Articles | Volume 16, issue 11
https://doi.org/10.5194/essd-16-5375-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-16-5375-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
26 Nov 2024
Data description paper |
| 26 Nov 2024
| 26 Nov 2024
Mapping rangeland health indicators in eastern Africa from 2000 to 2022
Instituto de Estadística, Facultad de Ciencias Económicas y Administrativas, Universidad Austral de Chile, Valdivia, Chile
School of Integrative Plant Science, Soil and Crop Sciences Section, Cornell University, Ithaca, NY, USA
Steven W. Wilcox
Department of Applied Economics, Utah State University, Logan, UT, USA
Patrick E. Clark
Northwest Watershed Research Center, USDA Agricultural Research Service, Boise, ID, USA
Francesco P. Fava
Department of Environmental Science and Policy, Università Degli Studi Di Milano, Milan, Italy
Nathaniel D. Jensen
The Global Academy of Agriculture and Food Systems, University of Edinburgh, Edinburgh, Scotland
Njoki Kahiu
Department of Plant and Environmental Sciences, New Mexico State University, Las Cruces, NM, USA
Chuan Liao
Department of Global Development, Cornell University, Ithaca, NY, USA
Benjamin Porter
Forest Ecosystem Monitoring Cooperative, University of Vermont, Burlington, VT, USA
Ying Sun
School of Integrative Plant Science, Soil and Crop Sciences Section, Cornell University, Ithaca, NY, USA
Christopher B. Barrett
Charles H. Dyson School of Applied Economics and Management, Cornell University, Ithaca, NY, USA
Jeb E. Brooks School of Public Policy, Cornell University, Ithaca, NY, USA
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Alouette van Hove, Kristoffer Aalstad, Vibeke Lind, Claudia Arndt, Vincent Odongo, Rodolfo Ceriani, Francesco Fava, John Hulth, and Norbert Pirk
EGUsphere, https://doi.org/10.5194/egusphere-2024-3994, https://doi.org/10.5194/egusphere-2024-3994, 2025
This preprint is open for discussion and under review for Biogeosciences (BG).
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Research on methane emissions from African livestock is limited. We used a probabilistic method fusing drone and flux tower observations with an atmospheric model to estimate emissions from various herds. This approach proved robust under non-stationary wind conditions and effective in estimating emissions as low as 100 g h-1. We also detected herd locations using spectral anomalies in satellite data. Our approach can be used to estimate diverse sources, thereby improving emission inventories.
Jiaming Wen, Giulia Tagliabue, Micol Rossini, Francesco Pietro Fava, Cinzia Panigada, Lutz Merbold, Sonja Leitner, and Ying Sun
EGUsphere, https://doi.org/10.5194/egusphere-2024-2529, https://doi.org/10.5194/egusphere-2024-2529, 2024
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Solar-induced chlorophyll fluorescence (SIF), a tiny optical signal emitted from the core photosynthetic machinery, has emerged as a promising tool to evaluate vegetation growth from satellites. We find satellite SIF can capture intra-seasonal (i.e., from days to weeks) vegetation dynamics of dryland ecosystems, while greenness-based vegetation indices cannot. This study generates novel insights for developing effective real-time vegetation monitoring systems to inform climate risk management.
Russell Doughty, Thomas P. Kurosu, Nicholas Parazoo, Philipp Köhler, Yujie Wang, Ying Sun, and Christian Frankenberg
Earth Syst. Sci. Data, 14, 1513–1529, https://doi.org/10.5194/essd-14-1513-2022, https://doi.org/10.5194/essd-14-1513-2022, 2022
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We describe and compare solar-induced chlorophyll fluorescence data produced by NASA from the Greenhouse Gases Observing Satellite (GOSAT) and the Orbiting Carbon Observatory-2 (OCO-2) and OCO-3 platforms.
Debsunder Dutta, David S. Schimel, Ying Sun, Christiaan van der Tol, and Christian Frankenberg
Biogeosciences, 16, 77–103, https://doi.org/10.5194/bg-16-77-2019, https://doi.org/10.5194/bg-16-77-2019, 2019
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Canopy structural and leaf photosynthesis parameterizations are often fixed over time in Earth system models and represent large sources of uncertainty in predictions of carbon and water fluxes. We develop a moving window nonlinear optimal parameter inversion framework using constraining flux and satellite reflectance observations. The results demonstrate the applicability of the approach for error reduction and capturing the seasonal variability of key ecosystem parameters.
Related subject area
Domain: ESSD – Land | Subject: Land Cover and Land Use
EARice10: a 10 m resolution annual rice distribution map of East Asia for 2023
A Sentinel-2 machine learning dataset for tree species classification in Germany
High-resolution mapping of global winter-triticeae crops using a sample-free identification method
A flux tower site attribute dataset intended for land surface modeling
Advances in LUCAS Copernicus 2022: enhancing Earth observations with comprehensive in situ data on EU land cover and 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
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
Revised and updated geospatial monitoring of twenty-first century forest carbon fluxes
20 m Africa Rice Distribution Map of 2023
Time-series of Landsat-based bi-monthly and annual spectral indices for continental Europe for 2000–2022
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
The Earth Topography 2022 (ETOPO 2022) Global DEM dataset
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
Above ground biomass dataset from SMOS L band vegetation optical depth and reference maps
ChinaSoyArea10m: a dataset of soybean-planting areas with a spatial resolution of 10 m across China from 2017 to 2021
Annual vegetation maps in Qinghai-Tibet Plateau (QTP) from 2000 to 2022 based on MODIS series satellite imagery
Physical, social, and biological attributes for improved understanding and prediction of wildfires: FPA FOD-Attributes dataset
ChatEarthNet: A Global-Scale Image-Text Dataset Empowering Vision-Language Geo-Foundation Models
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
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
Annual maps of forest cover in the Brazilian Amazon from analyses of PALSAR and MODIS images
Global 500 m seamless dataset (2000–2022) of land surface reflectance generated from MODIS products
The first map of crop sequence types in Europe over 2012–2018
WorldCereal: a dynamic open-source system for global-scale, seasonal, and reproducible crop and irrigation mapping
A new cropland area database by country circa 2020
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)
Mingyang Song, Lu Xu, Ji Ge, Hong Zhang, Lijun Zuo, Jingling Jiang, Yinhaibin Ding, Yazhe Xie, and Fan Wu
Earth Syst. Sci. Data, 17, 661–683, https://doi.org/10.5194/essd-17-661-2025, https://doi.org/10.5194/essd-17-661-2025, 2025
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We created a 10 m resolution rice distribution map for East Asia in 2023 (EARice10), achieving an overall accuracy (OA) of 90.48 % on validation samples. EARice10 shows strong consistency with statistical data (coefficient of determination, R2: 0.94–0.98) and existing datasets (R2: 0.79–0.98). It is the most up-to-date map, covering the four major rice-producing countries in East Asia at 10 m resolution.
