Abstract
Rapid and accurate estimation of plant potassium accumulation (PKA) using hyperspectral remote sensing is of significance for the precise management of crop K fertilizer. This study focused on the separation of non-negative matrix factorization (NMF) for hyperspectral reflectance from the ground and unmanned aerial vehicle (UAV) platforms and its mitigation effect on the water and soil background. Pure vegetation spectra were extracted from the canopy mixed spectra using NMF, and then a partial least-squares regression (PLSR) model was established based on the extracted vegetation spectra and rice PKA to construct an estimation model of rice PKA. The results showed that the green light and red edge bands contributed significantly to the rice PKA estimation. NMF could effectively extract pure vegetation and water and soil spectra from mixed spectra, and enhance the green peak, red valley, and red edge information of the extracted vegetation spectra. Compared with spectral indices, the PLSR performed best for ground and UAV data. Besides, the R2 of the PLSR model based on NMF-extracted vegetation spectra increased by 15.15% to 0.76%, and the verified RMSE and RE decreased by 16.93% and 16.77% to 3.19 g m−2 and 45.07%, respectively. Hyperspectral dataset testing from different years, growth stages and varieties, and UAV platforms showed that NMF could improve the estimation accuracy of rice PKA. This study showed that NMF could be applied to both ground and UAV hyperspectral platforms to improve the estimation accuracy of rice K nutrition.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Aasen, H., Burkart, A., Bolten, A., & Bareth, G. (2015). Generating 3D hyperspectral information with lightweight UAV snapshot cameras for vegetation monitoring: From camera calibration to quality assurance. ISPRS Journal of Photogrammetry and Remote Sensing, 108, 245–259. https://doi.org/10.1016/j.isprsjprs.2015.08.002.
Ahmad, I., & Maathuis, F. J. (2014). Cellular and tissue distribution of potassium: Physiological relevance, mechanisms and regulation. Journal of Plant Physiology, 171(9), 708–714. https://doi.org/10.1016/j.jplph.2013.10.016.
Albayrak, S. (2008). Use of reflectance measurements for the detection of N, P, K, ADF and NDF contents in sainfoin pasture. Sensors, 8(11), 7275–7286. https://doi.org/10.3390/s8117275.
Ali, S., Hafeez, A., Ma, X., Tung, S. A., Liu, A., Shah, A. N., et al. (2018). Potassium relative ratio to nitrogen considerably favors carbon metabolism in late-planted cotton at high planting density. Field Crops Research, 223(15), 48–56. https://doi.org/10.1016/j.fcr.2018.04.005.
Bablet, A., Vu, P. V. H., Jacquemoud, S., Viallefont-Robinet, F., Fabre, S., Briottet, X., et al. (2018). MARMIT: A multilayer radiative transfer model of soil reflectance to estimate surface soil moisture content in the solar domain (400–2500 nm). Remote Sensing of Environment, 217, 1–17. https://doi.org/10.1016/j.rse.2018.07.031.
Banayo, N. P. M. C., Haefele, S. M., Desamero, N. V., & Kato, Y. (2018). On-farm assessment of site-specific nutrient management for rainfed lowland rice in the Philippines. Field Crops Research, 220, 88–96. https://doi.org/10.1016/j.fcr.2017.09.011.
Baret, F., Jacquemoud, S., Guyot, G., & Leprieur, C. (1992). Modeled analysis of the biophysical nature of spectral shifts and comparison with information content of broad bands. Remote Sensing of Environment, 41, 133–142. https://doi.org/10.1016/0034-4257(92)90073-S.
Bartholomeus, H., Kooistra, L., Stevens, A., van Leeuwen, M., van Wesemael, B., Ben-Dor, E., et al. (2011). Soil organic carbon mapping of partially vegetated agricultural fields with imaging spectroscopy. International Journal of Applied Earth Observation and Geoinformation, 13(1), 81–88. https://doi.org/10.1016/j.jag.2010.06.009.
