High-Resolution Stress Detection in Crops: Integrating Satellite and Drone Remote Sensing for Resilient Agriculture
Main Article Content
Abstract
Satellite and drone-based remote sensing technologies are transforming how we detect plant stress by offering wide-area, non-contact monitoring tools. These systems can identify early signs of drought, heat, salinity, and nutrient stress using vegetation indices like NDVI, NDWI, and red-edge reflectance. In many developing regions, food systems remain vulnerable, yet field-based monitoring continues to be the standard, often too slow and limited to fully capture what is occurring across farms. The review is based on a structured synthesis of peer-reviewed studies and technical reports published between 2007 and 2025, selected through targeted keyword searches across Scopus, Web of Science, and Google Scholar, with emphasis on field-level applications in stress-prone agricultural systems. It examines how remote sensing is currently being applied to track abiotic stress across major crops, using case studies from India, China, Mali, and Sudan. These case studies help illustrate what is effective and where gaps still exist. Notably, NDVI and canopy temperature indices have shown strong correlations with drought severity and crop losses. Key challenges include the lack of stress indices tailored to local crops and soils, a limited connection between detected stress and yield outcomes, and the high costs or technical barriers associated with drone use. The review also outlines specific future research needs, such as how to detect multiple stressors simultaneously, enhance drought detection in drylands, and develop low-cost, accessible remote sensing tools that can assist smallholder farmers. These findings underscore the need for localized, affordable remote sensing solutions to bridge the gap between research and practice.
Article Details
This work is licensed under a Creative Commons Attribution 4.0 International License.
Courage Humphrey Ojeilua, University of Benin, Nigeria.
Department of Plant Biology and Biology
Favour N. Eze, Federal University of Technology, Akure, Nigeria.
Remote Sensing and GIS Department
Chijioke Cyriacus Ekechi, Tennessee Technological University
Department of Electrical and Computer Engineering
Oluwabukola Eunice Atijosan, State University of Ponta grossa, Parana, Brazil.
Department of Agronomy
Favour Chizurum Ukasoanya, Federal University of Technology Owerri, Nigeria.
Department of Mechatronics, Robotics, and Automation Engineering.
Lilian Chisom Chinwero, Enugu State University of Science and Technology, Nigeria.
Department of Science Education (Biology)
Somtochukwu Cyriacus Ekechi, Fanshawe College of Applied Arts and Technology, Nigeria.
Department of Information Technology
Adão, T., Hruška, J., Pádua, L., Bessa, J., Peres, E., Morais, R., & Sousa, J. (2017). Hyperspectral imaging: A review on UAV-based sensors, data processing and applications for agriculture and forestry. Remote Sensing, 9(11), 1110. https://doi.org/10.3390/rs9111110 DOI: https://doi.org/10.3390/rs9111110
Agarwal, A., Colwell, J., Dinnis, R., Andrea, V., Hill, T. R., Boonham, N., & Ankush Prashar. (2025). Infrared thermography in plant factories: Solving spatiotemporal variations via machine learning. Modern Agriculture, 3(1). https://doi.org/10.1002/moda.70012 DOI: https://doi.org/10.1002/moda.70012