Maximilian Freudenberg, Sebastian Schnell, and Paul Magdon
Earth Syst. Sci. Data, 17, 351–367, https://doi.org/10.5194/essd-17-351-2025, https://doi.org/10.5194/essd-17-351-2025, 2025
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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 the whole of Germany for the years 2015 to 2022 and comprises 48 species and 3 species groups.
Yangyang Fu, Xiuzhi Chen, Chaoqing Song, Xiaojuan Huang, Jie Dong, Qiongyan Peng, and Wenping Yuan
Earth Syst. Sci. Data, 17, 95–115, https://doi.org/10.5194/essd-17-95-2025, https://doi.org/10.5194/essd-17-95-2025, 2025
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This study proposed the Winter-Triticeae Crops Index (WTCI), which had great performance and stable spatiotemporal transferability in identifying winter-triticeae crops in 66 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.
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, 17, 117–134, https://doi.org/10.5194/essd-17-117-2025, https://doi.org/10.5194/essd-17-117-2025, 2025
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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.
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, 16, 5723–5735, https://doi.org/10.5194/essd-16-5723-2024, https://doi.org/10.5194/essd-16-5723-2024, 2024
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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
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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.
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
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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
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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
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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
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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
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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
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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
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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.
David A. Gibbs, Melissa Rose, Giacomo Grassi, Joana Melo, Simone Rossi, Viola Heinrich, and Nancy L. Harris
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-397, https://doi.org/10.5194/essd-2024-397, 2024
Revised manuscript accepted for ESSD
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Updated global maps of greenhouse gas emissions and sequestration by forests from 2001 onwards using satellite-derived data show that forests are strong net carbon sinks, capturing about as much CO2 each year on average as the United States emits from fossil fuels. After reclassifying fluxes to countries’ reporting categories for national greenhouse gas inventories, we found that roughly two-thirds of the total net flux from forests is anthropogenic and one-third is non-anthropogenic.
Jingling Jiang, Hong Zhang, Ji Ge, Lijun Zuo, Lu Xu, Minyang Song, Yinhaibin Ding, Yazhe Xie, and Wenjiang Huang
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-402, https://doi.org/10.5194/essd-2024-402, 2024
Revised manuscript accepted for ESSD
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This study employs temporal SAR data and optical imagery to conduct rice extraction experiments in 34 African countries with annual rice planting areas exceeding 5,000 hectares, achieving 20-meter resolution spatial distribution mapping of rice in Africa for 2023. The average classification accuracy on the validation set exceeded 85 %, and the R² values for linear fitting with existing statistical data all surpassed 0.9, demonstrating the effectiveness of the proposed mapping method.
Xuemeng Tian, Davide Consoli, Martijn Witjes, Florian Schneider, Leandro Parente, Murat Şahin, Yu-Feng Ho, Robert Minařík, and Tomislav Hengl
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-266, https://doi.org/10.5194/essd-2024-266, 2024
Revised manuscript accepted for ESSD
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Our study introduces a Landsat-based data cube simplifying access to detailed environmental data across Europe from 2000 to 2022, covering vegetation, water, soil, and crops. Our experiments demonstrate its effectiveness in developing environmental models and maps. Tailored feature selection is crucial for its effective use in environmental modeling. It aims to support comprehensive environmental monitoring and analysis, helping researchers and policymakers in managing environmental resources.
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
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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
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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.
Michael MacFerrin, Christopher Amante, Kelly Carignan, Matthew Love, and Elliot Lim
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-250, https://doi.org/10.5194/essd-2024-250, 2024
Revised manuscript accepted for ESSD
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Here we present Earth TOPOgraphy (ETOPO) 2022, the latest iteration of NOAA’s global, seamless topographic-bathymetric dataset. ETOPO 2022 is a significant upgrade in resolution and accuracy from previous ETOPO releases, freely available in multiple data formats and resolutions for all uses (public or private), excepting navigation.
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
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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
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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
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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.
Simon Boitard, Arnaud Mialon, Stéphane Mermoz, Nemesio J. Rodríguez-Fernández, Philippe Richaume, Julio César Salazar-Neira, Stéphane Tarot, and Yann H. Kerr
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-184, https://doi.org/10.5194/essd-2024-184, 2024
Revised manuscript accepted for ESSD
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Above Ground Biomass (AGB) is a critical component of the Earth carbon cycle. The presented dataset aims to help monitoring this essential climate variable with AGB time series from 2011 onward, derived with a carefully calibrated spatial relationship between the measurements of the Soil Moisture and Ocean Salinity (SMOS) mission and pre-existing AGB maps. The produced dataset has been extensively compared with other available AGB time series and can be used in AGB 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
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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.
