Energy and Water Cycles in the Third Pole

As the most prominent and complicated terrain on the globe, the Tibetan Plateau (TP) is often called the “Roof of the World”, “Third Pole” or “Asian Water Tower”. The energy and water cycles in the Third Pole have great impacts on the atmospheric circulation, Asian monsoon system and global climate change. On the other hand, the TP and the surrounding higher elevation area are also experiencing evident and rapid environmental changes under the background of global warming. As the headwater area of major rivers in Asia, the TP’s environmental changes—such as glacial retreat, snow melting, lake expanding and permafrost degradation—pose potential long-term threats to water resources of the local and surrounding regions. To promote quantitative understanding of energy and water cycles of the TP, several field campaigns, including GAME/Tibet, CAMP/Tibet and TORP, have been carried out. A large amount of data have been collected to gain a better understanding of the atmospheric boundary layer structure, turbulent heat fluxes and their coupling with atmospheric circulation and hydrological processes. The focus of this reprint is to present recent advances in quantifying land–atmosphere interactions, the water cycle and its components, energy balance components, climate change and hydrological feedbacks by in situ measurements, remote sensing or numerical modelling approaches in the “Third Pole” region

Detection of vegetation drying signals using diurnal variation of land surface temperature: Application to the 2018 East Asia heatwave

Satellite-based vegetation monitoring provides important insights regarding spatiotemporal variations in vegetation growth from a regional to continental scale. Most current vegetation monitoring methodologies rely on spectral vegetation indices (VIs) observed by polar-orbiting satellites, which provide one or a few observations per day. This study proposes a new methodology based on diurnal changes in land surface temperatures (LSTs) using Japan's geostationary satellite, Himawari-8/Advanced Himawari Imager (AHI). AHI thermal infrared observation provides LSTs at 10-min frequencies and ∼ 2 km spatial resolution. The DTC parameters that summarize the diurnal cycle waveform were obtained by fitting a diurnal temperature cycle (DTC) model to the time-series LST information for each day. To clarify the applicability of DTC parameters in detecting vegetation drying under humid climates, DTC parameters from in situ LSTs observed at vegetation sites, as well as those from Himawari-8 LSTs, were evaluated for East Asia. Utilizing the record-breaking heat wave that occurred in East Asia in 2018 as a case study, the anomalies of DTC parameters from the Himawari-8 LSTs were compared with the drying signals indicated by VIs, latent heat fluxes (LE), and surface soil moisture (SM). The results of site-based and satellite-based analyses revealed that DTR (diurnal temperature range) correlates with the evaporative fraction (EF) and SM, whereas Tmax (daily maximum LST) correlates with LE and VIs. Regarding other temperature-related parameters, T0 (LST around sunrise), Ta (temperature rise during daytime), and δT (temperature fall during nighttime) are unstable in quantification by DTC model. Moreover, time-related parameters, such as tm (time reaching Tmax), are more sensitive to topographic slope and geometric conditions than surface thermal properties at humid sites in East Asia, although they correlate with EF and SM at a semi-arid site in Australia. Additionally, the spatial distribution of the DTR anomaly during the 2018 heat wave corresponds with the drying signals indicated as negative SM anomalies. Regions with large positive anomalies in Tmax and DTR correspond to area with visible damage to vegetation, as indicated by negative VI anomalies. Hence, combined Tmax and DTR potentially detects vegetation drying indetectable by VIs, thereby providing earlier and more detailed vegetation monitoring in both humid and semi-arid climates

Remote Sensing of Land Surface Phenology

Land surface phenology (LSP) uses remote sensing to monitor seasonal dynamics in vegetated land surfaces and retrieve phenological metrics (transition dates, rate of change, annual integrals, etc.). LSP has developed rapidly in the last few decades. Both regional and global LSP products have been routinely generated and play prominent roles in modeling crop yield, ecological surveillance, identifying invasive species, modeling the terrestrial biosphere, and assessing impacts on urban and natural ecosystems. Recent advances in field and spaceborne sensor technologies, as well as data fusion techniques, have enabled novel LSP retrieval algorithms that refine retrievals at even higher spatiotemporal resolutions, providing new insights into ecosystem dynamics. Meanwhile, rigorous assessment of the uncertainties in LSP retrievals is ongoing, and efforts to reduce these uncertainties represent an active research area. Open source software and hardware are in development, and have greatly facilitated the use of LSP metrics by scientists outside the remote sensing community. This reprint covers the latest developments in sensor technologies, LSP retrieval algorithms and validation strategies, and the use of LSP products in a variety of fields. It aims to summarize the ongoing diverse LSP developments and boost discussions on future research prospects

Earth Observations for Addressing Global Challenges

"Earth Observations for Addressing Global Challenges" presents the results of cutting-edge research related to innovative techniques and approaches based on satellite remote sensing data, the acquisition of earth observations, and their applications in the contemporary practice of sustainable development. Addressing the urgent tasks of adaptation to climate change is one of the biggest global challenges for humanity. As His Excellency António Guterres, Secretary-General of the United Nations, said, "Climate change is the defining issue of our time—and we are at a defining moment. We face a direct existential threat." For many years, scientists from around the world have been conducting research on earth observations collecting vital data about the state of the earth environment. Evidence of the rapidly changing climate is alarming: according to the World Meteorological Organization, the past two decades included 18 of the warmest years since 1850, when records began. Thus, Group on Earth Observations (GEO) has launched initiatives across multiple societal benefit areas (agriculture, biodiversity, climate, disasters, ecosystems, energy, health, water, and weather), such as the Global Forest Observations Initiative, the GEO Carbon and GHG Initiative, the GEO Biodiversity Observation Network, and the GEO Blue Planet, among others. The results of research that addressed strategic priorities of these important initiatives are presented in the monograph

