ADVANCING CROP STRESS MONITORING AND FORECAST USING UNMANNED AERIAL VEHICLE (UAV) MEASUREMENTS OF SOLAR-INDUCED CHLOROPHYLL FLUORESCENCE (SIF) AND THE PHOTOCHEMICAL REFLECTANCE INDEX (PRI)

• Sponsoring Institution: National Institute of Food and Agriculture
• Project Status: TERMINATED
• Funding Source: AFRI COMPETITIVE GRANT
• Reporting Frequency: Annual
• Accession No.: 1015558
• Grant No.: 2018-67012-27985
• Project No.: NYC-125575
• Proposal No.: 2017-07107
• Program Code: A7201
• Project Start Date: May 1, 2018
• Project End Date: Apr 30, 2020
• Grant Year: 2018

Project Director
Chang, C. Y.

Recipient Organization
CORNELL UNIVERSITY
(N/A)
ITHACA,NY 14853

Performing Department
CALS - SIPS - Soil and Crop Sc

Non Technical Summary
Improving the sensitivity of methods for crop monitoring and yield forecasting is crucial for the agricultural community, economy and food security as the frequency of extreme weather events increases. In recent years, there is growing popularity of versatile unmanned aerial vehicle (UAV) platforms that can be used to remotely sense crops to monitor parameters such as crop yield, water status, and plant health. However, traditional remote monitoring methods rely on greenness-based indices such as the normalized difference vegetation index (NDVI), which is not sensitive to short-term plant stress response. A promising new alternative is the complementary coupling of sun-induced chlorophyll fluorescence (SIF), which is a direct functional proxy of photosynthesis, and the photochemical reflectance index (PRI), which provides information about photoprotection. We will measure SIF, PRI and NDVI in wet and dry areas in a rain-fed crop field using aerial measurements and validate them on the ground with measurements of plant physiology. We aim to demonstrate the effectiveness of the combination of SIF and PRI as a sensitive real-time indicator of crop stress. We also aim to encourage discourse about the development of cutting-edge UAV-based platforms to educators and farmers who can provide feedback about their needs and increase knowledge transfer. This project will develop a novel UAV-based crop monitoring system using the latest technologies in remote sensing aimed to improve early stress detection to mitigate effects of drought stress on crops.

Animal Health Component

0%

Research Effort Categories

Basic

(N/A)

Applied

50%

Developmental

50%

Classification

Knowledge Area (KA)	Subject of Investigation (SOI)	Field of Science (FOS)	Percent
203	1510	1020	100%

Knowledge Area
203 - Plant Biological Efficiency and Abiotic Stresses Affecting Plants;

Subject Of Investigation
1510 - Corn;

Field Of Science
1020 - Physiology;

Keywords

uav.

crop monitoring

drought stress

photosynthesis

remote sensing

Goals / Objectives
The major goal of this project is to develop a novel UAV-based platform that provides real-time monitoring of photosynthesis and water stress using the combination of sun-induced chlorophyll fluorescence (SIF) and photochemical reflectance index (PRI). Currently, UAVs that are used for crop monitoring rely upon multispectral cameras to measure the normalized difference vegetation index (NDVI) which assesses greenness of the plants as an indicator of plant health. However, greenness does not necessarily indicate strong physiological performance of plants. In contrast, SIF and PRI are two spectral signatures that directly provide information about photosynthesis and photoprotection, two highly dynamic processes that respond rapidly to environmental change and plant water status during the growing season. This project aims to demonstrate the effective sensitivity of SIF to crop stress, and present an improved, versatile crop monitoring platform.To achieve this goal, we will pursue the following objectives:Objective 1: Develop a UAV-based system that synergistically uses sun-induced chlorophyll fluorescence (SIF) and photochemical reflectance index (PRI) to perform real-time crop monitoring with high spatial and temporal resolution.Objective 2: Assess the added value of coupling SIF and PRI measurements in comparison with traditional remote sensing methods such as NDVI for monitoring crop yield and early detection of crop stress. Changes recorded in SIF, PRI and NDVI will be ground-truthed using leaf-level physiological measurements.Objective 3: Improve public awareness of cutting-edge developments in agricultural research, and encourage discourse with local farmers and educators on tools to improve preparednessagainst drought stress.

