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This book and the individual contributions contained in it are protected under copyright by the Publisher (other than as may be noted herein). Notices Knowledge and best practice in this field are constantly changing. As new research and experience broaden our understanding, changes in research methods, professional practices, or medical treatment may become necessary. Practitioners and researchers must always rely on their own experience and knowledge in evaluating and using any information, methods, compounds, or experiments described herein. In using such information or methods they should be mindful of their own safety and the safety of others, including parties for whom they have a professional responsibility.

The present study explores the possibility of using Landsat imagery for mapping tropical forest types with relevance to forest ecosystem services. The central part in the classification process is the use of multi-date image data and pre-classification image smoothing. The study argues that multi-date imagery contains information on phenological and canopy structural properties, and shows how the use of multi-date imagery has a significant impact on classification accuracy. Furthermore, the study shows the value of applying small kernel smoothing filters to reduce in-class spectral variability and enhance between-class spectral separability. Making use of these approaches and a maximum likelihood algorithm, six tropical forest types were classified with an overall accuracy of 90.94%, and with individual forest classes mapped with accuracies above 75.19% (user's accuracy) and above 74.17% (producer's accuracy).

Abstract. Sentinel-3 is the first satellite altimetry mission to operate both in synthetic aperture radar (SAR) mode and in open-loop tracking mode nearly globally. Both features are expected to improve the ability of the altimeters to observe inland water bodies. Additionally, the two-satellite constellation offers a unique compromise between spatial and temporal resolution with over 65 000 potential water targets sensed globally. In this study, we evaluate the possibility of extracting river water surface elevation (WSE) at catchment level from Sentinel-3A and Sentinel-3B radar altimetry using Level-1b and Level-2 data from two public platforms: the Copernicus Open Access Hub (SciHub) and Grid Processing on Demand (GPOD). The objectives of the study are to demonstrate that by using publicly available processing platforms, such databases can be created to suit specific study areas for any catchment and with a wide range of applications in hydrology. We select the Zambezi River as a study area. In the Zambezi basin, 156 virtual stations (VSs) contain useful WSE information in both datasets. The root-mean-square deviation (RMSD) is between 2.9 and 31.3 cm at six VSs, where in situ data are available, and all VSs reflect the observed WSE climatology throughout the basin. Some VSs are exclusive to either the SciHub or GPOD datasets, highlighting the value of considering multiple processing options beyond global altimetry-based WSE databases. In particular, we show that the processing options available on GPOD affect the number of useful VSs; specifically, extending the size of the receiving window considerably improved data at 13 Sentinel-3 VSs. This was largely related to the implementation of GPOD parameters. While correct on-board elevation information is crucial, the postprocessing options must be adapted to handle the steep changes in the receiving window position. Finally, we extract Sentinel-3 observations over key wetlands in the Zambezi basin. We show that clear seasonal patterns are captured in the Sentinel-3 WSE, reflecting flooding events in the floodplains. These results highlight the benefit of the high spatiotemporal resolution of the dual-satellite constellation.

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Abstract:The Water Observation and Information System (WOIS) is an open source software tool for monitoring, assessing and inventorying water resources in a cost-effective manner using Earth Observation (EO) data. The WOIS has been developed by, among others, the authors of this paper under the TIGER-NET project, which is a major component of the TIGER initiative of the European Space Agency (ESA) and whose main goal is to support the African Earth Observation Capacity for Water Resource Monitoring. TIGER-NET aims to support the satellite-based assessment and monitoring of water resources from watershed to cross-border basin levels through the provision of a free and powerful software package, with associated capacity building, to African authorities. More than 28 EO data processing solutions for water resource management tasks have been developed, in correspondence with the requirements of the participating key African water authorities, and demonstrated with dedicated case studies utilizing the software in OPEN ACCESSRemote Sens. 2014, 6 7820 operational scenarios. They cover a wide range of themes and information products, including basin-wide characterization of land and water resources, lake water quality monitoring, hydrological modeling and flood forecasting and mapping. For each monitoring task, step-by-step workflows were developed, which can either be adjusted by the user or largely automatized to feed into existing data streams and reporting schemes. The WOIS enables African water authorities to fully exploit the increasing EO capacity offered by current and upcoming generations of satellites, including the Sentinel missions.

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