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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, 2020, Vol. 17, No. 1, pp. 113-127

General methods and results of ground hyperspectral studies of seasonal changes in the reflective properties of crops and certain types of weeds

R.Yu. Danilov 1 , O.Yu. Kremneva 1 , V.Ya. Ismailov 1 , V.A. Tret'yakov 2 , A.A. Rizvanov 2 , V.V. Krivoshein 2 , A.A. Pachkin 1 
1 All-Russian Research Institute of Biological Plant Protection, Krasnodar, Russia
2 Central Research Institute of Mechanical Engineering, Korolev, Russia
Accepted: 27.11.2019
DOI: 10.21046/2070-7401-2020-17-1-113-127
The paper presents the results of experimental studies aimed at assessing the possibility of taxonomic identification and determination of morphophysiological changes of different types of cultivated and weed plants in field agrocenoses based on the analysis of data from ground-based hyperspectral measurements of plant objects. To implement the tasks, a field validation measurement technique has been developed for the reflection spectra of the studied objects using the Ocean Optics Maya 2000-Pro automated spectrometer, which allows on-line measurements of the spectral density of objects in the spectral density range from 350 to 1200 nm with high spectral resolution ~1 nm reflected solar radiation. In order to organize hyperspectral surveys, test plots with different species composition of cultivated and weed plants were arranged on the experimental fields of the scientific crop rotation of the All-Russian Scientific Research Institute of Biological Plant Protection. As a result of measurements carried out on test plots, the reflection spectra were obtained for certain types of cultivated and weed plants. On the basis of their processing, the database of the spectral characteristics of crops and weeds has been developed. The analysis of the obtained data revealed the peculiarities of changes in the reflectivity of cultivated crops and weeds for individual periods of the growing season. The obtained data are important for the validation of remote space observations using multispectral and hyperspectral instrumentation.
Keywords: spectrometer, ground hyperspectral measurements, cultivated and weedy plants, spectral density of energy brightness, spectral characteristics of plants
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References:

  1. Abrosimov A. V., Dvorkin B. A., Perspektivy primeneniya dannykh DZZ iz kosmosa dlya povysheniya effektivnosti sel’skogo khozyaistva v Rossii (Prospects of application of remote sensing data from space to improve the efficiency of agriculture in Russia), Geomatika, 2009, No. 4, pp. 45–49.
  2. Akopov A. K., Baula G. G., Krivoshein V. V., Krotkov A. Yu., Tretyakov V. A., Razrabotka metodiki nazemnykh validatsionnykh izmerenii spektrov sel’skokhozyaistvennykh kul’tur (The Development of Methodology for Ground Validation Measurements of Range of Crops), Kosmonavtika i raketostroenie, 2015, No. 6(85), pp. 45–50.
  3. Al’bedo i uglovye kharakteristiki otrazheniya podstilayushchei poverkhnosti i oblakov (Albedo and angular reflection characteristics of the underlying surface and clouds), Kondratyev K. Ya. (ed.), Leningrad: Gidrometeoizdat, 1981, 233 p.
  4. Antonov V. N., Sladkikh L. A., Monitoring sostoyaniya posevov i prognozirovanie urozhainosti yarovoi pshenitsy po dannym DZZ (Monitoring of crop condition and forecasting of productivity of spring wheat by remote sensing data), Geomatika, 2009, No. 4, pp. 50–53.
  5. Anshakov G. P., Zhuravel Yu. N., Fedoseev A. A., Effektivnost’ ispol’zovaniya mul’tispektral’nykh i giperspektral’nykh dannykh distantsionnogo zondirovaniya v zadachakh monitoringa okruzhayushchei sredy (The effectiveness of using multispectral and hyperspectral remote sensing data for environmental monitoring), Vestnik Samarskogo gosudarstvennogo aerokosmicheskogo universiteta, 2013, No. 4(42), pp. 38–48.
