Ecohydrology, Environmental Data Science, Soil physics, Soil moisture, Soil infiltration
64
Scopus Publications
Scopus Publications
Global hotspots of particulate organic carbon losses under climate change Siyi Sun, M. Francesca Cotrufo, R. A. Viscarra Rossel, Carsten W. Mueller, Morimaru Kida, Ailsa G. Hardie, Alec Mackay, Alexander H. Krichels, Wulf Amelung, Amit Kumar, Azamat Suleymanov, Baoku Shi, Bernard Jackson Cosby, César Plaza, César Terrer, Chang Liang, Chang Liao, Christopher Just, Ding Guo, Emanuele Lugato, Enqing Hou, Fan Ding, Fazhu Zhao, Feng Tao, Fernando T. Maestre, Franco Bilotto, Fuzhong Wu, Gisela V. García, Gongwen Luo, Guangxuan Han, Guillermo A. Studdert, Guillermo Hernandez-Ramirez, Guoxiang Niu, Gervasio Piñeiro, Gustavo Saiz, Haikuo Zhang, Hamada Abdelrahman, Haodi Xu, Inma Lebron, Irina Kurganova, Jennifer Blesh, Jeppe Å. Kristensen, Ji Liu, Jiacong Zhou, Jianping Wu, Jitendra Ahirwal, Junji Cao, Jørgen E. Olesen, Karin Kauer, Katerina Georgiou, Kees Jan van Groenigen, Kristof Van Oost, Kwame Agyei Frimpong, Lei Deng, Liane G. Benning, Liang Guo, Lizzie Mujuru, Manuel Delgado-Baquerizo, Maoz Dor, Mehdi Rahmati, Min Luo, Olga Kalinina, Olli Hyvärinen, Pablo García-Palacios, Paige Hansen, Patra Rounak, Pengpeng Duan, Pengzhi Zhao, Peter M. Homyak, Rajan Ghimire, Renaldas Žydelis, Roland Bol, Ronaldo Vibart, Ruiying Chang, Ruyi Luo, Sebastián Villarino, Shuai Xue, Shuli Niu, Shuotong Chen, Tengfei Yu, Steven J. Hall, Thomas Kätterer, Tida Ge, Vusumuzi Erick Mbanjwa, Vyacheslav M. Semenov, Weixing Liu, Weiyu Shi, Wei Zhang, Wolfgang Wanek, Wolfram Buss, Xiangrong Cheng, Xiankai Lu, Xiaojun Shi, Xiaoli Cheng, Xiaorong Wei, Xiaotong Liu, Xuhui Zhou, Yahya Kooch, Yangquanwei Zhong, Yanjiang Cai, Yan Yang, Yiqi Luo, Yixuan Zhang, Yunbin Qin, Yunting Fang, Yuting Liang, Yuyi Li, Zengming Chen, Zhanfeng Liu, Zhaoliang Song, Zhongkui Luo, Zhisheng An, Ji Chen Nature Communications, 2026 Soil organic carbon (SOC) comprises particulate (POC) and mineral-associated organic carbon (MAOC), which differ in formation, stabilization, and loss mechanisms. While the current global distribution of POC and MAOC is characterized, their vulnerability under future climate scenarios remains unclear. Using 3284 topsoil (0-30 cm) observations from six continents, we identify high-latitude soils as global hotspots of SOC vulnerability under shared socioeconomic pathway scenarios (SSP126, SSP245, and SSP585). Under a high-emission scenario (SSP585), high-latitude soils are projected to lose substantial POC by 2100, accounting for about 81 ± 10% of total SOC losses. These declines are driven by the high proportion of SOC stored as POC (fPOC) and its high temperature sensitivity. We show that fPOC is a robust indicator of SOC vulnerability to climate change. Globally, the projected POC decline corresponds to a cumulative carbon dioxide (CO2) release of 81.34 Pg CO2-equivalent by 2100, highlighting the importance of preserving POC to mitigate climate feedbacks. High-latitude soils are future soil organic carbon loss hotspots, with losses dominated by particulate organic carbon (POC). The fraction of POC in total SOC (fPOC) is a key indicator, emphasizing the climate importance of preserving POC.
