The potential of 2D Kalman filtering for soil moisture data assimilation-Reference-Cited by-同舟云学术

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The potential of 2D Kalman filtering for soil moisture data assimilation

Published:2015-12 Issue: Volume:171 Page:137-148
ISSN:0034-4257
Container-title:Remote Sensing of Environment
language:en
Short-container-title:Remote Sensing of Environment

Author:

Gruber Alexander,Crow Wade,Dorigo Wouter,Wagner Wolfgang

Funder

ESA's Climate Change Initiative (CCI)

European Union's Seventh Framework Programme

Publisher

Elsevier BV

Subject

Computers in Earth Sciences,Geology,Soil Science

Reference24 articles.

1. Improved prediction of quasi-global vegetation conditions using remotely-sensed surface soil moisture;Bolten;Geophysical Research Letters,2012

2. Assimilation of surface-and root-zone ascat soil moisture products into rainfall–runoff modeling;Brocca;IEEE Transactions on Geoscience and Remote Sensing,2012

3. An improved approach for estimating observation and model error parameters in soil moisture data assimilation;Crow;Water Resources Research,2010

4. The auto-tuned land data assimilation system (atlas);Crow;Water Resources Research,2014

5. Impact of incorrect model error assumptions on the sequential assimilation of remotely sensed surface soil moisture;Crow;Journal of Hydrometeorology,2006

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1. Downscaling soil moisture in regions with high soil heterogeneity: the solution based on ensemble learning with sequential and parallel learner;Science of The Total Environment;2024-11

2. A global-scale intercomparison of Triple Collocation Analysis- and ground-based soil moisture time-variant errors derived from different rescaling techniques;Remote Sensing of Environment;2023-02

3. A Hidden Markov Model based unscented Kalman Filtering framework for ecosystem health prediction: A case study in Shanghai-Hangzhou Bay Urban Agglomeration;Ecological Indicators;2022-05

4. Uncertainty Estimation for SMAP Level-1 Brightness Temperature Assimilation at Different Timescales;IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing;2022

5. Filtering grayscale images using the Kalman filter;E3S Web of Conferences;2022

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