ipaast – reading lists

To help build shared understandings across the domains of precision agriculture and archaeology, the ipaast project is compiling reading lists to provide basic background on key areas in research and practice. Some lists have been prepared specifically for a project workshop while others are for general reference.

Reading lists – September 2021 Workshop on Cropmarks and Crop Development Monitoring

Remote Sensing in Precision Agriculture


Hunt, E.R., Daughtry, C.S.T., 2018. What good are unmanned aircraft systems for agricultural remote sensing and precision agriculture? International Journal of Remote Sensing 39, 5345–5376. https://doi.org/10.1080/01431161.2017.1410300

Mulla, D.J., 2013. Twenty five years of remote sensing in precision agriculture: Key advances and remaining knowledge gaps. Biosystems Engineering, Special Issue: Sensing Technologies for Sustainable Agriculture 114, 358–371. https://doi.org/10.1016/j.biosystemseng.2012.08.009

Sishodia, R.P., Ray, R.L., Singh, S.K., 2020. Applications of Remote Sensing in Precision Agriculture: A Review. Remote Sensing 12, 3136. https://doi.org/10.3390/rs12193136

Webber, H., Heyd, V., Horton, M., Bell, M., Matthews, W., Chadburn, A., 2019. Precision farming and archaeology. Archaeol Anthropol Sci 11, 727–734. https://doi.org/10.1007/s12520-017-0564-8

Weiss, M., Jacob, F., Duveiller, G., 2020. Remote sensing for agricultural applications: A meta-review. Remote Sensing of Environment 236, 111402. https://doi.org/10.1016/j.rse.2019.111402

Aerial Photography

Yield estimation – with Structure from Motion (SfM)

Bendig, J., Bolten, A., Bennertz, S., Broscheit, J., Eichfuss, S., Bareth, G., 2014. Estimating Biomass of Barley Using Crop Surface Models (CSMs) Derived from UAV-Based RGB Imaging. Remote Sensing 6, 10395–10412. https://doi.org/10.3390/rs61110395

Chu, T., Starek, M.J., Brewer, M.J., Murray, S.C., Pruter, L.S., 2018. Characterizing canopy height with UAS structure-from-motion photogrammetry—results analysis of a maize field trial with respect to multiple factors. Remote Sensing Letters 9, 753–762. https://doi.org/10.1080/2150704X.2018.1475771

Hunt, E.R., Daughtry, C.S.T., 2018. What good are unmanned aircraft systems for agricultural remote sensing and precision agriculture? International Journal of Remote Sensing 39, 5345–5376. https://doi.org/10.1080/01431161.2017.1410300

Hunt, E.R., Rondon, S.I., 2017. Detection of potato beetle damage using remote sensing from small unmanned aircraft systems. JARS 11, 026013. https://doi.org/10.1117/1.JRS.11.026013

Li, W., Niu, Z., Chen, H., Li, D., Wu, M., Zhao, W., 2016. Remote estimation of canopy height and aboveground biomass of maize using high-resolution stereo images from a low-cost unmanned aerial vehicle system. Ecological Indicators 67, 637–648. https://doi.org/10.1016/j.ecolind.2016.03.036

Xie, T., Li, J., Yang, C., Jiang, Z., Chen, Y., Guo, L., Zhang, J., 2021. Crop height estimation based on UAV images: Methods, errors, and strategies. Computers and Electronics in Agriculture 185, 106155. https://doi.org/10.1016/j.compag.2021.106155

Multispectral Imaging

Crop Monitoring

de Oca, A.M., Arreola, L., Flores, A., Sanchez, J., Flores, G., 2018. Low-cost multispectral imaging system for crop monitoring, in: 2018 International Conference on Unmanned Aircraft Systems (ICUAS). Presented at the 2018 International Conference on Unmanned Aircraft Systems (ICUAS), pp. 443–451. https://doi.org/10.1109/ICUAS.2018.8453426

Houborg, R., McCabe, M.F., 2016. High-Resolution NDVI from Planet’s Constellation of Earth Observing Nano-Satellites: A New Data Source for Precision Agriculture. Remote Sensing 8, 768. https://doi.org/10.3390/rs8090768

Katsigiannis, P., Galanis, G., Dimitrakos, A., Tsakiridis, N., Kalopesas, C., Alexandridis, T., Chouzouri, A., Patakas, A., Zalidis, G., 2016. Fusion of spatio-temporal UAV and proximal sensing data for an agricultural decision support system, in: Fourth International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2016). Presented at the Fourth International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2016), International Society for Optics and Photonics, p. 96881R. https://doi.org/10.1117/12.2244856