Project Methods
System design: Two platforms for measuring SIF, PRI and NDVI will be developed from February to May 2018. These platforms will rely on the combination of the FLAME spectrometer (Ocean Optics), configured to record a wavelength range from 350-1100nm at 1.37nm FWHM spectral resolution, which will be used to measure NDVI and PRI, and the high-resolution QE-PRO spectrometer (Ocean Optics), configured to record a wavelength range from 730-784nm at 0.13nm FWHM spectral resolution, which will be used to measure SIF in the far-red region. The UAV-based platform will integrate the FLAME and QE-PRO with a Raspberry Pi 3 mini-computer, which will control the spectrometers and store the data onboard the UAV. This portable spectrometer package will be tested on the 3DR Solo and DJI Matrice 600 PRO UAVs, which are both multi-rotor drones. The ground-based platform will be mounted on a tower and will control the FLAME and QE-PRO spectrometers with a CR1000 datalogger. The tower will also be equipped with sensors for measuring photosynthetically active radiation (PAR), absorbed PAR (APAR), temperature and relative humidity. The tower platform was field-tested in 2017 with two QE-PROs and will be modified to include the FLAME in 2018.Experimental setup: Two field experiments will be performed during the growing seasons of 2018 and 2019. These experiments will involve airborne and ground-level monitoring of photosynthesis and stress within rain-fed corn fields located at Cornell Musgrave Research Farm, in Aurora, NY. Prior to planting, areas will be identified within the field that represent naturally recurring "wet" and "dry" spots due to local topography (e.g. hills vs. low spots). Five wet and five dry areas in the field will be used as plots and their locations will be marked using GPS. Soil moisture probes will be installed in each plot to record soil water availability over the growing season.Remote sensing measurements: Spectral measurements used for the calculation of SIF, PRI and NDVI will be performed using both UAV and ground-based platforms. Sixteen UAV campaigns will be conducted from June through September on a weekly basis and will target the ten identified plots. The tower-based platform, which will set up to monitor the whole field, will be situated at the center of the field adjacent to a flux tower maintained by colleagues Jeff Melkonian and Susan Riha. Both towers will be set at 4m height (a minimum of 2m above the top of the canopy when the maize is at full height) and will record continuous measurements for the full growing season. The flux tower will measure canopy-level fluxes of carbon dioxide and water, and will be used to calculate gross primary productivity (GPP) of the field.Ground measurements for validation: To assess the actual photosynthetic response of the plants, leaf-level measurements of photosynthetic gas exchange, chlorophyll fluorescence and leaf water potential will be collected from three plants per plot in the window between 11am - 3pm (+ 2 hours from solar noon) on a weekly basis, concurrent with UAV campaigns. Sun-exposed and shaded leaf samples will also be collected during these windows, which will be flash-frozen in liquid nitrogen and stored at -80°C until analysis. The samples will be transported to the lab of Prof. Ingo Ensminger at the University of Toronto Mississauga where they will be assessed using high pressure liquid chromatography (HPLC) for chlorophylls, carotenes, and xanthophyll pigments. Pigment extractions and HPLC analysis will be performed following the protocol described in Junker et al. (2016).Data analysis: Far-red SIF will be retrieved using the single-vector decomposition (SVD) method following Guanter et al. (2013). NDVI will be calculated as NDVI = (NIR - Red) / (NIR + Red) where Red is the amount of red light and NIR is the amount of near-infrared light reflected by vegetation (Rouse et al. 1974). PRI will be calculated as PRI = (R570 - R531)/(R­­570 - R531) where R­570 is reflectance at a xanthophyll-insensitive band at 570nm, and R531 is reflectance at a xanthophyll-sensitive band at 531nm (Gamon et al. 1992). Flux data will be de-spiked and pre-processed following Papale et al. (2006); GPP will be calculated from the processed flux data using the REddyProc package (Reichstein and Moffat 2014). Results from ground-level measurements and UAVs will be averaged and analyzed using two-way ANOVA using time and treatment (wet vs. dry) as factors. The leaf-level measurements will be correlated with data with UAV and tower measurements to compare how well SIF, NDVI and PRI can track changes in photosynthesis and water stress over the growing season.Efforts: Through conferences and papers, my research findings will benefit members of the scientific community interested in topics such as developing remote sensing methods for assessing photosynthesis in diverse environments and biomes for improving global carbon models, and developing UAV platforms for stress monitoring in farmland and managed forests. The outcomes of this project will also contribute to ongoing efforts by the scientific community to improve understanding of photosynthesis at the canopy level, which will be invaluable in upscaling for satellite-based detection of photosynthesis using SIF as a proxy.Through CCE webinars and events and extension efforts through the Geospatial Sciences Program Work Team established by Grantham and Hoskins, I will share my research findings and updates to farmers and educators in the New York region who are interested in learning about using UAV for crop yield and stress monitoring.Through presentations at local public venues such as Ithaca's Science Cabaret and the Seed to Supper program established by CCE, I can share my research with local members of the public of all ages who are interested in science and plant health. Both of these programs are free to the public. Science Cabaret is a monthly gathering where professors, graduate students and other members of the Ithaca community can present informal talks on current topics in science to interested members of the public. The Seed to Supper program teaches people how to grow their own food on a budget with an aim of improving overall food security.Evaluations: Project milestones include completion of the UAV system by end of May, 2018; submission of the methods paper by end of 2018; presentation of results at conferences and seminars during 2019; and submission of experiment results paper by end of 2019. My mentor will advise me during design of experimental field setups, training routines and field measurement protocols; review presentations and posters and offer guidance to improve effective presentation skills in advance of conference and workshop presentations; and review paper drafts to improve writing skills. I will test student trainees for technical replication proficiency prior to each field experiment. Students will present short 5 minute updates of project progress at biweekly lab meetings, at which my mentor and I will advise on improving data analysis and presentation skills. Students will write a short research report on the summer field work. My mentor and I will review and return comments to the students to guide them with writing effective scientific reports. At the end of their internships in 2018 and 2019, students will provide us with evaluations and feedback which will help me to improve my mentoring. With the assistance of Grantham and Hoskins, I will work to improve my communication with and solicit feedback from the geospatial sciences and farmer communities.