Bioucas-Dias, J. M., Plaza, A., Dobigeon, N., Parente, M., Du, Q., Gader, P., et al. (2012). Hyperspectral unmixing overview: Geometrical, statistical, and sparseregression-based approaches. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 5(2), 354–379. https://doi.org/10.1109/JSTARS.2012.2194696.
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.
Chappell, A., Webb, N. P., Guerschman, J. P., Thomas, D. T., Mata, G., Handcock, R. N., et al. (2018). Improving ground cover monitoring for wind erosion assessment using MODIS BRDF parameters. Remote Sensing of Environment, 204, 756–768. https://doi.org/10.1016/j.rse.2017.09.026.
Chen, Q., He, A. B., Wang, W. Q., Peng, S. B., Huang, J. L., Cui, K. H., et al. (2018). Comparisons of regeneration rate and yields performance between inbred and hybrid rice cultivars in a direct seeding rice-ratoon rice system in central China. Field Crops Research, 223, 164–170. https://doi.org/10.1016/j.fcr.2018.04.010.
Demidchik, V. (2014). Mechanisms and physiological roles of K+ efflux from root cells. Journal of Plant Physiology, 171(9), 696–707. https://doi.org/10.1016/j.jplph.2014.01.015.
Ge, Y., Atefi, A., Zhang, H., Miao, C., Ramamurthy, R. K., Sigmon, B., et al. (2019). High-throughput analysis of leaf physiological and chemical traits with VIS-NIR-SWIR spectroscopy: a case study with a maize diversity panel. Plant Methods, 15, 66. https://doi.org/10.1186/s13007-019-0450-8.
Geladi, P., & Kowalski, B. R. (1986). Partial least-squares regression: a tutorial. Analytica Chimica Acta, 185, 1–17. https://doi.org/10.1016/0003-2670(86)80028-9.
Gómez-Casero, M. T., López-Granados, F., Peña-Barragán, J. M., Jurado-Expósito, M., Torres, L. G., & Fernández-Escobar, R. (2007). Assessing nitrogen and potassium deficiencies in olive orchards through discriminant analysis of hyperspectral data. Journal of the American Society for Horticultural Science, 132(5), 611–618. https://doi.org/10.21273/JASHS.132.5.611.
Gomez, C., Adeline, K., Bacha, S., Driessen, B., Gorretta, N., Lagacherie, P., et al. (2018). Sensitivity of clay content prediction to spectral configuration of VNIR/SWIR imaging data, from multispectral to hyperspectral scenarios. Remote Sensing of Environment, 204, 18–30. https://doi.org/10.1016/j.rse.2017.10.047.
Hou, W. F., Xue, X. X., Li, X. K., Khan, M. R., Yan, J. Y., Ren, T., et al. (2019). Interactive effects of nitrogen and potassium on: Grain yield, nitrogen uptake and nitrogen use efficiency of rice in low potassium fertility soil in China. Field Crops Research, 236, 14–23. https://doi.org/10.1016/j.fcr.2019.03.006.
Hu, P. C., Guo, W., Chapman, S. C., Guo, Y., & Zheng, B. Y. (2019). Pixel size of aerial imagery constrains the applications of unmanned aerial vehicle in crop breeding. ISPRS Journal of Photogrammetry and Remote Sensing, 154, 1–9. https://doi.org/10.1016/j.isprsjprs.2019.05.008.
Kawamura, K., Mackay, A. D., Tuohy, M. P., Betteridge, K., Sanches, I. D., & Inoue, Y. (2011). Potential for spectral indices to remotely sense phosphorus and potassium content of legume-based pasture as a means of assessing soil phosphorus and potassium fertility status. International Journal of Remote Sensing, 32(1), 103–124. https://doi.org/10.1080/01431160903439908.
Kekulandara, D. S., Sirisena, D. N., Bandaranayake, P. C. G., Samarasinghe, G., Wissuwa, M., & Suriyagoda, L. D. B. (2018). Variation in grain yield, and nitrogen, phosphorus and potassium nutrition of irrigated rice cultivars grown at fertile and low-fertile soils. Plant and Soil, 434(1–2), 107–123. https://doi.org/10.1007/s11104-018-3663-0.