Guangsheng Zhou, Hongrui Ren, Lei Zhang, Xiaomin Lv, and Mengzi Zhou
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-193, https://doi.org/10.5194/essd-2024-193, 2024
Revised manuscript accepted for ESSD
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This study developed a new approach to long-time continuous annual vegetation mapping from remote sensing imagery, and mapped the vegetation of the Qinghai-Tibet Plateau (QTP) from 2000 to 2022 through the MOD09A1 product. The overall accuracy of continuous annual QTP vegetation mapping reached 80.9%, with the reference annual 2020 reaching an accuracy of 86.5% and a Kappa coefficient of 0.85. The study supports the use of remote sensing data to mapping a long-time continuous annual vegetation.
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
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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.
Zhenghang Yuan, Zhitong Xiong, Lichao Mou, and Xiao Xiang Zhu
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-140, https://doi.org/10.5194/essd-2024-140, 2024
Revised manuscript accepted for ESSD
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ChatEarthNet is an image-text dataset that provides high-quality, detailed natural language descriptions for global-scale satellite data. It consists of 163,488 image-text pairs with captions generated by ChatGPT-3.5, and an additional 10,000 image-text pairs with captions generated by ChatGPT-4V(ision). This dataset has significant potential for training and evaluating vision-language geo-foundation models in remote sensing.
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
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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
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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
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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
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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.
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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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WorldCereal is a global mapping system that addresses food security challenges. It provides seasonal updates on crop areas and irrigation practices, enabling informed decision-making for sustainable agriculture. Our global products offer insights into temporary crop extent, seasonal crop type maps, and seasonal irrigation patterns. WorldCereal is an open-source tool that utilizes space-based technologies, revolutionizing global agricultural mapping.
Francesco N. Tubiello, Giulia Conchedda, Leon Casse, Pengyu Hao, Giorgia De Santis, and Zhongxin Chen
Earth Syst. Sci. Data, 15, 4997–5015, https://doi.org/10.5194/essd-15-4997-2023, https://doi.org/10.5194/essd-15-4997-2023, 2023
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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
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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
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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
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Historical, fine-grained geospatial datasets on built-up areas are rarely available, constraining studies of urbanization, settlement evolution, or the dynamics of human–environment interactions to recent decades. In order to provide such historical data, we used publicly available cadastral building data for Spain and created a series of gridded surfaces, measuring age, physical, and land-use-related features of the built environment in Spain and the evolution of settlements from 1900 to 2020.
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
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The UK Land Cover Map 2021 (LCM2021) is a UK-wide land cover data set, with 21- and 10-class versions. It is intended to support a broad range of UK environmental research, including ecological and hydrological research. LCM2021 was produced by classifying Sentinel-2 satellite imagery. LCM2021 is distributed as a suite of products to facilitate easy use for a range of applications. To support research at different spatial scales it includes 10 m, 25 m and 1 km resolution products.
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
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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
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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.
Cited articles
Abatzoglou, J. T., Dobrowski, S. Z., Parks, S. A., and Hegewisch, K. C.: TerraClimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958–2015, Sci. Data, 5, 1–12, https://doi.org/10.1038/sdata.2017.191, 2018.
Adams, E. C., Parache, H. B., Cherrington, E., Ellenburg, W. L., Mishra, V., Lucey, R., and Nakalembe, C.: Limitations of remote sensing in assessing vegetation damage due to the 2019–2021 desert locust upsurge, Front. Climate, 3, 714273, https://doi.org/10.3389/fclim.2021.714273, 2021.
AghaKouchak, A., Farahmand, A., Melton, F. S., Teixeira, J., Anderson, M. C., Wardlow, B. D., and Hain, C. R.: Remote sensing of drought: Progress, challenges and opportunities, Rev. Geophys., 53, 452-480, https://doi.org/10.1002/2014RG000456, 2015.
Allred, B. W., Bestelmeyer, B. T., Boyd, C. S., Brown, C., Davies, K. W., Duniway, M. C., Ellsworth, L. M., Erickson, T. A., Fuhlendorf, S. D., Griffiths, T. V., Jansen, V., Jones, M. O., Karl, J., Knight, A., Maestas, J. D., Maynard, J. J., McCord, S. E., Naugle, D. E., Starns, H. D., Twidwell, D., and Uden, D. R.: Improving Landsat predictions of rangeland fractional cover with multitask learning and uncertainty, Methods Ecol. Evol., 12, 841–849, https://doi.org/10.1111/2041-210X.13564, 2021.
Allred, B. W., Creutzburg, M. K., Carlson, J. C., Cole, C. J., Dovichin, C. M., Duniway, M. C., Jones, M. O., Maestas, J. D., Naugle, D. E., Nauman, T. W., Okin, G. S., Reeves, M. C., Rigge, M., Savage, S. L., Twidwell, D., Uden, D. R., and Zhou, B.: Guiding principles for using satellite-derived maps in rangeland management, Rangelands, 44, 78–86, https://doi.org/10.1016/j.rala.2021.09.004, 2022.
Angassa, A.: Effects of grazing intensity and bush encroachment on herbaceous species and rangeland condition in southern Ethiopia, Land. Degrad. Dev., 25, 438–451, https://doi.org/10.1002/ldr.2160, 2014.
Angassa, A. and Oba, G.: Herder perceptions on impacts of range enclosures, crop farming, fire ban and bush encroachment on the rangelands of Borana, Southern Ethiopia, Hum. Ecol., 36, 201–215, https://doi.org/10.1007/s10745-007-9156-z, 2008.
Barbier, E. B. and Hochard, J. P.: Land degradation and poverty, Nat. Sustain., 1, 623–631, https://doi.org/10.1038/s41893-018-0155-4, 2018.
Beal, T., Gardner, C. D., Herrero, M., Iannotti, L. L., Merbold, L., Nordhagen, S., and Mottet, A.: Friend or foe? The role of animal-source foods in healthy and environmentally sustainable diets, J. Nutr., 153, 409–425, https://doi.org/10.1016/j.tjnut.2022.10.016, 2023.
Belgiu, M., and Drăguţ, 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.
