Articles | Volume 17, issue 2
https://doi.org/10.5194/essd-17-741-2025
© Author(s) 2025. 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-17-741-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
26 Feb 2025
Data description paper |
| 26 Feb 2025
| 26 Feb 2025
Time series of Landsat-based bimonthly and annual spectral indices for continental Europe for 2000–2022
Xuemeng Tian
CORRESPONDING AUTHOR
OpenGeoHub, Doorwerth, the Netherlands
Laboratory of Geo-Information Science and Remote Sensing, Wageningen University & Research, Wageningen, the Netherlands
Davide Consoli
OpenGeoHub, Doorwerth, the Netherlands
Martijn Witjes
OpenGeoHub, Doorwerth, the Netherlands
Florian Schneider
Thünen Institute of Climate-Smart Agriculture, Braunschweig, Germany
Leandro Parente
OpenGeoHub, Doorwerth, the Netherlands
Murat Şahin
OpenGeoHub, Doorwerth, the Netherlands
Yu-Feng Ho
OpenGeoHub, Doorwerth, the Netherlands
Robert Minařík
OpenGeoHub, Doorwerth, the Netherlands
Tomislav Hengl
OpenGeoHub, Doorwerth, the Netherlands
Related authors
Tomislav Hengl, Davide Consoli, Xuemeng Tian, Travis W. Nauman, Madlene Nussbaum, Mustafa Serkan Isik, Leandro Parente, Yu-Feng Ho, Rolf Simoes, Surya Gupta, Alessandro Samuel-Rosa, Taciara Zborowski Horst, José Lucas Safanelli, and Nancy Harris
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-336, https://doi.org/10.5194/essd-2025-336, 2025
Preprint under review for ESSD
Short summary
Short summary
We used satellite data and thousands of soil samples to create detailed global maps showing how soil changes over time. These maps reveal important patterns in soil health, such as a significant global loss of soil carbon in the past 25 years. Our results help track land degradation and support better land restoration efforts. This work provides a new global tool for understanding and protecting soil, a key resource for food, water, and climate.
Tomislav Hengl, Davide Consoli, Xuemeng Tian, Travis W. Nauman, Madlene Nussbaum, Mustafa Serkan Isik, Leandro Parente, Yu-Feng Ho, Rolf Simoes, Surya Gupta, Alessandro Samuel-Rosa, Taciara Zborowski Horst, José Lucas Safanelli, and Nancy Harris
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-336, https://doi.org/10.5194/essd-2025-336, 2025
Preprint under review for ESSD
Short summary
Short summary
We used satellite data and thousands of soil samples to create detailed global maps showing how soil changes over time. These maps reveal important patterns in soil health, such as a significant global loss of soil carbon in the past 25 years. Our results help track land degradation and support better land restoration efforts. This work provides a new global tool for understanding and protecting soil, a key resource for food, water, and climate.
Surya Gupta, Tomislav Hengl, Peter Lehmann, Sara Bonetti, and Dani Or
Earth Syst. Sci. Data, 13, 1593–1612, https://doi.org/10.5194/essd-13-1593-2021, https://doi.org/10.5194/essd-13-1593-2021, 2021
Niels H. Batjes, Eloi Ribeiro, Ad van Oostrum, Johan Leenaars, Tom Hengl, and Jorge Mendes de Jesus
Earth Syst. Sci. Data, 9, 1–14, https://doi.org/10.5194/essd-9-1-2017, https://doi.org/10.5194/essd-9-1-2017, 2017
Short summary
Short summary
Soil is an important provider of ecosystem services. Yet this natural resource is being threatened. Professionals, scientists, and decision makers require quality-assessed soil data to address issues such as food security, land degradation, and climate change. Procedures for safeguarding, standardising, and subsequently serving of consistent soil data to underpin broad-scale mapping and modelling are described. The data are freely accessible at doi:10.17027/isric-wdcsoils.20160003.
Related subject area
Domain: ESSD – Land | Subject: Land Cover and Land Use
GloUCP: a global 1 km spatially continuous urban canopy parameters for the WRF model
CCD-Rice: a long-term paddy rice distribution dataset in China at 30 m resolution
U-Surf: a global 1 km spatially continuous urban surface property dataset for kilometer-scale urban-resolving Earth system modeling
The Earth Topography 2022 (ETOPO 2022) global DEM dataset
The 20 m Africa rice distribution map of 2023
A 30m resolution annual cropland extent dataset of Africa in recent decades of the 21st century
Revised and updated geospatial monitoring of 21st century forest carbon fluxes
ChatEarthNet: a global-scale image–text dataset empowering vision–language geo-foundation models
GLC_FCS10: a global 10-m land-cover dataset with a fine classification system from Sentinel-1 and Sentinel-2 time-series data in Google Earth Engine
Aboveground biomass dataset from SMOS L-band vegetation optical depth and reference maps
Statistical Atlas of European Agriculture: Gridded Data from the Agricultural Census 2020 and the Spatial Distribution of CAP Contextual Indicators
GMIE: a global maximum irrigation extent and central pivot irrigation system dataset derived via irrigation performance during drought stress and deep learning methods
Annual vegetation maps in the Qinghai–Tibet Plateau (QTP) from 2000 to 2022 based on MODIS series satellite imagery
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
An annual 30 m cultivated pasture dataset of the Tibetan Plateau from 1988 to 2021
Large-scale forest stand height mapping in the northeastern U.S. and China using L-band spaceborne repeat-pass InSAR and GEDI LiDAR data
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
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
The GIEMS-MethaneCentric database: a dynamic and comprehensive global product of methane-emitting aquatic areas
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
Global agricultural lands in the year 2015
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
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
ChinaRiceCalendar – seasonal crop calendars for early-, middle-, and late-season rice in China
Harmonized European Union subnational crop statistics can reveal climate impacts and crop cultivation shifts
GLC_FCS30D: the first global 30 m land-cover dynamics monitoring product with a fine classification system for the period from 1985 to 2022 generated using dense-time-series Landsat imagery and the continuous change-detection method
A global estimate of monthly vegetation and soil fractions from spatiotemporally adaptive spectral mixture analysis during 2001–2022
A 2020 forest age map for China with 30 m resolution
Country-level estimates of gross and net carbon fluxes from land use, land-use change and forestry
A global FAOSTAT reference database of cropland nutrient budgets and nutrient use efficiency (1961–2020): nitrogen, phosphorus and potassium
Annual maps of forest cover in the Brazilian Amazon from analyses of PALSAR and MODIS images
Weilin Liao, Yanman Li, Xiaoping Liu, Yuhao Wang, Yangzi Che, Ledi Shao, Guangzhao Chen, Hua Yuan, Ning Zhang, and Fei Chen
Earth Syst. Sci. Data, 17, 2535–2551, https://doi.org/10.5194/essd-17-2535-2025, https://doi.org/10.5194/essd-17-2535-2025, 2025
Short summary
Short summary
The currently available urban canopy parameter (UCP) datasets are limited to just a few cities for urban climate simulations by the Weather Research and Forecasting (WRF) model. To address this gap, we develop a global 1 km spatially continuous UCP dataset (GloUCP) which provides superior spatial coverage and higher accuracy in capturing urban morphology across diverse regions. It has great potential to support further advancements in urban climate modeling and related applications.
Ruoque Shen, Qiongyan Peng, Xiangqian Li, Xiuzhi Chen, and Wenping Yuan
Earth Syst. Sci. Data, 17, 2193–2216, https://doi.org/10.5194/essd-17-2193-2025, https://doi.org/10.5194/essd-17-2193-2025, 2025
Short summary
Short summary
Rice is a vital staple crop that plays a crucial role in food security in China. However, long-term high-resolution rice distribution maps in China are lacking. This study developed a new rice-mapping method, mitigating the impact of cloud contamination and missing data in optical remote sensing observations on rice mapping. The resulting dataset, CCD-Rice (China Crop Dataset-Rice), achieved high accuracy and showed a strong correlation with statistical data.