Lee, D. D., & Seung, H. S. (1999). Learning the parts of objects by non-negative matrix factorization. Nature, 401, 788–791.
Li, D., Cheng, T., Zhou, K., Zheng, H. B., Yao, X., Tian, Y. C., et al. (2017a). WREP: A wavelet-based technique for extracting the red edge position from reflectance spectra for estimating leaf and canopy chlorophyll contents of cereal crops. ISPRS Journal of Photogrammetry and Remote Sensing, 129, 103–117. https://doi.org/10.1016/j.isprsjprs.2017.04.024.
Li, D., Wang, X., Zheng, H. B., Zhou, K., Yao, X., Tian, Y. C., et al. (2018). Estimation of area- and mass-based leaf nitrogen contents of wheat and rice crops from water-removed spectra using continuous wavelet analysis. Plant Methods, 14, 76. https://doi.org/10.1186/s13007-018-0344-1.
Li, Y. L., Liu, Y., Wu, S. W., Wang, C. K., Xu, A. A., & Pan, X. Z. (2017b). Hyper-spectral estimation of wheat biomass after alleviating of soil effects on spectra by non-negative matrix factorization. European Journal of Agronomy, 84, 58–66. https://doi.org/10.1016/j.eja.2016.12.003.
Liu, Y., Pan, X. Z., Shi, R. J., Li, Y. L., & Li, Z. T. (2015). Predicting soil salt content over partially vegetated surfaces using non-negative matrix factorization. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(11), 1–12. https://doi.org/10.1109/JSTARS.2015.2478490.
Liu, Y., Pan, X. Z., Shi, R. J., Li, Y. L., Wang, C. K., & Li, Z. T. (2016). Estimation of soil salt content over partially vegetated areas based on blind source separation. Acta Pedologica Sinica, 53(2), 322–331. https://doi.org/10.11766/trxb201508270275. (In Chinese).
Liu, H. Y., Zhu, H. C., & Wang, P. (2017). Quantitative modelling for leaf nitrogen content of winter wheat using UAV-based hyperspectral data. International Journal of Remote Sensing, 38(8–10), 2117–2134. https://doi.org/10.1080/01431161.2016.1253899.
Lu, D. J., Li, C. Z., Sokolwski, E., Magen, H., Chen, X. Q., Wang, H. Y., et al. (2017). Crop yield and soil available potassium changes as affected by potassium rate in rice–wheat systems. Field Crops Research, 214, 38–44. https://doi.org/10.1016/j.fcr.2017.08.025.
Lu, J. S., Yang, T. C., Su, X., Qi, H., Yao, X., Cheng, T., et al. (2020). Monitoring leaf potassium content using hyperspectral vegetation indices in rice leaves. Precision Agriculture, 21, 324–348. https://doi.org/10.1007/s11119-019-09670-w.
Ma, Q., Bell, R., & Biddulph, B. (2018). Potassium application alleviates grain sterility and increases yield of wheat (Triticum aestivum) in frost-prone Mediterranean-type climate. Plant and Soil, 434(1–2), 203–216. https://doi.org/10.1007/s11104-018-3620-y.
Mahajan, G. R., Sahoo, R. N., Pandey, R. N., Gupta, V. K., & Kumar, D. (2014). Using hyperspectral remote sensing techniques to monitor nitrogen, phosphorus, sulphur and potassium in wheat (Triticum aestivum L.). Precision Agriculture, 15(5), 499–522. https://doi.org/10.1007/s11119-014-9348-7.
Meganem, I., Deville, Y., Hosseini, S., Deliot, P., & Briottet, X. (2014). Linear-quadratic blind source separation using NMF to unmix urban hyperspectral images. IEEE Transactions on Signal Processing, 62(7), 1822–1833. https://doi.org/10.1109/TSP.2014.2306181.