Bestelmeyer, B. T., Ash, A., Brown, J. R., Densambuu, B., Fernández-Giménez, M., Johanson, J., Levi, M., Lopez, D., Peinetti, R., Rumpff, L., and Shaver, P.: State and transition models: theory, applications, and challenges. Rangeland systems: Processes, management and challenges, in: Rangeland Systems, Springer Series on Environmental Management, edited by: Briske, D., Springer, Cham, 303–345, https://doi.org/10.1007/978-3-319-46709-2_9, 2017.
Blackwell, P. J.: East Africa's Pastoralist Emergency: is climate change the straw that breaks the camel's back?, Third World Q., 31, 1321–1338, https://doi.org/10.1080/01436597.2010.541085, 2010.
Blanco, P. D., del Valle, H. F., Bouza, P. J., Metternicht, G. I., and Hardtke, L. A.: Ecological site classification of semiarid rangelands: Synergistic use of Landsat and Hyperion imagery, Int. J. Appl. Earth Obs. Geoinf., 29, 11–21, https://doi.org/10.1016/j.jag.2013.12.011, 2014.
Brandt, M., Tucker, C. J., Kariryaa, A., Rasmussen, K., Abel, C., Small, J., Chave, J., Rasmussen, L. V., Hiernaux, P., Diouf, A. A., Kergoat, L., Mertz, O., Igel, C., Gieseke, F., Schöning, J., Li, S., Melocik, K., Meyer, J., Sinno, S., Romero, E., Glennie, E., Montagu, A., Dendoncker, M., and Fensholt, R.: An unexpectedly large count of trees in the West African Sahara and Sahel, Nature, 587, 78–82, https://doi.org/10.1038/s41586-020-2824-5, 2020.
Browning, D. M., Maynard, J. J., Karl, J. W., and Peters, D. C.: Breaks in MODIS time series portend vegetation change: verification using long-term data in an arid grassland ecosystem, Ecol. Appl., 27, 1677–1693, https://doi.org/10.1002/eap.1561, 2017.
Browning, D. M., Snyder, K. A., and Herrick, J. E.: Plant phenology: Taking the pulse of rangelands, Rangelands, 41, 129–134, https://doi.org/10.1016/j.rala.2019.02.001, 2019.
Buchhorn, M., Smets, B., Bertels, L., De Roo, B., Lesiv, M., Tsendbazar, N. E., Linlin, L., and Tarko, A.: Copernicus Global Land Service: Land Cover 100m: Version 3 Globe 2015–2019: Product User Manual, Geneve, Switzerland, 22 pp., Zenodo, https://doi.org/10.5281/zenodo.3938963, 2020.
Carlotto, M. J.: Effect of errors in ground truth on classification accuracy, Int. J. Remote Sens., 30, 4831–4849, https://doi.org/10.1080/01431160802672864, 2009.
Cheng, Y., Vrieling, A., Fava, F., Meroni, M., Marshall, M., and Gachoki, S.: Phenology of short vegetation cycles in a Kenyan rangeland from PlanetScope and Sentinel-2, Remote Sens. Environ., 248, 112004, https://doi.org/10.1016/j.rse.2020.112004, 2020.
Claverie, M., Ju, J., Masek, J. G., Dungan, J. L., Vermote, E. F., Roger, J. C., Skakun, S. V., and Justice, C.: The Harmonized Landsat and Sentinel-2 surface reflectance data set, Remote Sens. Environ., 219, 145–161, https://doi.org/10.1016/j.rse.2018.09.002, 2018.
Coffer, M. M., Schaeffer, B. A., Zimmerman, R. C., Hill, V., Li, J., Islam, K. A., and Whitman, P. J.: Performance across WorldView-2 and RapidEye for reproducible seagrass mapping, Remote Sens. Environ., 250, 112036, https://doi.org/10.1016/j.rse.2020.112036, 2020.
Cooley, S. W., Smith, L. C., Stepan, L., and Mascaro, J.: Tracking dynamic northern surface water changes with high-frequency planet CubeSat imagery, Remote Sens., 9, 1306, https://doi.org/10.3390/rs9121306, 2017.
Drusch, M., Del Bello, U., Carlier, S., Colin, O., Fernandez, V., Gascon, F., and Bargellini, P.: Sentinel-2: ESA's optical high-resolution mission for GMES operational services, Remote Sens. Environ., 120, 25–36, 2012.
Dube, T., Shoko, C., Sibanda, M., Madileng, P., Maluleke, X. G., Mokwatedi, V. R., Tibane, L., and Tshebesebe, T.: Remote sensing of invasive Lantana camara (verbenaceae) in semiarid savanna rangeland ecosystems of south africa, Rangeland Ecol. Manag., 73, 411–419, https://doi.org/10.1016/j.rama.2020.01.003, 2020.
Ellis, E. C., Wang, H., Xiao, H. S., Peng, K., Liu, X. P., Li, S. C., Ouyang, H., Cheng, X., and Yang, L. Z.: Measuring long-term ecological changes in densely populated landscapes using current and historical high resolution imagery, Remote Sens. Environ., 100, 457–473, https://doi.org/10.1016/j.rse.2005.11.002, 2006.
Elmes, A., Alemohammad, H., Avery, R., Caylor, K., Eastman, J. R., Fishgold, L., Friedl, M. A., Jain, M., Kohli, D., Laso Bayas, J. C., Lunga, D., McCarty, J. L., Pontius, R. G., Reinmann, A. B., Rogan, J., Song, L., Stoynova, H., Ye, S., Yi, Z. F., and Estes, L.: Accounting for training data error in machine learning applied to Earth observations, Remote Sens., 12, 1034, https://doi.org/10.3390/rs12061034, 2020.
Fava, F. and Vrieling, A.: Earth observation for drought risk financing in pastoral systems of sub-Saharan Africa, Curr. Opin. Env. Sust., 48, 44–52, https://doi.org/10.1016/j.cosust.2020.09.006, 2021.
