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

dc.contributor.authorHanadé Houmma, I.
dc.contributor.authorGadal, S.
dc.contributor.authorEl Mansouri, L.
dc.contributor.authorGarba, M.
dc.contributor.authorGbetkom, P.G.
dc.contributor.authorMamane Barkawi, M.B.
dc.contributor.authorHadria, R.
dc.date.accessioned2024-03-14T08:21:48Z
dc.date.available2024-03-14T08:21:48Z
dc.date.issued2023-06-16
dc.identifier.citationHanadé 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.identifier.urihttps://hdl.handle.net/20.500.12478/8439
dc.description.abstractThis 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.description.sponsorshipIslamic Development Bank
dc.format.extent1-43
dc.language.isoen
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
cg.contributor.affiliationAix-Marseille University
cg.contributor.affiliationHassan II Institute of Agronomy and Veterinary, Morocco
cg.contributor.affiliationInternational Institute of Tropical Agriculture
cg.contributor.affiliationUniversite de Toulouse
cg.contributor.affiliationAbdou Moumouni University
cg.contributor.affiliationNational Institute of Agricultural Research, Morocco
cg.coverage.regionAfrica
cg.coverage.regionAfrica South of Sahara
cg.coverage.hubHeadquarters and Western Africa Hub
cg.authorship.typesCGIAR and developing country institute
cg.iitasubjectForestry
cg.iitasubjectMeteorology and Climatology
cg.iitasubjectNatural Resource Management
cg.journalGeomatics, Natural Hazards and Risk
cg.notesOpen Access Journal
cg.accessibilitystatusOpen Access
cg.reviewstatusPeer Review
cg.usagerightslicenseCreative Commons Attribution-NonCommercial 4.0 (CC BY-NC 4.0)
cg.targetaudienceScientists
cg.identifier.doihttps://doi.org/10.1080/19475705.2023.2223384
cg.iitaauthor.identifierGarba Maman: 0000-0002-3377-3064
cg.futureupdate.requiredNo
cg.identifier.issue1: 2223384
cg.identifier.volume14


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