Yifan Cheng, Lei Zhao, TC Chakraborty, Keith Oleson, Matthias Demuzere, Xiaoping Liu, Yangzi Che, Weilin Liao, Yuyu Zhou, and Xinchang “Cathy” Li
Earth Syst. Sci. Data, 17, 2147–2174, https://doi.org/10.5194/essd-17-2147-2025, https://doi.org/10.5194/essd-17-2147-2025, 2025
Short summary
Short summary
The absence of globally consistent and spatially continuous urban surface input has long hindered large-scale high-resolution urban climate modeling. Using remote sensing, cloud computing, and machine learning, we developed U-Surf, a 1 km dataset providing key urban surface properties worldwide. U-Surf enhances urban representation across scales and supports kilometer-scale urban-resolving Earth system modeling unprecedentedly, with broader applications in urban studies and beyond.
Michael MacFerrin, Christopher Amante, Kelly Carignan, Matthew Love, and Elliot Lim
Earth Syst. Sci. Data, 17, 1835–1849, https://doi.org/10.5194/essd-17-1835-2025, https://doi.org/10.5194/essd-17-1835-2025, 2025
Short summary
Short summary
Here we present Earth TOPOgraphy (ETOPO) 2022, the latest iteration of NOAA's global seamless topographic–bathymetric dataset. ETOPO 2022 is a significant upgrade regarding resolution and accuracy from previous ETOPO releases and is freely available in multiple data formats and resolutions for all uses (public or private), excepting navigation.
Jingling Jiang, Hong Zhang, Ji Ge, Lijun Zuo, Lu Xu, Mingyang Song, Yinhaibin Ding, Yazhe Xie, and Wenjiang Huang
Earth Syst. Sci. Data, 17, 1781–1805, https://doi.org/10.5194/essd-17-1781-2025, https://doi.org/10.5194/essd-17-1781-2025, 2025
Short summary
Short summary
This study uses temporal synthetic aperture radar (SAR) data and optical imagery to conduct rice-mapping experiments in 34 African countries with rice-planting areas exceeding 5000 ha in 2022, achieving a 20 m resolution spatial distribution mapping for 2023. The average classification accuracy based 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.
Zihang Lou, Dailiang Peng, Zhou Shi, Hongyan Wang, Yaqiong Zhang, Xue Yan, Zhongxing Chen, Su Ye, Le Yu, Jinkang Hu, Yulong Lv, Hao Peng, Yizhou Zhang, and Bing Zhang
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-133, https://doi.org/10.5194/essd-2025-133, 2025
Revised manuscript accepted for ESSD
Short summary
Short summary
This study created the first detailed annual maps of Africa's cropland extent from 2000 to 2022 in 30 m resolution to support global efforts against hunger and sustainable farming. Our findings show Africa's cropland grew by 8.5 % over two decade, while 11.5 % of cropland was abandoned by 2018 – revealing hidden challenges in agricultural sustainability. These yearly, field-sized maps help governments track where farming grows or shrinks, plan food supplies, and protect vital cropland.
David A. Gibbs, Melissa Rose, Giacomo Grassi, Joana Melo, Simone Rossi, Viola Heinrich, and Nancy L. Harris
Earth Syst. Sci. Data, 17, 1217–1243, https://doi.org/10.5194/essd-17-1217-2025, https://doi.org/10.5194/essd-17-1217-2025, 2025
Short summary
Short summary
Updated global maps of greenhouse gas (GHG) 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 USA emitted from fossil fuels in 2019. After reclassifying fluxes to countries’ reporting categories for national GHG inventories, we found that roughly two-thirds of the net CO2 flux from forests is anthropogenic and one-third is non-anthropogenic.
Zhenghang Yuan, Zhitong Xiong, Lichao Mou, and Xiao Xiang Zhu
Earth Syst. Sci. Data, 17, 1245–1263, https://doi.org/10.5194/essd-17-1245-2025, https://doi.org/10.5194/essd-17-1245-2025, 2025
Short summary
Short summary
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.
Xiao Zhang, Liangyun Liu, Tingting Zhao, Wenhan Zhang, Linlin Guan, Ming Bai, and Xidong Chen
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-73, https://doi.org/10.5194/essd-2025-73, 2025
Revised manuscript accepted for ESSD
Short summary
Short summary
This work describes a novel global 10 m land-cover dataset with fine classification system, which contains 30 land-cover subcategories and achieves the fulfilling performance over the globe.
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, 17, 1101–1119, https://doi.org/10.5194/essd-17-1101-2025, https://doi.org/10.5194/essd-17-1101-2025, 2025
Short summary
Short summary
Aboveground biomass (AGB) is a critical component of the Earth's carbon cycle. The presented dataset aims to help monitor 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.
Nicolas Lampach, Jon Olav Skoien, Helena Ramos, Julien Gaffuri, Renate Koeble, Linda See, and Marijn Van der Velde
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-59, https://doi.org/10.5194/essd-2025-59, 2025
Revised manuscript accepted for ESSD
Short summary
Short summary
Eurostat and JRC developed a new methodology to make geospatial data from agricultural census available to users, while ensuring that no confidential information from individuals is disclosed. The geospatial data presented in the article correspond to the contextual indicators of the monitoring and evaluation framework of the Common Agricultural Policy. Our exploratory analysis reveals several interesting patterns which contribute to the broader debate on the future of European agriculture.
Fuyou Tian, Bingfang Wu, Hongwei Zeng, Miao Zhang, Weiwei Zhu, Nana Yan, Yuming Lu, and Yifan Li
Earth Syst. Sci. Data, 17, 855–880, https://doi.org/10.5194/essd-17-855-2025, https://doi.org/10.5194/essd-17-855-2025, 2025
Short summary
Short summary
Our study introduces GMIE, a high-resolution global map of irrigated cropland at 100 m resolution, covering 403.17 Mha and utilizing irrigation performance under drought stress. We found that 23.4 % of global cropland is irrigated, with the most extensive areas in India, China, the United States, and Pakistan. We identified the distribution of central pivot systems commonly used in the United States and Saudi Arabia. This new map can better support water management and food security globally.
Guangsheng Zhou, Hongrui Ren, Lei Zhang, Xiaomin Lv, and Mengzi Zhou
Earth Syst. Sci. Data, 17, 773–797, https://doi.org/10.5194/essd-17-773-2025, https://doi.org/10.5194/essd-17-773-2025, 2025
Short summary
Short summary
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 using the MOD09A1 product. The overall accuracy of continuous annual QTP vegetation mapping reached 83.3%, with the reference annual 2020 data reaching an accuracy of 83.3% and a kappa coefficient of 0.82. The study supports the use of remote sensing data to mapping long-time continuous annual vegetation.
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
Short summary
Short summary
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
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 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
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 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
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.
Binghong Han, Jian Bi, Shengli Tao, Tong Yang, Yongli Tang, Mengshuai Ge, Hao Wang, Zhenong Jin, Jinwei Dong, Zhibiao Nan, and Jin-Sheng He
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-620, https://doi.org/10.5194/essd-2024-620, 2025
Revised manuscript accepted for ESSD
Short summary
Short summary
The Tibetan Plateau is an important pastoral area where cultivated pastures play an increasingly important role. However, little is known about the spatial distribution of the cultivated pasture due to the difficulty in distinguishing them from natural grasslands with remote sensing. For the first time, we have mapped the cultivated pastures on the plateau at a resolution of 30 meters with decent accuracy. This data set is valuable to scientists, policy makers, conservationists and pastoralists.
