Revealing the potential of spectral and textural predictor variables in a neural network-based rainfall retrieval technique-Reference-Cited by-同舟云学术

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Revealing the potential of spectral and textural predictor variables in a neural network-based rainfall retrieval technique

Published:2017-04-05 Issue:7 Volume:8 Page:647-656
ISSN:2150-704X
Container-title:Remote Sensing Letters
language:en
Short-container-title:Remote Sensing Letters

Author:

Meyer Hanna,Kühnlein Meike,Reudenbach Christoph,Nauss Thomas

Funder

Federal Ministry of Education and Research (BMBF)

Publisher

Informa UK Limited

Subject

Electrical and Electronic Engineering,Earth and Planetary Sciences (miscellaneous)

Link

https://www.tandfonline.com/doi/pdf/10.1080/2150704X.2017.1312026

Reference40 articles.

1. Cloud classification using the textural features of Meteosat images

2. GOES Multispectral Rainfall Algorithm (GMSRA)

3. PERSIANN-MSA: A Precipitation Estimation Method from Satellite-Based Multispectral Analysis

4. Evaluating the Utility of Multispectral Information in Delineating the Areal Extent of Precipitation

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1. Integrating LEO and GEO Observations: Toward Optimal Summertime Satellite Precipitation Retrieval;Journal of Hydrometeorology;2023-11

2. Towards improved precipitation estimation with the GOES‐16 advanced baseline imager: Algorithm and evaluation;Quarterly Journal of the Royal Meteorological Society;2022-09-27

3. Exploring the Temporal Information From GEO Satellites for Estimating Precipitation With Convolutional Neural Networks;IEEE Geoscience and Remote Sensing Letters;2022

4. Effect of environmental covariable selection in the hydrological modeling using machine learning models to predict daily streamflow;Journal of Environmental Management;2021-07

5. Importance of spatial predictor variable selection in machine learning applications – Moving from data reproduction to spatial prediction;Ecological Modelling;2019-11

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