# 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  
Satellite remote sensing (SRS) has provided major advances in understanding the water, energy, and carbon cycles, and through this, the understanding of the climate system and its change. In particular, SRS has contributed significantly to the generation of Essential Climate Variables (ECVs) for addressing the closure of the aforementioned three climate cycles. Closing the cycles allows us to identify gaps and where the SRS ECVs contribute to the fundamental understanding of the natural processes. This subsequently supports the improvement of forecasting climate change impacts. On the other hand, closing the cycles through SRS observation remains an outstanding scientific issue that requires high quality Climate Data Records (CDRs) of ECVs [21; 22]. It is well recognized that satellite data often contain uncertainties caused by biases in sensors and retrieval algorithms [23], as well as inconsistencies between consecutive satellite missions with the same sensors, or between different sensors aiming at the same ECVs. [PERSON] et al. [24] evaluated the performance of three satellite precipitation products over Africa (i.e., TRMM-TMPA, PERSIANN, and CMORPH), using the GPCP-1 dd as a reference dataset for years 2001 to 2013. They identified different types of errors for each season as a function of spatial classification (latitudinal bands, climatic zones, and topography) as well as its relationship with the Intertropical Convergence Zone (ITCZ) and the East African Monsoon. A bias correction based on these identified errors were implemented to correct the satellite precipitation products. The bias-corrected precipitation products can follow the position of the ITCZ oscillating seasonally over the equator, illustrating that the performance of satellite precipitation products can be improved substantially after taking into account different types of errors. To address the challenge of deriving soil moisture (SM) data with a complete coverage of the Tibetan Plateau (TP) for an extended period of time for climate change studies, [PERSON] et al. [25] combined satellite-observed, model-simulated, and in situ measured SM over the Tibetan Plateau. The in situ SM networks, combined with the classification of climate zones over the TP, were used to produce the in situ measured soil moisture climatology at the plateau scale, which was subsequently used to scale the model-simulated SM. The bias-corrected, model-simulated SM were then used to scale the satellite SM products. The three climatology-scaled SM products were blended objectively, by applying the triple collocation and least squares method. The final blended SM can replicate the SM dynamics across different climatic zones, from sub-humid regions to semi-arid and arid regions over the TP. This approach indicates the need to constrain model-simulated SM estimates with in situ measurements before their further application in scaling climatology of satellite SM products. [PERSON] and [PERSON] [26] used the theory of Apparent Thermal Inertia (ATI), under bare surface or sparse vegetation-covered land surface, to downscale FengYun-3B (FY-3B) soil moisture product from 25-km to 1-km spatial resolution. This downscaling was achieved by establishing the relationship between soil moisture and the ATI, the coefficients of which were obtained directly from 25-km FY-3B SM product and the ATI derived from MODIS data. The downscaled SM showed promising results when compared with the in situ SM measurements. They concluded that the accuracy of the FY-3B 25-km SM product determines the reliability of the coefficients of regression between SM and ATI, and that the proposed method is more applicable to areas with NDVI values smaller than 0.4. This limitation is mainly due to the fact that MODIS can only sense vegetation conditions, which reflect more the root zone soil water content than the surface soil moisture. The use of high resolution SAR data was further suggested to downscale SM over densely vegetated areas.