Progress 06/01/17 to 05/31/22
Outputs
Target Audience:The target audience for this research includes, but is not limited to: • Industry, including center pivot and drip irrigation industry; unmanned aerial vehicle (UAV) manufacturers and remote sensor developers. • Producers; • State and Federal government agencies interested in supporting variable rate irrigation (VRI) technologies; and • State Government agencies, Natural Resources Districts (NE) and Water Conservation districts regulating groundwater quality and pumping. The research will be of interest to irrigation companies which aim at using big data and decision support models to automate irrigation and increase efficiency of water use in agriculture fields. The project team reached key stakeholders through multiple channels, including presentations at local, regional, and international conferences, publication of agricultural extension and academic journal articles, field tours, demonstrations, professional workshops, and student mentoring. These efforts are detailed in the "Products" and "Other Products" sections. Changes/Problems:We were granted a no-cost extension due to the Covid19 pandemic shutdown that affected our field research. What opportunities for training and professional development has the project provided?During the duration of this project, several students, Co-PIs, and PI attended and presented this research at various conferences including the American Society of Agricultural and Biological Engineers Annual International Meeting (ASABE, 2017-2022), three different conferences various conferences with the Society of Photo-Optical Instrumentation Engineers (SPIE, 2020-2022), the U.S. Committee on Irrigation and Drainage (USCID, 2019) meeting, a special session on UAS systems at the Irrigation Association Conference in Long Beach (2018) and the Remote Sensing and Hydrology Symposium (RSHS, 2018). One student helped coordinate and develop content for various UAS training and extension events. One Post-doctoral, two doctoral, one master's, and two undergraduate students worked at some point on this project during its duration. Three graduate students studied and obtained UAS pilots' licenses due to this research. How have the results been disseminated to communities of interest?The results of this research have been presented at several conferences as well as through numerous publications listed in the "Products and Publications" section, including thesis/dissertation and peer-reviewed journal publications. What do you plan to do during the next reporting period to accomplish the goals?
Nothing Reported
Impacts
What was accomplished under these goals?
The first objective was to collect near real-time, remotely sensed thermal infrared, three-band multi-spectral, and optical/visual imagery over large-scale row crop systems using a fixed wing UAS with field verification through a ground sensor system. This objective explored the relatively new technology of UAS and how to collect data beneficial to agricultural production and irrigation management. The field/ground-based sensor nodes helped verify the data collected by the UAS sensor system so that the data could be confidently used in further applications related to irrigation management. The work completed showed that fixed wing and multirotor UAS systems, can be outfitted with sensors to collect data and images over production agricultural fields. Methodologies were developed on how to integrate stand-alone sensors into UAS, best methods for collecting data, and insights on calibration and post-processing of data/images into usable data sources for modeling purposes. These results will benefit producers, researchers, consultants, and industry who are wanting to develop their own UAS capabilities. Three different UAS systems were explored (fixed wing, multirotor, and hybrid system) which went beyond what was proposed in this objective. Each UAS was equipped with a RedEdge multispectral reflectance and FLIR thermal infrared imagers used to collect images over corn and soybean fields. The high spatial resolution of the multispectral and thermal images was much higher than what satellites and many manned aircraft remote sensing systems acquire. A calibration model was developed for the UAS thermal images using the ground-based infrared thermometers from the sensor nodes to increase the accuracy of measured surface temperature from the UAS-based thermal imager. Accurate surface temperatures are important in crop water management applications using energy balance models, as the surface temperature of a crop is highly correlated to crop water stress. During the duration of this project, 177 UAS flights were conducted over the variable rate irrigation (VRI) equipped center pivot field and the three carbon sequestration (CSP) fields under a corn/soybean rotation both irrigated and rainfed, that contained Ameriflux eddy covariance flux towers. In total, approximately 24,000 acres were covered with the UAS remote sensing systems, providing a high spatiotemporal resolution dataset. Two other laboratory calibration experiments were conducted; one involved soil moisture sensors and their calibration and the second was on the RedEdge multispectral sensor calibration. These experiments led to two additional journal publications in Transactions of the ASABE and the journal Precision Agriculture. The second objective was to extract crop feature data from the information obtained under objective one, for use in energy balance/evapotranspiration and soil water content simulation models. Updated remote sensing-based relationships for estimating crop biophysical parameters such as crop height, leaf area index, and fraction of vegetation cover were developed. There parameters are used when managing cropping systems and are especially important for estimating the evapotranspiration (ET). Having accurate estimations of these parameters will benefit producers, researchers, consultants, and industry who are wanting to better track crop growth and subsequent applications that utilize these relationships such as biomass and yield estimation. The UAS thermal and multispectral images were used to estimate the crop biophysical parameters and ET with the two-source energy balance model over the CSP fields which contained eddy covariance towers that provided measured ET. Estimation of ET using the two-source energy balance model was tested using UAS thermal images that were calibrated using three different methods. Overall, the UAS thermal calibration model developed in objective one provided more accurate surface temperature measurements which resulted in better agreement between estimated and measured daily ET. The more accurate estimates of daily ET throughout the growing season improved the capabilities of the Spatial EvapoTranspiration Modeling Interface (SETMI) to model the soil water content for irrigation scheduling. These improvements to SETMI were put into practice in objective three with the VRI irrigation management study. The results of the two-source energy balance model analysis will be published in late 2022. The third objective tested the SETMI model for managing a variable rate irrigation (VRI) system. SETMI tracks the soil water content by accounting for the flux of water entering/leaving the root zone of a crop and field. One of the most important and hardest to measure flux, ET, can be modeled using various approaches including remote sensing, demonstrated in objective two. SETMI maintains a water balance in the root zone of the crop by accounting for the various inputs and outputs. The root zone water content is used to determine the timing and amount of irrigation requirements. SETMI provides the information necessary to apply varying amounts of water throughout a field resulting in the prescriptions for programming the center pivot controller of the VRI system. This objective included a multi-year field study where variable rate/uniform irrigation was managed using different approaches. During the first two seasons of the project, the SETMI model was used for monitoring VRI equipped center pivots in both eastern and western NE. Several improvements to the model were added. In the 2019/2020 seasons, the field study included six different treatments for managing irrigation. The first VRI approach utilized SETMI water balance approach informed by Planet satellite imagery. The second VRI approach utilized the SETMI hybrid model informed by UAS multispectral/thermal imagery. A third VRI approach used an industry software. A rainfed treatment and two uniform treatments were also implemented. The first uniform treatment used GS1 soil moisture sensors to inform irrigation scheduling. The second uniform treatment used a crop consultant to resemble what a typical producer might use in irrigation decision making. During the growing seasons, six sensor node stations equipped with an infrared thermometer, three soil moisture sensors, and spectral reflectance sensors were installed in each half of the field. Canopy stress (canopy temperature - air temperature) was determined and the upper and lower baselines for determination of crop water stress index (CWSI) were developed. Continuous NDVI trends for corn were accessed during the growing season demonstrating that the rainfed corn senesced earlier than the irrigated corn. During the two-year study, neutron probe soil volumetric water content measurements were collected throughout the seasons to quantify the accuracy of the estimated soil water content and ET by the SETMI model. The VRI field study assessed the different irrigation approaches by evaluating irrigation applied, grain yield, change in soil water content, and seasonal cumulative ET. The approaches using SETMI with UAS and Planet images resulted in similar crop yield in comparison to the uniform irrigation treatment managed by the crop consultant, however, the SETMI approaches applied statistically less water (p < 0.05). The second uniform irrigation treatment managed using the sensor network also produced similar yield and irrigation results to the SETMI approaches. Results demonstrated that SETMI informed with UAS, or Planet images could track soil water content well enough to inform VRI management decisions. The work is beneficial to producers who have their own VRI systems, that could use SETMI to manage timing of their VRI applications. Companies who have their own decision support system for managing VRI could also benefit through improvements to their own decision support system based on the findings of this research.