Soil cultivation for potatoes. A global survey of cultivation practices Mark A. Stalham, Shaunagh Slack, Ryan Barrett, Ranjan Bhattacharyya, Karina M.V. Cavalieri-Polizeli, Rosario Fuentes del Río, Iain Kirkwood, John E. McPhee, Simon McWilliam, Mark J. Pavek, Mehdi Rahmati, Lautaro Rios, Kirstie Speed, Martin Steyn, Mike Thornton, Lucy Tillier, Barry White, Philip Wright, Ying Zhao, Blair M. McKenzie Soil and Tillage Research, 2026
AI in soil moisture remote sensing Carsten Montzka, Luca Brocca, Hao Chen, Narendra N. Das, Antara Dasgupta, Mehdi Rahmati, Thomas Jagdhuber International Journal of Applied Earth Observation and Geoinformation, 2026 • First detailed review of state-of-the-art in AI in soil moisture remote sensing. • AI is already a constant factor in soil moisture remote sensing. • Current trends and research avenues to improve AI in soil moisture remote sensing. • Guidelines to inspire and guide future research efforts in the field are provided. Soil moisture, a pivotal component of the hydrological cycle, exerts a profound influence on land surface exchange processes, but its spatial variability poses challenges for large-scale field observations, increasing reliance on satellite-based retrievals. However, spaceborne estimates face limitations due to model uncertainties and sensor-related constraints. Recent advances in artificial intelligence (AI) offer promising alternatives to traditional methods by enabling data-driven estimation of soil moisture without strong physical assumptions. Thus, a critical review of emerging AI-based soil moisture retrieval methods with respect to their advantages and disadvantages is vital to ensure the best utilization of such tools for soil moisture sensing, especially with novel sensors and data constantly being generated. In this comprehensive review, we furnish the first structured overview of AI methods and their applications in soil moisture retrievals from remote sensing. AI is able to enhance soil moisture retrieval by learning complex (highly nonlinear) relationships between satellite observations and ground reference data, to support time series reconstruction by filling gaps in data sets, to estimate subsurface soil moisture conditions from surface signals and auxiliary inputs, to enable spatial scaling by translating soil moisture estimates across different resolutions using multi-resolution data, to predict temporal dynamics as a soil moisture forecast, and to contribute to broader assessments of the water cycle and beyond by integrating soil moisture with further hydrological variables. Future directions for each method are also identified to address the scientific challenges of soil moisture retrieval and help focus the research community on the key open questions in the new era of rapidly expanding AI applications.
Soil moisture retrieval from Sentinel-1: Lessons learned after more than a decade in orbit Mehdi Rahmati, Anna Balenzano, Michel Bechtold, Luca Brocca, Anke Fluhrer, Thomas Jagdhuber, Kleanthis Karamvasis, David Mengen, Rolf H. Reichle, Seung-bum Kim, Ruhollah Taghizadeh-Mehrjardi, Jeffrey Walker, Liujun Zhu, Carsten Montzka Remote Sensing of Environment, 2026 Soil moisture is a critical variable for hydrology, agriculture and climate. However, large-scale soil moisture observation remains difficult due to sparse in situ networks and the inability of optical sensors to capture it under cloud cover. Synthetic aperture radar (SAR) missions, e.g., Sentinel-1, yield unique all-weather, day and night observations with a fine spatial and temporal resolution that makes them of interest for development of global soil moisture monitoring. Consequently, this review discusses the application of C-band SAR observations from the Sentinel-1 satellite mission to estimate high-resolution near-surface soil moisture. First, the importance of SAR backscatter monitoring from Sentinel-1 is emphasized. Next, the current state-of-the-art in soil moisture retrieval from Sentinel-1 is presented. Although considerable progress has been made in near-surface soil moisture retrieval, several limitations remain. Factors such as the effects of vegetation and surface roughness on the signal, sensor and scattering model limitations, spatial and temporal constraints, and