# Using Remote Sensing Techniques to Improve Hydrological Predictions in a Rapidly Changing World
## 3 Editorial Summary and Comments  
There are various ways that remote sensing techniques are used to improve hydrological simulations and predictions, enhancing our collective efforts. The efforts can be summarized into, but not limited to, the following categories: (1) Detecting hydrological and other related changes using state-of-the-art remote sensing techniques; (2) mapping eco-hydrological and hydrological processes and their driving factors using large samples and high-resolution datasets; (3) understanding hydrological processes in a rapidly changing world using hydrological modelling together with high-to-moderate-resolution (several meters to hundred meters) remote sensing data; (4) improving hydrological prediction skills by modifying hydrological model structures to incorporate remote sensing data and using various model calibrations against remote sensing data; (5) developing hydrological modelling frameworks using advanced cloud cluster computation techniques and tools, such as the Google Earth Engine; (6) using remote sensing data together with data assimilation or/and machine learning techniques to improve predictions of various hydrological variables and hydrological signatures; and (7) using remote sensing techniques for water-related studies, such as on the water-food-energy security nexus. Although the ten papers published in this Special Issue only cover parts of the summarized categories, we believe that continued efforts in using remote sensing techniques in hydrology definitely promote the development of hydrology, particularly in the fields of fusion of hydrological model and remote sensing and ground observations that cover all of the categories 1-7 (Figure 1).  
In fact, the first guest editor and his team (we) had put lots of efforts in these categories and shared some researches below. For instance, we used the Google Earth Engine platform to develop a carbon-water coupled model (PML-V2) for estimating the actual evapotranspiration and gross primary production products across the global land surface with 500 m and 8-day resolution for the period of 2002 to 2020 [16]. This is a particular example followed in the categories of 2, 3, and 5. Another example of category 4 is to develop state-of-the-art model-data fusion techniques for predicting runoff in ungauged catchments. We used remotely sensed ET data only to calibrate hydrological model parameters. Since it does not require observed streamflow data for model calibration, it has the great potential runoff prediction in poorly gauged or ungauged regions. We demonstrate that this approach is very useful in Australia [17] and China [7] after bias corrections. Last but not least, we modify the rainfall-runoff modelling structure for better incorporating remote sensing data. The reasoning is that traditional rainfall-runoff models do not have a structure to simulate the impact of Land Use and Land Cover Change (LUCC), and are not reliable to simulate hydrological processes with rapid LUCC. We modified traditional rainfall-runoff models by changing their submodule for describing soil moisture and actual evapotranspiration processes [18; 19]. The modified rainfall-runoff models improve hydrological simulations noticeably in the catchments experiencing rapid land cover changes [18].