About this Research Topic
The Hydrological cycle, also known as water cycle, involves the continuous circulation of water in the Earth-atmosphere system. Accurate measurements of various hydrological cycle components (e.g. precipitation, evapotranspiration, soil moisture and water storage changes) are essential for understanding the hydrological processes and for further for sustainable water resources management. Hydrological cycle components are characterized by significant variability both in time and space. The conventional in-situ measurements from gauge stations are generally considered to be the most accurate measurements, however, scientific communities are often encountered challenged with the limited availability and capability of in-situ measurements. Specifically, the network of gauge stations is often sparse and overall the number of stations is still on decreasing trend over the globe. The point-based feature makes gauge measurements insufficient to capture the spatial and temporal variability of hydrological cycle components. Therefore, alternative or complementary data sources should be investigated to fill the data gaps.
Satellite remote sensing has been shown great capability of estimating various hydrological cycle components at different temporal and spatial scales. Various communities have recognized the importance of satellite remote sensing, but they have been stressing the need for improvements in accuracy and particularly the spatial resolution because the spatial resolution of remotely sensed products is still often too coarse for many applications. To this regard, a specific topic “spatial downscaling” has emerged; over last decades, considerable efforts have been made to develop various spatial downscaling algorithms to improve the spatial resolution of remotely sensed estimates.
Machine learning and geostatistical methods have been innovatively utilized and demonstrated promising to advance the spatial downscaling in satellite remote sensing community. Together with the algorithms development in spatial downscaling, further pertinent research question arise: how to accurately evaluate the skill of downscaled remote sensing products? In general, there are three ways to answer this question, (1) by direct comparison of targeted components with in-situ measurements or other high-quality reference datasets, (2) by indirect evaluation through applications; for example, applying downscaled estimates of precipitation to force hydrological models and then performing evaluation in terms of simulated streamflow instead of direct comparison of targeted precipitation, and (3) by statistical error estimator, e.g., triple collocation analysis. However, all aforementioned approaches contain known limitations and, hence, there is a clear need for the development of novel procedures for fair evaluation particularly considering the limitations (e.g. representativeness and availability) of ground measurements form gauge stations.
The aim of this Research Topic is to present and discuss novel procedures in spatial downscaling of remotely sensed hydrological cycle components with emphasis on algorithms development, innovative evaluation and application of downscaled estimates. The targeted hydrological cycle components include precipitation, evapotranspiration, soil moisture and water storage change. All suitable article types, particularly Original Research, Review, Methods, and Data Report, are welcome in this Research Topic.
Satellite remote sensing has been shown great capability of estimating various hydrological cycle components at different temporal and spatial scales. Various communities have recognized the importance of satellite remote sensing, but they have been stressing the need for improvements in accuracy and particularly the spatial resolution because the spatial resolution of remotely sensed products is still often too coarse for many applications. To this regard, a specific topic “spatial downscaling” has emerged; over last decades, considerable efforts have been made to develop various spatial downscaling algorithms to improve the spatial resolution of remotely sensed estimates.
Machine learning and geostatistical methods have been innovatively utilized and demonstrated promising to advance the spatial downscaling in satellite remote sensing community. Together with the algorithms development in spatial downscaling, further pertinent research question arise: how to accurately evaluate the skill of downscaled remote sensing products? In general, there are three ways to answer this question, (1) by direct comparison of targeted components with in-situ measurements or other high-quality reference datasets, (2) by indirect evaluation through applications; for example, applying downscaled estimates of precipitation to force hydrological models and then performing evaluation in terms of simulated streamflow instead of direct comparison of targeted precipitation, and (3) by statistical error estimator, e.g., triple collocation analysis. However, all aforementioned approaches contain known limitations and, hence, there is a clear need for the development of novel procedures for fair evaluation particularly considering the limitations (e.g. representativeness and availability) of ground measurements form gauge stations.
The aim of this Research Topic is to present and discuss novel procedures in spatial downscaling of remotely sensed hydrological cycle components with emphasis on algorithms development, innovative evaluation and application of downscaled estimates. The targeted hydrological cycle components include precipitation, evapotranspiration, soil moisture and water storage change. All suitable article types, particularly Original Research, Review, Methods, and Data Report, are welcome in this Research Topic.
Keywords: hydrological cycle, remote sensing, spatial downscaling, algorithm development, geostatistics
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