Abstract
Unoccupied aerial system (UAS) imagery may serve as an additional tool towards management zone delineation. This is because UAS data collection is relatively flexible. However, it is unclear how useful UASs can be towards generating management zones, relative to preexisting tools (e.g. apparent soil electrical conductivity or ECa). The purpose of this study, therefore, was to evaluate UAS imagery, relative to ECa, in terms of their ability to: 1) predict cotton traits (i.e. height, seed cotton yield), and 2) define cotton management zones based on these traits. Single-season UAS images from multispectral/thermal sensors were collected and processed into Normalized Difference Vegetation Index (NDVI) and radiometric surface temperature (Tr), respectively. Management zones were also delineated using digital camera (RGB) imagery collected at periods before planting and near harvest. RGB management zones were delineated by a novel open boll mapping approach. In-season NDVI and Tr layers were significant (P < 0.01) predictors of canopy height. Additionally, NDVI and Tr maps produced statistically different management zones during flowering and boll filling growth stages in terms of yield (P = 0.001 or less). Open boll layers were all more accurate predictors of cotton seed yield than ECa data—these two layers also produced statistically distinct management zones. ANOVA tests revealed that, given ECa alone, adding UAS information via the RGB open boll map resulted in a significantly different yield prediction model (P < 0.001). These results suggest that UAS imagery can offer valuable information for cotton management zone delineation that other techniques cannot.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Data availability
Data and material needed to create most of the figures and tables are provided as supplementary data. Some exceptions (e.g. Fig. 3) were created using QGIS visualization.
Code availability
R code (available on request) was used to create most of the figures and tables.
References
Adamchuk, V. I., Hummel, J. W., Morgan, M. T., & Upadhyaya, S. K. (2004). On-the-go soil sensors for precision agriculture. Computers and Electronics in Agriculture, 44, 71–91. https://doi.org/10.1016/j.compag.2004.03.002
Ahmad, I. S., Reid, J. F., Noguchi, N., & Hansen, A. C. (1999). Nitrogen sensing for precision agriculture using chlorophyll maps. In ASAE/CSAE-SCGR annual international meeting (pp. 18–21). Washington DC, USA.
Allen, R. G., Pereira, L. S., Raes, D., & Smith, M. (1998). Crop evapotranspiration: Guidelines for computing crop water requirements. Irrigation and Drainage, 56, 300.
Berni, J. A. J., Zarco-Tejada, P. J., Suárez, 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, 722–738. https://doi.org/10.1109/TGRS.2008.2010457
Bivand, R. S., & Wong, D. W. S. (2018). Comparing implementations of global and local indicators of spatial association. TEST, 27, 716–748. https://doi.org/10.1007/s11749-018-0599-x
Blackmer, T. M., & Schepers, J. S. (1995). Use of a chlorophyll meter to monitor nitrogen status and schedule fertigation for corn. Journal of Production Agriculture, 8(1), 56–60. https://doi.org/10.2134/jpa1995.0056
Breusch, T. S., & Pagan, A. R. (1979). A Simple test for heteroscedasticity and random coefficient variation. Econometrica, 47(5), 1287–1294. https://doi.org/10.2307/1911963
Brevik, E. C., Calzolari, C., Miller, B. A., Pereira, P., Kabala, C., Baumgarten, A., & Jordán, A. (2016). Soil mapping, classification, and pedologic modeling: History and future directions. Geoderma, 264, 256–274. https://doi.org/10.1016/j.geoderma.2015.05.017
Burnham, K. P., & Anderson, D. R. (2002). Model selection and multimodel inference (2nd ed.). New York: Springer.