Publications
- Type:
Conference Papers and Presentations
Status:
Accepted
Year Published:
2021
Citation:
Singh, J., D. M. Heeren, Y. Ge, G. Bai, C. M. U. Neale, M. S. Maguire, and S. Bhatti. 2021. Sensor-based irrigation of maize and soybean in East-Central Nebraska under a sub-humid climate. ASABE Annual International Meeting (virtual), Paper No. 21001044. 12 pages.
- Type:
Conference Papers and Presentations
Status:
Accepted
Year Published:
2021
Citation:
Singh, J., Ge, Y., Heeren, D. M., Bai G., Neale C. M. U., Woldt, W. E., Maguire, M. S., & Kashyap, S. P. Unmanned Aerial Vehicle Data Mule over a Sensor Node Station Network in Maize and Soybean. 2021 American Society of Agricultural and Biological Engineers (ASABE) Annual International Meeting, Virtual and On Demand (July 12 July 16, 2021)
- Type:
Conference Papers and Presentations
Status:
Accepted
Year Published:
2021
Citation:
Kashyap, S. P., Heeren, D. M., Woldt, W. E., Neale, C. M. U., Irmak, S, Shi, Y., Maguire, M. S., Bhatti, S., & Singh, J. High-Frequency Unmanned Aircraft Flights for Crop Canopy Imaging During Moisture-Stress and Subsequent Irrigation. 2021 American Society of Agricultural and Biological Engineers (ASABE) Annual International Meeting, Virtual and On Demand (July 12 July 16, 2021)
- Type:
Journal Articles
Status:
Published
Year Published:
2022
Citation:
Maguire, M. S., Neale, C. M. U., Woldt, W. E., Heeren, D. M. (2022). Managing spatial irrigation using remote-sensing-based evapotranspiration and soil water adaptive control model. Agricultural water management, 272, 107838. doi.org/10.1016/j.agwat.2022.107838
- Type:
Journal Articles
Status:
Published
Year Published:
2021
Citation:
Singh, J., Ge, Y., Heeren, D. M., Walter-Shea, E., Neale, C. M. U., Irmak, S., Woldt, W. E., Bai, G., Bhatti, S., & Maguire, M. S. (2021). Inter-relationships between water depletion and temperature differential in row crop canopies in a sub-humid climate. Agricultural water management, 256, 107061. doi:10.1016/j.agwat.2021.107061
- Type:
Journal Articles
Status:
Published
Year Published:
2022
Citation:
Singh, J., Ge, Y., Heeren, D. M., Walter-Shea, E., Neale, C. M. U., Irmak, S., & Maguire, M. S. (2022). Unmanned Aerial System-Based Data Ferrying over a Sensor Node Station Network in Maize. Sensors, 22(5), 1863. MDPI AG. doi.org/10.3390/s22051863
- Type:
Theses/Dissertations
Status:
Published
Year Published:
2021
Citation:
Maguire, M. S. (2021). Leveraging Unmanned Aerial System Remote Sensing to Inform Energy and Water Balance Models for Spatial Soil Water Content Monitoring and Irrigation Management [Unpublished Ph.D. dissertation]. University of Nebraska Lincoln. https://digitalcommons.unl.edu/biosysengdiss/115/
- Type:
Theses/Dissertations
Status:
Published
Year Published:
2021
Citation:
Singh, J. (2021). Design and Evaluation of Unmanned Aerial System-Based Wireless Sensor Network for Irrigation Management [Unpublished Ph.D. dissertation]. University of Nebraska Lincoln. https://digitalcommons.unl.edu/biosysengdiss/120/
- Type:
Theses/Dissertations
Status:
Published
Year Published:
2021
Citation:
Kashyap, S. P. (2021). High-Frequency Unmanned Aircraft Flights for Crop Canopy Imaging During Diurnal Moisture Stress [Unpublished M.S. thesis]. University of Nebraska Lincoln. https://digitalcommons.unl.edu/biosysengdiss/121/
- Type:
Conference Papers and Presentations
Status:
Accepted
Year Published:
2021
Citation:
Maguire, M., Neale, C. M. U., & Woldt, W. "Comparison of modeled evapotranspiration from the SETMI hybrid model informed with multispectral and thermal infrared imagery acquired with an unmanned aerial system," Proc. SPIE 11856, Remote Sensing for Agriculture, Ecosystems, and Hydrology XXIII, 118560J (12 September 2021); https://doi.org/10.1117/12.2604207
- Type:
Conference Papers and Presentations
Status:
Accepted
Year Published:
2022
Citation:
Maguire, M. S. & Neale, C. M. U. "Irrigation scheduling using hybrid remote sensing-based evapotranspiration model informed by unmanned aerial system acquired multispectral and thermal imagery," Proc. SPIE 12114, Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping VII, 121140H (3 June 2022); https://doi.org/10.1117/12.2623262
|
Progress 06/01/20 to 05/31/21
Outputs
Target Audience:Overall, there were far fewer events during this reporting period due to the covid-19 pandemic. Many events were either postponed, moved to an online platform, or entirely canceled. Two graduate students supported by the project each virtually presented different research goals at two different conferences. Changes/Problems:
Nothing Reported
What opportunities for training and professional development has the project provided?Two doctoral, one master's, and two undergraduate students worked on this project during the current reporting period. One doctoral student attended the 2020 American Society of Agricultural and Biological Engineers (ASABE) Annual International Meeting hosted virtually and presented on work related to Objective 1. Another doctoral student attended an International Society for Optics and Photonics (SPIE) conference and presented on the estimation of crop ET using the two-source energy balance model and UAS images (Objective 2). Two graduate students studied and obtained UAS pilots' licenses due to this research. There were far fewer training and professional development events during this reporting period due to the covid-19 pandemic. Many events were either postponed, moved to an online platform, or entirely canceled. How have the results been disseminated to communities of interest?Overall, there were far fewer events during this reporting period due to the covid-19 pandemic. Many events were either postponed, moved to an online platform, or entirely canceled. Two graduate students supported by the project each virtually presented different research goals at two different conferences. What do you plan to do during the next reporting period to accomplish the goals?While the official project ending date was May 2021, several dissertations and journal articles will be published in the coming months to share the findings of this research. A no-cost extension was requested to continue with another year of data collection.
Impacts
What was accomplished under these goals?
