Dr. Alfonso F. Torres-Rua Profile

Dr. Alfonso F. Torres-Rua

Assistant Professor at Utah State Univ

SPIE Involvement:

Conference Program Committee | Editor | Author

Area of Expertise:

UAV , remote sensing , agriculture , water mangement , machine learning , irrigation

Websites:

Company Website

Profile Summary

A Peruvian native, he is passionate about finding practical solutions for agriculture, water using remote sensing and modeling.
Teaching Interests involves remote sensing (unmanned aerial vehicles and satellites) production and analysis for agricultural water management, with emphasis on spatial crop status and water use estimation and monitoring at small and large scale. Research Interests include integration of ground and remote sensing for agricultural production and water management, as well as basin-scale integration.

Publications (21)

Proceedings Article | 4 June 2022 Presentation + Paper

Presentation + Paper

Exploratory analysis of vineyard leaf water potential against sUAS multispectral and temperature information

Rui Gao, Maria Mar Alsina, Alfonso Torres-Rua, Lawrence Hipps, William Kustas, William White, Martha Anderson, Joseph Alfieri, Nick Dokoozlian, Hector Nieto, Feng Gao, Lynn McKee, John Prueger, Luis Sanchez, Andrew Mcelrone, Nicolas Bambach Ortiz, Calvin Coopmans, Ian Gowing

Proceedings Volume 12114, 121140K (2022) https://doi.org/10.1117/12.2622995

KEYWORDS: Near infrared, Vegetation, Remote sensing, Data modeling, Sensors, Agriculture, Performance modeling, Statistical analysis, Feature extraction

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Proceedings Article | 30 May 2022 Presentation

Presentation

Assessment of TSEB-PT and -2T in ET partitioning estimation over California commercial vineyards based on sUAS information

Rui Gao, Alfonso F. Torres-Rua, Lawrence Hipps, William P. Kustas, Martha Anderson, William A. White, Joseph Alfieri, Maria Mar Alsina, Nick Dokoozlian, Hector Nieto, Feng Gao, Lynn G. McKee, John H. Prueger, Luis Sanchez, Andrew J. Mcelrone, Nicolas Bambach-Ortiz, Calvin Coopmans, Ian Gowing

Proceedings Volume 12114, 121140I (2022) https://doi.org/10.1117/12.2622993

KEYWORDS: Remote sensing, Soil science, Satellites, Performance modeling, Climatology, Carbon, Atmospheric sensing, Agriculture

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SPIE Journal Paper | 7 March 2022

Estimation of bell pepper evapotranspiration using two-source energy balance model based on high-resolution thermal and visible imagery from unmanned aerial vehicles

Emre Tunca, Eyüp Selim Köksal, Alfonso Torres-Rua, William Kustas, Hector Nieto

JARS, Vol. 16, Issue 02, 022204, (March 2022) https://doi.org/10.1117/12.10.1117/1.JRS.16.022204

KEYWORDS: Unmanned aerial vehicles, Spatial resolution, Soil science, Agriculture, Thermal modeling, Thermography, Wind energy, Heat flux, Temperature metrology, Solar radiation models

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Proceedings Article | 12 April 2021 Presentation

Presentation

Challenges and opportunities of unmanned aerial vehicles in agriculture in the next decade

Alfonso Torres-Rua

Proceedings Volume 11747, 117470B (2021) https://doi.org/10.1117/12.2588153

KEYWORDS: Unmanned aerial vehicles, Agriculture

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Proceedings Article | 12 April 2021 Presentation + Paper

Presentation + Paper

Evapotranspiration partitioning assessment using a machine-learning-based leaf area index and the two-source energy balance model with sUAV information

Rui Gao, Alfonso Torres-Rua, Ayman Nassar, Joseph Alfieri, Mahyar Aboutalebi, Lawrence Hipps, Nicolas Bambach Ortiz, Andrew McElrone, Calvin Coopmans, William Kustas, William White, Lynn McKee, Maria del Mar Alsina, Nick Dokoozlian, Luis Sanchez, John Prueger, Hector Nieto, Nurit Agam

