Журнал

About Editorial Board Information for authors and reviewers Publication ethics Contacts User Account: send / review a manuscript
Archive
Volume 21, 2024
Number 1 Number 2 Number 3 Number 4 Number 5
Volume 20, 2023
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 19, 2022
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 18, 2021
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 17, 2020
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6 Number 7
Volume 16, 2019
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 15, 2018
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6 Number 7
Volume 14, 2017
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6 Number 7
Volume 13, 2016
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 12, 2015
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 11, 2014
Number 1 Number 2 Number 3 Number 4
Volume 10, 2013
Number 1 Number 2 Number 3 Number 4
Volume 9, 2012
Number 1 Number 2 Number 3 Number 4 Number 5
Volume 8, 2011
Number 1 Number 2 Number 3 Number 4
Volume 7, 2010
Number 1 Number 2 Number 3 Number 4
Issue 6, 2009
Volume 1 Volume 2
Issue 5, 2008
Volume 1 Volume 2
Issue 4, 2007
Volume 1 Volume 2
Issue 3, 2006
Volume 1 Volume 2
Issue 2, 2005
Volume 1 Volume 2
Issue 1, 2004
Volume 1
Search
Find:
русский
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, 2017, Vol. 14, No. 4, pp. 132-145

Accuracy assessment for winter crops mapping in spring-summer growing season with MODIS data

D.E. Plotnikov 1 , S.A. Bartalev 1 , E.A. Loupian 1 , V.A. Tolpin 1 
1 Space Research Institute RAS, Moscow, Russia
Accepted: 26.07.2017
DOI: 10.21046/2070-7401-2017-14-4-132-145
Over the past few years IKI RAS was able to develop methods for automatic winter crops mapping with time series of remote sensing data. Fifteen-year-long time series of winter crops maps were calculated for the area of their sustainable cultivation with a spatial resolution of 250 meters. With VEGA family systems development, primarily aimed at crops remote monitoring and their state analysis, it was made possible to store and assess ground truth data for dozens of thousands of cropland parcels sown with winter crops and other crop species. The paper describes the approach for and results of validation for automatically calculated winter crops maps based on representative and spatially distributed ground truth data for dozens of regions in Russian Federation. It is shown, that region-level accuracy of winter crops maps validated against in-situ data ranges from 70 to 98%. Scatter plots show high correlation of maps-extracted and official regional statistics data. The results obtained show that these maps are suitable for winter crops state assessment at the “rayon” (sub-region) level as well as for winter crops area estimation for large agricultural regions.
Keywords: remote sensing, mapping methods, winter crops, validation
Full text

References:

  1. Bartalev S.A., Egorov V.A., Zharko V.O., Loupian E.A., Plotnikov D.E., Khvostikov S.A., Shabanov N.V., Sputnikovoe kartografirovanie rastitel’nogo pokrova Rossii (Land cover mapping over Russia using Earth observation data), Moscow: IKI RAN, 2017, 208 p.
  2. Bartalev S.A., Egorov V.A., Loupian E.A., Plotnikov D.E., Uvarov I.A., Raspoznavanie pakhotnykh zemel’ na osnove mnogoletnikh sputnikovykh dannykh spektroradiometra MODIS i lokal’no-adaptivnoi klassifikatsii (Recognition of arable lands using multi-annual satellite data from spectroradiometermodis and locally adaptive supervised classification), Komp’yuternayaoptika, 2011, Vol. 35, No. 1, pp. 103–116.
  3. Vorob’eva N.S., Sergeev V.V., Chernov A.V., Informatsionnaya tekhnologiya rannego raspoznavaniya vidov sel’skokhozyaistvennykh kul’tur po kosmicheskim snimkam (Information technology of early crop identification by using satellite images), Komp’yuternaya optika, 2016, Vol. 40, No. 6, pp. 929–938. DOI: 10.18287/2412-6179-2016-40-6-929-938.
  4. Loupian E.A., Bartalev S.A., Krasheninnikova Yu.S., Nablyudenie anomal’no rannego razvitiya sel’skokhozyaistvennykh kul’tur v yuzhnykh regionakh Rossii vesnoi 2016 goda na osnove dannykh distantsionnogo monitoringa (Observing an abnormally early development of crops in the southern regions of Russia in spring 2016 using remote monitoring data), Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2016, Vol. 13, No. 2, pp. 240–243. DOI: 10.21046/2070-7401-2016-13-2-240-243.
  5. Loupian E.A., Bartalev S.A., Krasheninnikova Yu.S., Plotnikov D.E., Tolpin V.A., Nablyudenie rannego razvitiya ozimykh kul’tur v yuzhnykh regionakh evropeiskoi chasti Rossii vesnoi 2017 goda na osnove dannykh distantsionnogo monitoringa (Observation of early development of winter crops in spring 2017 in southern regions of Russia based on remote sensing data), Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2017, Vol. 14, No. 2, pp. 268–272. DOI: 10.21046/2070-7401-2017-14-2-268-272.
  6. Loupian E.A., Bartalev S.A., Tolpin V.A., Zharko V.O., Krasheninnikova Yu.S., Oksyukevich A.Yu., Ispol’zovanie sputnikovogo servisa VEGA v regional’nykh sistemakh distantsionnogo monitoringa (VEGA satellite service applications in regional remote monitoring systems), Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2014, Vol. 11, No. 3, pp. 215–232.
  7. Loupian E.A., Proshin A.A., Burtsev M.A., Balashov I.V., Bartalev S.A., Efremov V.Yu., Kashnitskii A.V., Mazurov A.A., Matveev A.M., Sudneva O.A., Sychugov I.G., Tolpin V.A., Uvarov I.A., Tsentr kollektivnogo pol’zovaniya sistemami arkhivatsii, obrabotki i analiza sputnikovykh dannykh IKI RAN dlya resheniya zadach izucheniya i monitoringa okruzhayushchei sredy (IKI center for collective use of satellite data archiving, processing and analysis systems aimed at solving the problems of environmental study and monitoring), Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2015, Vol. 12, No. 5, pp. 247–267.
  8. Loupian E.A., Savin I.Yu., Bartalev S.A., Tolpin V.A., Balashov I.V., Plotnikov D.E., Sputnikovyi servismonitoringa sostoyaniya rastitel’nosti (VEGA) (Satellite service for vegetation monitoring VEGA), Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2011, Vol. 8, No. 1, pp. 190–198.
  9. Plotnikov D.E., Bartalev S.A., Loupian E.A., Metod detektirovaniya letne-osennikh vskhodov ozimykh kul’tur po dannym radiometra MODIS (The method for wintercrops mapping in summer-fall growing season with MODIS radiometer data), Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2008, Issue 5, Vol. 2, pp. 322–330.
  10. Plotnikov D.E., Bartalev S.A., Khvostikov S.A., Metod iterativnoi ekspansii obuchayushchei vyborki i ego primenenie dlya raspoznavaniya ozimykh kul’tur po sputnikovym dannym (The method of training set iterative expansion and its usage for wintercrops mapping with remote sensing data), 11th vserossiiskaya otkrytaya konferentsiya “Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa” (11th All-Russia Open Conference ‘‘Current Problems in Remote Sensing of the Earth from Space’’), Moscow, IKI RAN, 11–15 Nov. 2013, Book of Abstracts, 2013, p. 346.
  11. Plotnikov D.E., Miklashevich T.S., Bartalev S.A., Vosstanovlenie vremennykh ryadov dannykh distantsionnykh izmerenii metodom polinomial’noi approksimatsii v skol’zyashchem okne peremennogo razmera (Using local polynomial approximation within moving window for remote sensing data time-series smoothing and data gaps recovery), Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2014, Vol. 11, No. 2, pp. 103–110.
  12. Terekhin E.A., Primenenie dannykh distantsionnogo zondirovaniya dlya monitoringa posevov ozimykh kul’tur Belgorodskoi oblasti (Remote sensing data application for wintercrops monitoring in Belgorod oblast), Geografiya i prirodnyeresursy, 2015, No. 3, pp. 175–181.
  13. Tolpin V.A., Loupian E.A., Bartalev S.A., Plotnikov D.E., Matveev A.M., Vozmozhnosti analiza sostoyaniya sel’skokhozyaistvennoi rastitel’nosti s ispol’zovaniem sputnikovogo servisa VEGA (Possibilities of agricultural vegetation condition analysis with the VEGA satellite service), Optika atmosfery i okeana, 2014, Vol. 27, No. 7 (306), pp. 581–586.
  14. Bartalev S.A., Egorov V.A., Loupian E.A., Khvostikov S.A., A new locally-adaptive classification method LAGMA for large-scale land cover mapping using remote-sensing data, Remote Sensing Letters, 2014, Vol. 5 (1), pp. 55–64. DOI: 10.1080/2150704X.2013.870675.
  15. Bartalev S.A., Plotnikov D.E., Loupian E.A., Mapping of arable land in Russia using multiyear time series of MODIS data and the LAGMA classification technique, Remote Sensing Letters, 2016, Vol. 7, No. 3, pp. 269–278. DOI: 10.1080/2150704X.2015.1130874.
  16. Breiman L., Random Forests, Machine Learning, 2001, Vol. 45 (1), DOI: 10.1023/A:1010933404324.
  17. Mingwei Z., Qingbo Z., Zhongxin C., Jia L., Yong Z., Chongfa C., Crop discrimination in Northern China with double cropping systems using Fourier analysis of time-series MODIS data, International Journal of Applied Earth Observation and Geoinformation, 2008, Vol. 10, pp. 476–485.
  18. Richardson A.J., Weigand C.L., Distinguishing vegetation from soil background information, Photogrammetric Engineering and Remote Sensing, 1977, Vol. 43, pp. 1541–1552.
  19. Skakun S., Franch B., Vermote E., Roger J-C., Becker-Reshef I., Justice C., Kussul N., Early season large-area winter crop mapping using MODIS NDVI data, growing degree days information and a Gaussian mixture model, Remote Sensing of Environment, 2017, Vol. 195, pp. 244–258. DOI: 10.1016/j.rse.2017.04.026.
  20. Stehman S.V., Selecting and Interpreting Measures of Thematic Classification Accuracy, Remote Sensing of Environment, 1997, Vol. 62, pp. 77–89. DOI: 10.1016/S0034-4257(97)00083-7.
  21. Wardlow B., Egbert S., Large-area crop mapping using time-series MODIS 250 m NDVI data: An assessment for the U.S. Central Great Plains, Remote Sensing of Environment, 2008, Vol. 112, pp. 1096–1116.