OBJECTIVE 1: During the 2020 growing season, 64 UAS flights were conducted over four research fields. This includes the Variable Rate Irrigation Field VRI (18), the Carbon Sequestration Project Center Pivot Fields CSP1 (15) and CSP2 (15), and the rainfed field CSP3 (16). The CSP fields contain Ameriflux eddy covariance flux towers. In total, approximately 9,040 acres were covered, creating a multispectral and thermal infrared dataset for corn and soybean supplemented by sensor nodes in the field. The team examined the correlation between UAS and field sensor node data, which included comparing thermal images to ground-based infrared thermometers. The data gathered provided insight into the sensor operation, concluding that a warming period was needed for the thermal camera to thermally stabilize. This study resulted in the development of calibration models for the UAS thermal camera that was published in the journal of Remote Sensing in April 2021 (Maguire et al., 2021). OBJECTIVE 2: The 2020 UAS thermal and multispectral images were used to estimate the crop biophysical parameters and ET with the two-source energy balance model over the CSP1, CS2, and CSP3 fields. These three fields contained eddy covariance towers that provided measured ET. The UAS thermal images were calibrated using three different methods (calibration model developed under Objective 1, MODTRAN, and no calibration) and used to inform the two-source energy balance model to estimate daily ET over CSP1, CSP2, and CSP3. Overall, the UAS thermal calibration model developed under Objective 1 provided more accurate surface temperature measurements which resulted in better agreement between estimated and measured daily ET. The increased accuracy in estimated ET provided by the two-source energy balance model resulted in better estimates of daily ET throughout the growing season, which improved the capabilities of SETMI to model the current soil water content for irrigation scheduling. These improvements to SETMI were put into practice under Objective 3 with the VRI irrigation management study. The results of the two-source energy balance model analysis will be published in late 2022. OBJECTIVE 3: The remote sensing based spatial evapotranspiration model, known as SETMI, was further developed with enhanced capabilities to simulate soil water balance precisely using unmanned aerial systems (UAS) imagery inputs, and with an improved approach to simulate deep percolation. SETMI was also updated to have the capability to run the soil water balance in three-layered soils to account for multiple horizons within the crop or vegetation root zone. The 2020 season included six different irrigation treatments, including a rainfed, two uniform irrigation, and three variable rate irrigation treatments with two variable rate treatments utilizing SETMI. In 2020, the research team was successful in collecting frequent UAS thermal and multispectral imagery which was utilized in the model for irrigation management. In addition to UAS images, Planet satellite reflectance images were used to inform the SETMI model for irrigation management. Planet satellite offers near-daily reflectance images at a 3 m spatial resolution which provided the SETMI model with plentiful remotely-sensed images to model the various crop biophysical parameters needed for SETMI to schedule variable rate irrigations. The UAS and Planet images could be strategically used for real-time irrigation management. SETMI was used to calculate spatial basal crop coefficient and schedule real-time irrigation during the 2020 season in corn and soybean using the UAS and Planet imagery along with soil water content measurements. The treatments using SETMI with UAS and Planet inputs resulted in similar crop yield produced with less applied depth of water compared to the uniform irrigation treatment managed by a professional crop consultant. The second uniform irrigation treatment managed using the sensor network also produced similar yield and irrigation results to the SETMI treatments. This research demonstrated that the SETMI model could track the soil water content well given that the start of the season soil water content was known. The results of the irrigation study are to be published in two Ph.D. dissertations in the second half of 2021. Four years of data collection at the Eastern NE Research & Extension Center (ENREC) in Mead, NE are leading to multiple publications, and supported two master's and three Ph.D. projects.
Publications
- Type:
Journal Articles
Status:
Published
Year Published:
2021
Citation:
Maguire, M. S., Neale, C. M. U., & Woldt, W. E. (2021). Improving Accuracy of Unmanned Aerial System Thermal Infrared Remote Sensing for Use in Energy Balance Models in Agriculture Applications. Remote Sensing, 13(9), 1635. doi.org/10.3390/rs13091635
- Type:
Journal Articles
Status:
Published
Year Published:
2020
Citation:
Singh, Jasreman & Heeren, Derek & Rudnick, Daran & Woldt, Wayne & Bai, Geng Frank & Ge, Yufeng & Luck, Joe. (2020). Soil Structure and Texture Effects on the Precision of Soil Water Content Measurements with a Capacitance- Based Electromagnetic Sensor. Transactions of the ASABE. 63. 141-152. 10.13031/trans.13496.
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2020
Citation:
Mitch Maguire, Christopher M. U. Neale, Wayne Woldt, "Modeling energy balance fluxes and evapotranspiration of maize and soybean using multispectral and thermal infrared imagery acquired with an unmanned aerial system," Proc. SPIE 11528, Remote Sensing for Agriculture, Ecosystems, and Hydrology XXII, 115280I (20 September 2020); https://doi.org/10.1117/12.2577131
- Type:
Other
Status:
Other
Year Published:
2020
Citation:
Singh J, Heeren D, Ge Y, Bai G (2020) Capturing Spatial Variability in Maize and Soybean using Stationary Sensor Nodes, Poster Presentation UNL Spring Research Fair, University of Nebraska-Lincoln.
|
Progress 06/01/19 to 05/31/20
Outputs
Target Audience:The target audience for this research includes, but it not limited to: Industry, including pivot and unmanned aerial vehicle (UAV) manufacturers; Producers; Government agencies interested in supporting variable rate irrigation (VRI) technologies; and Government agencies regulating groundwater quality and pumping. The research will be of interest to irrigation companies which aim at using big data and decision support models to automate irrigation and increase efficiency of water use in agriculture fields. The project team reaches key stakeholders through multiple channels, including presentations at local, regional, and international conferences, publication of agricultural extension and academic journal articles, field tours, demonstrations, professional workshops, and student mentoring. These efforts are detailed in the "Products" and "Other Products" sections. Changes/Problems:The main change has been the approval of the no-cost extension, allowing us to conduct an additional season of data collection to test the real-time scheduling approach on the VRI equipped pivot using models informed with the UAS imagery. What opportunities for training and professional development has the project provided?Two doctoral students, one MS student, and one undergraduate intern are currently working on this project, either directly or indirectly. The students have been active in collecting field data, giving presentations and developing journal manuscripts from the project. How have the results been disseminated to communities of interest?Various activities have supported diffusion of the technologies over the past year, including five peer-reviewed journal articles and six conference presentations. Journal articles reached a range of academic communities with publications in Precision Agriculture, Agricultural Water Management, Frontiers in Big Data, Transactions of the ASABE, and Applied Engineering in Agriculture. Presentations reached stakeholders at a variety of conferences, including the AGU Fall Meeting, the ASABE Annual International Meeting, the U.S. Committee on Irrigation and Drainage, DWFI's Annual Research Forum, and the Midwest Big Data Hubs All-Hands Meeting. What do you plan to do during the next reporting period to accomplish the goals?Field research is ongoing in the 2020 growing season with the use of sensor node station data for execution of treatments. Soil water content and weather information from sensor nodes will be used to support the remote sensing based model. During the 2020 growing season, the team will be researching six different irrigation treatments: Uniform (using soil moisture from sensor nodes); Rainfed; VRI scheduled with SETMI using UAS inputs; VRI scheduled with SETMI using Satellite inputs; LINDSAY VRI methodology Farm Manager (represents traditional management based on agronomic background). Each year the team is moving closer to the aim of the project, to manage VRI real-time based on unmanned aircraft. The research team will measure canopy temperature continuously to observe plant stress reaction in real time and track stress points. We will also conduct weekly or bi-weekly measurement of neutron probes. UNL is testing the concept of the data mule network amongst sensor nodes on the ground. This will be operational in 2020 growing season.
Impacts
What was accomplished under these goals?
