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Use of Infrared Thermometry to Measure Canopy-Air Temperature Difference at Partial Cover to Assess Crop Water Stress Index

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Use of Infrared Thermometry to Measure Canopy-Air Temperature Difference at Partial Cover to Assess Crop Water Stress Index Use of infrared thermometry to measure canopy-air temperature difference at partial cover to assess crop water stress index Item Type Thesis-Reproduction (electronic); text Authors Almeida, Julio Augusto Pires,1958- Publisher The University of Arizona. Rights Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author. Download date 01/10/2021 21:11:52 Link to Item http://hdl.handle.net/10150/191890 USE OF INFRARED THERMOMETRY TO MEASURE CANOPY- AIR TEMPERATURE DIFFERENCE AT PARTIAL COVER TO ASSESS CROP WATER STRESS INDEX by Julio Augusto Pires Almeida A Thesis Submitted to the Faculty of the DEPARTMENT OF AGRICULTURAL ENGINEERING In Partial Fulfillment of the Repuirements for the Degree of MASTER OF SCIENCE In the Graduate College THE UNIVERSITY OF ARIZONA 1986 STATEMENT BY AUTHOR This thesis has been submitted in partial fulfillment of requirements for an advanced degree at The University of Arizona and is deposited in the University Library to be made available to borrowers under rules of the Library. Brief quotations from this thesis are allowable without special permission, provided that accurate acknowledgment of source is made. Requests for permission for extended quotation from or reproduction of this manuscript in whole or in part may be granted by the head of the major department or the Dean of the Graduate College when in his/her judgment the proposed use of the material is in the interests of scholarship. In all other instances, however, permission must be obtained from the author. SIGNED: APPROVAL BY THESIS DIRECTOR Irsis has been approved on the date shown below: Professor of Agricultural Engineering To my mother and father Zulmira and Americo Almeida 111 ACKNOWLEDGMENTS I am certainly indebted to Dr. Donald C. Slack, my advisor, for his invaluable and indispensible assistance during the research phase and preparation of this thesis. My gratitude is also extended to Dr. Fangmeier, Dr. Matthias, Dr. Yitayew, Hamid Jalali-Farahari, Mark Killen, Steve Hughes, Charles Defer, Lou Stevens and others who were so willing to help at one time or another during the data collection phase of this research. iv TABLE OF CONTENTS Page LIST OF TABLES vii LIST OF ILLUSTRATIONS ix ABSTRACT xi CHAPTER 1. INTRODUCTION AND OBJECTIVES 1 2. LITERATURE REVIEW 4 Infrared Thermometry 4 Theoretical Approach 8 Empirical Approach 11 Digital Imagery 12 Line Source Sprinkler 14 3. METHODS AND MATERIALS 16 Materials 16 Methods 18 4. RESULTS AND DISCUSSION 26 Sensitivity Analysis 26 Measured Versus Predicted Canopy Temperatures 34 Crop Water Stress Index 50 Partial Cover 50 Full Cover 54 5. SUMMARY AND CONCLUSIONS 55 APPENDIX A: CALIBRATING IRT1 FOR NEGATIVE TEMPERATURES 59 APPENDIX B: CALIBRATING IRT1 FOR AREA VERSUS DISTANCE 61 APPENDIX C: CALIBRATING IRT1 AND IRT2 63 vi TABLE OF CONTENTS--Continued Page APPENDIX D: CALIBRATING THE DIGITAL CAMERA (DC) FOR AREA VERSUS DISTANCE 66 APPENDIX E: NEUTRON PROBE CALIBRATION 69 APPENDIX F: TABLES OF RESULTS 71 APPENDIX G: RAW DATA 86 SELECTED BIBLIOGRAPHY 96 LIST OF TABLES Table Page 1. Correlations between observed and predicted temperature by technique used 35 2. Rejections and acceptancies of null hypothesis at three confidence levels 42 3. Canopy cover range versus correlation coefficient for each set of data (technique) 44 Bi. IRT1 calibration data for distance versus area 62 Cl. IRT1 and IRT2 calibration data . 64 Dl. DC area versus distance calibration data 67 El. Neutron probe calibration data 70 Fl. Sensitivity analysis of equation (4) to its parameters . 72 F2. Analysis of field data, FARM.SE.1 78 F3. Analysis of laboratory data AETL.1 79 F4. Analysis of laboratory data AETL.4 80 F5. Analysis of laboratory data AETL.8A1 81 F6. Analysis of laboratory data AETL.COMP 82 vii viii LIST OF TABLES--Cintinued Table Page F7. CWSI values for field data (FARM.SE.1) 83 F8. CWSI values for laboratory data (AETL.8A1) 84 F9. CWSI values for field data FARM.SE.1 using Idso et al. (1982) relationship for cotton at full cover: intercept = 1.49 slope = 2.09 85 G1. Field data for FARM.NE.1, cotton at full cover 87 G2. Field data for FARM.SE.1, AETL., AETL.4, AETL.8A1, AETL.COMP, cotton at partial cover 88 LIST OF ILLUSTRATIONS Figure Page 1. Field plot layout 22 2. Sensitivity of the predicted canopy temperature to a 10 percent change in canopy cover as shown by canopy cover versus absolute value of observed minus predicted temperatures 28 3. Sensitivity of the predicted canopy temperature to a 10 percent change in soil temperature as shown by canopy cover versus absolute value of observed minus predicted temperatures 29 4. Sensitivity of the predicted canopy temperature to a 10 percent change in composite temperature as shown by canopy cover versus absolute value of observed minus predictd