uncertainties, e.g. in data assimilation, pose challenges to its usage. While Artificial Intelligence (AI)-based retrieval methods have shown promise, their interpretability, dependence on large datasets, vulnerability to data quality, and computational burden have been major challenges. Beyond methods that rely on backscatter, there have been recent works indicating that SAR interferometric observables have the potential to estimate soil moisture, especially in arid and semi-arid regions where these are particularly sensitive to moisture changes. To address these challenges, this paper recommends integrating Sentinel-1 with other satellite mission data for a multi-sensor data integration approach (e.g., Sentinel-2 and Soil Moisture Active Passive - SMAP data), refining physical and semi-empirical models, developing advanced AI techniques able to consider physical principles, and combining with emerging data from other high temporal resolution SAR missions (e.g., NASA-ISRO SAR). The review concludes with identification of key research priorities, including standardization of retrieval frameworks, improved validation efforts on standardized reference sets, and cloud processing for real-time user cases. Overall, the review provides a thorough foundation for understanding, refining, and advancing Sentinel-1 based soil moisture retrieval methods. • A decade of Sentinel-1-based retrievals has aided the global soil moisture record. • Vegetation effects issues and data assimilation conflicts require further research. • Non-homogeneous landscapes require an enhanced representation of soil roughness. • In arid & semi-arid areas, InSAR-derived soil moisture retrievals appear promising. • Operational readiness hinges on stronger validation and new observation modes.
Effective science communication in the face of water crises: a community perspective on challenges and best practice in HELPING Christina Orieschnig, Soham Adla, Kwok Pan Chun, Saumya Srivastava, Khosro Morovati, Ben C. Howard, Thanti Octavianti, José Gescilam Uchôa, Zheng Duan, Paola Mazzoglio, Anandharuban Panchanathan, Borbála Széles, Gerbrand Koren, Georgia A. Papacharalampous, Dhiraj Pradhananga, Konstantinos Soulis, Hajar Choukrani, Hamouda Dakhlaoui, Alper Elçi, Xinyang Fan, Sina Khatami, Eduardo Mario Mendiondo, Tarryn Payne, Mehdi Rahmati, Tirthankar Roy, Christopher Skinner, Claudia Teutschbein, Roland Yonaba, Tanveer Mehedi Adyel, Ignacio Aguirre, Hasnat Aslam, Abinesh Ganapathy, Jagriti Jain, Albert Nkwasa, Fiachra O’Loughlin, Ilias Pechlivanidis, Alonso Pizarro, Ashutosh Sharma, Hristos Tyralis, Shuchi Vora, Satwiki Adla, Miriam Bertola, Vinicius Boico, Natalie Ceperley, Benjamin Dewals, Moritz Heinle, Soren Jessen, Florian Kaiselgruber, Neha Lakhwan, Mayowa Benjamen Lateef, Ashish Mishra, Pamba Ojera, Valeriya Ovcharuk, Apoorva Singh, Abhinav Wadhwa, Suwash Chandra Acharya, Sotiria Alexandri, Eduardo Rico Carranza, Yonca Cavus, Nilay Dogulu, Abdoulaye Faty, Joaquin Jorquera, Viraj Rane, Massimiliano Zappa Hydrological Sciences Journal, 2026 Addressing global water crises demands effective communication across diverse audiences, especially in initiatives such as the scientific decade HELPING by the International Association of Hydrological Sciences (IAHS). This study synthesizes insights from the hydrological community, gathered through interviews, workshops and a digital survey. We identify key challenges and best practices across three inter-related domains of communication: science–society interactions, policy–science interfaces and transdisciplinary research communication. Effective science–society interaction depends on community trust-building, transparent communication of uncertainty and inclusive engagement strategies. Strong policy–science interfaces benefit from bridging institutions and dedicated knowledge brokers. Transdisciplinary work improves when disciplinary siloing is reduced through common language and co-production. We summarize our findings in the FUSS framework, which promotes messages that are few, unambiguous, short and well-structured. We argue that advancing hydrological science in the face of water crises requires moving beyond one-way communication towards more dialogic, inclusive and context-sensitive approaches.
