EGU2020-15941
https://doi.org/10.5194/egusphere-egu2020-15941
EGU General Assembly 2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Data assimilation of remote sensing data for farm scale maize fertilization in northern Italy

Calogero Schillaci, Edoardo Tomasoni, Marco Acutis, and Alessia Perego
Calogero Schillaci et al.
  • University of Milan, DISAA, Agricultural and Environmental Sciences Production, Landscape, Agroenergy Department, MILANO, Italy (calogero.schillaci@unimi.it)

To improve nitrogen fertilization is well known that vegetation indices can offer a picture of the nutritional status of the crop. In this study, field management information (maize sowing and harvesting dates, tillage, fertilization) and estimated vegetation indices VI (Sentinel 2 derived Leaf Area Index LAI, Normalized Difference Vegetation Index NDVI, Fraction of Photosynthetic radiation fPAR) were analysed to develop a batch-mode VIs routine to manage high dimensional temporal and spatial data for Decision Support Systems DSS in precision agriculture, and to optimize the maize N fertilization in the field. The study was carried out in maize (2017-2018) on a farm located in Mantua (northern Italy); the soil is a Vertic Calciustepts with a fine silty texture with moderate content of carbonates. A collection of Sentinel 2 images (with <25% cloud cover) were processed using Graph Processing Tool (GPT). This tool is used through the console to execute Sentinel Application Platform (SNAP) raster data operators in batch-mode. The workflow applied on the Sentinel images consisted in: resampling each band to 10m pixel size, splitting data into subsets according to the farm boundaries using Region of Interest (ROI). Biophysical Operator based on Biophysical Toolbox was used to derive LAI, fPAR for the estimation of maize vegetation indices from emergence until senescence. Yield data were acquired with a volumetric yield sensing in a combine harvester. Fertilization plans were then calculated for each field prior to the side-dressing fertilization. The routine is meant as a user-friendly tool to obtain time series of assimilated VIs of middle and high spatial resolution for field crop fertilization. It also overcomes the failures of the open source graphic user interface of SNAP. For the year 2018, yield data were related to the 34 LAI derived from Sentinel 2a products at 10 m spatial resolution (R2=0.42). This result underlined a trend that can be further studied to define a cluster strategy based on soil properties. As a further step, we will test whether spatial differences in assimilated VIs, integrated with yield data, can guide the nitrogen top-dress fertilization in quantitative way more accurately than a single image or a collection of single images.

How to cite: Schillaci, C., Tomasoni, E., Acutis, M., and Perego, A.: Data assimilation of remote sensing data for farm scale maize fertilization in northern Italy, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-15941, https://doi.org/10.5194/egusphere-egu2020-15941, 2020

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