# Preface: Land Surface Processes and Interactions--From HCMM to Sentinel Missions and Beyond
## 2 Recent Advances in Monitoring Land Surfaces and Interactions
### Water Cycle and Climate from Space
From the perspective of radiative transfer theory, [PERSON] et al. [PERSON] et al. (2017) discussed how to determine the optimal mounting depth of soil moisture and soil temperature (SMST) sensors, for calculating soil effective temperature (_Teff_) from satellite microwave sensors at the L-band, based on [PERSON]'s multilayer _Teff_ scheme [PERSON] et al. (2017); [PERSON] et al. (2017). It was found that, on average, nearly 20% of the _Teff_ signal cannot be captured by the Maqu in situ SMST network, using the currently assumed common installation configurations (i.e., 5 cm, 10 cm, 20 cm, 40 cm, and 80 cm). Furthermore, they identified the optimal mounting depths for the SMST observation pair as 5 cm and 20 cm by calculating _Teff_ at the center station in the Maqu Network. The findings can help to establish an optimal SMST network and improve the representativeness of the existing networks regarding the calculation of _Teff_. [PERSON] et al. [PERSON] et al. (2017) explored the linkage between soil moisture and the Qinghai-Xizang Plateau monsoon evolution, and proposed a new plateau monsoon index (ZPMI). The ZPMI was compared with the existing Plateau Monsoon Indices (PMI): the Traditional Plateau Monsoon Index (TPMI), the Dynamic Plateau Monsoon Index (DPMI), and the QPMI (i.e., PMI developed by [PERSON] et al.). The results show that the onset and retreat of plateau monsoons determined by the TPMI are approximately 1-2 months earlier than those of the ZPMI and DPMI, while the ZPMI can better reflect seasonal and inter-annual variations in precipitation over the plateau. They found that the plateau summer and winter monsoons have similar inter-annual and inter-decadal variation characteristics and show a rising trend, although the increasing trend of the summer monsoon is more significant. The analysis of ZPMI and SM in April and May revealed that when the SM over the central and eastern plateau is higher than normal, the plateau summer monsoon is stronger, and vice versa. [PERSON] et al. [PERSON] et al. (2017) assessed the orographic variability in glacial thickness changes at the Tibetan Plateau using ICESat laser altimetry, in combination with the SRTM DEM and the GLIMS glacier mask. Using the ICESat elevations acquired over terrain with a slope below 20\({}^{\circ}\) and a roughness at the footprint scale below 15 m, they identified 90 glaciated areas and investigated their changes. The results show that most of observed glaciated areas over the Tibetan Plateau are thinning, except for some glaciers in the northwest. Particularly, most north-facing glaciers increase in thickness. They concluded that glacial thickness changes depend strongly on the relative position in a mountain range. Due to satellite configuration and errors from spatial sampling, daily time series of microwave radiometer data obtained in one-orbit direction are suffering observation gaps. On the other hand, such time series carry information about the surface and atmosphere signal including emittance and attenuation. [PERSON] et al. [PERSON] et al. (2017) developed the Time Series Analysis Procedure (TSAP) to extract the surface signal from the noisy time series, based on the properties of the Discrete Fourier Transform (DFT). In particular, the Polarization Difference Brightness Temperature (PDBT) at 37 GHz data were used to explain the implementation of TSAP. The use of PDBT is beneficial for noise reduction, as the PDBT range between dryland and open water is about 20 k. Furthermore, since the PDBT at 37 GHz is mainly attenuated by hydrometeors that yield precipitation, the time series of rain-gauge data can help to reveal the spectral feature of the atmospheric signal. Thus, the spectral features of the surface signal were identified in the PDBT time series with the help of the rain-gauge data, as well as the Harmonic Analysis of Time Series (HANTS) algorithm correcting for atmospheric influence.