: A
typological framework of non-floodplain wetlands for global collaborative
research and sustainable use, Environ. Res. Lett., 17, 113002, https://doi.org/10.1088/1748-9326/ac9850, 2022.
Cheng, F. Y. and Basu, N. B.: Biogeochemical hotspots: Role of small water
bodies in landscape nutrient processing, Water Resour. Res., 53,
5038–5056, https://doi.org/10.1002/2016WR020102, 2017.
Cheng, F. Y., Van Meter, K. J., Byrnes, D. K., and Basu, N. B.: Maximizing
US nitrate removal through wetland protection and restoration, Nature, 588,
625–630, https://doi.org/10.1038/s41586-020-03042-5, 2020.
Christensen, J. R., Golden, H. E., Alexander, L. C., Pickard, B. R., Fritz,
K. M., Lane, C. R., Weber, M. H., Kwok, R. M., and Keefer, M. N.: Headwater
streams and inland wetlands: Status and advancements of geospatial datasets
and maps across the United States, Earth-Sci. Rev., 235, 104230,
https://doi.org/10.1016/j.earscirev.2022.104230, 2022.
Cohen, M. J., Creed, I. F., Alexander, L., Basu, N. B., Calhoun, A. J. K.,
Craft, C., D'Amico, E., DeKeyser, E., Fowler, L., Golden, H. E., Jawitz, J.
W., Kalla, P., Kirkman, L. K., Lane, C. R., Lang, M., Leibowitz, S. G.,
Lewis, D. B., Marton, J., McLaughlin, D. L., Mushet, D. M., Raanan-Kiperwas,
H., Rains, M. C., Smith, L., and Walls, S. C.: Do geographically isolated
wetlands influence landscape functions?, P. Natl. Acad.
Sci. USA, 113, 1978–1986, https://doi.org/10.1073/pnas.1512650113, 2016.
Colvin, S. A. R., Sullivan, S. M. P., Shirey, P. D., Colvin, R. W.,
Winemiller, K. O., Hughes, R. M., Fausch, K. D., Infante, D. M., Olden, J.
D., Bestgen, K. R., Danehy, R. J., and Eby, L.: Headwater Streams and
Wetlands are Critical for Sustaining Fish, Fisheries, and Ecosystem
Services, Fisheries, 44, 73–91, 2019.
Cowardin, L. M., Carter, V., Golet, F. C., and LaRoe, E. T.: Classification
of Wetlands and Deepwater habitats of The United States, Fish and Wildlife
Service, Washington DCFWS/OBS-79/31, https://www.fws.gov/wetlands/documents/classification-of-wetlands-and-deepwater-habitats-of-the-united-states.pdf (last access: 5 July 2023), 1979.
Creed, I. F., Lane, C. R., Serran, J. N., Alexander, L. C., Basu, N. B.,
Calhoun, A. J. K., Christensen, J. R., Cohen, M. J., Craft, C., D'Amico, E.,
DeKeyser, E., Fowler, L., Golden, H. E., Jawitz, J. W., Kalla, P., Kirkman,
L. K., Lang, M., Leibowitz, S. G., Lewis, D. B., Marton, J., McLaughlin, D.
L., Raanan-Kiperwas, H., Rains, M. C., Rains, K. C., and Smith, L.:
Enhancing protection for vulnerable waters, Nat. Geosci., 10, 809–815, https://doi.org/10.1038/ngeo3041, 2017.
Cunha, D. G. F., Magri, R. A. F., Tromboni, F., Ranieri, V. E. L., Fendrich,
A. N., Campanhão, L. M. B., Riveros, E. V., and Velázquez, J. A.:
Landscape patterns influence nutrient concentrations in aquatic systems:
citizen science data from Brazil and Mexico, Freshwater Sci., 38,
365–378, https://doi.org/10.1086/703396, 2019.
Dahl, T. E.: Wetlands – Losses in the United States, 1780's to 1980's, U.S.
Department of Interior, Fish and Wildlife Service Washington DC, https://www.fws.gov/wetlands/documents/wetlands-losses-in-the-united-states-1780s-to-1980s.pdf (last access: 5 July 2023), 1990.
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.
Davidson, N. C., Fluet-Chouinard, E., and Finlayson, C. M.: Global extent
and distribution of wetlands: trends and issues, Mar. Freshwater
Res., 69, 620–627, 2018.
De Groot, R., Stuip, M., Finlayson, M., and Davidson, N.: Valuing Wetlands:
Guidance for Valuing the Benefits Derived from Wetland Ecosystem Services,
Ramsar Convention Secretariat, Gland, Switzerland and Secretariat of the
Convention on Biological Diversity, Montreal, Canada, Gland,
SwitzerlandRamsar Technical Report No. 3/CBD Technical Series No. 27, 2006.