Al-Najadi, R., Al-Mulla, Y., Al-Abri, I., & Al-Sadi, A. M. (2025). Effectiveness of drone-based thermal sensors in optimizing controlled environment agriculture performance under arid conditions. Scientific Reports, 15(1). https://doi.org/10.1038/s41598-025-94432-0 DOI: https://doi.org/10.1038/s41598-025-94432-0
Ariante, G., & Core, G. D. (2025). Unmanned aircraft systems (UASs): Current state, emerging technologies, and future trends. Drones, 9(1), 59–59. https://doi.org/10.3390/drones9010059 DOI: https://doi.org/10.3390/drones9010059
Barnabás, B., Jäger, K., & Fehér, A. (2008). The effect of drought and heat stress on reproductive processes in cereals. Plant, Cell & Environment, 31(1), 11–38. https://doi.org/10.1111/j.1365-3040.2007.01727.x DOI: https://doi.org/10.1111/j.1365-3040.2007.01727.x
Berger, K., Machwitz, M., Kycko, M., Kefauver, S. C., Wittenberghe, S. V., Gerhards, M., Verrelst, J., Atzberger, C., der Tol , C. van, Damm, A., Rascher, U., Herrmann, I., Paz, V. S., Fahrner, S., Pieruschka, R., Prikaziuk, E., Buchaillot, M. L., Halabuk, A., Celesti, M., & Koren, G. (2022). Multi-sensor spectral synergies for crop stress detection and monitoring in the optical domain: A review. Remote Sensing of Environment, 280, 113198–113198. https://doi.org/10.1016/j.rse.2022.113198 DOI: https://doi.org/10.1016/j.rse.2022.113198
Berni, J. A. J., Zarco-Tejada, P. J., Sepulcre-Cantó, G., Fereres, E., & Villalobos, F. A. (2009). Mapping canopy conductance and CWSI in olive orchards using high-resolution thermal remote sensing imagery. Remote Sensing of Environment, 113(11), 2380–2388. https://doi.org/10.1016/j.rse.2009.06.018 DOI: https://doi.org/10.1016/j.rse.2009.06.018
Berni, J., Zarco-Tejada, P. J., Suarez, L., & Fereres, E. (2009). Thermal and narrowband multispectral remote sensing for vegetation monitoring from an unmanned aerial vehicle. IEEE Transactions on Geoscience and Remote Sensing, 47(3), 722–738. https://doi.org/10.1109/tgrs.2008.2010457 DOI: https://doi.org/10.1109/TGRS.2008.2010457
Brewer, T. (2012). Remote sensing of vegetation: Principles, techniques and applications. Soil Use and Management, 28(1), 134–134. https://doi.org/10.1111/j.1475-2743.2011.00383.x DOI: https://doi.org/10.1111/j.1475-2743.2011.00383.x
Carracelas, G., Ballester, C., Marchesi, C., Roel, A., & Hornbuckle, J. (2024). Assessing drone-based remote sensing indices for monitoring rice nitrogen plant status under different irrigation techniques. Agronomy, 14(12), 2976. https://doi.org/10.3390/agronomy14122976 DOI: https://doi.org/10.3390/agronomy14122976
Chaiyana, A., Hanchoowong, R., Srihanu, N., Prasanchum, H., Kangrang, A., Hormwichian, R., Kaewplang, S., Koedsin, W., & Huete, A. (2024). Leveraging remotely sensed and climatic data for improved crop yield prediction in the Chi Basin, Thailand. Sustainability, 16(6), 2260. https://doi.org/10.3390/su16062260 DOI: https://doi.org/10.3390/su16062260
Chandel, N. S., Tiwari, P. S., Singh, K. P., Jat, D., Gaikwad, B. B., Tripathi, H., & Golhani, K. (2019). Yield prediction in wheat (Triticum aestivum L.) using spectral reflectance indices. Current Science, 116(2), 272–272. https://doi.org/10.18520/cs/v116/i2/272-278 DOI: https://doi.org/10.18520/cs/v116/i2/272-278
