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Full-Text Articles in Engineering
Incorporation Of Unmanned Aerial Vehicle (Uav) Point Cloud Products Into Remote Sensing Evapotranspiration Models, Mahyar Aboutalebi, Alfonso F. Torres-Rua, Mac Mckee, William P. Kustas, Héctor Nieto, Maria Mar Alsina, Alex White, John H. Prueger, Lynn Mckee, Joseph Alfieri, Lawrence E. Hipps, Calvin Coopmans, Nick Dokoozlian
Incorporation Of Unmanned Aerial Vehicle (Uav) Point Cloud Products Into Remote Sensing Evapotranspiration Models, Mahyar Aboutalebi, Alfonso F. Torres-Rua, Mac Mckee, William P. Kustas, Héctor Nieto, Maria Mar Alsina, Alex White, John H. Prueger, Lynn Mckee, Joseph Alfieri, Lawrence E. Hipps, Calvin Coopmans, Nick Dokoozlian
Civil and Environmental Engineering Faculty Publications
In recent years, the deployment of satellites and unmanned aerial vehicles (UAVs) has led to production of enormous amounts of data and to novel data processing and analysis techniques for monitoring crop conditions. One overlooked data source amid these efforts, however, is incorporation of 3D information derived from multi-spectral imagery and photogrammetry algorithms into crop monitoring algorithms. Few studies and algorithms have taken advantage of 3D UAV information in monitoring and assessment of plant conditions. In this study, different aspects of UAV point cloud information for enhancing remote sensing evapotranspiration (ET) models, particularly the Two-Source Energy Balance Model (TSEB), over …
Evaluation Of The Uncertainty In Satellite-Based Crop State Variable Retrievals Due To Site And Growth Stage Specific Factors And Their Potential In Coupling With Crop Growth Models, Nathaniel Levitan, Yanghui Kang, Mutlu Özdogan, Vincenzo Magliulo, Paulo Castillo, Fred Moshary, Barry Gross
Evaluation Of The Uncertainty In Satellite-Based Crop State Variable Retrievals Due To Site And Growth Stage Specific Factors And Their Potential In Coupling With Crop Growth Models, Nathaniel Levitan, Yanghui Kang, Mutlu Özdogan, Vincenzo Magliulo, Paulo Castillo, Fred Moshary, Barry Gross
Publications and Research
Coupling crop growth models and remote sensing provides the potential to improve our understanding of the genotype x environment x management (G X E X M) variability of crop growth on a global scale. Unfortunately, the uncertainty in the relationship between the satellite measurements and the crop state variables across different sites and growth stages makes it diffcult to perform the coupling. In this study, we evaluate the effects of this uncertainty with MODIS data at the Mead, Nebraska Ameriflux sites (US-Ne1, US-Ne2, and US-Ne3) and accurate, collocated Hybrid-Maize (HM) simulations of leaf area index (LAI) and canopy light use …
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
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
AggieAir Publications
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 …
Improving Retrievals Of Crop Vegetation Parameters From Remote Sensing Data, Nathaniel Levitan
Improving Retrievals Of Crop Vegetation Parameters From Remote Sensing Data, Nathaniel Levitan
Dissertations and Theses
Agricultural systems are difficult to model because crop growth is driven by the strongly nonlinear interaction of Genotype x Environment x Management (G x E x M) factors. Due to the nonlinearity in the interaction of these factors, the amount of data necessary to develop and utilize models to accurately predict the performance of agricultural systems at an operational scale is large. Satellite remote sensing provides the potential to vastly increase the amount of data available for modelling agricultural systems as a result of its high revisit time and spatial coverage. Unfortunately, there have been significant difficulties in deploying remote …