A new multivariate agricultural drought composite index based on random forest algorithm and remote sensing data developed for Sahelian agrosystems

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dc.creatorHanade Houmma, I.
dc.creatorGadal, S.
dc.creatorEl Mansouri, L.
dc.creatorGarba, M.
dc.creatorGbetkom, P.G.
dc.creatorMamane Barkawi, M.B.
dc.creatorHadria, R.
dc.date2023-12-31
dc.date2024-03-01T14:53:26Z
dc.date2024-03-01T14:53:26Z
dc.date.accessioned2026-06-27T15:53:23Z
dc.descriptionThis manuscript aims to develop a new multivariate composite index for monitoring agricultural drought. To achieve this, the AVHRR, VIIRS, CHIRPS data series over a period of 40 years, rainfall and crop yield data as references were used. Variables include parameters for vegetative stress (SVCI, PV, SMN), water stress (PCI, RDI, NRDI), and heat stress (SMT, TCI, STCI), and a new variable related to environmental conditions was calculated through a normalized rainfall efficiency index. Then, random forest algorithm was used to determine the weights of each component of the model by considering interannual fluctuations in cereal yields as an impact variable. The multivariate composite model was compared to the VHI, NVSWI and SPI-12 indices for validation. The results show a large spatiotemporal concordance between the MDCI and the validation indices with a maximum correlation of 0.95 with the VHI and a highly significant p value (< 2.2e-16). Validation of the MDCI model by SPI-12 shows a significantly higher statistically significant relationship than that observed between SPI and VHI and NVSWI. P value range from 3.531e-05 to 6.137e-06 with correlations that vary between 0.6 and 0.64 depending on the station. It is also highly correlated with the Palmer drought severity index (PDSI) and climatic water deficit index (CWDI), with R = 0.85 and p value < 5.8e-10 and R = 0.72 and p value < 1.9e-6, respectively. Finally, the study provides a new direction for multivariate modeling of agricultural drought that should be further explored under various agroclimatic conditions.
dc.formatapplication/pdf
dc.identifierhttps://hdl.handle.net/10568/139779
dc.identifier.urihttp://hdl.handle.net/123456789/116509
dc.languageen
dc.publisherInforma UK Limited
dc.rightsOpen Access
dc.sourceHanadé Houmma, I., Gadal, S., El Mansouri, L., Garba, M., Gbetkom, P.G., Mamane Barkawi, M.B. & Hadria, R. (2023). A new multivariate agricultural drought composite index based on random forest algorithm and remote sensing data developed for Sahelian agrosystems. Geomatics, Natural Hazards and Risk, 14(1): 2223384, 1-34.
dc.subjectdrought
dc.subjectremote sensing
dc.subjectforests
dc.subjectsahel
dc.titleA new multivariate agricultural drought composite index based on random forest algorithm and remote sensing data developed for Sahelian agrosystems
dc.typeJournal Article

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Instituto Internacional de Agricultura Tropical (IITA)