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ISSN 2070-7401 (Print), ISSN 2411-0280 (Online)
Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa
CURRENT PROBLEMS IN REMOTE SENSING OF THE EARTH FROM SPACE

  

Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2018, Vol. 15, No. 7, pp. 102-113

Assessment of the chemical state of soil environment from remote sensing data of the Earth

I.M. Mikhaylenko 1 , V.N. Timoshin 1 
1 Agrophysical Research Institute, Saint Petersburg, Russia
Accepted: 12.10.2018
DOI: 10.21046/2070-7401-2018-15-7-102-113
Managing parameters of the chemical state of the soil through the introduction of mineral fertilizers is the most important technological component in modern systems of precision farming. This problem has not been positively resolved so far. Here, a significant deterrent is the lack of methods and means for estimating the parameters of the chemical state over large areas of agricultural fields. The purpose of this study is to create a scientific and methodological basis for solving the problem of estimating the parameters of the chemical state of the soil based on Earth remote sensing data (ERS). Due to the inaccessibility of a soil chemical condition for remote sensing, the evaluation of its parameters is carried out in two stages. At the first stage, the mass and chemical parameters of the state of sowing of an agricultural crop are estimated, and at the second stage, estimates of the parameters of the chemical state of the soil are constructed from these estimates. The proposed method is based on mathematical models: parameters of the mass and chemical parameters of the sowing state (ERS), the dependence of the yield on the parameters of the chemical state of the soil for individual crops. At the same time, an optimal filtering algorithm was used to build parameter estimates based on remote sensing data. The obtained estimates can be used to control the parameters of the chemical state of the soil in precision farming systems.
Keywords: precision farming, agrotechnology management, remote sensing of the Earth, mathematical models, algorithms for estimating parameters
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References:

  1. Kazakov I. E. Metody optimizatsii stokhasticheskikh sistem (Methods of optimization of stochastic systems), Moscow: Nauka, 1987, 384 p.
  2. Mikhaylenko I. M., Upravleniye sistemami tochnogo zemledeliya (Management of precision farming systems), St. Petersburg: SPbGU, 2005, 233 p.
  3. Mikhaylenko I. M., Osnovnye zadachi otsenivaniya sostoyaniya posevov i pochvennoy sredy po dannym kosmicheskogo zondirovaniya (The main tasks of assessing the state of crops and soil environment from space probing data), Ekologicheskiye sistemy i pribory, 2011, No. 8, pp. 17–25.
  4. Mikhaylenko I. M., Kurashvili A. E., Prognozirovanie sostoyaniya travostoya v sisteme upravleniya kachestvom kormov v molochnom zhivotnovodstve (Prediction of grass stand state in the feed quality management system in dairy cattle breeding), Bulletin of the RSHA, 2008, No. 2, pp. 10–13.
  5. Tochnoye sel’skoye khozyaystvo (Precision Agriculture), D. Shpaar, A. V. Zakharenko, V. P. Yakushev (eds.), St. Petersburg: VIZR, 2009, 397 p.
  6. Akhtman Y., Golubeva E., Tutubalina O., Zimin M., Application of hyperspectural images and ground data for precision farming, Geography, Environment, Sustainability, 2017, pp. 117–128.
  7. Jaud M., Le Dantec N., Ammann J., Grandjean P., Constantin D., Akhtman Y., Barbieux K., Allemand P., Delacourt C., Merminod B., Direct Georeferencing of a Pushbroom, Lightweight Hyperspectral System for Mini-UAV Applications, Remote Sensing, 2018, No. 10(2), pp. 204–219.
  8. Kalman R. E., A New Approach to Linear Filtering and Prediction Problems, Trans. ASME ― J. Basic Engineering, 1960, No. 82, pp. 35–45.
  9. Mikhailenko I. M., Assessment of crop and soil state using satellite remote sensing data, Intern. J. Information Technology and Operations Management, 2013, Vol. 1, No. 5, pp. 41–52.
  10. Quemada M., Gabriel J., Zarco-Tejada P., Airborne hyperspectural images and ground-level optical sensors as assessment tools for maize nitrogen fertilization, Remote Sensing, 2014, No. 6, pp. 2940–2962.
  11. Zarco-Tejada P. J., Guillén-Climent M. L., Hernández-Clemente R., Catalina A., González M. R., Martín P., Estimating leaf carotenoid content in vineyards using high resolution hyperspectral imagery acquired from an unmanned aerial vehicle (UAV), Agricultural and Forest Meteorology, 2013, Vol. 171–172, pp. 281–294.