Misopolinos, L., Zalidis, C., Liakopoulos, V., Stavridou, D., Katsigiannis, P., Alexandridis, T., Zalidis, G., 2015. Development of a UAV system for VNIR-TIR acquisitions in precision agriculture. Proceedings of SPIE – The International Society for Optical Engineering 9535. https://doi.org/10.1117/12.2192660


Hunt, E.R., Rondon, S.I., 2017. Detection of potato beetle damage using remote sensing from small, unmanned aircraft systems. JARS 11, 026013. https://doi.org/10.1117/1.JRS.11.026013

Kashyap, B., Kumar, R., 2021. Sensing Methodologies in Agriculture for Monitoring Biotic Stress in Plants Due to Pathogens and Pests. Inventions 6, 29. https://doi.org/10.3390/inventions6020029


Gašparović, M., Zrinjski, M., Barković, Đ., Radočaj, D., 2020. An automatic method for weed mapping in oat fields based on UAV imagery. Computers and Electronics in Agriculture 173, 105385. https://doi.org/10.1016/j.compag.2020.105385

Krähmer, H., Andreasen, C., Economou-Antonaka, G., Holec, J., Kalivas, D., Kolářová, M., Novák, R., Panozzo, S., Pinke, G., Salonen, J., Sattin, M., Stefanic, E., Vanaga, I., Fried, G., 2020. Weed surveys and weed mapping in Europe: State of the art and future tasks. Crop Protection 129, 105010. https://doi.org/10.1016/j.cropro.2019.105010

Wu, Z., Chen, Y., Zhao, B., Kang, X., Ding, Y., 2021. Review of Weed Detection Methods Based on Computer Vision. Sensors 21, 3647. https://doi.org/10.3390/s21113647

Nitrogen application

Ali, M.M., Al-Ani, A., Eamus, D., Tan, D.K.Y., 2017. Leaf nitrogen determination using non-destructive techniques–A review. Journal of Plant Nutrition 40, 928–953. https://doi.org/10.1080/01904167.2016.1143954

Colaço, A.F., Bramley, R.G.V., 2018. Do crop sensors promote improved nitrogen management in grain crops? Field Crops Research 218, 126–140. https://doi.org/10.1016/j.fcr.2018.01.007

Scharf, P.C., Shannon, D.K., Palm, H.L., Sudduth, K.A., Drummond, S.T., Kitchen, N.R., Mueller, L.J., Hubbard, V.C., Oliveira, L.F., 2011. Sensor-Based Nitrogen Applications Out-Performed Producer-Chosen Rates for Corn in On-Farm Demonstrations. Agronomy Journal 103, 1683–1691. https://doi.org/10.2134/agronj2011.0164

Hyperspectral Imaging

Crop monitoring

Caballero, D., Calvini, R., Amigo, J.M., 2020. Chapter 3.3 – Hyperspectral imaging in crop fields: precision agriculture, in: Amigo, J.M. (Ed.), Data Handling in Science and Technology, Hyperspectral Imaging. Elsevier, pp. 453–473. https://doi.org/10.1016/B978-0-444-63977-6.00018-3

Lu, B., Dao, P.D., Liu, J., He, Y., Shang, J., 2020. Recent Advances of Hyperspectral Imaging Technology and Applications in Agriculture. Remote Sensing 12, 2659. https://doi.org/10.3390/rs12162659

Thermal Imaging

Drought / irrigation monitoring

Maes, W.H., Steppe, K., 2019. Perspectives for Remote Sensing with Unmanned Aerial Vehicles in Precision Agriculture. Trends in Plant Science 24, 152–164. https://doi.org/10.1016/j.tplants.2018.11.007

Khanal, S., Fulton, J., Shearer, S., 2017. An overview of current and potential applications of thermal remote sensing in precision agriculture. Computers and Electronics in Agriculture 139, 22–32. https://doi.org/10.1016/j.compag.2017.05.001


Yield estimation

Jin, S., Sun, X., Wu, F., Su, Y., Li, Y., Song, S., Xu, K., Ma, Q., Baret, F., Jiang, D., Ding, Y., Guo, Q., 2021. Lidar sheds new light on plant phenomics for plant breeding and management: Recent advances and future prospects. ISPRS Journal of Photogrammetry and Remote Sensing 171, 202–223. https://doi.org/10.1016/j.isprsjprs.2020.11.006

Lefsky, M.A., Cohen, W.B., Harding, D.J., Parker, G.G., Acker, S.A., Gower, S.T., 2002. Lidar remote sensing of above-ground biomass in three biomes. Global Ecology and Biogeography 11, 393–399. https://doi.org/10.1046/j.1466-822x.2002.00303.x