Progress 05/01/18 to 04/30/20

Outputs
Target Audience:Target audience reached by this project included members of the solar-induced chlorophyll fluorescence (SIF) remote sensing research community,students both at Cornell University and other institutions. Efforts: During this project, PD Chang trained and mentored the following students: - Master's students from Cornell Department of Electrical and Computer Engineering (ECE): Samhita Marri, Shuhua Li, Shalki Srivastava, Mei Yang. Participation through ECE course "Design with Embedded Operating Systems" taught by Professor Joseph Skovira; worked on system hardware and software development. - Cornell University undergraduate student: Ajay Gupta. Participation through "Design with Embedded Operating Systems"; worked on system software development. - Potsdam University undergraduate student: Julie LeVonne. Participation through summer undergraduate research internship offered by Professor Ying Sun; worked on leaf-level plant physiology measurements. - visiting Ph.D. students Longlong Yu, Cong Wang, Yangyang Fan, Ruiqing Zhou. Externally funded by the China Scholarships Council; Yu, Wang, and Fan worked on leaf-level plant physiology measurements; Zhou worked on hardware system development. - Ph.D. student from Cornell School of Integrative Plant Science: Jiaming Wen. Externally funded by USAID, SURP, and NASA FINESS; worked on leaf-level plant physiology measurements. PD Chang has contributed the novel UAS platform developed during this project for use in an AFRI-Food and Agriculture Cyberinformatics and Tools project proposed by Drs. Kelly Robbins and Michael Gore (both from Cornell University). This project proposal was recently submitted in July 2020. A number of colleagues have additionally expressed interest in utilizing our UAV platform for future projects, including Dr. Jacob Washburn (USDA), Dr. Ravi Vallaru (University of Lincoln), Dr. Ingo Ensminger (University of Toronto), Dr. Christine Goodale (Cornell), and Dr. Michael Gore (Cornell). Furthermore, PD Chang and advisor Dr. Ying Sun will use the project outcomes incourses to be taught at Cornell University, funded by the following NSF NRT grant: Co-PI: Sun, Y. (PI: Scanlon, M.) 09/2019-08/2024. NRT: Team Training to Develop New Hardware and Software Applications for Digital Plant Science Across Multiple Scales. NSF Through this project, PD Chang has gained visibility in the SIF remote sensing community through her research outcomes, as well as invaluable experience as a mentor. Our research group at Cornell University, led by Dr. Ying Sun, has strengthened interdepartmental collaboration at Cornell between our department, the School of Integrative Plant Science, and the Department of Electrical and Computer Engineering. We have also increased visibility of our research group both within Cornell and externally with international institutions through working with the Cornell Initiative for Digital Agriculture, and internationally through planned future collaborations. Changes/Problems: Nothing Reported What opportunities for training and professional development has the project provided?During this project, PD Chang had extensive opportunity to trainand mentorstudents, which provided invaluable experience for managing her own research group in future. Professional development was achieved through planned collaborations with colleagues Jacob Washburn (USDA-ARS);Ravi Vallaru (University of Lincoln, UK);Michael Gore, Kelly Robbins, and Christine Goodale (Cornell University);Ingo Ensminger (University of Toronto, Canada).Furthermore, PD Chang has greatly expanded her professional network and engaged with top researchers in the SIF/remote sensing community during and following the publication of her two first-author research articles (Chang et al. 2020 JGR Biogeosciences and Chang et al. 2020 Agr For Meteorol), including among others Christian Frankenberg (NASA JPL/Caltech), Luis Guanter (Univ. Valencia, Spain), Micol Rossini (Univ. Milano-Bicocca, Italy),Troy