The overall goal of this research is to advance variable rate irrigation (VRI) technology across the Great Plains and Midwest through improved net water use efficiency of irrigated, row crop agriculture supported by unmanned aircraft systems (UAS) through remote sensing, integrated sensor systems, and adaptive modeling and simulation.The research will study the integration of technical systems and information technology to assist complex, variable rate irrigation decisions in typical large crop production areas and growing seasons. In the 2019 variable rate irrigation (VRI) field study, six different approaches were used to manage irrigation, with two new VRI management approaches introduced. The first new VRI approach utilized the Spatial EvapoTranspiration Modeling Interface (SETMI) model informed by Planet satellite imagery. Planet satellite is a relatively new satellite platform that had not been used in past VRI management research. The Planet satellite platform supplied daily multispectral imagery with a 3-meter spatial resolution. The second new VRI approach utilized the SETMI hybrid model informed by UAS multispectral and thermal imagery. There were 22 Planet satellite images and 17 UAS images acquired on different dates in the 2019 growing season used to inform the SETMI treatments. A third VRI approach used the Lindsay FieldNet Advisor software. In addition to the VRI approaches, a rainfed treatment along with two uniform treatments were implemented. The first uniform treatment used GS1 soil moisture sensors to inform when irrigation was needed. The second uniform treatment used what we called a "common" approach which was meant to resemble what a typical producer might use in irrigation decision making. The common approach used a crop consultant for determining when to irrigate corn and plant and soil observation for determining when to irrigate soybean. Neutron probe readings for soil volumetric water content were performed 9 times in the maize, and 10 times in the soybeans. Four rain gauges around the field were used to monitor rainfall for irrigation scheduling. During the 2019 growing season, six sensor node stations were installed in maize and soybean. Out of the six sensor node stations, two of the stations were installed in rainfed (non-irrigated), and other four were installed in irrigated (one in each - UAS, Satellite, Uniform, and Lindsay) treatments for both maize and soybean. Each sensor node station was equipped with IRT, three soil moisture sensors, and spectral reflectance sensors. So, the soil moisture content, canopy temperature, and normalized difference vegetation index (NDVI) for each of the sensor node stations was reported throughout the growing season. Soil moisture depletion was determined for all the 12 points in the field. In addition, the canopy stress (canopy temperature - air temperature) was determined and then upper and lower baselines for determination of crop water stress index (CWSI) were developed as outlined by Idso et al., 1981. The crop water stress index was reported for different stages of growth during the growing season and it was found that the CWSI values for rainfed plots were higher for most of the growing season. The relationship between the canopy stress and soil moisture depletion was also evaluated, and it was found that with the increase in soil moisture depletion the canopy temperature stress increases. However, the relationship was stronger for the rainfed treatments in both maize and soybean. The continuous NDVI trends for maize were accessed during the growing season and it was observed that the rainfed treatments had lower NDVI than the irrigated treatments during the latter part of the growing season due to irrigation applications in the latter part. The NDVI trends also demonstrated that due to no irrigation the rainfed maize senesced much earlier than the irrigated maize. For the year 2019, the maize yield for rainfed plots was significantly (p-value<0.5) lower than the irrigated plots with no significant yield difference between the irrigated plots. However, the rainfed yield was significantly lower than the uniform and satellite treatments for soybean. An additional 38 UAS flights were conducted over three Carbon Sequestration Project (CSP) fields during the growing season. Each CSP fields contains an eddy covariance system used for measuring energy balance fluxes and evapotranspiration. The UAS imagery collected over the CSP fields is being used to validate the two-source energy balance (TSEB) model informed by UAS imagery by comparing the model surface energy balance fluxes to the measured eddy covariance fluxes. The TSEB model is a main component of the SETMI hybrid model. The data collected over the CSP fields will help validate the TSEB model with UAS imagery.
Publications
- Type:
Journal Articles
Status:
Published
Year Published:
2020
Citation:
Barker, J. B., W. E. Woldt, B. D. Wardlow, M. S. Maguire, B. C. Leavitt, C. M. U. Neale, and D. M. Heeren. 2020. Calibration of a common shortwave multispectral camera system for quantitative agricultural applications. Precision Agriculture 21: 922-935, doi: 10.1007/s11119-019-09701-6.
- Type:
Journal Articles
Status:
Published
Year Published:
2020
Citation:
Bhatti, S., D. M. Heeren, J. B. Barker, C. M. U. Neale, W. E. Woldt, M. S. Maguire, and D. R. Rudnick. 2020. Site-specific irrigation management in a sub-humid climate using a spatial evapotranspiration model with satellite and airborne imagery. Agricultural Water Management 230, doi: 10.1016/j.agwat.2019.105950.
- Type:
Journal Articles
Status:
Published
Year Published:
2020
Citation:
Singh, J., D. M. Heeren, D. R. Rudnick, W. E. Woldt, G. Bai, Y. Ge, and J. D. Luck. 2020. Soil structure and texture effects on the precision of soil water content measurements with a capacitance-based electromagnetic sensor. Transactions of the ASABE 63(1): 141-152, doi: 10.13031/trans.13496.
- Type:
Journal Articles
Status:
Published
Year Published:
2019
Citation:
Barker, J. B., S. Bhatti, D. M. Heeren, C. M. U. Neale, and D. R. Rudnick. 2019. Variable rate irrigation of maize and soybean in West-Central Nebraska under full and deficit irrigation. Frontiers in Big Data 2(34), doi: 10.3389/fdata.2019.00034.
- Type:
Conference Papers and Presentations
Status:
Other
Year Published:
2019
Citation:
Bhatti, S., D. M. Heeren, J. B. Barker, C. M. U. Neale, W. E. Woldt, M. S. Maguire, and D. R. Rudnick. December 9-13, 2019. Site-specific irrigation management in a sub-humid climate using a spatial evapotranspiration model with satellite and airborne imagery. American Geophysical Union (AGU) Fall Meeting, San Francisco, Calif.
- Type:
Conference Papers and Presentations
Status:
Other
Year Published:
2019
Citation:
Singh, J., D. M. Heeren, Y. Ge, J. B. Barker, W. E. Woldt, C. M. U. Neale, G. Bai, D. R. Rudnick, J. D. Luck, G. E. Meyer. July 7-10, 2019. Soil structure and soil texture effects on soil water content measurements by a capacitance based electromagnetic sensor. ASABE Annual International Meeting, Boston, Mass.
- Type:
Conference Papers and Presentations
Status:
Other
Year Published:
2019
Citation:
Maguire, M., Woldt, W.E., Neale, C.M.U. (2019) Improving Variable Rate Irrigation Efficiency Using a Real-time Soil Moisture Adaptive Control Model Informed by Sensors Deployed on Unmanned Aircraft. Midwest Big Data Hubs All-Hands Meeting, Salina, KS.
- Type:
Conference Papers and Presentations
Status:
Other
Year Published:
2019
Citation:
Maguire, M., Neale, C.M.U., Woldt, W.E. (2019) Assessment of Thermal Infrared Sensors and their Accuracy in Low Altitude Unmanned Aerial System Remote Sensing. ASABE AIM, Boston, MA.
- Type:
Conference Papers and Presentations
Status:
Other
Year Published:
2019
Citation:
Maguire, M., Neale, C.M.U., Woldt, W.E. (2019) Modeling Evapotranspiration using Multispectral and Thermal Infrared Imagery Acquired with a Low Altitude Unmanned Aerial System. USCID, Reno, NV.
- Type:
Other
Status:
Published
Year Published:
2020
Citation:
(VIDEO) Kashyap, S. P., Woldt, W. E., Heeren, D. M., April (2020). High-Frequency Unmanned Aircraft Flights for Crop Canopy Imaging During Moisture-Stress and Subsequent Irrigation. Research Presentation Text and Video - Daugherty Water for Food Global Institutes Annual Research Forum. 2020.
