Dr. E. Raymond Hunt Profile

Dr. E. Raymond Hunt

Research Physical Scientist at USDA Agricultural Research Service

SPIE Involvement:

Author

SPIE Journal Paper | 12 February 2019 Open Access

Estimating vegetation water content during the Soil Moisture Active Passive Validation Experiment 2016

Michael Cosh, William White, Andreas Colliander, Thomas Jackson, John Prueger, Brian Hornbuckle, E. Raymond Hunt, Heather McNairn, Jarrett Powers, Victoria Walker, Paul Bullock

JARS, Vol. 13, Issue 01, 014516, (February 2019) https://doi.org/10.1117/12.10.1117/1.JRS.13.014516

KEYWORDS: Vegetation, Soil science, Agriculture, Earth observing sensors, Landsat, Clouds, Satellites, Remote sensing, Climatology, Calibration

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Vegetation water content (VWC) is an important land surface parameter that is used in retrieving surface soil moisture from microwave satellite platforms. Operational approaches utilize relationships between VWC and satellite vegetation indices for broad categories of vegetation, i.e., “agricultural crops,” based on climatological databases. Determining crop type–specific equations for water content could lead to improvements in the soil moisture retrievals. Data to address this issue are lacking, and as a part of the calibration and validation program for NASA’s Soil Moisture Active Passive (SMAP) Mission, field experiments are conducted in northern central Iowa and southern Manitoba to investigate the performance of the SMAP soil moisture products for these intensive agricultural regions. Both sites are monitored for soil moisture, and the calibration and validation assessments had indicated performance issues in both domains. One possible source could be the characterization of the vegetation. In this investigation, Landsat 8 data are used to compute a normalized difference water index for the entire summer of 2016 that is then integrated with extensive VWC sampling to determine how to best characterize daily estimates of VWC for improved algorithm implementation. In Iowa, regression equations for corn and soybean are developed that provided VWC with root mean square error (RMSE) values of 1.37 and 1.10 kg / m2, respectively. In Manitoba, corn and soybean equations are developed with RMSE values of 0.55 and 0.25 kg / m2. Additional crop-specific equations are developed for winter wheat (RMSE of 0.07 kg / m2), canola (RMSE of 0.90 kg / m2), oats (RMSE of 0.74 kg / m2), and black beans (RMSE of 0.31 kg / m2). Overall, the conditions are judged to be typical with the exception of soybeans, which had an exceptionally high biomass as a result of significant rainfall as compared to previous studies in this region. Future implementation of these equations into algorithm development for satellite and airborne radiative transfer modeling will improve the overall performance in agricultural domains.

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Proceedings Article | 17 May 2016 Paper

Insect detection and nitrogen management for irrigated potatoes using remote sensing from small unmanned aircraft systems

E. Raymond Hunt, Silvia Rondon, Philip Hamm, Robert Turner, Alan Bruce, Josh Brungardt

Proceedings Volume 9866, 98660N (2016) https://doi.org/10.1117/12.2224139

KEYWORDS: Nitrogen, Remote sensing, Agriculture, Vegetation, Image analysis, Image acquisition, Spatial resolution, Sensors, Visualization, NOx, Feature extraction, Near infrared

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SPIE Journal Paper | 3 December 2012

Remote sensing of fuel moisture content from canopy water indices and normalized dry matter index

E. Raymond Hunt, Lingli Wang, John Qu, Xianjun Hao

JARS, Vol. 6, Issue 01, 061705, (December 2012) https://doi.org/10.1117/12.10.1117/1.JRS.6.061705

KEYWORDS: Remote sensing, Reflectivity, Data modeling, Infrared radiation, Sensors, Solar radiation models, Thermal modeling, Infrared imaging, Agriculture, Absorption

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SPIE Journal Paper | 30 October 2012

Changes of crop rotation in Iowa determined from the United States Department of Agriculture, National Agricultural Statistics Service cropland data layer product

