Identifying favourable conditions for farm scale trafficability and grass growth using a combined Sentinel-2 and soil moisture deficit approach

Abstract

Read online

In Atlantic Europe, on poorly drained grasslands soils, compaction negatively affects soil health when trafficked in wet conditions, while optimum grass growth cannot be achieved in excessively dry conditions. In Ireland, daily soil moisture deficit (SMD) information is forecasted at regional scale for all soil drainage classes. Optimal paddock conditions can occur between trafficking (10 mm) and optimum grass growth (50 mm) SMD thresholds for an identified drainage class. The objective of this farm scale study is to improve the identification of optimum conditions in time and space by combining high resolution spatial soil moisture estimates with soil drainage class specific SMD data. For that purpose, Sentinel- 2 (S-2) data was used in a modified Optical Trapezoid Model (OPTRAM) to derive normalised surface soil moisture (nSSM) estimates at farm level. In-situ soil moisture sensors providing daily estimates of volumetric soil moisture were used for validation of OPTRAM with an RMSE of 0.05. Cumulative 7-day SMD prior to the date of each S-2 image was analysed for each year from 2017-2021 to select nSSM maps corresponding to negative, 0 or −0 and positive SMD. Results established a relationship between nSSM and SMD indicating optimal conditions changed spatially and temporally. The months of April, May, August and September always presented at least 35% of the farm area available for optimum management operations. Future refinement of this methodology utilising daily high resolution remote sensing data could provide near real-time information for farmers.

Keywords

• grassland
• heavy soils
• remote sensing
• decision support
• OPTRAM