Yanghai Yu, Yang Lei, Paul Siqueira, Xiaotong Liu, Denuo Gu, Anmin Fu, Yong Pang, Wenli Huang, and Jiancheng Shi
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-596, https://doi.org/10.5194/essd-2024-596, 2025
Revised manuscript accepted for ESSD
Short summary
Short summary
This paper presents a global-to-local method to improve forest height estimates by fusing InSAR and GEDI data. The large-scale ability was tested on open-access ALOS-1 data, where a two-fold solution is used to address temporal gap between GEDI and ALOS data. Produced products of 30 m gridded forest height mosaics for the northeastern U.S. and China show improved accuracy at 3–4 m/ha and 20 % enhancement over interpolated GEDI maps. The prototype is promising to fuse GEDI and future NISAR data.
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
Short summary
Short summary
The Land Use/Cover Area frame Survey (LUCAS) Copernicus 2022 is a large and systematic in situ field survey of 137 966 polygons over the European Union in 2022. The data contain 82 land cover classes and 40 land use classes.
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.
Juliette Bernard, Catherine Prigent, Carlos Jimenez, Etienne Fluet-Chouinard, Bernhard Lehner, Elodie Salmon, Philippe Ciais, Zhen Zhang, Shushi Peng, and Marielle Saunois
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-466, https://doi.org/10.5194/essd-2024-466, 2024
Revised manuscript accepted for ESSD
Short summary
Short summary
Wetlands are responsible for about a third of global emissions of methane, a potent greenhouse gas. We have developed the GIEMS-MethaneCentric (GIEMS-MC) dataset to represent the dynamics of wetland extent on a global scale (0.25°x0.25° resolution, monthly time step). This updated resource combines satellite data and existing wetland databases, covering 1992 to 2020. Consistent maps of other methane-emitting surface waters (lakes, rivers, reservoirs, rice paddies) are also provided.
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.
Zia Mehrabi, Kaitai Tong, Julie Fortin, Radost Stanimirova, Mark Friedl, and Navin Ramankutty
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-279, https://doi.org/10.5194/essd-2024-279, 2024
Revised manuscript accepted for ESSD
Short summary
Short summary
We present a global geospatial database of cropland and pastures representing the year 2015. We made it using machine learning models to merge satellite-based land cover data with agricultural census data that we compiled. This database is an update to an earlier version representing the year 2000. It can be used to study issues such as land use, food security, climate change and biodiversity loss. We provide a reproducible code base to easily update the product for future years.
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.
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.
Hui Li, Xiaobo Wang, Shaoqiang Wang, Jinyuan Liu, Yuanyuan Liu, Zhenhai Liu, Shiliang Chen, Qinyi Wang, Tongtong Zhu, Lunche Wang, and Lizhe Wang
Earth Syst. Sci. Data, 16, 1689–1701, https://doi.org/10.5194/essd-16-1689-2024, https://doi.org/10.5194/essd-16-1689-2024, 2024
Short summary
Short summary
Utilizing satellite remote sensing data, we established a multi-season rice calendar dataset named ChinaRiceCalendar. It exhibits strong alignment with field observations collected by agricultural meteorological stations across China. ChinaRiceCalendar stands as a reliable dataset for investigating and optimizing the spatiotemporal dynamics of rice phenology in China, particularly in the context of climate and land use changes.
Giulia Ronchetti, Luigi Nisini Scacchiafichi, Lorenzo Seguini, Iacopo Cerrani, and Marijn van der Velde
Earth Syst. Sci. Data, 16, 1623–1649, https://doi.org/10.5194/essd-16-1623-2024, https://doi.org/10.5194/essd-16-1623-2024, 2024
Short summary
Short summary
We present a dataset of EU-wide harmonized subnational crop area, production, and yield statistics with information on data sources, processing steps, missing and derived data, and quality checks. Statistical records (344 282) collected from 1975 to 2020 for soft and durum wheat, winter and spring barley, grain maize, sunflower, and sugar beet were aligned with the EUROSTAT crop legend and the 2016 territorial classification for 961 regions. Time series have a median length of 21 years.
Xiao Zhang, Tingting Zhao, Hong Xu, Wendi Liu, Jinqing Wang, Xidong Chen, and Liangyun Liu
Earth Syst. Sci. Data, 16, 1353–1381, https://doi.org/10.5194/essd-16-1353-2024, https://doi.org/10.5194/essd-16-1353-2024, 2024
Short summary
Short summary
This work describes GLC_FCS30D, the first global 30 m land-cover dynamics monitoring dataset, which contains 35 land-cover subcategories and covers the period of 1985–2022 in 26 time steps (its maps are updated every 5 years before 2000 and annually after 2000).
Qiangqiang Sun, Ping Zhang, Xin Jiao, Xin Lin, Wenkai Duan, Su Ma, Qidi Pan, Lu Chen, Yongxiang Zhang, Shucheng You, Shunxi Liu, Jinmin Hao, Hong Li, and Danfeng Sun
Earth Syst. Sci. Data, 16, 1333–1351, https://doi.org/10.5194/essd-16-1333-2024, https://doi.org/10.5194/essd-16-1333-2024, 2024
Short summary
Short summary
To provide multifaceted changes under climate change and anthropogenic impacts, we estimated monthly vegetation and soil fractions in 2001–2022, providing an accurate estimate of surface heterogeneous composition, better than vegetation index and vegetation continuous-field products. We find a greening trend on Earth except for the tropics. A combination of interactive changes in vegetation and soil can be adopted as a valuable measurement of climate change and anthropogenic impacts.
Kai Cheng, Yuling Chen, Tianyu Xiang, Haitao Yang, Weiyan Liu, Yu Ren, Hongcan Guan, Tianyu Hu, Qin Ma, and Qinghua Guo
Earth Syst. Sci. Data, 16, 803–819, https://doi.org/10.5194/essd-16-803-2024, https://doi.org/10.5194/essd-16-803-2024, 2024
Short summary
Short summary
To quantify forest carbon stock and its future potential accurately, we generated a 30 m resolution forest age map for China in 2020 using multisource remote sensing datasets based on machine learning and time series analysis approaches. Validation with independent field samples indicated that the mapped forest age had an R2 of 0.51--0.63. Nationally, the average forest age is 56.1 years (standard deviation of 32.7 years).
Wolfgang Alexander Obermeier, Clemens Schwingshackl, Ana Bastos, Giulia Conchedda, Thomas Gasser, Giacomo Grassi, Richard A. Houghton, Francesco Nicola Tubiello, Stephen Sitch, and Julia Pongratz
Earth Syst. Sci. Data, 16, 605–645, https://doi.org/10.5194/essd-16-605-2024, https://doi.org/10.5194/essd-16-605-2024, 2024
Short summary
Short summary
We provide and compare country-level estimates of land-use CO2 fluxes from a variety and large number of models, bottom-up estimates, and country reports for the period 1950–2021. Although net fluxes are small in many countries, they are often composed of large compensating emissions and removals. In many countries, the estimates agree well once their individual characteristics are accounted for, but in other countries, including some of the largest emitters, substantial uncertainties exist.
