Assessment of Surface Soil Moisture Using High-Resolution Multi-Spectral Imagery and Artificial Neural Networks

Hassan-Esfahani, Leila; Torres‐Rua, Alfonso F.; Jensen, Austin M.; McKee, Mac

doi:10.3390/rs70302627

Cited by 192 publications

(118 citation statements)

References 49 publications

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“…Likewise, monitoring soil water content is critical for determining efficient irrigation scheduling. The topsoil moisture content can be derived using RGB, NIR, and thermal bands [81]. The effective amount of water stored in the subsurface can be obtained by exploiting mathematical relationships between surface measurements and the root zone soil moisture, such as the Soil Moisture Analytical Relationship (SMAR) [82,83].…”

Section: Vegetation Monitoring and Precision Agriculturementioning

confidence: 99%

On the Use of Unmanned Aerial Systems for Environmental Monitoring

et al. 2018

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Environmental monitoring plays a central role in diagnosing climate and management impacts on natural and agricultural systems; enhancing the understanding of hydrological processes; optimizing the allocation and distribution of water resources; and assessing, forecasting, and even preventing natural disasters. Nowadays, most monitoring and data collection systems are based upon a combination of ground-based measurements, manned airborne sensors, and satellite observations. These data are utilized in describing both small-and large-scale processes, but have spatiotemporal constraints inherent to each respective collection system. Bridging the unique spatial and temporal divides that limit current monitoring platforms is key to improving our understanding of environmental systems. In this context, Unmanned Aerial Systems (UAS) have considerable potential to radically improve environmental monitoring. UAS-mounted sensors offer an extraordinary opportunity to bridge the existing gap between field observations and traditional air-and space-borne remote sensing, by providing high spatial detail over relatively large areas in a cost-effective way and an entirely new capacity for enhanced temporal retrieval. As well as showcasing recent advances in the field, there is also a need to identify and understand the potential limitations of UAS technology. For these platforms to reach their monitoring potential, a wide spectrum of unresolved issues and application-specific challenges require focused community attention. Indeed, to leverage the full potential of UAS-based approaches, sensing technologies, measurement protocols, postprocessing techniques, retrieval algorithms, and evaluation techniques need to be harmonized. The aim of this paper is to provide an overview of the existing research and applications of UAS in natural and agricultural ecosystem monitoring in order to identify future directions, applications, developments, and challenges.

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Section: Vegetation Monitoring and Precision Agriculturementioning

confidence: 99%

On the Use of Unmanned Aerial Systems for Environmental Monitoring

et al. 2018

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“…ANNs have shown promise in everyday problems such as handwritten zip code recognition [16], fingerprint identification [17], and image recognition [18]; as well as more complicated problems such as lung cancer classification based on MRI images [19], estimating surface soil moisture from high-resolution aerial images of cropland [20], and stock market predition [21].…”

Section: Neural Network Historymentioning

confidence: 99%

Automated Isotope Identification Algorithm Using Artificial Neural Networks

Kamuda

Stinnett

Sullivan

2017

IEEE Trans. Nucl. Sci.

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There is a need to develop an algorithm that can determine the relative activities of a mixture of many isotopes in a low-resolution gamma-ray spectrum. While techniques for this task exist, they require a human operator and are too slow to use on very large datasets of spectra. Pattern recognition algorithms such as neural networks are prime candidates for automated isotope identification using low-resolution gamma-ray spectra. While algorithms based on feature extraction such as peak finding or ROI algorithms work well for well calibrated high resolution detectors, for low-resolution detectors it may be more beneficial to use algorithms that incorporate more abstract features of the spectrum. This is especially true when analyzing a mixture of isotopes where peak overlap and Compton continuum effects occlude features of interest. To solve this, an artificial neural network (ANN) was trained to predict the presence and relative activities of isotopes from a mixture of many isotopes. The

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“…Although L-band microwave soil moisture products can partially overcome the influence of dense vegetation, optical remote sensing has its advantages in exemption from complicated polarization information exploration or exhaustive field observations on soil surface roughness. Thus, many soil moisture remote sensing achievements have been made on optical soil moisture remote sensing [22][23][24][25][26]. Nevertheless, clouds, thick fogs, mists, darkness, absence of revisiting and many other factors have prevented optical sensors from operating over a required location at the required moment.…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning Based Reconstruction Method for Satellite Remote Sensing of Soil Moisture Images with In Situ Observations

et al. 2017

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Surface soil moisture is an important environment variable that is dominant in a variety of research and application areas. Acquiring spatiotemporal continuous soil moisture observations is therefore of great importance. Weather conditions can contaminate optical remote sensing observations on soil moisture, and the absence of remote sensors causes gaps in regional soil moisture observation time series. Therefore, reconstruction is highly motivated to overcome such contamination and to fill in such gaps. In this paper, we propose a novel image reconstruction algorithm that improved upon the Satellite and In situ sensor Collaborated Reconstruction (SICR) algorithm provided by our previous publication. Taking artificial neural networks as a model, complex and highly variable relationships between in situ observations and remote sensing soil moisture is better projected. With historical data for the network training, feedforward neural networks (FNNs) project in situ soil moisture to remote sensing soil moisture at better performances than conventional models. Consequently, regional soil moisture observations can be reconstructed under full cloud contamination or under a total absence of remote sensors. Experiments confirmed better reconstruction accuracy and precision with this improvement than with SICR. The new algorithm enhances the temporal resolution of high spatial resolution remote sensing regional soil moisture observations with good quality and can benefit multiple soil moisture-based applications and research.

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Assessment of Surface Soil Moisture Using High-Resolution Multi-Spectral Imagery and Artificial Neural Networks

Cited by 192 publications

References 49 publications

On the Use of Unmanned Aerial Systems for Environmental Monitoring

On the Use of Unmanned Aerial Systems for Environmental Monitoring

Automated Isotope Identification Algorithm Using Artificial Neural Networks

A Machine Learning Based Reconstruction Method for Satellite Remote Sensing of Soil Moisture Images with In Situ Observations

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