Das, A. C., Shahriar, S. A., Chowdhury, M. A., Hossain, M. L., Mahmud, S., Tusar, M. K., Ahmed, R., & Salam, M. A. (2023). Assessment of remote sensing-based indices for drought monitoring in the north-western region of Bangladesh. Heliyon, 9(2), e13016. https://doi.org/10.1016/j.heliyon.2023.e13016 DOI: https://doi.org/10.1016/j.heliyon.2023.e13016
Delaney, B., Tansey, K., & Whelan, M. (2025). Satellite remote sensing techniques and limitations for identifying bare soil. Remote Sensing, 17(4), 630–630. https://doi.org/10.3390/rs17040630 DOI: https://doi.org/10.3390/rs17040630
Delegido, J., Verrelst, J., Meza, C. M., Rivera, J. P., Alonso, L., & Moreno, J. (2013). A red-edge spectral index for remote sensing estimation of green LAI over agroecosystems. European Journal of Agronomy, 46, 42–52. https://doi.org/10.1016/j.eja.2012.12.001 DOI: https://doi.org/10.1016/j.eja.2012.12.001
Dong, H., Dong, J., Sun, S., Bai, T., Zhao, D., Yin, Y., Shen, X., Wang, Y., Zhang, Z., & Wang, Y. (2024). Crop water stress detection based on UAV remote sensing systems. Agricultural Water Management, 303, 109059–109059. https://doi.org/10.1016/j.agwat.2024.109059 DOI: https://doi.org/10.1016/j.agwat.2024.109059
Drusch, M., Del Bello, U., Carlier, S., Colin, O., Fernandez, V., Gascon, F., Hoersch, B., Isola, C., Laberinti, P., Martimort, P., Meygret, A., Spoto, F., Sy, O., Marchese, F., & Bargellini, P. (2012). Sentinel-2: ESA’s optical high-resolution mission for GMES operational services. Remote Sensing of Environment, 120, 25–36. https://doi.org/10.1016/j.rse.2011.11.026 DOI: https://doi.org/10.1016/j.rse.2011.11.026
Ecke, S., Dempewolf, J., Frey, J., Schwaller, A., Endres, E., Klemmt, H.-J., Tiede, D., & Seifert, T. (2022). UAV-Based forest health monitoring: A systematic review. Remote Sensing, 14(13), 3205. https://doi.org/10.3390/rs14133205 DOI: https://doi.org/10.3390/rs14133205
Elhag, K., & Zhang, W. (2018). Monitoring and assessment of drought focused on its impact on sorghum yield over Sudan by using meteorological drought indices for the period 2001–2011. Remote Sensing, 10(8), 1231. https://doi.org/10.3390/rs10081231 DOI: https://doi.org/10.3390/rs10081231
Guo, F., Feng, Q., Yang, S., & Yang, W. (2024). Estimation of potato canopy leaf water content in various growth stages using UAV hyperspectral remote sensing and machine learning. Frontiers in Plant Science, 15. https://doi.org/10.3389/fpls.2024.1458589 DOI: https://doi.org/10.3389/fpls.2024.1458589
Hemati, M., Hasanlou, M., Mahdianpari, M., & Mohammadimanesh, F. (2021). A systematic review of Landsat data for change detection applications: 50 years of monitoring the Earth. Remote Sensing, 13(15), 2869. https://doi.org/10.3390/rs13152869 DOI: https://doi.org/10.3390/rs13152869
Jang, G., Kim, J., Yu, J.-K., Kim, H.-J., Kim, Y., Kim, D.-W., Kim, K.-H., Lee, C. W., & Chung, Y. S. (2020). Review: Cost-Effective unmanned aerial vehicle (UAV) platform for field plant breeding application. Remote Sensing, 12(6), 998. https://doi.org/10.3390/rs12060998 DOI: https://doi.org/10.3390/rs12060998
Jones, H. G. (2002). Use of infrared thermography for monitoring stomatal closure in the field: Application to grapevine. Journal of Experimental Botany, 53(378), 2249–2260. https://doi.org/10.1093/jxb/erf083 DOI: https://doi.org/10.1093/jxb/erf083