Cameron I. Ludemann, Nathan Wanner, Pauline Chivenge, Achim Dobermann, Rasmus Einarsson, Patricio Grassini, Armelle Gruere, Kevin Jackson, Luis Lassaletta, Federico Maggi, Griffiths Obli-Laryea, Martin K. van Ittersum, Srishti Vishwakarma, Xin Zhang, and Francesco N. Tubiello
Earth Syst. Sci. Data, 16, 525–541, https://doi.org/10.5194/essd-16-525-2024, https://doi.org/10.5194/essd-16-525-2024, 2024
Short summary
Short summary
Nutrient budgets help identify the excess or insufficient use of fertilizers and other nutrient sources in agriculture. They allow the calculation of indicators, such as the nutrient balance (surplus or deficit) and nutrient use efficiency, that help to monitor agricultural productivity and sustainability. This article describes a global cropland nutrient budget that provides data on 205 countries and territories from 1961 to 2020 (data available at https://www.fao.org/faostat/en/#data/ESB).
Yuanwei Qin, Xiangming Xiao, Hao Tang, Ralph Dubayah, Russell Doughty, Diyou Liu, Fang Liu, Yosio Shimabukuro, Egidio Arai, Xinxin Wang, and Berrien Moore III
Earth Syst. Sci. Data, 16, 321–336, https://doi.org/10.5194/essd-16-321-2024, https://doi.org/10.5194/essd-16-321-2024, 2024
Short summary
Short summary
Forest definition has two major biophysical parameters, i.e., canopy height and canopy coverage. However, few studies have assessed forest cover maps in terms of these two parameters at a large scale. Here, we assessed the annual forest cover maps in the Brazilian Amazon using 1.1 million footprints of canopy height and canopy coverage. Over 93 % of our forest cover maps are consistent with the FAO forest definition, showing the high accuracy of these forest cover maps in the Brazilian Amazon.
Cited articles
Baumann, P.: On the analysis-readiness of spatio-temporal earth data and suggestions for its enhancement, Environ. Model. Softw., 176, 106017, https://doi.org/10.1016/j.envsoft.2024.106017, 2024. a
Beeson, P. C., Daughtry, C. S., and Wallander, S. A.: Estimates of conservation tillage practices using landsat archive, Remote Sens., 12, 2665, https://doi.org/10.3390/rs12162665, 2020. a, b
Bolton, D. K., Gray, J. M., Melaas, E. K., Moon, M., Eklundh, L., and Friedl, M. A.: Continental-scale land surface phenology from harmonized Landsat8 and Sentinel-2 imagery, Remote Sens. Environ., 240, 111685, https://doi.org/10.1016/j.rse.2020.111685, 2020. a, b
Broeg, T., Don, A., Gocht, A., Scholten, T., Taghizadeh-Mehrjardi, R., and Erasmi, S.: Using local ensemble models and Landsat bare soil composites for large-scale soil organic carbon maps in cropland, Geoderma, 444, 116850, https://doi.org/10.1016/j.geoderma.2024.116850, 2024. a
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., et al.: 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. a
Brown, M., De Beurs, K., and Marshall, M.: Global phenological response to climate change in crop areas using satellite remote sensing of vegetation, humidity and temperature over 26 years, Remote Sens. Environ., 126, 174–183, https://doi.org/10.1016/j.rse.2012.08.009, 2012. a
Caparros-Santiago, J. A., Rodriguez-Galiano, V., and Dash, J.: Land surface phenology as indicator of global terrestrial ecosystem dynamics: A systematic review, ISPRS J. Photogram. Remote Sens., 171, 330–347, https://doi.org/10.1016/j.isprsjprs.2020.11.019, 2021. a, b, c
Castaldi, F., Chabrillat, S., Don, A., and van Wesemael, B.: Soil organic carbon mapping using LUCAS topsoil database and Sentinel-2 data: An approach to reduce soil moisture and crop residue effects, Remote Sens., 11, 2121, https://doi.org/10.3390/rs11182121, 2019. a
Chahal, I., Hooker, D., Deen, B., Janovicek, K., and Van Eerd, L.: Long-term effects of crop rotation, tillage, and fertilizer nitrogen on soil health indicators and crop productivity in a temperate climate, Soil Till. Res., 213, 105121, https://doi.org/10.1016/j.still.2021.105121, 2021. a
Chatenoux, B., Richard, J.-P., Small, D., Roeoesli, C., Wingate, V., Poussin, C., Rodila, D., Peduzzi, P., Steinmeier, C., Ginzler, C., Psomas, A., Schaepman, M. E., and Giuliani, G.: The Swiss data cube, analysis ready data archive using earth observations of Switzerland, Sci. Data, 8, 295, https://doi.org/10.1038/s41597-021-01076-6, 2021. a, b
Chen, Y., Song, X., Wang, S., Huang, J., and Mansaray, L. R.: Impacts of spatial heterogeneity on crop area mapping in Canada using MODIS data, ISPRS J. Photogram. Remote Sens., 119, 451–461, https://doi.org/10.1016/j.isprsjprs.2016.07.007, 2016. a
Chen, Y., Ma, L., Yu, D., Zhang, H., Feng, K., Wang, X., and Song, J.: Comparison of feature selection methods for mapping soil organic matter in subtropical restored forests, Ecol. Indicat., 135, 108545, https://doi.org/10.1016/j.ecolind.2022.108545, 2022. a
Consoli, D., Parente, L., Simoes, R., Şahin, M., Tian, X., Witjes, M., Sloat, L., and Hengl, T.: A computational framework for processing time-series of Earth Observation data based on discrete convolution: global-scale historical Landsat cloud-free aggregates at 30 m spatial resolution, Peer J., 12, e18585, https://doi.org/10.7717/peerj.18585, 2024. a, b, c, d, e, f, g, h, i
d'Andrimont, R., Yordanov, M., Martinez-Sanchez, L., Eiselt, B., Palmieri, A., Dominici, P., Gallego, J., Reuter, H. I., Joebges, C., Lemoine, G., and van der Velde, M.: Harmonised LUCAS in-situ land cover and use database for field surveys from 2006 to 2018 in the European Union, Sci. Data, 7, 352, https://doi.org/10.6084/m9.figshare.12841202, 2020. a
d'Andrimont, R., Verhegghen, A., Lemoine, G., Kempeneers, P., Meroni, M., and Van der Velde, M.: From parcel to continental scale–A first European crop type map based on Sentinel-1 and LUCAS Copernicus in-situ observations, Remote Sens. Environ., 266, 112708, https://doi.org/10.1016/j.rse.2021.112708, 2021. a, b
Daughtry, C. S., Serbin, G., Reeves III, J. B., Doraiswamy, P. C., and Hunt Jr., E. R.: Spectral reflectance of wheat residue during decomposition and remotely sensed estimates of residue cover, Remote Sens., 2, 416–431, https://doi.org/10.3390/rs2020416, 2010. a
De Beurs, K. M. and Henebry, G. M.: Land surface phenology and temperature variation in the International Geosphere–Biosphere Program high-latitude transects, Global Change Biol., 11, 779–790, https://doi.org/10.1111/j.1365-2486.2005.00949.x, 2005. a
De Beurs, K. M. and Henebry, G. M.: Spatio-Temporal Statistical Methods for Modelling Land Surface Phenology, in: Phenological Research, edited by: Hudson, I. and Keatley, M.,Springer, Dordrecht, https://doi.org/10.1007/978-90-481-3335-2_9, 2010. a, b