Alan Stern, Paul Doraiswamy, E. Raymond Hunt

JARS, Vol. 6, Issue 01, 063590, (October 2012) https://doi.org/10.1117/12.10.1117/1.JRS.6.063590

KEYWORDS: Agriculture, Bioalcohols, Landsat, Remote sensing, MODIS, Carbon, Soil science, Carbon sequestration, Biofuels, Earth observing sensors

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Proceedings Article | 24 October 2012 Paper

Remote sensing of fuel moisture content from the ratios of canopy water indices with a foliar dry matter index

E. Hunt, Lingli Wang, John Qu, Xianjun Hao

Proceedings Volume 8513, 851302 (2012) https://doi.org/10.1117/12.930077

KEYWORDS: Reflectivity, Curium, Remote sensing, Vegetation, Data modeling, Infrared radiation, Absorption, Solids, Computer simulations

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Proceedings Article | 19 October 2012 Paper

Assessing the extent of conservation tillage in agricultural landscapes

C. S. Daughtry, P. Beeson, S. Milak, B. Akhmedov, A. Sadeghi, E. R. Hunt, M. Tomer

Proceedings Volume 8531, 853116 (2012) https://doi.org/10.1117/12.974611

KEYWORDS: Reflectivity, Absorption, Agriculture, Photography, Remote sensing, Landsat, Spectroscopy, Soil science, Vegetation, Earth observing sensors

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Crop residue (or plant litter) on the soil surface can decrease soil erosion and runoff and improve soil quality. Quantification of crop residue cover is required to evaluate the effectiveness of conservation tillage practices as well as the extent of biofuel harvesting. Remote sensing techniques can provide reliable assessment od crop residue cover over large fields. With Landsat Thematic Mapper bands, crop residues can be brighter or darker than soils depending on soil type, crop type, moisture content, and residue age. With hyperspectral reflectance data, relatively narrow absorption features, centered near 2100 and 2300 nm, can be detected that are associated with cellulose and lignin concentrations. These features are evident in reflectance spectra of crop residues, but not in reflectance spectra of soils. Our objectives were to: (1) estimate crop residue cover using remotely sensed data over an agricultural site in central Iowa, and (2) evaluate alternative, less labor-intensive sampling schemes for acquiring crop residue cover surface reference data. We acquired EO-1 Hyperion imaging spectrometer data over agricultural fields in central Iowa shortly after planting in May 2004 and 2005. Crop residue cover was also measured in corn and soybean fields using line-point transects. The cellulose absorption index (CAI), which measured the relative intensity of the absorption feature near 2100 nm, was calculated using three relatively narrow bands centered at 2030, 2100, and 2210 nm. Results showed that crop residue cover was linearly related to CAI. Changes in the slopes of the regression line from year to year were related to scene moisture conditions. Tillage intensity classes corresponding to conventional tillage (≤ 30% cover) and conservation tillage (> 30% cover) were correctly identified in 75-82% of the fields. In addition, by combining information from previous season’s crop classification with crop residue cover after planting, an inventory of soil tillage intensity by previous crop was generated for the whole Hyperion scene for each year. Inventories and maps of tillage intensity are required for field- and watershed scale models to evaluate management practices that maximize production and minimize environmental impact.

Proceedings Article | 16 September 2011 Paper

Comparison of hyperspectral retrievals with vegetation water indices for leaf and canopy water content

E. Raymond Hunt, Craig S. Daughtry, John Qu, Lingli Wang, Xianjun Hao

Proceedings Volume 8156, 815606 (2011) https://doi.org/10.1117/12.895293

KEYWORDS: Absorption, Vegetation, Reflectivity, Feature extraction, Liquids, Atmospheric corrections, Infrared radiation, Soil science, Spectrometers, Sensors

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Proceedings Article | 12 August 2010 Paper

Remote sensing of leaf chlorophyll content at multiple scales using red, green, and blue bands