Jones, H. G., Serraj, R., Loveys, B. R., Xiong, L., Wheaton, A., & Price, A. H. (2009). Thermal infrared imaging of crop canopies for the remote diagnosis and quantification of plant responses to water stress in the field. Functional Plant Biology, 36(11), 978. https://doi.org/10.1071/fp09123 DOI: https://doi.org/10.1071/FP09123
Jones, S. E., Ayanlade, T. T., Fallen, B., Jubery, T. Z., Singh, A., Ganapathysubramanian, B., Sarkar, S., & Singh, A. K. (2024). Multi‐sensor and multi‐temporal high‐throughput phenotyping for monitoring and early detection of water‐limiting stress in soybean. The Plant Phenome Journal, 7(1). https://doi.org/10.1002/ppj2.70009 DOI: https://doi.org/10.1002/ppj2.70009
Karnieli, A., Agam, N., Pinker, R. T., Anderson, M., Imhoff, M. L., Gutman, G. G., Panov, N., & Goldberg, A. (2010). Use of NDVI and land surface temperature for drought assessment: Merits and limitations. Journal of Climate, 23(3), 618–633. https://doi.org/10.1175/2009JCLI2900.1 DOI: https://doi.org/10.1175/2009JCLI2900.1
Katrina Ariel Henn, & Peduzzi, A. (2024). Surface heat monitoring with high-resolution UAV thermal imaging: Assessing accuracy and applications in urban environments. Remote Sensing, 16(5), 930–930. https://doi.org/10.3390/rs16050930 DOI: https://doi.org/10.3390/rs16050930
Khaliq, A., Comba, L., Biglia, A., Ricauda Aimonino, D., Chiaberge, M., & Gay, P. (2019). Comparison of satellite and UAV-based multispectral imagery for vineyard variability assessment. Remote Sensing, 11(4), 436. https://doi.org/10.3390/rs11040436 DOI: https://doi.org/10.3390/rs11040436
Krause, S., & Sanders, T. G. (2023). Mapping tree water deficit with UAV thermal imaging and meteorological data. Remote Sensing in Earth Systems Sciences, 6(3-4), 275–296. https://doi.org/10.1007/s41976-023-00094-9 DOI: https://doi.org/10.1007/s41976-023-00094-9
Lausch, A., Erasmi, S., King, D., Magdon, P., & Heurich, M. (2017). Understanding forest health with remote sensing- Part II—A review of approaches and data models. Remote Sensing, 9(2), 129. https://doi.org/10.3390/rs9020129 DOI: https://doi.org/10.3390/rs9020129
Li, D., Miao, Y., Gupta, S. K., Rosen, C. J., Yuan, F., Wang, C., Wang, L., & Huang, Y. (2021). Improving potato yield prediction by combining cultivar information and UAV remote sensing data using machine learning. Remote Sensing, 13(16), 3322. https://doi.org/10.3390/rs13163322 DOI: https://doi.org/10.3390/rs13163322
Li, Y., Qu, T., Wang, Y., Zhao, Q., Jia, S., Yin, Z., Guo, Z., Wang, G., Li, F., & Zhang, W. (2024). UAV-Based remote sensing to evaluate daily water demand characteristics of maize: A case study from yuci lifang organic dry farming experimental base in jinzhong city, china. Agronomy, 14(4), 729. https://doi.org/10.3390/agronomy14040729 DOI: https://doi.org/10.3390/agronomy14040729
Lobell, D. B., Di Tommaso, S., You, C., Yacoubou Djima, I., Burke, M., & Kilic, T. (2019). Sight for sorghums: Comparisons of satellite- and ground-based sorghum yield estimates in Mali. Remote Sensing, 12(1), 100. https://doi.org/10.3390/rs12010100 DOI: https://doi.org/10.3390/rs12010100
Marino, S. (2022). Assessing the agronomic subfield variability by Sentinel-2 NDVI time-series and landscape position. Agronomy, 13(1), 44. https://doi.org/10.3390/agronomy13010044 DOI: https://doi.org/10.3390/agronomy13010044
