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Abstract: Regularly updated land cover information at continental or national scales is a requirement for various land management applications as well as biogeochemical and climate modeling exercises. However, monitoring or updating of map products with sufficient spatial detail is currently not widely practiced due to inadequate time-series coverage for most regions of the Earth. Classifications of coarser spatial resolution data can be automatically generated on an annual or finer time scale. However, discrete land cover classifications of such data cannot sufficiently quantify land surface heterogeneity or change. This study presents a methodology for continuous and discrete land cover mapping using moderate spatial resolution time series data sets. The method automatically selects sample data from higher spatial resolution maps and generates multiple decision trees. The leaves of decision trees are interpreted considering the sample distribution of all classes yielding class membership maps, which can be used as estimates for the diversity of classes in a coarse resolution cell. Results are demonstrated for the heterogeneous, small-patch landscape of Germany and the bio-climatically varying landscape of South Africa. Results have overall classification accuracies of 80%. A sensitivity analysis of individual modules of the classification process indicates the importance of appropriately chosen features, sample data balanced among classes, and an appropriate method to combine individual classifications. The comparison of classification results over several years not only indicates the method''s consistency, but also its potential to detect land cover changes. [Copyright &y& Elsevier]

Abstract: Over the past decade, soil moisture products derived from microwave remote sensing data have gained increasing attention for the monitoring of agricultural, hydrological and climate processes. ERS-1 and ERS-2 scatterometer-derived time series of data available since 1992 enable the user to analyze the change of soil moisture patterns over time and to monitor events deviating from the normal. In this paper, ERS scatterometer-derived soil moisture products are compared with known occurrences of heavy rain and drought related to the strong El Niño (EN) event of 1997/1998. We investigated globally if the typical EN-related short term climate changes can be depicted in soil moisture data. EN consequences and their manifestation in the data are presented for the Americas, Africa and Asia. Furthermore, the weather situation during the EN event in China is compared to the scatterometer-derived soil moisture data set in more detail. It could be observed that most EN-related climate variations on the different continents are clearly reflected by remote sensing soil moisture data. Heavy rain and drought conditions reported in daily media, science papers, and forecasts by the can be observed in anomaly data sets showing the deviation of soil moisture from the average. This also applies for the strong EN-related flood, which hit China in 1998 and following La Niña-related droughts. These analyses thus serve to demonstrate that information relevant to climatology is contained in the satellite scatterometer-derived soil moisture product, as well as in anomaly time series, derived from the existing soil moisture time series. Our procedure thus consisted of soil moisture time series calculation, soil moisture anomaly calculation, creation of time series and anomaly maps and the comparison of anomalies with meteorological data on three continents for the time period of outstanding El Nino years. The soil moisture data of TU Wien is available via the internet free of charge. [Copyright &y& Elsevier]