Engineering Commons^™

Modeling Leaf Area Index And Canopy Height Using Growing Degree Days, Ellie J. Kuhn

Biological and Agricultural Engineering Undergraduate Honors Theses

As the global population increases and food security is recognized as a critical issue, crop growth prediction models help ensure the sustainability of reliable food sources. Using a prediction model based on temperature and simple, measurable field parameters, e.g., Leaf Area Index (LAI) or Canopy Height (H_can), may allow farmers and others to intervene mid-season with fertilizer, irrigation, or other inputs to obtain a better harvest.

This study aims to create a general model that could predict LAI and H_can values for numerous rice varieties using Growing Degree Days (GDD) as the time scale. The models use …

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

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

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

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 …

Assessment Of Different Methods For Shadow Detection In High-Resolution Optical Imagery And Evaluation Of Shadow Impact On Calculation Of Ndvi And Evapotranspiration, Mahyar Aboutalebi, Alfonso F. Torres-Rua, William P. Kustas, Héctor Nieto, Calvin Coopmans, Mac Mckee

AggieAir Publications

Significant efforts have been made recently in the application of high-resolution remote sensing imagery (i.e., sub-meter) captured by unmanned aerial vehicles (UAVs) for precision agricultural applications for high-value crops such as wine grapes. However, at such high resolution, shadows will appear in the optical imagery effectively reducing the reflectance and emission signal received by imaging sensors. To date, research that evaluates procedures to identify the occurrence of shadows in imagery produced by UAVs is limited. In this study, the performance of four different shadow detection methods used in satellite imagery was evaluated for high-resolution UAV imagery collected over a California …

Assimilation Of Sar And Optical Data Into An Agro-Meteorological Model For Monitoring Yield Of Corn, Maël Ameline, Rémy Fieuzal, Julie Betbeber, Jean-Franҫois Berthoumieu, Frédéric Baup

International Congress on Environmental Modelling and Software

This study aims to assess the contribution of SAR and optical satellite images to estimate corn biophysical parameters and crop yield at field scale. Satellite data are assimilated into a simple agro-meteorological model named SAFY-WB (Simple Algorithm For Yield estimates combined with a Water Balance model), in order to simulate leaf area index, dry mass and yield. The method has been assessed thanks to data collected over two study areas (both located in the South-West of France), during two experimental campaigns: MCM’10 and MCM’15 (Multispectral Crop Monitoring), respectively performed in 2010 and 2015. SAR backscattering coefficients were acquired in the …

Agroecosystem And Ecosystem Resiliency To Extreme Hydrometeorological And Climate Events, Katherine M. Werner

Department of Agricultural and Biological Systems Engineering: Dissertations, Theses, and Student Research

The purpose of this study was to understand how a large-scale representation of agroecosystems and ecosystems respond to extreme hydrometeorological and climate extreme events (EHCE) within the Platte River Basin. The ability for agroecosystems and ecosystems to adapt to a changing and variable climate is vital for global water, energy, and food security. Two model experiments were performed, the first with a time span from 2000 to 2013 in which a dynamic leaf area index (LAI) MODIS15A2 product was implemented. The second experiment ran from 1950 through 2013 used a climatological fixed seasonal cycle calculated as the average from the …