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Remote sensing

An artist's rendering shows a gold-coated satellite with solar panels above Earth's surface against the backdrop of the dark space sky
Posted inEditors' Highlights

Transforming Hydrology by Integrating Sensors and Disciplines

by Tissa Illangasekare 29 November 202130 November 2021

Satellite sensing has transformed hydrology by providing global information on variables and fluxes. Breakthroughs will come from integrating sensing information and cross-disciplinary approaches.

Map of the study site in the northern California Coast Ranges.
Posted inEditors' Highlights

Thickness and Strength of Slow-moving Landslides Revealed

by O. Marc 22 April 202128 September 2021

Hundreds of slow-moving landslides’ deformation patterns were inverted to obtain their thickness and frictional strength, revealing that larger landslides are weaker and thinner than smaller ones.

Two maps of the Missouri river basin showing estimates of river discharge in a hydrological model (top) and when the model incorporates satellite data (bottom).
Posted inEditors' Highlights

Gauging Ungauged River Basins with Smart Remote Sensing

by Marc F. P. Bierkens 10 March 202131 March 2022

A clever combination of hydrologic modelling and discharge estimates from the Landsat satellite provides good discharge estimates throughout the Missouri river basin.

An AmeriFlux instrument tower rises above treetops in a New Mexico piñon-juniper forest
Posted inScience Updates

Measuring, Monitoring, and Modeling Ecosystem Cycling

by L. R. Hawkins, J. Kumar, X. Luo, D. Sihi and S. Zhou 5 August 202029 September 2021

Scientists leverage long-term environmental measurements, emerging satellite observations, and recent modeling advances to examine changes in ecosystem carbon and water cycling.

Charts comparing the Standardized Precipitation-Evapotranspiration Index and the standardized simulations at multiple timescales for the study area, which is shown on a map on the left
Posted inEditors' Highlights

Ensemble Learning Estimates Terrestrial Water Storage Changes

by Jonathan H. Jiang 2 July 202027 June 2020

Ensemble learning models for estimating past changes of terrestrial water storage from climate are presented and tested in the Pearl River basin, China.

An illustration of rainfall estimates from ground-based radar and spaceborne Tropical Rainfall Measuring Mission (TRMM) radar
Posted inEditors' Highlights

Machine Learning Improves Satellite Rainfall Estimates

by Valeriy Ivanov 31 October 201924 October 2019

A new deep learning approach bridges ground rain gauge and radar data with spaceborne radar observations of Tropical Rainfall Measuring Mission to improve precipitation estimation.

Young plant shoot growing in soil
Posted inEditors' Vox

The Paramount Societal Impact of Soil Moisture

by M. Tuller, E. Babaeian, S. B. Jones, C. Montzka, Harry Vereecken and M. Sadeghi 23 July 20195 January 2022

Recent technological innovations offer new opportunities for soil moisture characterization and monitoring from the pedon to global scales.

A visualization from space of rivers in Southeast and East Asia from a new global river map.
Posted inResearch Spotlights

A More Accurate Global River Map

by E. Underwood 12 July 201927 April 2022

A new map of global river systems is based on crowdsourcing and the latest topography data sets.

Tropical storm brews over Seychelles archipelago
Posted inResearch Spotlights

Precipitation in the Tropics: A New View

by Terri Cook 10 April 2019

The first study to simultaneously investigate precipitation and cloud structures in tropical weather systems concludes observation systems significantly overestimate the height of raining clouds.

A student collects soil moisture data from an instrument in the field.
Posted inResearch Spotlights

Are Soil Moisture and Latent Heat Overcoupled in Land Models?

by Aaron Sidder 9 April 2019

A novel statistical approach demonstrates how to reduce bias in remote sensing estimates of soil moisture and latent heat flux coupling strength and clarifies the relationship between the variables.

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