Carlson, T. N., & Ripley, D. A. (1997). On the relation between NDVI, fractional vegetation cover, and leaf area index. Remote Sensing of Environment, 62(3), 241–252. https://doi.org/10.1016/S0034-4257(97)00104-1
Charrad, M., Ghazzali, N., Boiteau, V., & Niknafs, A. (2014). NbClust: An R Package for determining the relevant number of clusters in a data set. Journal of Statistical Software, 61(6), 1–36. https://doi.org/10.18637/jss.v061.i06
Chen, R., Chu, T., Landivar, J. A., Yang, C., & Maeda, M. M. (2018). Monitoring cotton (Gossypium hirsutum L.) germination using ultrahigh-resolution UAS images. Precision Agriculture. https://doi.org/10.1007/s11119-017-9508-7
Cohen, Y., Alchanatis, V., Saranga, Y., Rosenberg, O., Sela, E., & Bosak, A. (2017). Mapping water status based on aerial thermal imagery: comparison of methodologies for upscaling from a single leaf to commercial fields. Precision Agriculture, 18(5), 801–822. https://doi.org/10.1007/s11119-016-9484-3
Cordoba, M. A., Bruno, C., Costa, J. L., Peralta, N. R., & Balzarini, M. G. (2016). Protocol for multivariate homogeneous zone delineation in precision agriculture. Biosystems Engineering, 143, 95–107. https://doi.org/10.1016/j.biosystemseng.2015.12.008
Corwin, D. L., Lesch, S. M., Shouse, P. J., Soppe, R., & Ayars, J. E. (2003). Identifying soil properties that influence cotton yield using soil sampling directed by apparent soil electrical conductivity. Agronomy Journal, 95, 352–364. https://doi.org/10.2134/agronj2003.3520
Corwin, D. L., & Lesch, S. M. (2005). Apparent soil electrical conductivity measurements in agriculture. Computers and Electronics in Agriculture, 46, 11–43. https://doi.org/10.1016/j.compag.2004.10.005
Corwin, D. L., & Scudiero, E. (2016). Field-scale apparent soil electrical conductivity. Methods of Soil Analysis, 1(1), 1–29. https://doi.org/10.2136/methods-soil.2015.0038
Cotton Australia. (2018). Interesting cotton facts. Cotton Library. Retrieved July 29, 2019, from https://cottonaustralia.com.au/cotton-library/fact-sheets/cotton-fact-file-interesting-cotton-facts.
Duan, T., Zheng, B., Guo, W., Ninomiya, S., Guo, Y., & Chapman, S. C. (2017). Comparison of ground cover estimates from experiment plots in cotton, sorghum and sugarcane based on images and ortho-mosaics captured by UAV. Functional Plant Biology, 44, 169–183. https://doi.org/10.1071/FP16123
Erickson, B., & Lowenberg-Deboer, J. (2020). 2020 Precision Agriculture Dealership Survey. Purdue University.
Fleming, K. L., Heermann, D. F., & Westfall, D. G. (2004). Evaluating soil color with farmer input and apparent soil electrical conductivity for management zone delineation. Agronomy Journal, 96, 1581–1587. https://doi.org/10.2134/agronj2004.1581
Fuchs, M., & Tanner, C. B. (1966). Infrared thermometry of vegetation. Agronomy Journal, 58(6), 597–601. https://doi.org/10.2134/agronj1966.00021962005800060014x
Fulton, J., Hawkins, E., Taylor, R., & Franzen, A. (2018). Yield monitoring and mapping. In D. K. Shannon, D. E. Clay, & N. R. Kitchen (Eds.), Precision agriculture basics (1st ed., pp. 63–78). ASA/CSA/SSSA.
Green, F. H. W., Harding, R. J., & Oliver, H. R. (1984). The relationship of soil temperature to vegetation height. Journal of Climatology, 4(7), 229–240. https://doi.org/10.1002/joc.3370040302
Guo, W. (2018). Spatial and temporal trends of irrigated cotton yield in the southern High Plains. Agronomy, 8(12), 298. https://doi.org/10.3390/agronomy8120298
Guo, W., Maas, S. J., & Bronson, K. F. (2012). Relationship between cotton yield and soil electrical conductivity, topography, and Landsat imagery. Precision Agriculture, 13(6), 678–692. https://doi.org/10.1007/s11119-012-9277-2
Hake, K., Burch, T., & Mauney, J. (1989). Making sense out of stalks. Physiology Today. National Cotton Council., 1–4.
