Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Crop Detection Using Time Series of Sentinel-2 and Sentinel-1 and Existing Land Parcel Information Systems

Herman Snevajs
,
Karel Charvat
* ORCID logo ,
Vincent Onckelet
,
Jiri Kvapil
,
Frantisek Zadrazil
,
Hana Kubickova
,
Jana Seidlova
,
Iva Bartlova
Version 1 : Received: 22 December 2021 / Approved: 14 January 2022 / Online: 14 January 2022 (11:18:59 CET)

A peer-reviewed article of this Preprint also exists.

Snevajs, H.; Charvat, K.; Onckelet, V.; Kvapil, J.; Zadrazil, F.; Kubickova, H.; Seidlova, J.; Bartlova, I. Crop Detection Using Time Series of Sentinel-2 and Sentinel-1 and Existing Land Parcel Information Systems. Remote Sens. 2022, 14, 1095. Snevajs, H.; Charvat, K.; Onckelet, V.; Kvapil, J.; Zadrazil, F.; Kubickova, H.; Seidlova, J.; Bartlova, I. Crop Detection Using Time Series of Sentinel-2 and Sentinel-1 and Existing Land Parcel Information Systems. Remote Sens. 2022, 14, 1095.

Abstract

Satellite Crop Detection technologies are focused on detection of different types of crops on the field in the early stage before harvesting. Crop detection is usually done on a time series of satellite data by classification of the desired fields. Currently, data obtained from Remote Sensing (RS) are used to solve tasks related to the identification of the type of agricultural crops, also modern technologies using AI methods are desired in the postprocessing part. In this challenge Sentinel-1 and Sentinel-2 time series data were used due to their periodic availability. Our focus was to develop methodology for classification of time series of Sentinel 2 and Sentinel 1 data and compare how accuracy of classification can be increased, but also how to guarantee availability of data. We analyse phenology of single crops and on the basis of this analysis we started to provide crop classification. Original crop classifications were made from Enhanced Vegetation Index (EVI) layers made from Sentinel-2 time-series data and then we added also . To increase accuracy we also integrate into the process parcel borders and provide classification of fields..

Keywords

crop detection; Sentinel 1; Sentinel 2; supervised classification; unsupervised classification; time series; agriculture; food security

Subject

Environmental and Earth Sciences, Remote Sensing

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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