Matsushita, B., Yang, W., Chen, J., Onda, Y., & Qiu, G. (2007). Sensitivity of the enhanced vegetation index (EVI) and normalized difference vegetation index (NDVI) to topographic effects: A case study in high-density cypress forest. Sensors, 7(11), 2636–2651. https://doi.org/10.3390/s7112636 DOI: https://doi.org/10.3390/s7112636
McCabe, M. F., Rodell, M., Alsdorf, D. E., Miralles, D. G., Uijlenhoet, R., Wagner, W., Lucieer, A., Houborg, R., Verhoest, N. E. C., Franz, T. E., Shi, J., Gao, H., & Wood, E. F. (2017). The future of Earth observation in hydrology. Hydrology and Earth System Sciences, 21(7), 3879–3914. https://doi.org/10.5194/hess-21-3879-2017 DOI: https://doi.org/10.5194/hess-21-3879-2017
Nawar, S., Buddenbaum, H., Hill, J., & Kozak, J. (2014). Modeling and mapping of soil salinity with reflectance spectroscopy and Landsat data using two quantitative methods (PLSR and MARS). Remote Sensing, 6(11), 10813–10834. https://doi.org/10.3390/rs61110813 DOI: https://doi.org/10.3390/rs61110813
Neinavaz, E., Schlerf, M., Darvishzadeh, R., Gerhards, M., & Skidmore, A. K. (2021). Thermal infrared remote sensing of vegetation: Current status and perspectives. International Journal of Applied Earth Observation and Geoinformation, 102, 102415. https://doi.org/10.1016/j.jag.2021.102415 DOI: https://doi.org/10.1016/j.jag.2021.102415
Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., & McGuinness, L. A. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. British Medical Journal, 372(71). https://doi.org/10.1136/bmj.n71 DOI: https://doi.org/10.31222/osf.io/v7gm2
Patil, P. P., Jagtap, M. P., Khatri, N., Madan, H., Aditya Abhiram Vadduri, & Tarun Patodia. (2024). Exploration and advancement of NDDI leveraging NDVI and NDWI in Indian semi-arid regions: A remote sensing-based study. Case Studies in Chemical and Environmental Engineering, 9, 100573–100573. https://doi.org/10.1016/j.cscee.2023.100573 DOI: https://doi.org/10.1016/j.cscee.2023.100573
Roy, D. P., Wulder, M. A., Loveland, T. R., C.E., W., Allen, R. G., Anderson, M. C., Helder, D., Irons, J. R., Johnson, D. M., Kennedy, R., Scambos, T. A., Schaaf, C. B., Schott, J. R., Sheng, Y., Vermote, E. F., Belward, A. S., Bindschadler, R., Cohen, W. B., Gao, F., & Hipple, J. D. (2014). Landsat-8: Science and product vision for terrestrial global change research. Remote Sensing of Environment, 145, 154–172. https://doi.org/10.1016/j.rse.2014.02.001 DOI: https://doi.org/10.1016/j.rse.2014.02.001
Salamí, E., Barrado, C., & Pastor, E. (2014). UAV flight experiments applied to the remote sensing of vegetated areas. Remote Sensing, 6(11), 11051–11081. https://doi.org/10.3390/rs61111051 DOI: https://doi.org/10.3390/rs61111051
Satapathy, T., Dietrich, J., & Ramadas, M. (2024). Agricultural drought monitoring and early warning at the regional scale using a remote sensing-based combined index. Environmental Monitoring and Assessment, 196(11). https://doi.org/10.1007/s10661-024-13265-y DOI: https://doi.org/10.1007/s10661-024-13265-y
Sharma, H., Sidhu, H., & Bhowmik, A. (2025). Remote sensing using unmanned aerial vehicles for water stress detection: A review focusing on specialty crops. Drones, 9(4), 241. https://doi.org/10.3390/drones9040241 DOI: https://doi.org/10.3390/drones9040241
Sishodia, R. P., Ray, R. L., & Singh, S. K. (2020). Applications of remote sensing in precision agriculture: A review. Remote Sensing, 12(19), 3136. https://doi.org/10.3390/rs12193136 DOI: https://doi.org/10.3390/rs12193136
