Intelligent Agriculture

Remote Estimation of Sugar Beet Biomass Condition

Ilya M. Mikhailenko

Laboratory of Information and Measuring Systems, Agrophysical Research Institute of the Russian Academy of Sciences, St. Petersburg 195220, Russia

Valery N. Timoshin

Laboratory of Information and Measuring Systems, Agrophysical Research Institute of the Russian Academy of Sciences, St. Petersburg 195220, Russia

DOI: https://doi.org/10.36956/ia.v1i1.1807

Received: 5 March 2025 | Revised: 10 April 2025 | Accepted: 18 April 2025 | Published Online: 25 April 2025

---

Abstract

The presented article considers the problem of estimating the parameters of root crop biomass based on Earth remote sensing data. The underground commercial part of the biomass of this type of crops is inaccessible to optical remote sensing. The authors develop a classical approach to estimating the parameters of the state of dynamic systems based on mathematical models. In their previous works, this approach was implemented by the authors to assess crops with above-ground commercial biomass. Such crops are cereals and perennial grasses. To assess the biomass of crops with an underground commercial part, the authors proposed using three mathematical models. The first, main one, is the model of the dynamics of the biomass of a root crop, reflecting the relationship between the above-ground part of the biomass and the mass of root crops. The second is a dynamic model of the parameters of the soil environment, reflecting the removal of nutrients and moisture by the biomass of the root crop. The third is a model of optical remote sensing, reflecting the relationship between the reflectance parameters in the red and near infrared optical ranges with the parameters of the above-ground part of the biomass. Since underground biomass is inaccessible to Earth remote sensing, special requirements are imposed on the model of biomass parameter dynamics. This model must have the property of observability, which ensures the assessment of all components of the root crop biomass when probing its above-ground part. The presence of three mathematical models allows simultaneous assessment of the root crop biomass parameters and soil environment parameters with the closure of the assessment algorithm on real Earth remote sensing data. The proposed methodology and algorithms are quite applicable to other root crops, such as carrots, potatoes, etc.

Keywords: Earth Remote Sensing; Root Crops; Biomass Parameters; Mathematical Models

---

References

[1] Ren, S.J., Guo, H., Wu, S., et al., 2023. Winter wheat planted area monitoring and yield modeling using MODIS data in the Huang-Huai-Hai Plain, China. Computers and Electronics in Agriculture. 182, 106049. DOI: https://doi.org/10.1016/j.compag.2021.106049

[2] Gong, X., Li, T., Wang, B., et al., 2025. Beyond the remote sensing ecological index: A comprehensive ecological quality evaluation using a Deep-learning-based Remote Sensing Ecological Index. Remote Sensing. 17(3), 558. DOI: https://doi.org/10.3390/rs17030558

[3] Mikhailenko, I.M., 2011. The main tasks of assessing the state of crops and the soil environment based on space sensing data. Ecological Systems and Devices. 8, 17–25.

[4] Mikhailenko, I.M., Timoshin, V.N., 2018. Assessing the chemical state of the soil environment based on remote sensing data. Modern Problems of Remote Sensing of the Earth from Space. 18(4), 125–134. DOI: https://doi.org/10.21046/2070-7401-2018-15-7-102-113

[5] Mikhailenko, I.M., Timoshin, V.N., 2018. Mathematical modeling and assessment of the chemical state of the soil environment based on remote sensing data. International Research Journal. 9(2), 26–38. DOI: https://doi.org/10.23670/IRJ.2018.75.9.029

[6] Mikhailenko, I.M., Timoshin, V.N., 2018. Making decisions on the date of forage harvesting based on Earth remote sensing data and adjustable mathematical models. Modern Problems of Earth Remote Sensing from Space. 15(1), 164–175. DOI: https://doi.org/10.21046/2070-7401-2018-15-1-169-182

[7] Mikhailenko, I.M., Timoshin, V.N., 2017. Managing sowing dates based on Earth remote sensing data.Modern Problems of Earth Remote Sensing from Space. 14(5), 178–189.

[8] Mikhailenko, I.M., 2013. Assessment of crop and soil state using satellite remote sensing data. International Journal of Information Technology & Operations Management. 1(5), 41–52.

[9] Mikhailenko, I.M., 2011. The main tasks of assessing the state of sowing and soil environment according to space sounding data. Environmental Systems and Development. 8, 17–25.

