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Plant Science Today (PST)

Review Articles

Vol. 12 No. 3 (2025)

Applications of thermal infrared remote sensing in agriculture

DOI
https://doi.org/10.14719/pst.9186
Submitted
29 April 2025
Published
25-06-2025 — Updated on 01-07-2025
Versions

Abstract

Agriculture is a fundamental sector globally, particularly in developing countries, as it provides food, feed and non-food products essential for economic and societal stability. Advancements in remote sensing technologies have greatly enhanced agricultural productivity and management. Thermal infrared remote sensing (TIRS) is a transformative tool in agriculture, enabling precise monitoring of crop and soil conditions by capturing and analysing the emitted radiation in the thermal infrared spectrum (3–14 μm). This technology offers critical
insights into crop and soil health. Unlike optical sensing, thermal remote sensing supports crop water stress assessment, soil moisture detection, irrigation scheduling, evapotranspiration monitoring, drought stress analysis, disease detection, soil property mapping, crop maturity assessment, yield estimation, tile drainage mapping and residue cover analysis. Integrating TIRS with multispectral and hyperspectral imaging enhances agricultural decision-making, optimises resource allocation and improves crop health. Future research should prioritize AI-driven real-time data processing by integrating machine learning, UAV-based imaging and IoT-enabled monitoring
systems. These advancements can enhance precision agriculture, optimize resource use and improve crop stress detection. As technological innovations continue to evolve, thermal remote sensing is poised to play a pivotal role in sustainable agricultural management, offering valuable insights to improve efficiency and resilience in farming practices.

References

  1. 1. Alazzai WK, Abood BSZ, Al-Jawahry HM, Obaid MK. Precision farming: The power of AI and IoT technologies. E3S Web of Conferences. 2024;491:04006. https://doi.org/10.1051/e3sconf/202449104006
  2. 2. Farella MM, Fisher JB, Jiao W, Key KB, Barnes ML. Thermal remote sensing for plant ecology from leaf to globe. J Ecol. 2022;110:1996-2014. https://doi.org/10.1111/1365-2745.13957
  3. 3. Lillesand T, Kiefer RW, Chipman J. Remote sensing and image interpretation: John Wiley & Sons; 2015.
  4. 4. Ishimwe R, Abutaleb K, Ahmed F. Applications of thermal imaging in agriculture-A Review. Adv Remote Sens. 2014;03(03):128-40. https://doi.org/10.4236/ars.2014.33011
  5. 5. Jensen JR. Remote sensing of the environment: An earth resource perspective 2/e: Pearson Education India; 2009.
  6. 6. Ullah S, Schlerf M, Skidmore AK, Hecker C. Identifying plant species using mid-wave infrared (2.5-6 μm) and thermal infrared (8-14 μm) emissivity spectra. Remote Sens Environ. 2012; 118:95-102. https://doi.org/10.1016/j.rse.2011.11.008
  7. 7. Khanal S, Fulton J, Shearer S. An overview of current and potential applications of thermal remote sensing in precision agriculture. Comput Electron Agric. 2017;139:22-32. https://doi.org/10.1016/j.compag.2017.05.001
  8. 8. Prakash A. Thermal remote sensing: Concepts, issues and applications. ISPRS Arch. 2000; XXXII (Part B1):239-43.
