Articles | Volume 15, issue 1
https://doi.org/10.5194/essd-15-113-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-15-113-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
09 Jan 2023
Data description paper |
| 09 Jan 2023
| 09 Jan 2023
MDAS: a new multimodal benchmark dataset for remote sensing
Jingliang Hu
Data Science in Earth Observation (SiPEO), Technical University of Munich (TUM), 80333 Munich, Germany
Rong Liu
Data Science in Earth Observation (SiPEO), Technical University of Munich (TUM), 80333 Munich, Germany
Danfeng Hong
Remote Sensing Technology Institute (IMF), German Aerospace Center (DLR), 82234 Wessling, Germany
now at: Key Laboratory of Computational Optical Imaging Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, 100094 Beijing, China
Andrés Camero
Remote Sensing Technology Institute (IMF), German Aerospace Center (DLR), 82234 Wessling, Germany
Helmholtz AI, 85764 Neuherberg, Germany
Data Science in Earth Observation (SiPEO), Technical University of Munich (TUM), 80333 Munich, Germany
now at: Key Laboratory of Computational Optical Imaging Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, 100094 Beijing, China
Mathias Schneider
Remote Sensing Technology Institute (IMF), German Aerospace Center (DLR), 82234 Wessling, Germany
Franz Kurz
Remote Sensing Technology Institute (IMF), German Aerospace Center (DLR), 82234 Wessling, Germany
Karl Segl
German Research Center for Geosciences (GFZ), Helmholtz Center Potsdam, Telegrafenberg A17, 14473 Potsdam, Germany
Data Science in Earth Observation (SiPEO), Technical University of Munich (TUM), 80333 Munich, Germany
Remote Sensing Technology Institute (IMF), German Aerospace Center (DLR), 82234 Wessling, Germany
Related authors
No articles found.
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.
Viola Steidl, Jonathan Louis Bamber, and Xiao Xiang Zhu
The Cryosphere, 19, 645–661, https://doi.org/10.5194/tc-19-645-2025, https://doi.org/10.5194/tc-19-645-2025, 2025
Short summary
Short summary
Glacier ice thickness is difficult to measure directly but is essential for glacier evolution modelling. In this work, we employ a novel approach combining physical knowledge and data-driven machine learning to estimate the ice thickness of multiple glaciers in Spitsbergen, Barentsøya, and Edgeøya in Svalbard. We identify challenges for the physics-aware machine learning model and opportunities for improving the accuracy and physical consistency that would also apply to other geophysical tasks.
Veronika Gstaiger, Nils Machinia, Nina Merkle, Dominik Rosenbaum, Ronald Nippold, Manuel Muehlhaus, Pablo d’Angelo, Corentin Henry, Xiangtian Yuan, Reza Bahmanyar, Franz Kurz, and Christa-Maria Krieg
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., X-3-2024, 163–168, https://doi.org/10.5194/isprs-annals-X-3-2024-163-2024, https://doi.org/10.5194/isprs-annals-X-3-2024-163-2024, 2024
Yifan Tian, Yao Sun, and Xiao Xiang Zhu
Abstr. Int. Cartogr. Assoc., 7, 171, https://doi.org/10.5194/ica-abs-7-171-2024, https://doi.org/10.5194/ica-abs-7-171-2024, 2024
Erik Loebel, Mirko Scheinert, Martin Horwath, Angelika Humbert, Julia Sohn, Konrad Heidler, Charlotte Liebezeit, and Xiao Xiang Zhu
The Cryosphere, 18, 3315–3332, https://doi.org/10.5194/tc-18-3315-2024, https://doi.org/10.5194/tc-18-3315-2024, 2024
Short summary
Short summary
Comprehensive datasets of calving-front changes are essential for studying and modeling outlet glaciers. Current records are limited in temporal resolution due to manual delineation. We use deep learning to automatically delineate calving fronts for 23 glaciers in Greenland. Resulting time series resolve long-term, seasonal, and subseasonal patterns. We discuss the implications of our results and provide the cryosphere community with a data product and an implementation of our processing system.
Weiyan Lin, Jiasong Zhu, Yuansheng Hua, Qingyu Li, Lichao Mou, and Xiao Xiang Zhu
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-1-2024, 371–378, https://doi.org/10.5194/isprs-archives-XLVIII-1-2024-371-2024, https://doi.org/10.5194/isprs-archives-XLVIII-1-2024-371-2024, 2024
Tian Li, Konrad Heidler, Lichao Mou, Ádám Ignéczi, Xiao Xiang Zhu, and Jonathan L. Bamber
Earth Syst. Sci. Data, 16, 919–939, https://doi.org/10.5194/essd-16-919-2024, https://doi.org/10.5194/essd-16-919-2024, 2024
Short summary
Short summary
Our study uses deep learning to produce a new high-resolution calving front dataset for 149 marine-terminating glaciers in Svalbard from 1985 to 2023, containing 124 919 terminus traces. This dataset offers insights into understanding calving mechanisms and can help improve glacier frontal ablation estimates as a component of the integrated mass balance assessment.
