Self‐Validating Deep Learning for Recovering Terrestrial Water Storage From Gravity and Altimetry Measurements-Reference-Cited by-同舟云学术

Self‐Validating Deep Learning for Recovering Terrestrial Water Storage From Gravity and Altimetry Measurements

Published:2020-08-28 Issue:17 Volume:47 Page:
ISSN:0094-8276
Container-title:Geophysical Research Letters
language:en
Short-container-title:Geophys. Res. Lett.

Author:

Irrgang Christopher¹^ORCID,Saynisch‐Wagner Jan¹^ORCID,Dill Robert¹^ORCID,Boergens Eva¹^ORCID,Thomas Maik¹²

Affiliation:

1. Section 1.3: Earth System Modelling Helmholtz Centre Potsdam – GFZ German Research Centre for Geosciences Potsdam Germany

2. Institute of Meteorology Freie Universität Berlin Berlin Germany

Funder

Helmholtz-Gemeinschaft

Publisher

American Geophysical Union (AGU)

Subject

General Earth and Planetary Sciences,Geophysics

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1029/2020GL089258

Reference55 articles.

1. Abadi M. Barham P. Chen J. Chen Z. Davis A. Dean J. Devin M. Ghemawat S. Irving G. &Isard M.(2016).Tensorflow: A system for large‐scale machine learning. In12th symposium on operating systems design and implementation pp.265–283.

2. Forecasting GRACE Data over the African Watersheds Using Artificial Neural Networks

3. Viewing Forced Climate Patterns Through an AI Lens

4. Measurement of Inland Surface Water from Multi-mission Satellite Radar Altimetry: Sustained Global Monitoring for Climate Change

5. Observing water level extremes in the Mekong River Basin: The benefit of long-repeat orbit missions in a multi-mission satellite altimetry approach

Cited by 12 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Using machine learning techniques to reconstruct the signal observed by the GRACE mission based on AMSR-E microwave data;Miscellanea Geographica;2024-04-01

2. Global high-resolution total water storage anomalies from self-supervised data assimilation using deep learning algorithms;Nature Water;2024-02-12

3. Hysteresis response of groundwater depth on the influencing factors using an explainable learning model framework with Shapley values;Science of The Total Environment;2023-12

4. STA-Net: Reconstruct Missing Temperature Data of Meteorological Stations Using a Spatiotemporal Attention Neural Network;Communications in Computer and Information Science;2023-11-13

5. When it comes to Earth observations in AI for disaster risk reduction, is it feast or famine? A topical review;Environmental Research Letters;2023-09-01