Steady infiltration rate spatial modeling from remote sensing data and terrain attributes in southeast Brazil

This paper aims to describe the development of steady infiltration rate (SIR) spatial prediction models using accessible input data. The models were created from SIR data collected through simulated rainfall at 71 points in part of the Cachimbal stream watershed (a Paraiba do Sul River tributary watershed) in Rio de Janeiro state-Brazil, using as covariates: terrain attributes derived from digital elevation model (DEM), remote sensing data and soil class, physical and chemical attributes maps. Itwas discussed how different land uses and soil degradation levels affect SIR and how NDVI can be used to represent themon SIR modeling. Among the soil physical properties, bulk density (BD) and total sand (TS) were selected as covariates. SIR was higherwhen lower the bulk density and higher the sand content. Soil types play a big role in SIR, highlighting the Gleissolos Haplicos (Gleysols) as the soil class that presented the lower average SIR values and the Latossolos Vermelho Amarelos and Nitossolos Haplicos (Ferralsols and Nitisols) that presented the highest. Topographic position Index (TPI), curvature, and TopographicWetness Index (TWI) were the terrain covariates used in the models. Their usage indicate lower SIR in concave, lower and wetter parts of the landscape. The results demonstrated that is possible to achieve satisfactory results for SIR spatialmodeling using easily accessible data (remote sensing and terrain attributes), but soil information is also necessary to develop better prediction models. (C) 2019 Elsevier B.V. All rights reserved.