Articles | Volume 16, issue 17
https://doi.org/10.5194/gmd-16-4957-2023
© 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/gmd-16-4957-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model evaluation paper
| 
31 Aug 2023
Model evaluation paper |
| 31 Aug 2023
| 31 Aug 2023
Uncertainty estimation for a new exponential-filter-based long-term root-zone soil moisture dataset from Copernicus Climate Change Service (C3S) surface observations
Adam Pasik
Department of Geodesy and Geoinformation, TU Wien, Wiedner Hauptstraße 8, 1040 Vienna, Austria
Alexander Gruber
CORRESPONDING AUTHOR
Department of Geodesy and Geoinformation, TU Wien, Wiedner Hauptstraße 8, 1040 Vienna, Austria
Wolfgang Preimesberger
Department of Geodesy and Geoinformation, TU Wien, Wiedner Hauptstraße 8, 1040 Vienna, Austria
Domenico De Santis
Research Institute for Geo-Hydrological Protection, National Research Council, Via della Madonna Alta 126, 06128 Perugia, Italy
Wouter Dorigo
Department of Geodesy and Geoinformation, TU Wien, Wiedner Hauptstraße 8, 1040 Vienna, Austria
Related authors
No articles found.
Andreas Güntner, Ehsan Sharifi, Julian Haas, Eva Boergens, Feifei Cao, Christoph Dahle, Neda Darbeheshti, Henryk Dobslaw, Inés Dussaillant, Wouter Dorigo, Frank Flechtner, Adrian Jäggi, Miriam Kosmale, Martin Lasser, Kari Luojus, Ulrich Meyer, Adam Pasik, Wolfgang Preimesberger, Claudia Ruz Vargas, and Michael Zemp
Earth Syst. Sci. Data, 18, 7787–7816, https://doi.org/10.5194/essd-18-7787-2026, https://doi.org/10.5194/essd-18-7787-2026, 2026
Short summary
Short summary
We provide a data set that illustrates how the amount of water that is stored in the subsurface as groundwater varies in time over the continents of the Earth. An important part of the data comes from satellites that track changes in water by its gravity effect. The data allow for assessing how groundwater as the most important freshwater resource for mankind and ecosystems is affected by climate variability, climate change and withdrawal by human activities.
Ruxandra-Maria Zotta, Moritz Clemens Müller, Raul Lezameta, Sophia Walther, Matthias Forkel, and Wouter Dorigo
EGUsphere, https://doi.org/10.5194/egusphere-2026-6254, https://doi.org/10.5194/egusphere-2026-6254, 2026
This preprint is open for discussion and under review for Biogeosciences (BG).
Short summary
Short summary
We developed a satellite-based method to estimate how much carbon plants take up through photosynthesis worldwide from 2002 to 2020. Using microwave observations, land cover and machine learning, we found that land-cover information improved estimates most, while deeper soil moisture better captured water-related changes in short vegetation. The approach complements existing products, but the total amount of plant carbon uptake remains uncertain.
Marco Dionigi, Tommaso Moramarco, Domenico De Santis, Christian Massari, Silvia Barbetta, and Stefania Camici
EGUsphere, https://doi.org/10.5194/egusphere-2026-5965, https://doi.org/10.5194/egusphere-2026-5965, 2026
This preprint is open for discussion and under review for Natural Hazards and Earth System Sciences (NHESS).
Short summary
Short summary
Flood forecasting is challenging in river basins with sparse monitoring networks. We developed a database of pre-computed flood simulations for the Crati River basin in southern Italy, combining stochastically generated rainfall and air temperature with a hydrological model run under different soil moisture conditions. Satellite soil moisture observations select the most relevant simulations, providing river flows in real time. The same approach could be applied to other poorly gauged basins.
Diaa Sheishah, Enas Abdelsamei, Viktoria Blanka-Vegi, Károly Barta, Ahmed M. Ali, Khaldoun Abualhin, Djamil Al-Halbouni, Wouter Dorigo, and Gyorgy Sipos
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2026-563, https://doi.org/10.5194/essd-2026-563, 2026
Preprint under review for ESSD
Short summary
Short summary
Soil moisture controls drought, crop water supply, and groundwater recharge, but it is difficult to monitor below the surface. We used national monitoring data from 117 stations and six soil depths to build an open database that links soil moisture, soil type, water retention, and expected electromagnetic responses. The results identify a key mid-depth buffering zone and provide a practical reference for future drought monitoring, irrigation planning, and non-invasive soil-water surveys.
Jawairia A. Ahmad, Christopher M. Taylor, Bethan L. Harris, Dávid D. Kovács, Seonaid Anderson, Adrià Amell, Colin Moldenhauer, Steven Wells, and Wouter Dorigo
EGUsphere, https://doi.org/10.5194/egusphere-2026-4687, https://doi.org/10.5194/egusphere-2026-4687, 2026
This preprint is open for discussion and under review for Hydrology and Earth System Sciences (HESS).
Short summary
Short summary
Evaluating the ability of satellite-observed soil moisture products to represent fine-scale land surface information shows that active microwave radar based product from ASCAT captures spatial variability at fine resolution which passive microwave based products miss. Rainfall dataset evaluation using fine-scale soil moisture information highlights the limitations in ERA5 precipitation. Further, fine-scale soil moisture information improves nowcasting of convective storms over dry regions.
Pierre Laluet, Jacopo Dari, Louise Busschaert, Zdenko Heyvaert, Gabrielle De Lannoy, Pia Langhans, Sara Modanesi, Christian Massari, Luca Brocca, Carla Saltalippi, Renato Morbidelli, Clément Albergel, and Wouter Dorigo
Earth Syst. Sci. Data, 18, 4833–4853, https://doi.org/10.5194/essd-18-4833-2026, https://doi.org/10.5194/essd-18-4833-2026, 2026
Short summary
Short summary
We developed a long-term dataset collection of irrigation water use based on about two decades of satellite observations, three distinct approaches, and many input datasets. The collection provides monthly estimates for major agricultural regions and helps describe how irrigation varies across locations, seasons, and years. It offers a foundation for improving how irrigation is quantified, compared across methods, and integrated into large-scale hydrological and climate studies.
Martin Hirschi, Dominik Michel, Dominik L. Schumacher, Wolfgang Preimesberger, and Sonia I. Seneviratne
Earth Syst. Sci. Data, 18, 4855–4870, https://doi.org/10.5194/essd-18-4855-2026, https://doi.org/10.5194/essd-18-4855-2026, 2026
Short summary
Short summary
Drier summers and more frequent droughts were experienced in Switzerland in the last two decades. We present a comprehensive set of in situ soil moisture measurements from the Swiss Soil Moisture Experiment (SwissSMEX) network, covering the period 2010–2025. We document recent measures that have been taken to secure the network and its long-term time series. We use this curated data to analyse reported drying trends.
Elena Leonarduzzi, Alexander Gruber, and Reed M. Maxwell
EGUsphere, https://doi.org/10.5194/egusphere-2026-2956, https://doi.org/10.5194/egusphere-2026-2956, 2026
This preprint is open for discussion and under review for Hydrology and Earth System Sciences (HESS).
Short summary
Short summary
Soil moisture is essential for natural hazards prediction, agriculture, and weather and climate forecasting, but current measurements cannot provide the needed detail across large areas. Our approach combines machine learning, physics-based hydrological modeling, and observations to produce high-resolution soil moisture maps, overcoming key limitations of existing approaches. This provides a promising path toward high-resolution soil moisture products, beneficial for many applications.
Oscar M. Baez-Villanueva, Alfredo Crespo-Otero, Sara Modanesi, Pierre Laluet, Sergio Vicente-Serrano, Jaap Schellekens, Jacopo Dari, Hylke E. Beck, Wouter Dorigo, Christian Massari, Chiara Corbari, Joppe Massant, Kwint Delbare, Olivier Bonte, Aaron Boone, Diego Fernández-Prieto, and Diego G. Miralles
EGUsphere, https://doi.org/10.5194/egusphere-2026-1856, https://doi.org/10.5194/egusphere-2026-1856, 2026
Short summary
Short summary
We developed a new method to estimate daily land evaporation at high resolution across the Iberian Peninsula, explicitly accounting for irrigation. By combining satellite and meteorological data, we show that irrigation can strongly increase evaporation in agricultural areas. The results better match ground observations and improve understanding of water use. This approach can support farming decisions and water management at the regional scale, and will be extended to global applications.
Ehsan Modiri, Oldrich Rakovec, Pallav Kumar Shrestha, Almudena García-García, Leandro Avila, Katie Blackford, Elizabeth Cooper, Bram Droppers, Paolo Filippucci, Milan Fischer, Matěj Orság, Pietro Stradiotti, Luca Brocca, Douglas B. Clark, Wouter Dorigo, Stefan Kollet, Jian Peng, Niko Wanders, and Luis Samaniego
EGUsphere, https://doi.org/10.5194/egusphere-2026-1012, https://doi.org/10.5194/egusphere-2026-1012, 2026
Short summary
Short summary
Drought impacts water supply, agriculture, and ecosystems, yet hydrological models often disagree on when and where drought occurs. This study tested whether satellite observations can improve how models represent soil moisture drought in the Rhine River basin. Using several models and major drought events, we show that satellite data improve spatial realism and reveal important differences among models, helping to better understand uncertainty in drought monitoring and early warning.
