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Open AccessArticle

A Comparison between Standard and Functional Clustering Methodologies: Application to Agricultural Fields for Yield Pattern Assessment

Institute of Methodologies for Environmental Analysis, National Research Council of Italy, C.da Santa Loja, Tito Scalo, 85050 Potenza, Italy
Istituto per le Applicazioni del Calcolo “M. Picone”, National Research Council of Italy, 80100 Naples, Italy
Department of Agriculture Forestry and Nature (DAFNE), University of Tuscia, 01100 Viterbo, Italy
Institute for Electromagnetic Sensing of the Environment, National Research Council of Italy, 20133 Milano, Italy
Georges Lemaître Centre for Earth and Climate, Earth and Life Institute, Universite Catholique de Louvain, 1348 Louvain-la-Neuve, Belgium
Author to whom correspondence should be addressed.
Remote Sens. 2018, 10(4), 585;
Received: 23 February 2018 / Revised: 3 April 2018 / Accepted: 9 April 2018 / Published: 10 April 2018
(This article belongs to the Special Issue Analysis of Multi-temporal Remote Sensing Images)
The recognition of spatial patterns within agricultural fields, presenting similar yield potential areas, stable through time, is very important for optimizing agricultural practices. This study proposes the evaluation of different clustering methodologies applied to multispectral satellite time series for retrieving temporally stable (constant) patterns in agricultural fields, related to within-field yield spatial distribution. The ability of different clustering procedures for the recognition and mapping of constant patterns in fields of cereal crops was assessed. Crop vigor patterns, considered to be related to soils characteristics, and possibly indicative of yield potential, were derived by applying the different clustering algorithms to time series of Landsat images acquired on 94 agricultural fields near Rome (Italy). Two different approaches were applied and validated using Landsat 7 and 8 archived imagery. The first approach automatically extracts and calculates for each field of interest (FOI) the Normalized Difference Vegetation Index (NDVI), then exploits the standard K-means clustering algorithm to derive constant patterns at the field level. The second approach applies novel clustering procedures directly to spectral reflectance time series, in particular: (1) standard K-means; (2) functional K-means; (3) multivariate functional principal components clustering analysis; (4) hierarchical clustering. The different approaches were validated through cluster accuracy estimates on a reference set of FOIs for which yield maps were available for some years. Results show that multivariate functional principal components clustering, with an a priori determination of the optimal number of classes for each FOI, provides a better accuracy than those of standard clustering algorithms. The proposed novel functional clustering methodologies are effective and efficient for constant pattern retrieval and can be used for a sustainable management of agricultural fields, depending on farming systems and environmental conditions in different regions. View Full-Text
Keywords: clustering methods; Landsat time series; high-resolution maps; agricultural fields clustering methods; Landsat time series; high-resolution maps; agricultural fields
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MDPI and ACS Style

Pascucci, S.; Carfora, M.F.; Palombo, A.; Pignatti, S.; Casa, R.; Pepe, M.; Castaldi, F. A Comparison between Standard and Functional Clustering Methodologies: Application to Agricultural Fields for Yield Pattern Assessment. Remote Sens. 2018, 10, 585.

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