  9. 9. Zhu L, Suomalainen J, Liu J, Hyyppä J, Kaartinen H, Haggren H. A review: Remote sensing sensors. In: Rustamov RB, Hasanova S, Zeynalova MH, editors. Multi-purposeful application of geospatial data. Intechopen; 2018. p. 9-42. https://doi.org/10.5772/intechopen.71049
  10. 10. Hendel IG, Ross GM. Efficacy of remote sensing in early forest fire detection: A thermal sensor comparison. Can J Remote Sens. 2020;46(4):414-28. https://doi.org/10.1080/07038992.2020.1776597
  11. 11. Sagan V, Maimaitijiang M, Sidike P, Eblimit K, Peterson KT, Hartling S, et al. UAV-based high resolution thermal imaging for vegetation monitoring, and plant phenotyping using ICI 8640 P, FLIR Vue Pro R 640, and thermomap cameras. Remote Sens. 2019;11(3):330. https://doi.org/10.3390/rs11030330
  12. 12. Anderson MC, Hain C, Otkin J, Zhan X, Mo K, Svoboda M, et al. An intercomparison of drought indicators based on thermal remote sensing and NLDAS-2 simulations with US drought monitor classifications. J Hydrometeorol. 2013;14(4):1035-56. https://doi.org/10.1175/JHM-D-12-0140.1
  13. 13. Messina G, Modica G. Applications of UAV thermal imagery in precision agriculture: State of the art and future research outlook. Remote Sens. 2020;12(9):1491. https://doi.org/10.3390/rs12091491
  14. 14. Maguire MS, Neale CMU, Woldt WE. Improving accuracy of unmanned aerial system thermal infrared remote sensing for use in energy balance models in agriculture applications. Remote Sens. 2021;13(9):1635. https://doi.org/10.3390/rs13091635
  15. 15. Halliday D, Resnick R, Walker J. Fundamentals of physics: John Wiley & Sons; 2013.
  16. 16. Kuenzer C, Dech S. Thermal infrared remote sensing: Sensors, Methods, Applications. Springer Nature; 2013. https://doi.org/10.1007/978-94-007-6639-6
  17. 17. Jacob F, Petitcolin Fo, Schmugge T, Vermote E, French A, Ogawa K. Comparison of land surface emissivity and radiometric temperature derived from MODIS and ASTER sensors. Remote Sens Environ. 2004;90(2):137-52. https://doi.org/10.1016/j.rse.2003.11.015
  18. 18. Palazzi V, Gelati F, Vaglioni U, Alimenti F, Mezzanotte P, Roselli L. Leaf-compatible autonomous RFID-based wireless temperature sensors for precision agriculture. 2019 IEEE topical conference on wireless sensors and sensor networks (WiSNet); 2019: IEEE. https://doi.org/10.1109/WISNET.2019.8711808
  19. 19. Udelhoven T, Schlerf M, Segl K, Mallick K, Bossung C, Retzlaff R, et al. A satellite-based imaging instrumentation concept for hyperspectral thermal remote sensing. Sensors. 2017;17(7):1542. https://doi.org/10.3390/s17071542
  20. 20. Sishodia RP, Ray RL, Singh SK. Applications of remote sensing in precision agriculture: A review. Remote Sens. 2020;12(19):3136. https://doi.org/10.3390/rs12193136
  21. 21. Sundaresan J, Santosh K, Déri A, Roggema R, Singh R. Geospatial technologies and climate change: Springer; 2014. https://doi.org/10.1007/978-3-319-01689-4
  22. 22. Wang J, Wang Y, Li G, Qi Z. Integration of remote sensing and machine learning for precision agriculture: A comprehensive perspective on applications. Agronomy. 2024;14(9):1975. https://doi.org/10.3390/agronomy14091975
  23. 23. Szpakowski D, Jensen J. A review of the applications of remote sensing in fire ecology. Remote Sens. 2019;11(22):2638. https://doi.org/10.3390/rs11222638
  24. 24. Aryalekshmi B, Biradar RC, Mohammed Ahamed J. Thermal imaging techniques in agricultural applications. Int J Innov Tech Explor Engin. 2019;8(12):2162-68. https://doi.org/10.35940/ijitee.L2949.1081219
  25. 25. Gerhards M, Schlerf M, Mallick K, Udelhoven T. Challenges and future perspectives of multi-/hyperspectral thermal infrared remote sensing for crop water-stress detection: A review. Remote Sens. 2019;11(10):1240. https://doi.org/10.3390/rs11101240
