TY - JOUR A1 - Koehler, Jonas A1 - Kuenzer, Claudia T1 - Forecasting spatio-temporal dynamics on the land surface using Earth Observation data — a review JF - Remote Sensing N2 - Reliable forecasts on the impacts of global change on the land surface are vital to inform the actions of policy and decision makers to mitigate consequences and secure livelihoods. Geospatial Earth Observation (EO) data from remote sensing satellites has been collected continuously for 40 years and has the potential to facilitate the spatio-temporal forecasting of land surface dynamics. In this review we compiled 143 papers on EO-based forecasting of all aspects of the land surface published in 16 high-ranking remote sensing journals within the past decade. We analyzed the literature regarding research focus, the spatial scope of the study, the forecasting method applied, as well as the temporal and technical properties of the input data. We categorized the identified forecasting methods according to their temporal forecasting mechanism and the type of input data. Time-lagged regressions which are predominantly used for crop yield forecasting and approaches based on Markov Chains for future land use and land cover simulation are the most established methods. The use of external climate projections allows the forecasting of numerical land surface parameters up to one hundred years into the future, while auto-regressive time series modeling can account for intra-annual variances. Machine learning methods have been increasingly used in all categories and multivariate modeling that integrates multiple data sources appears to be more popular than univariate auto-regressive modeling despite the availability of continuously expanding time series data. Regardless of the method, reliable EO-based forecasting requires high-level remote sensing data products and the resulting computational demand appears to be the main reason that most forecasts are conducted only on a local scale. In the upcoming years, however, we expect this to change with further advances in the field of machine learning, the publication of new global datasets, and the further establishment of cloud computing for data processing. KW - forecast KW - Earth Observation KW - land surface KW - land use KW - land cover KW - time series KW - machine learning KW - Markov chains KW - modeling Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-216285 SN - 2072-4292 VL - 12 IS - 21 ER - TY - JOUR A1 - Uereyen, Soner A1 - Kuenzer, Claudia T1 - A review of earth observation-based analyses for major river basins JF - Remote Sensing N2 - Regardless of political boundaries, river basins are a functional unit of the Earth’s land surface and provide an abundance of resources for the environment and humans. They supply livelihoods supported by the typical characteristics of large river basins, such as the provision of freshwater, irrigation water, and transport opportunities. At the same time, they are impacted i.e., by human-induced environmental changes, boundary conflicts, and upstream–downstream inequalities. In the framework of water resource management, monitoring of river basins is therefore of high importance, in particular for researchers, stake-holders and decision-makers. However, land surface and surface water properties of many major river basins remain largely unmonitored at basin scale. Several inventories exist, yet consistent spatial databases describing the status of major river basins at global scale are lacking. Here, Earth observation (EO) is a potential source of spatial information providing large-scale data on the status of land surface properties. This review provides a comprehensive overview of existing research articles analyzing major river basins primarily using EO. Furthermore, this review proposes to exploit EO data together with relevant open global-scale geodata to establish a database and to enable consistent spatial analyses and evaluate past and current states of major river basins. KW - major river basins KW - catchment KW - watershed KW - Earth observation KW - remote sensing KW - spatial analyses KW - land surface KW - surface water Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-193849 SN - 2072-4292 VL - 11 IS - 24 ER -