Y. Sun, A. Kruspe, L. Meng, Y. Tian, E. J. Hoffmann, S. Auer, and X. X. Zhu
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-1-W2-2023, 225–232, https://doi.org/10.5194/isprs-archives-XLVIII-1-W2-2023-225-2023, https://doi.org/10.5194/isprs-archives-XLVIII-1-W2-2023-225-2023, 2023
M. Mühlhaus, F. Kurz, A. R. Guridi Tartas, R. Bahmanyar, S. M. Azimi, and J. Hellekes
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., X-1-W1-2023, 371–378, https://doi.org/10.5194/isprs-annals-X-1-W1-2023-371-2023, https://doi.org/10.5194/isprs-annals-X-1-W1-2023-371-2023, 2023
J. Zhao, F. Roth, B. Bauer-Marschallinger, W. Wagner, M. Chini, and X. X. Zhu
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., X-1-W1-2023, 911–918, https://doi.org/10.5194/isprs-annals-X-1-W1-2023-911-2023, https://doi.org/10.5194/isprs-annals-X-1-W1-2023-911-2023, 2023
Yao Sun, Stefan Auer, Liqiu Meng, and Xiao Xiang Zhu
Abstr. Int. Cartogr. Assoc., 6, 250, https://doi.org/10.5194/ica-abs-6-250-2023, https://doi.org/10.5194/ica-abs-6-250-2023, 2023
S. Zhao, S. Saha, and X. X. Zhu
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2022, 1407–1413, https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-1407-2022, https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-1407-2022, 2022
S. Saha, J. Gawlikowski, and X. X. Zhu
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2022, 423–428, https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-423-2022, https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-423-2022, 2022
T. Krauß, F. Kurz, and H. Runge
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-1-2022, 85–91, https://doi.org/10.5194/isprs-annals-V-1-2022-85-2022, https://doi.org/10.5194/isprs-annals-V-1-2022-85-2022, 2022
F. Kurz, P. Mendes, V. Gstaiger, R. Bahmanyar, P. d’Angelo, S. M. Azimi, S. Auer, N. Merkle, C. Henry, D. Rosenbaum, J. Hellekes, H. Runge, F. Toran, and P. Reinartz
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-1-2022, 221–226, https://doi.org/10.5194/isprs-annals-V-1-2022-221-2022, https://doi.org/10.5194/isprs-annals-V-1-2022-221-2022, 2022
N. Merkle, C. Henry, S. M. Azimi, and F. Kurz
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-2-2022, 283–289, https://doi.org/10.5194/isprs-annals-V-2-2022-283-2022, https://doi.org/10.5194/isprs-annals-V-2-2022-283-2022, 2022
T. Beker, H. Ansari, S. Montazeri, Q. Song, and X. X. Zhu
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-3-2022, 85–92, https://doi.org/10.5194/isprs-annals-V-3-2022-85-2022, https://doi.org/10.5194/isprs-annals-V-3-2022-85-2022, 2022
K. R. Traoré, A. Camero, and X. X. Zhu
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-3-2022, 217–224, https://doi.org/10.5194/isprs-annals-V-3-2022-217-2022, https://doi.org/10.5194/isprs-annals-V-3-2022-217-2022, 2022
Y. Xie, K. Schindler, J. Tian, and X. X. Zhu
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2021, 247–254, https://doi.org/10.5194/isprs-archives-XLIII-B2-2021-247-2021, https://doi.org/10.5194/isprs-archives-XLIII-B2-2021-247-2021, 2021
S. M. Azimi, R. Kiefl, V. Gstaiger, R. Bahmanyar, N. Merkle, C. Henry, D. Rosenbaum, and F. Kurz
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2021, 433–440, https://doi.org/10.5194/isprs-archives-XLIII-B2-2021-433-2021, https://doi.org/10.5194/isprs-archives-XLIII-B2-2021-433-2021, 2021
C. Henry, J. Hellekes, N. Merkle, S. M. Azimi, and F. Kurz
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2021, 479–485, https://doi.org/10.5194/isprs-archives-XLIII-B2-2021-479-2021, https://doi.org/10.5194/isprs-archives-XLIII-B2-2021-479-2021, 2021
P. Ebel, S. Saha, and X. X. Zhu
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2021, 243–249, https://doi.org/10.5194/isprs-archives-XLIII-B3-2021-243-2021, https://doi.org/10.5194/isprs-archives-XLIII-B3-2021-243-2021, 2021
S. Saha, L. Kondmann, and X. X. Zhu
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-3-2021, 311–316, https://doi.org/10.5194/isprs-annals-V-3-2021-311-2021, https://doi.org/10.5194/isprs-annals-V-3-2021-311-2021, 2021
D. Hong, J. Yao, X. Wu, J. Chanussot, and X. Zhu
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2020, 423–428, https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-423-2020, https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-423-2020, 2020
N. Merkle, V. Gstaiger, E. Schröter, P. d’Angelo, S. M. Azimi, U. Kippnich, C. Barthel, and F. Kurz
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2020, 1243–1249, https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-1243-2020, https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-1243-2020, 2020
J. Hu, L. Mou, and X. X. Zhu
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2020, 1569–1574, https://doi.org/10.5194/isprs-archives-XLIII-B2-2020-1569-2020, https://doi.org/10.5194/isprs-archives-XLIII-B2-2020-1569-2020, 2020