  26. 26. Santesteban L, Di Gennaro S, Herrero-Langreo A, Miranda C, Royo J, Matese A. High-resolution UAV-based thermal imaging to estimate the instantaneous and seasonal variability of plant water status within a vineyard. Agric Water Manag. 2017;183:49-59. https://doi.org/10.1016/j.agwat.2016.08.026
  27. 27. Parihar G, Saha S, Giri LI. Application of infrared thermography for irrigation scheduling of horticulture plants. Smart Agric Technol. 2021;1:100021. https://doi.org/10.1016/j.atech.2021.100021
  28. 28. Garcia-Vasquez AC, Mokari E, Samani Z, Fernald A. Using UAV-thermal imaging to calculate crop water use and irrigation efficiency in a flood-irrigated pecan orchard. Agric Water Manag. 2022;272:107824. https://doi.org/10.1016/j.agwat.2022.107824
  29. 29. Taghvaeian S, Andales AA, Allen LN, Kisekka I, O'Shaughnessy SA, Porter DO, et al. Irrigation scheduling for agriculture in the United States: The progress made and the path forward. Trans ASABE. 2020;63(5):1603-18. https://doi.org/10.13031/trans.14110
  30. 30. Gu Z, Qi Z, Burghate R, Yuan S, Jiao X, Xu J. Irrigation scheduling approaches and applications: A review. J Irrig Drain Eng. 2020;146(6):04020007. https://doi.org/10.1061/(ASCE)IR.1943-4774.0001464
  31. 31. Quebrajo L, Pérez-Ruiz M, Pérez-Urrestarazu L, Martinez G, Egea G. Linking thermal imaging and soil remote sensing to enhance irrigation management of sugar beet. Biosyst Eng. 2018;165:77-87. https://doi.org/10.1016/j.biosystemseng.2017.08.013
  32. 32. Gerhards M, Rock G, Schlerf M, Udelhoven T. Water stress detection in potato plants using leaf temperature, emissivity, and reflectance. Int J Appl Earth Obs Geoinf. 2016;53:27-39. https://doi.org/10.1016/j.jag.2016.08.004
  33. 33. Zhou Z, Majeed Y, Naranjo GD, Gambacorta EM. Assessment for crop water stress with infrared thermal imagery in precision agriculture: A review and future prospects for deep learning applications. Comput Electron Agric. 2021;182:106019. https://doi.org/10.1016/j.compag.2021.106019
  34. 34. Payares LKA, Gomez‑del‑Campo M, Tarquis AM, García M. Thermal imaging from UAS for estimating crop water status in a Merlot vineyard in semi‑arid conditions. Irrig Sci. 2025;43:87-103. https://doi.org/10.1007/s00271-024-00955-1
  35. 35. Cho SB, Soleh HM, Choi JW, Hwang WH, Lee H, Cho YS, et al. Recent methods for evaluating crop water stress using AI Techniques: A review. Sensors. 2024;24(19):6313. https://doi.org/10.3390/s24196313
  36. 36. Dong H, Dong J, Sun S, Bai T, Zhao D, Yin Y, et al. Crop water stress detection based on UAV remote sensing systems. Agric Water Manag. 2024;303:109059. https://doi.org/10.1016/j.agwat.2024.109059
  37. 37. Mangus DL, Sharda A, Zhang N. Development and evaluation of thermal infrared imaging system for high spatial and temporal resolution crop water stress monitoring of corn within a greenhouse. Comput Electron Agric. 2016;121:149-59. https://doi.org/10.1016/j.compag.2015.12.007
  38. 38. Gebbers R, Adamchuk VI. Precision agriculture and food security. Science. 2010;327(5967):828-31. https://doi.org/10.1126/science.1183899
  39. 39. Ahmad U, Alvino A, Marino S. A review of crop water stress assessment using remote sensing. Remote Sens. 2021;13(20):4155. https://doi.org/10.3390/rs13204155
  40. 40. Idso S, Jackson R, Pinter Jr P, Reginato R, Hatfield J. Normalizing the stress-degree-day parameter for environmental variability. Agric Meteorol. 1981;24:45-55. https://doi.org/10.1016/0002-1571(81)90032-7
  41. 41. Jackson RD, Idso S, Reginato R, Pinter Jr P. Canopy temperature as a crop water stress indicator. Water Resour Res. 1981;17(4):1133-38. https://doi.org/10.1029/WR017i004p01133
  42. 42. Jones H, Schofield P. Thermal and other remote sensing of plant stress. Genet Plant Physiol. 2008;34(1-2):19-32.