Han, X., Thomasson, J. A., Bagnall, G. C., Pugh, N. A., Horne, D. W., Rooney, W. L., Jung, J., Chang, A., Malambo, L., Popescu, S. C., Gates, I. T., & Cope, D. A. (2018). Measurement and calibration of plant-height from fixed-wing UAV images. Sensors, 18, 4092. https://doi.org/10.3390/s18124092
Heijting, S., de Bruin, S., & Bregt, A. K. (2011). The arable farmer as the assessor of within-field soil variation. Precision Agriculture, 12(4), 488–507. https://doi.org/10.1007/s11119-010-9197-y
Hunt, E. R., Jr., & Daughtry, C. S. T. (2017). What good are unmanned aircraft systems for agricultural remote sensing and precision agriculture? International Journal of Remote Sensing. https://doi.org/10.1080/01431161.2017.1410300
Hurvich, C. M., & Tsai, C. L. (1989). Regression and time series model selection in small samples. Biometrika, 76(2), 297–307. https://doi.org/10.1093/biomet/76.2.297
Iqbal, F., Lucieer, A., & Barry, K. (2018). Simplified radiometric calibration for UAS-mounted multispectral sensor. European Journal of Remote Sensing, 51(1), 301–313. https://doi.org/10.1080/22797254.2018.1432293
Jiménez-Muñoz, J. C., Sobrino, J. A., Gillespie, A., Sabol, D., & Gustafson, W. T. (2006). Improved land surface emissivities over agricultural areas using ASTER NDVI. Remote Sensing of Environment, 103(4), 474–487. https://doi.org/10.1016/j.rse.2006.04.012
Johnson, J., MacDonald, S., Meyer, L., & Stone, L. (2018). The world and Untied States cotton outlook. Arlington, VA.
Lou, Z., Xin, F., Han, X., Lan, Y., Duan, T., & Fu, W. (2018). Effect of unmanned aerial vehicle flight height on droplet distribution, drift and control of cotton aphids and spider mites. Agronomy, 8, 187. https://doi.org/10.3390/agronomy8090187
Lund, E. D., Christy, C. D., & Drummond, P. E. (1999). Practical applications of soil electrical conductivity. In J. V. Stafford (Ed.), Precision Agriculture’99, Proceedings of the 2nd European Conference on Precision Agriculture. Odense, Denmark, July 11–15 (pp. 771–779). Sheffield Academic Press Ltd.
Maes, W. H., & Steppe, K. (2012). Estimating evapotranspiration and drought stress with ground-based thermal remote sensing in agriculture: A review. Journal of Experimental Botany, 63(13), 4671–4712. https://doi.org/10.1093/jxb/ers165
Maes, W. H., & Steppe, K. (2018). Perspectives for remote sensing with unmanned aerial vehicles in precision agriculture. Trends in Plant Science, 24(2), 152–164. https://doi.org/10.1016/j.tplants.2018.11.007
Manfreda, S., McCabe, M. F., Miller, P. E., Lucas, R., Madrigal, V. P., Mallinis, G, Ben-Dor, E., Helman, D., Estes, L., Ciraolo, G., Müllerová, J., Tauro, F., De Lima, M. I., De Lima, J. L. M. P., Maltese, A., Frances, F., Caylor, K., Kohv, M., Perks, M., ... Toth, B. (2018). On the use of unmanned aerial systems for environmental monitoring. Remote Sensing. https://doi.org/10.3390/rs10040641
Marino, S., & Alvino, A. (2018). Detection of homogeneous wheat areas using multi-temporal UAS images and ground truth data analyzed by cluster analysis and ground truth data analyzed by cluster analysis. European Journal of Remote Sensing, 51(1), 266–275. https://doi.org/10.1080/22797254.2017.1422280
Martínez-Casasnovas, J., Escolà, A., & Arnó, J. (2018). Use of farmer knowledge in the delineation of potential management zones in precision agriculture: A case study in maize (Zea mays L.). Agriculture, 8(6), 84. https://doi.org/10.3390/agriculture8060084
Matese, A., Di Gennaro, S. F., Miranda, C., Berton, A., & Santesteban, L. G. (2017). Evaluation of spectral-based and canopy-based vegetation indices from UAV and Sentinel 2 images to assess spatial variability and ground vine parameters. Advances in Animal Biosciences, 8(2), 817–822. https://doi.org/10.1017/S2040470017000929
McNeill, J. D. (1980). Electromagnetic terrain conductivity measurement at low induction numbers. Technical Note TN-6. (Vol. 76). Ontario, Canada.