Proceedings Volume 11747, 117470N (2021) https://doi.org/10.1117/12.2586259

KEYWORDS: Data modeling, Vegetation, Biological research, Systems modeling, Remote sensing, Machine learning, Ecosystems, Carbon, Autoregressive models, Atmospheric sensing

Read Abstract +

Accurate quantification of the partitioning of evapotranspiration (ET) into transpiration and evaporation fluxes is necessary to understanding ecosystem interactions among carbon, water, and energy flux components. ET partitioning can also support the description of atmosphere and land interactions and provide unique insights into vegetation water status. Previous studies have identified leaf area index (LAI) estimation as a key descriptor of biomass conditions needed for the estimation of transpiration and evaporation. LAI estimation in clumped vegetation systems, such as vineyards and orchards, has proven challenging and is strongly related to crop phenological status and canopy management. In this study, a feature extraction model based on previous research was built to generate a total of 202 preliminary variables at a 3.6-by-3.6- meter-grid scale based on submeter-resolution information from a small Unmanned Aerial Vehicle (sUAV) in four commercial vineyards across California. Using these variables, a machine learning model called eXtreme Gradient Boosting (XGBoost) was successfully built for LAI estimation. The XGBoost built-in function requires only six variables relating to vegetation indices and temperature to produce high-accuracy LAI estimation for the vineyard. Using the sixvariable XGBoost-based LAI map, two versions of the Two-Source Energy Balance (TSEB) model, TSEB-PT and TSEB- 2T were used for energy balance and ET partitioning. Comparing these results with the Eddy-Covariance (EC) tower data, showed that TSEB-PT outperforms TSEB-2T on the estimation of sensible heat flux (within 13% relative error) and surface heat flux (within 34% relative error), while TSEB-2T outperforms TSEB-PT on the estimation of net radiation (within 14% relative error) and latent heat flux (within 2% relative error). For the mature vineyard (north block), TSEB-2T performs better than TSEB-PT in partitioning the canopy latent heat flux with 6.8% relative error and soil latent heat flux with 21.7% relative error; however, for the younger vineyard (south block), TSEB-PT performs better than TSEB-2T in partitioning the canopy latent heat flux with 11.7% relative error and soil latent heat flux with 39.3% relative error.

Proceedings Article | 12 April 2021 Presentation

Presentation

Welcome and Introduction to SPIE Conference 11747

J. Alex Thomasson, Alfonso Torres-Rua

Proceedings Volume 11747, 1174702 (2021) https://doi.org/10.1117/12.2597580

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Proceedings Article | 12 April 2021 Presentation + Paper

Presentation + Paper

Development of high performance computing tools for estimation of high-resolution surface energy balance products using sUAS information

Ayman Nassar, Alfonso Torres-Rua, Venkatesh Merwade, Sayan Dey, Lan Zhao, I. Luk Kim, William Kustas, Hector Nieto, Lawrence Hipps, Rui Gao, Joseph Alfieri, John Prueger, Maria Mar Alsina, Lynn McKee, Calvin Coopmans, Luis Sanchez, Nick Dokoozlian, Nicolas Bambach Ortiz, Andrew Mcelrone

Proceedings Volume 11747, 117470K (2021) https://doi.org/10.1117/12.2587763

KEYWORDS: Atmospheric modeling, Performance modeling, Data processing, Data modeling, Computing systems, Remote sensing, MATLAB, Excel, Atmospheric sensing, Atmospheric monitoring

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Proceedings Article | 26 May 2020 Presentation + Paper

Presentation + Paper

Implications of soil and canopy temperature uncertainty in the estimation of surface energy fluxes using TSEB2T and high-resolution imagery in commercial vineyards

Ayman Nassar, Alfonso Torres-Rua, William Kustas, Hector Nieto, Mac McKee, Lawrence Hipps, Joseph Alfieri, John Prueger, Maria Mar Alsina, Lynn McKee, Calvin Coopmans, Luis Sanchez, Nick Dokoozlian

Proceedings Volume 11414, 114140F (2020) https://doi.org/10.1117/12.2558715

KEYWORDS: Solar radiation models, Vegetation, Agriculture, Wind energy, Data modeling, Remote sensing, Monte Carlo methods, Soil science, Atmospheric modeling, Heat flux

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Proceedings Article | 26 May 2020 Presentation + Paper

Presentation + Paper

To what extent does the Eddy Covariance footprint cutoff influence the estimation of surface energy fluxes using two source energy balance model and high-resolution imagery in commercial vineyards?

