Papers by Christopher M U Neale
The aim of this study was to retrieve empirical formulas for water quality of three coastal lakes... more The aim of this study was to retrieve empirical formulas for water quality of three coastal lakes using remote sensing data - HySpex airborne imaging spectrometer and Sentinel-2A data. The Lebsko Lake, the Gardno Lake and the Great Dolgie Lake are salt-water lakes located in Slowinski National Park in Poland on the Baltic Sea coast. They all are shallow and turbid reservoirs prone to cyanobacteria blooms and eutrophication. Hyperspectral remote sensing data were acquired by the HySpex airborne sensor (in the range of 400-2500 nm) on 03.08.2015, multispectral data was acquired on the same date by MSI imager from Sentinel-2A satellite (in the range of 443-2190 nm). The ground measurements campaign was conducted 2-4.08.2015. The ground measurements consisted of two parts. First part included spectral reflectance sampling with spectroradiometer ASD FieldSpec 3, which covered the wavelength range of 350-2500 nm at 5 nm intervals. In situ data were collected both for water and for specifi...
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Anais do IV Inovagri International Meeting - 2017, 2017
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80 páginasThe report contains findings and recommendations on the research and practice of irriga... more 80 páginasThe report contains findings and recommendations on the research and practice of irrigated agriculture in Brazil. The report presents five sections. Initially, it contains a description of visits to the several EMBRAPA research centers and to some of the irrigated regions and projects in the country. The discussion section contains some observations and comments on topics, important in policy and research areas for Brazil. A summary of the major comments and recommendations are presented in section 5
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Agricultural Water Management, 2021
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100-150 WORDS) As the world’s water resources come under increasing tension due to dual stressors... more 100-150 WORDS) As the world’s water resources come under increasing tension due to dual stressors of climate change and population growth, accurate knowledge of water consumption through evapotranspiration (ET) over a range in spatial scales will be critical in developing adaptation strategies. Remote sensing methods for monitoring consumptive water use (e.g, ET) are becoming increasingly important, especially in areas of significant water and food insecurity. One method to estimate ET from satellite-based methods, the Atmosphere Land Exchange Inverse (ALEXI) model uses the change in mid-morning land surface temperature to estimate the partitioning of sensible and latent heat fluxes which are then used to estimate daily ET. This presentation will outline several recent enhancements to the ALEXI modeling system, with a focus on global ET and drought monitoring.
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Water, 2019
Accurate estimates of sensible (H) and latent (LE) heat fluxes and actual evapotranspiration (ET)... more Accurate estimates of sensible (H) and latent (LE) heat fluxes and actual evapotranspiration (ET) are required for monitoring vegetation growth and improved agricultural water management. A large aperture scintillometer (LAS) was used to provide these estimates with the objective of quantifying the effects of surface heterogeneity due to soil moisture and vegetation growth variability. The study was conducted over drip-irrigated vineyards located in a semi-arid region in Albacete, Spain during summer 2007. Surface heterogeneity was characterized by integrating eddy covariance (EC) observations of H, LE and ET; land surface temperature (LST) and normalized difference vegetation index (NDVI) data from Landsat and MODIS sensors; LST from an infrared thermometer (IRT); a data fusion model; and a two-source surface energy balance model. The EC observations showed 16% lack of closure during unstable atmospheric conditions and was corrected using the residual method. The comparison between...
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Journal of Animal Science, 2020
Data were compiled from an experiment evaluating the effects of month of calving, wintering syste... more Data were compiled from an experiment evaluating the effects of month of calving, wintering system, and calf management on cow and calf performance to model the amount of water (green and blue) required to produce beef in Nebraska production systems. The referenced study was conducted over four years utilizing 217 cow/calf pairs per year. Cows were wintered on either native range or corn residue and month of calving was March, June, or August. Calves were managed as calf-feds or yearlings and only steer calves were included in the model. A 365-d period was utilized to estimate the cow’s contribution of water inputs based on dry matter intake throughout the year varying by production system. Diet characteristics, dry-matter intake, days on feed, average daily gain, and hot carcass weight were measurements used to estimate water utilization by the calves. The total water footprint was calculated by dividing the total amount of water used for each system (L) by the amount of boneless b...
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Earth and Space Science, 2019
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Agricultural Water Management, 2017
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The U.S. Agricultural Stabilization and Conservation Service (ASCS) in Logan, Utah, combines colo... more The U.S. Agricultural Stabilization and Conservation Service (ASCS) in Logan, Utah, combines color slide 35 mm aerial photography with color video imagery to perform routine (yearly) crop classification and land-cover mapping in Cache Valley. The classification can not be completed solely with the images acquired by aerial photography and the broad visible band video imagery. The process requires considerable effort and time. It was deemed worthwhile to investigate the feasibility of using digital multispectral video imagery from the USU system to classify crops and estimate land-cover through digital image processing within a GIS framework. This paper describes the methodology and some preliminary results.
