Código QR (código de barras bidimensional)

Evaluations of Machine Learning-Based CYGNSS Soil Moisture Estimates against SMAP Observations

This paper presents a machine learning (ML) framework to derive a quasi-global soil moisture (SM) product by direct use of the Cyclone Global Navigation Satellite System (CYGNSS)’s high spatio-temporal resolution observations over the tropics (within <inline-formula><math display="inline"><semantics...

Fuld beskrivelse

Na minha lista:
Principais autores: Volkan Senyurek, Fangni Lei, Dylan Boyd, Ali Cafer Gurbuz, Mehmet Kurum, Robert Moorhead
Format: Artigo
Sprog:Inglês
Udgivet: MDPI AG 2020-10-01
Serier:Remote Sensing
Fag:
GNSS-reflectometry
random forest
CYGNSS
soil moisture retrieval
ISMN
SMAP
Online adgang:https://www.mdpi.com/2072-4292/12/21/3503
Tags: Tilføj Tag
Ingen Tags, Vær først til at tagge denne postø!

Lignende værker