Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Average Value on a Rectangle

Average Value on a Rectangle

A snowfall map can represent how snow depth varies across a region, such as Colorado, over a fixed time period. Since different locations may receive different amounts of snow, the snowfall depth is described by a function of two variables. If f(x,y) represents the snow depth at a point in a rectangular region, then the average snowfall over the entire region is found by comparing the total accumulated snowfall with the area being measured.Total Snowfall over a RegionThe total snowfall over a...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Loss contexts enhance dorsolateral prefrontal interpersonal neural synchrony during successful human deceptive recommendations.

Communications biology·2026

Same author

Non-Urgent Messages Do Not Jump into My Headset Suddenly! Adaptive Notification Design in Mixed Reality.

IEEE transactions on visualization and computer graphics·2026

Same author

Conflict Resolution Strategies for Co-Manipulation of Virtual Objects Under Non-Disjoint Conditions.

IEEE transactions on visualization and computer graphics·2026

Same author

Downregulation of TDP43 by atovaquone inhibits oxidative phosphorylation and enhances sensitivity of triple-negative breast cancer to EGFR-TKIs.

Free radical biology & medicine·2026

Same author

Dentate gyrus microglial dynamics contributes to high-fat diet-induced depression-like behaviors in mice.

Neuropharmacology·2026

Same author

A decade-long seamless-continuity daily L-band soil moisture product derived from SMOS observations since 2010.

Scientific data·2026

Search research articles

Related Experiment Video

Updated: Jul 8, 2026

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM

Published on: October 11, 2016

A reduced latency regional gap-filling method for SMAP using random forest regression.

Xiaoyi Wang^1,2, Haishen Lü^1,2, Wade T Crow³

¹State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, National Cooperative Innovation Center for Water Safety and Hydro-science, College of Hydrology and Water Resources, Hohai University, Nanjing 210098, China.

|January 9, 2023

Summary

This study fills data gaps in Soil Moisture Active/Passive (SMAP) L3 soil moisture products using random forest models and near-real-time data. This enhances the spatiotemporal support for SMAP soil moisture data in crop-dominated regions.

Keywords:

Earth sciences Remote sensing Soil science

More Related Videos

Use of Principal Components for Scaling Up Topographic Models to Map Soil Redistribution and Soil Organic Carbon

Use of Principal Components for Scaling Up Topographic Models to Map Soil Redistribution and Soil Organic Carbon

Published on: October 16, 2018

Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images

Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images

Published on: January 7, 2019

Related Experiment Videos

Last Updated: Jul 8, 2026

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM

Published on: October 11, 2016

Use of Principal Components for Scaling Up Topographic Models to Map Soil Redistribution and Soil Organic Carbon

Use of Principal Components for Scaling Up Topographic Models to Map Soil Redistribution and Soil Organic Carbon

Published on: October 16, 2018

Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images

Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images

Published on: January 7, 2019

Area of Science:

Earth Science
Remote Sensing
Hydrology

Background:

The Soil Moisture Active/Passive (SMAP) mission provides crucial satellite-based soil moisture data.
Large spatial-temporal data gaps in SMAP products hinder near-real-time (NRT) applications.
Effective gap-filling methods are needed to improve the utility of SMAP soil moisture data.

Purpose of the Study:

To develop and validate a method for filling data gaps in the SMAP L3 soil moisture product.
To enhance the spatiotemporal coverage of SMAP soil moisture data for NRT applications.
To utilize NRT operational metadata and surface parameterization for soil moisture retrieval.

Main Methods:

Employed a random forest model to retrieve missing SMAP L3 soil moisture values.
Utilized NRT operational metadata (precipitation, skin temperature) and surface parameterization.
Tested the gap-filling method on SMAP descending (6 AM) and ascending (6 PM) orbits in a crop-dominated area (2015-2019).
Validated gap-filled estimates against in situ data and using triple collocation analysis.

Main Results:

Optimized random forest models achieved a high goodness of fit (R² ≥ 0.86).
The gap-filling scheme effectively addressed missing data points in SMAP L3 soil moisture.
Validation confirmed the reliability of the gap-filled soil moisture estimates.

Conclusions:

The proposed gap-filling scheme successfully enhances the spatiotemporal support for SMAP L3 soil moisture data.
This method, driven by low-latency data, offers a viable solution for NRT applications.
The study demonstrates the potential of machine learning approaches for improving satellite soil moisture data.