ipaast – reading lists 2

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 – November 2021 Workshop on Near Surface (Proximal) Soil Sensing

Topsoil-mapper sensor mounted on a tractor tilling a field of bare earth
(source: topsoil-mapper.com)

Near surface soil sensing in Precision Agriculture

Overviews

Mulder, V.L., de Bruin, S., Schaepman, M.E., Mayr, T.R., 2011. The use of remote sensing in soil and terrain mapping — A review. Geoderma 162, 1–19. https://doi.org/10.1016/j.geoderma.2010.12.018

Romero-Ruiz, A., Linde, N., Keller, T., Or, D., 2018. A Review of Geophysical Methods for Soil Structure Characterization. Reviews of Geophysics 56, 672–697. https://doi.org/10.1029/2018RG000611

Romero-Ruiz, A., Linde, Keller, Or, 2019. The Geophysical Signatures of Soil Structure [WWW Document]. Eos. URL http://eos.org/editors-vox/the-geophysical-signatures-of-soil-structure (accessed 2.11.22).

The Royal Society, 2020. Soil structure and its benefits: Chapter 2 Measurements

(Soil structure evidence synthesis report). London, Royal Society.

https://royalsociety.org/topics-policy/projects/soil-structure-and-its-benefits/ (accessed 18.12.21).

Viscarra Rossel, R.A., Adamchuk, V.I., Sudduth, K.A., McKenzie, N.J., Lobsey, C., 2011. Chapter Five – Proximal Soil Sensing: An Effective Approach for Soil Measurements in Space and Time, in: Sparks, D.L. (Ed.), Advances in Agronomy, Advances in Agronomy. Academic Press, pp. 243–291. https://doi.org/10.1016/B978-0-12-386473-4.00005-1

Zhang, M., Wang, N., Chen, L., 2021. Sensing Technologies and Automation for Precision Agriculture, in: Hamrita, T.K. (Ed.), Women in Precision Agriculture: Technological Breakthroughs, Challenges and Aspirations for a Prosperous and Sustainable Future, Women in Engineering and Science. Springer International Publishing, Cham, pp. 35–54. https://doi.org/10.1007/978-3-030-49244-1_2

Modelling soil variability

Chatterjee, S., Hartemink, A.E., Triantafilis, J., Desai, A.R., Soldat, D., Zhu, J., Townsend, P.A., Zhang, Y., Huang, J., 2021. Characterization of field-scale soil variation using a stepwise multi-sensor fusion approach and a cost-benefit analysis. CATENA 201, 105190. https://doi.org/10.1016/j.catena.2021.105190

Ji, W., Adamchuk, V.I., Chen, S., Mat Su, A.S., Ismail, A., Gan, Q., Shi, Z., Biswas, A., 2019. Simultaneous measurement of multiple soil properties through proximal sensor data fusion: A case study. Geoderma 341, 111–128. https://doi.org/10.1016/j.geoderma.2019.01.006

Wadoux, A.M.J.-C., Odeh, I.O.A., McBratney, A.B., 2021. Overview of Pedometrics, in: Reference Module in Earth Systems and Environmental Sciences. Elsevier. https://doi.org/10.1016/B978-0-12-822974-3.00001-X

Saifuzzaman, M., Adamchuk, V., Biswas, A., Rabe, N., 2021. High-density proximal soil sensing data and topographic derivatives to characterise field variability. Biosystems Engineering 211, 19–34. https://doi.org/10.1016/j.biosystemseng.2021.08.018

Management Zone delineation

Bjørn Møller, A., Koganti, T., Beucher, A., Iversen, B.V., Greve, M.H., 2021. Downscaling digital soil maps using electromagnetic induction and aerial imagery. Geoderma 385, 114852. https://doi.org/10.1016/j.geoderma.2020.114852

Corti, M., Marino Gallina, P., Cavalli, D., Ortuani, B., Cabassi, G., Cola, G., Vigoni, A., Degano, L., Bregaglio, S., 2020. Evaluation of In-Season Management Zones from High-Resolution Soil and Plant Sensors. Agronomy 10, 1124. https://doi.org/10.3390/agronomy10081124

de Assis Silva, S., dos Santos, R.O., de Queiroz, D.M., de Souza Lima, J.S., Pajehú, L.F., Medauar, C.C., 2021. Apparent soil electrical conductivity in the delineation of management zones for cocoa cultivation. Information Processing in Agriculture. https://doi.org/10.1016/j.inpa.2021.04.004