Foga, S., Scaramuzza, P. L., Guo, S., Zhu, Z., Dilley Jr, R. D., Beckmann, T., Schmidt, G. L., Dwyer, J. L., Hughes, M. J., and Laue, B.: Cloud detection algorithm comparison and validation for operational Landsat data products, Remote Sens. Environ., 194, 379–390, https://doi.org/10.1016/j.rse.2017.03.026, 2017.
Foody, G. M.: Status of land cover classification accuracy assessment, Remote Sens. Environ., 80, 185–201, https://doi.org/10.1016/S0034-4257(01)00295-4, 2002.
Foody, G. M.: The impact of imperfect ground reference data on the accuracy of land cover change estimation, Int. J. Remote Sens., 30, 3275–3281, https://doi.org/10.1080/01431160902755346, 2009.
Franks, S., Storey, J., and Rengarajan, R.: The new Landsat collection-2 digital elevation model, Remote Sens., 12, 3909, https://doi.org/10.3390/rs12233909, 2020.
Fuhlendorf, S. D., Engle, D. M., Elmore, R. D., Limb, R. F., and Bidwell, T. G.: Conservation of pattern and process: developing an alternative paradigm of rangeland management, Rangeland Ecol. Manag., 65, 579–589, https://doi.org/10.2111/REM-D-11-00109.1, 2012.
Gao, B. C.: NDWI – A normalized difference water index for remote sensing of vegetation liquid water from space, Remote Sens. Environ., 58, 257–266, https://doi.org/10.1016/S0034-4257(96)00067-3, 1996.
Ghafari, S., Ghorbani, A., Moameri, M., Mostafazadeh, R., and Bidarlord, M.: Composition and structure of species along altitude gradient in Moghan-Sabalan rangelands, Iran, J. Mt. Sci., 15, 1209–1228, https://doi.org/10.1007/s11629-017-4820-2, 2018.
Giuliani, G., Mazzetti, P., Santoro, M., Nativi, S., Van Bemmelen, J., Colangeli, G., and Lehmann, A.: Knowledge generation using satellite earth observations to support sustainable development goals (SDG): A use case on Land degradation, Int. J. Appl. Earth Obs., 88, 102068, https://doi.org/10.1016/j.jag.2020.102068, 2020.
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.
Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., and Moore, R.: Google Earth Engine: Planetary-scale geospatial analysis for everyone, Remote Sens. Environ., 202, 18–27, https://doi.org/10.1016/j.rse.2017.06.031, 2017.
Graetz, R. D., Carneggie, D. M., Hacker, R., Lendon, C., and Wilcox, D. G.: A quantitative evaluation of Landsat imagery of Australian rangelands, Rangel. J., 1, 53–59, https://doi.org/10.1071/RJ9760053, 1976.
Hansen, M. C., Townshend, J. R., DeFries, R. S., and Carroll, M.: Estimation of tree cover using MODIS data at global, continental and regional/local scales, Int. J. Remote Sens., 26, 4359–4380, https://doi.org/10.1080/01431160500113435, 2005.
Hill, M. J. and Guerschman, J. P.: The MODIS Global Vegetation Fractional Cover Product 2001–2018: Characteristics of Vegetation Fractional Cover in Grasslands and Savanna Woodlands, Remote Sens., 12, 406, https://doi.org/10.3390/rs12030406, 2020.
Hill, M. J. and Guerschman, J. P.: Global trends in vegetation fractional cover: Hotspots for change in bare soil and non-photosynthetic vegetation, Agr. Ecosyst. Environ., 324, 107719, https://doi.org/10.1016/j.agee.2021.107719, 2022.
Hoffman, T. and Vogel, C.: Climate change impacts on African rangelands, Rangelands, 30, 12–17, https://doi.org/10.2111/1551-501X(2008)30[12:CCIOAR]2.0.CO;2, 2008.
ILRI, IUCN, FAO, WWF, UNEP and ILC.: Rangelands Atlas. Nairobi Kenya: ILRI, ISBN 978-1-904722-67-0, 2021.
IPCC: Climate Change 2022: Impacts, Adaptation, and Vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, edited by: Pörtner, H.-O., Roberts, D. C., Tignor, M., Poloczanska, E. S., Mintenbeck, K., Alegría, A., Craig, M., Langsdorf, S., Löschke, S., Möller, V., Okem, A., and Rama, B., Cambridge University Press, Cambridge University Press, Cambridge, UK and New York, NY, USA, 3056 pp., https://doi.org/10.1017/9781009325844, 2022.
Jarvis, A., Reuter, H. I., Nelson, A., and Guevara, E.: Hole-filled seamless SRTM data Version 4, available from the CGIAR-CSI SRTM 90m, International Centre for Tropical Agriculture CIAT, https://srtm.csi.cgiar.org (last access: 10 November 2023), 2008.
Jones, M. O., Allred, B. W., Naugle, D. E., Maestas, J. D., Donnelly, P., Metz, L. J., Karl, J., Smith, R., Bestelmeyer, B., Boyd, C., Kerby, J. D., and McIver, J. D.: Innovation in rangeland monitoring: annual, 30 m, plant functional type percent cover maps for US rangelands, 1984–2017, Ecosphere, 9, e02430, https://doi.org/10.1002/ecs2.2430, 2018.
Jones, M. O., Naugle, D. E., Twidwell, D., Uden, D. R., Maestas, J. D., and Allred, B. W.: Beyond inventories: Emergence of a new era in rangeland monitoring, Rangeland Ecol. Manag., 73, 577–583, https://doi.org/10.1016/j.rama.2020.06.009, 2020.
Li, G., Han, W., Dong, Y., Zhai, X., Huang, S., Ma, W., Cui, X., and Wang, Y.: Multi-Year Crop Type Mapping Using Sentinel-2 Imagery and Deep Semantic Segmentation Algorithm in the Hetao Irrigation District in China, Remote Sens., 15, 875, https://doi.org/10.3390/rs15040875, 2023.
