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Unsupervised Learning of Robust Representations for Change Detection on Sentinel-2 Earth Observation Images Read chapter Read first chapter

2020 | OriginalPaper | Chapter

Producing Mid-Season Nitrogen Application Maps for Arable Crops, by Combining Sentinel-2 Satellite Images and Agrometeorological Data in a Decision Support System for Farmers. The Case of NITREOS

Authors : Emmanuel Lekakis, Dimitra Perperidou, Stylianos Kotsopoulos, Polimachi Simeonidou

Published in: Environmental Software Systems. Data Science in Action

Publisher: Springer International Publishing

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Abstract

NITREOS (Nitrogen Fertilization, Irrigation and Crop Growth Monitoring using Earth Observation Systems) is a farm management information system (FMIS) for organic and conventional agriculture which aims in enabling farmers to tackle crop abiotic stresses and control important growing parameters to ensure crop health and optimal yields. NITREOS employs a user friendly, web-based platform that integrates satellite remote sensing data, numerical weather predictions and agronomic models, and offers a suite of farm management advisory services to address the needs of smallholder farmers, agricultural cooperatives and agricultural consultants. This paper provides an analysis of different methodologies employed in the nitrogen fertilization service of NITREOS. The methods are based on the determination of the Nitrogen Fertilization Optimization Algorithm for cotton, maize and wheat crops. Available agro-meteorological data on two distinct agricultural regions were used for the calibration and validation of the recommended Nitrogen rates.

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previous chapter Redefining Agricultural Insurance Services Using Earth Observation Data. The Case of Beacon Project
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Metadata
Title
Producing Mid-Season Nitrogen Application Maps for Arable Crops, by Combining Sentinel-2 Satellite Images and Agrometeorological Data in a Decision Support System for Farmers. The Case of NITREOS
Authors
Emmanuel Lekakis
Dimitra Perperidou
Stylianos Kotsopoulos
Polimachi Simeonidou
Copyright Year
2020
Book
Environmental Software Systems. Data Science in Action

Print ISBN: 978-3-030-39814-9

Electronic ISBN: 978-3-030-39815-6

Copyright Year: 2020

DOI
https://doi.org/10.1007/978-3-030-39815-6_10

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