Deriving soil moisture information with optical remote sensing data in R
Convener: Iuliia BurdunECSECS | Co-conveners: Michel Bechtold, Viacheslav KomisarenkoECSECS

Soil moisture is a key variable needed for application in climatology and hydrology. Knowledge about soil moisture is important to understand the ecosystems feedback to global climate change. Remote sensing can assist with deriving spatial soil moisture data on a regular basis. Particularly, optical remote sensing can be used to estimate soil moisture with unprecedented satellite archives (>30 years of Landsat) at high spatial resolution (30 m) globally.
Optical Trapezoid Model (OPTRAM) has shown high accuracy in soil moisture estimation over mineral and organic soils. OPTRAM utilises NIR and SWIR spectral regions that are sensitive to the water content in soil and vegetation. In wetlands, OPTRAM can also be used to derive information about groundwater position. Deriving soil moisture information with OPTRAM is a complex task that requires skills in processing remote sensing images, coding and analysing spatiotemporal data.
In this workshop, we will present the workflow needed for OPTRAM calculation in open-source R software. We will guide the participants on several key points:
- sources to derive optical remote sensing data;
- spatial data wrangling;
- estimation of OPTRAM;
- plotting of spatial data;
- interpretation of results.