  6. Arkhipova O. E., Kachalina N. A., Tyutyunov Yu. V., Kovalev O. V., Otsenka zasorennosti antropogennykh fitotsenozov na osnove dannykh distantsionnogo zondirovaniya Zemli (na primere ambrozii polynnolistnoi) (Weediness Assessment of Anthropogenic Phytocenoses on the Basis of Satellite Remote Sensing Data (A Case Example of Common Ragweed)), Issledovaniya Zemli iz kosmosa, 2014, No. 6, pp. 15–26.
  7. Bartalev S. A., Loupian E. A., Neishtadt I. A., Savin I. Yu., Distantsionnaya otsenka parametrov sel’skokhozyaistvennykh zemel’ po sputnikovym dannym spektroradiometra MODIS (Remote estimation of agricultural land parameters by satellite data of MODIS Spectroradiometer), Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2005, Issue 2, Vol. 2, pp. 228–236.
  8. Baula G. G., Brychikhin M. N., Istomina M. I., Krotkov A. Yu., Szhenov E. Yu., Rizvanov A. A., Tretyakov V. A., Formirovanie bazy dannykh giperspektral’nykh opticheskikh kharakteristik sel’skokhozyaistvennykh kul’tur v ul’trafioletovoi, vidimoi i blizhnei infrakrasnoi oblastyakh spektra (Development of a Database of Hyperspectral Optical Characteristics of Agricultural Crops in the Ultraviolet, Visible and Near Infrared Regions’ Spectrum), Kosmonavtika i raketostroenie, 2013, No. 4(73), pp. 178–184.
  9. Voronina P. V., Mamash E. A., Klassifikatsiya tematicheskikh zadach monitoringa sel’skogo khozyaistva s ispol’zovaniem dannykh distantsionnogo zondirovaniya MODIS (Classification of thematic monitoring for agriculture problems using remote sensing MODIS data), Vychislitel’nye tekhnologii, 2014, Vol. 19, No. 3, pp. 76–102.
  10. Vygodskaya I. N., Gorshkova I. I., Teoriya i eksperiment v distantsionnykh issledovaniyakh rastitel’nosti (Theory and experiment in remote studies of vegetation), Moscow: Gidrometeoizdat, 1987, 246 p.
  11. Grigoriev A. N., Ryzhikov D. M., Obshchaya metodika i rezul’taty spektroradiometricheskogo issledovaniya otrazhatel’nykh svoistv borshchevika Sosnovskogo v diapazone 320–1100 nm v interesakh distantsionnogo zondirovaniya Zemli (General methodology and results of spectroradiometric research of reflective properties of the Heracleum Sosnowskyi in the range 320–1100 nm for Earth remote sensing), Sovremennyye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2018, Vol. 15, No. 1, pp. 183–192.
  12. Danilov R. Yu., Ismailov V. Ya., Tretyakov V. A., Kremneva O. Yu., Shumilov Yu. V., Rizvanov A. A., Krivoshein V. V., Kostenko I. A., Razrabotka pretsizionnykh tekhnologii fitosanitarnogo monitoringa agroekosistem na osnove ispol’zovaniya dannykh distantsionnogo giperspektral’nogo zondirovaniya Zemli (Development of precision technologies of agroecosystems phytosanitary monitoring based on the use of data of remote hyperspecral sensing of the Earth), Dostizheniya nauki i tekhniki APK, 2018, Vol. 32, No. 10, pp. 82–86.
  13. Derkacheva A. A., Tutubalina O. V., Zimin M. V., Golubeva E. I., Primenenie aviatsionnykh giperspektral’nykh snimkov i nazemnykh dannykh dlya tselei tochnogo zemledeliya (Application of aerial hyperspectral images and ground data for precision agriculture), Zemlya iz kosmosa — naibolee effektivnye resheniya, 2015, Issue S, pp. 43–46.
  14. Dospekhov B. A., Metodika polevogo opyta (s osnovami statisticheskoi obrabotki rezul’tatov issledovanii) (Methodology of field experience (with the basics of statistical processing of research results)), Kazan-Moscow: Agropromizdat, 1985, 351 p.