Soil infiltration variability across diverse soil reference groups, textures, and landuse types Farnaz Sharghi S., Sara L. Bauke, Mehdi Rahmati, Dymphie J. Burger, Harry Vereecken, Wulf Amelung Geoderma, 2025 • Soil reference groups explained infiltration variability better than texture or land use. • Soil texture alone poorly represents infiltration, despite common belief in literature. • Topsoil traits may clarify infiltration patterns across soil groups. Soil infiltration, a key process in the terrestrial water cycle, is typically measured pointwise, but is often upscaled by averaging across different soil groups or even texture classes, e.g., when parameterizing water movement in land surface models. We hypothesize that for upscaling, in addition to soil texture, infiltration rates/parameters vary also between different reference soil groups and landuse types. Therefore, we analyzed the between- and within-group variabilities of key infiltration parameters, e.g. saturated hydraulic conductivity ( K s ) and final infiltration rate ( i c ), derived from the Soil Water Infiltration Global (SWIG) database by calculating mutual information and a set of other commonly used statistical measures (e.g., standard deviation) among those classifiers. Results showed that soil texture alone is inadequate to scale up infiltration parameters, leading to lower mutual information and higher standard deviation values of 0.16 and 1.08 for i c , as well as to 0.16 and 3.65 for K s , respectively. Similarly, landuse also failed to explain the observed variation in infiltration parameters (with mutual information = 0.28 and 0.14 and standard deviation = 1.10 and 4.08 for i c and K s , respectively). In contrast, the World Reference Base soil group was superior to texture and landuse in explaining the observed variability of infiltration parameters, specifically for i c (with higher mutual information and lower standard deviation of 0.52 and 1.10, respectively). The integrated classification of texture, landuse and reference groups resulted in even higher mutual information and lower standard deviation values (with mutual information values of 0.66 and 0.54 for i c and K s , respectively). These results highlight that accounting for the soil classification beyond soil texture should be considered when scaling up the infiltration process.
Assessing evapotranspiration dynamics across central Europe in the context of land-atmosphere drivers Anke Fluhrer, Martin J. Baur, María Piles, Bagher Bayat, Mehdi Rahmati, David Chaparro, Clémence Dubois, Florian M. Hellwig, Carsten Montzka, Angelika Kübert, Marlin M. Mueller, Isabel Augscheller, Francois Jonard, Konstantin Schellenberg, Thomas Jagdhuber Biogeosciences, 2025 Evapotranspiration (ET) is an important variable for analyzing ecosystems, biophysical processes, and drought-related changes in the soil–plant–atmosphere system. In this study, we evaluated freely available ET products from satellite remote sensing (i.e., the Moderate resolution Imaging Spectroradiometer, MODIS; the ESA's Spinning Enhanced Visible and Infrared Imager, SEVIRI; and the Global Land Evaporation Amsterdam model, GLEAM) as well as modeling and reanalysis (i.e., the land component of the Earth system modeling product European Re-Analysis, ERA5-land, and Global Land Data Assimilation System version 2, GLDAS-2) together with in situ observations at eight Integrated Carbon Observation System (ICOS) stations across central Europe between 2017 and 2020. The land cover at the selected ICOS stations ranged from deciduous broad-leaf forests, evergreen needle-leaf forests, and mixed forests to agriculture. Trends in ET were analyzed together with soil moisture (SM) from the Soil Moisture Active Passive (SMAP) mission and the water vapor pressure deficit (VPD) from FLUXNET field measurements over 4 years, including a severe summer drought in 2018 and contrasting wet conditions in 2017. The analyses revealed the increased atmospheric aridity and decreased water supply for plant transpiration under drought conditions, showing that ET was generally lower and VPD higher in 2018 compared to in 2017. Across the study period, results indicate that during moisture-limited drought years, ET strongly decreases due to decreasing SM and increasing VPD. However, during normal or rather-wet years when SM is not limited, ET is mainly controlled by VPD and, hence, the atmospheric demand. The comparison of the different ET products based on time series, statistics, and extended triple collocation (ETC) shows generally good agreement, with ETC correlations between 0.39 and 0.99, as well as root-mean-square errors lower than 1.07 mm d−1. The greatest deviations were found at the agricultural managed sites Selhausen (Germany) and Bilos (France), with the former also showing the highest potential dependencies (error cross-correlation (ECC)) between the ET products (up to 7.6 and outside the acceptable range of −0.5 < ECC < 0.5). Thus, our results indicate that ET products differ most at stations with spatiotemporally varying land cover conditions (a variety of crops over growing periods and between seasons). This is because complex heterogeneity in land cover complicates the estimation of ET, while ET products agree well at evergreen needle-leaf stations with fewer temporal changes throughout the year and between years. The ET products from SEVIRI, ERA5-land, and GLEAM performed best when compared to ICOS observations, with either the lowest errors or the highest correlations.