Liao, C. and Clark, P. E.: Rangeland vegetation diversity and transition pathways under indigenous pastoralist management regimes in southern Ethiopia, Agr. Ecosyst. Environ., 252, 105–113, https://doi.org/10.1016/j.agee.2017.10.009, 2018.
Liao, C. and Fei, D.: Pastoralist Adaptation Practices under Non-Governmental Development Interventions in Southern Ethiopia, Rangeland J., 39, 189–200, https://doi.org/10.1071/RJ16015, 2017.
Liao, C., Clark, P. E., and DeGloria, S. D.: Bush encroachment dynamics and rangeland management implications in southern Ethiopia, Ecol. Evol., 8, 11694–11703, https://doi.org/10.1002/ece3.4621, 2018.
Lin, C., Jin, Z., Mulla, D., Ghosh, R., Guan, K., Kumar, V., and Cai, Y.: Toward Large-Scale Mapping of Tree Crops with High-Resolution Satellite Imagery and Deep Learning Algorithms: A Case Study of Olive Orchards in Morocco, Remote Sens., 13, 1740, https://doi.org/10.3390/rs13091740, 2021.
Liu, H. Q. and Huete, A.: A feedback based modification of the NDVI to minimize canopy background and atmospheric noise, IEEE T. Geosci. Remote., 33, 457–465, https://doi.org/10.1109/TGRS.1995.8746027, 1995.
Loveland, T. R. and Dwyer, J. L.: Landsat: Building a strong future, Remote Sens. Environ., 122, 22–29, https://doi.org/10.1016/j.rse.2011.09.022, 2012.
Magurran, A. E., Baillie, S. R., Buckland, S. T., Dick, J. M., Elston, D. A., Scott, E. M., Smith, R. I., Somerfield, P. J., and Watt, A. D.: Long-term datasets in biodiversity research and monitoring: assessing change in ecological communities through time, Trends Ecol. Evol., 25, 574–582, https://doi.org/10.1016/j.tree.2010.06.016, 2010.
Markham, B. L., Storey, J. C., Williams, D. L., and Irons, J. R. Landsat sensor performance: history and current status, IEEE T. Geosci. Remote Sens., 42, 2691–2694, https://doi.org/10.1109/TGRS.2004.840720, 2004.
Matongera, T. N., Mutanga, O., Sibanda, M., and Odindi, J.: Estimating and Monitoring Land Surface Phenology in Rangelands: A Review of Progress and Challenges, Remote Sens., 13, 2060, https://doi.org/10.3390/rs13112060, 2021.
McFeeters, S. K.: The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features, Int. J. Remote Sens., 17, 1425–1432, https://doi.org/10.1080/01431169608948714, 1996.
Müller, M.: Information retrieval for music and motion, Springer, Berlin, 313 pp., ISBN 978-3-540-74048-3, https://doi.org/10.1007/978-3-540-74048-3, 2007.
Mundia, C. N. and Aniya, M.: Analysis of land use/cover changes and urban expansion of Nairobi city using remote sensing and GIS, Int. J. Remote Sens., 26, 2831–2849, https://doi.org/10.1080/01431160500117865, 2005.
Nandintsetseg, B., Chang, J., Sen, O. L., Reyer, C. P., Kong, K., Yetemen, O., Ciais, P., and Davaadalai, J.: Future drought risk and adaptation of pastoralism in Eurasian rangelands, npj Clim. Atmos. Sci., 7, 82, https://doi.org/10.1038/s41612-024-00624-2, 2024.
Oba, G., Weladji, R. B., Lusigi, W. J., and Stenseth, N. C.: Scale-dependent effects of grazing on rangeland degradation in northern Kenya: a test of equilibrium and non-equilibrium hypotheses, Land Degrad. Dev., 14, 83–94, https://doi.org/10.1002/ldr.524, 2003.
Okal, H. A., Ngetich, F. K., and Okeyo, J. M.: Spatio-temporal characterization of droughts using selected indices in Upper Tana River watershed, Kenya, Sci. African, 7, e00275, https://doi.org/10.1016/j.sciaf.2020.e00275, 2020.
Ott, J. E., Kilkenny, F. F., Summers, D. D., and Thompson, T. W.: Long-term vegetation recovery and invasive annual suppression in native and introduced postfire seeding treatments, Rangeland Ecol. Manag., 72, 640–653, https://doi.org/10.1016/j.rama.2019.02.001, 2019.
Padial-Iglesias, M., Serra, P., Ninyerola, M., and Pons, X.: A Framework of Filtering Rules over Ground Truth Samples to Achieve Higher Accuracy in Land Cover Maps, Remote Sens., 13, 2662, https://doi.org/10.3390/rs13142662, 2021.
Pellant, M., Shaver, P. L., Pyke, D. A., Herrick, J. E., Lepak, N., Riegel, G., Kachergis, E., Newingham, B. A., Toledo, D., and Busby, F. E.: Interpreting Indicators of Rangeland Health, Version 5, Tech Ref 1734-6, U.S. Department of the Interior, Bureau of Land Management, National Operations Center, Denver, CO, https://pubs.er.usgs.gov/publication/70215720 (last access: 10 November 2023), 2020.
Pratt, D. J., Greenway, P. J., and Gwynne, M. D.: A classification of East African rangeland, with an appendix on terminology, J. Appl. Ecol., 3, 369–382, https://doi.org/10.2307/2401259, 1966.
Pricope, N. G., Husak, G., Lopez-Carr, D., Funk, C., and Michaelsen, J.: The climate-population nexus in the East African Horn: Emerging degradation trends in rangeland and pastoral livelihood zones, Global Environ. Chang., 23, 1525–1541, https://doi.org/10.1016/j.gloenvcha.2013.10.002, 2013.
Qi, J., Chehbouni, A., Huete, A. R., Kerr, Y. H., and Sorooshian, S.: A modified soil adjusted vegetation index, Remote Sens. Environ., 48, 119–126, https://doi.org/10.1016/0034-4257(94)90134-1, 1994.