Ayman Nassar, Alfonso Torres-Rua, William Kustas, Hector Nieto, Mac McKee, Lawrence Hipps, Joseph Alfieri, John Prueger, Maria Mar Alsina, Lynn McKee, Calvin Coopmans, Luis Sanchez, Nick Dokoozlian

Proceedings Volume 11414, 114140G (2020) https://doi.org/10.1117/12.2558777

KEYWORDS: Wind energy, Agriculture, Heat flux, Remote sensing, Soil science, Vegetation, Mathematical modeling, Statistical analysis, Turbulence, Receptors

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Validation of surface energy fluxes from remote sensing sources is performed using instantaneous field measurements obtained from eddy covariance (EC) instrumentation. An eddy covariance measurement is characterized by a footprint function / weighted area function that describes the mathematical relationship between the spatial distribution of surface flux sources and their corresponding magnitude. The orientation and size of each flux footprint / source area depends on the micro-meteorological conditions at the site as measured by the EC towers, including turbulence fluxes, friction velocity (ustar), and wind speed, all of which influence the dimensions and orientation of the footprint. The total statistical weight of the footprint is equal to unity. However, due to the large size of the source area / footprint, a statistical weight cutoff of less than one is considered, ranging between 0.85 and 0.95, to ensure that the footprint model is located inside the study area. This results in a degree of uncertainty when comparing the modeled fluxes from remote sensing energy models (i.e., TSEB2T) against the EC field measurements. In this research effort, the sensitivity of instantaneous and daily surface energy flux estimates to footprint weight cutoffs are evaluated using energy balance fluxes estimated with multispectral imagery acquired by AggieAir sUAS (small Unmanned Aerial Vehicle) over commercial vineyards near Lodi, California, as part of the ARS-USDA Agricultural Research Service’s Grape Remote Sensing Atmospheric Profile and Evapotranspiration eXperiment (GRAPEX) project. The instantaneous fluxes from the eddy covariance tower will be compared against instantaneous fluxes obtained from different TSEB2T aggregated footprint weights (cutoffs). The results indicate that the size, shape, and weight of pixels inside the footprint source area are strongly influenced by the cutoff values. Small cutoff values, such as 0.3 and 0.35, yielded high weights for pixels located within the footprint domain, while large cutoffs, such as 0.9 and 0.95, result in low weights. The results also indicate that the distribution of modelled LE values within the footprint source area are influenced by the cutoff values. A wide variation in LE was observed at high cutoffs, such as 0.90 and 0.95, while a low variation was observed at small cutoff values, such as 0.3. This happens due to the large number of pixel units involved inside the footprint domain when using high cutoff values, whereas a limited number of pixels are obtained at lower cutoff values.

Proceedings Article | 14 May 2020 Presentation + Paper

Presentation + Paper

Estimation of evapotranspiration and energy fluxes using a deep-learning-based high-resolution emissivity model and the two-source energy balance model with sUAS information

Alfonso Torres-Rua, Andres Ticlavilca, Mahyar Aboutalebi, Hector Nieto, Maria Mar Alsina, Alex White, John Prueger, Joseph Alfieri, Lawrence Hipps, Lynn McKee, William Kustas, Calvin Coopmans, Nick Dokoozlian

Proceedings Volume 11414, 114140B (2020) https://doi.org/10.1117/12.2558824

KEYWORDS: Unmanned aerial vehicles, Vegetation, Earth observing sensors, Landsat, Data modeling, Agriculture, Thermal modeling, Sensors, Cameras, Water