Deep Learning Identification of the Governing Equation for Water Flow in Heterogeneous Soils From Data Wenxiang Song, Liangsheng Shi, Leilei He, Yuanyuan Zha, Xiaolong Hu, Mehdi Rahmati, Harry Vereecken Water Resources Research, 2025 Despite the remarkable advances in using deep learning for describing and predicting soil water flow, these models inherently cannot deepen our understanding of its underlying physical mechanisms as they are black‐box approaches. To address this issue, a novel data‐driven equation discovery approach has recently been widely used to facilitate scientific discovery in geoscience disciplines, including soil hydrology. However, due to the inherent complexity of soils, current data‐driven discovery approaches cannot deal with heterogeneous soil scenarios. In this study, we present a new group sparse regression theory and a deep learning framework to extend previous studies to be able to identify the governing equations for soil water flow in heterogeneous soils from observational data. Specifically, we focus on discovering equations from only time series of volumetric soil water content data, which are easily accessible. To accommodate it, the underlying assumption of the generalized soil‐water content‐based governing equation is utilized, and a coarse‐grained group sparsity theory is developed. Furthermore, we incorporate the proposed group sparse regression into a new deep‐learning framework: Extended‐DeepGS (Extended Deep‐learning‐based Group Sparsity). Through deep‐learning identification, it realizes simultaneous reconstructions of soil moisture dynamics and governing equations. A series of comprehensive numerical experiments are designed and conducted to test the performance of the theory and framework, and the results show its robustness. We also summarize the potential effects of soil heterogeneity on the discovery of equations. Finally, we discuss the limitations of the approach, which may inform future developments.
Integrative Use of C- and L-Band SAR Data over European sites Francesco Mattia, Anna Balenzano, Giuseppe Satalino, Davide Palmisano, Antonella Belmonte, Carsten Montzka, Mehdi Rahmati, Michele Rinaldi, Sergio Ruggieri, Deodato Tapete, Julia Kubanek International Geoscience and Remote Sensing Symposium IGARSS, 2025
Soil is a living archive of the Earth system Mehdi Rahmati, Dani Or, Wulf Amelung, Sara L. Bauke, Roland Bol, Harrie-Jan Hendricks Franssen, Carsten Montzka, Jan Vanderborght, Harry Vereecken Nature Reviews Earth and Environment, 2023
Mixed formulation for an easy and robust numerical computation of sorptivity Laurent Lassabatere, Pierre-Emmanuel Peyneau, Deniz Yilmaz, Joseph Pollacco, Jesús Fernández-Gálvez, Borja Latorre, David Moret-Fernández, Simone Di Prima, Mehdi Rahmati, Ryan D. Stewart, Majdi Abou Najm, Claude Hammecker, Rafael Angulo-Jaramillo Hydrology and Earth System Sciences, 2023