DeVries, B., Huang, C., Lang, M., Jones, J., Huang, W., Creed, I., and
Carroll, M.: Automated Quantification of Surface Water Inundation in
Wetlands Using Optical Satellite Imagery, Remote Sens., 9, 807, https://doi.org/10.3390/rs9080807, 2017.
Dewitz, J.: National Land Cover Database (NLCD) 2016 Products: U.S.
Geological Survey data [data set], https://doi.org/10.5066/P96HHBIE, 2019.
Dottori, F., Salamon, P., Bianchi, A., Alfieri, L., Hirpa, F. A., and Feyen, L.: Development and evaluation of a framework for global flood hazard mapping, Adv. Water Resour., 94, 87–102, https://doi.org/10.1016/j.advwatres.2016.05.002, 2016.
Drenkhan, F., Buytaert, W., Mackay, J. D., Barrand, N. E., Hannah, D. M., and Huggel, C.: Looking beyond glaciers to understand mountain water security, Nat. Sustain., 6, 130–138, https://doi.org/10.1038/s41893-022-00996-4, 2023.
ESA: Worldwide Land Cover Mapping, ESA [data set] https://esa-worldcover.org/en, last access: 22 Deccember 2022a.
ESRI: World Terrain Base,
https://www.arcgis.com/home/item.html?id=be2e229ffc864c868a78f5ca68ca5b8e,
last access: 22 December 2022.
Evenson, G. R., Golden, H. E., Lane, C. R., McLaughlin, D. L., and D'Amico,
E.: Depressional Wetlands Affect Watershed Hydrological, Biogeochemical, and
Ecological Functions, Ecol. Appl., 28, 953–966, https://doi.org/10.1002/eap.1701, 2018a.
Evenson, G. R., Jones, C. N., McLaughlin, D. L., Golden, H. E., Lane, C. R.,
DeVries, B., Alexander, L. C., Lang, M. W., McCarty, G. W., and Sharifi, A.:
A watershed-scale model for depressional wetland-rich landscapes, J.
Hydrol., 1, 100002,
https://doi.org/10.1016/j.hydroa.2018.10.002, 2018b.
Evenson, G. R., Golden, H. E., Christensen, J. R., Lane, C. R., Rajib, A.,
D'Amico, E., Mahoney, D. T., White, E., and Wu, Q.: Wetland restoration
yields dynamic nitrate responses across the Upper Mississippi river basin,
Environ. Res. Commun., 3, 095002, https://doi.org/10.1088/2515-7620/ac2125,
2021.
Fan, Y., Li, H., and Miguez-Macho, G.: Global Patterns of Groundwater Table Depth, Science, 339, 940–943, https://doi.org/10.1126/science.1229881, 2013.
Fewtrell, T. J., Bates, P. D., Horritt, M., and Hunter, N. M.: Evaluating
the effect of scale in flood inundation modelling in urban environments,
Hydrol. Process., 22, 5107–5118,
https://doi.org/10.1002/hyp.7148, 2008.
Fluet-Chouinard, E., Lehner, B., Rebelo, L.-M., Papa, F., and Hamilton, S.
K.: Development of a global inundation map at high spatial resolution from
topographic downscaling of coarse-scale remote sensing data, Remote Sens.
Environ., 158, 348–361,
https://doi.org/10.1016/j.rse.2014.10.015, 2015.
Fluet-Chouinard, E., Stocker, B. D., Zhang, Z., Malhotra, A., Melton, J. R.,
Poulter, B., Kaplan, J. O., Goldewijk, K. K., Siebert, S., Minayeva, T.,
Hugelius, G., Joosten, H., Barthelmes, A., Prigent, C., Aires, F., Hoyt,
Davidson, A. M. N., Finlayson, C. M., Lehner, B., Jackson, R. B., and McIntyre, P. B.:
Extensive global wetland loss over the past three centuries, Nature, 614,
281–286, 2023.
Fossey, M. and Rousseau, A. N.: Can isolated and riparian wetlands mitigate
the impact of climate change on watershed hydrology? A case study approach,
J. Environ. Manage., 184, 327–339,
https://doi.org/10.1016/j.jenvman.2016.09.043, 2016.
Gardner, R.: What the US Supreme Court decision means for wetlands, Nature,
618, 215, 10.1038/d41586-023-01827-y, 2023.
Golden, H. E., Sander, H. A., Lane, C. R., Zhao, C., Price, K., D'Amico, E.,
and Christensen, J. R.: Relative effects of geographically isolated wetlands
on streamflow: a watershed-scale analysis, Ecohydrol., 9, 21–38, https://doi.org/10.1002/eco.1608, 2016.
Golden, H. E., Creed, I. F., Ali, G., Basu, N. B., Neff, B. P., Rains, M.
C., McLaughlin, D. L., Alexander, L. C., Ameli, A. A., Christensen, J. R.,
Evenson, G. R., Jones, C. N., Lane, C. R., and Lang, M.: Integrating
geographically isolated wetlands into land management decisions, Front.
