- Article
(24861 KB) - Metadata XML
- September 15, 2026
- |
- Home
- |
- Sitemap
- |
- The ISPRS Foundation
- |
- 𝕏
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Articles | Volume X-1/W1-2023
https://doi.org/10.5194/isprs-annals-X-1-W1-2023-911-2023
© 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/isprs-annals-X-1-W1-2023-911-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
05 Dec 2023
| 05 Dec 2023
A PRELIMINARY COMPARISON OF TWO EXCLUSION MAPS FOR LARGE-SCALE FLOOD MAPPING USING SENTINEL-1 DATA
Chair of Data Science in Earth Observation, Technical University of Munich, Arcisstraße 21, Munich 80333, Germany
Department of Geodesy and Geoinformation, TU Wien, Wiedner Hauptstr 8, Vienna A-1040, Austria
B. Bauer-Marschallinger
Department of Geodesy and Geoinformation, TU Wien, Wiedner Hauptstr 8, Vienna A-1040, Austria
W. Wagner
Department of Geodesy and Geoinformation, TU Wien, Wiedner Hauptstr 8, Vienna A-1040, Austria
M. Chini
Department of Environmental Research and Innovation, Luxembourg Institute of Science and Technology, 5 Avenue des Hauts-Fourneaux, Esch-sur-Alzette 4362, Luxembourg
X. X. Zhu
Chair of Data Science in Earth Observation, Technical University of Munich, Arcisstraße 21, Munich 80333, Germany
Related authors
No articles found.
Nils Lehmann, Cesar Aybar, Ando Shah, Marcello Passaro, Jonathan L. Bamber, and Xiao Xiang Zhu
Earth Syst. Sci. Data, 18, 6815–6840, https://doi.org/10.5194/essd-18-6815-2026, https://doi.org/10.5194/essd-18-6815-2026, 2026
Short summary
Short summary
Different satellites, ocean floats and model reanalysis products record sea surface height, temperature, salt content and winds, but each archive stores its data differently, so combining them is tedious and time consuming. We therefore combine these data sources into a consistent collection, so that data retrieval across sources becomes easier. Ocean studies are consequently easier to reproduce and compare, and hopefully support research and better understanding of ocean and climate processes.
Paco Frantzen, Susan Steele-Dunne, Roland Lindorfer, Sebastian Hahn, Mariette Vreugdenhil, and Wolfgang Wagner
EGUsphere, https://doi.org/10.5194/egusphere-2026-5133, https://doi.org/10.5194/egusphere-2026-5133, 2026
This preprint is open for discussion and under review for Earth Observation (EO).
Short summary
Short summary
Soil moisture content is important for vegetation health and evaporation, and can be estimated using microwave instruments on satellites. For one of such instruments, ASCAT, the surface reflective properties must be estimated to retrieve soil moisture. We assess whether a new method for estimating reflective properties improves the ASCAT soil moisture estimates, by comparing them with measurements from soil moisture probes. We found that the new method improves ASCAT soil moisture estimates.
Clay Harrison, Sebastian Hahn, Roland Lindorfer, Thomas Melzer, Raffaele Crapolicchio, and Wolfgang Wagner
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., XI-3-2026, 411–417, https://doi.org/10.5194/isprs-annals-XI-3-2026-411-2026, https://doi.org/10.5194/isprs-annals-XI-3-2026-411-2026, 2026
Sebastian Hahn, Thomas Melzer, and Wolfgang Wagner
Earth Syst. Sci. Data, 18, 4393–4423, https://doi.org/10.5194/essd-18-4393-2026, https://doi.org/10.5194/essd-18-4393-2026, 2026
Short summary
Short summary
This article presents the latest version of the H SAF (Satellite Application Facility on Support to Operational Hydrology and Water Management) ASCAT (Advanced Scatterometer) SSM (surface soil moisture) datasets, unifying the NRT (near real-time) product with the historical offline data record. This release now applies the latest retrieval algorithm to both data streams, creating a consistent and unified data stream that is further complemented by a new, high-resolution 6.25 km sampling SSM product. The H SAF ASCAT SSM datasets are publicly available from https://hsaf.meteoam.it.