Magney (UC Davis), Katja Grossmann (Univ. Heidelberg, Germany), Philipp Kohler (NASA JPL/Caltech), Grayson Badgley (Columbia University), Taylor Jones (Boston University). Mentored students included: - Master's students from Cornell Department of Electrical and Computer Engineering (ECE): Samhita Marri, Shuhua Li, Shalki Srivastava, Mei Yang. Participation through ECE course "Design with Embedded Operating Systems" taught by Professor Joseph Skovira; worked on system hardware and software development. - Cornell University undergraduate student: Ajay Gupta. Participation through "Design with Embedded Operating Systems"; worked on system software development. - Potsdam University undergraduate student: Julie LeVonne. Participation through summer undergraduate research internship offered by Professor Ying Sun; worked on leaf-level plant physiology measurements. - visiting Ph.D. students Longlong Yu, Cong Wang, Yangyang Fan, Ruiqing Zhou. Externally funded by the China Scholarships Council; Yu, Wang, and Fan worked on leaf-level plant physiology measurements; Zhou worked on hardware system development. - Ph.D. student from Cornell School of Integrative Plant Science: Jiaming Wen. Externally funded by USAID, SURP, and NASA FINESS; worked on leaf-level plant physiology measurements. How have the results been disseminated to communities of interest?As listed in Objective 3 above, measurements from the 2019 field campaign and a description of the novel UAV system were first reported at the NIFA Project Director's Meeting / National Association of Plant Breeders Annual Meeting, which was held in Pine Mountain, Georgia on August 25, 2019. Students from across Cornell University's campus as well as visiting students from Wageningen University, the Netherlands, the University of São Paolo, Brazil, and the University of California, Davis, were also introduced to the project by PD Chang and advisor Prof. Ying Sun through a demonstration during a Digital Agriculture Hackathon hosted by the Cornell Initiative for Digital Agriculture on February 28, 2020. PD Chang has additionally shared the published outcomes of this project with the international SIF remote sensing community through the AmeriFluxand SIF-community email listservs as well as on Twitter (@chryschang).Findings from this project will also be presented at the Ecological Society of America Annual Meeting which will be held during the first week of August 2020. Following further system development, we will construct a user-friendly website portal and work with Cornell Extension personnel to connect and share our work with the NY farm community. Furthermore, PD Chang and advisor Ying Sun will use the project outcomes as part ofcourses to be taught at Cornell University, which are funded by the following NSF NRT grant: Co-PI: Sun, Y. (PI: Scanlon, M.) 09/2019-08/2024. NRT: Team Training to Develop New Hardware and Software Applications for Digital Plant Science Across Multiple Scales. NS 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 goal of this project was to produce a UAV-based platform that provides real-time monitoring of photosynthesis and water stress with both high spatial and temporal resolution using SIF and PRI, validated by ground-based physiological measurements. This was pursued through the following three objectives: Objective 1: Develop a UAV-based system that synergistically uses solar-induced chlorophyll fluorescence (SIF) and the photochemical reflectance index (PRI) for agricultural applications at high spatial resolution in real time. The UAV platform was developed from Fall 2018 to Spring 2019. The system consists of two hyperspectral spectrometers connected by a single-bifurcated fiber optic that rotates between downwelling and upwelling radiation acquisition using a pair of servo motors. These components are controlled by a Raspberry Pi 3B+ microcomputer, with software based on an existing tower system (Gu et al. 2019) that is triggered during flight using customized DJI