Ecol. Environ., 15, 319–327, https://doi.org/10.1002/fee.1504, 2017.
Golden, H. E., Rajib, A., Lane, C. R., Christensen, J. R., Wu, Q., and
Mengistu, S.: Non-floodplain Wetlands Affect Watershed Nutrient Dynamics: A
Critical Review, Environ. Sci. Technol., 53, 7203–7214, https://doi.org/10.1021/acs.est.8b07270, 2019.
Golden, H. E., Lane, C. R., Rajib, A., and Wu, Q.: Improving global flood
and drought predictions: integrating non-floodplain wetlands into watershed
hydrologic models, Environ. Res. Lett., 16, 091002, https://doi.org/10.1088/1748-9326/ac1fbc, 2021.
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.
Hamunyela, E., Hipondoka, M., Persendt, F., Sevelia Nghiyalwa, H., Thomas,
C., and Matengu, K.: Spatio-temporal characterization of surface water
dynamics with Landsat in endorheic Cuvelai-Etosha Basin (1990–2021), ISPRS
J. Photogramm., 191, 68–84,
https://doi.org/10.1016/j.isprsjprs.2022.07.007, 2022.
Herold, M., Groenestijn, A. V., Kooistra, L., Kalogirou, V., and Arino, O.: Land Cover CCI, Product User Guide Version 2.0, https://maps.elie.ucl.ac.be/CCI/viewer/download/ESACCI-LC-Ph2-PUGv2_2.0.pdf (last access: May 2022), 2015.
Hoch, J. M. and Trigg, M. A.: Advancing global flood hazard simulations by
improving comparability, benchmarking, and integration of global flood
models, Environ. Res. Lett., 14, 034001, https://doi.org/10.1088/1748-9326/aaf3d3, 2019.
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.
Horritt, M. S. and Bates, P. D.: Evaluation of 1D and 2D numerical models
for predicting river flood inundation, J. Hydrol., 268, 87–99,
https://doi.org/10.1016/S0022-1694 (02)00121-X, 2002.
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, W., DeVries, B., Huang, C., Lang, M., Jones, J., Creed, I., and
Carroll, M.: Automated Extraction of Surface Water Extent from Sentinel-1
Data, Remote Sens., 10, 797, https://doi.org/10.3390/rs10050797, 2018.
IPCC: Intergovernmental Panel on Climate Change 2014: Impacts, adaptation,
and vulnerability, Cambridge University Press, Cambridge, U.K., ISBN 978-1-107-05807-1, 2014.
Jafarzadegan, K., Merwade, V., and Saksena, S.: A geomorphic approach to
100-year floodplain mapping for the Conterminous United States, J.
Hydrol., 561, 43–58,
https://doi.org/10.1016/j.jhydrol.2018.03.061, 2018.
Jakubínský, J., Prokopová, M., Raška, P., Salvati, L.,
Bezak, N., Cudlín, O., Cudlín, P., Purkyt, J., Vezza, P.,
Camporeale, C., Danìk, J., Pástor, M., and Lepeška, T.:
Managing floodplains using nature-based solutions to support multiple
ecosystem functions and services, WIREs Water, 8, e1545,
https://doi.org/10.1002/wat2.1545, 2021.
Jin, S., Homer, C., Yang, L., Danielson, P., Dewitz, J., Li, C., Zhu, Z.,
Xian, G., and Howard, D.: Overall Methodology Design for the United States
National Land Cover Database 2016 Products, Remote Sens., 11, 2971, https://doi.org/10.3390/rs11242971, 2019.
Jones, C. N., Evenson, G. R., McLaughlin, D. L., Vanderhoof, M. K., Lang, M.
W., McCarty, G. W., Golden, H. E., Lane, C. R., and Alexander, L. C.:
Estimating restorable wetland water storage at landscape scales,
Hydrol. Process., 32, 305–313, https://doi.org/10.1002/hyp.11405, 2018.
Karra, K., Kontgis, C., Statman-Weil, Z., Mazzariello, J. C., Mathis, M.,
and Brumby, S. P. Global land use/land cover with Sentinel 2 and deep
learning, in: 2021 IEEE International Geoscience and Remote Sensing
Symposium IGARSS, IEEE, 4704–4707,
doi.org/10.1109/IGARSS47720.2021.9553499, 2021.
Kam, S. P.: Valuing the role of living aquatic resources to rural
livelihoods in multiple-use, seasonally-inundated wetlands in the Yellow
River Basin of China, for improved governance, CGIAR Challenge Program on
Water & Food, Colombo, Sri Lanka, https://hdl.handle.net/10568/3859 (last access: 24 May 2023), 2010.