McNeill, J. D. (1992). Rapid, accurate mapping of soil salinity by electromagnetic ground conductivity meters. In Advances in measurement of soil physical properties: Bringing theory into practice (pp. 209–229). ASA/CSA/SSSA.
Minasny, B., & McBratney, A. B. (2006). A conditioned Latin hypercube method for sampling in the presence of ancillary information. Computers and Geosciences, 32(9), 1378–1388. https://doi.org/10.1016/j.cageo.2005.12.009
Moran, P. (1950). Notes on continuous stochastic phenomena. Biometrika, 37(1), 17–23. https://doi.org/10.2307/2332142
Mulla, D. J. (2013). Twenty five years of remote sensing in precision agriculture: Key advances and remaining knowledge gaps. Biosystems Engineering, 114(4), 358–371. https://doi.org/10.1016/j.biosystemseng.2012.08.009
Nawar, S., Corstanje, R., Halcro, G., Mulla, D., & Mouazen, A. M. (2017). Delineation of soil management zones for variable-rate fertilization: A review. Advances in agronomy (1st ed., Vol. 143). Elsevier Inc. https://doi.org/10.1016/bs.agron.2017.01.003
O’Connor, J., Smith, M., & James, M. R. (2017). Cameras and settings for aerial surveys in the geosciences: Optimising image data. Progress in Physical Geography, 41, 325–344. https://doi.org/10.1177%2F0309133317703092
Pádua, L., Marques, P., Hruska, J., Adao, T., Peres, E., Morais, R., & Sousa, J. J. (2018). Multi-temporal vineyard monitoring through UAV-based RGB imagery. Remote Sensing, 10, 1907. https://doi.org/10.3390/rs10121907
Patterson, T. C. (2007). Google Earth as a (not just) geography education tool. Journal of Geography, 106, 145–152. https://doi.org/10.1080/00221340701678032
Pettigrew, W. T. (2008). The effect of higher temperatures on cotton lint yield production and fiber quality. Crop Science, 48(1), 278–285. https://doi.org/10.2135/cropsci2007.05.0261
Plant, R. E., Munk, D. S., Roberts, B. R., Vargas, R. L., Rains, D. W., Travis, R. L., & Hutmacher, R. B. (2000). Relationships between remotely sensed reflectance data and cotton growth and yield. Transactions of the ASABE, 43(3), 535–546. https://doi.org/10.13031/2013.2733
Pritsolas, J., Pearson, R., Connor, J., & Kyveryga, P. (2016). Challenges and successes when generating in-season multi-temporal calibrated aerial imagery. In Proceedings of the 13th international conference on precision agriculture (pp. 1–15). St. Louis, USA: International Society of Precision Agriculture.
R Core Development Team. (2018). R: A Language and Environment for Statistical Computing. Vienna.
Rhoades, J. D., Manteghi, N. A., Shouse, P. J., & Alves, W. J. (1989). Soil electrical conductivity and soil salinity: New formulations and calibrations. Soil Science Society of America Journal, 53, 433–439. https://doi.org/10.2136/sssaj1989.03615995005300020020x
Ribeiro-Gomes, K., Hernández-López, D., Ortega, J. F., Ballesteros, R., Poblete, T., & Moreno, M. A. (2017). Uncooled thermal camera calibration and optimization of the photogrammetry process for UAV applications in agriculture. Sensors, 17, 2173. https://doi.org/10.3390/s17102173
Robinson, D. A., Lebron, I., Lesch, S. M., & Shouse, P. (2004). Minimizing drift in electrical conductivity measurements in high temperature environments using the EM-38. Soil Science Society of America Journal, 68(2), 339–345. https://doi.org/10.2136/sssaj2004.3390
Rouse, J. W., Hass, R. H., Schell, J. A., & Deering, D. W. (1973). Monitoring vegetation systems in the Great Plains with ERTS. In Third earth resources technology satellite (ERTS) symposium (Vol. 1, pp. 309–317). U.S. Gov. Printing Office.
