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

• Published in: Geoderma Regional Vol. 20; p. e00242
• Main Authors: Moraes, André Geraldo de Lima, de Carvalho, Daniel Fonseca, Antunes, Mauro Antônio Homem, Ceddia, Marcos Bacis, Flanagan, Dennis C.
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.03.2020
• Subjects: Acrisols; Cambisols; Rainfall simulator; Vegetation indexes; Rainfall simulator; Cambisols; Vegetation indexes; Acrisols
• ISSN: 2352-0094; 2352-0094
• DOI: 10.1016/j.geodrs.2019.e00242

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 Paraíba 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. It was discussed how different land uses and soil degradation levels affect SIR and how NDVI can be used to represent them on SIR modeling. Among the soil physical properties, bulk density (BD) and total sand (TS) were selected as covariates. SIR was higher when lower the bulk density and higher the sand content. Soil types play a big role in SIR, highlighting the Gleissolos Háplicos (Gleysols) as the soil class that presented the lower average SIR values and the Latossolos Vermelho Amarelos and Nitossolos Háplicos (Ferralsols and Nitisols) that presented the highest. Topographic position Index (TPI), curvature, and Topographic Wetness 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 spatial modeling using easily accessible data (remote sensing and terrain attributes), but soil information is also necessary to develop better prediction models. •Development of spatial SIR models that use input data of relative easy acquisition.•Discusses the correlation between NDVI and field observed SIR.•Proposes different SIR models, to be used according to available input data.