de Jong, R. and de Bruin, S.: Linear trends in seasonal vegetation time series and the modifiable temporal unit problem, Biogeosciences, 9, 71–77, https://doi.org/10.5194/bg-9-71-2012, 2012. a
Demattê, J. A., Safanelli, J. L., Poppiel, R. R., Rizzo, R., Silvero, N. E. Q., Mendes, W. D. S., Bonfatti, B. R., Dotto, A. C., Salazar, D. F. U., Mello, F. A. D. O., da Silveira Paiva, A. F., Barros Souza, A., dos Santos, N. V., Nascimento, C. M., de Mello, D. C., Bellinaso, H., Neto, L. G., Amorim, M. T. A., de Resende, M. E. B., da Souza Vieira, J., de Queiroz, L. G., Gallo, B. C., Sayão, V. M., and da Silva Lisboa, C. J.: Bare earth's surface spectra as a proxy for soil resource monitoring, Sci. Rep., 10, 4461, https://doi.org/10.1038/s41598-020-61408-1, 2020. a, b, c, d, e, f, g
Diek, S., Fornallaz, F., Schaepman, M. E., and De Jong, R.: Barest pixel composite for agricultural areas using landsat time series, Remote Sens., 9, 1245, https://doi.org/10.3390/rs9121245, 2017. a, b
Dvorakova, K., Heiden, U., Pepers, K., Staats, G., van Os, G., and van Wesemael, B.: Improving soil organic carbon predictions from a Sentinel–2 soil composite by assessing surface conditions and uncertainties, Geoderma, 429, 116128, https://doi.org/10.1016/j.geoderma.2022.116128, 2023. a
Edlinger, A., Garland, G., Banerjee, S., Degrune, F., García-Palacios, P., Hallin, S., Herzog, C., Jansa, J., Kost, E., Maestre, F. T., Sánchez-Pescador, D., Philippot, L., Rillig, M., Romdhane, S., Saghaï, A., Spor, A., Frossard, E., van der Heijden, M., Hartman, K., and Held, A.: Edlinger_etal_database.xlsx, Figshare [data set], https://doi.org/10.6084/m9.figshare.15134328.v4, 2022. a
Edlinger, A., Garland, G., Banerjee, S., Degrune, F., García-Palacios, P., Herzog, C., Sánchez Pescador, D., Romdhane, S., Ryo, M., Saghaï, A., Hallin, S., Maestre, F. T., Philippot, L., Rillig, M., and van der Heijden, M.: The impact of agricultural management on soil aggregation and carbon storage is regulated by climate. Digging deeper Dataset, Figshare [data set], https://doi.org/10.6084/m9.figshare.19762114.v5, 2023. a
Eskandari, I., Navid, H., and Rangzan, K.: Evaluating spectral indices for determining conservation and conventional tillage systems in a vetch-wheat rotation, Int. Soil Water Conserv. Res., 4, 93–98, https://doi.org/10.1016/j.iswcr.2016.04.002, 2016. a
Estel, S., Kuemmerle, T., Alcántara, C., Levers, C., Prishchepov, A., and Hostert, P.: Mapping farmland abandonment and recultivation across Europe using MODIS NDVI time series, Remote Sens. Environ., 163, 312–325, https://doi.org/10.1016/j.rse.2015.03.028, 2015. a
Ettehadi Osgouei, P., Kaya, S., Sertel, E., and Alganci, U.: Separating built-up areas from bare land in mediterranean cities using Sentinel-2A imagery, Remote Sens., 11, 345, https://doi.org/10.3390/rs11030345, 2019. a, b
Eurostat: Agricultural practices (ef_mp_prac) [Data set], Eurostat [data set], https://doi.org/10.2908/EF_MP_PRAC, 2024. a
Ganguly, S., Friedl, M. A., Tan, B., Zhang, X., and Verma, M.: Land surface phenology from MODIS: Characterization of the Collection 5 global land cover dynamics product, Remote Sens. Environ., 114, 1805–1816, https://doi.org/10.1016/j.rse.2010.04.005, 2010. a
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. a, b
Giuliani, G., Chatenoux, B., De Bono, A., Rodila, D., Richard, J.-P., Allenbach, K., Dao, H., and Peduzzi, P.: Building an earth observations data cube: lessons learned from the swiss data cube (sdc) on generating analysis ready data (ard), Big Earth Data, 1, 100–117, https://doi.org/10.1080/20964471.2017.1398903, 2017. a, b, c
Giuliani, G., Cazeaux, H., Burgi, P.-Y., Poussin, C., Richard, J.-P., and Chatenoux, B.: SwissEnveo: A FAIR national environmental data repository for earth observation open science, Data Sci. J., 20, 22–22, https://doi.org/10.5334/dsj-2021-022, 2021. a, b, c
Gomes, L. C., Faria, R. M., de Souza, E., Veloso, G. V., Schaefer, C. E. G., and Fernandes Filho, E. I.: Modelling and mapping soil organic carbon stocks in Brazil, Geoderma, 340, 337–350, https://doi.org/10.1016/j.geoderma.2019.01.007, 2019. a
Gomes, V. C., Queiroz, G. R., and Ferreira, K. R.: An overview of platforms for big earth observation data management and analysis, Remote Sens., 12, 1253, https://doi.org/10.3390/rs12081253, 2020. a
Gómez, C., White, J. C., and Wulder, M. A.: Optical remotely sensed time series data for land cover classification: A review, ISPRS J. Photogram. Remote Sens., 116, 55–72, https://doi.org/10.1016/j.isprsjprs.2016.03.008, 2016. a
Google Sheet link: Landsat-based Spectral Indices Data Cube – Access Catalog, Google Sheet link [data set], https://docs.google.com/spreadsheets/d/1QTA6OkkYlZljfHst_inCrkC7DJcMAyHnM9k0iHulwpg/edit?usp=sharing (last access: 27 June 2024), 2024. a
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. a
He, X., Yang, L., Li, A., Zhang, L., Shen, F., Cai, Y., and Zhou, C.: Soil organic carbon prediction using phenological parameters and remote sensing variables generated from Sentinel-2 images, Catena, 205, 105442, https://doi.org/10.1016/j.catena.2021.105442, 2021. a
Heiden, U., d'Angelo, P., Schwind, P., Karlshöfer, P., Müller, R., Zepp, S., Wiesmeier, M., and Reinartz, P.: Soil reflectance composites – improved thresholding and performance evaluation, Remote Sens., 14, 4526, https://doi.org/10.3390/rs14184526, 2022. a
Henebry, G. M. and De Beurs, K. M.: Remote sensing of land surface phenology: A prospectus, in: Phenology: An integrative environmental science, Springer, 385–411, https://doi.org/10.1007/978-94-007-6925-0_21, 2013. a, b, c
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. a
Huete, A. R.: A soil-adjusted vegetation index (SAVI), Remote Sens. Environ., 25, 295–309, https://doi.org/10.1016/0034-4257(88)90106-X, 1988. a, b
Iosifescu Enescu, I., de Espona, L., Haas-Artho, D., Kurup Buchholz, R., Hanimann, D., Rüetschi, M., Karger, D. N., Plattner, G.-K., Hägeli, M., Ginzler, C., Zimmermann, N. E., and Pellissier, L.: Cloud optimized raster encoding (core): A web-native streamable format for large environmental time series, Geomatics, 1, 369–382, https://doi.org/10.3390/geomatics1030021, 2021. a
Kansakar, P. and Hossain, F.: A review of applications of satellite earth observation data for global societal benefit and stewardship of planet earth, Space Policy, 36, 46–54, https://doi.org/10.1016/j.spacepol.2016.05.005, 2016. a
