ALL-RUSSIA OPEN ANNUAL CONFERENCES ON
CURRENT PROBLEMS IN REMOTE SENSING OF THE EARTH FROM SPACE
Principal physics, methods and techniques for monitoring the environment, potentially dangerous phenomena and objects
рус
Proceedings of the 16th Conference (November 12-16, 2018, Moscow, Russia)
Development of Seasonal NDVI Profiles References for Main Agricultural Crops
Sergey A. Khvostikov, Sergey A. Bartalev
Space Research Institute, Russian Academy of Sciences, Moscow, Russia
khvostikov@d902.iki.rssi.ru
DOI 10.21046/rorse2018.55
NDVI vegetation index can describe crop dynamic and is widely used in agriculture monitoring. This paper presents method for evaluation of NDVI seasonal profiles references for different crops using big archive of field data with significant level of errors. The NDVI reference profiles are created at the level of administrative districts using available field data and represent typical vegetation index dynamic of each crop in current year. The crop references of neighbor regions are additionally cross-compared to filter incorrect data. This paper presents a method for verification of field data using evaluated NDVI seasonal profiles. This method was tested for more than 400000 fields and expert analysis showed accuracy score at 92%.
Keywords: NDVI seasonal profiles , crop references, clusterization, verification
References: - [1] Loupian E.A., Proshin A.A., Burtsev M.A., Balashov I.V., Bartalev S.A., Efremov V.Yu., Kashnitskiy A.V., Mazurov A.A., Matveev A.M., Sudneva O.A., Sychugov I.G., Tolpin V.A., Uvarov I.A. 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, 12(5), pp. 263-284. (In Russian).
- [2] Plotnikov D.E., Miklashevich T.S., Bartalev S.A. 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, 11(2), pp. 103-110. (In Russian).
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Section 1. Methods of modeling various phenomena focused on assimilation of remote sensing data
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