Read Abstract +

Surface temperature is necessary for the estimation of energy fluxes and evapotranspiration from satellites and airborne data sources. For example, the Two-Source Energy Balance (TSEB) model uses thermal information to quantify canopy and soil temperatures as well as their respective energy balance components. While surface (also called kinematic) temperature is desirable for energy balance analysis, obtaining this temperature is not straightforward due to a lack of spatially estimated narrowband (sensor-specific) and broadband emissivities of vegetation and soil, further complicated by spectral characteristics of the UAV thermal camera. This study presents an effort to spatially model narrowband and broadband emissivities for a microbolometer thermal camera at UAV information resolution (~0.15 m) based on Landsat and NASA HyTES information using a deep learning (DL) model. The DL model is calibrated using equivalent optical Landsat / UAV spectral information to spatially estimate narrowband emissivity values of vegetation and soil in the 7–14- nm range at UAV resolution. The resulting DL narrowband emissivity values were then used to estimate broadband emissivity based on a developed narrowband-broadband emissivity relationship using the MODIS UCSB Emissivity Library database. The narrowband and broadband emissivities were incorporated into the TSEB model to determine their impact on the estimation of instantaneous energy balance components against ground measurements. The proposed effort was applied to information collected by the Utah State University AggieAir small Unmanned Aerial Systems (sUAS) Program as part of the ARS-USDA GRAPEX Project (Grape Remote sensing Atmospheric Profile and Evapotranspiration eXperiment) over a vineyard located in Lodi, California. A comparison of resulting energy balance component estimates, with and without the inclusion of high-resolution narrowband and broadband emissivities, against eddy covariance (EC) measurements under different scenarios are presented and discussed.

Proceedings Article | 27 April 2020 Presentation

Presentation

Deriving daily evapotranspiration from multiple unmanned aerial vehicle (UAV) thermal imageries and high-frequency ground thermal measurements (Conference Presentation)

Mahyar Aboutalebi, Alfonso Torres-Rua, Mac McKee, William Kustas, Hector Nieto, Maria Alsina, Alex White, John H. Prueger, Lynn McKee, Joseph Alfieri, Lawrence Hipps, Calvin Coopmans, Nick Dokoozlian

Proceedings Volume 11414, 114140E (2020) https://doi.org/10.1117/12.2558630

KEYWORDS: Unmanned aerial vehicles, Thermography, Wind energy, Remote sensing, Thermal modeling, Agriculture, Shortwaves, Soil science, Temperature sensors, Sensors

Read Abstract +

Evapotranspiration (ET) derived from remote sensing-based models represents ½ hourly to hourly value that is upscaled to a daily scale for practical applications in the fields of agricultural and water management. Several upscaling methods such as Gaussian fitting curve, sine approach, and evaporative fraction approach have been developed to extrapolate remote sensing-based ET values to daily scale by assuming constant daytime ratios (e.g., self-preservation of available energy partitioning). This simple assumption can result in uncertainties in the performance of those methods and can be violated for unstable conditions such as a cloudy day. Studies showed that, for example, diurnal variation of incoming shortwave radiation will change from a Gaussian distribution to a multimodal distribution on a cloudy day. Besides, when remote-sensing ET outputs are directly upscaled to daily scale and compared with eddy covariance measurements, a fixed footprint of eddy covariance is assumed, while the actual eddy covariance footprint is dynamic and changes with wind speed, direction and atmospheric stability. In this study, a new method is proposed to spatially and temporally simulate canopy and soil temperature for each time step (e.g., 1-hour) based on the temperature pattern recorded by IRT temperature sensors and UAV initial temperatures at the specific time of day. Next, the Two-Source Energy Balance (TSEB) model is executed for each time step of the daytime period (usually when net radiation >100 W/m^2) to calculate energy balance components. The integration of TSEB outputs over the daytime period leads to estimations of daily energy balance components. Since cloudy conditions affect temperatures recorded by IRT sensors, the proposed model is not sensitive to weather conditions. In addition, the proposed model physically simulates ET at each time step instead of directly extrapolating ET from a single remote sensing observation and model output This feature solves the limitations of comparing the direct extrapolation methods of instantaneous ET against eddy covariance measurements. The proposed approach is applied on information collected by the Utah State University AggieAir small Unmanned Aerial Systems (sUAS) Program as part of the ARS-USDA GRAPEX Project (Grape Remote sensing Atmospheric Profile and Evapotranspiration eXperiment) conducted since 2014 over multiple vineyards located in California. The estimated ET values from the TSEB model at hourly time steps and integrated over the daytime period are compared to eddy covariance measurements of ET. Additionally, hourly model output integrated over the daytime period compared to the different upscaling methods are presented and discussed.

