Simplified and Hybrid Remote Sensing-Based Delineation of Management Zones for Nitrogen Variable Rate Application in Wheat

Enhancing digital and precision agriculture is currently inevitable to overcome the economic and environmental challenges of the agriculture in the 21st century. The purpose of this study was to generate and compare management zones (MZ) based on the Sentinel-2 satellite data for variable rate appli...

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Autores principales:	Mohammad Rokhafrouz, Hooman Latifi, Ali A. Abkar, Tomasz Wojciechowski, Mirosław Czechlowski, Ali Sadeghi Naieni, Yasser Maghsoudi, Gniewko Niedbała
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	precision agriculture management zones remote sensing Sentinel-2 clustering winter wheat Agriculture (General) S1-972
Acceso en línea:	https://doaj.org/article/27218195734349b78f6ec2f218cf1313
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spelling	oai:doaj.org-article:27218195734349b78f6ec2f218cf13132021-11-25T15:59:10ZSimplified and Hybrid Remote Sensing-Based Delineation of Management Zones for Nitrogen Variable Rate Application in Wheat10.3390/agriculture111111042077-0472https://doaj.org/article/27218195734349b78f6ec2f218cf13132021-11-01T00:00:00Zhttps://www.mdpi.com/2077-0472/11/11/1104https://doaj.org/toc/2077-0472Enhancing digital and precision agriculture is currently inevitable to overcome the economic and environmental challenges of the agriculture in the 21st century. The purpose of this study was to generate and compare management zones (MZ) based on the Sentinel-2 satellite data for variable rate application of mineral nitrogen in wheat production, calculated using different remote sensing (RS)-based models under varied soil, yield and crop data availability. Three models were applied, including (1) a modified “RS- and threshold-based clustering”, (2) a “hybrid-based, unsupervised clustering”, in which data from different sources were combined for MZ delineation, and (3) a “RS-based, unsupervised clustering”. Various data processing methods including machine learning were used in the model development. Statistical tests such as the Paired Sample <i>T</i>-test, Kruskal–Wallis H-test and Wilcoxon signed-rank test were applied to evaluate the final delineated MZ maps. Additionally, a procedure for improving models based on information about phenological phases and the occurrence of agricultural drought was implemented. The results showed that information on agronomy and climate enables improving and optimizing MZ delineation. The integration of prior knowledge on new climate conditions (drought) in image selection was tested for effective use of the models. Lack of this information led to the infeasibility of obtaining optimal results. Models that solely rely on remote sensing information are comparatively less expensive than hybrid models. Additionally, remote sensing-based models enable delineating MZ for fertilizer recommendations that are temporally closer to fertilization times.Mohammad RokhafrouzHooman LatifiAli A. AbkarTomasz WojciechowskiMirosław CzechlowskiAli Sadeghi NaieniYasser MaghsoudiGniewko NiedbałaMDPI AGarticleprecision agriculturemanagement zonesremote sensingSentinel-2clusteringwinter wheatAgriculture (General)S1-972ENAgriculture, Vol 11, Iss 1104, p 1104 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	precision agriculture management zones remote sensing Sentinel-2 clustering winter wheat Agriculture (General) S1-972
spellingShingle	precision agriculture management zones remote sensing Sentinel-2 clustering winter wheat Agriculture (General) S1-972 Mohammad Rokhafrouz Hooman Latifi Ali A. Abkar Tomasz Wojciechowski Mirosław Czechlowski Ali Sadeghi Naieni Yasser Maghsoudi Gniewko Niedbała Simplified and Hybrid Remote Sensing-Based Delineation of Management Zones for Nitrogen Variable Rate Application in Wheat
description	Enhancing digital and precision agriculture is currently inevitable to overcome the economic and environmental challenges of the agriculture in the 21st century. The purpose of this study was to generate and compare management zones (MZ) based on the Sentinel-2 satellite data for variable rate application of mineral nitrogen in wheat production, calculated using different remote sensing (RS)-based models under varied soil, yield and crop data availability. Three models were applied, including (1) a modified “RS- and threshold-based clustering”, (2) a “hybrid-based, unsupervised clustering”, in which data from different sources were combined for MZ delineation, and (3) a “RS-based, unsupervised clustering”. Various data processing methods including machine learning were used in the model development. Statistical tests such as the Paired Sample <i>T</i>-test, Kruskal–Wallis H-test and Wilcoxon signed-rank test were applied to evaluate the final delineated MZ maps. Additionally, a procedure for improving models based on information about phenological phases and the occurrence of agricultural drought was implemented. The results showed that information on agronomy and climate enables improving and optimizing MZ delineation. The integration of prior knowledge on new climate conditions (drought) in image selection was tested for effective use of the models. Lack of this information led to the infeasibility of obtaining optimal results. Models that solely rely on remote sensing information are comparatively less expensive than hybrid models. Additionally, remote sensing-based models enable delineating MZ for fertilizer recommendations that are temporally closer to fertilization times.
format	article
author	Mohammad Rokhafrouz Hooman Latifi Ali A. Abkar Tomasz Wojciechowski Mirosław Czechlowski Ali Sadeghi Naieni Yasser Maghsoudi Gniewko Niedbała
author_facet	Mohammad Rokhafrouz Hooman Latifi Ali A. Abkar Tomasz Wojciechowski Mirosław Czechlowski Ali Sadeghi Naieni Yasser Maghsoudi Gniewko Niedbała
author_sort	Mohammad Rokhafrouz
title	Simplified and Hybrid Remote Sensing-Based Delineation of Management Zones for Nitrogen Variable Rate Application in Wheat
title_short	Simplified and Hybrid Remote Sensing-Based Delineation of Management Zones for Nitrogen Variable Rate Application in Wheat
title_full	Simplified and Hybrid Remote Sensing-Based Delineation of Management Zones for Nitrogen Variable Rate Application in Wheat
title_fullStr	Simplified and Hybrid Remote Sensing-Based Delineation of Management Zones for Nitrogen Variable Rate Application in Wheat
title_full_unstemmed	Simplified and Hybrid Remote Sensing-Based Delineation of Management Zones for Nitrogen Variable Rate Application in Wheat
title_sort	simplified and hybrid remote sensing-based delineation of management zones for nitrogen variable rate application in wheat
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/27218195734349b78f6ec2f218cf1313
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Simplified and Hybrid Remote Sensing-Based Delineation of Management Zones for Nitrogen Variable Rate Application in Wheat

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