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Using Digital Cameras on an Unmanned Aerial Vehicle to Derive Optimum Color Vegetation Indices for Leaf Nitrogen Concentration Monitoring in Winter Wheat

A Crop Group-Specific Pure Pixel Time Series for Europe

European Commission, Joint Research Centre (JRC), 21027 Ispra, Italy
French National Institute for Agricultural Research (INRA), UMR EMMAH, CEDEX 9, 84914 Avignon, France
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(22), 2668;
Received: 27 September 2019 / Revised: 1 November 2019 / Accepted: 9 November 2019 / Published: 15 November 2019
(This article belongs to the Section Remote Sensing in Agriculture and Vegetation)
Long timeseries of Earth observation data for the characterization of agricultural crops across large scales are of high interest to crop modelers, scientists, and decision makers in the fields of agricultural and environmental policy as well as crop monitoring and food security. They are particularly important for regression-based crop monitoring systems that rely on historic information. The major challenge lies in identifying pixels from satellite imagery that represent pure enough crop signals. Here, we present a data-driven semi-automatic approach to identify pure pixels of two crop groups (i.e., winter and spring crops and summer crops) based on a MODIS–NDVI timeseries. We applied this method to the European Union at a 250 m spatial resolution. Pre-processed and smoothed, daily normalized difference vegetation index (NDVI) data (2001–2017) were used to first extract the phenological data. To account for regional characteristics (varying climate, agro-management, etc.), these data were clustered by administrative units and by year using a Gaussian mixture model. The number of clusters was pre-defined using data from regional agricultural acreage statistics. After automatic labelling, clusters were filtered based on agronomic knowledge and phenological information extracted from the same timeseries. The resulting pure pixels were validated with two different datasets, one based on high-resolution Sentinel-2 data (5 sites, 2 years) and one based on a regional crop map (1 site, 7 years). For the winter and spring crop class, pixel purity amounted to 93% using the first validation dataset and to 73% using the second one, averaged over the different years. For summer crops, the respective values were 61% (91% without one critical validation site) and 72%. The phenological analyses revealed a clear trend towards an earlier NDVI peak (approximately −0.28 days/year) for winter and spring crops across Europe. We expect that this dataset will be useful for various applications, from crop model calibration to operational crop monitoring and yield forecasting. View Full-Text
Keywords: pixel purity; crop classification; crop monitoring; Gaussian mixture model; phenology; remote sensing; timeseries pixel purity; crop classification; crop monitoring; Gaussian mixture model; phenology; remote sensing; timeseries
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MDPI and ACS Style

Weissteiner, C.J.; López-Lozano, R.; Manfron, G.; Duveiller, G.; Hooker, J.; van der Velde, M.; Baruth, B. A Crop Group-Specific Pure Pixel Time Series for Europe. Remote Sens. 2019, 11, 2668.

AMA Style

Weissteiner CJ, López-Lozano R, Manfron G, Duveiller G, Hooker J, van der Velde M, Baruth B. A Crop Group-Specific Pure Pixel Time Series for Europe. Remote Sensing. 2019; 11(22):2668.

Chicago/Turabian Style

Weissteiner, Christof J., Raúl López-Lozano, Giacinto Manfron, Grégory Duveiller, Josh Hooker, Marijn van der Velde, and Bettina Baruth. 2019. "A Crop Group-Specific Pure Pixel Time Series for Europe" Remote Sensing 11, no. 22: 2668.

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