Pierre Laluet, Chiara Corbari, Oscar Baez-Villanueva, Sophia Walther, Yongqiang Zhang, Joaquín Muñoz-Sabater, Clément Albergel, and Wouter Dorigo
Hydrol. Earth Syst. Sci., 30, 1779–1799, https://doi.org/10.5194/hess-30-1779-2026, https://doi.org/10.5194/hess-30-1779-2026, 2026
Short summary
Short summary
We examined how well global datasets estimate evapotranspiration, the water moving from land to the air, in irrigated areas. We compared six widely used products with irrigation maps, satellite estimates, and field measurements across different climate regions. Some datasets better capture irrigation effects, especially those using temperature and vegetation satellite data. This work helps guide dataset selection and supports better inclusion of irrigation in hydrological and climate models.
Roland Hohensinn, Junyang Gou, Ulrich Meyer, Vincent Humphrey, Wouter Dorigo, Eva Boergens, Benedikt Soja, Alexander Gruber, Laura Jensen, Annette Eicker, Michael Rast, and Andreas Güntner
EGUsphere, https://doi.org/10.5194/egusphere-2026-975, https://doi.org/10.5194/egusphere-2026-975, 2026
Short summary
Short summary
This study provides a statistically traceable, uncertainty-aware assessment of global terrestrial and groundwater storage trends based on 21.5 years of GRACE/-FO observations using the Global Gravity-based Groundwater Product (G3P). By accounting for long-range memory and interannual variability, we isolate robust trends from climate variability and noise. The analysis refines continental budgets and identifies emerging groundwater gains alongside widespread persistent decline.
Domenico De Santis, Silvia Barbetta, Sumit Sen, Viviana Maggioni, Farhad Bahmanpouri, Ashutosh Sharma, Ankit Agarwal, Sagar Gupta, Francesco Avanzi, and Christian Massari
Nat. Hazards Earth Syst. Sci., 26, 1075–1104, https://doi.org/10.5194/nhess-26-1075-2026, https://doi.org/10.5194/nhess-26-1075-2026, 2026
Short summary
Short summary
A conceptual, semi-distributed hydrological model was tailored to simulate high flows in monsoon-dominated, glacier-influenced and flood-prone Himalayan basins. Multi-data calibration using satellite-based glacier mass loss and evapotranspiration estimates improved process realism in data-scarce environments. The proposed modelling approach captured key streamflow features despite significant input uncertainties, proving to be a useful tool for exploring the local hydrological response dynamics.
Bethan L. Harris, Christopher M. Taylor, Wouter Dorigo, Ruxandra-Maria Zotta, Darren Ghent, and Iván Noguera
Hydrol. Earth Syst. Sci., 29, 6917–6933, https://doi.org/10.5194/hess-29-6917-2025, https://doi.org/10.5194/hess-29-6917-2025, 2025
Short summary
Short summary
An improved understanding of land-atmosphere coupling processes during flash (rapid-onset) droughts is needed to aid the development of forecasts for these events. We use satellite observations to investigate the surface energy budget during flash droughts globally. The most intense events show a perturbed surface energy budget months before onset. In some regions, vegetation observations 1–2 months before onset provide information on the likelihood of heat extremes during an event.
Wolfgang Preimesberger, Pietro Stradiotti, and Wouter Dorigo
Earth Syst. Sci. Data, 17, 4305–4329, https://doi.org/10.5194/essd-17-4305-2025, https://doi.org/10.5194/essd-17-4305-2025, 2025
Short summary
Short summary
We introduce the official ESA CCI Soil Moisture GAPFILLED climate data record. A univariate interpolation algorithm is applied to predict missing data points without relying on ancillary variables. The dataset includes gap-free uncertainty estimates for all predictions and was validated with independent in situ reference measurements. Our data record is recommended for applications which require global long-term gap-free satellite soil moisture data.
Wolfgang Knorr, Matthew Williams, Tea Thum, Thomas Kaminski, Michael Voßbeck, Marko Scholze, Tristan Quaife, T. Luke Smallman, Susan C. Steele-Dunne, Mariette Vreugdenhil, Tim Green, Sönke Zaehle, Mika Aurela, Alexandre Bouvet, Emanuel Bueechi, Wouter Dorigo, Tarek S. El-Madany, Mirco Migliavacca, Marika Honkanen, Yann H. Kerr, Anna Kontu, Juha Lemmetyinen, Hannakaisa Lindqvist, Arnaud Mialon, Tuuli Miinalainen, Gaétan Pique, Amanda Ojasalo, Shaun Quegan, Peter J. Rayner, Pablo Reyes-Muñoz, Nemesio Rodríguez-Fernández, Mike Schwank, Jochem Verrelst, Songyan Zhu, Dirk Schüttemeyer, and Matthias Drusch
Geosci. Model Dev., 18, 2137–2159, https://doi.org/10.5194/gmd-18-2137-2025, https://doi.org/10.5194/gmd-18-2137-2025, 2025
Short summary
Short summary
When it comes to climate change, the land surface is where the vast majority of impacts happen. The task of monitoring those impacts across the globe is formidable and must necessarily rely on satellites – at a significant cost: the measurements are only indirect and require comprehensive physical understanding. We have created a comprehensive modelling system that we offer to the research community to explore how satellite data can be better exploited to help us capture the changes that happen on our lands.
Martin Hirschi, Pietro Stradiotti, Bas Crezee, Wouter Dorigo, and Sonia I. Seneviratne
Hydrol. Earth Syst. Sci., 29, 397–425, https://doi.org/10.5194/hess-29-397-2025, https://doi.org/10.5194/hess-29-397-2025, 2025
Short summary
Short summary
We investigate the potential of long-term satellite and reanalysis products for characterising soil drying by analysing their 2000–2022 soil moisture trends and their representation of agroecological drought events of this period. Soil moisture trends are globally diverse and partly contradictory between products. This also affects the products' drought-detection capacity. Based on the best-estimate products, consistent soil drying is observed over more than 40 % of the land area covered.
Ruxandra-Maria Zotta, Leander Moesinger, Robin van der Schalie, Mariette Vreugdenhil, Wolfgang Preimesberger, Thomas Frederikse, Richard de Jeu, and Wouter Dorigo
Earth Syst. Sci. Data, 16, 4573–4617, https://doi.org/10.5194/essd-16-4573-2024, https://doi.org/10.5194/essd-16-4573-2024, 2024
Short summary
Short summary
VODCA v2 is a dataset providing vegetation indicators for long-term ecosystem monitoring. VODCA v2 comprises two products: VODCA CXKu, spanning 34 years of observations (1987–2021), suitable for monitoring upper canopy dynamics, and VODCA L (2010–2021), for above-ground biomass monitoring. VODCA v2 has lower noise levels than the previous product version and provides valuable insights into plant water dynamics and biomass changes, even in areas where optical data are limited.
Samuel Scherrer, Gabriëlle De Lannoy, Zdenko Heyvaert, Michel Bechtold, Clement Albergel, Tarek S. El-Madany, and Wouter Dorigo
Hydrol. Earth Syst. Sci., 27, 4087–4114, https://doi.org/10.5194/hess-27-4087-2023, https://doi.org/10.5194/hess-27-4087-2023, 2023
Short summary
Short summary
We explored different options for data assimilation (DA) of the remotely sensed leaf area index (LAI). We found strong biases between LAI predicted by Noah-MP and observations. LAI DA that does not take these biases into account can induce unphysical patterns in the resulting LAI and flux estimates and leads to large changes in the climatology of root zone soil moisture. We tested two bias-correction approaches and explored alternative solutions to treating bias in LAI DA.
Remi Madelon, Nemesio J. Rodríguez-Fernández, Hassan Bazzi, Nicolas Baghdadi, Clement Albergel, Wouter Dorigo, and Mehrez Zribi
Hydrol. Earth Syst. Sci., 27, 1221–1242, https://doi.org/10.5194/hess-27-1221-2023, https://doi.org/10.5194/hess-27-1221-2023, 2023
Short summary
Short summary
We present an approach to estimate soil moisture (SM) at 1 km resolution using Sentinel-1 and Sentinel-3 satellites. The estimates were compared to other high-resolution (HR) datasets over Europe, northern Africa, Australia, and North America, showing good agreement. However, the discrepancies between the different HR datasets and their lower performances compared with in situ measurements and coarse-resolution datasets show the remaining challenges for large-scale HR SM mapping.
