Building a bridge between MODIS and VIIRS in measuring chlorophyll-a through linear regression: a case study in the Arabian Gulf

Albanai, Jasem A

doi:10.1007/s12517-023-11618-4

Cited by 1 publication

(1 citation statement)

References 33 publications

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“…Furthermore, the National Snow and Ice Data Center has been engaged in the construction of a time series utilizing data from both the MODIS and VIIRS missions. This effort can offer valuable insights for the research community, particularly in the context of climate change [11,12].…”

Section: Introductionmentioning

confidence: 99%

Transcriptome Data Analysis Applied to Grapevine Growth Stage Identification

Altimiras,

Pavéz,

Pourreza

et al. 2024

Agronomy

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In agricultural production, it is fundamental to characterize the phenological stage of plants to ensure a good evaluation of the development, growth and health of crops. Phenological characterization allows for the early detection of nutritional deficiencies in plants that diminish the growth and productive yield and drastically affect the quality of their fruits. Currently, the phenological estimation of development in grapevine (Vitis vinifera) is carried out using four different schemes: Baillod and Baggiolini, Extended BBCH, Eichhorn and Lorenz, and Modified E-L. Phenological estimation requires the exhaustive evaluation of crops, which makes it intensive in terms of labor, personnel, and the time required for its application. In this work, we propose a new phenological classification based on transcriptional measures of certain genes to accurately estimate the stage of development of grapevine. There are several genomic information databases for Vitis vinifera, and the function of thousands of their genes has been widely characterized. The application of advanced molecular biology, including the massive parallel sequencing of RNA (RNA-seq), and the handling of large volumes of data provide state-of-the-art tools for the determination of phenological stages, on a global scale, of the molecular functions and processes of plants. With this aim, we applied a bioinformatic pipeline for the high-throughput quantification of RNA-seq datasets and further analysis of gene ontology terms. We identified differentially expressed genes in several datasets, and then, we associated them with the corresponding phenological stage of development. Differentially expressed genes were classified using count-based expression analysis and clustering and annotated using gene ontology data. This work contributes to the use of transcriptome data and gene expression analysis for the classification of development in plants, with a wide range of industrial applications in agriculture.

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Section: Introductionmentioning

confidence: 99%