Engineering Commons^™

Evaluation Of A Hybrid Remote Sensing Evapotranspiration Model For Variable Rate Irrigation Management, J. Burdette Barker, Christopher M. U. Neale, Derek M. Heeren

Biological Systems Engineering: Papers and Publications

Accurate generation of spatial irrigation prescriptions is essential for implementation and evaluation of variable rate irrigation (VRI) technology. A hybrid remote sensing evapotranspiration (ET) model was evaluated for use in developing irrigation prescriptions for a VRI center pivot. The model is a combination of a two-source energy balance model and a reflectance based crop coefficient water balance model. Spatial ET and soil water depletion were modeled for a 10 km² area consisting of rainfed and irrigated maize fields in eastern Nebraska for 2013. Multispectral images from Landsat 8 Operational Land Imager and Thermal Infrared Sensor were used as model …

Slides: Food Production: Technical Challenges In Agricultural Water Conservation, Perry Cabot

Innovations in Managing Western Water: New Approaches for Balancing Environmental, Social and Economic Outcomes (Martz Summer Conference, June 11-12)

Presenter: Dr. Perry Cabot, Research Scientist and Extension Specialist, Colorado Water Institute, Colorado State University

35 slides

Estimating Chlorophyll With Thermal And Broadband Multispectral High Resolution Imagery From An Unmanned Aerial System Using Relevance Vector Machines For Precision Agriculture, Manal Elarab, Andres M. Ticlavilca, Alfonso F. Torres-Rua, Inga Maslova, Mac Mckee

Civil and Environmental Engineering Faculty Publications

Precision agriculture requires high-resolution information to enable greater precision in the management of inputs to production. Actionable information about crop and field status must be acquired at high spatial resolution and at a temporal frequency appropriate for timely responses. In this study, high spatial resolution imagery was obtained through the use of a small, unmanned aerial system called AggieAirTM. Simultaneously with the AggieAir flights, intensive ground sampling for plant chlorophyll was conducted at precisely determined locations. This study reports the application of a relevance vector machine coupled with cross validation and backward elimination to a dataset composed of reflectance from …

Assessment Of Surface Soil Moisture Using High-Resolution Multi-Spectral Imagery And Artificial Neural Networks, Leila Hassan-Esfahani, Alfonso F. Torres-Rua, Austin M. Jensen, Mac Mckee

Civil and Environmental Engineering Faculty Publications

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) = …

Comparison Of Single- And Dual-Polarization-Based Rainfall Estimates Using Nexrad Data For The Nasa Iowa Flood Studies Project, Bong Chul Seo, Brenda Dolan, Witold F. Krajewski, Steven A. Rutledge, Walter Petersen

Civil, Architectural and Environmental Engineering Faculty Research & Creative Works

This study compares and evaluates single-polarization (SP)- and dual-polarization (DP)-based radar rainfall (RR) estimates using NEXRAD data acquired during Iowa Flood Studies (IFloodS), a NASA GPM ground validation field campaign carried out in May-June 2013. The objective of this study is to understand the potential benefit of the DP quantitative precipitation estimation, which selects different rain-rate estimators according to radar-identified precipitation types, and to evaluate RR estimates generated by the recent research SP and DP algorithms. The Iowa Flood Center SP (IFC-SP) and Colorado State University DP(CSU-DP) products are analyzed and assessed using two high-density, high-quality rain gauge networks as …

Sparse Coding Based Dense Feature Representation Model For Hyperspectral Image Classification, Ender Oguslu, Guoqing Zhou, Zezhong Zheng, Khan Iftekharuddin, Jiang Li

Electrical & Computer Engineering Faculty Publications

We present a sparse coding based dense feature representation model (a preliminary version of the paper was presented at the SPIE Remote Sensing Conference, Dresden, Germany, 2013) for hyperspectral image (HSI) classification. The proposed method learns a new representation for each pixel in HSI through the following four steps: sub-band construction, dictionary learning, encoding, and feature selection. The new representation usually has a very high dimensionality requiring a large amount of computational resources. We applied the l₁/l_q regularized multiclass logistic regression technique to reduce the size of the new representation. We integrated the method with a linear …