Quintano, C., Fernández-Manso, A., Shimabukuro, Y. E., and Pereira, G.: Spectral unmixing, Int. J. Remote Sens., 33, 5307–5340, https://doi.org/10.1080/01431161.2012.661095, 2012.
Račič, M., Oštir, K., Zupanc, A., and Čehovin Zajc, L.: Multi-Year Time Series Transfer Learning: Application of Early Crop Classification, Remote Sens., 16, 270, https://doi.org/10.3390/rs16020270, 2024.
Reeves, M. C. and Baggett, L. S.: A remote sensing protocol for identifying rangelands with degraded productive capacity, Ecol. Indic., 43, 172–182, https://doi.org/10.1016/j.ecolind.2014.02.009, 2014.
Reid, R. S., Galvin, K. A., and Kruska, R. S.: Global significance of extensive grazing lands and pastoral societies: an introduction, in: Fragmentation in semi-arid and arid landscapes, edited by: Galvin, K. A., Reid, R. S, Behnke Jr., R. H., and Hobbs, N. T., Springer, Dordrecht, 1–24, https://doi.org/10.1007/978-1-4020-4906-4, 2008.
Rigge, M., Shi, H., Homer, C., Danielson, P., and Granneman, B.: Long-term trajectories of fractional component change in the Northern Great Basin, USA, Ecosphere, 10, e02762, https://doi.org/10.1002/ecs2.2762, 2019.
Rigge, M., Homer, C., Cleeves, L., Meyer, D. K., Bunde, B., Shi, H., Xian, G., Schell, S., and Bobo, M.: Quantifying western US rangelands as fractional components with multi-resolution remote sensing and in situ data, Remote Sens., 12, 412, https://doi.org/10.3390/rs12030412, 2020.
Rigge, M., Homer, C., Shi, H., Meyer, D., Bunde, B., Granneman, B., Postma, K., Danielson, P., Case, A., and Xian, G.: Rangeland fractional components across the Western United States from 1985 to 2018, Remote Sens., 13, 813, https://doi.org/10.3390/rs13040813, 2021.
Rogan, J., Miller, J., Stow, D., Franklin, J., Levien, L., and Fischer, C.: Land-cover change monitoring with classification trees using Landsat TM and ancillary data, Photogramm. Eng. Rem. S., 69, 793–804, https://doi.org/10.14358/PERS.69.7.793, 2003.
Roques, K. G., O’Connor, T. G., and Watkinson, A. R.: Dynamics of shrub encroachment in an African savanna: relative influences of fire, herbivory, rainfall and density dependence, J. Appl. Ecol., 38, 268–280, https://doi.org/10.1046/j.1365-2664.2001.00567.x, 2001.
Roy, D. P., Kovalskyy, V., Zhang, H. K., Vermote, E. F., Yan, L., Kumar, S. S., and Egorov, A.: Characterization of Landsat-7 to Landsat-8 reflective wavelength and normalized difference vegetation index continuity, Remote Sens. Environ., 185, 57–70, https://doi.org/10.1016/j.rse.2015.12.024, 2016.
Safanelli, J. L., Poppiel, R. R., Ruiz, L. F. C., Bonfatti, B. R., Oliveira Mello, F. A. D., Rizzo, R., and Demattê, J. A.: Terrain Analysis in Google Earth Engine: A Method Adapted for High-Performance Global-Scale Analysis, ISPRS Int. Geo-Inf., 9, 400, https://doi.org/10.3390/ijgi9060400, 2020.
Sankey, T. T., Leonard, J. M., and Moore, M. M.: Unmanned aerial vehicle-based rangeland monitoring: examining a century of vegetation changes, Rangeland Ecol. Manag., 72, 858–863, https://doi.org/10.1016/j.rama.2019.04.002, 2019.
Sankey, T. T., Leonard, J., Moore, M. M., Sankey, J. B., and Belmonte, A.: Carbon and ecohydrological priorities in managing woody encroachment: UAV perspective 63 years after a control treatment, Environ. Res. Lett., 16, 124053, https://doi.org/10.1088/1748-9326/ac3796, 2021.
Sayre, N. F., McAllister, R. R., Bestelmeyer, B. T., Moritz, M., and Turner, M. D.: Earth stewardship of rangelands: coping with ecological, economic, and political marginality, Front. Ecol. Environ., 11, 348–354, https://doi.org/10.1890/120333, 2013.
Schmidt, G. L., Jenkerson, C., Masek, J. G., Vermote, E., and Gao, F.: Landsat ecosystem disturbance adaptive processing system (LEDAPS) algorithm description, 17 pp., https://pubs.usgs.gov/of/2013/1057/ (last access: 10 November 2023), 2013.
Seidel, M. and Hlawitschka, M.: An R-Based function for modeling of end member compositions, Math. Geosci., 47, 995–1007, https://doi.org/10.1007/s11004-015-9609-7, 2015.
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.
Soto, G. E., Wilcox, S., Clark, P. E., Fava, F. P., Jensen, N. M., Kahiu, N., Liao, C., Porter, B., Sun, Y., and Barrett, C. B.: Mapping Rangeland Health Indicators in East Africa from 2000 to 2022, Zenodo [data set], https://doi.org/10.5281/zenodo.7106166, 2023.
Steele, C. M., Bestelmeyer, B. T., Burkett, L. M., Smith, P. L., and Yanoff, S.: Spatially explicit representation of state-and-transition models, Rangeland Ecol. Manage., 65, 213–222, https://doi.org/10.2111/REM-D-11-00047.1, 2012.
Sudmanns, M., Tiede, D., Lang, S., Bergstedt, H., Trost, G., Augustin, H., Baraldi, A., and Blaschke, T.: Big Earth data: disruptive changes in Earth observation data management and analysis?, Int. J. Digit. Earth, 13, 832–850, https://doi.org/10.1080/17538947.2019.1585976, 2020.