Luisa Schmidt, Matthias Forkel, Ruxandra-Maria Zotta, Samuel Scherrer, Wouter A. Dorigo, Alexander Kuhn-Régnier, Robin van der Schalie, and Marta Yebra
Biogeosciences, 20, 1027–1046, https://doi.org/10.5194/bg-20-1027-2023, https://doi.org/10.5194/bg-20-1027-2023, 2023
Short summary
Short summary
Vegetation attenuates natural microwave emissions from the land surface. The strength of this attenuation is quantified as the vegetation optical depth (VOD) parameter and is influenced by the vegetation mass, structure, water content, and observation wavelength. Here we model the VOD signal as a multi-variate function of several descriptive vegetation variables. The results help in understanding the effects of ecosystem properties on VOD.
Taylor Smith, Ruxandra-Maria Zotta, Chris A. Boulton, Timothy M. Lenton, Wouter Dorigo, and Niklas Boers
Earth Syst. Dynam., 14, 173–183, https://doi.org/10.5194/esd-14-173-2023, https://doi.org/10.5194/esd-14-173-2023, 2023
Short summary
Short summary
Multi-instrument records with varying signal-to-noise ratios are becoming increasingly common as legacy sensors are upgraded, and data sets are modernized. Induced changes in higher-order statistics such as the autocorrelation and variance are not always well captured by cross-calibration schemes. Here we investigate using synthetic examples how strong resulting biases can be and how they can be avoided in order to make reliable statements about changes in the resilience of a system.
Matthias Forkel, Luisa Schmidt, Ruxandra-Maria Zotta, Wouter Dorigo, and Marta Yebra
Hydrol. Earth Syst. Sci., 27, 39–68, https://doi.org/10.5194/hess-27-39-2023, https://doi.org/10.5194/hess-27-39-2023, 2023
Short summary
Short summary
The live fuel moisture content (LFMC) of vegetation canopies is a driver of wildfires. We investigate the relation between LFMC and passive microwave satellite observations of vegetation optical depth (VOD) and develop a method to estimate LFMC from VOD globally. Our global VOD-based estimates of LFMC can be used to investigate drought effects on vegetation and fire risks.
Leander Moesinger, Ruxandra-Maria Zotta, Robin van der Schalie, Tracy Scanlon, Richard de Jeu, and Wouter Dorigo
Biogeosciences, 19, 5107–5123, https://doi.org/10.5194/bg-19-5107-2022, https://doi.org/10.5194/bg-19-5107-2022, 2022
Short summary
Short summary
The standardized vegetation optical depth index (SVODI) can be used to monitor the vegetation condition, such as whether the vegetation is unusually dry or wet. SVODI has global coverage, spans the past 3 decades and is derived from multiple spaceborne passive microwave sensors of that period. SVODI is based on a new probabilistic merging method that allows the merging of normally distributed data even if the data are not gap-free.
Robin van der Schalie, Mendy van der Vliet, Clément Albergel, Wouter Dorigo, Piotr Wolski, and Richard de Jeu
Hydrol. Earth Syst. Sci., 26, 3611–3627, https://doi.org/10.5194/hess-26-3611-2022, https://doi.org/10.5194/hess-26-3611-2022, 2022
Short summary
Short summary
Climate data records of surface soil moisture, vegetation optical depth, and land surface temperature can be derived from passive microwave observations. The ability of these datasets to properly detect anomalies and extremes is very valuable in climate research and can especially help to improve our insight in complex regions where the current climate reanalysis datasets reach their limitations. Here, we present a case study over the Okavango Delta, where we focus on inter-annual variability.
Benjamin Wild, Irene Teubner, Leander Moesinger, Ruxandra-Maria Zotta, Matthias Forkel, Robin van der Schalie, Stephen Sitch, and Wouter Dorigo
Earth Syst. Sci. Data, 14, 1063–1085, https://doi.org/10.5194/essd-14-1063-2022, https://doi.org/10.5194/essd-14-1063-2022, 2022
Short summary
Short summary
Gross primary production (GPP) describes the conversion of CO2 to carbohydrates and can be seen as a filter for our atmosphere of the primary greenhouse gas CO2. We developed VODCA2GPP, a GPP dataset that is based on vegetation optical depth from microwave remote sensing and temperature. Thus, it is mostly independent from existing GPP datasets and also available in regions with frequent cloud coverage. Analysis showed that VODCA2GPP is able to complement existing state-of-the-art GPP datasets.
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.
Sara Modanesi, Christian Massari, Alexander Gruber, Hans Lievens, Angelica Tarpanelli, Renato Morbidelli, and Gabrielle J. M. De Lannoy
Hydrol. Earth Syst. Sci., 25, 6283–6307, https://doi.org/10.5194/hess-25-6283-2021, https://doi.org/10.5194/hess-25-6283-2021, 2021
Short summary
Short summary
Worldwide, the amount of water used for agricultural purposes is rising and the quantification of irrigation is becoming a crucial topic. Land surface models are not able to correctly simulate irrigation. Remote sensing observations offer an opportunity to fill this gap as they are directly affected by irrigation. We equipped a land surface model with an observation operator able to transform Sentinel-1 backscatter observations into realistic vegetation and soil states via data assimilation.
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.
Cited articles
Adeaem, Gößwein, B., Hahn, S., Preimesberger, W., and BM, B.: TUW-GEO/pyswi: v1.0 (v1.0), Zenodo [code], https://doi.org/10.5281/zenodo.7534919, 2023. a
Albergel, C., Rüdiger, C., Pellarin, T., Calvet, J.-C., Fritz, N., Froissard, F., Suquia, D., Petitpa, A., Piguet, B., and Martin, E.: From near-surface to root-zone soil moisture using an exponential filter: an assessment of the method based on in-situ observations and model simulations, Hydrol. Earth Syst. Sci., 12, 1323–1337, https://doi.org/10.5194/hess-12-1323-2008, 2008. a, b, c, d, e, f, g, h, i
Al Bitar, A. and Mahmoodi, A.: Algorithm Theoretical Basis Document (ATBD) for
the SMOS Level 4 Root Zone Soil Moisture (Version v30_01), Tech. Rep.,
https://doi.org/10.5281/zenodo.4298572, 2020. a
Alday, J. G., Camarero, J. J., Revilla, J., and Resco de Dios, V.: Similar
diurnal, seasonal and annual rhythms in radial root expansion across two
coexisting Mediterranean oak species, Tree Physiol., 40, 956–968,
https://doi.org/10.1093/treephys/tpaa041, 2020. a
Al-Yaari, A., Dayau, S., Chipeaux, C., Aluome, C., Kruszewski, A., Loustau, D.,
and Wigneron, J.-P.: The AQUI Soil Moisture Network for Satellite Microwave
Remote Sensing Validation in South-Western France, Remote Sensing, 10, 1839,
https://doi.org/10.3390/rs10111839, 2018. a
Baldwin, D., Manfreda, S., Keller, K., and Smithwick, E.: Predicting root zone
soil moisture with soil properties and satellite near-surface moisture data
across the conterminous United States, J. Hydrol., 546, 393–404,
https://doi.org/10.1016/j.jhydrol.2017.01.020, 2017. a
Bauer Marschallinger, B.: Algorithm Theoretical Basis Document,
CGLOPS1_ATBD_SWI1km-V1 l1.30, Tech. rep., Copernicus Global Land
Operations, 2022. a
Bauer-Marschallinger, B., Paulik, C., Hochstöger, S., Mistelbauer, T.,
Modanesi, S., Ciabatta, L., Massari, C., Brocca, L., and Wagner, W.: Soil
Moisture from Fusion of Scatterometer and SAR: Closing the Scale Gap with
Temporal Filtering, Remote Sensing, 10, 1030, https://doi.org/10.3390/rs10071030, 2018. a
Beck, H. E., de Jeu, R. A. M., Schellekens, J., van Dijk, A. I. J. M., and
Bruijnzeel, L. A.: Improving Curve Number Based Storm Runoff Estimates Using
Soil Moisture Proxies, IEEE J. Sel. Top. Appl., 2, 250–259,
https://doi.org/10.1109/JSTARS.2009.2031227, 2009. a, b, c
Bell, J. E., Palecki, M. A., Baker, C. B., Collins, W. G., Lawrimore, J. H.,
Leeper, R. D., Hall, M. E., Kochendorfer, J., Meyers, T. P., Wilson, T., and
Diamond, H. J.: U.S. Climate Reference Network Soil Moisture and Temperature
Observations, J. Hydrometeorol., 14, 977–988,
https://doi.org/10.1175/JHM-D-12-0146.1, 2013. a
Beven, K.: On the concept of model structural error, Water Sci.