- Type:
Journal Articles
Status:
Published
Year Published:
2019
Citation:
OShaughnessy, S. A., S. R. Evett, P. D. Colaizzi, M. A. Andrade, T. H. Marek, D. M. Heeren, F. R. Lamm, and J. L. LaRue. 2019. Identifying advantages and disadvantages of variable rate irrigation an updated review. Applied Engineering in Agriculture 35(6): 837-852, doi: 10.13031/aea.13128.
|
Progress 06/01/18 to 05/31/19
Outputs
Target Audience:The target audience for this research includes, but it not limited to: Industry, including pivot and unmanned aerial vehicle (UAV) manufacturers; Producers; Government agencies interested in supporting variable rate irrigation (VRI) technologies; and Government agencies regulating groundwater quality and pumping. The research will be of interest to irrigation companies which aim at using big data and decision support models to automate irrigation and increase efficiency of water use in agriculture fields. The project team reaches key stakeholders through multiple channels, including presentations at local, regional, and international conferences, publication of agricultural extension and academic journal articles, field tours, demonstrations, professional workshops, and student mentoring. These efforts are detailed in the "Products" and "Other Products" sections. Changes/Problems:In the initial funded proposal, a sub-award was identified for the University of Colorado-Boulder (CU) for the design and implementation of a data mule approach to gathering field data from the UAV. As the project has progressed, UNL has been performing the activities identified as part of the sub-award. After discussions with the co-PI at CU and their agreement to relinquish their funding, the project PI will be officially requesting to NIFA in the near-future, that the funds be re-allocated for a no-cost extension of the grant and an additional year of field research. What opportunities for training and professional development has the project provided?Three doctoral students and three undergraduate interns are currently working on this project. In the past year, two students graduated with their master's degree and one postdoctoral research associate completed his postdoc appointment. The two students who graduated are continuing to work on the project for their PhD research. The three doctoral students, each working primarily on one of the three project goals, attended the 2019 American Society of Agricultural and Biological Engineers (ASABE) Annual International Meeting in Boston, Massachusetts (July 7 - July 10, 2019) and the 2019 Water for Food Global Conference in Lincoln, Nebraska. At ASABE, each student gave a presentation. At the Water for Food conference, each student presented a research poster. ? Additionally, two graduate students studied and obtained UAS pilots' licenses due to this research. How have the results been disseminated to communities of interest?University Extension activities have supported diffusion of the technologies, including activities with: 2019 Nebraska GIS/LIS Biennial Symposium, Nebraska Broadband Task Force, ENREC open house, Midwest Big Data Hub All-Hands Meeting/workshop, Flights to Maps UAS workshop, Kansas State University visitors, and a delegation from Australia. Specifically, at the 2018 American Society of Agricultural and Biological Engineers (ASABE) conference, Dr. Burdette and Dr Derek Heeren co-hosted a session on VRI to approximately 60 stakeholders. A VRI review paper cited our research work, due to publication from session ("Identifying advantages and disadvantages of variable rate irrigation - an updated review." Applied Engineering in Agriculture. DOI: 10.13031/aea.13128). The project PI, Dr. Christopher Neale, presented on the use of UAVs in agriculture at the Irrigation Association conference in Long Beach, CA in Dec 2018, where he met the USDA program manager. The three graduate students currently supported by the project all each presented a poster on the three goals of the research at the 2019 Water for Food Global Conference in Lincoln, NE. During the poster session, these students had an opportunity to interact with other researchers. What do you plan to do during the next reporting period to accomplish the goals?Field research is ongoing in the 2019 growing season with the use of sensor node station data for execution of treatments. Soil water content and weather information from sensor nodes will be used to support the remote sensing based model. Efforts will be made test a data download system through the use of unmanned aerial vehicles from the sensor node stations in the field. During the 2019 growing season, the team will be researching six different irrigation treatments: Uniform (using soil moisture from sensor nodes) Rainfed VRI scheduled with SETMI using UAS inputs VRI scheduled with SETMI using Satellite inputs LINDSAY VRI methodology Farm Manager; represents traditional management based on agronomic background Each year the team is moving closer to the aim of the project, to manage VRI real-time based on unmanned aircraft. In the 2019 season, we are using unmanned aircraft to schedule VRI, but keeping the satellite method for comparison. We are also sharing knowledge with an industry partner, LINDSAY corporation. By also comparing to a treatment that reflects common practice, we are provide context in interpreting benefits of VRI as a best management practice. The research team will measure canopy temperature continuously to observe plant stress reaction in real time and track stress points. We will also conduct weekly measurement of neutron probes. UNL is developing a data mule network amongst sensor nodes on the ground. This will be operational in 2019 growing season. Additionally, a new graduate student, Suresh Kashyap, has been recruited to work on the project.
Impacts
What was accomplished under these goals?
OBJECTIVE 1: During the 2018 growing season, 57 UAS flights were conducted over four research fields. This includes the Variable Rate Irrigation Field VRI (18), the Carbon Sequestration Project Center Pivot Fields CSP1 (14) and CSP2 (13), and the rainfed field CSP3 (12). The CSP fields contain Ameriflux eddy covariance flux towers. In total, approximately 7,410 acres were covered, creating a multispectral and thermal infrared dataset for corn and soybean supplemented by sensor nodes in the field. The team is currently examining the correlation between UAS and field sensor node data, including comparing thermal imagery to sensor nodes and energy balance and evapotranspiration estimates to those measured by the flux towers The research team conducted multiple laboratory experiments, including a test to calibrate two UAS thermal cameras (FLIR Duo Pro R and ThermalCapture Fusion Zoom) . in a controlled environment. The purpose was to assess accuracy and develop calibration models to be applied to UAS imagery for increasing accuracy of surface temperature measurements. Data gathered will provide insights into the sensor operation, for example how long the thermal cameras should be on, warming up, before they become thermally stabilized. The field assessment of these thermal camera calibration models is currently underway during the 2019 growing season, with a forthcoming publication planned. Two other laboratory calibration experiments were conducted, including on soil moisture sensors and multispectral sensors. This research will lead to two additional publications. The soil moisture results are under review in Transactions of the ASABE and the multispectral results are submitted to the journal Precision Agriculture. OBJECTIVE 2: The research team used UAS multispectral imagery to assess and enhance estimation models for leaf area index, crop height, and fraction of cover to be used in the SETMI hybrid model. The results are described in a Master's of Science thesis chapter and forthcoming journal publication. The MS student and thesis information are as follows: Maguire, Mitch. "An Evaluation of Unmanned Aerial System Multispectral and Thermal Infrared Data as Information for Agricultural Crop and Irrigation Management." July, 2018. . The impact of research towards objective two is to improve irrigation scheduling. Because a drone collects higher spatial and temporal resolution than LANDSAT satellite, this research will improve estimation of biophysical parameters used for irrigation management. OBJECTIVE 3: The remote sensing based spatial evaporation model, known as SETMI, was further developed with enhanced capabilities to simulate soil water balance precisely using unmanned aerial systems (UAS) imagery inputs, and with an improved approach to simulate deep percolation. SETMI was also updated to have the capability to run the soil water balance in three-layered soils to account for multiple horizons within the crop or vegetation root zone. In 2018, the research team was successful in collecting frequent UAS thermal and multispectral imagery which was utilized in the model for irrigation management. Good quality Landsat imagery was available only on few days during the 2018 season due to te occurrence of multiple cloudy days. Despite long stretches of cloudy days, the UAS could be strategically used to collect imagery and for real-time irrigation management. SETMI was used to calculate spatial basal crop coefficient and schedule real-time irrigation during the 2018 season in corn and soybean using the UAS and Landsat imagery along with soil water content measurements from four representative locations in the field. The treatment using SETMI with UAS inputs resuted in similar crop yield as other treatments with equal applied depth of water as the uniform irrigation treatment managed using precise neutron probe soil water content measurements. SETMI using Landsat inputs significantly reduced water withdrawals for soybean while producing comparable yield to other methods. This research has demonstrated the importance of incorporating soil water measurements into VRI management. The incorporation of soil water content into the model significantly reduced irrigation requirements by increasing the simulated soil water on soil water measurement days. This aided the model by decreasing the model drift from actual conditions. Two years of data collection at the Eastern NE Research & Extension Center (ENREC) in Mead, NE are leading to multiple publications, and supported a masters of science thesis project. Data gathered from ENREC are analyzed in the thesis chapter and forthcoming journal publication under review in Ag Water Management. The MS student and thesis information are as follows: Bhatti, Sandeep. "Variable Rate Irrigation Using a Spatial Evapotranspiration Model With Remote Sensing Imagery and Soil Water Content Measurements." December, 2018. . Results of two years of scheduling a VRI equipped center pivot in Brule, NE using the SETMI model are under review as journal manuscript in Frontiers and Big Data. The impact of research towards objective three is in generating VRI prescriptions according to soil variability and crop needs. Currently, creating VRI prescriptions is expensive and there is no clear solution from industry. Most VRI exists as static prescription based on fixed parameters. The best tools on the market use spatial soil water balance, but still do not include sensor input (both field and remote sensing). By using remote sensing data as an input for irrigation scheduling, the team updates our crop coefficient based on actual field conditions instead of using textbook models for crop coefficient. This create more accurate and precise VRI prescriptions, which can be updated multiple times (i.e., weekly) throughout a growing season.
