Results 71 to 80 of about 5,604,864 (173)

Data Assimilation to Extract Soil Moisture Information from SMAP Observations

Remote Sensing, 2017
This study compares different methods to extract soil moisture information through the assimilation of Soil Moisture Active Passive (SMAP) observations.
Jana Kolassa, Rolf H. Reichle, Qing Liu, Michael Cosh, David D. Bosch, Todd G. Caldwell, Andreas Colliander, Chandra Holifield Collins, Thomas J. Jackson, Stan J. Livingston, Mahta Moghaddam, Patrick J. Starks   +11 more
doaj   +1 more source

Pedotransfer functions in the era of machine learning: Data limitations, spatial scale, transferability, and future directions

Vadose Zone Journal, Volume 25, Issue 5, September/October 2026.
Abstract Pedotransfer functions (PTFs) are predictive relationships that estimate difficult‐to‐measure soil hydraulic properties from readily available data such as texture, bulk density, and organic matter content. This review synthesizes PTF development from early regression‐based approaches to modern machine‐learning techniques, including artificial
Frank Anyoka Adekilae, Vinit Sehgal
wiley   +1 more source

Development of a New Generic AI Model for Spatio‐Temporal Prediction of Soil Moisture and Soil Water Isotopes

Water Resources Research, Volume 62, Issue 9, September 2026.
Abstract Understanding water fluxes and mixing dynamics in the unsaturated zone is crucial for ecohydrological studies. Yet, observations of soil moisture and soil water isotopes remain limited in spatial extent and temporal resolution, restricting modeling studies on the critical zone. A data‐driven Artificial Intelligence (AI) approach was applied in
Hyekyeng Jung, Dörthe Tetzlaff, Kristina Yordanova, Chris Soulsby   +3 more
wiley   +1 more source

Evaluation of the Bayesian Downscaling Algorithm for Achieving Higher Resolution Soil Moisture Data

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
The NASA-launched Soil Moisture Active Passive satellite mission (SMAP) had the objective to globally characterize soil moisture with an intermediate resolution (9 km), through the integration of radar (3 km) and radiometer (36 km) observations. The SMAP
Xiaoling Wu, Jeffrey P. Walker, Nan Ye
doaj   +1 more source

The Role of Assimilating Deep Learning‐Enhanced AMSR‐E/2 Observations in European Soil Moisture Reanalysis

Water Resources Research, Volume 62, Issue 9, September 2026.
Abstract Soil moisture is an essential climate variable that controls the exchange of water and energy between land and atmosphere, directly impacting land surface feedbacks. Satellite‐derived soil moisture provides valuable large‐scale information, but long‐term applications are limited by sensor lifespans, retrieval noise, and systematic biases ...
Visakh Sivaprasad, Johannes Keller, Yorck Ewerdwalbesloh, Anne Springer, Harrie‐Jan Hendricks Franssen, Harry Vereecken, Carsten Montzka   +6 more
wiley   +1 more source

Development of low latency, high resolution SMAP soil moisture retrievals in support of near-real-time applications

Frontiers in Earth Science
Soil moisture plays a crucial role in weather forecasting and climate prediction, and provides useful information for flood and drought monitoring, and agricultural irrigation scheduling. In recent decades, soil moisture measurements have been enabled by
Yonghwan Kwon, Yonghwan Kwon, Yonghwan Kwon, Pang-Wei Liu, Pang-Wei Liu, Mahdi Navari, Mahdi Navari, Rajat Bindlish, Eric M. Kemp, Eric M. Kemp, Jerry W. Wegiel, Jerry W. Wegiel, Ehsan Jalilvand, Ehsan Jalilvand, Sujay V. Kumar   +14 more
doaj   +1 more source

Remote Sensing Hydrology Revisited: Advances, Persistent Challenges, and a Science‐Driven Agenda for the Next Decade

Water Resources Research, Volume 62, Issue 9, September 2026.
Abstract In the WRR 50th Anniversary Special Collection, Lettenmaier et al. (2015, https://doi.org/10.1002/2015WR017616) provided a timely account of how satellite remote sensing evolved from a niche capability in hydrology into a mainstream driver of hydrologic discovery, as the community shifted from imagery toward validated Earth Observation (EO ...
Yang Hong, Dennis P. Lettenmaier, Efi Foufoula‐Georgiou, Ana P. Barros, George J. Huffman, James S. Famiglietti, Dawen Yang, Tamlin M. Pavelsky, Soroosh Sorooshian, Taikan Oki, Siyu Zhu, Mengye Chen, Songkun Yan   +12 more
wiley   +1 more source

Beyond SMAP: Can GNSS‐R Platforms Ensure Continuity of Global Soil Moisture Observations?

Water Resources Research, Volume 62, Issue 9, September 2026.
Abstract The absence of a definitive follow‐on strategy for the Soil Moisture Active Passive (SMAP) mission, which has already surpassed its planned mission lifetime, presents a significant challenge for the hydrological science community. GNSS‐Reflectometry (GNSS‐R), which exploits reflected satellite navigation signals, has emerged as a rapidly ...
Ehsan Jalilvand, Sujay V. Kumar, Rajat Bindlish, Jerry W. Wegiel, Mehmet Kurum, Hyunglok Kim, Pang‐Wei Liu, Mahdi Navari   +7 more
wiley   +1 more source

Integrated SMAP and SMOS Soil Moisture Observations [PDF]

, 2019
Soil Moisture Active Passive (SMAP) mission and the Soil Moisture and Ocean Salinity (SMOS) missions provide brightness temperature and soil moisture estimates every 2-3 days.
Bindlish, Rajat, Jackson, Thomas J., Chan, Steven, Kerr, Yann, Colliander, Andreas   +4 more
core  

The 43-year (1978-2020) global 9km remotely sensed soil moisture product

, 2022
The 43-year global 9km remotely sensed soil moisture product is estimated by the fusion of two kinds of microwave soil moisture products using the spatial temporal fusion model (STFM). One product is the Climate Change Initiative (CCI) 0.25° passive soil
Xinghua, Li, Lili, Li, Hongtao, Jiang, Huanfeng, Shen   +3 more
core   +1 more source