Assessment of optimal irrigation water allocation for pressurized irrigation system using water balance approach, learning machines, and remotely sensed data

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

doi:10.1016/j.agwat.2015.02.005

Cited by 71 publications

(38 citation statements)

References 20 publications

(17 reference statements)

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“…A key characteristic of these learning machine algorithms is their use of the underlying statistical characteristics of available information (predictors and predictands) without prior assumptions of their relationship. Examples of learning machine algorithms in spatial applications used in water resources and soil moisture applications can be found in [6,8,[14][15][16][17][18][19][20][21], among others. The RVM, a Bayesian regression algorithm, was applied in several of these studies.…”

Section: Introductionmentioning

confidence: 99%

“…represents an existing information source that can significantly enhance agriculture and water management [1][2][3][4][5]. Soil moisture (or soil water content) is an important variable because, along with evapotranspiration estimates, soil moisture can support estimation of current and future irrigation water needs using water balance techniques [1,[6][7][8].…”

Section: Introductionmentioning

confidence: 99%

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Estimation of Surface Soil Moisture in Irrigated Lands by Assimilation of Landsat Vegetation Indices, Surface Energy Balance Products, and Relevance Vector Machines

Torres‐Rua

Ticlavilca

Bachour

et al. 2016

Water

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Spatial surface soil moisture can be an important indicator of crop conditions on farmland, but its continuous estimation remains challenging due to coarse spatial and temporal resolution of existing remotely-sensed products. Furthermore, while preceding research on soil moisture using remote sensing (surface energy balance, weather parameters, and vegetation indices) has demonstrated a relationship between these factors and soil moisture, practical continuous spatial quantification of the latter is still unavailable for use in water and agricultural management. In this study, a methodology is presented to estimate volumetric surface soil moisture by statistical selection from potential predictors that include vegetation indices and energy balance products derived from satellite (Landsat) imagery and weather data as identified in scientific literature. This methodology employs a statistical learning machine called a Relevance Vector Machine (RVM) to identify and relate the potential predictors to soil moisture by means of stratified cross-validation and forward variable selection. Surface soil moisture measurements from irrigated agricultural fields in Central Utah in the 2012 irrigation season were used, along with weather data, Landsat vegetation indices, and energy balance products. The methodology, data collection, processing, and estimation accuracy are presented and discussed.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Estimation of Surface Soil Moisture in Irrigated Lands by Assimilation of Landsat Vegetation Indices, Surface Energy Balance Products, and Relevance Vector Machines

Torres‐Rua

Ticlavilca

Bachour

et al. 2016

Water

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“…In the current study, several learning machine algorithms have been applied to identify the correlation of AggieAir inputs with the agricultural variables within the study area. Artificial Neural Networks (ANNs), Bayesian ANNs and RVMs are the main algorithms that have been explored [1,2,4,5].…”

Section: Learning Machinesmentioning

confidence: 99%

“…Although users benefit from the free and real-time source of information, they have to cope with some limitations, such as having pre-defined spectral bands (which are not necessarily appropriate for agricultural applications), coarse spatial resolution (30m by 30m), and large temporal gaps between the overpasses (16 days in the case of Landsat) [1]. Although aircraft remote sensing of agricultural conditions is not perfect and suffers from some other limitations, it may avoid some of the limitations of Landsat imagery [2].…”

Section: Introductionmentioning

confidence: 99%

Development of unmanned aerial systems for use in precision agriculture: The AggieAir experience

Torres‐Rua

Arab²,

Hassan-Esfahani

et al. 2015

2015 IEEE Conference on Technologies for Sustainability (SusTech)

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Researchers at the Utah Water ResearchLaboratory at Utah State University have developed a small unmanned aerial system (UAS) called "AggieAir" for use as a precision remote sensing tool. The AggieAir TM UAS platform can be launched and landed almost anywhere. It carries scientific-grade cameras that capture imagery in the visual (red/green/blue), near infrared, and infrared (thermal) spectra. It has been applied in several western states to provide high-resolution imagery in support of research on water, natural resources, and agricultural problems. This paper focuses on uses of AggieAir in the generation of information for use in agricultural operations, especially with respect to precision agriculture. Examples of the use of AggieAir multi-spectral imagery include high-resolution estimation of evapotranspiration rates, crop tissue nitrogen and chlorophyll, surface and root-zone soil moisture, and crop leaf canopy volume.

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“…The methods were applied by using a 5-fold cross validation method for data generalization. Other water resources related studies have utilized Bowden's approach and concluded that it ensures that the training, testing, and validation sets are representative of the same population [52][53][54][55][56][57].…”

Section: Division Set Up In Ann Model Architecturementioning

confidence: 99%

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

et al. 2015

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Many crop production management decisions can be informed using data from high-resolution aerial images that provide information about crop health as influenced by soil fertility and moisture. Surface soil moisture is a key component of soil water balance, which addresses water and energy exchanges at the surface/atmosphere interface; however, high-resolution remotely sensed data is rarely used to acquire soil moisture values. In this study, an artificial neural network (ANN) model was developed to quantify the effectiveness of using spectral images to estimate surface soil moisture. The model produces acceptable estimations of surface soil moisture (root mean square error (RMSE) = 2.0, mean absolute error (MAE) = 1.8, coefficient of correlation (r) = 0.88, coefficient of performance (e) = 0.75 and coefficient of determination (R 2 ) = 0.77) by combining field measurements with inexpensive and readily available remotely sensed inputs. The spatial data (visual spectrum, near infrared, infrared/thermal) are produced by the AggieAir™ platform, which includes an unmanned aerial vehicle (UAV) that enables users to gather aerial imagery at a low price and high spatial and temporal resolutions. This study reports the development of an ANN model that translates AggieAir™ imagery into estimates of surface soil moisture for a large field irrigated by a center pivot sprinkler system.

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Assessment of optimal irrigation water allocation for pressurized irrigation system using water balance approach, learning machines, and remotely sensed data

Cited by 71 publications

References 20 publications

Estimation of Surface Soil Moisture in Irrigated Lands by Assimilation of Landsat Vegetation Indices, Surface Energy Balance Products, and Relevance Vector Machines

Estimation of Surface Soil Moisture in Irrigated Lands by Assimilation of Landsat Vegetation Indices, Surface Energy Balance Products, and Relevance Vector Machines

Development of unmanned aerial systems for use in precision agriculture: The AggieAir experience

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

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