Q. Li, Y. Shi, S. Auer, R. Roschlaub, K. Möst, M. Schmitt, and X. X. Zhu
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-2-2020, 517–524, https://doi.org/10.5194/isprs-annals-V-2-2020-517-2020, https://doi.org/10.5194/isprs-annals-V-2-2020-517-2020, 2020
L. Mou, Y. Hua, P. Jin, and X. X. Zhu
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-2-2020, 533–540, https://doi.org/10.5194/isprs-annals-V-2-2020-533-2020, https://doi.org/10.5194/isprs-annals-V-2-2020-533-2020, 2020
C. Qiu, P. Gamba, M. Schmitt, and X. X. Zhu
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-3-2020, 787–794, https://doi.org/10.5194/isprs-annals-V-3-2020-787-2020, https://doi.org/10.5194/isprs-annals-V-3-2020-787-2020, 2020
M. Schmitt, J. Prexl, P. Ebel, L. Liebel, and X. X. Zhu
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-3-2020, 795–802, https://doi.org/10.5194/isprs-annals-V-3-2020-795-2020, https://doi.org/10.5194/isprs-annals-V-3-2020-795-2020, 2020
J. Knöttner, D. Rosenbaum, F. Kurz, P. Reinartz, and A. Brunn
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2-W7, 95–101, https://doi.org/10.5194/isprs-annals-IV-2-W7-95-2019, https://doi.org/10.5194/isprs-annals-IV-2-W7-95-2019, 2019
M. Schmitt, L. H. Hughes, C. Qiu, and X. X. Zhu
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2-W7, 145–152, https://doi.org/10.5194/isprs-annals-IV-2-W7-145-2019, https://doi.org/10.5194/isprs-annals-IV-2-W7-145-2019, 2019
M. Schmitt, L. H. Hughes, C. Qiu, and X. X. Zhu
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2-W7, 153–160, https://doi.org/10.5194/isprs-annals-IV-2-W7-153-2019, https://doi.org/10.5194/isprs-annals-IV-2-W7-153-2019, 2019
P. d’Angelo and F. Kurz
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W13, 1643–1647, https://doi.org/10.5194/isprs-archives-XLII-2-W13-1643-2019, https://doi.org/10.5194/isprs-archives-XLII-2-W13-1643-2019, 2019
C. Kempf, J. Tian, F. Kurz, P. d’Angelo, and P. Reinartz
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W13, 43–50, https://doi.org/10.5194/isprs-archives-XLII-2-W13-43-2019, https://doi.org/10.5194/isprs-archives-XLII-2-W13-43-2019, 2019
F. Kurz, T. Krauß, H. Runge, D. Rosenbaum, and P. d’Angelo
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W13, 61–66, https://doi.org/10.5194/isprs-archives-XLII-2-W13-61-2019, https://doi.org/10.5194/isprs-archives-XLII-2-W13-61-2019, 2019
C.-Y. Sheu, F. Kurz, and P. Angelo
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-1, 147–154, https://doi.org/10.5194/isprs-annals-IV-1-147-2018, https://doi.org/10.5194/isprs-annals-IV-1-147-2018, 2018
S. Azimi, E. Vig, F. Kurz, and P. Reinartz
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-1, 19–23, https://doi.org/10.5194/isprs-archives-XLII-1-19-2018, https://doi.org/10.5194/isprs-archives-XLII-1-19-2018, 2018
F. Kurz, D. Waigand, P. Pekezou-Fouopi, E. Vig, C. Henry, N. Merkle, D. Rosenbaum, V. Gstaiger, S. Azimi, S. Auer, P. Reinartz, and S. Knake-Langhorst
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-1, 251–256, https://doi.org/10.5194/isprs-archives-XLII-1-251-2018, https://doi.org/10.5194/isprs-archives-XLII-1-251-2018, 2018
G. Abdi, F. Samadzadegan, and F. Kurz
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B6, 193–199, https://doi.org/10.5194/isprs-archives-XLI-B6-193-2016, https://doi.org/10.5194/isprs-archives-XLI-B6-193-2016, 2016
F. Kurz, D. Rosenbaum, H. Runge, D. Cerra, G. Mattyus, and P. Reinartz
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B2, 521–525, https://doi.org/10.5194/isprs-archives-XLI-B2-521-2016, https://doi.org/10.5194/isprs-archives-XLI-B2-521-2016, 2016
Related subject area
Domain: ESSD – Land | Subject: Land Cover and Land Use
ChatEarthNet: a global-scale image–text dataset empowering vision–language geo-foundation models
Aboveground biomass dataset from SMOS L-band vegetation optical depth and reference maps
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
Time series of Landsat-based bimonthly and annual spectral indices for continental Europe for 2000–2022
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
CCD-Rice: A long-term paddy rice distribution dataset in China at 30 m resolution
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
GloUCP: A global 1 km spatially continuous urban canopy parameters for the WRF model
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
U-Surf: A Global 1 km spatially continuous urban surface property dataset for kilometer-scale urban-resolving Earth system modeling
20 m Africa Rice Distribution Map of 2023
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
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
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
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.
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.
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.