Li, W., Niu, Z., Huang, N., Wang, C., Gao, S., Wu, C., 2015. Airborne LiDAR technique for estimating biomass components of maize: A case study in Zhangye City, Northwest China. Ecological Indicators 57, 486–496. https://doi.org/10.1016/j.ecolind.2015.04.016

Zarco-Tejada, P.J., Diaz-Varela, R., Angileri, V., Loudjani, P., 2014. Tree height quantification using very high resolution imagery acquired from an unmanned aerial vehicle (UAV) and automatic 3D photo-reconstruction methods. European Journal of Agronomy 55, 89–99. https://doi.org/10.1016/j.eja.2014.01.004

Wireless Sensor Network (WSN)

Soil / air monitoring

Boursianis, A.D., Papadopoulou, M.S., Diamantoulakis, P., Liopa-Tsakalidi, A., Barouchas, P., Salahas, G., Karagiannidis, G., Wan, S., Goudos, S.K., 2020. Internet of Things (IoT) and Agricultural Unmanned Aerial Vehicles (UAVs) in smart farming: A comprehensive review. Internet of Things 100187. https://doi.org/10.1016/j.iot.2020.100187

Elijah, O., Rahman, T.A., Orikumhi, I., Leow, C.Y., Hindia, M.N., 2018. An Overview of Internet of Things (IoT) and Data Analytics in Agriculture: Benefits and Challenges. IEEE Internet of Things Journal 5, 3758–3773. https://doi.org/10.1109/JIOT.2018.2844296

Palazzi, V., Bonafoni, S., Alimenti, F., Mezzanotte, P., Roselli, L., 2019. Feeding the World with Microwaves: How Remote and Wireless Sensing Can Help Precision Agriculture. IEEE Microwave Magazine 20, 72–86. https://doi.org/10.1109/MMM.2019.2941618

Remote Sensing in Archaeology and Heritage Management


Adamopoulos, E., Rinaudo, F., 2020. UAS-Based Archaeological Remote Sensing: Review, Meta-Analysis and State-of-the-Art. Drones 4, 46. https://doi.org/10.3390/drones4030046

Campana, S., 2017. Drones in Archaeology. State-of-the-art and Future Perspectives. Archaeological Prospection 24, 275–296. https://doi.org/10.1002/arp.1569

Verhoeven, G., Cowley, D., Traviglia, A., 2021. Archaeological Remote Sensing in the 21st Century: (Re)Defining Practice and Theory. https://doi.org/10.3390/rs13081431;

https://www.researchgate.net/publication/353211741_Archaeological_Remote_Sensing_in_the_21st_Century_ReDefining_Practice_and_Theory#fullTextFileContent [Full text]

Webber, H., Heyd, V., Horton, M., Bell, M., Matthews, W., Chadburn, A., 2019. Precision farming and archaeology. Archaeological and Anthropological Sciences 11, 727–734. https://doi.org/10.1007/s12520-017-0564-8

Aerial Photography

Feature detection

Küng, O., Strecha, C., Beyeler, A., Zufferey, J.-C., Floreano, D., Fua, P., Gervaix, F., 2012. The accuracy of automatic photogrammetric techniques on ultra-light uav imagery, in: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Presented at the International Conference on Unmanned Aerial Vehicle in Geomatics (UAV-g) (Volume XXXVIII-1/C22) – 14–16 September, Zurich, Switzerland, Copernicus GmbH, pp. 125–130. https://doi.org/10.5194/isprsarchives-XXXVIII-1-C22-125-2011

Verhoeven, G., Doneus, M., Briese, Ch., Vermeulen, F., 2012. Mapping by matching: a computer vision-based approach to fast and accurate georeferencing of archaeological aerial photographs. Journal of Archaeological Science 39, 2060–2070. https://doi.org/10.1016/j.jas.2012.02.022

Verhoeven, G., Vermeulen, F., 2016. Engaging with the Canopy—Multi-Dimensional Vegetation Mark Visualisation Using Archived Aerial Images. Remote Sensing 8, 752. https://doi.org/10.3390/rs8090752


Agapiou, A., Lysandrou, V., Hadjimitsis, D.G., 2020. Earth Observation Contribution to Cultural Heritage Disaster Risk Management: Case Study of Eastern Mediterranean Open Air Archaeological Monuments and Sites. Remote Sensing 12, 1330. https://doi.org/10.3390/rs12081330

