Dynamics of Irrigated Land Expansion in the Ouémé River Basin Using Field and Remote Sensing Data in the Google Earth Engine

David Houéwanou Ahoton (), Taofic Bacharou, Aymar Yaovi Bossa, Luc Ollivier Sintondji, Benjamin Bonkoungou and Voltaire Midakpo Alofa
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David Houéwanou Ahoton: Doctoral School of Agricultural and Water Sciences (DSAWS), University of Abomey-Calavi, Cotonou 01 BP 526, Benin
Taofic Bacharou: Polytechnic School of Abomey-Calavi, University of Abomey-Calavi, Cotonou 01 BP 2009, Benin
Aymar Yaovi Bossa: National Water Institute, University of Abomey-Calavi, Cotonou 01 BP 526, Benin
Luc Ollivier Sintondji: National Water Institute, University of Abomey-Calavi, Cotonou 01 BP 526, Benin
Benjamin Bonkoungou: Doctoral School of Agricultural and Water Sciences (DSAWS), University of Abomey-Calavi, Cotonou 01 BP 526, Benin
Voltaire Midakpo Alofa: Doctoral School of Agricultural and Water Sciences (DSAWS), University of Abomey-Calavi, Cotonou 01 BP 526, Benin

Land, 2024, vol. 13, issue 11, 1-17

Abstract: The availability of reliable and quantified information on the spatiotemporal distribution of irrigated land at the river basin scale is an essential step towards sustainable management of water resources. This research aims to assess the spatiotemporal extent of irrigated land in the Ouémé River basin using Landsat multi-temporal images and ground truth data. A methodology was built around the use of supervised classification and the application of an algorithm based on the logical expression and thresholding of a combination of surface temperature (Ts) and normalized difference vegetation index (NDVI). The findings of the supervised classification showed that agricultural areas were 16,003 km 2 , 19,732 km 2 , and 22,850 km 2 for the years 2014, 2018, and 2022, respectively. The irrigated land areas were 755 km 2 , 1143 km 2 , and 1883 km 2 for the same years, respectively. A significant increase in irrigated areas was recorded throughout the study period. The overall accuracy values of 79%, 82%, and 83% obtained during validation of the irrigated land maps indicate a good performance of the algorithm. The results suggest a promising application of the algorithm to obtain up-to-date information on the distribution of irrigated land in several regions of Africa.

Keywords: random forest; irrigated land; agricultural areas; Ts; NDVI; Ouémé River basin (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:gam:jlands:v:13:y:2024:i:11:p:1926-:d:1522128

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