Improved digital soil mapping with multitemporal remotely sensed satellite data fusion: A case study in Iran

Solmaz Fathololoumi; Ali Reza Vaezi; Seyed Kazem Alavipanah; Ardavan Ghorbani; Daniel Saurette; Asim Biswas

doi:10.1016/j.scitotenv.2020.137703

Improved digital soil mapping with multitemporal remotely sensed satellite data fusion: A case study in Iran

Sci Total Environ. 2020 Jun 15:721:137703. doi: 10.1016/j.scitotenv.2020.137703. Epub 2020 Mar 7.

Authors

Solmaz Fathololoumi¹, Ali Reza Vaezi², Seyed Kazem Alavipanah³, Ardavan Ghorbani⁴, Daniel Saurette⁵, Asim Biswas⁶

Affiliations

¹ Department of Soil Science, Faculty of Agriculture, University of Zanjan, Iran; School of Environmental Sciences, University of Guelph, Canada; Faculty of Agriculture and Natural Resources, University of Mohaghegh Ardabili, Ardabil, Iran. Electronic address: fathololomi.s@znu.ac.ir.
² Department of Soil Science, Faculty of Agriculture, University of Zanjan, Iran. Electronic address: vaezi.alireza@znu.ac.ir.
³ Department of Remote Sensing and GIS, Faculty of Geography, University of Tehran, Iran; Department of Geography, Humboldt University Berlin, Unter den Linden 6, 10099 Berlin, Germany. Electronic address: salavipa@ut.ac.ir.
⁴ Faculty of Agriculture and Natural Resources, University of Mohaghegh Ardabili, Ardabil, Iran. Electronic address: a_ghorbani@uma.ac.ir.
⁵ School of Environmental Sciences, University of Guelph, Canada. Electronic address: dsaurett@uoguelph.ca.
⁶ School of Environmental Sciences, University of Guelph, Canada. Electronic address: biswas@uoguelph.ca.

PMID: 32172111
DOI: 10.1016/j.scitotenv.2020.137703

Abstract

Modeling and mapping of soil properties are critical in many environmental, climatic, ecological and hydrological applications. Digital soil mapping (DSM) techniques are now commonly applied to predict soil properties with limited data by developing predictive relationships with environmental covariates. Most studies derive covariates from a digital elevation model (named static covariates). Many works also include single-day remotely sensed satellite imagery. However, multitemporal satellite images can capture information about soil properties over time and bring additional information in predicting soil properties in DSM. We refer to covariates derived from multitemporal satellite images as dynamic covariates. The objective of this study was to assess the performance of DSM when using terrain derivatives (static covariates), single-date remotely sensed satellite indices (limited dynamic covariates), multitemporal satellite indices (dynamic covariates), and combinations of terrain derivatives and satellite indices (covariate fusion) as covariates in predicting soil properties and estimating uncertainty. Three soil properties are considered in this study: organic carbon (OC), sand content, and calcium carbonate equivalent (CCE). Inclusion of single and/or multitemporal remotely sensed satellite indices improved the prediction of soil properties over traditionally used terrain indices. Significant improvements were observed in the prediction of soil properties using two models, Cubist and random forest (RF). The increase in the R² values for Cubist and RF were 126% and 78% for OC, 110% and 54% for sand, and 87% and 32% for CCE. The RMSE decreased by 34% and 27% for OC, 25% and 12% for sand, and 39% and 19% for CCE, when compared to the terrain indices only model. This also reduced the uncertainty of estimation and mapping. These clearly showed the advantage of using multitemporal satellite data fusion rather than simply using static terrain indices for DSM of soil properties to deliver a great potential in improving soil modeling and mapping for many applications.

Keywords: Cross validation; Cubist; Digital soil mapping; Landsat; Random forest; Soil properties.