Onboard-SDK-ROS software. As intended, the system is capable of acquiring far-red SIF and hyperspectral reflectance and provides a live feed of acquired spectra, RGB camera, system performance, and GPS position in the field during flight. This system was completed in time for a planned field campaign in Summer 2019. Objective 2: Assess the added value of coupling SIF and PRI measurements in comparison with traditional remote sensing methods such as the normalized difference vegetation index (NDVI) for monitoring crop yield and early detection of crop stress. A coordinated field campaign of UAV flights coupled with ground measurements was undertaken in Summer 2019 to assess crop status of soybean and maize via remote sensing (SIF, PRI, NDVI) along with crop physiology (leaf-level gas exchange and chlorophyll fluorescence, leaf water potential, soil moisture, plant height and vegetative stage, leaf area index). Measurements revealed that a) SIF outperformed NDVI for differentiating among maize plots of differing height and yield potential and b) different diurnal patterns of SIF and PRI were observed among soybean and maize crops at different growth stages. Furthermore, the UAV system exhibited strong correlation with the existing SIF tower system established at our field site. In combination, our findings reveal that the novel UAV and tower SIF systems can synergistically provide continuous, long-term coverage with a large complement of auxiliary sensors for site monitoring (tower) along with high spatial resolution coverage (UAV). We anticipate this to be advantageous for future agricultural and research applications. These field campaigns also revealed differing diurnal dynamics of SIF caused by canopy row orientation. By combining tower, UAV and ground measurements, we are able to greatly improve our mechanistic understanding of diurnal SIF dynamics. Our current manuscript (in progress) proposes that SIF is strongly affected by canopy structure which affects both total absorbed photosynthetically active radiation and energy partitioning among fluorescence, photosynthesis and non-photochemical quenching. Our next manuscript will evaluate the performance of SIF and PRI for detecting drought stress. Objective 3: Improve public awareness of cutting-edge developments in agricultural research, and encourage discourse with local farmers and educators on tools to improve preparedness against drought stress. Measurements from the 2019 field campaign and a description of the novel UAV system were first reported at the NIFA Project Director's Meeting / National Association of Plant Breeders Annual Meeting, which was held in Pine Mountain, Georgia on August 25, 2019. Students from across Cornell University's campus as well as visiting students from Wageningen University, the Netherlands, the University of São Paolo, Brazil, and the University of California, Davis, were also introduced to the project by PD Chang and Prof. Ying Sun through a demonstration during a Digital Agriculture Hackathon hosted by the Cornell Initiative for Digital Agriculture on February 28, 2020. Findings from this project will also be presented at the Ecological Society of America Annual Meeting which will be held during the first week of August 2020. However, the platform still requires further development before it is ready for public use. We will continue to work towards the last objective to connect with the farm community. In addition, the work from this project has directly contributed to three funded grants led by advisor Dr. Ying Sun: Sun, Y. 09/2019-08/2022. Dynamics of Chlorophyll Fluorescence and Its Relationship with Photosynthesis from Leaf to Continent: Theory Meets Data. NSF-MSB Sun, Y. (PI: Parazoo, N.) 09/2019-08/2022. Resolving the diurnal cycle of Solar-Induced Chlorophyll Fluorescence (SIF) from stomate to landscape. NASA JPL-SURP Sun, Y. (Co-PIs: C. Gomes, A. Ortiz-Bobea) 09/2019-08/2022. Remote-Sensing Based Framework for Farm-Scale In-Season Crop Yield Forecast. Cornell-CIDA

