TEMPLINK

Ground surface temperature (GST), measured at 5 cm into the ground, is a key parameter controlling all the subsurface biophysical processes at the complex land-atmosphere interaction. GST is important for multiple geosciences and agricultural applications, being essential for understanding the climate change impacts on various environments. Due to the high heterogeneity of land surfaces, it remains a challenge to monitor the GST small-scale variability over large areas. MODerate resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) product provides the global daily temperature at the top of the surface cover at 1-km spatial resolution. However, the remote sensing LST cannot directly express the GST. This gap will address by exploiting the relationship between GST and LST, and developing a data-based model to derive the GST from MODIS LST. An innovative multidisciplinary and interdisciplinary approach will be used by combining (i) remote sensing, (ii) thermo-hydrological dynamics, and (iii) cutting-edge numerical modelling (GEOtop model and machine learning algorithms). The model will simplify and improve the spatiotemporal observations of the soil subsurface temperature with a significant socio-economic benefit for a broad set of end users (e.g. policy makers, farmers, scientists). The model will be developed at a local scale (Mazia Valley, Italian Alps) and applied at a regional scale (Qinghai-Tibet Plateau - QTP) to test its worldwide applicability. The host provides the research infrastructure (computational power), the necessary data (in-situ GST and MODIS LST), and expertise in numerical modelling for developing and validating the model.

Collected observation will contribute to the International Long Term Ecological Research (ILTER) and soil and near-surface temperature (SoilTemp) monitoring networks.