Abstract
It is widely acknowledged that the global stock of soil and environmental resources are diminishing and under threat. This issue stems from current and historical unsustainable management practices, leading to degraded landscapes, which is further compounded by increased pressures upon them from ever-increasing anthropogenic activities. To curb the trajectory toward a collapse of our ecosystems, systematic ways are needed to assess the condition of our natural resources, how much they might have changed, and to what extent this might impact on the life sustaining functions we derive from our environment and the extent of our food producing systems. Some solutions to these issues come in the form of measurement, mapping and monitoring technology, which facilitates powerful ways in which to be informed about and to understand and assess the condition of our landscapes so that they can be managed strategically or simply improved. This Special Issue showcases from several locations across the globe, detailed examples of what is achievable at the convergence of big data brought about by remote and proximal sensing platforms, advanced statistical modelling and computing infrastructure to understand and monitor our ecosystems better. These utilities not only provide high-resolution abilities to map the extent and changes to our food producing systems, they also have yielded new ways to determine land-use and climate effects on the fate of soil carbon across living generations and to identify hydrological risk strategies in otherwise data-poor urban environments. Leveraging the availability of remote sensing data is telling, but the papers in this Special Issue also highlight the sophistication of modelling capabilities to deliver not only highly detailed maps of temporal dynamic soil phenomena but ways to draw new inferences from sparse and disparate model input data. The challenges of restoring our ecosystems are immense and sobering. However, we are well equipped and capable of confronting these pervasive issues in objective and data-informed ways that have previously never been possible.
Highlights
Anthropogenic activities on the earth system to fulfill increasing demands for food and clean water for the world’s population have accelerated changes in the soil and ecosystem
Field observation data coupled with earth observing remote and proximal sensors, provide exciting new opportunities to extract new knowledge. This special issue looks for application of rich remote sensing time series data in combination with statistical models that enable space and time mapping of soil and the environment
Both [2,3] exploit the use of time series remote sensing data compiled and delivered through such platforms as the Google Earth Engine (GEE)
Summary
Anthropogenic activities on the earth system to fulfill increasing demands for food and clean water for the world’s population have accelerated changes in the soil and ecosystem. Field observation data coupled with earth observing remote and proximal sensors, provide exciting new opportunities to extract new knowledge This special issue looks for application of rich remote sensing time series data in combination with statistical models that enable space and time mapping of soil and the environment. We received, reviewed, and published 7 articles from around the world that provide new insight and approaches to leverage big data and advanced statistical modelling algorithms to better understand and monitor our ecosystems These studies demonstrate capabilities in high-resolution mapping of the extent and phenology of high value cropping enterprises. They have yielded ways to determine land-use and climate effects on the fate of soil carbon stocks spatially and across human generations, and ways to identify hydrological risk strategies in otherwise data-poor urban environments. Papers in this Special Issue deploy sophisticated modelling workflows to deliver highly detailed maps of temporal dynamic soil phenomena but ways to draw new inferences from sparse and disparate model input data that will enable more informed and nuanced management of our degraded and threaten ecosystems
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