Technol., 52, 167–175, https://doi.org/10.2166/wst.2005.0165, 2005. a
Beyrich, F. and Adam, W.: Site and Data Report for the Lindenberg Reference
Site in CEOP – Phase 1, Tech. Rep. 230, Deutscher Wetterdienst, Offenbach am
Main, 2007. a
Biddoccu, M., Ferraris, S., Opsi, F., and Cavallo, E.: Long-term monitoring of
soil management effects on runoff and soil erosion in sloping vineyards in
Alto Monferrato (North–West Italy), Soil Till. Res., 155,
176–189, https://doi.org/10.1016/j.still.2015.07.005, 2016. a
Bircher, S., Skou, N., Jensen, K. H., Walker, J. P., and Rasmussen, L.: A soil moisture and temperature network for SMOS validation in Western Denmark, Hydrol. Earth Syst. Sci., 16, 1445–1463, https://doi.org/10.5194/hess-16-1445-2012, 2012. a
Blöschl, G., Blaschke, A. P., Broer, M., Bucher, C., Carr, G., Chen, X., Eder, A., Exner-Kittridge, M., Farnleitner, A., Flores-Orozco, A., Haas, P., Hogan, P., Kazemi Amiri, A., Oismüller, M., Parajka, J., Silasari, R., Stadler, P., Strauss, P., Vreugdenhil, M., Wagner, W., and Zessner, M.: The Hydrological Open Air Laboratory (HOAL) in Petzenkirchen: a hypothesis-driven observatory, Hydrol. Earth Syst. Sci., 20, 227–255, https://doi.org/10.5194/hess-20-227-2016, 2016. a
Bogena, H., Kunkel, R., Puetz, T., Vereecken, H., Krueger, E., Zacharias, S.,
Dietrich, P., Wollschlaeger, U., Kunstmann, H., Papen, H., Schmid, H. P.,
Munch, J. C., Priesack, E., Schwank, M., Bens, O., Brauer, A., Borg, E., and
Hajnsek, I.: TERENO – Long-term monitoring network for terrestrial
environmental research, Hydrol. Wasserbewirts., 56, 138–143,
2012. a
Bogena, H., Montzka, C., Huisman, J., Graf, A., Schmidt, M., Stockinger, M.,
von Hebel, C., Hendricks-Franssen, H., van der Kruk, J., Tappe, W., Lücke,
A., Baatz, R., Bol, R., Groh, J., Pütz, T., Jakobi, J., Kunkel, R., Sorg,
J., and Vereecken, H.: The TERENO-Rur Hydrological Observatory: A Multiscale
Multi-Compartment Research Platform for the Advancement of Hydrological
Science, Vadose Zone J., 17, 180055, https://doi.org/10.2136/vzj2018.03.0055,
2018. a
Bogena, H. R.: TERENO: German network of terrestrial environmental
observatories, Journal of Large-Scale Research Facilities, 2, A52–A52,
https://doi.org/10.17815/jlsrf-2-98, 2016. a
Bouaziz, L. J. E., Steele-Dunne, S. C., Schellekens, J., Weerts, A. H., Stam,
J., Sprokkereef, E., Winsemius, H. H. C., Savenije, H. H. G., and Hrachowitz,
M.: Improved Understanding of the Link Between Catchment-Scale Vegetation
Accessible Storage and Satellite-Derived Soil Water Index, Water Resour.
Res., 56, e2019WR026365, https://doi.org/10.1029/2019WR026365, 2020. a, b, c, d, e
Brocca, L., Melone, F., and Moramarco, T.: On the estimation of antecedent
wetness conditions in rainfall–runoff modelling, Hydrol. Process.,
22, 629–642, https://doi.org/10.1002/hyp.6629, 2008. a
Brocca, L., Melone, F., Moramarco, T., and Morbidelli, R.: Antecedent wetness
conditions based on ERS scatterometer data, J. Hydrol., 364,
73–87, https://doi.org/10.1016/j.jhydrol.2008.10.007, 2009. a
Brocca, L., Hasenauer, S., Lacava, T., Melone, F., Moramarco, T., Wagner, W.,
Dorigo, W., Matgen, P., Martínez-Fernández, J., Llorens, P., Latron, J.,
Martin, C., and Bittelli, M.: Soil moisture estimation through ASCAT and
AMSR-E sensors: An intercomparison and validation study across Europe, Remote
Sens. Environ., 115, 3390–3408, https://doi.org/10.1016/j.rse.2011.08.003,
2011. a, b, c, d
C3S: Algorithm Theoretical Baseline Document (ATBD) – Soil Moisture Service
D1.SM.2-v3.0, Tech. Rep., EODC, https://doi.org/10.24381/cds.d7782f18, 2020. a, b
Calvet, J.-C., Fritz, N., Froissard, F., Suquia, D., Petitpa, A., and Piguet,
B.: In situ soil moisture observations for the CAL/VAL of SMOS: the SMOSMANIA
network, in: 2007 IEEE International Geoscience and Remote Sensing Symposium, Barcelona, Spain, 23–28 July 2007,
1196–1199, https://doi.org/10.1109/IGARSS.2007.4423019, 2007. a
Calvet, J.-C., Fritz, N., Berne, C., Piguet, B., Maurel, W., and Meurey, C.: Deriving pedotransfer functions for soil quartz fraction in southern France from reverse modeling, SOIL, 2, 615–629, https://doi.org/10.5194/soil-2-615-2016, 2016. a
Canisius, F.: Calibration of Casselman, Ontario Soil Moisture Monitoring
Network, Tech. Rep., Agriculture and Agri-food Canada, 2011. a
Capello, G., Biddoccu, M., Ferraris, S., and Cavallo, E.: Effects of Tractor
Passes on Hydrological and Soil Erosion Processes in Tilled and Grassed
Vineyards, Water, 11, 2118, https://doi.org/10.3390/w11102118, 2019. a
Cappelaere, B., Descroix, L., Lebel, T., Boulain, N., Ramier, D., Laurent,
J.-P., Favreau, G., Boubkraoui, S., Boucher, M., Bouzou Moussa, I.,
Chaffard, V., Hiernaux, P., Issoufou, H., Le Breton, E., Mamadou, I.,
Nazoumou, Y., Oi, M., Ottlé, C., and Quantin, G.: The AMMA-CATCH experiment
in the cultivated Sahelian area of south-west Niger – Investigating water
cycle response to a fluctuating climate and changing environment, J.
Hydrol., 375, 34–51, https://doi.org/10.1016/j.jhydrol.2009.06.021, 2009. a
Cook, D. R.: Soil Water and Temperature System (SWATS) Instrument Handbook,
Tech. Rep., US Department of Energy, https://doi.org/10.2172/1251383,
2016a. a
Cook, D. R.: Soil Temperature and Moisture Profile (STAMP) System Handbook,
Tech. Rep., US Department of Energy, https://doi.org/10.2172/1332724,
2016b. a
Cook, D. R.: Surface Energy Balance System (SEBS) Instrument Handbook, Tech.
Rep., US Department of Energy, https://doi.org/10.2172/1004944, 2018. a
de Lange, R., Beck, R., van de Giesen, N., Friesen, J., de Wit, A., and Wagner,
W.: Scatterometer-Derived Soil Moisture Calibrated for Soil Texture With a
One-Dimensional Water-Flow Model, IEEE T. Geosci. Remote, 46, 4041–4049, https://doi.org/10.1109/TGRS.2008.2000796, 2008. a, b, c, d
Dente, L., Su, Z., and Wen, J.: Validation of SMOS Soil Moisture Products over
the Maqu and Twente Regions, Sensors, 12, 9965–9986,
https://doi.org/10.3390/s120809965, 2012. a, b, c
de Rosnay, P., Gruhier, C., Timouk, F., Baup, F., Mougin, E., Hiernaux, P.,
Kergoat, L., and LeDantec, V.: Multi-scale soil moisture measurements at the
Gourma meso-scale site in Mali, J. Hydrol., 375, 241–252,
https://doi.org/10.1016/j.jhydrol.2009.01.015, 2009. a
De Santis, D. and Biondi, D.: Error Propagation from Remotely Sensed Surface
Soil Moisture Into Soil Water Index Using an Exponential Filter, in: HIC
2018. 13th International Conference on Hydroinformatics, Palermo, Italy, 1–6 July 2018, edited by: Loggia,
G. L., Freni, G., Puleo, V., and Marchis, M. D., vol. 3 of EPiC Series
in Engineering, EasyChair, 520–525, https://doi.org/10.29007/kvhb, 2018. a, b, c, d, e, f
Dorigo, W., Xaver, A., Vreugdenhil, M., Gruber, A., Hegyiová, A.,
Sanchis-Dufau, A., Zamojski, D., Cordes, C., Wagner, W., and Drusch, M.:
Global Automated Quality Control of In Situ Soil Moisture Data from the
International Soil Moisture Network, Vadose Zone J., 12, vzj2012.0097,
https://doi.org/10.2136/vzj2012.0097, 2013. a
Dorigo, W., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L.,
Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, P. D., Hirschi,
M., Ikonen, J., de Jeu, R., Kidd, R., Lahoz, W., Liu, Y. Y., Miralles, D.,
Mistelbauer, T., Nicolai-Shaw, N., Parinussa, R., Pratola, C., Reimer, C.,
van der Schalie, R., Seneviratne, S. I., Smolander, T., and Lecomte, P.:
ESA CCI Soil Moisture for improved Earth system understanding: State-of-the
art and future directions, Remote Sens. Environ., 203, 185–215,
https://doi.org/10.1016/j.rse.2017.07.001, 2017. a
Dorigo, W., Dietrich, S., Aires, F., Brocca, L., Carter, S., Cretaux, J.-F.,
Dunkerley, D., Enomoto, H., Forsberg, R., Güntner, A., Hegglin, M. I.,
Hollmann, R., Hurst, D. F., Johannessen, J. A., Kummerow, C., Lee, T.,
Luojus, K., Looser, U., Miralles, D. G., Pellet, V., Recknagel, T., Vargas,
C. R., Schneider, U., Schoeneich, P., Schröder, M., Tapper, N., Vuglinsky,
V., Wagner, W., Yu, L., Zappa, L., Zemp, M., and Aich, V.: Closing the Water
Cycle from Observations across Scales: Where Do We Stand?, B.