Killough, B.: Overview of the open data cube initiative, in: IGARSS 2018 – 2018 IEEE international geoscience and remote sensing symposium, 22–27 July 2018, Valencia, Spain, 8629–8632, https://doi.org/10.1109/IGARSS.2018.8517694, 2018. a
Killough, B.: The impact of analysis ready data in the Africa regional data cube, in: IGARSS 2019 – 2019 IEEE International Geoscience and Remote Sensing Symposium, 28 July–2 August 2019, Yokohama, Japan, 5646–5649, https://doi.org/10.1109/IGARSS.2019.8898321, 2019. a
Kooch, Y., Amani, M., and Abedi, M.: Vegetation degradation threatens soil health in a mountainous semi-arid regiony, Sci. Total Environ., 830, 154827, https://doi.org/10.1016/j.scitotenv.2022.154827, 2022. a
Lacagnina, C., David, R., Nikiforova, A., Kuusniemi, M.-E., Cappiello, C., Biehlmaier, O., Wright, L., Schubert, C., Bertino, A., Thiemann, H., and Dennis, R.: Towards a data quality framework for EOSC authorship community, PhD thesis, EOSC Association, Zenodo, https://doi.org/10.5281/zenodo.7515816, 2022. a
Lawrence, I. and Lin, K.: A concordance correlation coefficient to evaluate reproducibility, Biometrics, 45, 255–268, https://doi.org/10.2307/2532051, 1989. a
Lewis, A., Oliver, S., Lymburner, L., Evans, B., Wyborn, L., Mueller, N., Raevksi, G., Hooke, J., Woodcock, R., Sixsmith, J., Wu, W., Tan, P., Li, F., Killough, B., Minchin, S., Roberts, D., Ayers, D., Bala, B., Dwyer, J., Dekker, A., Dhu, T., Hicks, A., Ip, A., Purss, M., Richards, C., Sagar, S., Trenham, C., Wang, P., and Wang, L.-W.: The Australian geoscience data cube – foundations and lessons learned, Remote Sens. Environ., 202, 276–292, https://doi.org/10.1016/j.rse.2017.03.015, 2017. a
Li, L., Friedl, M. A., Xin, Q., Gray, J., Pan, Y., and Frolking, S.: Mapping crop cycles in China using MODIS-EVI time series, Remote Sens., 6, 2473–2493, https://doi.org/10.3390/rs6032473, 2014. a, b, c
Liu, C., Zhang, Q., Tao, S., Qi, J., Ding, M., Guan, Q., Wu, B., Zhang, M., Nabil, M., Tian, F., Zeng, H., Zhang, N., Bavuudorj, G., Rukundo, E., Liu, W., Bofana, J., Beyene, A. N., and Elnashar, A.: A new framework to map fine resolution cropping intensity across the globe: Algorithm, validation, and implication, Remote Sens. Environ., 251, 112095, https://doi.org/10.1016/j.rse.2020.112095, 2020. a, b
Lokers, R., Knapen, R., Janssen, S., van Randen, Y., and Jansen, J.: Analysis of Big Data technologies for use in agro-environmental science, Environ. Model. Softw., 84, 494–504, https://doi.org/10.1016/j.envsoft.2016.07.017, 2016. a, b
Magdoff, F. and Van Es, H.: Building soils for better crops, in: vol. 4, Sustainable Agriculture Network Beltsville, https://www.sare.org/wp-content/uploads/Building-Soils-for-Better-Crops.pdf (last access: 28 November 2024), 2000. a
Maynard, J. J. and Levi, M. R.: Hyper-temporal remote sensing for digital soil mapping: Characterizing soil-vegetation response to climatic variability, Geoderma, 285, 94–109, https://doi.org/10.1016/j.geoderma.2016.09.024, 2017. a, b
McBratney, A. B., Santos, M. M., and Minasny, B.: On digital soil mapping, Geoderma, 117, 3–52, https://doi.org/10.1016/S0016-7061(03)00223-4, 2003. a, b
Meroni, M., d'Andrimont, R., Vrieling, A., Fasbender, D., Lemoine, G., Rembold, F., Seguini, L., and Verhegghen, A.: Comparing land surface phenology of major European crops as derived from SAR and multispectral data of Sentinel-1 and-2, Remote Sens. Environ., 253, 112232, https://doi.org/10.1016/j.rse.2020.112232, 2021. a, b
Montero, D., Aybar, C., Mahecha, M. D., Martinuzzi, F., Söchting, M., and Wieneke, S.: A standardized catalogue of spectral indices to advance the use of remote sensing in Earth system research, Scientific Data, 10, 197, https://doi.org/10.1038/s41597-023-02096-0, 2023 (code available at: https://github.com/awesome-spectral-indices/, last access: 27 June 2024). a, b, c, d, e
Myneni, R. and Williams, D.: On the relationship between FAPAR and NDVI, Remote Sens. Environ., 49, 200–211, https://doi.org/10.1016/0034-4257(94)90016-7, 1994. a
Mzid, N., Pignatti, S., Huang, W., and Casa, R.: An analysis of bare soil occurrence in arable croplands for remote sensing topsoil applications, Remote Sens., 13, 474, https://doi.org/10.3390/rs13030474, 2021. a, b, c, d
Nazari, S., Rostaminia, M., Ayoubi, S., Rahmani, A., and Mousavi, S. R.: Efficiency of Different Feature Selection Methods in Digital Mapping of Subgroup and Soil Family Classes with Data Mining Algorithms, Water Soil, 34, 973–987, https://doi.org/10.22067/jsw.v34i4.85748, 2020. a
Nguyen, L. H., Joshi, D. R., Clay, D. E., and Henebry, G. M.: Characterizing land cover/land use from multiple years of Landsat and MODIS time series: A novel approach using land surface phenology modeling and random forest classifier, Remote Sens. Environ., 238, 111017, https://doi.org/10.1016/j.rse.2018.12.016, 2020. a
Obuchowicz, C., Poussin, C., and Giuliani, G.: Change in observed long-term greening across Switzerland – evidence from a three decades NDVI time-series and its relationship with climate and land cover factors, Big Earth Data, 8, 1–32, https://doi.org/10.1080/20964471.2023.2268322, 2023. a, b
Patel, J. H. and Oza, M. P.: Deriving crop calendar using NDVI time-series, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-8, 869–873, https://doi.org/10.5194/isprsarchives-XL-8-869-2014, 2014. a
Pietrzykowski, M. and Krzaklewski, W.: An assessment of energy efficiency in reclamation to forest, Ecol. Eng., 30, 341–348, https://doi.org/10.1016/j.ecoleng.2007.04.003, 2007. a, b
Pôças, I., Gonçalves, J., Marcos, B., Alonso, J., Castro, P., and Honrado, J. P.: Evaluating the fitness for use of spatial data sets to promote quality in ecological assessment and monitoring, Int. J. Geogr. Inform. Sci., 28, 2356–2371, https://doi.org/10.1080/13658816.2014.924627, 2014. a
Poussin, C., Massot, A., Ginzler, C., Weber, D., Chatenoux, B., Lacroix, P., Piller, T., Nguyen, L., and Giuliani, G.: Drying conditions in Switzerland – indication from a 35-year Landsat time-series analysis of vegetation water content estimates to support SDGs, Big Earth Data, 5, 445–475, https://doi.org/10.1080/20964471.2021.1974681, 2021. a, b
Poussin, C., Timoner, P., Chatenoux, B., Giuliani, G., and Peduzzi, P.: Improved Landsat-based snow cover mapping accuracy using a spatiotemporal NDSI and generalized linear mixed model, Sci. Remote Sens., 7, 100078, https://doi.org/10.1016/j.srs.2023.100078, 2023. a
