Agriculture 2021, 11, 999. https://doi.org/10.3390/agriculture11100999 www.mdpi.com/journal/agriculture Article Review on Multitemporal Classification Methods of Satellite Images for Crop and Arable Land Recognition Joanna Pluto-Kossakowska Faculty of Geodesy and Cartography, Warsaw University of Technology, 00-661 Warsaw, Poland; joanna.kossakowska@pw.edu.pl Abstract: This paper presents a review of the conducted research in the field of multitemporal clas- sification methods used for the automatic identification of crops and arable land using optical sat- ellite images. The review and systematization of these methods in terms of the effectiveness of the obtained results and their accuracy allows for the planning towards further development in this area. The state of the art analysis concerns various methodological approaches, including selection of data in terms of spatial resolution, selection of algorithms, as well as external conditions related to arable land use, especially the structure of crops. The results achieved with use of various ap- proaches and classifiers and subsequently reported in the literature vary depending on the crops and area of analysis and the sources of satellite data. Hence, their review and systematic conclusions are needed, especially in the context of the growing interest in automatic processes of identifying crops for statistical purposes or monitoring changes in arable land. The results of this study show no significant difference between the accuracy achieved from different machine learning algo- rithms, yet on average artificial neural network classifiers have results that are better by a few per- cent than others. For very fragmented regions, better results were achieved using Sentinel-2, SPOT- 5 rather than Landsat images, but the level of accuracy can still be improved. For areas with large plots there is no difference in the level of accuracy achieved from any HR images. Keywords: crop detection; machine learning; satellite image classification 1. Introduction The aim of this paper is to systematise the present achievements in the field of crop and arable land recognition with use of multitemporal classification based on machine learning algorithms using optical satellite images. Recognition and classification of differ- ent crops in a particular area and environmental conditions involves the accuracy and optimalisation of these processes. Such factors as crop types and agriculture structure, classifier methods, and optical sensors are the focus of this paper. Certain trends as well as indications of further development and research directions, whose aim is to automatise crop identification processes, transpire from the projects and implementation experience described so far. Research and studies into arable areas could be categorised into three main groups depending on spatial resolution of data. The first category consists in monitoring change and its dynamics in arable land on a continental scale with a resolution of 1 km, with use of data from sensors such as, for instance, NOAA/AVHRR, VEGETATION, MODIS [1,2]. The second category includes studies into identification and classification of crop types, conducted on a regional scale [3]. Currently, this approach is frequently adopted by sci- entists and developers using widely available data of medium resolution of several dozen metres, which is still called high resolution (for instance SPOT or Sentinel-2 of 10 m to 60 m resolution or Landsat of 30 m resolution). The third category in the context of spatial resolution consideration are studies with use of very high resolution imaging–of 1 m or Citation: Pluto-Kossakowska, J. Review on Multitemporal Classification Methods of Satellite Images for Crop and Arable Land Recognition. Agriculture 2021, 11, 999. https://doi.org/10.3390/ agriculture11100999 Academic Editors: Maciej Zaborowicz and Dawid Wojcieszak Received: 24 August 2021 Accepted: 7 October 2021 Published: 13 October 2021 Publisher’s Note: MDPI stays neu- tral with regard to jurisdictional claims in published maps and institu- tional affiliations. Copyright: © 2021 by the author. Li- censee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and con- ditions of the Creative Commons At- tribution (CC BY) license (http://cre- ativecommons.org/licenses/by/4.0/).