Publications
- Type:
Theses/Dissertations
Status:
Published
Year Published:
2018
Citation:
S. Bhatti. 2018. Variable rate irrigation using a spatial evapotranspiration model with remote sensing imagery and soil water content measurements. MS Thesis. Department of Biological Systems Engineering, University of Nebraska-Lincoln, Lincoln, Nebr. .
- Type:
Theses/Dissertations
Status:
Published
Year Published:
2018
Citation:
M. S. Maguire. 2018. An evaluation of unmanned aerial system multispectral and thermal infrared data as information for agricultural crop and irrigation management. MS Thesis. Department of Biological Systems Engineering, University of Nebraska-Lincoln, Lincoln, Nebr. .
- Type:
Journal Articles
Status:
Published
Year Published:
2018
Citation:
Barker, J. B., C. M. U. Neale, D. M. Heeren, and A. E. Suyker. 2018. Evaluation of a hybrid reflectance-based crop coefficient and energy balance evapotranspiration model for irrigation management. Transactions of the ASABE 61(2): 533-548, doi: 10.13031/trans.12311.
- Type:
Journal Articles
Status:
Published
Year Published:
2018
Citation:
OShaughnessy, S. A., S. R. Evett, P. D. Colaizzi, M. A. Andrade, T. H. Marek, D. M. Heeren, F. R. Lamm, and J. L. LaRue. 2019. Identifying advantages and disadvantages of variable rate irrigation an updated review. Applied Engineering in Agriculture (in press), doi: 10.13031/aea.13128.
- Type:
Journal Articles
Status:
Under Review
Year Published:
2019
Citation:
Bhatti, S., D. M. Heeren, J. B. Barker, C. M. U. Neale, W. E. Woldt, M. S. Maguire, and D. R. Rudnick. 2019. Site-specific irrigation management in a sub-humid climate using a spatial evapotranspiration model with satellite and airborne imagery. Agricultural Water Management (in review).
- Type:
Journal Articles
Status:
Under Review
Year Published:
2019
Citation:
Barker, J. B., S. Bhatti1, D. M. Heeren, C. M. U. Neale, and D. R. Rudnick. 2019. Variable rate irrigation of maize and soybean in West-Central Nebraska under full and deficit irrigation. Frontiers in Big Data (in review).
- Type:
Journal Articles
Status:
Under Review
Year Published:
2019
Citation:
Singh, J., D. M. Heeren, D. R. Rudnick, W. E. Woldt, Y. Ge, and J. D. Luck. 2019. Soil structure and texture effects on the precision of soil water content measurements by a capacitance-based electromagnetic sensor. Transactions of the ASABE (in review).
- Type:
Journal Articles
Status:
Under Review
Year Published:
2019
Citation:
Barker, J. B., W. E. Woldt, B. D. Wardlow, M. S. Maguire, B. C. Leavitt, C. M. U. Neale, and D. M. Heeren. 2019. Calibration of a common shortwave multispectral camera system for quantitative agricultural applications. Precision Agriculture (in revisions).
- Type:
Conference Papers and Presentations
Status:
Other
Year Published:
2019
Citation:
Maguire, M., Neale, C.M.U., Woldt, W. 2019. Assessment of Thermal Infrared Sensors and their Accuracy in Low Altitude Unmanned Aerial System Remote Sensing. Presented at ASABE AIM 2019. Boston, MA, USA.
- Type:
Conference Papers and Presentations
Status:
Other
Year Published:
2019
Citation:
Bhatti, S., Kumari, A., Sarangi, A., Heeren, D. M., Kaur, R., Neale, C.M.U., Singh, M. July 7-10, 2019. Integrated Soil Moisture and Canopy Temperature Sensing System for Irrigation Scheduling. Presented at ASABE AIM 2019. Boston, MA, USA.
- Type:
Other
Status:
Other
Year Published:
2019
Citation:
(POSTER) Bhatti, S., Barker, J.B., Heeren, D.M., Neale, C.M.U, Rudnick, D.R., Woldt, W.E., Boldt, A.L. April 29-30, 2019. Performance of Variable Rate Irrigation Management Using Spatial Evapotranspiration Model with Satellite Imagery Updated Using Soil Moisture Content Measurements. Presented at Water for Food Global Conference. Lincoln, NE, USA.
- Type:
Other
Status:
Other
Year Published:
2019
Citation:
(POSTER) Singh, J., Ge, Y., Bai, G., Barker, J.B., Heeren, D.M., and Neale, C.M.U. April 29-30, 2019. In-field soil and plant sensor network to improve variable rate irrigation decision-making. Presented at Water for Food Global Conference. Lincoln, NE, USA.
- Type:
Other
Status:
Other
Year Published:
2018
Citation:
(POSTER) Bhatti, S., Barker, J.B., Heeren, D.M., Neale, C.M.U., Rudnick, D.R., Woldt, W.E., Boldt, A.L. October 24-26, 2018. Variable rate irrigation with spatial evapotranspiration model using imagery from satellite and unmanned aerial systems. Presented at Nebraska Water Center Great Plains Regional Water Symposium. Lincoln, NE, USA.
- Type:
Other
Status:
Other
Year Published:
2018
Citation:
(POSTER) Singh, J., Ge, Y., Bai, G., Barker, J.B., Heeren, D.M., Neale, C.M.U. October 24-26, 2018. In-field soil and plant sensor network to improve variable rate irrigation decision-making. Presented at Nebraska Water Center Great Plains Regional Water Symposium. Lincoln, NE, USA.
- Type:
Other
Status:
Other
Year Published:
2018
Citation:
(POSTER) Li, J., Barker, J.B., Bhatti, S., Possignolo, I., Heeren, D.M., Boldt, A.L., Yan, H. October 24-26, 2018. Comparison of methods for calculating deep percolation. Presented at Nebraska Water Center Great Plains Regional Water Symposium. Lincoln, NE, USA.
- Type:
Other
Status:
Other
Year Published:
2018
Citation:
(POSTER) Li, J., Barker, J.B., Possignolo, I.P., Heeren, D.M., Boldt, A.L., Bhatti, S., Yan, H. June 6, 2018. Comparison of methods for calculating deep percolation. Presented at Marena Oklahoma In Situ Sensor Testbed (MOISST) Workshop. Lincoln, NE, USA.
