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A framework for disaggregating remote-sensing cropland into rainfed and irrigated classes at continental scale

OAR@ICRISAT

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Relation http://oar.icrisat.org/12380/
https://doi.org/10.1016/j.jag.2023.103607
https://doi.org/10.1016/j.jag.2023.103607
 
Title A framework for disaggregating remote-sensing cropland into rainfed and irrigated classes at continental scale
 
Creator Owusu, A
Kagone, S
Leh, M
Velpuri, N M
Gumma, M K
Ghansah, B
Thilina-Prabhath, P
Akpoti, K
Mekonnen, K
Tinonetsana, P
Mohammed, I
 
Subject Remote Sensing
 
Description Agriculture consumes the largest share of freshwater globally; therefore, distinguishing between rainfed and irrigated croplands is essential for agricultural water management and food security. In this study, a framework incorporating the Budyko model was used to differentiate between rainfed and irrigated cropland areas in Africa for eight remote sensing landcover products and a high-confidence cropland map (HCCM). The HCCM was generated for calibration and validation of the crop partitioning framework as an alternative to individual cropland masks which exhibit high disagreement. The accuracy of the framework in partitioning the HCCM was evaluated using an independent validation dataset, yielding an overall accuracy rate of 73 %. The findings of this study indicate that out of the total area covered by the HCCM (2.36 million km2), about 461,000 km2 (19 %) is irrigated cropland. The partitioning framework was applied on eight landcover products, and the extent of irrigated areas varied between 19 % and 30 % of the total cropland area. The framework demonstrated high precision and specificity scores, indicating its effectiveness in correctly identifying irrigated areas while minimizing the misclassification of rainfed areas as irrigated. This study provides an enhanced understanding of rainfed and irrigation patterns across Africa, supporting efforts towards achieving sustainable and resilient agricultural systems. Consequently, the approach outlined expands on the suite of remote sensing landcover products that can be used for agricultural water studies in Africa by enabling the extraction of irrigated and rainfed cropland data from landcover products that do not have disaggregated cropland classes.
 
Publisher Elsevier
 
Date 2023-12-15
 
Type Article
PeerReviewed
 
Format application/pdf
 
Language en
 
Rights cc_by_nc_nd
 
Identifier http://oar.icrisat.org/12380/1/International%20Journal%20of%20Applied%20Earth%20Observation%20and%20Geoinformation_126_1-16_2024.pdf
Owusu, A and Kagone, S and Leh, M and Velpuri, N M and Gumma, M K and Ghansah, B and Thilina-Prabhath, P and Akpoti, K and Mekonnen, K and Tinonetsana, P and Mohammed, I (2023) A framework for disaggregating remote-sensing cropland into rainfed and irrigated classes at continental scale. International Journal of Applied Earth Observation and Geoinformation, 126. pp. 1-15. ISSN 1569-8432