# Preface: Land Surface Processes and Interactions--From HCMM to Sentinel Missions and Beyond
## 2 Recent Advances in Monitoring Land Surfaces and Interactions
### Drought Detection  
Drought has wreaked havoc on human societies throughout history, leading to fatal disasters including devastated crops, famine, conflicts, and wars. The occurrence of drought has been associated with climatic shifts that caused low rainfall, while being distinguished as meteorological, soil moisture, and hydrological drought, depending on the dominant controlling factors from the climatic perspective [5; 6]. Many remote sensing-based indices have been developed in past decades to describe different aspects of droughs, while most of them neglect the changes in total water storage in an area and can result in certain bias. An integrated approach, not only considering the water and energy cycles but also the biogeochemical cycle, is needed to achieve a holistic view of the mechanisms behind drought occurrence. [PERSON] et al. [7] used spectroscopic techniques and radiative transfer model (RTM) inversion to monitor the response to drought stress in a _Poa pratensis_ (or meadow grass) canopy exposed to various levels of soil moisture deficit. The canopy reflectance and destructive measurements of LAI (Leaf Area Index) and leaf chlorophyll content (Cab) were taken. They found that spectroscopic techniques can reveal plant stress up to six days earlier than visual inspection. Furthermore, with the RTM model inversion by means of an iterative optimization approach, they found that the first strong sign of water stress on the retrieved grass properties was detected as a change of leaf water content, followed by Cab and leaf dry matter content in the earlier stages. The results from this study indicate the potentials of combining spectroscopic techniques and RTM model inversion to detect vegetation stress before they become visibly apparent, potentially contributing to an effective early warning system for drought detection. [PERSON] et al. [8] investigated the corn response to climate stress via identifying the NDVI (Normalized Difference Vegetation Index) residual, which was derived by using the concept of \"normal\" phenology over a decade. They found a significant relationship between crop yield and NDVI residual during the pre-silking period. The latter was deemed as the indicator to reflect crop stress during the early growing stage, as well as the early in-season yield prediction. They concluded that dry weather may hamper potential crop growth, while the oversupply of rainfall at the end of the growing season does not contribute to crop growth. However, the above-normal precipitation earlier in the growing season does reduce the risk of yield loss at the watershed scale, indicating a potential agriculture management strategy for adapting to the projected increase in periods of extreme droughs. Exploring a new approach to estimate the time lag between anomalies in precipitation and vegetation activity, [PERSON] et al. [9] investigated the spatial distribution of the differences of the time lags between a wet year and a dry year in a drought-prone region of China. They used the phase spectrum of the cross-spectral density to measure the time lag in the response of vegetation activity to precipitation. This cross-spectral analysis approach can simultaneously determine the strength of the relationship and the phase lag for all significant periodic components. These results provide useful insights on vegetation response to precipitation as well as additional information for areas which are more susceptible to drought, particularly considering that drought usually originates from a lack of sufficient precipitation. The developed novel method can be applied to estimate the time lag between time series of satellite observations capturing paired forcing and response signals, in addition to providing useful information to improve early drought detection. Using SPOT-5 Take-5 (as a proxy of Sentinel-2), [PERSON] et al.