Viola Steidl, Jürgen Kusche, Fupeng Li, and Xiao Xiang Zhu
EGUsphere, https://doi.org/10.5194/egusphere-2026-2312, https://doi.org/10.5194/egusphere-2026-2312, 2026
Short summary
Short summary
Terrestrial water storage is an indicator of water availability, but forecasting its changes is difficult as it depends on processes acting at varying spatial and temporal scales. We introduce a hierarchical machine learning model that represents the Earth at two scales, the grid scale and the hydrological basin scale, to forecast global water storage changes up to six months ahead. We identify where model skill fades and which challenges to address to improve its forecasts.
Yang Mu, Zhitong Xiong, Yi Wang, Muhammad Shahzad, Franz Essl, Holger Kreft, Mark van Kleunen, and Xiao Xiang Zhu
Earth Syst. Sci. Data, 18, 1379–1403, https://doi.org/10.5194/essd-18-1379-2026, https://doi.org/10.5194/essd-18-1379-2026, 2026
Short summary
Short summary
To better protect our planet's forests, we need to know what trees are where. We created GlobalGeoTree, a massive public dataset linking 6.3 million tree locations worldwide with satellite data. This dataset helps computers learn to identify tree species from space, supporting biodiversity monitoring and climate action. Our baseline model shows this is a promising path to understanding global forests.
Dariela A. Vázquez Rodríguez, Pavan Muguda Sanjeevamurthy, Wolfgang Wagner, Sebastian Hahn, and Fabiola D. Yépez-Rincón
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., X-3-W3-2025, 101–108, https://doi.org/10.5194/isprs-annals-X-3-W3-2025-101-2026, https://doi.org/10.5194/isprs-annals-X-3-W3-2025-101-2026, 2026
Xiao Xiang Zhu, Sining Chen, Fahong Zhang, Yilei Shi, and Yuanyuan Wang
Earth Syst. Sci. Data, 17, 6647–6668, https://doi.org/10.5194/essd-17-6647-2025, https://doi.org/10.5194/essd-17-6647-2025, 2025
Short summary
Short summary
We introduce GlobalBuildingAtlas, a publicly available dataset offering global and complete coverage of building polygons (GBA.Polygon), heights (GBA.Height) and Level of Detail 1 3D models (GBA.LoD1). This is the first open dataset to offer high quality, consistent, and complete building data in 2D and 3D at the individual building level on a global scale. With more than 2.75 billion buildings worldwide, it surpasses the most comprehensive database to date by more than 1 billion buildings.
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.
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
Florian Roth, Bernhard Bauer-Marschallinger, Mark Edwin Tupas, Christoph Reimer, Peter Salamon, and Wolfgang Wagner
Nat. Hazards Earth Syst. Sci., 23, 3305–3317, https://doi.org/10.5194/nhess-23-3305-2023, https://doi.org/10.5194/nhess-23-3305-2023, 2023
Short summary
Short summary
In August and September 2022, millions of people were impacted by a severe flood event in Pakistan. Since many roads and other infrastructure were destroyed, satellite data were the only way of providing large-scale information on the flood's impact. Based on the flood mapping algorithm developed at Technische Universität Wien (TU Wien), we mapped an area of 30 492 km2 that was flooded at least once during the study's time period. This affected area matches about the total area of Belgium.
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
Jingliang Hu, Rong Liu, Danfeng Hong, Andrés Camero, Jing Yao, Mathias Schneider, Franz Kurz, Karl Segl, and Xiao Xiang Zhu
Earth Syst. Sci. Data, 15, 113–131, https://doi.org/10.5194/essd-15-113-2023, https://doi.org/10.5194/essd-15-113-2023, 2023
Short summary
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.
M. Tupas, C. Navacchi, F. Roth, B. Bauer-Marschallinger, F. Reuß, and W. Wagner
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-4-W1-2022, 495–502, https://doi.org/10.5194/isprs-archives-XLVIII-4-W1-2022-495-2022, https://doi.org/10.5194/isprs-archives-XLVIII-4-W1-2022-495-2022, 2022
Ashwini Petchiappan, Susan C. Steele-Dunne, Mariette Vreugdenhil, Sebastian Hahn, Wolfgang Wagner, and Rafael Oliveira
Hydrol. Earth Syst. Sci., 26, 2997–3019, https://doi.org/10.5194/hess-26-2997-2022, https://doi.org/10.5194/hess-26-2997-2022, 2022
Short summary
Short summary
This study investigates spatial and temporal patterns in the incidence angle dependence of backscatter from the ASCAT C-band scatterometer and relates those to precipitation, humidity, and radiation data and GRACE equivalent water thickness in ecoregions in the Amazon. The results show that the ASCAT data record offers a unique perspective on vegetation water dynamics exhibiting sensitivity to moisture availability and demand and phenological change at interannual, seasonal, and diurnal scales.
