F. Reyes

G. Chiodini

N. Mzid

R. Casa

S. Pascucci

S. Pignatti

English

Archivio istituzionale della ricerca - Università di Modena e Reggio Emilia

An Enhanced Yield Potential Spatial Clustering Method,  accounting for Seasonality, Heterogeneous Morphology and Climate Variability.  An application in the Umbria Region (Central Italy) for the SMARTAGRI PROJECT Francesco Reyes1, Nada Mzid1, Raffaele Casa1, S. Pascucci2, S. Pignatti2, G. Chiodini3 1 Department of Agricultural and Forestry Science, University of Tuscia, Viterbo, Italy 2Consiglio Nazionale delle Ricerche, Rome, Italy 3Consorzio Agrario e Progetto SMARTAGRI, Perugia, Italy Email: freyes@unitus.it Results   State of the Art Homogeneous management of heterogeneous fields: • Inefficiencies and environmental risks (e.g. use excess of resources; N leaching)  Use of clustering:  • recognize spatial patterns in crop fields  • identify of homogeneous zones for management (e.g. fertility) • understand causes of spatial and temporal variability in crop yields • bulk clustering incorporates contrasting & confounding factors Study area:   Farms in hilly area (Umbria region), Central Italy  Vector files of agricultural fields  Imagery (5 years EO) • Landsat8-8 : 30m resolution, 10 days frequency, 2013-2015 • Sentinel-2 : 10m resolution, 5 days frequency, 2015-2019  Meteorological data: • Daily precipitation, 2013-2019 Introduction Data Pre-treatment Fields clipping Agricultural fields (Vector) Earth Explorer website (Landsat) EO imagery (last 5 cropping seasons) EO imagery (Rasters) Cloud masking NDVI time series for fields (Rasters) Sentinel Hub sen2r R package (Sentinel 2) Search peak NDVI [01 Mar - 01 Jun] Non-irrigated crops [01 Feb – 30 Jun] Methods SPI wet dry Agricultural fields (Vector) Local meteo data (precipitation) Meteorological database Calculation climatic index:  Standardized Precipitation Index Classification of years Dry SPI <-1 Years of  NDVI series Normal -1<SPI <1 Wet SPI >1 Dry NDVI series Z-scores normalization Normal Wet K-means clustering K-means clustering K-means clustering Whole series simple K-means clustering >= N3 per clustering NDVI classified according to climatic year Vegetation responds to weather and soil conditions • seasonal climatic conditions may improve our understanding of spatio-temporal yield patterns  Objectives The SMARTAGRI platform is created based on a Regional Development Plan of the Umbria region in order to assist farmers in the agricultural management practices  This research project aims at: • improving clustering method by accounting for crop seasonality, climatic variability, heterogeneous morphology for applications in the context of the SMARTAGRI platform  Data Set Description Discussion Clustering results clearly differenciate seasonality-based from bulk clustering Both clustering methods in some cases failed to correctly classify high/low yielding field areas.  Pros • Considering separate years improved understanding of spatio-temporal variability and identification of different yield areas in some cases.  Cons • Need larger number of images from different climatic years for sound classification • Sentinel-2 time series was too short (<5 years) so it was intergrated with Landsat (30 m) • Wrong classification of climatic years risks to increase the error in respect to bulk clustering  Future works • Include topography in the clustering • Identify climatic indexes suitable to better represent interannual crop response to climate (e.g. SPEI) • Use longer and more homogeneous S2 time series • Consider multitemporal yield maps and soil properties maps for validation • Possibly select images based on proxy of phenological stage • Often clusters near borders (mixed pixels)  remove edge pixels 2018 wet Clustering All years Normal Wet Yield (t/ha) Yield Map DEM All years N15 Normal N3 Wet N8 Elevation Biavati Tamburini 1 Tamburini 2 SMART AGRI 2012 dry Clustering All years N24 Dry N7 Normal N3 Wet N12 All years Dry Normal Wet Yield (t/ha) Yield Map DEM Elevation 2018 wet Clustering All years Normal Wet Yield (t/ha) Yield Map DEM All years N20 Normal N6 Wet N12 Elevation Acknowledgements  We acknowledge the farmers Tamburini and Biavati for providing yield map data and useful discussions. 

An Enhanced Yield Potential Spatial Clustering Method, accounting for Seasonality, Heterogeneous Morphology and Climate Variability.
An application in the Umbria Region (Central Italy) for the SMARTAGRI PROJECT

https://iris.unimore.it/bitstream/11380/1292075/1/ECPA%202019%20Poster.%20Reyes%20et%20al.%2c%20Spatial%20Clustering.pdf

An Enhanced Yield Potential Spatial Clustering Method, accounting for Seasonality, Heterogeneous Morphology and Climate Variability. An application in the Umbria Region (Central Italy) for the SMARTAGRI PROJECT

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Archivio istituzionale della ricerca - Università di Modena e Reggio Emilia