Proceedings Article | 14 May 2019 Paper

Paper

Validation of digital surface models (DSMs) retrieved from unmanned aerial vehicle (UAV) point clouds using geometrical information from shadows

Mahyar Aboutalebi, Alfonso Torres-Rua, Mac McKee, William Kustas, Héctor Nieto, Calvin Coopmans

Proceedings Volume 11008, 110080L (2019) https://doi.org/10.1117/12.2519694

KEYWORDS: Unmanned aerial vehicles, Statistical analysis, Error analysis, LIDAR, Agriculture, Satellites, Remote sensing, 3D modeling

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Proceedings Article | 14 May 2019 Paper

Paper

The impact of shadows on partitioning of radiometric temperature to canopy and soil temperature based on the contextual two-source energy balance model (TSEB-2T)

Mahyar Aboutalebi, Alfonso F. Torres-Rua, Mac McKee, Hector Nieto, William Kustas, Calvin Coopmans

Proceedings Volume 11008, 1100804 (2019) https://doi.org/10.1117/12.2519685

KEYWORDS: Unmanned aerial vehicles, Vegetation, 3D modeling, Image resolution, Near infrared, Cameras

Read Abstract +

Tests of the most recent version of the two-source energy balance model have demonstrated that canopy and soil temperatures can be retrieved from high-resolution thermal imagery captured by an unmanned aerial vehicle (UAV). This work has assumed a linear relationship between vegetation indices (VIs) and radiometric temperature in a square grid (i.e., 3.6 m x 3.6 m) that is coarser than the resolution of the imagery acquired by the UAV. In this method, with visible, near infrared (VNIR), and thermal bands available at the same high-resolution, a linear fit can be obtained over the pixels located in a grid, where the x-axis is a vegetation index (VI) and the y-axis is radiometric temperature. Next, with an accurate VI threshold that separates soil and vegetation pixels from one another, the corresponding soil and vegetation temperatures can be extracted from the linear equation. Although this method is simpler than other approaches, such as TSEB with Priestly-Taylor (TSEB-PT), it could be sensitive to VIs and the parameters that affect VIs, such as shadows. Recent studies have revealed that, on average, the values of VIs, such as normalized difference vegetation index (NDVI) and leaf area index (LAI), that are located in sunlit areas are greater than those in shaded areas. This means that involving or compensating for shadows will affect the linear relationship parameters (slope and bias) between radiometric temperature and VI, as well as thresholds that separate soil and vegetation pixels. This study evaluates the impact of shadows on the retrieval of canopy and soil temperature data from four UAV images before and after applying shadow compensation techniques. The retrieved temperatures, using the TSEB-2T approach, both before and after shadow correction, are compared to the average temperature values for both soil and canopy in each grid. The imagery was acquired by the Utah State University AggieAir UAV system over a commercial vineyard located in California as part of the USDA Agricultural Research Service Grape Remote sensing Atmospheric Profile and Evapotranspiration Experiment (GRAPEX) Program during 2014 to 2016. The results of this study show when it is necessary to employ shadow compensation methods to retrieve vegetation and soil temperature directly.