Nieves-Cordones, M., Aleman, F., Martinez, V., & Rubio, F. (2014). K+ uptake in plant roots. The systems involved, their regulation and parallels in other organisms. Journal of Plant Physiology, 171(9), 688–695. https://doi.org/10.1016/j.jplph.2013.09.021.
Ouerghemmi, W., Gomez, C., Naceur, S., & Lagacherie, P. (2011). Applying blind source separation on hyperspectral data for clay content estimation over partially vegetated surfaces. Geoderma, 163(3–4), 227–237. https://doi.org/10.1016/j.geoderma.2011.04.019.
Ouerghemmi, W., Gomez, C., Naceur, S., & Lagacherie, P. (2016). Semi-blind source separation for the estimation of the clay content over semi-vegetated areas using VNIR/SWIR hyperspectral airborne data. Remote Sensing of Environment, 181, 251–263. https://doi.org/10.1016/j.rse.2016.04.013.
Pandey, P., Ge, Y., Stoerger, V., & Schnable, J. C. (2017). High throughput in vivo analysis of plant leaf chemical properties using hyperspectral imaging. Frontiers in Plant Science, 8, 1348. https://doi.org/10.3389/fpls.2017.01348.
Pimstein, A., Karnieli, A., Bansal, S. K., & Bonfil, D. J. (2011). Exploring remotely sensed technologies for monitoring wheat potassium and phosphorus using field spectroscopy. Field Crops Research, 121(1), 125–135. https://doi.org/10.1016/j.fcr.2010.12.001.
Roberts, D. A., Gardner, M., Church, R., Ustin, S., Scheer, G., & Green, R. O. (1998). Mapping chaparral in the santa monica mountains using multiple endmember spectral mixture models. Remote Sensing of Environment, 65(3), 267–279. https://doi.org/10.1016/S0034-4257(98)00037-6.
Römheld, V., & Kirkby, E. A. (2010). Research on potassium in agriculture: needs and prospects. Plant and Soil, 335(1–2), 155–180. https://doi.org/10.1007/s11104-010-0520-1.
Sanches, I. D., Tuohy, M. P., Hedley, M. J., & Mackay, A. D. (2012). Seasonal prediction of in situ pasture macronutrients in New Zealand pastoral systems using hyperspectral data. International Journal of Remote Sensing, 34(1), 276–302. https://doi.org/10.1080/01431161.2012.713528.
Severtson, D., Callow, N., Flower, K., Neuhaus, A., Olejnik, M., & Nansen, C. (2016). Unmanned aerial vehicle canopy reflectance data detects potassium deficiency and green peach aphid susceptibility in canola. Precision Agriculture, 17(6), 659–677. https://doi.org/10.1007/s11119-016-9442-0.
Shi, C., & Wang, L. (2014). Incorporating spatial information in spectral unmixing: A review. Remote Sensing of Environment, 149, 70–87. https://doi.org/10.1016/j.rse.2014.03.034.
Singh, V. K., Dwivedi, B. S., Tiwari, K. N., Majumdar, K., Rani, M., Singh, S. K., et al. (2014). Optimizing nutrient management strategies for rice–wheat system in the Indo-Gangetic Plains of India and adjacent region for higher productivity, nutrient use efficiency and profits. Field Crops Research, 164, 30–44. https://doi.org/10.1016/j.fcr.2014.05.007.
Singh, V. K., Dwivedi, B. S., Yadvinder, S., Singh, S. K., Mishra, R. P., Shukla, A. K., et al. (2018). Effect of tillage and crop establishment, residue management and K fertilization on yield, K use efficiency and apparent K balance under rice-maize system in north-western India. Field Crops Research, 224, 1–12. https://doi.org/10.1016/j.fcr.2018.04.012.
Stadler, A., Rudolph, S., Kupisch, M., Langensiepen, M., van der Kruk, J., & Ewert, F. (2015). Quantifying the effects of soil variability on crop growth using apparent soil electrical conductivity measurements. European Journal of Agronomy, 64, 8–20. https://doi.org/10.1016/j.eja.2014.12.004.