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
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
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
Paolo Filippucci, Luca Brocca, Raphael Quast, Luca Ciabatta, Carla Saltalippi, Wolfgang Wagner, and Angelica Tarpanelli
Hydrol. Earth Syst. Sci., 26, 2481–2497, https://doi.org/10.5194/hess-26-2481-2022, https://doi.org/10.5194/hess-26-2481-2022, 2022
Short summary
Short summary
A high-resolution (1 km) rainfall product with 10–30 d temporal resolution was obtained starting from SM data from Sentinel-1. Good performances are achieved using observed data (gauge and radar) over the Po River Valley, Italy, as a benchmark. The comparison with a product characterized by lower spatial resolution (25 km) highlights areas where the high spatial resolution of Sentinel-1 has great benefits. Possible applications include water management, agriculture and index-based insurances.
Stefan Schlaffer, Marco Chini, Wouter Dorigo, and Simon Plank
Hydrol. Earth Syst. Sci., 26, 841–860, https://doi.org/10.5194/hess-26-841-2022, https://doi.org/10.5194/hess-26-841-2022, 2022
Short summary
Short summary
Prairie wetlands are important for biodiversity and water availability. Knowledge about their variability and spatial distribution is of great use in conservation and water resources management. In this study, we propose a novel approach for the classification of small water bodies from satellite radar images and apply it to our study area over 6 years. The retrieved dynamics show the different responses of small and large wetlands to dry and wet periods.
Wouter Dorigo, Irene Himmelbauer, Daniel Aberer, Lukas Schremmer, Ivana Petrakovic, Luca Zappa, Wolfgang Preimesberger, Angelika Xaver, Frank Annor, Jonas Ardö, Dennis Baldocchi, Marco Bitelli, Günter Blöschl, Heye Bogena, Luca Brocca, Jean-Christophe Calvet, J. Julio Camarero, Giorgio Capello, Minha Choi, Michael C. Cosh, Nick van de Giesen, Istvan Hajdu, Jaakko Ikonen, Karsten H. Jensen, Kasturi Devi Kanniah, Ileen de Kat, Gottfried Kirchengast, Pankaj Kumar Rai, Jenni Kyrouac, Kristine Larson, Suxia Liu, Alexander Loew, Mahta Moghaddam, José Martínez Fernández, Cristian Mattar Bader, Renato Morbidelli, Jan P. Musial, Elise Osenga, Michael A. Palecki, Thierry Pellarin, George P. Petropoulos, Isabella Pfeil, Jarrett Powers, Alan Robock, Christoph Rüdiger, Udo Rummel, Michael Strobel, Zhongbo Su, Ryan Sullivan, Torbern Tagesson, Andrej Varlagin, Mariette Vreugdenhil, Jeffrey Walker, Jun Wen, Fred Wenger, Jean Pierre Wigneron, Mel Woods, Kun Yang, Yijian Zeng, Xiang Zhang, Marek Zreda, Stephan Dietrich, Alexander Gruber, Peter van Oevelen, Wolfgang Wagner, Klaus Scipal, Matthias Drusch, and Roberto Sabia
Hydrol. Earth Syst. Sci., 25, 5749–5804, https://doi.org/10.5194/hess-25-5749-2021, https://doi.org/10.5194/hess-25-5749-2021, 2021
Short summary
Short summary
The International Soil Moisture Network (ISMN) is a community-based open-access data portal for soil water measurements taken at the ground and is accessible at https://ismn.earth. Over 1000 scientific publications and thousands of users have made use of the ISMN. The scope of this paper is to inform readers about the data and functionality of the ISMN and to provide a review of the scientific progress facilitated through the ISMN with the scope to shape future research and operations.
The International Society for Photogrammetry and Remote Sensing is a non-governmental organization devoted to the development of international cooperation for the advancement of photogrammetry and remote sensing and their applications. The Society operates without any discrimination on grounds of race, religion, nationality, or political philosophy.
Useful Links
Useful External Links
Our Contact
c/o
Leibniz University Hannover
Institute of Photogrammetry and GeoInformation
Nienburger Str. 1
D-30167 Hannover
GERMANY
Email: isprs-sg@isprs.org