Kremenetski, K. V., Velichko, A. A., Borisova, O. K., MacDonald, G. M.,
Smith, L. C., Frey, K. E., and Orlova, L. A.: Peatlands of the Western
Siberian lowlands: current knowledge on zonation, carbon content and Late
Quaternary history, Quaternary Sci. Rev., 22, 703–723, 2003.
Kundzewicz, Z. W., Hegger, D. L. T., Matczak, P., and Driessen, P. P. J.:
Opinion: Flood-risk reduction: Structural measures and diverse strategies,
P. Natl. Acad. Sci. USA, 115, 12321–12325, https://doi.org/10.1073/pnas.1818227115, 2018.
Lane, C. R. and D'Amico, E.: Identification of Putative Geographically
Isolated Wetlands of the Conterminous United States, JAWRA J.
Am. Water Resour. As., 52, 705–722, https://doi.org/10.1111/1752-1688.12421,
2016.
Lane, C. R., Leibowitz, S. G., Autrey, B. C., LeDuc, S. D., and Alexander,
L. C.: Hydrological, Physical, and Chemical Functions and Connectivity of
Non-Floodplain Wetlands to Downstream Waters: A Review, J.
Am. Water Resour. As., 54, 346–371, https://doi.org/10.1111/1752-1688.12633,
2018.
Lane, C. R., D'Amico, E., Christensen, J. R., Golden, H. E., Wu, Q., and
Rajib, A.: Global non-floodplain wetlands, U.S. EPA Office of Research and Development [data set],
https://gaftp.epa.gov/EPADataCommons/ORD/Global_NonFloodplain_Wetlands/ (last access: 24 May 2023) and https://doi.org/10.23719/1528331,
2023a.
Lane, C. R., Creed, I. F., Golden, H. E., Leibowitz, S. G., Mushet, D. M.,
Rains, M. C., Wu, Q., D'Amico, E., Alexander, L. C., Ali, G. A., Basu, N.
B., Bennett, M. G., Christensen, J. R., Cohen, M. J., Covino, T. P.,
DeVries, B., Hill, R. A., Jencso, K., Lang, M. W., McLaughlin, D. L.,
Rosenberry, D. O., Rover, J., and Vanderhoof, M. K.: Vulnerable Waters are
Essential to Watershed Resilience, Ecosystems, 26, 1–28, https://doi.org/10.1007/s10021-021-00737-2,
2023b.
Lehner, B. and Doll, P.: Development and validation of a global database of
lakes, reservoirs and wetlands, J. Hydrol., 296, 1–22, 2004.
Lehner, B. and Grill, G.: Global river hydrography and network routing:
baseline data and new approaches to study the world's large river systems,
Hydrol. Process., 27, 2171–2186,
https://doi.org/10.1002/hyp.9740, 2013.
Leibowitz, S.: Geographically Isolated Wetlands: Why We Should Keep the
Term, Wetlands, 35, 997–1003, https://doi.org/10.1007/s13157-015-0691-x, 2015.
Leibowitz, S. G.: Isolated wetlands and their functions: an ecological
perspective, Wetlands, 22, 517–531, 2003.
Leibowitz, S. G., Hill, R. A., Creed, I. F., Compton, J. E., Golden, H. E.,
Weber, M. H., Rains, M. C., Jones, J., C. E., Lee, E. H., Christensen, J.
R., Bellmore, R. A., and Lane, C. R.: National hydrologic connectivity
classification links wetlands with stream water quality, Nature Water, 1,
370–380, https://doi.org/10.1038/s44221-023-00057-w, 2023.
Liu, D., Cao, C., Chen, W., Ni, X., Tian, R., and Xing, X.: Monitoring and
predicting the degradation of a semi-arid wetland due to climate change and
water abstraction in the Ordos Larus relictus National Nature Reserve,
China, Geomat. Nat. Haz. Risk, 8, 367–383, https://doi.org/10.1080/19475705.2016.1220024, 2017.
Makungu, E. and Hughes, D. A.: Understanding and modelling the effects of
wetland on the hydrology and water resources of large African river basins,
J. Hydrol., 603, 127039,
https://doi.org/10.1016/j.jhydrol.2021.127039, 2021.
Martinis, S., Groth, S., Wieland, M., Knopp, L., and Rättich, M.:
Towards a global seasonal and permanent reference water product from
Sentinel-1/2 data for improved flood mapping, Remote Sens. Environ.,
278, 113077, https://doi.org/10.1016/j.rse.2022.113077, 2022.
Marton, J. M., Creed, I. F., Lewis, D., Lane, C. R., Basu, N., Cohen,
M. J., and Craft, C.: Geographically isolated wetlands are important biogeochemical
reactors on the landscape, BioScience, 65, 408–418, https://doi.org/10.1093/biosci/biv009,
2015.
McCauley, L. A., Anteau, M. J., van der Burg, M. P., and Wiltermuth, M. T.:
Land use and wetland drainage affect water levels and dynamics of remaining
wetlands, Ecosphere, 6, art92, https://doi.org/10.1890/ES14-00494.1, 2015.
