research-article

Remote sensing of cropping practice in Northern Italy using time-series from Sentinel-2

Authors:

Thor-Bjørn Ottosen,

Suzanne T.E. Lommen,

Carsten Ambelas SkjøthAuthors Info & Claims

Volume 157, Issue C

Pages 232 - 238

https://doi.org/10.1016/j.compag.2018.12.031

Published: 01 February 2019 Publication History

Graphical abstract

Display Omitted

Highlights

•

Gross features of the phenology of fields can be detected with Sentinel-2.

•

High weed infestations can be detected from NDVI time series from Sentinel-2.

•

Seven phenological classes of cropping practice were mapped for two Sentinel-2 tiles.

•

Two thirds of the winter crops show some sign of weed infestation.

•

The thematic accuracy of the land use map is assessed to 69%.

Abstract

Maps of cropping practice, including the level of weed infestation, are useful planning tools e.g. for the assessment of the environmental impact of the crops, and Northern Italy is an important example due to the large and diverse agricultural production and the high weed infestation. Sentinel-2A is a new satellite with a high spatial and temporal resolution which potentially allows the creation of detailed maps of cropping practice including weed infestation. To explore the applicability of Sentinel-2A for mapping cropping practice, we analysed the Normalised Differential Vegetation Index (NDVI) time series from five weed-infested crop fields as well as the areas designated as non-irrigated agricultural land in Corine Land Cover, which also contributed to an increased understanding of the cropping practice in the region. The analysis of the case studies showed that the temporal resolution of Sentinel-2A was high enough to distinguish the gross features of the cropping practice, and that high weed infestations can be detected at this spatial resolution. The analysis of the entire region showed the potential for mapping cropping practice using Sentinel-2. In conclusion, Sentinel-2A is to some extent applicable for mapping cropping practice with reasonable thematic accuracy.

References

[1]

Y. Auda, C. Déchamp, G. Dedieu, F. Blasco, D. Duisit, E.J.L. Pontier, Détection des plantes envahissantes par télédétection : un cas d'étude, l'ambroisie en région Rhône-Alpes, France, Int. J. Remote Sens. 29 (2008) 1109–1124.

[2]

Y. Auda, O. Hagolle, J.-P. Gastellu-Etchegorry, S. Rakotoniaina, R. Roux, H. Meon, C. Dechamp, Contribution of multi-temporal very high resolution images to Ambrosia Artemisiifolia L. remote sensing, Allergo J. 17 (2008) 380.

[3]

A. Bégué, D. Arvor, B. Bellon, J. Betbeder, D.P.D. de Abelleyra, R. Ferraz, V. Lebourgeois, C. Lelong, M. Simões, R. Verón, S., Remote sensing and cropping practices: a review, Remote Sens. 10 (2018).

[4]

M. Benza, J.R. Weeks, D.A. Stow, D. López-Carr, K.C. Clarke, A pattern-based definition of urban context using remote sensing and GIS, Remote Sens. Environ. 183 (2016) 250–264.

[5]

Bossard, M., Feranec, J. & Otahel, J., 1994. CORINE Land Cover. Technical Guide.

[6]

L. Chen, Z. Jin, R. Michishita, J. Cai, T. Yue, B. Chen, B. Xu, Dynamic monitoring of wetland cover changes using time-series remote sensing imagery, Ecol. Inf. 24 (2014) 17–26.

[7]

S. Ciappetta, A. Ghiani, F. Gilardelli, M. Bonini, S. Citterio, R. Gentili, Invasion of Ambrosia artemisiifolia in Italy: Assessment via analysis of genetic variability and herbarium data, Flora – Morphol., Distrib., Functional Ecol. Plants 223 (2016) 106–113.

[8]

Clerc, S., & MPC Team, 2017. Sentinel 2 Data Quality Report.

[9]

R.G. Congalton, K. Green, Assessing the Accuracy of Remotely Sensed Data: Principles and Practices, CRC Press/Taylor & Francis, 2009.

[10]

Davies, D.L., Bouldin, D.W., 1979. A cluster separation measure. IEEE Trans. Pattern Anal. Mach. Intell., PAMI-1, pp. 224–227.

[11]

D.A. de Alwis, Z.M. Easton, H.E. Dahlke, W.D. Philpot, T.S. Steenhuis, Unsupervised classification of saturated areas using a time series of remotely sensed images, Hydrol. Earth Syst. Sci. 11 (2007) 1609–1620.

