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Towards Predictive Modeling of Sorghum Biomass Yields Using Fraction of Absorbed Photosynthetically Active Radiation Derived from Sentinel-2 Satellite Imagery and Supervised Machine Learning Techniques

1
CREA Research Center for Cereal and Industrial Crops, Bologna 40128, Italy
2
Vlaamse instelling voor technologisch onderzoek N.V., MOL 2400, Belgium
3
Department for Sustainability, Italian National Agency for New Technologies, Energy and Sustainable Economic Development (ENEA), Rotondella (MT) 75026, Italy
*
Author to whom correspondence should be addressed.
Agronomy 2019, 9(4), 203; https://doi.org/10.3390/agronomy9040203
Received: 23 March 2019 / Revised: 13 April 2019 / Accepted: 18 April 2019 / Published: 20 April 2019
(This article belongs to the Special Issue Remote Sensing Applications for Agriculture and Crop Modelling)
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Abstract

Sorghum crop is grown under tropical and temperate latitudes for several purposes including production of health promoting food from the kernel and forage and biofuels from aboveground biomass. One of the concerns of policy-makers and sorghum growers is to cost-effectively predict biomass yields early during the cropping season to improve biomass and biofuel management. The objective of this study was to investigate if Sentinel-2 satellite images could be used to predict within-season biomass sorghum yields in the Mediterranean region. Thirteen machine learning algorithms were tested on fortnightly Sentinel-2A and Sentinel-2B estimates of the fraction of Absorbed Photosynthetically Active Radiation (fAPAR) in combination with in situ aboveground biomass yields from demonstrative fields in Italy. A gradient boosting algorithm implementing the xgbtree method was the best predictive model as it was satisfactorily implemented anywhere from May to July. The best prediction time was the month of May followed by May–June and May–July. To the best of our knowledge, this work represents the first time Sentinel-2-derived fAPAR is used in sorghum biomass predictive modeling. The results from this study will help farmers improve their sorghum biomass business operations and policy-makers and extension services improve energy planning and avoid energy-related crises. View Full-Text
Keywords: sorghum biomass; prediction modeling; machine learning; fAPAR; Sentinel-2 satellite imagery; big data technology; remote sensing sorghum biomass; prediction modeling; machine learning; fAPAR; Sentinel-2 satellite imagery; big data technology; remote sensing
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Habyarimana, E.; Piccard, I.; Catellani, M.; De Franceschi, P.; Dall’Agata, M. Towards Predictive Modeling of Sorghum Biomass Yields Using Fraction of Absorbed Photosynthetically Active Radiation Derived from Sentinel-2 Satellite Imagery and Supervised Machine Learning Techniques. Agronomy 2019, 9, 203.

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