Am. Meteorol. Soc., 102, E1897–E1935,
https://doi.org/10.1175/BAMS-D-19-0316.1, 2021a. a, b
Dorigo, W., Himmelbauer, I., Aberer, D., Schremmer, L., Petrakovic, I., Zappa, L., Preimesberger, W., Xaver, A., Annor, F., Ardö, J., Baldocchi, D., Bitelli, M., Blöschl, G., Bogena, H., Brocca, L., Calvet, J.-C., Camarero, J. J., Capello, G., Choi, M., Cosh, M. C., van de Giesen, N., Hajdu, I., Ikonen, J., Jensen, K. H., Kanniah, K. D., de Kat, I., Kirchengast, G., Kumar Rai, P., Kyrouac, J., Larson, K., Liu, S., Loew, A., Moghaddam, M., Martínez Fernández, J., Mattar Bader, C., Morbidelli, R., Musial, J. P., Osenga, E., Palecki, M. A., Pellarin, T., Petropoulos, G. P., Pfeil, I., Powers, J., Robock, A., Rüdiger, C., Rummel, U., Strobel, M., Su, Z., Sullivan, R., Tagesson, T., Varlagin, A., Vreugdenhil, M., Walker, J., Wen, J., Wenger, F., Wigneron, J. P., Woods, M., Yang, K., Zeng, Y., Zhang, X., Zreda, M., Dietrich, S., Gruber, A., van Oevelen, P., Wagner, W., Scipal, K., Drusch, M., and Sabia, R.: The International Soil Moisture Network: serving Earth system science for over a decade, Hydrol. Earth Syst. Sci., 25, 5749–5804, https://doi.org/10.5194/hess-25-5749-2021, 2021b. a, b
Dorigo, W., Preimesberger, W., Moesinger, L., Pasik, A., Scanlon, T., Hahn, S.,
Van der Schalie, R., Van der Vliet, M., De Jeu, R., Kidd, R.,
Rodriguez-Fernandez, N., and Hirschi, M.: ESA Soil Moisture Climate Change
Initiative: COMBINED Product, Version 05.3, Centre for Environmental Data
Analysis [data set],
https://catalogue.ceda.ac.uk/uuid/e43aead9947549078c2d108b2c3632b2 (last access: 28 August 2023),
2021c. a
Dorigo, W. A., Wagner, W., Hohensinn, R., Hahn, S., Paulik, C., Xaver, A., Gruber, A., Drusch, M., Mecklenburg, S., van Oevelen, P., Robock, A., and Jackson, T.: The International Soil Moisture Network: a data hosting facility for global in situ soil moisture measurements, Hydrol. Earth Syst. Sci., 15, 1675–1698, https://doi.org/10.5194/hess-15-1675-2011, 2011. a
Famiglietti, J. S., Ryu, D., Berg, A. A., Rodell, M., and Jackson, T. J.: Field
observations of soil moisture variability across scales, Water Resour.
Res., 44, W01423, https://doi.org/10.1029/2006WR005804, 2008. a
Flammini, A., Corradini, C., Morbidelli, R., Saltalippi, C., Picciafuoco, T.,
and Giráldez, J. V.: Experimental Analyses of the Evaporation Dynamics in
Bare Soils under Natural Conditions, Water Resour. Manag., 32,
1153–1166, https://doi.org/10.1007/s11269-017-1860-x, 2018a. a
Flammini, A., Morbidelli, R., Saltalippi, C., Picciafuoco, T., Corradini, C.,
and Govindaraju, R. S.: Reassessment of a semi-analytical field-scale
infiltration model through experiments under natural rainfall events, J. Hydrol., 565, 835–845, https://doi.org/10.1016/j.jhydrol.2018.08.073,
2018b. a
Ford, T. W., Harris, E., and Quiring, S. M.: Estimating root zone soil moisture using near-surface observations from SMOS, Hydrol. Earth Syst. Sci., 18, 139–154, https://doi.org/10.5194/hess-18-139-2014, 2014. a, b, c, d
Fuchsberger, J., Kirchengast, G., and Kabas, T.: WegenerNet high-resolution weather and climate data from 2007 to 2020, Earth Syst. Sci. Data, 13, 1307–1334, https://doi.org/10.5194/essd-13-1307-2021, 2021. a
Galle, S., Grippa, M., Peugeot, C., Bouzou Moussa, I., Cappelaere,
B., Demarty, J., Mougin, E., Lebel, T., and Chaffard, V.: AMMA-CATCH
a Hydrological, Meteorological and Ecological Long Term Observatory on West
Africa: Some Recent Results, in: AGU Fall Meeting Abstracts, vol. 2015,
GC42A–01, 2015. a
GCOS: The Global Observing System for Climate: Implementation needs, World
Meteorological Organization, 214,
https://public.wmo.int/en/resources/library/global-observing-system-climate-implementation-needs (last access: 28 August 2023),
2016. a
González-Zamora, A., Sánchez, N., Pablos, M., and Martínez-Fernández, J.:
CCI soil moisture assessment with SMOS soil moisture and in situ data under
different environmental conditions and spatial scales in Spain, Remote
Sens. Environ., 225, 469–482, https://doi.org/10.1016/j.rse.2018.02.010, 2019. a
Grillakis, M. G., Koutroulis, A. G., Alexakis, D. D., Polykretis, C., and
Daliakopoulos, I. N.: Regionalizing Root-Zone Soil Moisture Estimates From
ESA CCI Soil Water Index Using Machine Learning and Information on Soil,
Vegetation, and Climate, Water Resour. Res., 57, e2020WR029249,
https://doi.org/10.1029/2020WR029249, 2021. a, b, c, d
Gruber, A., Dorigo, W., Crow, W., and Wagner, W.: Triple Collocation-Based
Merging of Satellite Soil Moisture Retrievals, IEEE T.