- Type:
Conference Papers and Presentations
Status:
Other
Year Published:
2018
Citation:
Barker, J. B., S. Bhatti, D. M. Heeren, and C. M. U. Neale. June 6, 2018. VRI Irrigation scheduling. Presented at Marena Oklahoma In Situ Sensor Testbed (MOISST) Workshop. Lincoln, NE, USA.
- Type:
Conference Papers and Presentations
Status:
Other
Year Published:
2018
Citation:
Heeren, D. M., Barker, J.B., Bhatti, S., Maguire, M., Woldt, W.E., Neale, C.M.U. September 14, 2018. Variable rate irrigation (VRI): Benefits, limitations, and management practices. Presented to Delegation of Irrigators from New Zealand. Lincoln, NE, USA.
- Type:
Conference Papers and Presentations
Status:
Other
Year Published:
2019
Citation:
Woldt, W.E. April 24, 2019. Unmanned Aircraft: Considerations and Lessons Learned from Seven Years of Flight Operations. Presented at 2019 Nebraska GIS/LIS Biennial Symposium. Omaha, NE, USA.
- Type:
Conference Papers and Presentations
Status:
Other
Year Published:
2019
Citation:
Woldt, W.E. March 29, 2019. Unmanned Aircraft: Another Source of Big Data. Presented to the Nebraska Rural Broadband Task Force. ENREC, Mead, NE, USA.
- Type:
Conference Papers and Presentations
Status:
Other
Year Published:
2018
Citation:
Smith, S., Neale, C.M.U. Dec 2018. Using Drones to Improve Irrigation Management. Presented at Irrigation Association Annual Meeting. Long Beach, CA, USA.
- Type:
Conference Papers and Presentations
Status:
Other
Year Published:
2019
Citation:
Singh, J., Heeren, D.M., Rudnick, D.R., Barker, J.B., Woldt, W.E., Ge, Y., Luck, J.D., Bai, G., Meyer, G., Neale, C.M.U. July 7-10, 2019. Soil Structure and Texture Effects on the Precision of Soil Water Content Measurements by a Capacitance-based Electromagnetic Sensor. Presented at ASABE AIM 2019. Boston, MA, USA.
- Type:
Conference Papers and Presentations
Status:
Other
Year Published:
2018
Citation:
Bhatti, S., Heeren, D. M., Barker, J. B., Neale, C.M.U., Rudnick, D.R., Woldt, W.E., Ge, Y., Luck, J.D., Meyer, G.E., Munoz-Arriola, F., Boldt, A.L., Maguire, M. S. July 29-August 1, 2018. Variable rate irrigation management using a spatial evapotranspiration model. Presented at ASABE AIM 2018, Detroit, MI, USA.
- Type:
Conference Papers and Presentations
Status:
Other
Year Published:
2018
Citation:
Barker, J. B., Maguire, M. S., Neale, C.M.U., Woldt, W. E., Wardlow, B. D., Leavitt, B. C., Heeren, D. M. July 29-August 1, 2018. Calibration of an unmanned-aircraft-mounted shortwave multispectral camera system for use in evapotranspiration modeling. Presented at ASABE AIM 2018, Detroit, MI, USA.
- Type:
Conference Papers and Presentations
Status:
Other
Year Published:
2018
Citation:
Singh, J., Ge, Y., Bai, G. July 29-August 1, 2018. In-Field Soil and Plant Sensor Network to Improve Variable Rate Irrigation Efficiency. American Society of Agricultural and Biological Engineers (ASABE) Presented at ASABE AIM 2018, Detroit, MI, USA.
- Type:
Conference Papers and Presentations
Status:
Other
Year Published:
2018
Citation:
Maguire, M. S., Barker, J. B., Neale, C.M.U., Woldt, W.E., Suyker, A.E. July 29-August 1, 2018. Modeling Evapotranspiration using Multispectral and Thermal Infrared Imagery Acquired with a Low Altitude Unmanned Aerial System. Presented at ASABE AIM 2018, Detroit, MI, USA.
- Type:
Conference Papers and Presentations
Status:
Other
Year Published:
2019
Citation:
Heeren, D.M., Barker, J.B., Bhatti, S., Rudnick, D.R., Munoz-Arriola, F. February 26-27, 2019. Impact of variable rate irrigation (VRI) on consumptive use of water resources. Presented at Central Plains Irrigation Association (CPIA) Central Plains Irrigation Conference. Kearney, NE, USA.
|
Progress 06/01/17 to 05/31/18
Outputs
Target Audience:The target audience for this research includes, but it not limited to: Industry, including pivot and unmanned aerial vehicle (UAV) manufacturers; Producers; Government agencies interested in supporting variable rate irrigation (VRI) technologies; and Government agencies regulating groundwater quality and pumping. The research team reaches key stakeholders through multiple channels, including presentations at local, regional, and international conferences, publication of agricultural extension and academic journal articles, field tours, demonstrations, professional workshops, and student mentoring. These efforts are detailed in the "Products" and "Other Products" sections. Changes/Problems:
Nothing Reported
What opportunities for training and professional development has the project provided?Nine undergraduate and four graduate students gained research and field experience by working on this project in Year 1. Two graduate students, Sandeep Bhatti and Jasreman Singh, received financial support from the Daugherty Water for Food Global Institute to conduct research. Bhatti also received funds from the USGS. Additionally, of these thirteen students, five were from developing countries. Any knowledge and skills they gain during their time at the University of Nebraska and working on the VRI project will be transferred back to their home countries at the conclusion of their visit. The project also supported the research and master's thesis for two students in the Biological Systems Engineering department at the University of Nebraska. Additionally, co-PI Dr. Wayne Woldt held a training workshop on applications of Unmanned Aircraft Systems in agriculture in Mead, Nebraska on April 23, 2018. Professionals seeking continuing education attended this workshop. Attendees included planners, engineers, information systems professionals, and agricultural financing professionals. How have the results been disseminated to communities of interest?Dissemination has included presentations at technical conferences, presentations to industry groups, and peer reviewed journal manuscript(s), described in the "Products" section. What do you plan to do during the next reporting period to accomplish the goals?Plans for the next reporting period include full implementation of the sensor network. The project team will incorporate the UAV imagery into the model and irrigation scheduling experiment, along with improved calibration of UAV thermal infrared imagery. Implementation of the UAV data mule concept will also occur. The researchers will prepare at least two journal manuscripts. Two graduate students conducting research related to the project will graduate with their master's degrees.
Impacts
What was accomplished under these goals?