Climate Change and Environmental Sustainability- Volume 5

This volume of Climate Change and Environmental Sustainability covers topics on greenhouse gas emissions, climatic impacts, climate models and prediction, and analytical methods. Issues related to two major greenhouse gas emissions, namely of carbon dioxide and methane, particularly in wetlands and agriculture sector, and radiative energy flux variations along with cloudiness are explored in this volume. Further, climate change impacts such as rainfall, heavy lake-effect snowfall, extreme temperature, impacts on grassland phenology, impacts on wind and wave energy, and heat island effects are explored. A major focus of this volume is on climate models that are of significance to projection and to visualise future climate pathways and possible impacts and vulnerabilities. Such models are widely used by scientists and for the generation of mitigation and adaptation scenarios. However, dealing with uncertainties has always been a critical issue in climate modelling. Therefore, methods are explored for improving climate projection accuracy through addressing the stochastic properties of the distributions of climate variables, addressing variational problems with unknown weights, and improving grid resolution in climatic models. Results reported in this book are conducive to a better understanding of global warming mechanisms, climate-induced impacts, and forecasting models. We expect the book to benefit decision makers, practitioners, and researchers in different fields and contribute to climate change adaptation and mitigation

Data-driven model development in environmental geography - Methodological advancements and scientific applications

Die Erfassung räumlich kontinuierlicher Daten und raum-zeitlicher Dynamiken ist ein Forschungsschwerpunkt der Umweltgeographie. Zu diesem Ziel sind Modellierungsmethoden erforderlich, die es ermöglichen, aus limitierten Felddaten raum-zeitliche Aussagen abzuleiten. Die Komplexität von Umweltsystemen erfordert dabei die Verwendung von Modellierungsstrategien, die es erlauben, beliebige Zusammenhänge zwischen einer Vielzahl potentieller Prädiktoren zu berücksichtigen. Diese Anforderung verlangt nach einem Paradigmenwechsel von der parametrischen hin zu einer nicht-parametrischen, datengetriebenen Modellentwicklung, was zusätzlich durch die zunehmende Verfügbarkeit von Geodaten verstärkt wird. In diesem Zusammenhang haben sich maschinelle Lernverfahren als ein wichtiges Werkzeug erwiesen, um Muster in nicht-linearen und komplexen Systemen zu erfassen. Durch die wachsende Popularität maschineller Lernverfahren in wissenschaftlichen Zeitschriften und die Entwicklung komfortabler Softwarepakete wird zunehmend der Fehleindruck einer einfachen Anwendbarkeit erzeugt. Dem gegenüber steht jedoch eine Komplexität, die im Detail nur durch eine umfassende Methodenkompetenz kontrolliert werden kann. Diese Problematik gilt insbesondere für Geodaten, die besondere Merkmale wie vor allem räumliche Abhängigkeit aufweisen, womit sie sich von "gewöhnlichen" Daten abheben, was jedoch in maschinellen Lernanwendungen bisher weitestgehend ignoriert wird. Die vorliegende Arbeit beschäftigt sich mit dem Potenzial und der Sensitivität des maschinellen Lernens in der Umweltgeographie. In diesem Zusammenhang wurde eine Reihe von maschinellen Lernanwendungen in einem breiten Spektrum der Umweltgeographie veröffentlicht. Die einzelnen Beiträge stehen unter der übergeordneten Hypothese, dass datengetriebene Modellierungsstrategien nur dann zu einem Informationsgewinn und zu robusten raum-zeitlichen Ergebnissen führen, wenn die Merkmale von geographischen Daten berücksichtigt werden. Neben diesem übergeordneten methodischen Fokus zielt jede Anwendung darauf ab, durch adäquat angewandte Methoden neue fachliche Erkenntnisse in ihrem jeweiligen Forschungsgebiet zu liefern. Im Rahmen der Arbeit wurde eine Vielzahl relevanter Umweltmonitoring-Produkte entwickelt. Die Ergebnisse verdeutlichen, dass sowohl hohe fachwissenschaftliche als auch methodische Kenntnisse unverzichtbar sind, um den Bereich der datengetriebenen Umweltgeographie voranzutreiben. Die Arbeit demonstriert erstmals die Relevanz räumlicher Überfittung in geographischen Lernanwendungen und legt ihre Auswirkungen auf die Modellergebnisse dar. Um diesem Problem entgegenzuwirken, wird eine neue, an Geodaten angepasste Methode zur Modellentwicklung entwickelt, wodurch deutlich verbesserte Ergebnisse erzielt werden können. Diese Arbeit ist abschließend als Appell zu verstehen, über die Standardanwendungen der maschinellen Lernverfahren hinauszudenken, da sie beweist, dass die Anwendung von Standardverfahren auf Geodaten zu starker Überfittung und Fehlinterpretation der Ergebnisse führt. Erst wenn Eigenschaften von geographischen Daten berücksichtigt werden, bietet das maschinelle Lernen ein leistungsstarkes Werkzeug, um wissenschaftlich verlässliche Ergebnisse für die Umweltgeographie zu liefern