Variance-Based Fusion of VCI and TCI for Efficient Classification of Agriculture Drought Using Landsat Data in the High Atlas (Morocco, North Africa)

Ezzahra, Fathallah Fatima; Ahmed, Algouti; Abdellah, Algouti

Variance-Based Fusion of VCI and TCI for Efficient Classification of Agriculture Drought Using Landsat Data in the High Atlas (Morocco, North Africa)

2023

Ezzahra, Fathallah Fatima | Ahmed, Algouti | Abdellah, Algouti

Drought assessment using drought indices has been widely carried out for drought monitoring. Remote sensing-based indices use remotely sensed data to map drought conditions in a particular area or region. Therefore, the objective of the present study is to make a study on drought risk based on the calculation of an indicator from biophysical parameters extracted from NOAA/AVHRR satellite data, namely TCI and VCI, to obtain a better understanding of the differentiation between each index, and their application for drought monitoring in the High Atlas of Marrakech on the Chchaoua Morocco watershed during 1980-2020. Landsat oli7 and8 data were used to construct the indices. The result showed that each index proved to be a useful, fast, sufficient, and inexpensive tool for drought monitoring. However, each index has its differences. The TCI was found to be drought sensitive during the dry season or in months when high temperatures occurred. While VCI detected drought more sensitively in the rainy season as well (December-January-February to May) than TCI and VCI. Meanwhile, VCI, including the improved TCI, combined two indicators to better understand drought occurrence. These indices were calculated using GIS, QGis, ArcGis satellite imagery scenes, and Landsat. After a comparative study of these years, from 1984 to 2020, the evolution of the VCI and TCI was highlighted.

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