temperatures 30 5. Sensitivity of the predicted canopy temperature to a 10 percent change in canopy emissivity as shown by canopy cover versus absolute value of observed minus predicted temperatures 31 6. Relationship between observed and predicted canopy temperatures for field data (FARM.SE.1) 36 7. Relationship between observed and predicted canopy temperatures for laboratory data (AETL.1) 37 8. Relationship between observed and predicted canopy temperatures for laboratory data (AETL.4) 38 ix X LIST OF ILLUSTRATIONS--Continued Figure Page 9. Relationship between observed and predicted canopy temperatures for laboratory data (AETL.COMP) 39 10. Relationship between observed and predicted canopy temperatures for laboratory data (AETL.8A1) 40 11. Relationship between canopy cover and absolute value of observed minus predicted canopy temperatures for field data (FARM.SE.1) 4 5 12. Relationship between canopy cover and absolute value of observed minus predicted canopy temperatures for laboratory data (AETL.1) 46 13 Relationship between canopy cover and absolute value of observed minus predicted canopy temperatures data (AETL.4) 47 14 Relationship between canopy cover and absolute value of observed minus predicted canopy temperatures for laboratory data (AETL.COMP) 14 8 15. Relationship between canopy cover and absolute value of observed minus predicted canopy temperatures for laboratory data (AETL.8A1) 49 16. VPD versus canopy - air temperature difference for partial field and laboratory data 51 ABSTRACT A study on the potential for extracting canopy temperature from composite scenes of plant and soil background using infrared thermometry was carried out. Using field and laboratory data, Heilman's equation was tested for its sensitivity and ability to predict actual canopy temperature. Using Idso's approach, crop water stress index values were calculated for partial and full canopies. The study showed that under partial canopy situations calculated leaf temperature is very sensitive to soil background and composite scene temperatures and moderately sensitive to canopy emissivity and cover. Negative values as well as values greater than unity for crop water stress index were calculated for partial canopy conditions. Negative values were also reported for the full canopy cover conditions thus, establishing a need for better estimates of data used in calculation of crop water stress index values. Infrared thermometry does not show much promise as an irrigation scheduling technique for canopies under partial cover using the approach investigated in this study. xi CHAPTER 1 INTRODUCTION AND OBJECTIVES Timing of irrigation, or irrigation scheduling, is considered one of the most important and difficult tasks of irrigation science. Many methods have been developed for timing irrigation, the most common ones being soil based, climatic based and plant based (Jackson, 1983; Geiser et al., 1982). Soil based techniques have generally consisted of a means of directly or indirectly estimating soil moisture (i.e., neutron probes, tensiometers, gravimetric) and then replenishing the soil with water when a certain management allowed depletion level has been reached. Generally, the level of depletion is set at 50 to 60 percent (Geiser et al., 1982); however, it is dependent on crop, soil type, management and, water availability. Climatic based methods have been developed as alternatives to soil based methods. They are based on the evaporative demands incurred on the plants by the environment and, because they may include a plant factor, they are sometimes considered better than soil based approaches. Since the objectives of irrigation is to minimize plant stress, it would be to no one's surprise that the best scheduling technique should be considered plant based. Examples of plant based methods would be the pressure bomb, leaf diffusion porometer, leaf water content and, temperature. Of all these methods, temperature 1 2 is the one which probably holds a more promising future. The temperature method has caught the attention of many researchers interested in irrigation scheduling in the last decade. Most of the above mentioned techniques are based on measurements made on individual plant parts, whereas the temperature method can be representative of a large number of complete plants over a representative field area. Researchers like Tanner (1963) and Gates (1964) have long recognized the importance of temperature as an indicator of plant water status and tried to use it not only as a means of predicting crop yield but also, as a means of irrigation timing. Other researchers that have used crop temperature as a means of irrigation timing include Isdo et al. (1981), Idso (1982), Geiser et al. (1982), Jackson (1981), Hiler and Clark (1971) and, Hiler et al. (1974). The development of small portable infrared thermometers (IRT) has made canopy temperature an easily measured parameter in the field. Such IRT's are accurate to within +0.5 ° C. This development has opened the way for the much needed research in this subject.
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