Xuemeng Tian, Davide Consoli, Martijn Witjes, Florian Schneider, Leandro Parente, Murat Şahin, Yu-Feng Ho, Robert Minařík, and Tomislav Hengl
Earth Syst. Sci. Data, 17, 741–772, https://doi.org/10.5194/essd-17-741-2025, https://doi.org/10.5194/essd-17-741-2025, 2025
Short summary
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.
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.
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.
Ruoque Shen, Qiongyan Peng, Xiangqian Li, Xiuzhi Chen, and Wenping Yuan
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-584, https://doi.org/10.5194/essd-2024-584, 2024
Revised manuscript accepted for ESSD
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 using to address the challenges of cloud contamination and missing data in optical remote sensing observations. The resulting dataset, CCD-Rice (China Crop Dataset-Rice), achieved high accuracy and showed strong correlation with statistical data.
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.
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 Discuss., https://doi.org/10.5194/essd-2024-408, https://doi.org/10.5194/essd-2024-408, 2024
Revised manuscript accepted for ESSD
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.
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.
Yifan Cheng, Lei Zhao, Tirthankar Chakraborty, Keith Oleson, Matthias Demuzere, Xiaoping Liu, Yangzi Che, Weilin Liao, Yuyu Zhou, and Xinchang Li
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-416, https://doi.org/10.5194/essd-2024-416, 2024
Revised manuscript accepted for ESSD
Short summary
Short summary
Absence of globally consistent and spatially continuous urban surface properties have long prevented large-scale high-resolution urban climate modeling. We developed the U-Surf data, a 1km-resolution dataset that provides key urban surface properties worldwide. U-Surf enhances urban representation in models, enables city-to-city comparison, and supports kilometer-scale Earth system modeling. Its broader applications can be extended to machine learning and many other non-climatic practices.
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
Short summary
Short summary
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.
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.
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
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 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
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.
Xiangan Liang, Qiang Liu, Jie Wang, Shuang Chen, and Peng Gong
Earth Syst. Sci. Data, 16, 177–200, https://doi.org/10.5194/essd-16-177-2024, https://doi.org/10.5194/essd-16-177-2024, 2024
Short summary
Short summary
The state-of-the-art MODIS surface reflectance products suffer from temporal and spatial gaps, which make it difficult to characterize the continuous variation of the terrestrial surface. We proposed a framework for generating the first global 500 m daily seamless data cubes (SDC500), covering the period from 2000 to 2022. We believe that the SDC500 dataset can interest other researchers who study land cover mapping, quantitative remote sensing, and ecological science.
Rémy Ballot, Nicolas Guilpart, and Marie-Hélène Jeuffroy
Earth Syst. Sci. Data, 15, 5651–5666, https://doi.org/10.5194/essd-15-5651-2023, https://doi.org/10.5194/essd-15-5651-2023, 2023
Short summary
Short summary
Assessing the benefits of crop diversification – a key element of agroecological transition – on a large scale requires a description of current crop sequences as a baseline, which is lacking at the scale of Europe. To fill this gap, we used a dataset that provides temporally and spatially incomplete land cover information to create a map of dominant crop sequence types for Europe over 2012–2018. This map is a useful baseline for assessing the benefits of future crop diversification.
Kristof Van Tricht, Jeroen Degerickx, Sven Gilliams, Daniele Zanaga, Marjorie Battude, Alex Grosu, Joost Brombacher, Myroslava Lesiv, Juan Carlos Laso Bayas, Santosh Karanam, Steffen Fritz, Inbal Becker-Reshef, Belén Franch, Bertran Mollà-Bononad, Hendrik Boogaard, Arun Kumar Pratihast, Benjamin Koetz, and Zoltan Szantoi
Earth Syst. Sci. Data, 15, 5491–5515, https://doi.org/10.5194/essd-15-5491-2023, https://doi.org/10.5194/essd-15-5491-2023, 2023
Short summary
Short summary
WorldCereal is a global mapping system that addresses food security challenges. It provides seasonal updates on crop areas and irrigation practices, enabling informed decision-making for sustainable agriculture. Our global products offer insights into temporary crop extent, seasonal crop type maps, and seasonal irrigation patterns. WorldCereal is an open-source tool that utilizes space-based technologies, revolutionizing global agricultural mapping.
Francesco N. Tubiello, Giulia Conchedda, Leon Casse, Pengyu Hao, Giorgia De Santis, and Zhongxin Chen
Earth Syst. Sci. Data, 15, 4997–5015, https://doi.org/10.5194/essd-15-4997-2023, https://doi.org/10.5194/essd-15-4997-2023, 2023
Short summary
Short summary
We describe a new dataset of cropland area circa the year 2020, with global coverage and country detail. Data are generated from geospatial information on the agreement characteristics of six high-resolution cropland maps. By helping to highlight features of cropland characteristics and underlying causes for agreement across land cover products, the dataset can be used as a tool to help guide future mapping efforts towards improved agricultural monitoring.
Martin Schwartz, Philippe Ciais, Aurélien De Truchis, Jérôme Chave, Catherine Ottlé, Cedric Vega, Jean-Pierre Wigneron, Manuel Nicolas, Sami Jouaber, Siyu Liu, Martin Brandt, and Ibrahim Fayad
Earth Syst. Sci. Data, 15, 4927–4945, https://doi.org/10.5194/essd-15-4927-2023, https://doi.org/10.5194/essd-15-4927-2023, 2023
Short summary
Short summary
As forests play a key role in climate-related issues, their accurate monitoring is critical to reduce global carbon emissions effectively. Based on open-access remote-sensing sensors, and artificial intelligence methods, we created high-resolution tree height, wood volume, and biomass maps of metropolitan France that outperform previous products. This study, based on freely available data, provides essential information to support climate-efficient forest management policies at a low cost.
