Ground Truths to Support Remote-Sensing Inference of Irrigation Benefits and Effects in Rwanda

Remote sensing can map actual irrigation areas using vegetation indices [11]. Ground truthing is critical for achieving maximum accuracy in crop acreage assessment over a large area. Machine learning practitioners are developing and validating techniques for generating inferences about infrastructur...

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Bibliographic Details
Published in2023 IEEE AFRICON pp. 1 - 6
Main Authors Bologo, Fidelis L., Rawn, Barry, Raji, Tunmise
Format Conference Proceeding
LanguageEnglish
Published IEEE 20.09.2023
Subjects
Crops
ground truth
Irrigation
Machine learning
Remote sensing
Sensors
vegetation indices
Vegetation mapping
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Summary:Remote sensing can map actual irrigation areas using vegetation indices [11]. Ground truthing is critical for achieving maximum accuracy in crop acreage assessment over a large area. Machine learning practitioners are developing and validating techniques for generating inferences about infrastructure interventions' human and agricultural effects. The limitations of these practices and their accuracy need to be evaluated through parallel analysis and comparative analysis before remote sensing inferences can be fully trusted. This paper reports progress toward determining effective methods using remote sensing vegetation indices to study the effect of rainfall and irrigation on croplands in Rwanda.
ISSN:2153-0033
DOI:10.1109/AFRICON55910.2023.10293712