McKenna, O. P., Mushet, D. M., Rosenberry, D. O., and LaBaugh, J. W.:
Evidence for a climate-induced ecohydrological state shift in wetland
ecosystems of the southern Prairie Pothole Region, Climatic Change, 145,
273–287, https://doi.org/10.1007/s10584-017-2097-7, 2017.
McLaughlin, D. L., Kaplan;, D. A., and Cohen, M. J.: A significant nexus:
Geographically isolated wetlands influence landscape hydrology, Water
Resour. Res., 50, 7153–7166, https://doi.org/10.1002/2013WR015002, 2014.
Merken, R., Deboelpaep, E., Teunen, J., Saura, S., and Koedam, N.: Wetland
Suitability and Connectivity for Trans-Saharan Migratory Waterbirds, PLOS
ONE, 10, e0135445, https://doi.org/10.1371/journal.pone.0135445, 2015.
Messager, M. L., Lehner, B., Grill, G., Nedeva, I., and Schmitt, O.:
Estimating the volume and age of water stored in global lakes using a
geo-statistical approach, Nat. Commun., 7, 13603, https://doi.org/10.1038/ncomms13603, 2016.
Millennial Ecosystem Assessment: Ecosystems and Human Well-Being: Wetlands and Water Synthesis, World Resources Institute, Washington, D.C., ISBN 1-56973-597-2, 2005.
Mudashiru, R. B., Sabtu, N., Abustan, I., and Balogun, W.: Flood hazard mapping
methods: A review, J. Hydrol., 603, 126846, https://doi.org/10.1016/j.jhydrol.2021.126846, 2021.
Mushet, D., Calhoun, A., Alexander, L., Cohen, M., DeKeyser, E., Fowler, L.,
Lane, C., Lang, M., Rains, M., and Walls, S.: Geographically Isolated
Wetlands: Rethinking a Misnomer, Wetlands, 35, 423–431, https://doi.org/10.1007/s13157-015-0631-9, 2015.
Mushet, D. M., Alexander, L. C., Bennett, M., Schofield, K., Christensen, J.
R., Ali, G., Pollard, A., Fritz, K., and Lang, M. W.: Differing Modes of
Biotic Connectivity within Freshwater Ecosystem Mosaics, J.
Am. Water Resour. As., 55, 307–317, https://doi.org/10.1111/1752-1688.12683, 2019.
Nardi, F., Annis, A., Di Baldassarre, G., Vivoni, E. R., and Grimaldi, S.:
GFPLAIN250m, a global high-resolution dataset of Earth's floodplains,
Sci. Data, 6, 180309, https://doi.org/10.1038/sdata.2018.309, 2019.
National Landcover Database (NLCD): 2019 NLCD Land Cover (CONUS), National Landcover Database (NLCD) [data set],
https://www.mrlc.gov/data, last access: 22 December 2022.
Nitzsche, K. N., Kalettka, T., Premke, K., Lischeid, G., Gessler, A., and
Kayler, Z. E.: Land-use and hydroperiod affect kettle hole sediment carbon
and nitrogen biogeochemistry, Sci. Total Environ., 574, 46–56,
https://doi.org/10.1016/j.scitotenv.2016.09.003, 2017.
Olefeldt, D., Hovemyr, M., Kuhn, M. A., Bastviken, D., Bohn, T. J., Connolly, J., Crill, P., Euskirchen, E. S., Finkelstein, S. A., Genet, H., Grosse, G., Harris, L. I., Heffernan, L., Helbig, M., Hugelius, G., Hutchins, R., Juutinen, S., Lara, M. J., Malhotra, A., Manies, K., McGuire, A. D., Natali, S. M., O'Donnell, J. A., Parmentier, F.-J. W., Räsänen, A., Schädel, C., Sonnentag, O., Strack, M., Tank, S. E., Treat, C., Varner, R. K., Virtanen, T., Warren, R. K., and Watts, J. D.: The Boreal–Arctic Wetland and Lake Dataset (BAWLD), Earth Syst. Sci. Data, 13, 5127–5149, https://doi.org/10.5194/essd-13-5127-2021, 2021.
Pappenberger, F., Dutra, E., Wetterhall, F., and Cloke, H. L.: Deriving global flood hazard maps of fluvial floods through a physical model cascade, Hydrol. Earth Syst. Sci., 16, 4143–4156, https://doi.org/10.5194/hess-16-4143-2012, 2012.
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.
Prigent, C., Papa, F., Aires, F., Rossow, W. B., and Matthews, E.: Global
inundation dynamics inferred from multiple satellite observations,
1993–2000, J. Geophys. Res.-Atmos., 112, D12107,
https://doi.org/10.1029/2006JD007847, 2007.