Radeloff, V. C., Roy, D. P., Wulder, M. A., Anderson, M., Cook, B., Crawford, C. J., Friedl, M., Gao, F., Gorelick, N., Hansen, M., Healey, S., Hostert, P., Hulley, G., Huntington, J. L., Johnson, D. M., Neigh, C., Lyapustin, A., Lymburner, L., Pahlevan, N., Pekel, J.-F., Scambos, T. A., Schaaf, C., Strobl, P., Woodcock, C. E., Zhang, H. K., and Zhu, Z.: Need and vision for global medium-resolution Landsat and Sentinel-2 data products, Remote Sens. Environ., 300, 113918, https://doi.org/10.1016/j.rse.2023.113918, 2024. a
Ranghetti, L., Busetto, L., Crema, A., Fasola, M., Cardarelli, E., and Boschetti, M.: Testing estimation of water surface in Italian rice district from MODIS satellite data, Int. J. Appli. Earth Obs. Geoinf., 52, 284–295, https://doi.org/10.1016/j.jag.2016.06.018, 2016. a, b, c
Reddy, N. M., Peerzada, I. A., Moonis, M., and Singh, O.: Application of biophysical, soil, and vegetation indices to better understand forest dynamics and develop strategies for forest conservation, in: Forest Dynamics and Conservation: Science, Innovations and Policies, Springer, 399–415, https://doi.org/10.1007/978-981-19-0071-6_19, 2022. a
Rhyma, P., Norizah, K., Hamdan, O., Faridah-Hanum, I., and Zulfa, A.: Integration of normalised different vegetation index and Soil-Adjusted Vegetation Index for mangrove vegetation delineation, Remote Sens. Appl.: Soc. Environ., 17, 100280, https://doi.org/10.1016/j.rsase.2019.100280, 2020. a
Rizzo, R., Wadoux, A. M.-C., Demattê, J. A., Minasny, B., Barrón, V., Ben-Dor, E., Francos, N., Savin, I., Poppiel, R., Silvero, N. E. Q., da Silva, F., Rosin, N. A., Rosas, J. T. F., Greschuk, L. T., Ballester, M. V. R., Gómez, A. M. R., Belllinaso, H., Safanelli, J. L., Chabrillat, S., Fiorio, P. R., Das, B. S., Malone, B. P., Zalidis, G., Tziolas, N., Tsakiridis, N., Karyotis, K., Samarinas, N., Kalopesa, E., Gholizadeh, A., Shepherd, K. D., Milewski, R., Vaudour, E., Wang, C., and Salama, E. S. M.: Remote sensing of the Earth's soil color in space and time, Remote Sens. Environ., 299, 113845, https://doi.org/10.1016/j.rse.2023.113845, 2023. a
Robinson, N. P., Allred, B. W., Smith, W. K., Jones, M. O., Moreno, A., Erickson, T. A., Naugle, D. E., and Running, S. W.: Terrestrial primary production for the conterminous United States derived from Landsat 30 m and MODIS 250 m, Remote Sens. Ecol. Conserv., 4, 264–280, https://doi.org/10.1002/rse2.74, 2018. a
Rogge, D., Bauer, A., Zeidler, J., Mueller, A., Esch, T., and Heiden, U.: Building an exposed soil composite processor (SCMaP) for mapping spatial and temporal characteristics of soils with Landsat imagery (1984–2014), Remote Sens. Environ., 205, 1–17, https://doi.org/10.1016/j.rse.2017.11.004, 2018. a, b
Safanelli, J. L., Chabrillat, S., Ben-Dor, E., and Demattê, J. A.: Multispectral models from bare soil composites for mapping topsoil properties over Europe, Remote Sens., 12, 1369, https://doi.org/10.3390/rs12091369, 2020. a, b
Salcedo-Sanz, S., Ghamisi, P., Piles, M., Werner, M., Cuadra, L., Moreno-Martínez, A., Izquierdo-Verdiguier, E., Muñoz-Marí, J., Mosavi, A., and Camps-Valls, G.: Machine learning information fusion in Earth observation: A comprehensive review of methods, applications and data sources, Inform. Fusion, 63, 256–272, https://doi.org/10.1016/j.inffus.2020.07.004, 2020. a, b
Salomonson, V. V. and Appel, I.: Development of the Aqua MODIS NDSI fractional snow cover algorithm and validation results, IEEE T. Geosci. Remote, 44, 1747–1756, https://doi.org/10.1109/TGRS.2006.876029, 2006. a, b
Seifert, C. A. and Lobell, D. B.: Response of double cropping suitability to climate change in the United States, Environ. Res. Lett., 10, 024002, https://doi.org/10.1088/1748-9326/10/2/024002, 2015. a
Sen, P. K.: Estimates of the regression coefficient based on Kendall's tau, J. Am. Stat. Assoc., 63, 1379–1389, https://doi.org/10.1080/01621459.1968.10480934, 1968. a
Sharma, P., Singh, A., Kahlon, C. S., Brar, A. S., Grover, K. K., Dia, M., and Steiner, R. L.: The role of cover crops towards sustainable soil health and agriculture – A review paper, Am. J. Plant Sci., 9, 1935–1951, https://doi.org/10.4236/ajps.2018.99140, 2018. a
Shi, J., Yang, L., Zhu, A.-X., Qin, C., Liang, P., Zeng, C., and Pei, T.: Machine-Learning Variables at Different Scales vs. Knowledge-based Variables for Mapping Multiple Soil Properties, Soil Sci. Soc. Am. J., 82, 645–656, https://doi.org/10.2136/sssaj2017.11.0392, 2018. a
Siebert, S., Portmann, F. T., and Döll, P.: Global patterns of cropland use intensity, Remote Sens., 2, 1625–1643, https://doi.org/10.3390/rs2071625, 2010. a
Sonmez, N. K. and Slater, B.: Measuring intensity of tillage and plant residue cover using remote sensing, Eur. J. Remote Sens., 49, 121–135, https://doi.org/10.5721/EuJRS20164907, 2016. a
Sothe, C., Gonsamo, A., Arabian, J., and Snider, J.: Large scale mapping of soil organic carbon concentration with 3D machine learning and satellite observations, Geoderma, 405, 115402, https://doi.org/10.1016/j.geoderma.2021.115402, 2022. a
Stern, C., Abernathey, R., Hamman, J., Wegener, R., Lepore, C., Harkins, S., and Merose, A.: Pangeo Forge: Crowdsourcing Analysis-Ready, Cloud Optimized Data Production, Front. Clim., 3, 782909, https://doi.org/10.3389/fclim.2021.782909, 2022. a
Storey, E. A., Stow, D. A., and O'Leary, J. F.: Assessing postfire recovery of chamise chaparral using multi-temporal spectral vegetation index trajectories derived from Landsat imagery, Remote Sens. Environ., 183, 53–64, https://doi.org/10.1016/j.rse.2016.05.018, 2016. a
Szabo, S., Gácsi, Z., and Balazs, B.: Specific features of NDVI, NDWI and MNDWI as reflected in land cover categories, Acta Geogr. Debrecina Landsc. Environ. Ser., 10, 194–202, https://doi.org/10.21120/LE/10/3-4/13, 2016. a
Taha, A. A. and Hanbury, A.: Metrics for evaluating 3D medical image segmentation: analysis, selection, and tool, BMC Med. Imag., 15, 1–28, https://doi.org/10.1186/s12880-015-0068-x, 2015. a
Theil, H.: A rank-invariant method of linear and polynomial regression analysis, Indagationes mathematicae, P. Roy. Neth. Acad. Sci., 53, Part I: 386–392, Part II: 521–525, Part III: 1397–1412, 1950. a
Tian, X., Ho, Y.-F., Witjes, M., Parente, L., Hengl, T., and Minarik, R.: Pan-EU Landmask: 10 m Resolution Geospatial Land Coverage with Administrative Boundary details on country and regional level (Version v20230722) [Data set], Zenodo [data set], https://doi.org/10.5281/zenodo.8171860, 2023. a, b