Geosci. Remote, 55, 6780–6792, https://doi.org/10.1109/TGRS.2017.2734070,
2017. a, b
Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., and Dorigo, W.: Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology, Earth Syst. Sci. Data, 11, 717–739, https://doi.org/10.5194/essd-11-717-2019, 2019. a, b
Gruber, A., De Lannoy, G., Albergel, C., Al-Yaari, A., Brocca, L., Calvet,
J.-C., Colliander, A., Cosh, M., Crow, W., Dorigo, W., Draper, C., Hirschi,
M., Kerr, Y., Konings, A., Lahoz, W., McColl, K., Montzka, C.,
Muñoz-Sabater, J., Peng, J., Reichle, R., Richaume, P., Rüdiger, C.,
Scanlon, T., van der Schalie, R., Wigneron, J.-P., and Wagner, W.:
Validation practices for satellite soil moisture retrievals: What are (the)
errors?, Remote Sens. Environ., 244, 111806,
https://doi.org/10.1016/j.rse.2020.111806, 2020. a
Gupta, H. V., Kling, H., Yilmaz, K. K., and Martinez, G. F.: Decomposition of
the mean squared error and NSE performance criteria: Implications for
improving hydrological modelling, J. Hydrol., 377, 80–91,
https://doi.org/10.1016/j.jhydrol.2009.08.003, 2009. a
Hajdu, I., Yule, I., Bretherton, M., Singh, R., and Hedley, C.: Field
performance assessment and calibration of multi-depth AquaCheck
capacitance-based soil moisture probes under permanent pasture for hill
country soils, Agr. Water Manage., 217, 332–345,
https://doi.org/10.1016/j.agwat.2019.03.002, 2019. a
Hollinger, S. E. and Isard, S. A.: A Soil Moisture Climatology of Illinois,
J. Climate, 7, 822–833,
https://doi.org/10.1175/1520-0442(1994)007<0822:ASMCOI>2.0.CO;2, 1994. a
Ikonen, J., Vehviläinen, J., Rautiainen, K., Smolander, T., Lemmetyinen, J., Bircher, S., and Pulliainen, J.: The Sodankylä in situ soil moisture observation network: an example application of ESA CCI soil moisture product evaluation, Geosci. Instrum. Method. Data Syst., 5, 95–108, https://doi.org/10.5194/gi-5-95-2016, 2016. a
Ikonen, J., Smolander, T., Rautiainen, K., Cohen, J., Lemmetyinen, J.,
Salminen, M., and Pulliainen, J.: Spatially Distributed Evaluation of ESA CCI
Soil Moisture Products in a Northern Boreal Forest Environment, Geosciences,
8, 51, https://doi.org/10.3390/geosciences8020051, 2018. a
Jackson, T. J., Cosh, M. H., Bindlish, R., Starks, P. J., Bosch, D. D.,
Seyfried, M., Goodrich, D. C., Moran, M. S., and Du, J.: Validation of
Advanced Microwave Scanning Radiometer Soil Moisture Products, IEEE
T. Geosci. Remote, 48, 4256–4272,
https://doi.org/10.1109/TGRS.2010.2051035, 2010. a
Jensen, K. H. and Refsgaard, J. C.: HOBE: The Danish Hydrological Observatory,
Vadose Zone J., 17, 180059, https://doi.org/10.2136/vzj2018.03.0059, 2018. a
Jin, R., Li, X., Yan, B., Li, X., Luo, W., Ma, M., Guo, J., Kang, J., Zhu, Z.,
and Zhao, S.: A Nested Ecohydrological Wireless Sensor Network for Capturing
the Surface Heterogeneity in the Midstream Areas of the Heihe River Basin,
China, IEEE Geosci. Remote S., 11, 2015–2019,
https://doi.org/10.1109/LGRS.2014.2319085, 2014. a
Kang, C. S., Kanniah, K. D., and Kerr, Y. H.: Calibration of SMOS Soil Moisture
Retrieval Algorithm: A Case of Tropical Site in Malaysia, IEEE T. Geosci. Remote, 57, 3827–3839,
https://doi.org/10.1109/TGRS.2018.2888535, 2019. a
Kang, J., Li, X., Jin, R., Ge, Y., Wang, J., and Wang, J.: Hybrid Optimal
Design of the Eco-Hydrological Wireless Sensor Network in the Middle Reach of
the Heihe River Basin, China, Sensors, 14, 19095–19114,
https://doi.org/10.3390/s141019095, 2014. a
Kirchengast, G., Kabas, T., Leuprecht, A., Bichler, C., and Truhetz, H.:
WegenerNet: A Pioneering High-Resolution Network for Monitoring Weather and
Climate, B. Am. Meteorol. Soc., 95, 227–242,
https://doi.org/10.1175/BAMS-D-11-00161.1, 2014. a
Larson, K. M., Small, E. E., Gutmann, E. D., Bilich, A. L., Braun, J. J., and
Zavorotny, V. U.: Use of GPS receivers as a soil moisture network for water
cycle studies, Geophys. Res. Lett., 35, L24405, https://doi.org/10.1029/2008GL036013,
2008. a
Leavesley, G., David, O., Garen, D., Lea, J., Marron, J., Pagano, T., Perkins,
T., and Strobel, M.: A modeling framework for improved agricultural water
supply forecasting, in: AGU Fall Meeting Abstracts, vol. 1, San Francisco,
CA, USA, 2008. a
Lebel, T., Cappelaere, B., Galle, S., Hanan, N., Kergoat, L., Levis, S., Vieux,
B., Descroix, L., Gosset, M., Mougin, E., Peugeot, C., and Seguis, L.:
AMMA-CATCH studies in the Sahelian region of West-Africa: An overview,
J. Hydrol., 375, 3–13, https://doi.org/10.1016/j.jhydrol.2009.03.020,
2009. a
Leys, C., Ley, C., Klein, O., Bernard, P., and Licata, L.: Detecting outliers:
Do not use standard deviation around the mean, use absolute deviation around
the median, J. Exp. Soc. Psychol., 49, 764–766,
https://doi.org/10.1016/j.jesp.2013.03.013, 2013. a
L'Heureux, J.: 2011 Installation Report for AAFC‐ SAGES Soil Moisture
Stations in Kenaston, SK, Tech. Rep., Agriculture and Agri-food Canada, 2011. a
Loew, A., Dall'Amico, J. T., Schlenz, F., and Mauser, W.: The Upper Danube soil moisture validation site: Measurements and activities,
Earth Observation and Water Cycle Science, 674, 56, 2009. a
Mahmood, R. and Hubbard, K. G.: Relationship between soil moisture of near
surface and multiple depths of the root zone under heterogeneous land uses
and varying hydroclimatic conditions, Hydrol. Process., 21, 3449–3462,
https://doi.org/10.1002/hyp.6578, 2007. a, b, c, d
Manfreda, S., Brocca, L., Moramarco, T., Melone, F., and Sheffield, J.: A physically based approach for the estimation of root-zone soil moisture from surface measurements, Hydrol. Earth Syst. Sci., 18, 1199–1212, https://doi.org/10.5194/hess-18-1199-2014, 2014. a
Marczewski, W., Slominski, J., Slominska, E., Usowicz, B., Usowicz, J., Romanov, S., Maryskevych, O., Nastula, J., and Zawadzki, J.: Strategies for validating and directions for employing SMOS data, in the Cal-Val project SWEX (3275) for wetlands, Hydrol. Earth Syst. Sci. Discuss., 7, 7007–7057, https://doi.org/10.5194/hessd-7-7007-2010, 2010. a
Martens, B., Miralles, D. G., Lievens, H., van der Schalie, R., de Jeu, R. A. M., Fernández-Prieto, D., Beck, H. E., Dorigo, W. A., and Verhoest, N. E. C.: GLEAM v3: satellite-based land evaporation and root-zone soil moisture, Geosci. Model Dev., 10, 1903–1925, https://doi.org/10.5194/gmd-10-1903-2017, 2017. a
Mattar, C., Santamaría-Artigas, A., Durán-Alarcón, C., Olivera-Guerra, L.,
Fuster, R., and Borvarán, D.: LAB-net the first Chilean soil moisture
network for remote sensing applications, in: Quantitative Remote Sensing
Symposium (RAQRS), 22–26 September 2014, Torrent, Spain, P4.35, 22–26, 2014. a
Mattar, C., Santamaría-Artigas, A., Durán-Alarcón, C., Olivera-Guerra, L.,
Fuster, R., and Borvarán, D.: The LAB-Net Soil Moisture Network: Application
to Thermal Remote Sensing and Surface Energy Balance, Data, 1, 6,
https://doi.org/10.3390/data1010006, 2016. a
Moghaddam, M., Entekhabi, D., Goykhman, Y., Li, K., Liu, M., Mahajan, A.,
Nayyar, A., Shuman, D., and Teneketzis, D.: A Wireless Soil Moisture Smart
