Inductively mapping expert-derived soil-landscape units within dambo wetland catenae using multispectral and topographic data." Geoderma 150

Abstract Constructing a cost-effective and detailed digital soil map of Africa will require the extensive utilization of both legacy soil data and legacy soil-landscape knowledge -which in Africa is primarily available from reconnaissance-scale catena or association maps and related studies. We evaluated a hybrid approach for disaggregating reconnaissance scale soil maps: rapid and inexpensive delineation of representative soil-landscape units in the field using expert knowledge, followed by the use of inductive, empirical, and correlative modeling techniques to map these landscape units. Our 2,214 km 2 study area, located in central Uganda, consisted predominantly of catenae that terminate in seasonal valley floor wetlands called dambos-a type of landscape that can be found throughout the African continent. For model training and validation, we identified four landscape classes in the field using published expert knowledge: well-drained uplands (red soils); sloping dambo wetland margins (gradient > 2%), frequently inundated dambo bottoms (hummocky microtopography), and flat dambo floors (default). Using binary decision trees (BDT) with multispectral and topographic remote sensing covariates, we created a 20 m resolution map of these four classes. Multispectral inputs included reflectance values, vegetation indices, and spectral mixture modeling fractions from Systèm

Содержание. Секция 07 - Оптика и спектроскопия

The remarkable complexity of soil and its importance to a wide range of ecosystem services presents major challenges to the modeling of soil processes. Although major progress in soil models has occurred in the last decades, models of soil processes remain disjointed between disciplines or ecosystem services, with considerable uncertainty remaining in the quality of predictions and several challenges that remain yet to be addressed. First, there is a need to improve exchange of knowledge and experience among the different disciplines in soil science and to reach out to other Earth science communities. Second, the community needs to develop a new generation of soil models based on a systemic approach comprising relevant physical, chemical, and biological processes to address critical knowledge gaps in our understanding of soil processes and their interactions. Overcoming these challenges will facilitate exchanges between soil modeling and climate, plant, and social science modeling communities. It will allow us to contribute to preserve and improve our assessment of ecosystem services and advance our understanding of climate-change feedback mechanisms, among others, thereby facilitating and strengthening communication among scientific disciplines and society. We review the role of modeling soil processes in quantifying key soil processes that shape ecosystem services, with a focus on provisioning and regulating services. We then identify key challenges in modeling soil processes, including the systematic incorporation of heterogeneity and uncertainty, the integration of data and models, and strategies for effective integration of knowledge on physical, chemical, and biological soil processes. We discuss how the soil modeling community could best interface with modern modeling activities in other disciplines, such as climate, ecology, and plant research, and how to weave novel observation and measurement techniques into soil models. We propose the establishment of an international soil modeling consortium to coherently advance soil modeling activities and foster communication with other Earth science disciplines. Such a consortium should promote soil modeling platforms and data repository for model development, calibration and intercomparison essential for addressing contemporary challenges