[12]

M. Drusch, U. del Bello, S. Carlier, O. Colin, V. Fernandez, F. Gascon, B. Hoersch, C. Isola, P. Laberinti, P. Martimort, A. Meygret, F. Spoto, O. Sy, F. Marchese, P. Bargellini, Sentinel-2: ESA's optical high-resolution mission for GMES operational services, Remote Sens. Environ. 120 (2012) 25–36.

[13]

S. Estel, T. Kuemmerle, C. Levers, M. Baumann, P. Hostert, Mapping cropland-use intensity across Europe using MODIS NDVI time series, Environ. Res. Lett. 11 (2016) 024015–024015.

[14]

I. Harris, P.D. Jones, T.J. Osborn, D.H. Lister, Updated high-resolution grids of monthly climatic observations - the CRU TS3.10 Dataset, Int. J. Climatol. 34 (2014) 623–642.

[15]

M.E. Jakubauskas, D.R. Legates, J.H. Kastens, Crop identification using harmonic analysis of time-series AVHRR NDVI data, Comput. Electron. Agric. 37 (2002) 127–139.

[16]

G. Kazinczi, I. Béres, R. Novák, K. Bíro, Z. Pathy, Common ragweed Ambrosia artemisiifolia: a review with special regards to the results in Hungary. I. Taxonomy, origin and distribution, morphology, life cycle and reproduction strategy, Herbologia 9 (2008) 55–91.

[17]

M. Laba, F. Tsai, D. Ogurcak, S. Smith, M.E. Richmond, Field determination of optimal dates for the discrimination of invasive wetland plant species using derivative spectral analysis, Photogramm. Eng. Remote Sens. 71 (2005) 603–611.

[18]

S.K. Langley, H.M. Cheshireb, K.S. Humes, A comparison of single date and multitemporal satellite image classifications in a semi-arid grassland, J. Arid Environ. 49 (2001) 401–411.

[19]

G. Latombe, P. Pyšek, J.M. Jeschke, T.M. Blackburn, S. Bacher, C. Capinha, M.J. Costello, M. Fernández, R.D. Gregory, D. Hobern, C. Hui, W. Jetz, S. Kumschick, C. McGrannachan, J. Pergl, H.E. Roy, R. Scalera, Z.E. Squires, J.R.U. Wilson, M. Winter, P. Genovesi, M.A. McGeoch, A vision for global monitoring of biological invasions, Biol. Conserv. 213 (2017) 295–308.

[20]

B. Leff, N. Ramankutty, J.A. Foley, Geographic distribution of major crops across the world, Global Biogeochem. Cycles 18 (2004) 18.

[21]

É. Lehoczky, J. Busznyák, G. Gólya, Study on the spread, biomass production, and nutrient content of ragweed with high-precision GNSS and GIS device system, Commun. Soil Sci. Plant Anal. 44 (2013) 535–545.

[22]

H. Müller-Schärer, S.T.E. Lommen, M. Rossinelli, M. Bonini, M. Boriani, G. Bosio, U. Schaffner, P. Hatcher, Ophraella communa, the ragweed leaf beetle, has successfully landed in Europe: fortunate coincidence or threat?, Weed Res. 54 (2014) 109–119.

[23]

J. Müllerová, J. Brůna, T. Bartaloš, P. Dvořák, M. Vítková, P. Pyšek, Timing is important: unmanned aircraft vs. satellite imagery in plant invasion monitoring, Front. Plant Sci. 8 (2017) 1–13.

[24]

R. Ngom, P. Gosselin, Development of a remote sensing-based method to map likelihood of common ragweed (ambrosia artemisiifolia) presence in urban areas, IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 7 (2014) 126–139.

[25]

T.T.H. Nguyen, C.A.J.M. de Bie, A. Ali, E.M.A. Smaling, T.H. Chu, Mapping the irrigated rice cropping patterns of the Mekong delta, Vietnam, through hyper-temporal SPOT NDVI image analysis, Int. J. Remote Sens. 33 (2012) 415–434.

[26]

E.C. Oerke, Crop losses to pests, J. Agric. Sci. 144 (2006) 31–43.