  15. Zimin M. V., Tutubalina O. V., Golubeva E. I., Rees G. U., Metodika nazemnogo spektrometrirovaniya rastenii Arktiki dlya deshifrirovaniya kosmicheskikh snimkov (Ground spectrometry of Arctic plants for the interpretation of space imagery), Vestnik Moskovskogo gosudarstvennogo universiteta, Ser. 5: Geografiya, 2014, No. 5, pp. 34–41.
  16. Ismailov E. Ya., Nadykta V. D., Ismailov V. Ya., Kostenko I. A., Shvets A. A., Giperspektral’nye issledovaniya porazheniya sel’skokhozyaistvennykh kul’tur fitopatogenami (Hyperspectral Monitoring of Arable Crop Phytopathogen Infestation), Kosmonavtika i raketostroenie, 2012, No. 3(68), pp. 98–103.
  17. Kachalina N. A., Arkhipova O. E., Grechishchev A. V., Otsenka zasorennosti agrofitotsenozov Rostovskoi oblasti s ispol’zovaniem giperspektral’nykh dannykh distantsionnogo zondirovaniya Zemli (Weediness assessment of anthropogenic phytocenoses in Rostov region using hyperspectral remote sensing data), Informatsiya i kosmos, 2016, No. 1, pp. 131–136.
  18. Kochubei S. M., Shadchin T. M., Kobets N. I., Spektral’nye svoistva rastenii kak osnova metodov distantsionnoi diagnostiki (Spectral properties of plants as the basis of remote diagnostics methods), Kiev: Naukova dumka, 1990, 134 p.
  19. Krinov E. A., Spektral’naya otrazhatel’naya sposobnost’ prirodnykh obrazovanii (Spectral reflectance of natural formations), Moscow: Izd. AN SSSR, 1947, 270 p.
  20. Luneva N. N., Geobotanicheskii uchet zasorennosti posevov sel’skokhozyaistvennykh kul’tur (Geobotanical records of weediness of agricultural crops), Metody monitoringa i prognoza razvitiya vrednykh organizmov, Moscow; Saint Petersburg, 2002, pp. 82–88.
  21. Makarenkov A. A., Algoritmy predvaritel’noi obrabotki informatsii ot aerokosmicheskikh sistem giperspektral’noi sʺemki Zemli: Diss. kand. tekhn. nauk (Algorithms of preliminary information processing from aerospace systems of hyperspectral earth survey. Cand. techn. sci. thesis), Ryazan, 2015, 141 p.
  22. Markov M. V., Sorno-polevaya rastitel’nost’ i metodika ee izucheniya (Weed-field vegetation and methods of its study), Kazan: Izd. Kazanskogo universiteta, 1970, 51 p.
  23. Mikhailenko I. M., Voronkov I. V., Metody obnaruzheniya sornyakov, boleznei i vreditelei rastenii po dannym distantsionnogo zondirovaniya (Methods for detection of weeds, pests and diseases of plants from remote sensing data), Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2016, Vol. 13, No. 3, pp. 72–83.
  24. Nagalevskii Yu. Ya., Chistyakov V. I., Fizicheskaya geografiya Krasnodarskogo kraya (Physical geography of the Krasnodar Territory), Krasnodar: Severnyi Kavkaz, 2003, 256 p.
  25. Pyankov S. V., Kalinin N. A., Sviyazov E. M., Smirnova A. A., Nekrasov I. B., Monitoring sostoyaniya sel’skokhozyaistvennykh kul’tur v Permskom krae po dannym distantsionnogo zondirovaniya Zemli (Monitoring of conditions of agricultural crops in the Perm Krai on information of the remote sensing of Earth), Vestnik Permskogo universiteta. Biologiya, 2009, Issue. 10(36), pp. 147–153.
  26. Rozenberg G. S., Saksonov S. V., Kuznetsova R. S., Senator S. A., Kosmicheskii monitoring v landshaftno-ekologicheskikh issledovaniyakh (Space monitoring in landscape-ecological researches), Izvestiya Samarskogo nauchnogo tsentra Rossiiskoi akademii nauk, 2012, Vol. 14, No. 1–1, pp. 9–14.