Proceedings Article | 14 May 2019 Presentation

Presentation

Challenges that beyond-visual-line-of-sight technology will create for UAS-based remote sensing in agriculture (Conference Presentation)

Mac McKee, Alfonso Torres-Rua, Mahyar Aboutalebi, Ayman Nassar, Calvin Coopmans, William Kustas, Feng Gao, Nicholas Dokoozlian, Luis Sanchez, Maria Alsina

Proceedings Volume 11008, 110080J (2019) https://doi.org/10.1117/12.2520248

KEYWORDS: Remote sensing, Agriculture, Calibration, Geographic information systems, Clouds, Sensors

Read Abstract +

Current technologies employed for use of small, unmanned aerial systems (sUAS), or “drones”, for remote sensing (RS) activities that support the information needs of agricultural operations are expensive, provide relatively small geographic coverage, and typically produce data of limited scientific quality. Research is currently underway that will someday yield sense-and-avoid technologies that will enable sUAS to detect and safely avoid potential collisions with conventional and UAS air traffic. Other on-going research will result in the development of a nation-wide air traffic management system that will fully integrate UAS flights into US airspace. Together, these two research products will allow safe, long-duration sUAS flights, or flights that are “beyond-visual-line-of-sight” (BVLOS). As BVLOS technologies come on line, there will likely be a decrease in the per-acre cost of sUAS-based RS, which, in turn, will spur incentives for development of sUAS that can fly much further and cover much more area than platforms that are now in common use. A decrease in per-acre RS costs well likely result in an increased demand for RS technology in support of agricultural operations. However, adoption of BVLOS technologies that support greater access to US airspace for agricultural RS applications will present challenges to commonly used sUAS RS approaches that do not scale well as sUAS geographical coverage and flight times increase. Of particular concern are radiometric calibration of imagery and the cost and time required for generation of high-quality orthomosaics, point cloud algorithms, effects of temporal changes in surface spectral and thermal response, and delivery of time-sensitive image products to the grower. This paper examines the implications of these future challenges for sUAS RS in agriculture and offers insights into their solution that are topics of research today. In addition to examining the topics of radiometric calibration and orthorectification, other aspects, such as field protocols in the design and execution of flights and the need for additional on-board sensors, will be discussed.

Proceedings Article | 14 May 2019 Paper

Paper

Estimation of soil moisture at different soil levels using machine learning techniques and unmanned aerial vehicle (UAV) multispectral imagery

Mahyar Aboutalebi, L. Niel Allen, Alfonso Torres-Rua, Mac McKee, Calvin Coopmans

Proceedings Volume 11008, 110080S (2019) https://doi.org/10.1117/12.2519743

KEYWORDS: Soil science, Unmanned aerial vehicles, Machine learning, Multispectral imaging, Computer programming, Data modeling

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Soil moisture is a key component of water balance models. Physically, it is a nonlinear function of parameters that are not easily measured spatially, such as soil texture and soil type. Thus, several studies have been conducted on the estimation of soil moisture using remotely sensed data and data mining techniques such as artificial neural networks (ANNs) and support vector machines (SVMs). However, all models developed based on these techniques are limited to site-specific applications where they are trained and their parameters are tuned. Moreover, since the system of non-linear equations produced by and conducted in the machine learning process are not accessible to researchers, each application of these machine learning approaches must repeat these training steps for any new study area. The fact that the results of this machine learning, black box approach cannot be easily transferred to different locations for extraction of soil moisture estimates is frustrating, and it can lead to inaccurate comparisons between methods or model performance. To overcome the Black-box issue, this study employed a powerful technique called genetic programming (GP), which is a combination of an evolutionary algorithm and artificial intelligence, to simulate soil moisture at different levels using high-resolution, multispectral imagery acquired with an unmanned aerial vehicle (UAV). The output of this approach is either a linear or nonlinear empirical equation that can be used by others. The performance of GP was compared with ANN and SVM modeling results. Several sets of high-resolution aerial imagery captured by the Utah State University AggieAir UAV system over two experimental pasture sites located in northern and southern Utah were used for this soil moisture estimation approach. The inputs used to train these models include the reflectance for the visible, near-infrared (NIR), and thermal bands. The results show (1) the performance of GP versus ANN and SVM and (2) the master equation provided by GP, which can be used in other locations and applications.