Publications

• Type: Journal Articles Status: Accepted Year Published: 2020 Citation: Chang C. Y., Zhou R., Kira O., Marri S., Skovira J., Gu L., Sun Y. An Unmanned Aerial System (UAS) for concurrent measurements of sun-induced chlorophyll fluorescence and hyperspectral reflectance to improve crop monitoring and stress detection. Agr. Forest Meteorol. Accepted with minor revisions, notified July 30, 2020.
• Type: Journal Articles Status: Published Year Published: 2020 Citation: Chang C. Y., Guanter L., Frankenberg C., K�hler P., Gu L., Magney, T. S., Grossmann, K., Sun Y. (2020) Systematic assessment of retrieval methods for ground solar-induced chlorophyll fluorescence (SIF) using high-frequency automated field spectroscopy. J. Geophys. Res: Biogeosciences 125: e2019JG005533. doi: 10.1029/2019JG005533
• Type: Journal Articles Status: Published Year Published: 2020 Citation: Wen J., K�hler P., Duveiller G., Parazoo N. C., Yu L., Chang C. Y., Sun Y. (2020) Generating a long-term record of high-resolution global solar-induced chlorophyll fluorescence (SIF) by harmonizing multiple satellite instruments: A case study for fusing GOME-2 and SCIAMACHY. Remote Sens. Environ. 239: 111644. doi: 10.1016/j.rse.2020.111644
• Type: Journal Articles Status: Published Year Published: 2019 Citation: Yu L., Wen J., Chang C. Y., Frankenberg C., Sun Y. (2019) High-resolution global contiguous SIF of OCO-2. Geophys. Res. Lett. 46: 1449-1458. doi: 10.1029/2018GL081109
• Type: Journal Articles Status: Published Year Published: 2019 Citation: Gu, L., Wood J. D., Chang C. Y., Sun, Y. (2019) Advancing Terrestrial Ecosystem Science with a Novel Automated Measurement System for Sun-Induced Chlorophyll Fluorescence for Integration with Eddy Covariance Flux Networks. J. Geophys. Res. 124: 127-146. doi: 10.1029/2018JG004742
• Type: Journal Articles Status: Published Year Published: 2019 Citation: Gu L., Han J., Wood J. D., Chang C. Y., Sun, Y. (2019) Sun-Induced Chlorophyll Fluorescence and its importance for modeling photosynthesis from the side of light reactions. New Phytol. 223: 1179-1191. doi: 10.1111/nph.15796
• Type: Journal Articles Status: Other Year Published: 2020 Citation: Chang C. Y., Han J., Wen J., Yu L., LeVonne J., Wang C., Fan Y., Ng K., Kira O., Melkonian J., Riha S., Gu L., Sun Y. Diurnal dynamics of Solar-induced chlorophyll fluorescence (SIF): Disentangling the confounding impacts from retrieval methods, canopy structures, and plant physiology. Manuscript in progress.
• Type: Conference Papers and Presentations Status: Accepted Year Published: 2020 Citation: Sun Y., Chang C. Y. (2020) Diurnal dynamics of Solar-Induced Chlorophyll Fluorescence (SIF): Disentangling the confounding impacts from retrieval methods, canopy structures, and plant physiology. Oral Presentation. Ecological Society of America Annual Meeting, August 2020, virtual meeting.