Goal 1: To collect near real-time, remotely sensed thermal infrared, three-band multi-spectral, and optical/visual imagery over large-scale row crop systems using a fixed wing UAS equipped with a novel triple sensor system, with verification through a field/ground sensor system. Field research for the 2017 crop season (Year 1) was completed between May and September 2017. The second year of field research for the 2018 crop season (Year 2) begun as of May 2018. High-quality Landsat images were obtained for eight dates at the ENREC Maize field, for seven dates at the ENREC Soybean field, and for 11 dates at the Brule Maize field. Unmanned aircraft shortwave reflectance imagery was collect on 27 days over the course of the 2017 growing season, covering a total of 38,160 acre-fields. Flights included up to four fields (the VRI study field, and three other research fields equipped with eddy covariance flux systems that were nearby). Calibration of UAV shortwave reflectance products was a focus particularly during the offseason between years 1 and 2. Calibration of the UAV thermal infrared also commenced. A sub award contract was initiated with the University of Colorado-Boulder, with an amount of $25,389 for Year 1. The award secured support for: 1) UAS preparation and mission planning: Provide guidance on aircraft preparation and flight training for UNL operations. 2) Flight path planning: Assist with flight path planning and analysis of coverage rates. Share best practices for flight planning, especially in low wind conditions or other off-nominal (e.g. cold weather) conditions. 3) Sensor network data mule: Model and plan aircraft motion for data collection from sensor networks. Advise on sensor data rates and communication protocols. Goal 2: To extract crop feature data from the information obtained under objective one for use in a crop energy balance/evapotranspiration and soil water content simulation model. Supplies were acquired in Year 1 for the soil water content measurement network. These supplies included 12 sensor nodes consisting of soil moisture sensors, canopy temperature sensors, air temperature/humidity sensors, data loggers, wireless communication module, DC batteries, solar radiation sensors, and other components such as structure for sensor support and weatherproof shield. In addition, supplies and components were obtained for the direct data retrieval system between UAS and WSN. Goal 3: To synthesize and test the output from the model for management of a variable rate pivot irrigation system, with evaluation of effectiveness of the method. In Year 1, three irrigation treatments were applied at two different locations. Three irrigation treatments were applied to soybean and maize production at the University of Nebraska's Eastern Nebraska Research and Extension Center (ENREC), near Mead, NE: 1) VRI using remote sensing (RS) model, 2) uniform, 3) rainfed. Two treatments were also applied in an additional test field at Brule, NE to a maize crop: 1) VRI using RS model, 2) uniform. Neutron probe data were collected on 12 dates in the ENREC Maize field, on 11 dates in the ENREC Soybean field, and on six dates at the Brule Maize field. Five irrigation prescriptions (IrrRx) were implemented in the ENREC Maize field, three IrrRx were implemented in the ENREC Soybean field, and 22 IrrRx were implemented in the Brule Maize field. Sensor Nodes at Mead collected on the following data: surface and air temperature, wind, solar radiation, volumetric soil water content, and surface shortwave reflectance. Modifications were made to the spatial water balance model to improve soil water content modeling capabilities, flexibility of model options, and incorporation of UAV imagery. Preliminary results show that seasonal irrigation depth applied for VRI treatment was higher than uniform treatment for ENREC soybeans. Significantly higher maize yield was found for VRI and "uniform 2" treatments over rainfed treatment at ENREC field.
Publications
- Type:
Theses/Dissertations
Status:
Accepted
Year Published:
2018
Citation:
Maguire, M. S. (2018). A THESIS: THERMAL INFRARED DATA AS INFORMATION FOR AGRICULTURAL CROP AND IRRIGATION MANAGEMENT. Presented to the Faculty of The Graduate College at the University of Nebraska In Partial Fulfillment of Requirements For the Degree of Master of Science. Lincoln, NE.
- Type:
Conference Papers and Presentations
Status:
Other
Year Published:
2018
Citation:
Maguire, M. S., Barker, J. B., Neale, C. M. U., Woldt, W. E. (2018) Modeling Evapotranspiration over Low Vegetation Crop Cover using Multispectral and Thermal Infrared Imagery Acquired with a Low Altitude Unmanned Aerial System. 2018 Remote Sensing and Hydrology Symposium. Cordoba, Spain.
- Type:
Conference Papers and Presentations
Status:
Other
Year Published:
2018
Citation:
Barker, J. B., S. Bhatti, D. M. Heeren, and C. M. U. Neale. May 8, 2018. Irrigation management using remote-sensing-based spatial evapotranspiration modeling in maize and soybean in Nebraska, USA. International Association of Hydrological Sciences (IAHS) Remote Sensing and Hydrology Symposium, Cordoba, Spain.
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2018
Citation:
Barker, J. B., C. M. U. Neale, D. M. Heeren, and A. E. Suyker. 2018. Evaluation of a hybrid reflectance-based crop coefficient and energy balance evapotranspiration model for irrigation management. Transactions of the ASABE 61(2): 533-548, doi: 10.13031/trans.12311.
- Type:
Journal Articles
Status:
Published
Year Published:
2018
Citation:
Barker, J. B., D. M. Heeren, C. M. U. Neale, and D. R. Rudnick. 2018. Evaluation of variable rate irrigation using a remote-sensing-based model. Agricultural Water Management 203: 63-74, doi: 10.1016/j.agwat.2018.02.022.
- Type:
Conference Papers and Presentations
Status:
Other
Year Published:
2018
Citation:
Woldt, W. E., C. M. U. Neale, D. M. Heeren, E. Frew and G. E. Meyer. May 2, 2018. Improving agricultural water efficiency with unmanned aircraft. Conference proceedings paper. Association for Unmanned Vehicle Systems International (AUVSI) XPONENTIAL trade show and conference, Denver, Colo.
- Type:
Conference Papers and Presentations
Status:
Other
Year Published:
2018
Citation:
Heeren, D. M., J. B. Barker, S. Bhatti, M. Maguire, W. E. Woldt, and C. M. U. Neale. April 4, 2018. Variable rate irrigation (VRI): Benefits, limitations, and management practices. Invited presentation. Nebraska Water Center (NWC) Water Seminar, Lincoln, Nebr.
- Type:
Conference Papers and Presentations
Status:
Other
Year Published:
2017
Citation:
Maguire, M. S., Woldt, W. E., Neale, C. M. U. (2017) A Production Field Scale Review of Agricultural Image Processing and Dual Sensor Integration for Unmanned Aircraft Systems. 2017 Robert B. Daugherty Water for Food Global Conference. Lincoln, NE.
- Type:
Conference Papers and Presentations
Status:
Other
Year Published:
2017
Citation:
Maguire, M. S., Woldt, W. E., Neale, C. M. U., Frew, E. W., & Meyer. G. E.
(2017). A Survey of Agricultural Image Processing for Unmanned Aircraft Systems. ASABE Paper No. 1701454. St. Joseph, MI: ASABE.
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2017
Citation:
Maguire, M. S., Woldt, W. E., Frew, E. W., Smith, J., & Elston, J. (2017).
Thermal Infrared and Multi-spectral Dual Sensor Integration for Unmanned Aircraft Systems. ASABE Paper No. 1701455. St. Joseph, MI: ASABE.
- Type:
Conference Papers and Presentations
Status:
Other
Year Published:
2018
Citation:
Heeren, D. M., J. B. Barker, M. Maguire, W. E. Woldt, and C. M. U. Neale. January 15, 2018. Drones are buzzing toward increased crop production. Invited presentation. IHE Delft Lunch Seminar, Delft, Netherlands.
- Type:
Conference Papers and Presentations
Status:
Other
Year Published:
2017
Citation:
Heeren, D. M. October 4, 2017. Variable rate irrigation (VRI): Potential benefits and management practices. Invited presentation. Department Colloquium, Biological Systems Engineering Department, Lincoln, Nebr.
- Type:
Other
Status:
Published
Year Published:
2017
Citation:
Bhatti, S., J. B. Barker, D. M. Heeren, C. M. U. Neale, D. R. Rudnick, W. E. Woldt, J. D. Luck, Y. Ge, G. E. Meyer, A. L. Boldt, and M. Maguire. October 26-27, 2017. Water and crop response to variable rate irrigation using remote sensing model and soil moisture content monitoring. Nebraska Water Center (NWC) Nebraska Water Symposium, Lincoln, Nebr. Poster presentation.
- Type:
Conference Papers and Presentations
Status:
Other
Year Published:
2017
Citation:
Barker, J. B., D. M. Heeren, C. M. U. Neale, D. L. Martin, T. E. Franz, and W. R. Kranz. July 16-19, 2017. Variable rate irrigation management of corn and soybean using a remote-sensing-based water balance. ASABE Annual International Meeting, Spokane, Wash.
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