Sensor Web Using Physics-Based Optimal Control: Concept and Initial
Demonstrations, IEEE J. Sel. Top. Appl., 3, 522–535, https://doi.org/10.1109/JSTARS.2010.2052918, 2010. a
Moghaddam, M., Silva, A., Clewley, D., Akbar, R., Hussaini, S., Whitcomb, J.,
Devarakonda, R., Shrestha, R., Cook, R., Prakash, G., Santhana Vannan, S.,
and Boyer, A.: Soil Moisture Profiles and Temperature Data from SoilSCAPE
Sites, USA [data set], https://doi.org/10.3334/ORNLDAAC/1339, 2016. a
Morbidelli, R., Corradini, C., Saltalippi, C., Flammini, A., and Rossi, E.: Infiltration-soil moisture redistribution under natural conditions: experimental evidence as a guideline for realizing simulation models, Hydrol. Earth Syst. Sci., 15, 2937–2945, https://doi.org/10.5194/hess-15-2937-2011, 2011. a
Morbidelli, R., Saltalippi, C., Flammini, A., Rossi, E., and Corradini, C.:
Soil water content vertical profiles under natural conditions: matching of
experiments and simulations by a conceptual model, Hydrol. Process.,
28, 4732–4742, https://doi.org/10.1002/hyp.9973, 2014. a
Morbidelli, R., Saltalippi, C., Flammini, A., Cifrodelli, M., Picciafuoco, T.,
Corradini, C., and Govindaraju, R. S.: In situ measurements of soil saturated
hydraulic conductivity: Assessment of reliability through rainfall–runoff
experiments, Hydrol. Process., 31, 3084–3094, https://doi.org/10.1002/hyp.11247,
2017. a
Mougin, E., Hiernaux, P., Kergoat, L., Grippa, M., de Rosnay, P., Timouk, F.,
Le Dantec, V., Demarez, V., Lavenu, F., Arjounin, M., Lebel, T., Soumaguel,
N., Ceschia, E., Mougenot, B., Baup, F., Frappart, F., Frison, P., Gardelle,
J., Gruhier, C., Jarlan, L., Mangiarotti, S., Sanou, B., Tracol, Y.,
Guichard, F., Trichon, V., Diarra, L., Soumaré, A., Koité, M., Dembélé,
F., Lloyd, C., Hanan, N., Damesin, C., Delon, C., Serça, D., Galy-Lacaux,
C., Seghieri, J., Becerra, S., Dia, H., Gangneron, F., and Mazzega, P.: The
AMMA-CATCH Gourma observatory site in Mali: Relating climatic variations to
changes in vegetation, surface hydrology, fluxes and natural resources,
J. Hydrol., 375, 14–33, https://doi.org/10.1016/j.jhydrol.2009.06.045, 2009. a
Muñoz Sabater, J.: ERA5-Land hourly data from 1981 to present, Copernicus
Climate Change Service (C3S) Climate Data Store (CDS) [data set],
https://doi.org/10.24381/cds.e2161bac, 2019. a
Muñoz Sabater, J.: ERA5-Land hourly data from 1950 to 1980, Copernicus Climate
Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.e2161bac,
2021. a
Muñoz-Sabater, J., Dutra, E., Agustí-Panareda, A., Albergel, C., Arduini, G., Balsamo, G., Boussetta, S., Choulga, M., Harrigan, S., Hersbach, H., Martens, B., Miralles, D. G., Piles, M., Rodríguez-Fernández, N. J., Zsoter, E., Buontempo, C., and Thépaut, J.-N.: ERA5-Land: a state-of-the-art global reanalysis dataset for land applications, Earth Syst. Sci. Data, 13, 4349–4383, https://doi.org/10.5194/essd-13-4349-2021, 2021. a, b, c
Musial, J., Dabrowska-Zielinska, K., Kiryla, W., Oleszczuk, R., Gnatowski, T.,
and Jaszczynski, J.: Derivation and validation of the high resolution
satellite soil moisture products: a case study of the Biebrza Sentinel-1
validation sites, Geoinformation Issues,
8, 37–53, https://doi.org/10.34867/gi.2016.4, 2016. a
Nguyen, H. H., Kim, H., and Choi, M.: Evaluation of the soil water content
using cosmic-ray neutron probe in a heterogeneous monsoon climate-dominated
region, Adv. Water Resour., 108, 125–138,
https://doi.org/10.1016/j.advwatres.2017.07.020, 2017. a
Ojo, E. R., Bullock, P. R., L'Heureux, J., Powers, J., McNairn, H., and
Pacheco, A.: Calibration and Evaluation of a Frequency Domain Reflectometry
Sensor for Real-Time Soil Moisture Monitoring, Vadose Zone J., 14,
vzj2014.08.0114, https://doi.org/10.2136/vzj2014.08.0114, 2015. a
Osenga, E. C., Arnott, J. C., Endsley, K. A., and Katzenberger, J. W.:
Bioclimatic and Soil Moisture Monitoring Across Elevation in a Mountain
Watershed: Opportunities for Research and Resource Management, Water
Resour. Res., 55, 2493–2503, https://doi.org/10.1029/2018WR023653, 2019. a
Osenga, E. C., Vano, J. A., and Arnott, J. C.: A community-supported weather
and soil moisture monitoring database of the Roaring Fork catchment of the
Colorado River Headwaters, Hydrol. Process., 35, e14081,
https://doi.org/10.1002/hyp.14081, 2021. a
Parinussa, R. M., Meesters, A. G. C. A., Liu, Y. Y., Dorigo, W., Wagner, W.,
and de Jeu, R. A. M.: Error Estimates for Near-Real-Time Satellite Soil
Moisture as Derived From the Land Parameter Retrieval Model, IEEE Geosci. Remote S., 8, 779–783, https://doi.org/10.1109/LGRS.2011.2114872,
2011. a
Pasik, A. J. and Preimesberger, W.: 2002–2020 Error-characterized Root-zone Soil Moisture (0–2 m) from C3S Surface Observations (1.6), TU Wien [data set], https://doi.org/10.48436/9gsg6-nn854, 2023. a
Pathe, C., Wagner, W., Sabel, D., Doubkova, M., and Basara, J.: Using ENVISAT
ASAR global mode data for surface soil moisture retrieval over Oklahoma, USA,
IEEE Trans. Geosci. Rem. Sens., 47, 468–480,
https://doi.org/10.1109/TGRS.2008.2004711, 2009. a
Pellarin, T., Calvet, J.-C., and Wagner, W.: Evaluation of ERS scatterometer
soil moisture products over a half-degree region in southwestern France,
Geophys. Res. Lett., 33, L17401, https://doi.org/10.1029/2006GL027231, 2006. a
Petropoulos, G. P. and McCalmont, J. P.: An Operational In Situ Soil Moisture
& Soil Temperature Monitoring Network for West Wales, UK: The WSMN Network,
Sensors, 17, 1481, https://doi.org/10.3390/s17071481, 2017. a
Preimesberger, W., Scanlon, T., Su, C.-H., Gruber, A., and Dorigo, W.:
Homogenization of Structural Breaks in the Global ESA CCI Soil Moisture
Multisatellite Climate Data Record, IEEE T. Geosci.
Remote, 59, 2845–2862, https://doi.org/10.1109/TGRS.2020.3012896, 2021. a
Raffelli, G., Previati, M., Canone, D., Gisolo, D., Bevilacqua, I., Capello,
G., Biddoccu, M., Cavallo, E., Deiana, R., Cassiani, G., and Ferraris, S.:
Local- and Plot-Scale Measurements of Soil Moisture: Time and Spatially
Resolved Field Techniques in Plain, Hill and Mountain Sites, Water, 9, 706,
https://doi.org/10.3390/w9090706, 2017. a
Reichle, R., DeLannoy, G., Koster, R. D., Crow, W. T., and Kimball, J.: SMAP L4
9 km EASE-Grid Surface and Root Zone Soil Moisture Geophysical Data, Version
3, Tech. Rep., TU Vienna, https://doi.org/10.5067/B59DT1D5UMB4, 2017. a
Rodell, M., Houser, P. R., Jambor, U., Gottschalck, J., Mitchell, K., Meng,
C.-J., Arsenault, K., Cosgrove, B., Radakovich, J., Bosilovich, M., Entin,
J. K., Walker, J. P., Lohmann, D., and Toll, D.: The Global Land Data
Assimilation System, B. Am. Meteorol. Soc., 85, 381–394, https://doi.org/10.1175/BAMS-85-3-381, 2004. a, b
Rüdiger, C., Hancock, G., Hemakumara, H. M., Jacobs, B., Kalma, J. D.,
Martinez, C., Thyer, M., Walker, J. P., Wells, T., and Willgoose, G. R.:
Goulburn River experimental catchment data set, Water Resour. Res., 43, W10403,
https://doi.org/10.1029/2006WR005837, 2007. a
Schaefer, G. L., Cosh, M. H., and Jackson, T. J.: The USDA Natural Resources
Conservation Service Soil Climate Analysis Network (SCAN), J.