E. Raymond Hunt, Daniel Long, Jan Eitel, C. Daughtry

Proceedings Volume 7809, 780902 (2010) https://doi.org/10.1117/12.859142

KEYWORDS: Reflectivity, Nitrogen, Remote sensing, Digital cameras, Sensors, Spectrometers, Agriculture, Environmental management, Absorption, Data acquisition

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Proceedings Article | 21 August 2009 Paper

Remote sensing of canopy water content: scaling from leaf data to MODIS

E. Raymond Hunt, John Qu, Xianjun Hao, Lingli Wang

Proceedings Volume 7454, 745409 (2009) https://doi.org/10.1117/12.825401

KEYWORDS: MODIS, Sun, Remote sensing, Vegetation, Infrared radiation, Reflectivity, Short wave infrared radiation, Sensors, Digital photography, Earth observing sensors

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Proceedings Article | 6 October 2007 Paper

Remote sensing of vegetation water content using shortwave infrared reflectances

E. Raymond Hunt, M. Tugrul Yilmaz

Proceedings Volume 6679, 667902 (2007) https://doi.org/10.1117/12.734730

KEYWORDS: Vegetation, Soil science, Short wave infrared radiation, Infrared radiation, Shortwaves, Reflectivity, MODIS, Remote sensing, Microwave radiation, Earth observing sensors

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SPIE Journal Paper | 1 January 2007

Using canopy reflectance models and spectral angles to assess potential of remote sensing to detect invasive weeds

E. Hunt, Craig Daughtry, Moon Kim, Amy Parker Williams

JARS, Vol. 1, Issue 01, 013506, (January 2007) https://doi.org/10.1117/12.10.1117/1.2536275

KEYWORDS: Reflectivity, Landsat, Remote sensing, Vegetation, Solar radiation models, Image classification, Sensors, Spectral models, Visible radiation, Earth observing sensors

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Proceedings Article | 27 September 2006 Paper

Remote sensing and geospatial modeling for monitoring invasive plant species

E. Raymond Hunt

Proceedings Volume 6298, 62980J (2006) https://doi.org/10.1117/12.679132

KEYWORDS: Data modeling, Remote sensing, Vegetation, Animal model studies, Statistical analysis, Hyperspectral imaging, Error analysis, Accuracy assessment, Visible radiation, Infrared imaging

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Proceedings Article | 14 August 2003 Paper

A case study of precision farming for nutrient management of corn

Alfonso Blanco, Ray Hunt, Richard Gomez, William Roper

Proceedings Volume 5097, (2003) https://doi.org/10.1117/12.502453

KEYWORDS: Sensors, Remote sensing, Nitrogen, Vegetation, Agriculture, Photography, Hyperspectral imaging, Reflectivity, Head, Environmental sensing

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Proceedings Article | 6 November 1996 Paper

Progress of the Wyoming Hyperspectral Imagery Pilot Project: analysis of AVIRIS data for rangeland assessment

E. Hunt, M. Barlow, C. Mahelona, W. Laycock, Steven Heising, R. Smith, J. Foreman

Proceedings Volume 2821, (1996) https://doi.org/10.1117/12.257181

KEYWORDS: Vegetation, Hyperspectral imaging, Sensors, Bidirectional reflectance transmission function, Image analysis, Remote sensing, Landsat, Mining, Spectroscopy, Earth observing sensors

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Showing 5 of 14 publications

Conference Committee Involvement (12)

Remote Sensing and Modeling of Ecosystems for Sustainability XV

22 August 2018 | San Diego, California, United States

Remote Sensing and Modeling of Ecosystems for Sustainability XIV

9 August 2017 | San Diego, California, United States

Remote Sensing and Modeling of Ecosystems for Sustainability XIII

31 August 2016 | San Diego, California, United States

Remote Sensing and Modeling of Ecosystems for Sustainability XII

11 August 2015 | San Diego, California, United States

Remote Sensing and Modeling of Ecosystems for Sustainability XI

18 August 2014 | San Diego, California, United States

Showing 5 of 12 Conference Committees

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