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Satellite data and supervised learning to prevent impact of drought on crop production: meteorological drought

Author: Ornella, L.
Author: Kruseman, G.
Author: Crossa, J.
Year: 2020
URI: https://hdl.handle.net/10883/20591
Abstract: Reiterated and extreme weather events pose challenges for the agricultural sector. The convergence of remote sensing and supervised learning (SL) can generate solutions for the problems arising from climate change. SL methods build from a training set a function that maps a set of variables to an output. This function can be used to predict new examples. Because they are nonparametric, these methods can mine large quantities of satellite data to capture the relationship between climate variables and crops, or successfully replace autoregressive integrated moving average (ARIMA) models to forecast the weather. Agricultural indices (AIs) reflecting the soil water conditions that influence crop conditions are costly to monitor in terms of time and resources. So, under certain circumstances, meteorological indices can be used as substitutes for AIs. We discuss meteorological indexes and review SL approaches that are suitable for predicting drought based on historical satellite data. We also include some illustrative case studies. Finally, we will survey rainfall products existing at the web and some alternatives to process the data: from high-performance computing systems able to process terabyte-scale datasets to open source software enabling the use of personal computers.
Format: PDF
Language: English
Publisher: IntechOpen
Copyright: CIMMYT manages Intellectual Assets as International Public Goods. The user is free to download, print, store and share this work. In case you want to translate or create any other derivative work and share or distribute such translation/derivative work, please contact CIMMYT-Knowledge-Center@cgiar.org indicating the work you want to use and the kind of use you intend; CIMMYT will contact you with the suitable license for that purpose.
Type: Book Chapter
Place of Publication: London (United Kingdom)
DOI: 10.5772/intechopen.85471
Keywords: Supervised Learning
Keywords: Wavelet
dc.relation.ispartof: Drought: detection and solutions
Agrovoc: REMOTE SENSING
Agrovoc: WEATHER DATA
Agrovoc: SATELLITES
Agrovoc: METEOROLOGICAL OBSERVATIONS
Agrovoc: DROUGHT


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  • Socioeconomics
    Including topics such as farming systems, markets, impact & targeting, innovations, and GIS

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