Atmos. Ocean. Tech., 24, 2073–2077,
https://doi.org/10.1175/2007JTECHA930.1, 2007. a
Schlenz, F., dall'Amico, J. T., Loew, A., and Mauser, W.: Uncertainty
Assessment of the SMOS Validation in the Upper Danube Catchment, IEEE
T. Geosci. Remote, 50, 1517–1529,
https://doi.org/10.1109/TGRS.2011.2171694, 2012. a
Shuman, D. I., Nayyar, A., Mahajan, A., Goykhman, Y., Li, K., Liu, M.,
Teneketzis, D., Moghaddam, M., and Entekhabi, D.: Measurement Scheduling for
Soil Moisture Sensing: From Physical Models to Optimal Control, P. IEEE, 98, 1918–1933, https://doi.org/10.1109/JPROC.2010.2052532, 2010. a
Smith, A. B., Walker, J. P., Western, A. W., Young, R. I., Ellett, K. M.,
Pipunic, R. C., Grayson, R. B., Siriwardena, L., Chiew, F. H. S., and
Richter, H.: The Murrumbidgee soil moisture monitoring network data set,
Water Resour. Res., 48, W07701, https://doi.org/10.1029/2012WR011976, 2012. a
Stefan, V.-G., Indrio, G., Escorihuela, M.-J., Quintana-Seguí, P., and Villar,
J. M.: High-Resolution SMAP-Derived Root-Zone Soil Moisture Using an
Exponential Filter Model Calibrated per Land Cover Type, Remote Sensing, 13, 1112,
https://doi.org/10.3390/rs13061112, 2021. a
Su, Z., Wen, J., Dente, L., van der Velde, R., Wang, L., Ma, Y., Yang, K., and Hu, Z.: The Tibetan Plateau observatory of plateau scale soil moisture and soil temperature (Tibet-Obs) for quantifying uncertainties in coarse resolution satellite and model products, Hydrol. Earth Syst. Sci., 15, 2303–2316, https://doi.org/10.5194/hess-15-2303-2011, 2011. a, b, c
Sure, A. and Dikshit, O.: Estimation of root zone soil moisture using passive
microwave remote sensing: A case study for rice and wheat crops for three
states in the Indo-Gangetic basin, J. Environ. Manage., 234,
75–89, https://doi.org/10.1016/j.jenvman.2018.12.109, 2019. a, b, c, d
Tagesson, T., Fensholt, R., Guiro, I., Rasmussen, M. O., Huber, S., Mbow, C.,
Garcia, M., Horion, S., Sandholt, I., Holm-Rasmussen, B., Göttsche, F. M.,
Ridler, M.-E., Olén, N., Lundegard Olsen, J., Ehammer, A., Madsen, M.,
Olesen, F. S., and Ardö, J.: Ecosystem properties of semiarid savanna
grassland in West Africa and its relationship with environmental variability,
Glob. Change Biol., 21, 250–264, https://doi.org/10.1111/gcb.12734, 2015. a
Taylor, J. R.: An Introduction to Error Analysis: The Study of Uncertainties in
Physical Measurements, 2nd edn., University Science Books, Sausalito, ISBN 9780935702750, 1997. a
Tobin, K. J., Torres, R., Crow, W. T., and Bennett, M. E.: Multi-decadal analysis of root-zone soil moisture applying the exponential filter across CONUS, Hydrol. Earth Syst. Sci., 21, 4403–4417, https://doi.org/10.5194/hess-21-4403-2017, 2017. a, b
Van Cleve, K., Chapin, F., Stuart, R., and W., R.: Bonanza Creek Long Term
Ecological Research Project Climate Database,
https://www.lter.uaf.edu/ (last access: 28 August 2023), 2015. a
van der Schalie, R., de Jeu, R., Kerr, Y., Wigneron, J.,
Rodríguez-Fernández, N., Al-Yaari, A., Parinussa, R., Mecklenburg, S., and
Drusch, M.: The merging of radiative transfer based surface soil moisture
data from SMOS and AMSR-E, Remote Sens. Environ., 189, 180–193,
https://doi.org/10.1016/j.rse.2016.11.026, 2017. a
Vreugdenhil, M., Dorigo, W., Broer, M., Haas, P., Eder, A., Hogan, P.,
Bloeschl, G., and Wagner, W.: Towards a high-density soil moisture network
for the validation of SMAP in Petzenkirchen, Austria, in: 2013 IEEE
International Geoscience and Remote Sensing Symposium – IGARSS, 21–26 July 2013, Melbourne, VIC, Australia,
1865–1868, https://doi.org/10.1109/IGARSS.2013.6723166, 2013. a
Vreugdenhil, M., Greimeister-Pfeil, I., Preimesberger, W., Camici, S., Dorigo,
W., Enenkel, M., van der Schalie, R., Steele-Dunne, S., and Wagner, W.:
Microwave remote sensing for agricultural drought monitoring: Recent
developments and challenges, Frontiers in Water, 4, 1045451,
https://doi.org/10.3389/frwa.2022.1045451, 2022. a
Wang, T., Franz, T. E., You, J., Shulski, M. D., and Ray, C.: Evaluating
controls of soil properties and climatic conditions on the use of an
exponential filter for converting near surface to root zone soil moisture
contents, J. Hydrol., 548, 683–696,
https://doi.org/10.1016/j.jhydrol.2017.03.055, 2017. a, b, c, d, e, f
Wigneron, J.-P., Dayan, S., Kruszewski, A., Aluome, C., AI-Yaari, M. G.-E. A.,
Fan, L., Guven, S., Chipeaux, C., Moisy, C., Guyon, D., and Loustau, D.: The
Aqui Network: Soil Moisture Sites in the “Les Landes” Forest and Graves
Vineyards (Bordeaux Aquitaine Region, France), in: IGARSS 2018–2018 IEEE
International Geoscience and Remote Sensing Symposium, 22–27 July 2018, Valencia, Spain, 3739–3742,
https://doi.org/10.1109/IGARSS.2018.8517392, 2018. a
Xaver, A., Zappa, L., Rab, G., Pfeil, I., Vreugdenhil, M., Hemment, D., and Dorigo, W. A.: Evaluating the suitability of the consumer low-cost Parrot Flower Power soil moisture sensor for scientific environmental applications, Geosci. Instrum. Method. Data Syst., 9, 117–139, https://doi.org/10.5194/gi-9-117-2020, 2020. a
Yang, K., Qin, J., Zhao, L., Chen, Y., Tang, W., Han, M., Lazhu, Chen, Z., Lv,
N., Ding, B., Wu, H., and Lin, C.: A Multiscale Soil Moisture and
Freeze–Thaw Monitoring Network on the Third Pole, B. Am.
Meteorol. Soc., 94, 1907–1916, https://doi.org/10.1175/BAMS-D-12-00203.1,
2013. a
Yang, Y., Bao, Z., Wu, H., Wang, G., Liu, C., Wang, J., and Zhang, J.: An
Exponential Filter Model-Based Root-Zone Soil Moisture Estimation Methodology
from Multiple Datasets, Remote Sensing, 14, 1785, https://doi.org/10.3390/rs14081785, 2022. a, b
Young, R., Walker, J., Yeoh, N., Smith, A.and Ellett, K., Merlin, O., and
Western, A.: Soil moisture and meteorological observations from the
murrumbidgee catchment, Tech. Rep., Department of Civil and Environmental
Engineering, The University of Melbourne, 2008. a
Zacharias, S., Bogena, H., Samaniego, L., Mauder, M., Fuß, R., Pütz, T.,
Frenzel, M., Schwank, M., Baessler, C., Butterbach-Bahl, K., Bens, O., Borg,
E., Brauer, A., Dietrich, P., Hajnsek, I., Helle, G., Kiese, R., Kunstmann,
H., Klotz, S., Munch, J. C., Papen, H., Priesack, E., Schmid, H. P.,
Steinbrecher, R., Rosenbaum, U., Teutsch, G., and Vereecken, H.: A Network of
Terrestrial Environmental Observatories in Germany, Vadose Zone J., 10,
955–973, https://doi.org/10.2136/vzj2010.0139, 2011. a
Zappa, L., Forkel, M., Xaver, A., and Dorigo, W.: Deriving Field Scale Soil
Moisture from Satellite Observations and Ground Measurements in a Hilly
Agricultural Region, Remote Sensing, 11, 2596, https://doi.org/10.3390/rs11222596, 2019. a
Zappa, L., Woods, M., Hemment, D., Xaver, A., and Dorigo, W.: Evaluation of
remotely sensed soil moisture products using crowdsourced measurements, in:
Eighth International Conference on Remote Sensing and Geoinformation of the
Environment (RSCy2020), edited by: Themistocleous, K., Papadavid, G.,
Michaelides, S., Ambrosia, V., and Hadjimitsis, D. G., vol. 11524, International Society for Optics and Photonics, SPIE,
115241U,
https://doi.org/10.1117/12.2571913, 2020. a
Zhao, T., Shi, J., Lv, L., Xu, H., Chen, D., Cui, Q., Jackson, T. J., Yan, G.,
Jia, L., Chen, L., Zhao, K., Zheng, X., Zhao, L., Zheng, C., Ji, D., Xiong,
C., Wang, T., Li, R., Pan, J., Wen, J., Yu, C., Zheng, Y., Jiang, L., Chai,
L., Lu, H., Yao, P., Ma, J., Lv, H., Wu, J., Zhao, W., Yang, N., Guo, P., Li,
Y., Hu, L., Geng, D., and Zhang, Z.: Soil moisture experiment in the Luan
River supporting new satellite mission opportunities, Remote Sens.
Environ., 240, 111 680, https://doi.org/10.1016/j.rse.2020.111680, 2020. a
Zheng, J., Zhao, T., Lü, H., Shi, J., Cosh, M. H., Ji, D., Jiang, L., Cui, Q.,
Lu, H., Yang, K., Wigneron, J.-P., Li, X., Zhu, Y., Hu, L., Peng, Z., Zeng,
Y., Wang, X., and Kang, C. S.: Assessment of 24 soil moisture datasets using
a new in situ network in the Shandian River Basin of China, Remote Sens.
Environ., 271, 112891, https://doi.org/10.1016/j.rse.2022.112891, 2022. a
Zreda, M., Desilets, D., Ferré, T. P. A., and Scott, R. L.: Measuring soil
moisture content non-invasively at intermediate spatial scale using
cosmic-ray neutrons, Geophys. Res. Lett., 35, L21402,
https://doi.org/10.1029/2008GL035655, 2008. a
Zreda, M., Shuttleworth, W. J., Zeng, X., Zweck, C., Desilets, D., Franz, T., and Rosolem, R.: COSMOS: the COsmic-ray Soil Moisture Observing System, Hydrol. Earth Syst. Sci., 16, 4079–4099, https://doi.org/10.5194/hess-16-4079-2012, 2012. a
Download
The requested paper has a corresponding corrigendum published. Please read the corrigendum first before downloading the article.
- Article
(2537 KB) - Full-text XML
Short summary
We apply the exponential filter (EF) method to satellite soil moisture retrievals to estimate the water content in the unobserved root zone globally from 2002–2020. Quality assessment against an independent dataset shows satisfactory results. Error characterization is carried out using the standard uncertainty propagation law and empirically estimated values of EF model structural uncertainty and parameter uncertainty. This is followed by analysis of temporal uncertainty variations.
We apply the exponential filter (EF) method to satellite soil moisture retrievals to estimate...
Altmetrics
Final-revised paper
Preprint