Zhuohong Li, Wei He, Mofan Cheng, Jingxin Hu, Guangyi Yang, and Hongyan Zhang
Earth Syst. Sci. Data, 15, 4749–4780, https://doi.org/10.5194/essd-15-4749-2023, https://doi.org/10.5194/essd-15-4749-2023, 2023
Short summary
Short summary
Nowadays, a very-high-resolution land-cover (LC) map with national coverage is still unavailable in China, hindering efficient resource allocation. To fill this gap, the first 1 m resolution LC map of China, SinoLC-1, was built. The results showed that SinoLC-1 had an overall accuracy of 73.61 % and conformed to the official survey reports. Comparison with other datasets suggests that SinoLC-1 can be a better support for downstream applications and provide more accurate LC information to users.
Cited articles
Adrian, J., Sagan, V., and Maimaitijiang, M.: Sentinel SAR-optical fusion for
crop type mapping using deep learning and Google Earth Engine, ISPRS J. Photogramm., 175, 215–235, 2021. a
Al-Najjar, H. A., Kalantar, B., Pradhan, B., Saeidi, V., Halin, A. A., Ueda,
N., and Mansor, S.: Land cover classification from fused DSM and UAV images
using convolutional neural networks, Remote Sensing, 11, 1461, https://doi.org/10.3390/rs11121461, 2019. a
Brachmann, J., Baumgartner, A., and Gege, P.: The Calibration Home Base for
Imaging Spectrometers, Journal of Large-Scale Research Facilities JLSRF, 2, https://doi.org/10.17815/jlsrf-2-137, 2016. a
d'Angelo, P. and Kurz, F.: Aircraft based real time bundle adjustment and
digital surface model generation, in: ISPRS Geospatial Week 2019,
1643–1647, https://elib.dlr.de/127049/ (last access: 2 January 2023), 2019. a
Du, B., Wei, Q., and Liu, R.: An improved quantum-behaved particle swarm
optimization for endmember extraction, IEEE T. Geosci.
Remote, 57, 6003–6017, 2019. a
Filipponi, F.: Sentinel-1 GRD preprocessing workflow, Proceedings, 18, 11, https://doi.org/10.3390/ECRS-3-06201, 2019. a
Ge, C., Du, Q., Sun, W., Wang, K., Li, J., and Li, Y.: Deep Residual
Network-Based Fusion Framework for Hyperspectral and LiDAR Data, IEEE J. Sel. Top. Appl., 14,
2458–2472, 2021. a
Guanter, L., Kaufmann, H., Segl, K., Foerster, S., Rogass, C., Chabrillat, S.,
Kuester, T., Hollstein, A., Rossner, G., Chlebek, C., Straif, C., Fischer,
S., Schrader, S., Storch, T., Heiden, U., Mueller, A., Bachmann, M., Mühle,
H., Müller, R., Habermeyer, M., Ohndorf, A., Hill, J., Buddenbaum, H.,
Hostert, P., Van der Linden, S., Leitão, P. J., Rabe, A., Doerffer, R.,
Krasemann, H., Xi, H., Mauser, W., Hank, T., Locherer, M., Rast, M., Staenz,
K., and Sang, B.: The EnMAP Spaceborne Imaging Spectroscopy Mission for Earth
Observation, Remote Sensing, 7, 8830–8857,
https://doi.org/10.3390/rs70708830, 2015. a
Hang, R., Li, Z., Ghamisi, P., Hong, D., Xia, G., and Liu, Q.: Classification
of hyperspectral and LiDAR data using coupled CNNs, IEEE T.
Geosci. Remote, 58, 4939–4950, 2020. a
Hong, D. and Zhu, X. X.: SULoRA: Subspace unmixing with low-rank attribute
embedding for hyperspectral data analysis, IEEE J. Sel. Top. Signal Process.,
12, 1351–1363, 2018. a
Hong, D., Yokoya, N., Chanussot, J., and Zhu, X.: CoSpace: Common subspace
learning from hyperspectral-multispectral correspondences, IEEE T. Geosci.