PRISM Climate Group: Parameter-elevation Regressions on Independent Slopes
Model, PRISM Climate Group [data set], http://prism.oregonstate.edu/, last access: 22 December 2022.
Rains, M. C., Leibowitz, S. G., Cohen, M. J., Creed, I. F., Golden, H. E.,
Jawitz, J. W., Kalla, P., Lane, C. R., Lang, M. W., and McLaughlin, D. L.:
Geographically isolated wetlands are part of the hydrological landscape,
Hydrol. Process., 30, 153–160, https://doi.org/10.1002/hyp.10610, 2016.
Rajib, A., Golden, H. E., Lane, C. R., and Wu, Q.: Surface depression and
wetland water storage improves major river basin hydrologic predictions,
Water Resour. Res., 56, e2019WR026561,
https://doi.org/10.1029/2019WR026561, 2020.
Rajib, A., Zheng, Q., Golden, H. E., Wu, Q., Lane, C. R., Christensen, J.
R., Morrison, R. R., Annis, A., and Nardi, F.: The changing face of
floodplains in the Mississippi River Basin detected by a 60-year land use
change dataset, Sci. Data, 8, 271, https://doi.org/10.1038/s41597-021-01048-w, 2021.
Robarts, R., Zhulidov, A., and Pavlov, D.: The State of knowledge about
wetlands and their future under aspects of global climate change: the
situation in Russia, Aquat. Sci., 75, 27–38, https://doi.org/10.1007/s00027-011-0230-7,
2013.
Rodrigues, L. N., Sano, E. E., Steenhuis, T. S., and Passo, D. P.:
Estimation of Small Reservoir Storage Capacities with Remote Sensing in the
Brazilian Savannah Region, Water Resour. Manage., 26, 873–882, https://doi.org/10.1007/s11269-011-9941-8, 2012.
Rodríguez-Rodríguez, M., Aguilera, H., Guardiola-Albert, C., and
Fernández-Ayuso, A.: Climate Influence Vs. Local Drivers in Surface
Water-Groundwater Interactions in Eight Ponds of Doñana National Park
(Southern Spain), Wetlands, 41, 25, https://doi.org/10.1007/s13157-021-01425-6, 2021.
Rudari, R., Silvestro, F., Campo, L., Rebora, N., Boni, G., and Herold, C.:
Improvement of the global flood model for the GAR 2015, United Nations
Office for Disaster Risk Reduction (UNISDR), Centro Internazionale in
Monitoraggio Ambientale (CIMA), UNEP GRID-Arendal (GRID-Arendal): Geneva,
Switzerland, 69, https://www.undrr.org/publication/improvement-global-food-model-gar-2015 (last access: 5 July 2023), 2015.
Sampson, C. C., Smith, A. M., Bates, P. D., Neal, J. C., Alfieri, L., and
Freer, J. E.: A high-resolution global flood hazard model, Water Resour.
Res., 51, 7358–7381, https://doi.org/10.1002/2015WR016954, 2015.
Samways, M. J., Deacon, C., Kietzka, G. J., Pryke, J. S., Vorster, C., and
Simaika, J. P.: Value of artificial ponds for aquatic insects in
drought-prone southern Africa: a review, Biodivers. Conserv., 29,
3131–3150, https://doi.org/10.1007/s10531-020-02020-7, 2020.
Sangwan, N. and Merwade, V.: A Faster and Economical Approach to Floodplain
Mapping Using Soil Information, J. Am. Water
Resour. As., 51, 1286–1304, https://doi.org/10.1111/1752-1688.12306, 2015.
Schofield, K. A., Alexander, L. C., Ridley, C. E., Vanderhoof, M. K., Fritz,
K. M., Autrey, B. C., DeMeester, J. E., Kepner, W. G., Lane, C. R.,
Leibowitz, S. G., and Pollard, A. I.: Biota Connect Aquatic Habitats
throughout Freshwater Ecosystem Mosaics, J. Am. Water
Resour. As., 54, 372–399, https://doi.org/10.1111/1752-1688.12634, 2018.
Serran, J. N., Creed, I. F., Ameli, A. A., and Aldred, D. A.: Estimating
rates of wetland loss using power-law functions, Wetlands, 38, 109–120, https://doi.org/10.1007/s13157-017-0960-y, 2017.
Shaw, D. A., Vanderkamp, G., Conly, F. M., Pietroniro, A., and Martz, L.:
The Fill–Spill Hydrology of Prairie Wetland Complexes during Drought and
Deluge, Hydrol. Process., 26, 3147–3156, https://doi.org/10.1002/hyp.8390, 2012.
Smith, L. L., Subalusky, A. L., Atkinson, C. L., Earl, J. E., Mushet, D. M.,
Scott, D. E., Lance, S. L., and Johnson, S. A.: Biological Connectivity of
Seasonally Ponded Wetlands across Spatial and Temporal Scales, J.