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Remote Sensing for Agriculture, Ecosystems, and Hydrology IX, 2007
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Presented at Emerging challenges and opportunities for irrigation managers: energy, efficiency an... more Presented at Emerging challenges and opportunities for irrigation managers: energy, efficiency and infrastructure: a USCID water management conference held on April 26-29, 2011 in Albuquerque, New Mexico.Includes bibliographical references.Releases from Rio Grande Project storage are made on demand by the U.S. Bureau of Reclamation for diversion into Elephant Butte Irrigation District (EBID), El Paso County Water Improvement District No.1, and Republic of Mexico canals and laterals. The diversions are charged against each district and Mexico's annual diversion allocation. As the Rio Grande Project implements and refines new operating procedures and the State of New Mexico continues efforts to implement Active Water Resource Management in the Lower Rio Grande, it is essential to have a high degree of confidence in the measurements of the water diverted from the Rio Grande. With this mission in mind, the New Mexico Interstate Stream Commission (NMISC) initiated a study to evaluate...
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Remote Sensing for Agriculture, Ecosystems, and Hydrology XXII, 2020
Estimating actual crop evapotranspiration (ET) is a critical component in tracking crop water ava... more Estimating actual crop evapotranspiration (ET) is a critical component in tracking crop water availability and managing irrigation. Various methods presently exist for modeling crop ET utilizing remotely sensed data and imagery as inputs. While more traditional remote sensing platforms like satellite and manned aircraft provide useful information, limitations exist due to lack of spatiotemporal resolution and high cost. Unmanned aerial systems (UAS) now offer greater opportunities for collecting remotely sensed data with enhanced spatiotemporal resolution that may lead to increased accuracy in estimating crop (ET), making near real-time irrigation management more feasible. The two-source energy balance (TSEB) model is one of the methods of estimating crop ET from surface energy balance fluxes. In this research, multispectral and thermal infrared imagery collected with a UAS over maize and soybean fields throughout the growing season at different vegetative stages were used in the TSEB model to estimate spatially distributed surface energy balance fluxes and daily crop ET. The estimated spatially distributed surface fluxes and ET were weighted and aggregated using a two-dimensional flux footprint and validated against measured fluxes from Eddy Covariance systems located within the fields.
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Remote Sensing of Environment, 2021
Abstract The combination of Unmanned/Unoccupied Aerial Vehicle (UAV) data and deep learning, espe... more Abstract The combination of Unmanned/Unoccupied Aerial Vehicle (UAV) data and deep learning, especially convolutional neural networks (CNNs), offers robust new tools for precision land cover mapping. However, its successful application is highly dependent on local experiences that are rarely documented, resulting in practical limitations during implementation. Cost-effective deep learning frameworks for fast deployment are required. This study presents a deep learning adaptation framework, named Auto-UNet++, trying to streamline wetland mapping tasks (including training data labeling and organizing). The framework treats mapping tasks as an intact semantic segmentation pipeline and then integrates automatic strategies into each step to reduce human intervention. These automatic strategies are achieved by standard computer vision techniques, including multi-view (MV) imaging—highly overlapped UAV images over an area (for labeling/voting), unsupervised clustering (for labeling), multi-scale CNN (for feature extraction), and attention mechanism—a CNN design used to select informative features from input (for feature exploration/selection). The framework was tested on playa wetland mapping in the Rainwater Basin, Nebraska, USA, with multispectral UAV datasets. Generally, the multi-scale CNN mapping task achieved a high of 87% overall accuracy and over 90% accuracy in water delineation. The results indicate that the multi-view and attention strategies have the potential to improve segmentation performance, and together with unsupervised learning, save considerable labor/expertise. Interestingly, evidence shows that the band/scale attention (weight) is adaptively associated with the land cover percentages per input image, indicating spatial contexts captured. This finding highlights the potential usages of the attention rule in automatic feature exploration, selection, and model interpretation. The framework illustrating a highly automated deep learning deployment on small MV datasets facilitates cost-effective wetland cover mapping. Although limitations exist, the study demonstrated the possibility of where/how conventional segmentation pipelines can be improved in typical UAV wetland mapping tasks. The framework and findings are useful for similar applications (including non-UAV studies) that only have limited time, labor, and expertise to implement sophisticated semantic segmentation models.
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While the current constellation of geostationary sensors provides near-global coverage (60N to 60... more While the current constellation of geostationary sensors provides near-global coverage (60N to 60S) – it requires merging data from 7 satellites [resolving time differences; view angles; atmospheric correction]. Polar orbiting sensors such as MODIS and VIIRS provide daily global coverage of LST at higher resolutions than GEO sensors but at only two times per day
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Nebraska. Department of Transportation, May 19, 2021
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Papers by Christopher M U Neale