Sensitivity of hyperspectral vegetation indices to rainfall seasonality in the Brazilian savannahs: an analysis using PRISMA data

Abstract

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.