Rouze, G., Neely, H. L., Morgan, C. L., Kustas, W., McKee, L., Prueger, J., Jung, J., Chang, A., Gates, I. T., Cope, D., Thomasson, J. A., Bagnall, G. C., Rajan, N., Mohanty, B. (in preparation). Evaluation of contextual and non-contextual Unmanned Aerial Vehicle (UAV) evapotranspiration across various pixel resolutions and soil types.
Rubio, E., Caselles, V., & Badenas, C. (1997). Emissivity measurements of several soils and vegetation types in the 8–14µm wave band: Analysis of two field methods. Remote Sensing of Environment, 59(3), 490–521. https://doi.org/10.1016/S0034-4257(96)00123-X
Schepers, A. R., Shanahan, J. F., Liebig, M. A., Schepers, J. S., Johnson, S. H., & Luchiari, A., Jr. (2004). Appropriateness of management zones for characterizing spatial variability of soil properties and irrigated corn yields acrossy. Agronomy Journal, 96, 195–203. https://doi.org/10.2134/agronj2004.1950
Scudiero, E., Teatini, P., Manoli, G., Braga, F., Skaggs, T. H., & Morari, F. (2018). Workflow to establish time-specific zones in precision agriculture by spatiotemporal integration of plant and soil sensing data. Agronomy, 8, 253. https://doi.org/10.3390/agronomy8110253
Shi, Y., Thomasson, J. A., Murray, S. C., Pugh, N. A., Rooney, W. L., Shafian, S., Rajan, N., Rouze, G., Morgan, C. L. S., Neely, H. L., Rana, A., Bagvathiannan, M. V., Henrickson, J., Bowden, E., Valasek, J., Olsenholler, J., Bishop, M. P., Sheridan, R., Putman, E. B., ... Yang, C. (2016). Unmanned aerial vehicles for high-throughput phenotyping and agronomic research. PLoS ONE,. https://doi.org/10.1371/journal.pone.0159781
Stanislav, S. (2010). A field-scale assessment of soil-specific seeding rates to optimize yield factors and water use in cotton. Masters Thesis. Texas A&M University.
Taskos, D. G., Koundouras, S., Stamatiadis, S., Zioziou, E., Nikolaou, N., Karakioulakis, K., Theodorou, N. (2015). Using active canopy sensors and chlorophyll meters to estimate grapevine nitrogen status and productivity. Precision Agriculture, 16(1), 77–98. https://doi.org/10.1007/s11119-014-9363-8
Thomasson, J. A., & Sui, R. (2003). Mississippi cotton yield monitor: three years of field-test results. Applied Engineering in Agriculture, 19(6), 631–636. https://doi.org/10.13031/2013.15655
Tisseyre, B., & Leroux, C. (2017). How significantly different are your within field zones? Advances in Animal Biosciences: Precision Agriculture, 8(2), 620–624. https://doi.org/10.1017/S2040470017000012
Torres-Rua, A. (2017). Vicarious calibration of sUAS microbolometer temperature imagery for estimation of radiometric land surface temperature. Sensors, 17(7), 1499. https://doi.org/10.3390/s17071499
Triantafilis, J., Kerridge, B., & Buchanan, S. M. (2009). Digital soil-class mapping from proximal and remotely sensed data at the field level. Agronomy Journal, 101(4), 841–853. https://doi.org/10.2134/agronj2008.0112
U.S. Census Bureau Trade Data. (2018). Global agricultural trade system. Retrieved July 29, 2019, from https://apps.fas.usda.gov/gats/default.aspx.