Nawar, S., Corstanje, R., Halcro, G., Mulla, D., Mouazen, A.M., 2017. Chapter Four – Delineation of Soil Management Zones for Variable-Rate Fertilization: A Review, in: Sparks, D.L. (Ed.), Advances in Agronomy. Academic Press, pp. 175–245. https://doi.org/10.1016/bs.agron.2017.01.003

Electromagnetic induction (EMI)

Mapping – soil attributes

Doolittle, J.A., Brevik, E.C., 2014. The use of electromagnetic induction techniques in soils studies. Geoderma 223–225, 33–45. https://doi.org/10.1016/j.geoderma.2014.01.027

Hanssens, D., Delefortrie, S., Bobe, C., Hermans, T., De Smedt, P., 2019. Improving the reliability of soil EC-mapping: Robust apparent electrical conductivity (rECa) estimation in ground-based frequency domain electromagnetics. Geoderma 337, 1155–1163. https://doi.org/10.1016/j.geoderma.2018.11.030

Heil, K., Schmidhalter, U., 2017. The Application of EM38: Determination of Soil Parameters, Selection of Soil Sampling Points and Use in Agriculture and Archaeology. Sensors 17, 2540. https://doi.org/10.3390/s17112540

Sanches, G.M., Otto, R., Adamchuk, V., S.G. Magalhães, P., 2022. Spatial variability of soil attributes by an electromagnetic induction sensor: A framework of multiple fields assessment under Brazilian soils. Biosystems Engineering 216, 229–240. https://doi.org/10.1016/j.biosystemseng.2022.02.017

Mapping – Moisture

Huang, J., Scudiero, E., Choo, H., Corwin, D.L., Triantafilis, J., 2016. Mapping soil moisture across an irrigated field using electromagnetic conductivity imaging. Agricultural Water Management 163, 285–294. https://doi.org/10.1016/j.agwat.2015.09.003

Mapping – compaction

Galambošová, J., Macák, M., Rataj, V., Barát, M., Misiewicz, P., 2020. Determining Trafficked Areas Using Soil Electrical Conductivity – A Pilot Study. Acta Technologica Agriculturae 23, 1–6. https://doi.org/10.2478/ata-2020-0001

Electrical Resistivity Tomography (ERT)

Mapping moisture (combined with GPR)

Attia al Hagrey, S., 2007. Geophysical imaging of root-zone, trunk, and moisture heterogeneity. Journal of Experimental Botany 58, 839–854. https://doi.org/10.1093/jxb/erl237

Time-lapse analysis monitoring moisture (with EMI)

Blanchy, G., Watts, C.W., Richards, J., Bussell, J., Huntenburg, K., Sparkes, D.L., Stalham, M., Hawkesford, M.J., Whalley, W.R., Binley, A., 2020. Time-lapse geophysical assessment of agricultural practices on soil moisture dynamics. Vadose Zone Journal 19, e20080. https://doi.org/10.1002/vzj2.20080

Ground Penetrating Radar (GPR)

Soil characterisation

Hubbard, S., Chen, J., Williams, K., Rubin, Y., Peterson, J., 2005. Environmental and Agricultural Applications of GPR, Proceedings of the 3rd International Workshop on Advanced Ground Penetrating Radar, IWAGPR 2005. https://doi.org/10.1109/AGPR.2005.1487843

Lombardi, F., Lualdi, M., 2019. Step-Frequency Ground Penetrating Radar for Agricultural Soil Morphology Characterisation. Remote Sensing 11, 1075. https://doi.org/10.3390/rs11091075

Mapping – soil water content

Algeo, J., Slater, L., Binley, A., Van Dam, R., Watts, C., 2018. A Comparison of Ground-Penetrating Radar Early-Time Signal Approaches for Mapping Changes in Shallow Soil Water Content. Vadose Zone Journal 17. https://doi.org/10.2136/vzj2018.01.0001