Proceedings Article | 14 May 2019 Presentation + Paper

Presentation + Paper

Estimation of surface thermal emissivity in a vineyard for UAV microbolometer thermal cameras using NASA HyTES hyperspectral thermal, and landsat and AggieAir optical data

Alfonso Torres-Rua, Mahyar Aboutalebi, Timothy Wright, Ayman Nassar, Pierre Guillevic, Lawrence Hipps, Feng Gao, Kevin Jim, Maria Mar Alsina, Calvin Coopmans, Mac McKee, William Kustas

Proceedings Volume 11008, 1100802 (2019) https://doi.org/10.1117/12.2518958

KEYWORDS: Earth observing sensors, Landsat, Microbolometers, Cameras, Unmanned aerial vehicles, Sensors, Reflectivity, Vegetation, Agriculture, Infrared sensors

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Proceedings Article | 16 July 2018 Presentation + Paper

Presentation + Paper

Inter-comparison of thermal measurements using ground-based sensors, UAV thermal cameras, and eddy covariance radiometers

Alfonso Torres-Rua, Hector Nieto, Chistopher Parry, Manal Elarab, Wesley Collatz, Calvin Coopmans, Lynn McKee, Mac McKee, William Kustas

Proceedings Volume 10664, 106640E (2018) https://doi.org/10.1117/12.2305832

KEYWORDS: Sensors, Unmanned aerial vehicles, Radiometry, Cameras, Calibration, Infrared sensors, Thermography, Agriculture, Remote sensing, Environmental sensing

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Proceedings Article | 21 May 2018 Presentation + Paper

Presentation + Paper

Implications of sensor inconsistencies and remote sensing error in the use of small unmanned aerial systems for generation of information products for agricultural management

Mac McKee, Ayman Nassar, Alfonso Torres-Rua, Mahyar Aboutalebi, William Kustas

Proceedings Volume 10664, 1066402 (2018) https://doi.org/10.1117/12.2305826

KEYWORDS: Sensors, Remote sensing, Vegetation, Agriculture, Near infrared, Monte Carlo methods, Sensor performance, Satellites, Error analysis, Reflectivity

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Proceedings Article | 21 May 2018 Paper

Paper

Behavior of vegetation/soil indices in shaded and sunlit pixels and evaluation of different shadow compensation methods using UAV high-resolution imagery over vineyards

Mahyar Aboutalebi, Alfonso Torres-Rua, Mac McKee, William Kustas, Hector Nieto, Calvin Coopmans

Proceedings Volume 10664, 1066407 (2018) https://doi.org/10.1117/12.2305883

KEYWORDS: Unmanned aerial vehicles, Near infrared, Vegetation, Agriculture, Earth observing sensors, Satellite imaging, Satellites, Optical filters, Image segmentation, Remote sensing

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Proceedings Article | 21 May 2018 Paper

Paper

Multispectral remote sensing for yield estimation using high-resolution imagery from an unmanned aerial vehicle

Mahyar Aboutalebi, Alfonso F. Torres-Rua, Niel Allen

Proceedings Volume 10664, 106640K (2018) https://doi.org/10.1117/12.2305888

KEYWORDS: Unmanned aerial vehicles, Vegetation, Data modeling, Near infrared, Remote sensing, Reflectivity, Global Positioning System, 3D modeling, Clouds, Image resolution

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Proceedings Article | 16 May 2017 Presentation + Paper

Presentation + Paper

Use of UAV for support of intensive agricultural management decisions: from science to commercial applications

Alfonso Torres-Rua

Proceedings Volume 10218, 102180A (2017) https://doi.org/10.1117/12.2267725

KEYWORDS: Unmanned aerial vehicles, Agriculture, Remote sensing, Landsat, Yield improvement, Water

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Showing 5 of 21 publications

Proceedings Volume Editor (3)

SPIE Conference Volume | 29 June 2022

Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping VII

SPIE Conference Volume | 5 May 2021

Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping VI

SPIE Conference Volume | 2 June 2020

Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping V

Conference Committee Involvement (6)

Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping X

15 April 2025 | Orlando, Florida, United States

Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping IX

22 April 2024 | National Harbor, Maryland, United States

Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping VIII

1 May 2023 | Orlando, Florida, United States

Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping VII

4 April 2022 | Orlando, Florida, United States

Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping VI

12 April 2021 | Online Only, Florida, United States

Showing 5 of 6 Conference Committees

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