Tian, X., Consoli, D., Parente, L., Yufeng, H., and Hengl, T.: Landsat-based spectral indices for pan-EU 2000–2022, Zenodo [data set], https://doi.org/10.5281/zenodo.10776891, 2024a. a, b
Tian, X., Hengl, T., yu-feng-ho, and d-consoli: AI4SoilHealth/SoilHealthDataCube: v20241204-1 (v20241204-1), Zenodo [code], https://doi.org/10.5281/zenodo.12907281, 2024b. a, b
Tucker, C. J.: Red and photographic infrared linear combinations for monitoring vegetation, Remote Sens. Environ., 8, 127–150, https://doi.org/10.1016/0034-4257(79)90013-0, 1979. a
Turmel, M.-S., Speratti, A., Baudron, F., Verhulst, N., and Govaerts, B.: Crop residue management and soil health: A systems analysis, Agricult. Syst., 134, 6–16, https://doi.org/10.1016/j.agsy.2014.05.009, 2015. a
Van Deventer, A., Ward, A., Gowda, P., and Lyon, J.: Using thematic mapper data to identify contrasting soil plains and tillage practices, Photogram. Eng. Remote Sens., 63, 87–93, 1997. a
Wadoux, A. M.-C., Minasny, B., and McBratney, A. B.: Machine learning for digital soil mapping: Applications, challenges and suggested solutions, Earth-Sci. Rev., 210, 103359, https://doi.org/10.1016/j.earscirev.2020.103359, 2020. a, b, c
Wagemann, J., Siemen, S., Seeger, B., and Bendix, J.: Users of open Big Earth data – An analysis of the current state, Comput. Geosci., 157, 104916, https://doi.org/10.1016/j.cageo.2021.104916, 2021. a, b, c, d
Wentz, E. A. and Shimizu, M.: Measuring spatial data fitness-for-use through multiple criteria decision making, Ann. Am. Assoc. Geogr., 108, 1150–1167, https://doi.org/10.1080/24694452.2017.1411246, 2018. a
White, M. A., Thornton, P. E., and Running, S. W.: A continental phenology model for monitoring vegetation responses to interannual climatic variability, Global Biogeochem. Cy., 11, 217–234, https://doi.org/10.1029/97GB00330, 1997. a
Wiesmeier, M., Urbanski, L., Hobley, E., Lang, B., von Lützow, M., Marin-Spiotta, E., van Wesemael, B., Rabot, E., Ließ, M., Garcia-Franco, N., Wollschläger, U., Vogel, H.-J., and Kögel-Knabner, I.: Soil organic carbon storage as a key function of soils-A review of drivers and indicators at various scales, Geoderma, 333, 149–162, https://doi.org/10.1016/j.geoderma.2018.07.026, 2019. a, b, c, d
Wu, J., Lin, L., Li, T., Cheng, Q., Zhang, C., and Shen, H.: Fusing Landsat 8 and Sentinel-2 data for 10-m dense time-series imagery using a degradation-term constrained deep network, Int. J. Appl. Earth Obs. Geoinf. 108, 102738, https://doi.org/10.1016/j.jag.2022.102738, 2022. a
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. a, b
Xiang, X., Du, J., Jacinthe, P.-A., Zhao, B., Zhou, H., Liu, H., and Song, K.: Integration of tillage indices and textural features of Sentinel-2A multispectral images for maize residue cover estimation, Soil Till. Res., 221, 105405, https://doi.org/10.1016/j.still.2022.105405, 2022. a
Xue, Z., Du, P., and Feng, L.: Phenology-driven land cover classification and trend analysis based on long-term remote sensing image series, IEEE J. Select. Top. Appl. Earth Obs. Remote Sens., 7, 1142–1156, https://doi.org/10.1109/JSTARS.2013.2294956, 2014. a
Yang, L., He, X., Shen, F., Zhou, C., Zhu, A.-X., Gao, B., Chen, Z., and Li, M.: Improving prediction of soil organic carbon content in croplands using phenological parameters extracted from NDVI time series data, Soil Till. Res., 196, 104465, https://doi.org/10.1016/j.still.2019.104465, 2020. a
Yang, R.-M., Liu, L.-A., Zhang, X., He, R.-X., Zhu, C.-M., Zhang, Z.-Q., and Li, J.-G.: The effectiveness of digital soil mapping with temporal variables in modeling soil organic carbon changes, Geoderma, 405, 115407, https://doi.org/10.1016/j.geoderma.2021.115407, 2022. a
Yang, T., Siddique, K. H., and Liu, K.: Cropping systems in agriculture and their impact on soil health – A review, Global Ecol. Conserv., 23, e01118, https://doi.org/10.1016/j.gecco.2020.e01118, 2020. a
Yang, X., Blower, J., Bastin, L., Lush, V., Zabala, A., Masó, J., Cornford, D., Díaz, P., and Lumsden, J.: An integrated view of data quality in Earth observation, Philos. T. Roy. Soc. A, 371, 20120072, https://doi.org/10.1098/rsta.2012.0072, 2013. a
Zepp, S., Heiden, U., Bachmann, M., Möller, M., Wiesmeier, M., and van Wesemael, B.: Optimized bare soil compositing for soil organic carbon prediction of topsoil croplands in Bavaria using Landsat, ISPRS J. Photogram. Remote Sens., 202, 287–302, https://doi.org/10.3390/rs13030474, 2023. a, b
Zeraatpisheh, M., Garosi, Y., Owliaie, H. R., Ayoubi, S., Taghizadeh-Mehrjardi, R., Scholten, T., and Xu, M.: Improving the spatial prediction of soil organic carbon using environmental covariates selection: A comparison of a group of environmental covariates, Catena, 208, 105723, https://doi.org/10.1016/j.catena.2021.105723, 2022. a, b, c
Zhang, X., Liu, L., Liu, Y., Jayavelu, S., Wang, J., Moon, M., Henebry, G. M., Friedl, M. A., and Schaaf, C. B.: Generation and evaluation of the VIIRS land surface phenology product, Remote Sens. Environ., 216, 212–229, https://doi.org/10.1016/j.rse.2018.06.047, 2018. a
Zhang, X., Liu, L., Chen, X., Gao, Y., Xie, S., and Mi, J.: GLC_FCS30: global land-cover product with fine classification system at 30 m using time-series Landsat imagery, Earth Syst. Sci. Data, 13, 2753–2776, https://doi.org/10.5194/essd-13-2753-2021, 2021. a
Zheng, B., Campbell, J., Serbin, G., and Daughtry, C.: Multitemporal remote sensing of crop residue cover and tillage practices: A validation of the minNDTI strategy in the United States, J. Soil Water Conserv., 68, 120–131, https://doi.org/10.2489/jswc.68.2.120, 2013. a
Zhou, J., Jia, L., Menenti, M., and Gorte, B.: On the performance of remote sensing time series reconstruction methods – A spatial comparison, Remote Sens. Environ., 187, 367–384, https://doi.org/10.1016/j.rse.2016.10.025, 2016. a, b
Zhou, T., Geng, Y., Ji, C., Xu, X., Wang, H., Pan, J., Bumberger, J., Haase, D., and Lausch, A.: Prediction of soil organic carbon and the C:N ratio on a national scale using machine learning and satellite data: A comparison between Sentinel-2, Sentinel-3 and Landsat-8 images, Sci. Total Environ., 755, 142661, https://doi.org/10.1016/j.scitotenv.2020.142661, 2021. a
Short summary
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 policy-makers in managing environmental resources.
Our study introduces a Landsat-based data cube simplifying access to detailed environmental data...
Altmetrics
Final-revised paper
Preprint