Zhou, L., Yu, D., Wang, Z., Wang, X., 2019. Soil Water Content Estimation Using High-Frequency Ground Penetrating Radar. Water 11, 1036. https://doi.org/10.3390/w11051036

In-situ Sensors

Monitoring soil properties

Briciu-Burghina, C., Zhou, J., Ali, M.I., Regan, F., 2022. Demonstrating the Potential of a Low-Cost Soil Moisture Sensor Network. Sensors 22, 987. https://doi.org/10.3390/s22030987

Hardie, M., 2020. Review of Novel and Emerging Proximal Soil Moisture Sensors for Use in Agriculture. Sensors 20, 6934. https://doi.org/10.3390/s20236934

Remote Sensing for Soils (Agriculture and Environment)

Spectral analysis

Mapping – soil properties

Pallottino, F., Antonucci, F., Costa, C., Bisaglia, C., Figorilli, S., Menesatti, P., 2019. Optoelectronic proximal sensing vehicle-mounted technologies in precision agriculture: A review. Computers and Electronics in Agriculture 162, 859–873. https://doi.org/10.1016/j.compag.2019.05.034

Silvero, N.E.Q., Demattê, J.A.M., Amorim, M.T.A., Santos, N.V. dos, Rizzo, R., Safanelli, J.L., Poppiel, R.R., Mendes, W. de S., Bonfatti, B.R., 2021. Soil variability and quantification based on Sentinel-2 and Landsat-8 bare soil images: A comparison. Remote Sensing of Environment 252, 112117. https://doi.org/10.1016/j.rse.2020.112117

Ge, Y., Thomasson, J.A., Sui, R., 2011. Remote sensing of soil properties in precision agriculture: A review. Front. Earth Sci. 5, 229–238. https://doi.org/10.1007/s11707-011-0175-0

Monitoring – Soil Organic Carbon

Angelopoulou, T., Tziolas, N., Balafoutis, A., Zalidis, G., Bochtis, D., 2019. Remote Sensing Techniques for Soil Organic Carbon Estimation: A Review. Remote Sensing 11, 676. https://doi.org/10.3390/rs11060676

Smith, P., Soussana, J.-F., Angers, D., Schipper, L., Chenu, C., Rasse, D.P., Batjes, N.H., van Egmond, F., McNeill, S., Kuhnert, M., Arias-Navarro, C., Olesen, J.E., Chirinda, N., Fornara, D., Wollenberg, E., Álvaro-Fuentes, J., Sanz-Cobena, A., Klumpp, K., 2020. How to measure, report and verify soil carbon change to realize the potential of soil carbon sequestration for atmospheric greenhouse gas removal. Global Change Biology 26, 219–241. https://doi.org/10.1111/gcb.14815

Zhang, G.-L., Liu, F., Song, X.-D., Zhao, Y.-G., 2016. Digital Soil Mapping Across Paradigms, Scales, and Boundaries: A Review, in: Zhang, G.-L., Brus, D., Liu, F., Song, X.-D., Lagacherie, P. (Eds.), Digital Soil Mapping Across Paradigms, Scales and Boundaries, Springer Environmental Science and Engineering. Springer, Singapore, pp. 3–10. https://doi.org/10.1007/978-981-10-0415-5_1

Modelling – Soil Organic Carbon

Smith, P., Smith, J.U., Powlson, D.S., McGill, W.B., Arah, J.R.M., Chertov, O.G., Coleman, K., Franko, U., Frolking, S., Jenkinson, D.S., Jensen, L.S., Kelly, R.H., Klein-Gunnewiek, H., Komarov, A.S., Li, C., Molina, J.A.E., Mueller, T., Parton, W.J., Thornley, J.H.M., Whitmore, A.P., 1997. A comparison of the performance of nine soil organic matter models using datasets from seven long-term experiments. Geoderma, Evaluation and Comparison of Soil Organic Matter Models 81, 153–225. https://doi.org/10.1016/S0016-7061(97)00087-6

Lidar

Surface mapping – for water and nutrient run-off

Turner, R., Panciera, R., Tanase, M.A., Lowell, K., Hacker, J.M., Walker, J.P., 2014. Estimation of soil surface roughness of agricultural soils using airborne LiDAR. Remote Sensing of Environment 140, 107–117. https://doi.org/10.1016/j.rse.2013.08.030