Results 71 to 80 of about 4,306 (212)

Mapping Evapotranspiration and Diffuse Recharge via Variational Assimilation of GOES LST and SMAP Soil Moisture

Water Resources Research, Volume 62, Issue 2, February 2026.
Abstract Evapotranspiration (ET) and groundwater recharge are fundamental components of the terrestrial water cycle, critical for understanding land–atmosphere interactions, ecosystem dynamics, and climate feedbacks. However, large‐scale mapping of these fluxes is hindered by sparse in situ observations and uncertainties in land surface models.
Asif Mahmood, Leila Farhadi
wiley   +1 more source

An Effective Monitoring of Evolving Groundwater Drought via Multivariate Data Assimilation and Machine Learning

Water Resources Research, Volume 62, Issue 2, February 2026.
Abstract Groundwater drought represents one of the most pervasive and difficult‐to‐monitor forms of water scarcity, threatening the reliability of freshwater supply for over 2 billion people worldwide, agricultural productivity, and ecosystem health.
Parnian Ghaneei, Hamid Moradkhani
wiley   +1 more source

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

Deriving Gridded Soil Moisture Estimates Using Earth Observation Data and a Process Informed Statistical Machine Learning Approach

Meteorological Applications, Volume 33, Issue 1, January/February 2026.
A process‐informed machine learning approach, which included dynamic and static earth observation data and trained on in situ data from the United Kingdom, was capable of reproducing measured in situ values from locations not included in the model training. The model was then employed to derive daily, gridded estimates of soil moisture for Ireland. The
Rowan Fealy, Kazeem Ishola, Tim McCarthy, Ajay Nair, Rafael de Andrade Moral   +4 more
wiley   +1 more source

Global Assessment of the SMAP Level-4 Surface and Root-Zone Soil Moisture Product Using Assimilation Diagnostics [PDF]

, 2017
The Soil Moisture Active Passive (SMAP) mission Level-4 Soil Moisture (L4_SM) product provides 3-hourly, 9-km resolution, global estimates of surface (0-5 cm) and root-zone (0-100 cm) soil moisture and related land surface variables from 31 March 2015 to
Ardizzone, Joseph V., Chakraborty, Purnendu, Collins, Douglas W., Conaty, Austin L., Crow, Wade T., Girotto, Manuela, Jones, Lucas A., Kimball, John S., Kolassa, Jana, Koster, Randal D., Lannoy, Gabrielle J. M. De, Lievens, Hans, Liu, Qing, Lucchesi, Robert A., Reichle, Rolf H., Smith, Edmond B.   +15 more
core   +2 more sources

Land data assimilation of satellite‐based surface soil moisture: Impact on atmospheric simulations over the contiguous United States

Quarterly Journal of the Royal Meteorological Society, Volume 152, Issue 775, January 2026 Part B.
Soil Moisture Active Passive soil moisture retrievals are assimilated into a land surface model and the resulting land reanalysis product is used to initialise the land component of coupled land‐atmosphere experiments. We show that the local impact of land data assimilation on the atmosphere is influenced by two factors: the magnitude by which it ...
Zdenko Heyvaert, Michel Bechtold, Wouter Dorigo, Jonas Mortelmans, Daniel Hagan, Joseph Santanello, Gabriëlle De Lannoy   +6 more
wiley   +1 more source

Advances in Assimilation of Satellite-Based Passive Microwave Observations for Soil-Moisture Estimation [PDF]

, 2012
Satellite-based microwave measurements have long shown potential to provide global information about soil moisture. The European Space Agency (ESA) Soil Moisture and Ocean Salinity (SMOS, [1]) mission as well as the future National Aeronautics and Space ...
De Lannoy, Gabrielle J. M., Draper, Clara, Koster, Randy, Liu, Qing, Pauwels, Valentijn, Reichle, Rolf H.   +5 more
core   +1 more source

Seasonal Dependence of SMAP Radiometer-Based Soil Moisture Performance as Observed over Core Validation Sites [PDF]

, 2019
The NASA SMAP (Soil Moisture Active Passive) mission provides a global coverage of soil moisture measurements based on its L-band microwave radiometer every 2-3 days at about 40 km resolution.
Asanuma, J., Berg, A., Bindlish, R., Bosch, D. D., Caldwell, T., Chan, S. K., Colliander, A., Cosh, M. H., Holifield Collins, C., Jackson, T. J., Jensen, K. H., Martinez-Fernandez, J., McNairn, H., O'Neill, P., Prueger, J. H., Seyfried, M. S., Starks, P. J., Su, Z., Thibeault, M., Walker, J. P., Yueh, S. H.   +20 more
core   +2 more sources

A Decadal Survey of the Near‐Surface Seismic Velocity Response to Hydrological Variations in Utah, United States

Journal of Geophysical Research: Solid Earth, Volume 131, Issue 1, January 2026.
Abstract Ongoing climate change is leading to an increase in prolonged droughts and severe weather events, which are particularly pronounced in semi‐arid regions, such as the western United States. These extremes could have lasting social and environmental impacts.
Kuan‐Fu Feng, Marine Denolle, Fan‐Chi Lin, Tonie van Dam   +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