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

Authors: DE LIMA MORAES, ANDRE - Universidade Federal Do Rio De Janeiro; DE CARVALHO, DANIEL - Universidade Federal Do Rio De Janeiro; HOMEM ANTUNES, MAURO - Universidade Federal Do Rio De Janeiro; BACIS CEDDIA, MARCOS - Universidade Federal Do Rio De Janeiro; Flanagan, Dennis

Submitted to: Geoderma Regional
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 10/15/2019
Publication Date: 12/24/2019
Citation: De Lima Moraes, A.G., De Carvalho, D.F., Homem Antunes, M.A., Bacis Ceddia, M., Flanagan, D.C. 2019. Steady infiltration rate spatial modeling from remote sensing data and terrain attributes. Geoderma Regional. 20:e00242. https://doi.org/10.1016/j.geodrs.2019.e00242.
DOI: https://doi.org/10.1016/j.geodrs.2019.e00242

Interpretive Summary: Rain that falls onto a soil surface either infiltrates or runs off. From an agricultural and environmental perspective, greater infiltration into a soil provides for more moisture for a growing crop, and less surface runoff that can cause soil erosion and carry sediment and water pollutants off-site. Determination of infiltration rates for soils in the field uses experimental equipment such as rainfall simulators, but these types of studies are expensive and time-consuming, and limited to a practical extent. Thus, equations are often used to estimate water infiltration rates into soils, as a function of soil texture (sand, silt, clay content), land management, cover, etc. In this study, we used field rainfall simulation experiments at 71 locations in the Médio Paraíba do Sul region of Brazil to measure infiltration rates; remotely-sensed satellite information, a digital elevation model, and detailed soil analysis data were then used to determine 6 statistical models to estimate steady infiltration rates (SIR). We found that remotely sensed data along with terrain features could be used to obtain satisfactory predictions of SIR; however, best modeled results were obtained when soil information was also utilized. These results impact soil scientists, engineers, students, and others involved in field soil and water conservation efforts, particularly for those in developing countries that do not have readily available soil survey information. The importance of detailed soil information is also highlighted.

Technical Abstract: The lack of high-resolution spatial data for environmental modeling is a large challenge, especially in developing countries such as Brazil. In this context, it is important to develop environmental models that use easily accessible data as input, such as remote sensing and terrain covariates, which are available worldwide. 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 DEM, remote sensing data and soil class, physical and chemical attributes maps. In this paper, we demonstrate 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.