Progress 05/01/18 to 04/30/19

Outputs
Target Audience:During May-October 2018, I mentored one undergraduate student and fourPh.D. students to perform plant physiology measurements used for ground-validation of canopy sun-induced chlorophyll fluorescence (SIF) measured using a tower-based system developed in early 2018. In November 2018, I presented some of the summer work at a departmental seminar for the Soil and Crop Sciences Section of Cornell's School of Integrative Plant Science.In December 2018, I presented a poster of the summer work to international colleagues at the American Geophysical Union Fall Meeting. During Fall 2018-Spring 2019, I mentored fourmaster's students from Cornell's Department of Electrical & Computer Engineering, and one visiting Ph.D. student from China, to develop a UAV-portable version of the SIF tower system as well as upgrade the tower system components. In March 2019, I was invited to present a guestlecture and two laboratory sectionsfor the course "Geographic Information Systems (GIS): Concepts and Applications", in which I introduced undergraduatestudents to concepts related tosolar-induced chlorophyll fluorescence, remote sensing,and UAV platform development. Changes/Problems: Nothing Reported What opportunities for training and professional development has the project provided?This project has provided me with opportunities for mentoring a number of students of diverse experience, skills, and cultural and educational backgrounds, collaborating with the Engineering department to develop both the tower and UAV-based SIF systems, and interact with other agriculture researchers as a part of Cornell's Digital Agriculture Initiative. In addition, the presentation opportunities described below for Objective 3 havebeen beneficial for my professional career development. During 2018, I also attended several teaching workshops hosted by Cornell's Center for Teaching Innovation for the purpose of career development. How have the results been disseminated to communities of interest?For Objective 3, I shared our workwith other researchers at Cornell University as part of Cornell's Digital Agriculture Initiative in July 2018, presented some of the ground-basedfindings to departmental colleagues at aseminar in November 2018 andwith the international scientific community at the American Geophysical Union Fall Meeting in December 2018, and presenteda lecture about remote sensing and theUAV platform development for undergraduate students studying GIS and remote sensing in March 2019.I have alsoset up a Twitter account with the goal of sharing our work with a general audience. What do you plan to do during the next reporting period to accomplish the goals?We will be completing the UAV development for Objective 1 within the next month, and complete a second growing season of tower-based measurements (and first growing season of UAV-based measurements), which will provide the data required to achieve Objective 2. For Objective 3, more outreach activities are planned for 2019-2020 after the completion of the 2019 growing season, duringwhich I willwork with Deb Grantham and Susan Hoskins from Cornell's Co-operative Extension and Paul Stachowski from the Musgrave Research Farm to share our work with localcommunitiesinterested inUAV applications for agriculture.

Impacts
What was accomplished under these goals? Significant progress has been made towards Objectives 1 and 2 with some progress toward Objective 3. For Objective 1, the first prototype of the UAV-based system is currently nearing completion and will be tested for robustness over the next month (June 2019), with field measurements planned starting in July. For Objective 2, tower-based measurements of SIF and reflectance along with leaf-level physiological measurements of photosynthesis, chlorophyll fluorescence and leaf water potential collected during the 2018 growing season are under analysis. These measurements will be repeated in 2019 using the new improved tower SIF system, complemented by the UAV-based measurements. I am currently working on a paper from the 2018 data. Objective 3 is described in more detail below.

Publications