  43. 43. Gutiérrez S, Diago M, Fernández-Novales J, Tardaguila J. On-the-go thermal imaging for water status assessment in commercial vineyards. Adv Anim Biosci. 2017;8(2):520-24. https://doi.org/10.1017/S204047001700108X
  44. 44. Egea G, Padilla-Díaz CM, Martinez-Guanter J, Fernández JE, Pérez-Ruiz M. Assessing a crop water stress index derived from aerial thermal imaging and infrared thermometry in super-high density olive orchards. Agric Water Manag. 2017;187:210-21. https://doi.org/10.1016/j.agwat.2017.03.030
  45. 45. Elsayed S, Elhoweity M, Ibrahim HH, Dewir YH, Migdadi HM, Schmidhalter U. Thermal imaging and passive reflectance sensing to estimate the water status and grain yield of wheat under different irrigation regimes. Agric Water Manag. 2017;189:98-110. https://doi.org/10.1016/j.agwat.2017.05.001
  46. 46. Bian J, Zhang Z, Chen J, Chen H, Cui C, Li X, et al. Simplified evaluation of cotton water stress using high resolution unmanned aerial vehicle thermal imagery. Remote Sens. 2019;11(3):267. https://doi.org/10.3390/rs11030267
  47. 47. Yue J, Tian J, Tian Q, Xu K, Xu N. Development of soil moisture indices from differences in water absorption between shortwave-infrared bands. ISPRS J Photogramm Remote Sens. 2019;154:216-30. https://doi.org/10.1016/j.isprsjprs.2019.06.012
  48. 48. Han L, Wang C, Liu Q, Wang G, Yu T, Gu X, et al. Soil moisture mapping based on multi-source fusion of optical, near-infrared, thermal infrared, and digital elevation model data via the bayesian maximum entropy framework. Remote Sens. 2020;12(23):3916. https://doi.org/10.3390/rs12233916
  49. 49. Khanal S, Kc K, Fulton JP, Shearer S, Ozkan E. Remote sensing in agriculture-accomplishments, limitations, and opportunities. Remote Sens. 2020;12(22):3783. https://doi.org/10.3390/rs12223783
  50. 50. Sharma N. Thermal remote sensing as a tool for irrigation scheduling. Agriculture & Food: e-Newsletter. 2020;2(4):97-98.
  51. 51. Mu T, Liu G, Yang X, Yu Y. Soil-moisture estimation based on multiple-source remote-sensing images. Remote Sens. 2023;15(1):139. https://doi.org/10.3390/rs15010139
  52. 52. Hassan-Esfahani L, Torres-Rua A, Jensen A, McKee M. Assessment of surface soil moisture using high-resolution multi-spectral imagery and artificial neural networks. Remote Sens. 2015;7(3):2627-46. https://doi.org/10.3390/rs70302627
  53. 53. Kashyap B, Kumar R. Sensing methodologies in agriculture for soil moisture and nutrient monitoring. IEEE Access. 2021;9:14095-121. https://doi.org/10.1109/ACCESS.2021.3052478
  54. 54. Liu Z, Zhao L, Peng Y, Wang G, Hu Y. Improving estimation of soil moisture content using a modified soil thermal inertia model. Remote Sens. 2020;12(11):1719. https://doi.org/10.3390/rs12111719
  55. 55. Zhang D, Zhou G. Estimation of soil moisture from optical and thermal remote sensing: A review. Sensors. 2016;16(8):1308. https://doi.org/10.3390/s16081308
  56. 56. Wang L, Qu JJ. Satellite remote sensing applications for surface soil moisture monitoring: A review. Front Earth Sci. 2009;3:237-47. https://doi.org/10.1007/s11707-009-0023-7
  57. 57. Carlson T. An overview of the "triangle method" for estimating surface evapotranspiration and soil moisture from satellite imagery. Sensors. 2007;7(8):1612-29. https://doi.org/10.3390/s7081612
  58. 58. Shafian S, Maas SJ. Index of soil moisture using raw landsat image digital count data in Texas high plains. Remote Sens. 2015;7(3):2352-72. https://doi.org/10.3390/rs70302352
  59. 59. Tariq A, Shu H, Siddiqui S, Imran M, Farhan M. Monitoring land use and land cover changes using geospatial techniques, a case study of Fateh Jang, Attock, Pakistan. Geogr Environ Sustain. 2021;14(1):41-52. https://doi.org/10.24057/2071-9388-2020-117
