Deep Learning-Based Soil Moisture Retrieval in CONUS Using CYGNSS Delay–Doppler Maps-Reference-Cited by-同舟云学术

Deep Learning-Based Soil Moisture Retrieval in CONUS Using CYGNSS Delay–Doppler Maps

Published:2022 Issue: Volume:15 Page:6867-6881
ISSN:1939-1404
Container-title:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
language:
Short-container-title:IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

Author:

Nabi M M¹^ORCID,Senyurek Volkan²^ORCID,Gurbuz Ali C.¹^ORCID,Kurum Mehmet¹^ORCID

Affiliation:

1. Department of Electrical and Computer Engineering, Information Processing and Sensing Lab, Mississippi State University, Mississippi State, MS, USA

2. Geosystems Research Institute, Mississippi State University, Mississippi State, MS, USA

Funder

National Science Foundation

USDA-ARS

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Atmospheric Science,Computers in Earth Sciences

Link

https://ieeexplore.ieee.org/ielam/4609443/9656571/9851513-aam.pdf

Reference46 articles.

1. Characterizing Coarse-Scale Representativeness of in situ Soil Moisture Measurements from the International Soil Moisture Network