Am. Water Resour. As., 55, 334–353, https://doi.org/10.1111/1752-1688.12682, 2019.
Strahler, A. N.: Quantitative analysis of watershed geomorphology, American
Geophysical Union Transactions, 38, 913–920, 1957.
Sullivan, S. M. P., Rains, M. C., and Rodewald, A. D.: Opinion: The proposed
change to the definition of “waters of the United States” flouts sound
science, P. Natl. Acad. Sci. USA, 116, 11558, https://doi.org/10.1073/pnas.1907489116, 2019.
Tayefi, V., Lane, S. N., Hardy, R. J., and Yu, D.: A comparison of one- and
two-dimensional approaches to modelling flood inundation over complex upland
floodplains, Hydrol. Process., 21, 3190–3202, https://doi.org/10.1002/hyp.6523, 2007.
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.
Tsendbazar, N., Herold, M., Li, L., Tarko, A., de Bruin, S., Masiliunas, D.,
Lesiv, M., Fritz, S., Buchhorn, M., Smets, B., Van De Kerchove, R., and
Duerauer, M.: Towards operational validation of annual global land cover
maps, Remote Sens. Environ., 266, 112686, https://doi.org/10.1016/j.rse.2021.112686, 2021.
Tullos, D.: Opinion: How to achieve better flood-risk governance in the
United States, P. Natl. Acad. Sci. USA, 115,
3731–3734, https://doi.org/10.1073/pnas.1722412115, 2018.
Uden, D. R., Allen, C. R., Bishop, A. A., Grosse, R., Jorgensen, C. F.,
LaGrange, T. G., Stutheit, R. G., and Vrtiska, M. P.: Predictions of future
ephemeral springtime waterbird stopover habitat availability under global
change, Ecosphere, 6, 1–26, https://doi.org/10.1890/ES15-00256.1, 2015.
United States Geological Survey (USGS): Watershed Boundary Dataset, United States Geological Survey (USGS) [data set],
https://www.usgs.gov/national-hydrography/access-national-hydrography-products,
last access: 22 December 2022a.
United States Geological Survey (USGS): National Elevation Dataset, United States Geological Survey (USGS) [data set],
https://www.usgs.gov/3d-elevation-program, last access: 22 December 2022b.
US Fish and Wildlife Service: National Wetlands Inventory, US Fish and Wildlife Service [data set], https://www.fws.gov/program/national-wetlands-inventory/data-download (last access: 24 May 2023), various dates.
Van Meter, K. J. and Basu, N. B.: Signatures of human impact: size
distributions and spatial organization of wetlands in the Prairie Pothole
landscape, Ecol. Appl., 25, 451–465, https://doi.org/10.1890/14-0662.1, 2015.
Van Meter, K. J., Basu, N. B., Tate, E., and Wyckoff, J.: Monsoon Harvests:
The Living Legacies of Rainwater Harvesting Systems in South India,
Environ. Sci. Technol., 48, 4217–4225, https://doi.org/10.1021/es4040182,
2014.
Vanderhoof, M. K. and Lane, C. R.: The potential role of very
high-resolution imagery to characterise lake, wetland and stream systems
across the Prairie Pothole Region, United States, Int. J. Remote Sens., 40, 5768–5798, https://doi.org/10.1080/01431161.2019.1582112, 2019.
Wania, R., Melton, J. R., Hodson, E. L., Poulter, B., Ringeval, B., Spahni, R., Bohn, T., Avis, C. A., Chen, G., Eliseev, A. V., Hopcroft, P. O., Riley, W. J., Subin, Z. M., Tian, H., van Bodegom, P. M., Kleinen, T., Yu, Z. C., Singarayer, J. S., Zürcher, S., Lettenmaier, D. P., Beerling, D. J., Denisov, S. N., Prigent, C., Papa, F., and Kaplan, J. O.: Present state of global wetland extent and wetland methane modelling: methodology of a model inter-comparison project (WETCHIMP), Geosci. Model Dev., 6, 617–641, https://doi.org/10.5194/gmd-6-617-2013, 2013.
Werner, M. G. F., Hunter, N. M., and Bates, P. D.: Identifiability of
distributed floodplain roughness values in flood extent estimation, J.
Hydrol., 314, 139–157,
https://doi.org/10.1016/j.jhydrol.2005.03.012, 2005.
Wickham, J., Stehman, S. V., Sorenson, D. G., Gass, L., and Dewitz, J. A.:
Thematic accuracy assessment of the NLCD 2016 land cover for the
conterminous United States, Remote Sens. Environ., 257, 112357,
https://doi.org/10.1016/j.rse.2021.112357, 2021.
Wing, O. E. J., Bates, P. D., Sampson, C. C., Smith, A. M., Johnson, K. A.,
and Erickson, T. A.: Validation of a 30 m resolution flood hazard model of
the conterminous United States, Water Resour. Res., 53, 7968–7986, https://doi.org/10.1002/2017WR020917, 2017.