  60. 60. Ghiat I, Mackey HR, Al-Ansari T. A review of evapotranspiration measurement models, techniques and methods for open and closed agricultural field applications. Water. 2021;13(18):2523. https://doi.org/10.3390/w13182523
  61. 61. García-Santos V, Sánchez JM, Cuxart J. Evapotranspiration acquired with remote sensing thermal-based algorithms: A state-of-the-art review. Remote Sens. 2022;14(14):3440. https://doi.org/10.3390/rs14143440
  62. 62. Derardja B, Khadra R, Abdelmoneim AAA, El-Shirbeny MA, Valsamidis T, De Pasquale V, et al. Advancements in remote sensing for evapotranspiration estimation: A comprehensive review of temperature-based models. Remote Sens. 2024;16(11):1927. https://doi.org/10.3390/rs16111927
  63. 63. Cheng J, Kustas WP. Using very high resolution thermal infrared imagery for more accurate determination of the impact of land cover differences on evapotranspiration in an irrigated agricultural area. Remote Sens. 2019;11(6):613. https://doi.org/10.3390/rs11060613
  64. 64. Knipper KR, Kustas WP, Anderson MC, Alfieri JG, Prueger JH, Hain CR, et al. Evapotranspiration estimates derived using thermal-based satellite remote sensing and data fusion for irrigation management in California vineyards. Irrig Sci. 2019;37:431-49. https://doi.org/10.1007/s00271-018-0591-y
  65. 65. Pan S, Pan N, Tian H, Friedlingstein P, Sitch S, Shi H, et al. Evaluation of global terrestrial evapotranspiration using state-of-the-art approaches in remote sensing, machine learning and land surface modeling. Hydrol Earth Syst Sci. 2020;24(3):1485-509. https://doi.org/10.5194/hess-24-1485-2020
  66. 66. Kullberg EG, DeJonge KC, Chávez JL. Evaluation of thermal remote sensing indices to estimate crop evapotranspiration coefficients. Agric Water Manag. 2017;179:64-73. https://doi.org/10.1016/j.agwat.2016.07.007
  67. 67. Alkaraki KF, Hazaymeh K. A comprehensive remote sensing-based agriculture drought condition indicator (CADCI) using machine learning. Environ Chall. 2023;11:100699. https://doi.org/10.1016/j.envc.2023.100699
  68. 68. Qin Q, Wu Z, Zhang T, Sagan V, Zhang Z, Zhang Y, et al. Optical and thermal remote sensing for monitoring agricultural drought. Remote Sens. 2021;13(24):5092. https://doi.org/10.3390/rs13245092
  69. 69. Hazaymeh K, Hassan QK. Remote sensing of agricultural drought monitoring: A state of art review. AIMS Environ Sci. 2016;3(4):604-30. https://doi.org/10.3934/environsci.2016.4.604
  70. 70. Choi M, Jacobs JM, Anderson MC, Bosch DD. Evaluation of drought indices via remotely sensed data with hydrological variables. J Hydrol. 2013;476:265-73. https://doi.org/10.1016/j.jhydrol.2012.10.042
  71. 71. Martinelli F, Scalenghe R, Davino S, Panno S, Scuderi G, Ruisi P, et al. Advanced methods of plant disease detection. A review. Agron Sustain Dev. 2015;35:1-25. https://doi.org/10.1007/s13593-014-0246-1
  72. 72. Shakeel Q, Bajwa RT, Rashid I, Aslam HMU, Iftikhar Y, Mubeen M, et al. Concept and application of infrared thermography for plant disease measurement. In: Ul Haq I, Ijaz S, editors. Trends in Plant Disease Assessment. Springer, Singapore; 2022. p. 109-25. https://doi.org/10.1007/978-981-19-5896-0_7
  73. 73. Choudhary A, Sharma S, Yadav P. Remote sensing: A tool of plant disease management. Just Agriculture Multidisciplinary Newsletter. 2022;2(7):1-6.
  74. 74. Omran ESE. Early sensing of peanut leaf spot using spectroscopy and thermal imaging. Arch Agron Soil Sci. 2017;63(7):883-96. https://doi.org/10.1080/03650340.2016.1247952
  75. 75. Oerke EC. Remote sensing of diseases. Annu Rev Phytopathol. 2020;58(1):225-52. https://doi.org/10.1146/annurev-phyto-010820-012832
  76. 76. Şahi̇n YS, Bütüner AK, Erdoğan H. Potential for early detection of powdery mildew in okra under field conditions using thermal imaging. Scientific Papers Series Management, Economic Engineering in Agriculture and Rural Development. 2023;23(3):867-70.