Stein, B. R., Thomas, V. A., Lorentz, L. J., & Strahm, B. D. (2014). Predicting macronutrient concentrations from loblolly pine leaf reflectance across local and regional scales. GIScience Remote Sensing, 51(3), 269–287. https://doi.org/10.1080/15481603.2014.912875.
Thomson, E. R., Malhi, Y., Bartholomeus, H., Oliveras, I., Gvozdevaite, A., Peprah, T., et al. (2018). Mapping the leaf economic spectrum across west African tropical forests using UAV-acquired hyperspectral imagery. Remote Sensing, 10(10), 1532. https://doi.org/10.3390/rs10101532.
Tian, Y. C., Gu, K. J., Chu, X., Yao, X., Cao, W. X., & Zhu, Y. (2013). Comparison of different hyperspectral vegetation indices for canopy leaf nitrogen concentration estimation in rice. Plant and Soil, 376(1–2), 193–209. https://doi.org/10.1007/s11104-013-1937-0.
Turner, D., Lucieer, A., & Wallace, L. (2014). Direct georeferencing of ultrahigh-resolution UAV imagery. IEEE Transactions on Geoscience and Remote Sensing, 52(5), 2738–2745. https://doi.org/10.1109/TGRS.2013.2265295.
Van Beek, J., Tits, L., Somers, B., Deckers, T., Janssens, P., & Coppin, P. (2015). Reducing background effects in orchards through spectral vegetation index correction. International Journal of Applied Earth Observation and Geoinformation, 34, 167–177. https://doi.org/10.1016/j.jag.2014.08.009.
Verhoef, W., & Bach, H. (2003). Simulation of hyperspectral and directional radiance images using coupled biophysical and atmospheric radiative transfer models. Remote Sensing of Environment, 87, 23–41. https://doi.org/10.1016/S0034-4257(03)00143-3.
Xu, X. Q., Lu, J. S., Zhang, N., Yang, T. C., He, J. Y., Yao, X., et al. (2019). Inversion of rice canopy chlorophyll content and leaf area index based on coupling of radiative transfer and Bayesian network models. ISPRS Journal of Photogrammetry and Remote Sensing, 150, 185–196. https://doi.org/10.1016/j.isprsjprs.2019.02.013.
Yang, G.J., Li, C.C., Wang, Y.J., Yuan, H.H., Feng, H.K., Xu, B., et al. (2017). The DOM generation and precise radiometric calibration of a UAV-mounted miniature snapshot hyperspectral imager. Remote Sensing, 9(7), 642.https://doi.org/10.3390/rs9070642
Yao, X., Huang, Y., Shang, G. Y., Zhou, C., Cheng, T., Tian, Y. C., et al. (2015). Evaluation of six algorithms to monitor wheat leaf nitrogen concentration. Remote Sensing, 7(11), 14939–14966. https://doi.org/10.3390/rs71114939.
Zhu, F. Y., Wang, Y., Xiang, S. M., Fan, B., & Pan, C. H. (2014). Structured sparse method for hyperspectral unmixing. ISPRS Journal of Photogrammetry and Remote Sensing, 88, 101–118. https://doi.org/10.1016/j.isprsjprs.2013.11.014.
Zorb, C., Senbayram, M., & Peiter, E. (2014). Potassium in agriculture–status and perspectives. Journal of Plant Physiology, 171(9), 656–669. https://doi.org/10.1016/j.jplph.2013.08.008.
Acknowledgements
This work was supported by the National Natural Science Foundation of China (31971784); the National Key R&D Program (2018YFD0300805), the Jiangsu Collaborative Innovation Center for Modern Crop Production, and the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD). We are grateful to the editor and two reviewers for their suggestions and comments, which significantly improved the quality of this paper.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Lu, J., Li, W., Yu, M. et al. Estimation of rice plant potassium accumulation based on non-negative matrix factorization using hyperspectral reflectance. Precision Agric 22, 51–74 (2021). https://doi.org/10.1007/s11119-020-09729-z
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11119-020-09729-z