[27]

Olsen, O., 2010. Agriculture and fisheries A regional picture of farming in Europe — what, where and how much? Eurostat Statistics in focus.

[28]

Ottosen, T.-B., Petch, G., Hanson, M., Skjøth, C. Accuracy assessment of high resolution maps of European conifer and broadleaved trees based on Sentinel-2. Remote Sensing of Environment (in preparation).

[29]

S. Rakotoniaina, Y. Auda, F. Blasco, C. Déchamp, Comparaison Des Méthodes de Classification Non Paramétrique (k-NN) et Contextuelle (ICM) Appliquées à La Cartographie Par Télédétection Du Niveau d ’ Infestation Par l ’ Ambroisie Comparison of the k-NN Nonparametric and the ICM Contextual Classification Methods Applied to the Remote Sensing Detection of Ragweed, Ambroisie, First Int. Ragweed Rev. 26 (2009) 77–87.

[30]

M. Smith, L. Cecchi, C.A. Skjøth, G. Karrer, B. Šikoparija, Common ragweed: A threat to environmental health in Europe, Environ. Int. 61 (2013) 115–126.

[31]

Sölter, U., Mathiassen, S., Verschwele, A., 2016. Combining cutting and herbicide application for Ambrosia artemisiifolia control. In: 27th German Conference on Weed Biology and Weed Control.

[32]

S.V. Stehman, Statistical rigor and practical utility in thematic map accuracy assessment, Photogramm. Eng. Remote Sens. 67 (2001) 727–734.

[33]

P. Villa, D. Stroppiana, G. Fontanelli, R. Azar, P. Brivio, In-season mapping of crop type with optical and X-band SAR data: a classification tree approach using synoptic seasonal features, Remote Sens. 7 (2015) 12859–12886.

[34]

J. Wickham, S.V. Stehman, L. Gass, J.A. Dewitz, D.G. Sorenson, B.J. Granneman, R.V. Poss, L.A. Baer, Thematic accuracy assessment of the 2011 National Land Cover Database (NLCD), Remote Sens. Environ. 191 (2017) 328–341.

[35]

X. Zhang, R. Sun, B. Zhang, Q. Tong, Land cover classification of the North China Plain using MODIS_EVI time series, ISPRS J. Photogramm. Remote Sens. 63 (2008) 476–484.

[36]

Y. Zhang, G.F. Hepner, The Dynamic-Time-Warping-based k-means++ clustering and its application in phenoregion delineation, Int. J. Remote Sens. 38 (2017) 1720–1736.

Index Terms

Remote sensing of cropping practice in Northern Italy using time-series from Sentinel-2

Index terms have been assigned to the content through auto-classification.

Recommendations

An automated early-season method to map winter wheat using time-series Sentinel-2 data: A case study of Shandong, China
Highlights
- An automated early-season method to map winter wheat using Sentinel-2 data is proposed.
Abstract
Timely and accurate information on winter wheat distribution and planting area is of great significance to food security, policy-making, and ecological function evaluation. However, several problems exist in the traditional winter ...
Monitoring Winter Wheat Phenology Using Time Series of Remote Sensing Data
ICIC '09: Proceedings of the 2009 Second International Conference on Information and Computing Science - Volume 01

The phenological dynamics of crops reflect the dynamics of the Earth’s agroecosystems. Crop phenology extraction of from time series of remote sensing data is a promising avenue to study the dynamics of crop development. In this paper, a three step ...
Agriculture Phenology Monitoring Using NDVI Time Series Based on Remote Sensing Satellites: A Case Study of Guangdong, China
Abstract—
This article presents the use of the Normalized Differences Vegetation Index (NDVI) time series based change detection method for agriculture phenology monitoring. NDVI make use of the multi-spectral remote sensing data band combinations ...

Comments

Information & Contributors

Information

Published In

cover image Computers and Electronics in Agriculture

Computers and Electronics in Agriculture Volume 157, Issue C

Feb 2019

616 pages

ISSN:0168-1699

Issue’s Table of Contents

Elsevier B.V.

Publisher

Elsevier Science Publishers B. V.

Netherlands

Publication History

Published: 01 February 2019

Author Tags

Qualifiers

Research-article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

0
Total Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 14 Oct 2024

Other Metrics

View Author Metrics

Citations

View Options

View options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

Media

Figures

Other

Tables

View Issue’s Table of Contents