  77. 77. Raza SE, Prince G, Clarkson JP, Rajpoot NM. Automatic detection of diseased tomato plants using thermal and stereo visible light images. PloS One. 2015;10(4):e0123262. https://doi.org/10.1371/journal.pone.0123262
  78. 78. Yao Z, He D, Lei Y. Thermal imaging for early non destructive detection of wheat stripe rust. 2018 ASABE Annual International Meeting. 2018;1801728. https://doi.org/10.13031/aim.201801728
  79. 79. Chaerle L, Hagenbeek D, De Bruyne E, Valcke R, Van Der Straeten D. Thermal and chlorophyll-fluorescence imaging distinguish plant-pathogen interactions at an early stage. Plant Cell Physiol. 2004;45(7):887-96. https://doi.org/10.1093/pcp/pch097
  80. 80. Zhang J, Huang Y, Pu R, Gonzalez-Moreno P, Yuan L, Wu K, et al. Monitoring plant diseases and pests through remote sensing technology: A review. Comput Electron Agric. 2019;165:104943. https://doi.org/10.1016/j.compag.2019.104943
  81. 81. Cao F, Liu F, Guo H, Kong W, Zhang C, He Y. Fast detection of Sclerotinia sclerotiorum on oilseed rape leaves using low-altitude remote sensing technology. Sensors. 2018;18(12):4464. https://doi.org/10.3390/s18124464
  82. 82. Hashim I, Shariff A, Bejo S, Muharam F, Ahmad K, Hashim H. Application of thermal imaging for plant disease detection. IOP Conf Ser: Earth Environ Sci. 2020;540:012052. https://doi.org/10.1088/1755-1315/540/1/012052
  83. 83. Jafari M, Minaei S, Safaie N. Detection of pre-symptomatic rose powdery-mildew and gray-mold diseases based on thermal vision. Infrared Phys Technol. 2017;85:170-83. https://doi.org/10.1016/j.infrared.2017.04.023
  84. 84. Oerke EC, Fröhling P, Steiner U. Thermographic assessment of scab disease on apple leaves. Precis Agric. 2011;12:699-715. https://doi.org/10.1007/s11119-010-9212-3
  85. 85. Stoll M, Schultz HR, Baecker G, Berkelmann-Loehnertz B. Early pathogen detection under different water status and the assessment of spray application in vineyards through the use of thermal imagery. Precis Agric. 2008;9:407-17. https://doi.org/10.1007/s11119-008-9084-y
  86. 86. Oerke EC, Steiner U, Dehne HW, Lindenthal M. Thermal imaging of cucumber leaves affected by downy mildew and environmental conditions. J Exp Bot. 2006;57(9):2121-32. https://doi.org/10.1093/jxb/erj170
  87. 87. Wang L, Poque S, Valkonen JP. Phenotyping viral infection in sweet potato using a high-throughput chlorophyll fluorescence and thermal imaging platform. Plant Methods. 2019;15:1-14. https://doi.org/10.1186/s13007-019-0501-1
  88. 88. Al-doski J, Mansor SB, Shafri H, Zulhaidi H. Thermal imaging for pests detecting-A review. Int J Agric Plant. 2016;2:10-30.