  27. Sid’ko A. F., Shevyrnogov A. P., Izuchenie sezonnoi zavisimosti spektral’noi yarkosti posevov sel’skokhozyaistvennykh kul’tur ot soderzhaniya khlorofilla i fiziologicheskikh parametrov rastenii (Study of seasonal dependence of spectral brightness of crops on chlorophyll content and physiological parameters of plants), Issledovanie Zemli iz kosmosa, 1998, No. 3, pp. 96–105.
  28. Sid’ko A. F., Pugacheva I. Y., Shevyrnogov A. P., Issledovanie dinamiki spektral’noi yarkosti posevov sel’skokhozyaistvennykh kul’tur v period vegetatsii na territorii Krasnoyarskogo kraya (Investigation of the Spectral Brightness Dynamics of Agricultural Crops during Vegetation Period at the Krasnoyarsk Territory), J. Siberian Federal University. Engineering and Technologies, 2009, Vol. 2, No. 1, pp. 100–111.
  29. Terekhin E. A., Spektral’nye otrazhatel’nye svoistva sel’skokhozyaistvennoi rastitel’nosti Belgorodskoi oblasti (po materialam kosmicheskoi sʺemki) (Spectral refletance properties of agriculiural vegetation of Belgorod region based on remote sensing data), Nauchnye vedomosti Belgorodskogo gosudarstvennogo universiteta. Ser.: Estestvennye nauki, 2012, Issue. 20, No. 15(134), pp. 188–193.
  30. Tretyakov V. A., Krotkov A. Yu., Krivoshein V. V., Danilov R. Yu., Uluchshenie protsessa tematicheskoi obrabotki giperspektral’noi informatsii (Improving the thematic processing of hyperspectral information), Tsifrovaya obrabotka signalov, 2017, No. 3, pp. 28–32.
  31. Chaban L. N., Vecheruk G. V., Gavrilova T. S., Issledovanie vozmozhnostei klassifikatsii rastitel’nogo pokrova po giperspektral’nym izobrazheniyam v paketakh tematicheskoi obrabotki dannykh distantsionnogo zondirovaniya (Classification Study hyperspectral vegetation images in thematic processing packages remote sensing data), Trudy Moskovskogo fiziko-tekhnicheskogo instituta, 2009, Vol. 1, No. 3, pp. 171–180.
  32. Chaban L. N., Vecheruk G. V., Kondranin T. V., Kudriavtsev C. B., Nikolenko A. A., Modelirovanie i tematicheskaya obrabotka izobrazhenii, identichnykh videodannym s gotovyashcheisya k zapusku i razrabatyvaemoi giperspektral’noi apparatury DZZ (Modeling and thematic processing of images identical to the imagery from workable and preparing for the space launch hyperspectral remote sensors), Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2012, Vol. 9, No. 2, pp. 111–121.
  33. Campbell J. B., Introduction to Remote Sensing, New York: Guilford Press, 1996. 622 p.
  34. Colwell R. N., Determining the Prevalence of Certain Cereal Crop Diseases by Means of Aerial Photography, Hilgardia, 1956, Vol. 26, No. 5, pp. 223–286.
  35. Govender M., Chetty K., Bulcock H., A review of hyperspectral remote sensing and its application in vegetation and water resource studies, Water SA, 2007, Vol. 33, No. 2, pp. 145–151.
  36. Hatfield J. L., Gitelson A. A., Schepers J. S., Walthall C. L., Application of Spectral Remote Sensing for Agronomic Decisions ― Celebrate the Centennial, A Supplement to Agronomy J., 2008, pp. 117–131.
  37. Krezhova D., Dikova B., Maneva S., Ground based hyperspectral remote sensing for disease detection of tobacco plants, Bulgarian J. Agricultural Science, 2014, Vol. 20, No. 5, pp. 1142–1150.
  38. Lillesand T. M., Kiefer R. M., Chipman J. W., Remote Sensing and Image Interpretation. Fifth Edition, New Jersey: John Wiley and Sons, Inc., 2003, 784 p.