Gaussian process based spatial modeling of soil moisture for dense soil moisture sensing network

Andugula, Prakash; Durbha, Surya S.; Lokhande, Avinash; Suradhaniwar, Saurabh

doi:10.1109/agro-geoinformatics.2017.8047014

Cited by 12 publications

(8 citation statements)

References 7 publications

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“…Sun et al, 2014;J. Yang et al, 2018), precipitation (Kleiber et al, 2012), and soil moisture (Andugula et al, 2017), etc. Challenges facing GPR include computational efficiency, expressive power, the use of non-Gaussian likehoods, and the scaling to large datasets.…”

Section: Appendix a Some Early Machine Learning Methods And Their Hyd...mentioning

confidence: 99%

A Transdisciplinary Review of Deep Learning Research and Its Relevance for Water Resources Scientists

Shen

2018

Water Resources Research

755

411

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Deep learning (DL), a new generation of artificial neural network research, has transformed industries, daily lives, and various scientific disciplines in recent years. DL represents significant progress in the ability of neural networks to automatically engineer problem‐relevant features and capture highly complex data distributions. I argue that DL can help address several major new and old challenges facing research in water sciences such as interdisciplinarity, data discoverability, hydrologic scaling, equifinality, and needs for parameter regionalization. This review paper is intended to provide water resources scientists and hydrologists in particular with a simple technical overview, transdisciplinary progress update, and a source of inspiration about the relevance of DL to water. The review reveals that various physical and geoscientific disciplines have utilized DL to address data challenges, improve efficiency, and gain scientific insights. DL is especially suited for information extraction from image‐like data and sequential data. Techniques and experiences presented in other disciplines are of high relevance to water research. Meanwhile, less noticed is that DL may also serve as a scientific exploratory tool. A new area termed AI neuroscience, where scientists interpret the decision process of deep networks and derive insights, has been born. This budding subdiscipline has demonstrated methods including correlation‐based analysis, inversion of network‐extracted features, reduced‐order approximations by interpretable models, and attribution of network decisions to inputs. Moreover, DL can also use data to condition neurons that mimic problem‐specific fundamental organizing units, thus revealing emergent behaviors of these units. Vast opportunities exist for DL to propel advances in water sciences.

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Section: Appendix a Some Early Machine Learning Methods And Their Hyd...mentioning

confidence: 99%

A Transdisciplinary Review of Deep Learning Research and Its Relevance for Water Resources Scientists

Shen

2018

Water Resources Research

755

411

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“…In addition to remotely sensed data, machine learning algorithms can also be used to leverage in situ moisture measurements. For example, Andugula et al (2017) used GPR to upscale point-based soil moisture measurements from a dense sensor network.…”

Section: Estimating Hydrologic Variablesmentioning

confidence: 99%

Machine learning for hydrologic sciences: An introductory overview

Liang

2021

WIREs Water

106

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The hydrologic community has experienced a surge in interest in machine learning in recent years. This interest is primarily driven by rapidly growing hydrologic data repositories, as well as success of machine learning in various academic and commercial applications, now possible due to increasing accessibility to enabling hardware and software. This overview is intended for readers new to the field of machine learning. It provides a non-technical introduction, placed within a historical context, to commonly used machine learning algorithms and deep learning architectures. Applications in hydrologic sciences are summarized next, with a focus on recent studies. They include the detection of patterns and events such as land use change, approximation of hydrologic variables and processes such as rainfall-runoff modeling, and mining relationships among variables for identifying controlling factors. The use of machine learning is also discussed in the context of integrated with process-based modeling for parameterization, surrogate modeling, and bias correction. Finally, the article highlights challenges of extrapolating robustness, physical interpretability, and small sample size in hydrologic applications.

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“…There are two reasons for us to use GPR in this study. First, the Gaussian process has been successfully applied to model spatially varying environmental processes such as precipitation (Kleiber, Katz, and Rajagopalan 2012), soil moisture (Andugula et al, 2017), and streamflow (Sun, Wang, and Xu 2014;Yang et al 2018). Because surface water distribution is related to precipitation, soil moisture, and streamflow, GPR can be useful to estimate the spatial distributions of water fraction.…”

Section: Gaussian Process Regressionmentioning

confidence: 99%

Automated estimation of daily surface water fraction from MODIS and Landsat images using Gaussian process regression

Liu

2021

International Journal of Remote Sensing

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Satellite remote sensing has been widely used to monitor surface water, but its application in observing rapid inundation changes remains challenging. Observations relying on only one sensor could hardly achieve both high temporal and high spatial resolutions. High spatial resolution images are not frequent enough to capture the fast-changing inundation, while high temporal resolution images do not provide sufficient detail on spatial variations. To address this resolution compromise for rapid response, here we propose a new automated method to estimate daily sub-pixel water cover, the surface water fraction, from Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat images. First, water indices are derived from MODIS and Landsat images, and the Landsat derived water indices are used to generate a binary water map. We then aggregate the Landsat water map to the coarser MODIS resolution, making a surface water fraction map. Second, the relationship between the MODIS derived water indices and the Landsat derived surface water fraction is fitted using a Gaussian Process Regression (GPR) model. Lastly, the fitted GPR model is applied to new MODIS imagery to estimate surface water fraction. The experiment results showed that the proposed method could provide water fraction with Root Mean Squared Errors of less than 7%. By mapping fractional water cover automatically and regularly, the proposed method could facilitate daily emergency responses and long-term analyses.

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Gaussian process based spatial modeling of soil moisture for dense soil moisture sensing network

Cited by 12 publications

References 7 publications

A Transdisciplinary Review of Deep Learning Research and Its Relevance for Water Resources Scientists

A Transdisciplinary Review of Deep Learning Research and Its Relevance for Water Resources Scientists

Machine learning for hydrologic sciences: An introductory overview

Automated estimation of daily surface water fraction from MODIS and Landsat images using Gaussian process regression

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