Winsemius, H. C., Van Beek, L. P. H., Jongman, B., Ward, P. J., and Bouwman, A.: A framework for global river flood risk assessments, Hydrol. Earth Syst. Sci., 17, 1871–1892, https://doi.org/10.5194/hess-17-1871-2013, 2013.
Winter, T. C.: The Vulnerability of Wetlands to Climate Change: A Hydrologic
Landscape Perspective, J. Am. Water Resour.
As., 36, 305–311, https://doi.org/10.1111/j.1752-1688.2000.tb04269.x, 2000.
Winter, T. C., Harvey, J. W., Franke, O. L., and Alley, W. M.: Ground Water and
Surface Water: A Single Resoure, U.S. Government Printing Office,
Washington, DC, https://doi.org/10.3133/cir1139, 1998.
Woznicki, S. A., Baynes, J., Panlasigui, S., Mehaffey, M., and Neale, A.:
Development of a spatially complete floodplain map of the conterminous
United States using random forest, Sci. Total Environ., 647,
942–953, https://doi.org/10.1016/j.scitotenv.2018.07.353, 2019.
Wu, Q., Lane, C. R., Wang, L., Vanderhoof, M. K., Christensen, J. R., and
Liu, H.: Efficient Delineation of Nested Depression Hierarchy in Digital
Elevation Models for Hydrological Analysis Using Level-Set Method,
J. Am. Water Resour. As., 55, 354–368, https://doi.org/10.1111/1752-1688.12689, 2019a.
Wu, Q., Lane, C. R., Li, X., Zhao, K., Zhou, Y., Clinton, N., DeVries, B.,
Golden, H. E., and Lang, M. W.: Integrating LiDAR data and multi-temporal
aerial imagery to map wetland inundation dynamics using Google Earth Engine,
Remote Sens. Environ., 228, 1–13,
https://doi.org/10.1016/j.rse.2019.04.015, 2019b.
Xi, Y., Peng, S., Ducharne, A., Ciais, P., Gumbricht, T., Jimenez, C.,
Poulter, B., Prigent, C., Qiu, C., Saunois, M., and Zhang, Z.: Gridded maps
of wetlands dynamics over mid-low latitudes for 1980–2020 based on
TOPMODEL, Sci. Data, 9, 347, https://doi.org/10.1038/s41597-022-01460-w, 2022.
Yamazaki, D., Kanae, S., Kim, H., and Oki, T.: A physically based description of
floodplain inundation dynamics in a global river routing model, Water
Resour. Res., 47, W04501, https://doi.org/10.1029/2010WR009726, 2011.
Yamazaki, D., Ikeshima, D., Sosa, J., Bates, P. D., Allen, G., and Pavelsky,
T.: MERIT Hydro: A high-resolution global hydrography map based on latest
topography datasets, Water Resour. Res., 55, 5053–5073, https://doi.org/10.1029/2019wr024873, 2019.
Zanaga, D., Van De Kerchove, R., De Keersmaecker, W., Souverijns, N.,
Brockmann, C., Quast, R., Wevers, J., Grosu, A., Paccini, A., Vergnaud, S.,
Cartus, O., Santoro, M., Fritz, S., Georgieva, I., Lesiv, M., Carter, S.,
Herold, M., Li, Linlin, Tsendbazar, N. E., Ramoino, F., and Arino, O.: ESA
WorldCover 10 m 2020 v100, Zenodo [data set],
https://doi.org/10.5281/zenodo.5571936, 2021.
Zedler, J. B. and Kercher, S.: Causes and consequences of invasive plants in
wetlands: Opportunities, opportunists, and outcomes, CRC CR Rev. Plant Sci., 23, 431–452, 2004.
Zhang, X., Liu, L., Zhao, T., Chen, X., Lin, S., Wang, J., Mi, J., and Liu, W.: GWL_FCS30: a global 30 m wetland map with a fine classification system using multi-sourced and time-series remote sensing imagery in 2020, Earth Syst. Sci. Data, 15, 265–293, https://doi.org/10.5194/essd-15-265-2023, 2023.
Zhao, G., Bates, P., Neal, J., and Pang, B.: Design flood estimation for global river networks based on machine learning models, Hydrol. Earth Syst. Sci., 25, 5981–5999, https://doi.org/10.5194/hess-25-5981-2021, 2021.
Zhu, Y., Xu, Y., Deng, X., Kwon, H., and Qin, Z.: Peatland Loss in Southeast
Asia Contributing to U.S. Biofuel's Greenhouse Gas Emissions, Environ.
Sci. Technol., 56, 13284–13293, https://doi.org/10.1021/acs.est.2c01561, 2022.
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.
Non-floodplain wetlands (NFWs) – wetlands located outside floodplains – confer...
Altmetrics
Final-revised paper
Preprint