Remote, 57, 4349–4359, 2019b. a
Hong, D., Yokoya, N., Ge, N., Chanussot, J., and Zhu, X.: Learnable manifold
alignment (LeMA): A semi-supervised cross-modality learning framework for
land cover and land use classification, ISPRS J. Photogramm. Remote Sens.,
147, 193–205, 2019c. a
Hong, D., Gao, L., Hang, R., Zhang, B., and Chanussot, J.: Deep encoder-decoder
networks for classification of hyperspectral and LiDAR data, IEEE Geosci. Remote S., 19, 5500205, https://doi.org/10.1109/LGRS.2020.3017414, 2020a. a
Hong, D., Wu, X., Ghamisi, P., Chanussot, J., Yokoya, N., and Zhu, X. X.:
Invariant attribute profiles: A spatial-frequency joint feature extractor for
hyperspectral image classification, IEEE T. Geosci. Remote, 58,
3791–3808, 2020b. a
Hong, D., Yokoya, N., Xia, G.-S., Chanussot, J., and Zhu, X. X.: X-ModalNet: A
semi-supervised deep cross-modal network for classification of remote sensing
data, ISPRS J. Photogramm. Remote Sens., 167, 12–23, 2020c. a
Hong, D., Gao, L., Yao, J., Yokoya, N., Chanussot, J., Heiden, U., and Zhang,
B.: Endmember-Guided Unmixing Network (EGU-Net): A General Deep Learning
Framework for Self-Supervised Hyperspectral Unmixing, IEEE Trans. Neural
Netw. Learn. Syst., 33, 6518–6531, https://doi.org/10.1109/TNNLS.2021.3082289, 2021a. a
Hong, D., Gao, L., Yao, J., Zhang, B., Plaza, A., and Chanussot, J.: Graph
convolutional networks for hyperspectral image classification, IEEE Trans.
Geosci. Remote Sens., 59, 5966–5978, 2021b. a
Hong, D., Gao, L., Yokoya, N., Yao, J., Chanussot, J., Qian, D., and Zhang, B.:
More Diverse Means Better: Multimodal Deep Learning Meets Remote-Sensing
Imagery Classification, IEEE T. Geosci. Remote, 59, 4340–4354,
2021c. a
Hong, D., He, W., Yokoya, N., Yao, J., Gao, L., Zhang, L., Chanussot, J., and
Zhu, X.: Interpretable Hyperspectral Artificial Intelligence: When nonconvex
modeling meets hyperspectral remote sensing, IEEE Geosci. Remote Sens. Mag.,
9, 52–87, 2021d. a
Hong, D., Hu, J., Yao, J., Chanussot, J., and Zhu, X. X.: Multimodal remote
sensing benchmark datasets for land cover classification with a shared and
specific feature learning model, ISPRS J. Photogramm., 178, 68–80, 2021e. a
Hong, D., Yao, J., Meng, D., Xu, Z., and Chanussot, J.: Multimodal GANs: Toward
crossmodal hyperspectral-multispectral image segmentation, IEEE T.
Geosci. Remote, 59, 5103–5113, 2021f. a
Hong, D., Yokoya, N., Chanussot, J., Xu, J., and Zhu, X. X.: Joint and
progressive subspace analysis (JPSA) with spatial-spectral manifold alignment
for semisupervised hyperspectral dimensionality reduction, IEEE Trans.
Cybern., 51, 3602–3615, 2021g. a
Hu, J., Ghamisi, P., and Zhu, X. X.: Feature extraction and selection of
sentinel-1 dual-pol data for global-scale local climate zone classification,
ISPRS Int. Geo-Inf., 7, 379, https://doi.org/10.3390/ijgi7090379, 2018. a
Hu, J., Hong, D., and Zhu, X. X.: MIMA: MAPPER-induced manifold alignment for
semi-supervised fusion of optical image and polarimetric SAR data, IEEE
T. Geosci. Remote, 57, 9025–9040, 2019. a
Hu, J., Liu, R., Hong, D., Camero, A., Yao, J., Schneider, M., Kurz, F., Segl,
K., and Zhu, X. X.: MDAS: A new multimodal benchmark dataset for remote
sensing, TUM [data set], https://doi.org/10.14459/2022mp1657312, 2022a. a, b
Hu, J., Liu, R., Hong, D., and Camero, A.: zhu-xlab/augsburg_Multimodal_Data_Set_MDaS: Accepted data set paper, Zenodo [code], https://doi.org/10.5281/zenodo.7428215, 2022b. a, b
Huang, R., Hong, D., Xu, Y., Yao, W., and Stilla, U.: Multi-Scale Local Context
Embedding for LiDAR Point Cloud Classification, IEEE Geosci. Remote S., 17, 721–725, 2020. a
Khodadadzadeh, M., Li, J., Prasad, S., and Plaza, A.: Fusion of hyperspectral
and LiDAR remote sensing data using multiple feature learning, IEEE J.
