@Article{AraújoGalvDala:2023:AnUsPR,
               author = "Ara{\'u}jo, Julibana de Abreu and Galv{\~a}o, L{\^e}nio Soares 
                         and Dalagnol, Ricardo",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Jet Propulsion 
                         Laboratory}",
                title = "Sensitivity of hyperspectral vegetation indices to rainfall 
                         seasonality in the Brazilian savannahs: an analysis using PRISMA 
                         data",
              journal = "Remote Sensing Letters",
                 year = "2023",
               volume = "14",
               number = "3",
                pages = "277--287",
                month = "mar.",
             abstract = "We evaluated the sensitivity of 14 narrowband vegetation indices 
                         (VIs) to rainfall seasonality over the Brazilian savannahs. Five 
                         images obtained in 2020 by the PRecursore IperSpettrale della 
                         Missione Applicativa (PRISMA) tracked the transition from the 
                         rainy (11 May) to the dry season (8 July, 17 August, and 4 
                         September), and towards the beginning of a new seasonal cycle on 3 
                         October. We considered two scenarios in the data analysis. First, 
                         we kept the PRISMA image from 11 May as a reference to evaluate 
                         the VI sensitivity with increasing water deficit from 8 July (49 
                         days without any precipitation) to 4 September (102 days). Second, 
                         we changed the reference image to 4 September to evaluate the 
                         largest VI responses on 3 October after the first rainfall. The 
                         first three VIs (ranked by F-values) having significant changes 
                         with increasing water deficit over grasslands were the Normalized 
                         Difference Vegetation Index (NDVI), Enhanced Vegetation Index 
                         (EVI), and Moisture Stress Index (MSI). The Vogelmann red edge 
                         index (VOG) and Red-Edge Normalized Difference Vegetation Index 
                         (RENDVI) presented the largest F-values over woodlands. In the 
                         second scenario, Red-edge Vegetation Stress Index (RVSI) and 
                         RENDVI were among the first five ranked VIs by t-values over both 
                         areas. The largest changes in VIs were generally observed over 
                         savannah grassland, which is the most sensitive physiognomy to 
                         water deficit. The lowest modifications were noted over riparian 
                         forests, which have access to waters from rivers. The 
                         vegetation-type dependence of the VI changes was also observed 
                         after the occurrence of rainfall. Results suggest the potential 
                         use of different VIs to obtain phenological metrics for more 
                         accurate savannah mapping in Brazil.",
                  doi = "10.1080/2150704X.2023.2189031",
                  url = "http://dx.doi.org/10.1080/2150704X.2023.2189031",
                 issn = "2150-704X",
             language = "en",
           targetfile = "Sensitivity of hyperspectral vegetation indices to rainfall 
                         seasonality in the Brazilian savannahs an analysis using PRISMA 
                         data.pdf",
        urlaccessdate = "12 maio 2024"
}