[10] Rachkulik, V.I., Sitnikova, M.V., 1981. Reflective properties and state of vegetation cover. Gidrometeoizdat: Leningrad, Russia. 287p.

[11] Farook, A.A., Afzaal, H., Benlamri, R., et al., 2023. Red-green-blue vegetation index into normalized difference vegetation index: a robust and low-cost approach for vegetation monitoring using machine vision and generative adversarial networks. Precision Agriculture. 24, 1097–1115. DOI: https://doi.org/10.1007/s11119-023-10001-3

[12] David, R.M., Rosser, N.J., Donoghue, D.N.M., 2022. Improving above ground biomass estimates of Southern Africa dryland forests by combining Sentinel-1 SAR and Sentinel-2 multispectral imagery. Remote Sensing of Environment. 282, 113232. DOI: https://doi.org/10.1016/j.rse.2022.113232

[13] Ponzoni, F.J., Borges da Silva, C., Benfica dos Santos, S., et al., 2014. Local illumination influence on vegetation indices and plant area index (PAI) relationships. Remote Sensing. 6(7), 6266–6282. DOI: https://doi.org/10.3390/rs6076266

[14] Zhang, S., Zhao, T., Xu, H., et al., 2024. GLC_FCS30D: the first global 30 m land-cover dynamics monitoring product with a fine classification system for the period from 1985 to 2022 generated using dense-time-series Landsat imagery and the continuous change-detection method. Earth System Science Data. 16, 1353–1381. DOI: https://doi.org/10.5194/essd-16-1353-2024

[15] Sims, D.A., Gamon, J.A., 2002. Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages. Remote Sensing of Environment. 81(2–3), 337–354. DOI: https://doi.org/10.1016/S0034-4257(02)00010-x

[16] Gao, W., Zheng, C., Liu, S., et al., 2022. NDVI-based vegetation dynamics and their responses to climate change and human activities from 1982 to 2020: A case study in the Mu Us Sandy Land, China. Ecological Indicators. 137, 108745. DOI: https://doi.org/10.1016/j.ecolind.2022.108745

[17] Kasimati, A., Psiroukis, V., Darrah, N., et al., 2023. Investigation of the similarities between NDVI maps from different proximal and remote sensing platforms in explaining vineyard variability. Precision Agriculture. 24, 1220–1240. DOI: https://doi.org/10.1007/s11119-022-09984-2

[18] Doornbos, J., Babur, O., Valente, J., 2025. Evaluating generalization of methods for artificially generating NDVI from UAV RGB imagery in vineyards. Remote Sensing. 17(3), 512. DOI: https://doi.org/10.3390/rs17030512

[19] Roy, B., Sagan, W., Khayreti, A., et al., 2024. Early detection of drought stress in durum wheat using hyperspectral imaging and photosystem sensing. Remote Sensing. 16(1), 155. DOI: https://doi.org/10.3390/rs16010155

[20] Penuelas, J., Baret, F., Filella, I., 1995. Semi-empirical indices to assess carotenoids/chlorophyll a ratio from leaf spectral reflectance. Photosynthetica. 31, 221–230.

[21] Quemada, M., Gabriel, J., Zarco-Tejada, P., 2014. Airborne hyperspectral images and ground-level optical sensors as assessment tools for maize nitrogen fertilization. Remote Sensing. 6, 2940–2962. DOI: https://doi.org/10.3390/rs6042940

[22] Rouse, J.W., Haas, R.H., Schell, J.A., et al., 1973. Monitoring vegetation systems in the Great Plains with ERTS. Third ERTS Symposium. NASA SP-351. 1, 309–317.

[23] Sami, K., Kushal, K.C., John, P.F., et al., 2020. Remote sensing in agriculture—accomplishments, limitations, and opportunities. Remote Sensing. 12(22), 3783. DOI: https://doi.org/10.3390/rs12223783

[24] Kochubey, S.M., Shadchina, T.M., Kobets, N.I., 1990. Spectral properties of plants as a basis for remote diagnostics methods. Naukova Dumka: Kyiv, Ukraine.

[25] Kazakov, I.E., 1987. Methods for optimizing stochastic systems. Science: Moscow, Russia. 349p.

[26] Kalman, R.E., 1960. A new approach to linear filtering and prediction problems. Transactions of the ASME – Journal of Basic Engineering. 82, 35–45.

[27] Eikhoff, P., 1975. Fundamentals of identification of control systems. Parameter and state estimation. Mir: Moscow, Russia. 681p.