Sel. Top. Appl., 8,
2971–2983, 2015. a
Köhler, C.: Airborne Imaging Spectrometer HySpex, Journal of Large-Scale
Research Facilities JLSRF, 2, https://doi.org/10.17815/jlsrf-2-151, 2016. a
Krauß, T., d'Angelo, P., Schneider, M., and Gstaiger, V.: The fully automatic optical processing system Catena at DLR, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-1/W1, 177–183, https://doi.org/10.5194/isprsarchives-XL-1-W1-177-2013, 2013. a
Kurz, F., Türmer, S., Meynberg, O., Rosenbaum, D., Runge, H., Reinartz, P.,
and Leitloff, J.: Low-cost Systems for real-time Mapping Applications,
Photogramm. Fernerkun., Schweizerbart Science Publishers, Stuttgart, Germany, 159–176,
https://doi.org/10.1127/1432-8364/2012/0109, 2012. a
Liu, R. and Zhu, X.: Endmember Bundle Extraction Based on Multiobjective
Optimization, IEEE T. Geosci. Remote, 59, 8630–8645, https://doi.org/10.1109/TGRS.2020.3037249, 2020. a
Liu, R., Zhang, L., and Du, B.: A novel endmember extraction method for
hyperspectral imagery based on quantum-behaved particle swarm optimization,
IEEE J. Sel. Top. Appl., 10, 1610–1631, 2017. a
Liu, X., Liu, Q., and Wang, Y.: Remote sensing image fusion based on two-stream
fusion network, Inform. Fusion, 55, 1–15, https://doi.org/10.1016/j.inffus.2019.07.010, 2020. a, b, c, d
Loncan, L., de Almeida, L. B., Bioucas-Dias, J. M., Briottet, X., Chanussot, J., Dobigeon, N., Fabre, S., Liao, W., Licciardi, G. A., Simões, M., Tourneret, J-Y., Veganzones, M. A., Vivone, G., Wei, Q., and Yokoya, N.:
Hyperspectral pansharpening: A review, IEEE Geoscience and remote sensing
magazine, 3, 27–46, https://doi.org/10.1109/MGRS.2015.2440094, 2015. a
Meraner, A., Ebel, P., Zhu, X. X., and Schmitt, M.: Cloud removal in Sentinel-2
imagery using a deep residual neural network and SAR-optical data fusion,
ISPRS J. Photogramm., 166, 333–346, 2020. a
Okujeni, A., van der Linden, S., and Hostert, P.: Berlin-urban-gradient dataset
2009 – an enmap preparatory flight campaign, EnMAP Flight Campaigns Technical Report, Potsdam: GFZ Data Services, https://doi.org/10.2312/enmap.2016.002, 2016. a
Paris, C. and Bruzzone, L.: A three-dimensional model-based approach to the
estimation of the tree top height by fusing low-density LiDAR data and very
high resolution optical images, IEEE T. Geosci. Remote, 53, 467–480, 2014. a
Rainforth, T. and Wood, F.: Canonical correlation forests, arXiv [preprint],
https://doi.org/10.48550/arXiv.1507.05444, 20 July 2015. a
Rasti, B., Hong, D., Hang, R., Ghamisi, P., Kang, X., Chanussot, J., and
Benediktsson, J.: Feature Extraction for Hyperspectral Imagery: The Evolution
from Shallow to Deep: Overview and Toolbox, IEEE Geosci. Remote Sens. Mag.,
8, 60–88, 2020. a
Richter, R.: Correction of satellite imagery over mountainous terrain, Appl.
Opt., 37, 4004–4015, 1998. a
Rottensteiner, F., Sohn, G., Jung, J., Gerke, M., Baillard, C., Benitez, S., and Breitkopf, U.: The ISPRS benchmark on urban object classification and 3D building reconstruction, ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., I-3, 293–298, https://doi.org/10.5194/isprsannals-I-3-293-2012, 2012. a
Schläpfer, D., Richter, R., and Feingersh, T.: Operational BRDF effects
correction for wide-field-of-view optical scanners (BREFCOR), IEEE
T. Geosci. Remote, 53, 1855–1864, 2014. a
Schwind, P., Schneider, M., and Müller, R.: Improving HySpex Sensor Co-registration Accuracy using BRISK and Sensor-model based RANSAC, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-1, 371–376, https://doi.org/10.5194/isprsarchives-XL-1-371-2014, 2014. a
Segl, K., Guanter, L., Kaufmann, H., Schubert, J., Kaiser, S., Sang, B., and
Hofer, S.: Simulation of Spatial Sensor Characteristics in the Context of the
EnMAP Hyperspectral Mission, IEEE T. Geosci. Remote, 48, 3046–3054, https://doi.org/10.1109/TGRS.2010.2042455, 2010. a, b
Segl, K., Guanter, L., Rogass, C., Kuester, T., Roessner, S., Kaufmann, H.,
Sang, B., Mogulsky, V., and Hofer, S.: EeteS – The EnMAP End-to-End
Simulation Tool, IEEE J. Sel. Top. Appl., 5, 522–530,
https://doi.org/10.1109/JSTARS.2012.2188994, 2012. a, b
Segl, K., Guanter, L., Gascon, F., Kuester, T., Rogass, C., and Mielke, C.:
S2eteS: An End-to-End Modeling Tool for the Simulation of Sentinel-2 Image
Products, IEEE T. Geosci. Remote, 53, 5560–5571,
https://doi.org/10.1109/TGRS.2015.2424992, 2015. a, b
Sheikholeslami, M. M., Nadi, S., Naeini, A. A., and Ghamisi, P.: An efficient
deep unsupervised superresolution model for remote sensing images, IEEE
J. Sel. Top. Appl.,
13, 1937–1945, 2020. a
Sumbul, G., Charfuelan, M., Demir, B., and Markl, V.: Bigearthnet: A
large-scale benchmark archive for remote sensing image understanding, in:
