Forecasting of Cereal Yields in a Semi-arid Area Using the Simple Algorithm for Yield Estimation (SAFY) Agro-Meteorological Model Combined with Optical SPOT/HRV Images
Identifieur interne : 000473 ( Main/Exploration ); précédent : 000472; suivant : 000474Forecasting of Cereal Yields in a Semi-arid Area Using the Simple Algorithm for Yield Estimation (SAFY) Agro-Meteorological Model Combined with Optical SPOT/HRV Images
Auteurs : Aicha Chahbi Bellakanji [Tunisie] ; Mehrez Zribi ; Zohra Lili-Chabaane ; Bernard MougenotSource :
- Sensors (Basel, Switzerland) [ 1424-8220 ] ; 2018.
Abstract
In semi-arid areas characterized by frequent drought events, there is often a strong need for an operational grain yield forecasting system, to help decision-makers with the planning of annual imports. However, monitoring the crop canopy and production capacity of plants, especially for cereals, can be challenging. In this paper, a new approach to yield estimation by combining data from the Simple Algorithm for Yield estimation (SAFY) agro-meteorological model with optical SPOT/ High Visible Resolution (HRV) satellite data is proposed. Grain yields are then statistically estimated as a function of Leaf Area Index (LAI) during the maximum growth period between 25 March and 5 April. The LAI is retrieved from the SAFY model, and calibrated using SPOT/HRV data. This study is based on the analysis of a rich database, which was acquired over a period of two years (2010–2011, 2012–2013) at the Merguellil site in central Tunisia (North Africa) from more than 60 test fields and 20 optical satellite SPOT/HRV images. The validation and calibration of this methodology is presented, on the basis of two subsets of observations derived from the experimental database. Finally, an inversion technique is applied to estimate the overall yield of the entire studied site.
Url:
DOI: 10.3390/s18072138
PubMed: 29970840
PubMed Central: 6068716
Affiliations:
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Le document en format XML
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<sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">Forecasting of Cereal Yields in a Semi-arid Area Using the Simple Algorithm for Yield Estimation (SAFY) Agro-Meteorological Model Combined with Optical SPOT/HRV Images</title>
<author><name sortKey="Chahbi Bellakanji, Aicha" sort="Chahbi Bellakanji, Aicha" uniqKey="Chahbi Bellakanji A" first="Aicha" last="Chahbi Bellakanji">Aicha Chahbi Bellakanji</name>
<affiliation><nlm:aff id="af1-sensors-18-02138">LR17AGR01 (LR GREEN-TEAM)/INAT, University Carthage, Avenue de la République, P.O. Box 77, Carthage, Tunis 1054, Tunisia;<email>chehbi.aicha@gmail.com</email>
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<affiliation><nlm:aff id="af1-sensors-18-02138">LR17AGR01 (LR GREEN-TEAM)/INAT, University Carthage, Avenue de la République, P.O. Box 77, Carthage, Tunis 1054, Tunisia;<email>chehbi.aicha@gmail.com</email>
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<front><div type="abstract" xml:lang="en"><p>In semi-arid areas characterized by frequent drought events, there is often a strong need for an operational grain yield forecasting system, to help decision-makers with the planning of annual imports. However, monitoring the crop canopy and production capacity of plants, especially for cereals, can be challenging. In this paper, a new approach to yield estimation by combining data from the Simple Algorithm for Yield estimation (SAFY) agro-meteorological model with optical SPOT/ High Visible Resolution (HRV) satellite data is proposed. Grain yields are then statistically estimated as a function of Leaf Area Index (LAI) during the maximum growth period between 25 March and 5 April. The LAI is retrieved from the SAFY model, and calibrated using SPOT/HRV data. This study is based on the analysis of a rich database, which was acquired over a period of two years (2010–2011, 2012–2013) at the Merguellil site in central Tunisia (North Africa) from more than 60 test fields and 20 optical satellite SPOT/HRV images. The validation and calibration of this methodology is presented, on the basis of two subsets of observations derived from the experimental database. Finally, an inversion technique is applied to estimate the overall yield of the entire studied site.</p>
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