Webster, R., & Oliver, M. A. (1992). Sample adequately to estimate variograms of soil properties. Journal of Soil Science, 43, 177–192. https://doi.org/10.1111/j.1365-2389.1992.tb00128.x
Westoby, M. J., Brasington, J., Glasser, N. F., Hambrey, M. J., & Reynolds, J. M. (2012). “Structure-from-Motion” photogrammetry: A low-cost, effective tool for geoscience applications. Geomorphology, 179, 300–314. https://doi.org/10.1016/j.geomorph.2012.08.021
Xia, T., Kustas, W. P., Anderson, M. C., Alfieri, J. G., Gao, F., McKee, L., Prueger, J. H., Geli, H. M., Neale, C. M. U., Sanchez, L., Mar- Alsina, M., & Wang, Z. (2016). Mapping evapotranspiration with high-resolution aircraft imagery over vineyards using one-and two-source modeling schemes. Hydrology and Earth System Sciences, 20(4), 1523–1545. https://doi.org/10.5194/hess-20-1523-2016
Xu, R., Li, C., & Paterson, A. H. (2019). Multispectral imaging and unmanned aerial systems for cotton plant phenotyping. PLoS ONE, 14(2), e0205083. https://doi.org/10.1371/journal.pone.0205083
Yeom, J., Jung, J., Chang, A., Maeda, M., & Landivar, J. (2018). Automated open cotton boll detection for yield estimation using unmanned aircraft vehicle (UAV) data. Remote Sensing, 10(12), 1–20. https://doi.org/10.3390/rs10121895
Yu, Q., Acheampong, M., Pu, R., Landry, S. M., Ji, W., & Dahigamuwa, T. (2018). Assessing effects of urban vegetation height on land surface temperature Tampa, Florida, USA. International Journal of Applied Earth Observation and Geoinformation, 73, 712–720. https://doi.org/10.1016/j.jag.2018.08.016
Zarco-Tejada, P. J., Ustin, S. L., & Whiting, M. L. (2005). Temporal and spatial relationships between within-field yield variability in cotton and high-spatial hyperspectral remote sensing imagery. Agronomy Journal, 97(3), 641–653. https://doi.org/10.2134/agronj2003.0257
Zhang, C., & Kovacs, J. M. (2012). The application of small unmanned aerial systems for precision agriculture: A review. Precision Agriculture, 13(6), 693–712. https://doi.org/10.1007/s11119-012-9274-5
Zhou, X., Zheng, H. B., Xu, X. Q., He, J. Y., Ge, X. K., Yao, X., Cheng, T., Zhu, Y., Cao, W. X., & Tian, Y. C. (2017). Predicting grain yield in rice using multi-temporal vegetation indices from UAV-based multispectral and digital imagery. ISPRS Journal of Photogrammetry and Remote Sensing, 130, 246–255. https://doi.org/10.1016/j.isprsjprs.2017.05.003
Acknowledgements
This work was supported by Texas A&M Agrilife Research. The authors would also like to personally thank the following people for their contributions towards data collection: the flight team (Andrew Vree, Ian Gates, Dale Cope), field workers (Nicole Shigley, Michael Hiefner). Special thanks are also in order for the undergraduate soil science class for collecting yield samples. Thanks to Cody Bagnall and Alex Thomasson for their contributions in GCP development and GPS data collection. Thanks to Jinha Jung and Anjin Chang for their orthomosaicking efforts for the RGB and multispectral imagery. Scott Stanislav was responsible for collecting ECa data and soil data during his time as a graduate student, under the direction of Cristine Morgan.
Disclaimer
Mention of a commercial product is solely for the purpose of providing specific information and should not be construed as a product endorsement by the authors or the institutions with which the authors are affiliated.
Author information
Authors and Affiliations
Contributions
GR partially designed the research, conceptualized the manuscript and wrote the manuscript. HN and CM partially designed the research and reviewed the manuscript. BK and MW reviewed and provided proofs to the manuscript.
Corresponding author
Ethics declarations
Conflict of interest
The authors declare no conflict of interest.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Appendix
Appendix
Depiction of zonal area used to define the erase spatial option within the materials and method. The red dot indicates the location of the center the sampling area, while the outer yellow region the area chosen for sampling. Note the lack bolls within the inner ring (Color figure online)
Comparisons between canopy height (y-axis) and vegetation fraction cover (x-axis) on July 1 (left) and July 13 (right)
Rights and permissions
About this article
Cite this article
Rouze, G., Neely, H., Morgan, C. et al. Evaluating unoccupied aerial systems (UAS) imagery as an alternative tool towards cotton-based management zones. Precision Agric 22, 1861–1889 (2021). https://doi.org/10.1007/s11119-021-09816-9
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11119-021-09816-9