IGARSS 2019–2019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan, 28 July–2 August 2019,
5901–5904, https://doi.org/10.1109/IGARSS.2019.8900532, 2019. a
Tupin, F. and Roux, M.: Detection of building outlines based on the fusion of
SAR and optical features, ISPRS J. Photogramm.,
58, 71–82, 2003. a
Wu, X., Hong, D., Tian, J., Chanussot, J., Li, W., and Tao, R.: ORSIm detector:
A novel object detection framework in optical remote sensing imagery using
spatial-frequency channel features, IEEE T. Geosci. Remote, 57,
5146–5158, 2019. a
Wu, X., Hong, D., Chanussot, J., Xu, Y., Tao, R., and Wang, Y.: Fourier-based
Rotation-invariant Feature Boosting: An Efficient Framework for Geospatial
Object Detection, IEEE Geosci. Remote S., 17, 302–306, 2020. a
Xia, G.-S., Yang, W., Delon, J., Gousseau, Y., Sun, H., and Maître, H.:
Structural high-resolution satellite image indexing, in: ISPRS TC VII
Symposium – 100 Years ISPRS, vol. 38, 298–303, 2010. a
Xia, G.-S., Hu, J., Hu, F., Shi, B., Bai, X., Zhong, Y., Zhang, L., and Lu, X.:
AID: A benchmark data set for performance evaluation of aerial scene
classification, IEEE T. Geosci. Remote, 55,
3965–3981, 2017. a
Xia, G.-S., Bai, X., Ding, J., Zhu, Z., Belongie, S., Luo, J., Datcu, M.,
Pelillo, M., and Zhang, L.: DOTA: A large-scale dataset for object detection
in aerial images, in: Proceedings of the IEEE conference on computer vision
and pattern recognition, Salt Lake City, Utah, US, 18–22 June 2018, 3974–3983, 2018. a
Xu, Y., Du, B., Zhang, L., Cerra, D., Pato, M., Carmona, E., Prasad, S.,
Yokoya, N., Hänsch, R., and Le Saux, B.: Advanced multi-sensor optical
remote sensing for urban land use and land cover classification: Outcome of
the 2018 IEEE GRSS data fusion contest, IEEE J. Sel. Top.
Appl., 12, 1709–1724, 2019. a
Yang, Y. and Newsam, S.: Bag-of-visual-words and spatial extensions for
land-use classification, in: Proceedings of the 18th SIGSPATIAL international
conference on advances in geographic information systems, San Jose, California, US, 2–5 November 2010, 270–279, https://doi.org/10.1145/1869790.1869829, 2010. a
Yao, J., Hong, D., Xu, L., Meng, D., Chanussot, J., and Xu, Z.:
Sparsity-Enhanced Convolutional Decomposition: A Novel Tensor-Based Paradigm
for Blind Hyperspectral Unmixing, IEEE T. Geosci. Remote, 60, 5505014, https://doi.org/10.1109/TGRS.2021.3069845, 2021. a
Zhang, D., Shao, J., Li, X., and Shen, H. T.: Remote sensing image
super-resolution via mixed high-order attention network, IEEE T.
Geosci. Remote, 59, 5183–5196, 2020. a
Zhang, S., Yuan, Q., Li, J., Sun, J., and Zhang, X.: Scene-adaptive remote
sensing image super-resolution using a multiscale attention network, IEEE
T. Geosci. Remote, 58, 4764–4779,
2020. a
Zhang, T., Zhang, X., Li, J., Xu, X., Wang, B., Zhan, X., Xu, Y., Ke, X., Zeng, T., Su, H., Ahmad, I., Pan, D., Liu, C., Zhou, Y., Shi., J., and Wei, S.: SAR Ship Detection Dataset (SSDD): Official Release and
Comprehensive Data Analysis, Remote Sensing, 13, 3690, https://doi.org/10.3390/rs13183690, 2021. a
Zhou, Y., Wetherley, E. B., and Gader, P. D.: Unmixing urban hyperspectral
imagery using probability distributions to represent endmember variability,
Remote Sens. Environ., 246, 111857, https://doi.org/10.1016/j.rse.2020.111857, 2020. a
Zhu, F.: Hyperspectral unmixing: ground truth labeling, datasets, benchmark
performances and survey, arXiv [preprint], https://doi.org/10.48550/arXiv.1708.05125, 17 August 2017. a
Zhu, X. X., Hu, J., Qiu, C., Shi, Y., Kang, J., Mou, L., Bagheri, H., Haberle, M., Hua, Y., Huang, R., Hughes, L., Li, H., Sun, Y., Zhang, G., Han, S., Schmitt, M., and Wang, Y.: So2Sat LCZ42: A Benchmark Data Set for the Classification of Global Local Climate Zones [software and data set], IEEE Geoscience and Remote Sensing Magazine, 8, 76–89, https://doi.org/10.1109/MGRS.2020.2964708, 2020. a, b
Zhuang, L., Lin, C.-H., Figueiredo, M. A., and Bioucas-Dias, J. M.:
Regularization parameter selection in minimum volume hyperspectral unmixing,
IEEE T. Geosci. Remote, 57, 9858–9877, 2019. a
Short summary
Multimodal data fusion is an intuitive strategy to break the limitation of individual data in Earth observation. Here, we present a multimodal data set, named MDAS, consisting of synthetic aperture radar (SAR), multispectral, hyperspectral, digital surface model (DSM), and geographic information system (GIS) data for the city of Augsburg, Germany, along with baseline models for resolution enhancement, spectral unmixing, and land cover classification, three typical remote sensing applications.
Multimodal data fusion is an intuitive strategy to break the limitation of individual data in...
Altmetrics
Final-revised paper
Preprint