Thenkabail, P. S. (Ed.): Remotely sensed data characterization, classification, and accuracies, CRC press, https://doi.org/10.1201/b19294, 2015.
Treitz, P. and Rogan, J.: Remote sensing for mapping and monitoring land-cover and land-use change-an introduction, Prog. Plann., 61, 269–279, https://doi.org/10.1016/S0305-9006(03)00066-7, 2004.
U.S. Geological Survey (USGS): Landsat 8 Data Users Handbook, Sioux falls, S.D., 114 pp., https://www.usgs.gov/media/files/landsat-8-data-users-handbook (last access: 10 November 2023), 2019.
U.S. Geological Survey (USGS): Landsat Collection 2 (ver. 1.1, January 15, 2021): U.S. Geological Survey Fact Sheet 2021–3002, Sioux falls, S.D., 4 pp., https://doi.org/10.3133/fs20213002, 2021.
U.S. Geological Survey (USGS): Landsat 8-9 Collection 2 (C2) Level 2 Science Product (L2SP) Guide. Version 5.0., Sioux falls, S.D., 43 pp., https://www.usgs.gov/media/files/landsat-8-9-collection-2-level-2-science-product-guide (last access: 10 November 2023), 2022.
Vetter, S.: Rangelands at equilibrium and non-equilibrium: recent developments in the debate, J. Arid Environ., 62, 321–341, https://doi.org/10.1016/j.jaridenv.2004.11.015, 2005.
Weikmann, G., Paris, C., and Bruzzone, L.: Multi-year crop type mapping using pre-trained deep long-short term memory and Sentinel 2 image time series, in: Image and Signal Processing for Remote Sensing XXVII, Vol. 11862, SPIE, 171–181, https://doi.org/10.1117/12.2600559, 2021.
Weltje, G. J.: End-member modeling of compositional data: Numerical-statistical algorithms for solving the explicit mixing problem, Math. Geol., 29, 503–549, https://doi.org/10.1007/BF02775085, 1997.
Weng, Q.: Land use change analysis in the Zhujiang Delta of China using satellite remote sensing, GIS and stochastic modelling, J. Environ. Manag., 64, 273–284, https://doi.org/10.1006/jema.2001.0509, 2002.
Williams, D. L., Goward, S., and Arvidson, T.: Landsat: Yesterday, today and tomorrow, Photogramm. Eng. Rem. S., 72, 1171–1178, https://doi.org/10.14358/PERS.72.10.1171, 2006.
Wulder, M. A., Masek, J. G., Cohen, W. B., Loveland, T. R., and Woodcock, C. E.: Opening the archive: How free data has enabled the science and monitoring promise of Landsat, Remote Sens. Environ., 122, 2–10, https://doi.org/10.1016/j.rse.2012.01.010, 2012.
Wulder, M. A., Loveland, T. R., Roy, D. P., Crawford, C. J., Masek, J. G., Woodcock, C. E., Alle, R. G., Anderson, M. C., Belward, A. S., Cohen, W. B., Dwyer, J., Erb, A., Gao, F., Griffiths, P., Helder, D., Hermosilla, T., Hipple, J. D., Hostert, P., Hughes, M. J., Huntington, J., Johnson, D. M., Kennedy, R., Kilic, A., Li, Z., Lymburner, L., McCorkel, J., Pahlevan, N., Scambos, T. A., Schaaf, C., Schott, J. R., Sheng, Y., Storey, J., Vermotev, E., Vogelmann, JJ., White, J. C., Wynne, R. H., and Zhu, Z.: Current status of Landsat program, science, and applications, Remote Sens. Environ., 225, 127–147, https://doi.org/10.1016/j.rse.2019.02.015, 2019.
Wulder, M. A., Roy, D. P., Radeloff, V. C., Loveland, T. R., Anderson, M. C., Johnson, D. M., Healey, S., Zhu, Z., Scambos, T. A., Pahlevan, N., Hansen, M., Gorelick, N., Crawford, C. J., Masek, J. G., Hermosilla, T., White, J. C., Belward, A. S., Schaaf, C., Woodcock, C. E., Huntington, J. L., Lymburner, L., Hostert, P., Gao, F., Lyapustin, A., Pekel, J. F., Strobl, P., and Cook, B. D.: Fifty years of Landsat science and impacts, Remote Sens. Environ., 280, 113195, https://doi.org/10.1016/j.rse.2022.113195, 2022.
Wynants, M., Kelly, C., Mtei, K., Munishi, L., Patrick, A., Rabinovich, A., Nasseri, M., Gilvear, D., Roberts, N., Boeckx, P., Wilson, G., Blake, W. H., and Ndakidemi, P.: Drivers of increased soil erosion in East Africa's agro-pastoral systems: changing interactions between the social, economic and natural domains, Reg. Environ. Change, 19, 1909–1921, https://doi.org/10.1007/s10113-019-01520-9, 2019.
Yang, X. and Lo, C. P.: Using a time series of satellite imagery to detect land use and land cover changes in the Atlanta, Georgia metropolitan area, Int. J. Remote Sens., 23, 1775–1798, https://doi.org/10.1080/01431160110075802, 2002.
Zabel, F., Delzeit, R., Schneider, J. M., Seppelt, R., Mauser, W., and Václavík, T.: Global impacts of future cropland expansion and intensification on agricultural markets and biodiversity, Nat. Comm., 10, 1–10, https://doi.org/10.1038/s41467-019-10775-z, 2019.
Zhou, B., Okin, G. S., and Zhang, J.: Leveraging Google Earth Engine (GEE) and machine learning algorithms to incorporate in situ measurement from different times for rangelands monitoring, Remote Sens. Environ., 236, 111521, https://doi.org/10.1016/j.rse.2019.111521, 2020.
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.
This paper uses machine learning and linear unmixing to produce rangeland health indicators:...
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