  89. 89. Ibrahim A, Yousry M, Saad M, Mahmoud M, Said M, Ameen A. Infrared thermal imaging as an innovative approach for early detection infestation of stored product insects in certain stored grains. Cercetări Agronomice în Moldova. 2020;LII(4):321-31. https://doi.org/10.46909/cerce-2019-0031
  90. 90. Manickavasagan A, Jayas D, White N. Thermal imaging to detect infestation by Cryptolestes ferrugineus inside wheat kernels. J Stored Prod Res. 2008;44(2):186-92. https://doi.org/10.1016/j.jspr.2007.10.006
  91. 91. Chelladurai V, Kaliramesh S, Jayas D. Detection of Callosobruchus maculatus (F.) infestation in mung bean (Vigna radiata) using thermal imaging technique. In: NABEC-CSBE/SCGAB 2012 Joint Meeting and Technical Conference Northeast Agricultural & Biological Engineering Conference Canadian Society for Bioengineering Lakehead University, Orillia, Ontario July 15-18, 2012. Available from: https://library.csbescgab.ca/docs/meetings/2012/CSBE12121.pdf
  92. 92. Yüzügüllü O, Fajraoui N, Liebisch F. Soil texture and pH mapping using remote sensing and support sampling. IEEE J Sel Top Appl Earth Obs Remote Sens. 2024;17:12685-12705. https://doi.org/10.1109/JSTARS.2024.3422494
  93. 93. Mirzaeitalarposhti R, Shafizadeh-Moghadam H, Taghizadeh-Mehrjardi R, Demyan MS. Digital soil texture mapping and spatial transferability of machine learning models using Sentinel-1, Sentinel-2, and terrain-derived covariates. Remote Sens. 2022;14(23):5909. https://doi.org/10.3390/rs14235909
  94. 94. Liu F, Zhang GL, Song X, Li D, Zhao Y, Yang J, et al. High-resolution and three-dimensional mapping of soil texture of China. Geoderma. 2020;361:114061. https://doi.org/10.1016/j.geoderma.2019.114061
  95. 95. Wang DC, Zhang GL, Zhao MS, Pan XZ, Zhao YG, Li DC, et al. Retrieval and mapping of soil texture based on land surface diurnal temperature range data from MODIS. PloS One. 2015;10(6): e0129977. https://doi.org/10.1371/journal.pone.0129977
  96. 96. Jensen T, Apan A, Zeller L. Crop maturity mapping using a low-cost low-altitude remote sensing system. Proceedings of the 2009 Surveying and Spatial Sciences Institute Biennial International Conference (SSC 2009); 2009.
  97. 97. Dunn B, Dunn T. Predicting rice crop maturity using remote sensing [Internet]. New South Wales: NSW Department of Primary Industries; 2021 [cited 2024 Dec 20]:137-139. Available from: https://www.dpi.nsw.gov.au/__data/assets/pdf_file/0009/1365192/SRR21-book-web-cm29Oct2021.pdf
  98. 98. Wang L, Gao R, Li C, Wang J, Liu Y, Hu J, et al. Mapping soybean maturity and biochemical traits using UAV-based hyperspectral images. Remote Sens. 2023;15(19):4807. https://doi.org/10.3390/rs15194807
  99. 99. Zhuo W, Huang J, Gao X, Ma H, Huang H, Su W, et al. Prediction of winter wheat maturity dates through assimilating remotely sensed leaf area index into crop growth model. Remote Sens. 2020;12(18):2896. https://doi.org/10.3390/rs12182896
  100. 100. Trentin C, Ampatzidis Y, Lacerda C, Shiratsuchi L. Tree crop yield estimation and prediction using remote sensing and machine learning: A systematic review. Smart Agric Technol. 2024:100556. https://doi.org/10.1016/j.atech.2024.100556
  101. 101. Ali AM, Abouelghar M, Belal A, Saleh N, Yones M, Selim AI, et al. Crop yield prediction using multi sensors remote sensing. Egypt J Remote Sens Space Sci. 2022;25(3):711-16. https://doi.org/10.1016/j.ejrs.2022.04.006
  102. 102. Abdul-Jabbar T, Ziboon A, Albayati M. Crop yield estimation using different remote sensing data: Literature review. IOP Conference Series: Earth Environ Sci. 2023: IOP Publishing. https://doi.org/10.1088/1755-1315/1129/1/012004
  103. 103. Maimaitijiang M, Sagan V, Sidike P, Hartling S, Esposito F, Fritschi FB. Soybean yield prediction from UAV using multimodal data fusion and deep learning. Remote Sens Environ. 2020;237:111599. https://doi.org/10.1016/j.rse.2019.111599
  104. 104. Leroux L, Baron C, Zoungrana B, Traoré SB, Seen DL, Bégué A. Crop monitoring using vegetation and thermal indices for yield estimates: Case study of a rainfed cereal in semi-arid West Africa. IEEE J Sel Top Appl Earth Obs Remote Sens. 2015;9(1):347-62. https://doi.org/10.1109/JSTARS.2015.2501343
  105. 105. Sakamoto T, Gitelson AA, Arkebauer TJ. MODIS-based corn grain yield estimation model incorporating crop phenology information. Remote Sens Environ. 2013;131:215-31. https://doi.org/10.1016/j.rse.2012.12.017
  106. 106. Mkhabela M, Bullock P, Raj S, Wang S, Yang Y. Crop yield forecasting on the Canadian Prairies using MODIS NDVI data. Agric Meteorol. 2011;151(3):385-93. https://doi.org/10.1016/j.agrformet.2010.11.012
  107. 107. Du WY, Zhang LD, Hu ZF, Shamaila Z, Zeng AJ, Song JL, et al. Utilization of thermal infrared image for inversion of winter wheat yield and biomass. Spectrosc Spect Anal. 2011;31(6):1476-80.
