Soil moisture estimation with distributed temperature sensing
Soil moisture is very important for land surface modeling. Since land surface processes are nonlinear, it is important to represent the spatial pattern of soil states. However, the gap between large scale (remote sensing) and point scale measurements hampers the improvement in remote sensing measurements and limits our knowledge about the spatial pattern of soil states. Distributed Temperature Sensing (DTS) could continuously measure temperature with high resolution (spatial, 0.25 m; temporal, 10 s) at large scales (>10 km). Therefore, DTS is considered to be a powerful tool for relating the in situ (point scale) and remote sensing (footprint scale) measurements, which could improve our understanding of our environment. His research focuses are: Soil states estimation by DTS; Soil moisture spatial pattern and scaling methods; Remote sensing measurements validation and augmentation.