Spatial downscaling of SMAP radiometer soil moisture using radar data: Application of machine learning to the SMAPEx and SMAPVEX campaigns

Spatial downscaling of SMAP radiometer soil moisture using radar data: Application of machine learning to the SMAPEx and SMAPVEX campaigns

This study developed a random forest approach for downscaling the coarse-resolution (36 km) soil moisture measured by The National Aeronautics and Space Administration (NASA) Soil Moisture Active Passive (SMAP) mission to 1 km spatial resolution, utilizing airborne remotely sensed data (radar backsc...

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Bibliografische gegevens
Hoofdauteurs: Elaheh Ghafari, Jeffrey P. Walker, Liujun Zhu, Andreas Colliander, Alireza Faridhosseini
Formaat: Artikel
Taal:English
Gepubliceerd in: Elsevier 2024-06-01
Reeks:Science of Remote Sensing
Onderwerpen:
Machine learning
Downscaling
Soil moisture
SMAP
Random forest model
SMAPEx
Online toegang:http://www.sciencedirect.com/science/article/pii/S2666017224000063

Internet

http://www.sciencedirect.com/science/article/pii/S2666017224000063

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