  108. 108. Hellebrand H, Linke M, Beuche H, Herold B, Geyer M. Horticultural products evaluated by thermography. AgEng, Warwick. 2000. p. 26-27.
  109. 109. Tilahun T, Seyoum WM. High-resolution mapping of tile drainage in agricultural fields using unmanned aerial system (UAS)-based radiometric thermal and optical sensors. Hydrology. 2021;8(1):2. https://doi.org/10.3390/hydrology8010002
  110. 110. Koganti T, Ghane E, Martinez LR, Iversen BV, Allred BJ. Mapping of agricultural subsurface drainage systems using unmanned aerial vehicle imagery and ground penetrating radar. Sensors. 2021;21(8):2800. https://doi.org/10.3390/s21082800
  111. 111. King KW, Williams MR, Fausey NR. Contributions of systematic tile drainage to watershed‐scale phosphorus transport. J Environ Qual. 2015;44(2):486-94. https://doi.org/10.2134/jeq2014.04.0149
  112. 112. Smith DR, King KW, Johnson L, Francesconi W, Richards P, Baker D, et al. Surface runoff and tile drainage transport of phosphorus in the midwestern United States. J Environ Qual. 2015;44(2):495-502. https://doi.org/10.2134/jeq2014.04.0176
  113. 113. Allred B, Eash N, Freeland R, Martinez L, Wishart D. Effective and efficient agricultural drainage pipe mapping with UAS thermal infrared imagery: A case study. Agric Water Manag. 2018;197:132-37. https://doi.org/10.1016/j.agwat.2017.11.011
  114. 114. Woo DK, Song H, Kumar P. Mapping subsurface tile drainage systems with thermal images. Agric Water Manag. 2019;218:94-101. https://doi.org/10.1016/j.agwat.2019.01.031
  115. 115. Rahmani SR, Schulze DG. Mapping subsurface tile lines on a research farm using aerial photography, paper maps, and expert knowledge. Agrosyst Geosci Environ. 2023;6(2):e20362. https://doi.org/10.1002/agg2.20362
  116. 116. de Paul OV. Remote sensing, surface residue cover and tillage practice. J Environ Prot. 2012;3(2):211-17. https://doi.org/10.4236/jep.2012.32026
  117. 117. Lin N, Ma X, Jiang R, Wu M, Zhang W. Estimation of maize residue cover using remote sensing based on adaptive threshold segmentation and Cat Boost algorithm. Agriculture. 2024;14(5):711. https://doi.org/10.3390/agriculture14050711
  118. 118. Barnes ML, Yoder L, Khodaee M. Detecting winter cover crops and crop residues in the midwest US using machine learning classification of thermal and optical imagery. Remote Sens. 2021;13(10):1998. https://doi.org/10.3390/rs13101998
  119. 119. Yang L, Lu B, Schmidt M, Natesan S, McCaffrey D. Applications of remote sensing for crop residue cover mapping. Smart Agric Technol. 2025;11:100880. https://doi.org/10.1016/j.atech.2025.100880
  120. 120. Sullivan D, Shaw J, Mask P, Rickman D, Guertal E, Luvall J, et al. Evaluation of multispectral data for rapid assessment of wheat straw residue cover. Soil Sci Soc Am J. 2004;68(6):2007-13. https://doi.org/10.2136/sssaj2004.2007

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