Tang, Z., Jin, Y., Brown, P. H., & Park, M. (2023). Estimation of tomato water status with photochemical reflectance index and machine learning: Assessment from proximal sensors and UAV imagery. Frontiers in Plant Science, 14. https://doi.org/10.3389/fpls.2023.1057733 DOI: https://doi.org/10.3389/fpls.2023.1057733
Thenkabail, P. S. (2016). Land resources monitoring, modeling, and mapping with remote sensing. CRC Press, Taylor & Francis Group. DOI: https://doi.org/10.1201/b19322
Vicente-Serrano, S. M., Beguería, S., & López-Moreno, J. I. (2010). A multiscalar drought index sensitive to global warming: The standardized precipitation evapotranspiration index. Journal of Climate, 23(7), 1696–1718. https://doi.org/10.1175/2009jcli2909.1 DOI: https://doi.org/10.1175/2009JCLI2909.1
Wang, R., Cherkauer, K., & Bowling, L. (2016). Corn response to climate stress detected with satellite-based NDVI time series. Remote Sensing, 8(4), 269. https://doi.org/10.3390/rs8040269 DOI: https://doi.org/10.3390/rs8040269
Wen, W., Timmermans, J., Chen, Q., & van Bodegom, P. M. (2022). Monitoring the combined effects of drought and salinity stress on crops using remote sensing in the Netherlands. Hydrology and Earth System Sciences, 26(17), 4537–4552. https://doi.org/10.5194/hess-26-4537-2022 DOI: https://doi.org/10.5194/hess-26-4537-2022
Yang, J. C., Magney, T. S., Yan, D., Knowles, J. F., Smith, W. K., Scott, R. L., & Barron‐Gafford, G. A. (2020). The photochemical reflectance index (PRI) captures the ecohydrologic sensitivity of a semiarid mixed conifer forest. Journal of Geophysical Research: Biogeosciences, 125(11). https://doi.org/10.1029/2019jg005624 DOI: https://doi.org/10.1029/2019JG005624
Yu, X., Huo, X., Qian, L., Du, Y., Liu, D., Cao, Q., Wang, W., Hu, X., Yang, X., & Fan, S. (2024). Combining UAV multispectral and thermal infrared data for maize growth parameter estimation. Agriculture, 14(11), 2004–2004. https://doi.org/10.3390/agriculture14112004 DOI: https://doi.org/10.3390/agriculture14112004
Zarco-Tejada, P. J., González-Dugo, V., & Berni, J. A. J. (2012). Fluorescence, temperature and narrow-band indices acquired from a UAV platform for water stress detection using a micro-hyperspectral imager and a thermal camera. Remote Sensing of Environment, 117, 322–337. https://doi.org/10.1016/j.rse.2011.10.007 DOI: https://doi.org/10.1016/j.rse.2011.10.007
Zhang, L., Niu, Y., Zhang, H., Han, W., Li, G., Tang, J., & Peng, X. (2019). Maize canopy temperature extracted from UAV thermal and RGB imagery and its application in water stress monitoring. Frontiers in Plant Science, 10. https://doi.org/10.3389/fpls.2019.01270 DOI: https://doi.org/10.3389/fpls.2019.01270
Zhao, H., Wang, J., Guo, J., Hui, X., Wang, Y., Cai, D., & Yan, H. (2024). Detecting water stress in winter wheat based on multifeature fusion from UAV remote sensing and stacking ensemble learning method. Remote Sensing, 16(21), 4100–4100. https://doi.org/10.3390/rs16214100 DOI: https://doi.org/10.3390/rs16214100
Zipper, S. C., Qiu, J., & Kucharik, C. J. (2016). Drought effects on US maize and soybean production: Spatiotemporal patterns and historical changes. Environmental Research Letters, 11(9), 094021. https://doi.org/10.1088/1748-9326/11/9/094021 DOI: https://doi.org/10.1088/1748-9326/11/9/094021
Article Sidebar