On Infiltration and Infiltration Characteristic Times Mehdi Rahmati, Borja Latorre, David Moret‐Fernández, Laurent Lassabatere, Nima Talebian, Dane Miller, Renato Morbidelli, Massimo Iovino, Vincenzo Bagarello, Mohammad Reza Neyshabouri, Ying Zhao, Jan Vanderborght, Lutz Weihermüller, Rafael Angulo Jaramillo, Dani Or, Martinus Th. van Genuchten, Harry Vereecken Water Resources Research, 2022
A scaling procedure for straightforward computation of sorptivity Laurent Lassabatere, Pierre-Emmanuel Peyneau, Deniz Yilmaz, Joseph Pollacco, Jesús Fernández-Gálvez, Borja Latorre, David Moret-Fernández, Simone Di Prima, Mehdi Rahmati, Ryan D. Stewart, Majdi Abou Najm, Claude Hammecker, Rafael Angulo-Jaramillo Hydrology and Earth System Sciences, 2021
Development and analysis of the Soil Water Infiltration Global database Mehdi Rahmati, Lutz Weihermüller, Jan Vanderborght, Yakov A. Pachepsky, Lili Mao, Seyed Hamidreza Sadeghi, Niloofar Moosavi, Hossein Kheirfam, Carsten Montzka, Kris Van Looy, Brigitta Toth, Zeinab Hazbavi, Wafa Al Yamani, Ammar A. Albalasmeh, Ma'in Z. Alghzawi, Rafael Angulo-Jaramillo, Antônio Celso Dantas Antonino, George Arampatzis, Robson André Armindo, Hossein Asadi, Yazidhi Bamutaze, Jordi Batlle-Aguilar, Béatrice Béchet, Fabian Becker, Günter Blöschl, Klaus Bohne, Isabelle Braud, Clara Castellano, Artemi Cerdà, Maha Chalhoub, Rogerio Cichota, Milena Císlerová, Brent Clothier, Yves Coquet, Wim Cornelis, Corrado Corradini, Artur Paiva Coutinho, Muriel Bastista de Oliveira, José Ronaldo de Macedo, Matheus Fonseca Durães, Hojat Emami, Iraj Eskandari, Asghar Farajnia, Alessia Flammini, Nándor Fodor, Mamoun Gharaibeh, Mohamad Hossein Ghavimipanah, Teamrat A. Ghezzehei, Simone Giertz, Evangelos G. Hatzigiannakis, Rainer Horn, Juan José Jiménez, Diederik Jacques, Saskia Deborah Keesstra, Hamid Kelishadi, Mahboobeh Kiani-Harchegani, Mehdi Kouselou, Madan Kumar Jha, Laurent Lassabatere, Xiaoyan Li, Mark A. Liebig, Lubomír Lichner, María Victoria López, Deepesh Machiwal, Dirk Mallants, Micael Stolben Mallmann, Jean Dalmo de Oliveira Marques, Miles R. Marshall, Jan Mertens, Félicien Meunier, Mohammad Hossein Mohammadi, Binayak P. Mohanty, Mansonia Pulido-Moncada, Suzana Montenegro, Renato Morbidelli, David Moret-Fernández, Ali Akbar Moosavi, Mohammad Reza Mosaddeghi, Seyed Bahman Mousavi, Hasan Mozaffari, Kamal Nabiollahi, Mohammad Reza Neyshabouri, Marta Vasconcelos Ottoni, Theophilo Benedicto Ottoni Filho, Mohammad Reza Pahlavan-Rad, Andreas Panagopoulos, Stephan Peth, Pierre-Emmanuel Peyneau, Tommaso Picciafuoco, Jean Poesen, Manuel Pulido, Dalvan José Reinert, Sabine Reinsch, Meisam Rezaei, Francis Parry Roberts, David Robinson, Jesús Rodrigo-Comino, Otto Corrêa Rotunno Filho, Tadaomi Saito, Hideki Suganuma, Carla Saltalippi, Renáta Sándor, Brigitta Schütt, Manuel Seeger, Nasrollah Sepehrnia, Ehsan Sharifi Moghaddam, Manoj Shukla, Shiraki Shutaro, Ricardo Sorando, Ajayi Asishana Stanley, Peter Strauss, Zhongbo Su, Ruhollah Taghizadeh-Mehrjardi, Encarnación Taguas, Wenceslau Geraldes Teixeira, Ali Reza Vaezi, Mehdi Vafakhah, Tomas Vogel, Iris Vogeler, Jana Votrubova, Steffen Werner, Thierry Winarski, Deniz Yilmaz, Michael H. Young, Steffen Zacharias, Yijian Zeng, Ying Zhao, Hong Zhao, Harry Vereecken Earth System Science Data, 2018
