Experimental determination of soil moisture on hyperspectral images
Podlipnov V.V., Shchedrin V.N., Babichev A.N., Vasilyev S.M., Blank V.A.

IPSI RAS – Branch of the FSRC “Crystallography and Photonics” RAS, Molodogvardeyskaya 151, 443001, Samara, Russia;
Samara National Research University, 34, Moskovskoye shosse, 443086, Samara, Russia  ;

Russian Scientific Research Institute of Land Improvement Problems, Baklanovsky ave., 190, Novocherkassk, Rostov region, Russian Federation, 346421

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Abstract:
The possibility of humidity determination based on the use of spectral distributions in the wavelength range of up to 1 µm is analyzed. We discuss the use of an imaging hyperspectrometer for precision farming. A field experiment to determine the soil moisture index under the vegetation cover is described. A procedure of precise calibration of the hyperspectrometer based on the use of a tunable laser is described. We show the possibility of the practical determination of humidity based on the use of spectra in the wavelength range of up to 1 µm.

Keywords:
hyperspectrometer, image processing, distribution histogram, remote sensing, NDVI, water band index, the Offner scheme, hyperspectral images.

Citation:
Podlipnov VV, Shchedrin VN, Babichev AN, Vasilyev SM, Blank VA. Experimental determination of soil moisture on hyperspectral images. Computer Optics 2018; 42(5): 877-884. DOI: 10.18287/2412-6179-2017-42-5-877-884.

References:

  1. Achata E, Esquerre C, O’Donnell C, Gowen A. A study on the application of near infrared hyperspectral chemical imaging for monitoring moisture content and water activity in low moisture systems. Molecules 2015; 20(2): 2611-2621. DOI: 10.3390/molecules20022611.
  2. Donets VV, Kochubey SM, Yatsenko VA, Kazantsev TA, Brovchenko VV. Creation of the spectral hardware-software complex for subsatellite validation of the controlled from distance research of vegetation [In Russian]. Information processing systems 2013; 8(115): 36-42.
  3. Cherepanov A, Druzhinina E. Spectral characteristics of vegetation and vegetation indexes [In Russian]. Geomatics 2009; 3: 28-32.
  4. Antonov V, Sladkih L. Crop monitoring and spring wheat yields forecasting basing on remote sensing data. Geomatics 2009; 4(5): 50-53.
  5. Maiorova VI, Bannikov AM, Grishko DA, Jarenov IS, Leonov VV, Toporkov AG, Harlan AA. Monitoring condition of agricultural fields based on prediction of NDVI with the use of multi-spectral and hyper-spectral data from space imagery [In Russian]. Science and Education 2013; 7: 199-228. DOI: 10.7463/0713.0577991.
  6. Genc H, Genc L, Turhan H. Vegetation indices as indicators of damage by the sunnpest (Hemiptera: Scutelleridae) to field grown wheat. African Journal of Biotechnology 2008; 7 (2): 173-180. DOI: 10.5897/AJB2008.000-5018.
  7. Pushkin AA, Sidelnik NYa, Kovalevskiy SV. The using of the satellite imagery for the assessment of the forest fire danger. Proceedings of BSTU 2015; 1(174): 36-40.
  8. Pushkin AA, Sidelnik NYa, Kovalevskiy SV, Iljuchik MA, Melnik PGr. Spectral indices for forest fire danger assessment based on satellite imagery and with using GIS-technologies in conditions of the rational nature use [In Russian]. Bioeconomy and ecobiopolitic 2016; 1(2): 163-170.
  9. Mitrofanov E, Shashnev I, Bubnenkov D. Application of narrowband vegetation indices for assessment of forest greenery [In Russian]. Bulletin MSRU 2012; 4: 118-122.
  10. Vinogradov AN, Egorov VV, Kalinin AP, Rodionov AI, Rodionov ID. On board visual and near UV-band hyperspectrometr with high spatial resolution [In Russian]. Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa 2012; 9(3): 101-107.
  11. Vinogradov AN, Egorov VV, Kalinin AP, Rodionov AI, Rodionov ID, Rodionova IP. Design and examination of the near infrared band hyperspectrometer BIK1 [In Russian]. Moscow: SRI RAS; 2017. ISBN: 978-5-00015-015-3.
  12. Karpeev SV, Khonina SN, Kharitonov SI. Study of the diffraction grating on the convex surface as a dispersive element [In Russian]. Computer Optics 2015; 39(2): 211-217. DOI: 10.18287/0134-2452-2015-39-2-211-217.
  13. Karpeev SV, Khonina SN, Murdagulov AR, Petrov MV. Alignment and study of prototypes of the Offner hyperspectrometer [In Russian]. Vestnik of Samara University 2016; 15(1): 197-206. DOI: 10.18287/2412-7329-2016-151-197-206.
  14. Balakay GT, Vasilyev SM, Babichev AN. The concept of new generation irrigation machines for precision irrigation technology. Scientific Journal of Russian Scientific Research Institute of Land Improvement Problems 2017; 2(26): 1-18. Source: < http://www.rosniipm-sm.ru/archive?n=476&id=477 >.
  15. Babichev AN, Balakay GT, Monastyrskiy VA. Irrigation regimes real time control in crop yield productivity programming. Scientific Journal of Russian Scientific Research Institute of Land Improvement Problems 2017; 3(27): 83-96. Source: < http://www.rosniipm-sm.ru/archive?n=491&id=498 >.
  16. Balakay GT, Balakay NI. Calculation and correction methodologies of crop irrigation schedule. Scientific Journal of Russian Scientific Research Institute of Land Improvement Problems 2017; 1(25): 32-49. Source: .
  17.  Shchedrin VN, Senchukov GA, Gostishchev VD. Prospects of land reclamation and its role for providing Food Security in Russia. Scientific Journal of Russian Scientific Research Institute of Land Improvement Problems 2013; 4(12): 1-14. Source: < http://www.rosniipm-sm.ru/archive?n=205&id=206 >.
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