Tapete, D., Traviglia, A., Delpozzo, E., Cigna, F., 2021. Regional-Scale Systematic Mapping of Archaeological Mounds and Detection of Looting Using COSMO-SkyMed High Resolution DEM and Satellite Imagery. Remote Sensing 13, 3106. https://doi.org/10.3390/rs13163106

Zaina, F., Nabati Mazloumi, Y., 2021. A multi-temporal satellite-based risk analysis of archaeological sites in Qazvin plain (Iran). Archaeological Prospection 2021; 1–17. https://doi.org/10.1002/arp.1818

Multispectral Imaging

Feature detection

Abate, N., Frisetti, A., Marazzi, F., Masini, N., Lasaponara, R., 2021. Multitemporal–Multispectral UAS Surveys for Archaeological Research: The Case Study of San Vincenzo Al Volturno (Molise, Italy). Remote Sensing 13, 2719. https://doi.org/10.3390/rs13142719

Agapiou, A., 2020. Optimal Spatial Resolution for the Detection and Discrimination of Archaeological Proxies in Areas with Spectral Heterogeneity. Remote Sensing 12, 136. https://doi.org/10.3390/rs12010136

Agapiou, A., Hadjimitsis, D.G., Sarris, A., Georgopoulos, A., Alexakis, D.D., 2013. Optimum temporal and spectral window for monitoring crop marks over archaeological remains in the Mediterranean region. Journal of Archaeological Science 40, 1479–1492. https://doi.org/10.1016/j.jas.2012.10.036

Moriarty, C., Cowley, D.C., Wade, T., Nichol, C.J., 2019. Deploying multispectral remote sensing for multi-temporal analysis of archaeological crop stress at Ravenshall, Fife, Scotland. Archaeological Prospection 26, 33–46. https://doi.org/10.1002/arp.1721

Feature detection (soil)

Zhurbin, I.V., Borisov, A.V., 2020. Non-destructive approach for studying medieval settlements destroyed by ploughing: combining aerial photography, geophysical and soil surveys. Archaeological Prospection 27, 343–360. https://doi.org/10.1002/arp.1778


Lim, J.S., Gleason, S., Jones, W., Church, W., 2021. Nuna Nalluyuituq (The Land Remembers): Remembering landscapes and refining methodologies through community-based remote sensing in the Yukon-Kuskokwim Delta, Southwest Alaska. Archaeological Prospection 28, 339–355. https://doi.org/10.1002/arp.1840


Tapete, D., Cigna, F., 2019. Detection of Archaeological Looting from Space: Methods, Achievements and Challenges. Remote Sensing 11, 2389. https://doi.org/10.3390/rs11202389


Feature detection

Doneus, M., Verhoeven, G., Atzberger, C., Wess, M., Ruš, M., 2014. New ways to extract archaeological information from hyperspectral pixels. Journal of Archaeological Science 52, 84–96. https://doi.org/10.1016/j.jas.2014.08.023

Feature detection (soil)

Thabeng, O.L., Adam, E., Merlo, S., 2019. Spectral Discrimination of Archaeological Sites Previously Occupied by Farming Communities Using In Situ Hyperspectral Data. Journal of Spectroscopy 2019, e5158465. https://doi.org/10.1155/2019/5158465


Feature detection (crop)

James, K., Nichol, C.J., Wade, T., Cowley, D., Gibson Poole, S., Gray, A., Gillespie, J., 2020. Thermal and Multispectral Remote Sensing for the Detection and Analysis of Archaeologically Induced Crop Stress at a UK Site. Drones 4, 61. https://doi.org/10.3390/drones4040061


Feature detection

Historic England 2018 Using Airborne Lidar in Archaeological Survey: The Light Fantastic. Swindon. Historic England. https://HistoricEngland.org.uk/research/methods/airborne-remote-sensing/lidar/

Roiha, J., Heinaro, E., Holopainen, M., 2021. The Hidden Cairns—A Case Study of Drone-Based ALS as an Archaeological Site Survey Method. Remote Sensing 13, 2010. https://doi.org/10.3390/rs13102010


Chase, A.S.Z., Chase, D.Z., Chase, A.F., 2017. LiDAR for Archaeological Research and the Study of Historical Landscapes, in: Masini, N., Soldovieri, F. (Eds.), Sensing the Past: From Artifact to Historical Site, Geotechnologies and the Environment. Springer International Publishing, Cham, pp. 89–100. https://doi.org/10.1007/978-3-319-50518-3_4

Lozić, E., 2021. Application of Airborne LiDAR Data to the Archaeology of Agrarian Land Use. The Case Study of the Early Medieval Microregion of Bled (Slovenia). Remote Sensing 13, 3228. https://doi.org/10.3390/rs13163228