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

First Provisional Land Surface Reflectance Product from Geostationary Satellite Himawari-8 AHI

A provisional surface reflectance (SR) product from the Advanced Himawari Imager (AHI) on-board the new generation geostationary satellite (Himawari-8) covering the period between July 2015 and December 2018 is made available to the scientific community. The Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm is used in conjunction with time series Himawari-8 AHI observations to generate 1-km gridded and tiled land SR every 10 minutes during day time. This Himawari-8 AHI SR product includes retrieved atmospheric properties (e.g., aerosol optical depth at 0.47µm and 0.51µm), spectral surface reflectance (AHI bands 1–6), parameters of the RTLS BRDF model, and quality assurance flags. Product evaluation shows that Himawari-8 AHI data on average yielded 35% more cloud-free, valid pixels in a single day when compared to available data from the low earth orbit (LEO) satellites Terra/Aqua with MODIS sensor. Comparisons of Himawari-8 AHI SR against corresponding MODIS SR products (MCD19A1) over a variety of land cover types with the similar viewing geometry show high consistency between them, with correlation coefficients (r) being 0.94 and 0.99 for red and NIR bands, respectively. The high-frequency geostationary data are expected to facilitate studies of ecosystems on daily to diurnal time scales, complementing observations from networks such as the FLUXNET

Remote sensing of the terrestrial carbon cycle: A review of advances over 50 years

Highlights We review 50 years of history and advances in remote sensing of C fluxes and stocks We present an overview of terrestrial C cycle, remote sensing, and key milestones We review remote sensing platforms/sensors, data, methods, findings, and challenges We also discuss the uncertainty and validation of the C flux and stock estimates A forward-looking perspective and insights for future research are provided Abstract Quantifying ecosystem carbon fluxes and stocks is essential for better understanding the global carbon cycle and improving projections of the carbon-climate feedbacks. Remote sensing has played a vital role in this endeavor during the last five decades by quantifying carbon fluxes and stocks. The availability of satellite observations of the land surface since the 1970s, particularly the early 1980s, has made it feasible to quantify ecosystem carbon fluxes and stocks at regional to global scales. Here we provide a review of the advances in remote sensing of the terrestrial carbon cycle from the early 1970s to present. First, we present an overview of the terrestrial carbon cycle and remote sensing of carbon fluxes and stocks. Remote sensing data acquired in a broad wavelength range (visible, infrared, and microwave) of the electromagnetic spectrum have been used to estimate carbon fluxes and/or stocks. Second, we provide a historical overview of the key milestones in remote sensing of the terrestrial carbon cycle. Third, we review the platforms/sensors, methods, findings, and challenges in remote sensing of carbon fluxes. The remote sensing data and techniques used to quantify carbon fluxes include vegetation indices, light use efficiency models, terrestrial biosphere models, data-driven (or machine learning) approaches, solar-induced chlorophyll fluorescence (SIF), land surface temperature, and atmospheric inversions. Fourth, we review the platforms/sensors, methods, findings, and challenges in passive optical, microwave, and lidar remote sensing of biomass carbon stocks as well as remote sensing of soil organic carbon. Fifth, we review the progresses in remote sensing of disturbance impacts on the carbon cycle. Sixth, we also discuss the uncertainty and validation of the resulting carbon flux and stock estimates. Finally, we offer a forward-looking perspective and insights for future research and directions in remote sensing of the terrestrial carbon cycle. Remote sensing is anticipated to play an increasingly important role in carbon cycling studies in the future. This comprehensive and insightful review on 50 years of remote sensing of the terrestrial carbon cycle is timely and valuable and can benefit scientists in various research communities (e.g., carbon cycle, remote sensing, climate change, ecology) and inform ecosystem and carbon management, carbon-climate projections, and climate policymaking

Arctic warming-induced cold damage to East Asian terrestrial ecosystems

The global mean temperature is increasing due to the increase in greenhouse gases in the atmosphere, but paradoxically, many regions in the mid-latitudes have experienced cold winters recently. Here we analyse multiple observed and modelled datasets to evaluate links between Arctic temperature variation and cold damage in the East Asian terrestrial ecosystem. We find that winter warming over the Barents-Kara Sea has led to simultaneous negative temperature anomalies over most areas in East Asia and negative leaf area index anomalies in southern China where mostly subtropical evergreen forests are growing. In addition to these simultaneous impacts, spring vegetation activity and gross primary productivity were also reduced over evergreen and deciduous trees, and spring phenological dates are delayed. Earth System model simulations reveal that cold damage becomes stronger under greenhouse warming; therefore Arctic warming-induced cold stress should be considered in forest and carbon management strategies

The Orbiting Carbon Observatory (OCO-2) Tracks 2-3 Peta-Gram Increase in Carbon Release to the Atmosphere During the 2014-2016 El Nino

The powerful El Nio event of 2015-2016 - the third most intense since the 1950s - has exerted a large impact on the Earth's natural climate system. The column-averaged CO2 dry-air mole fraction (XCO2) observations from satellites and ground based networks are analyzed together with in situ observations for the period of September 2014 to October 2016. From the differences between satellite (OCO-2) observations and simulations using an atmospheric chemistry-transport model, we estimate that, relative to the mean annual fluxes for 2014, the most recent El Nio has contributed to an excess CO2 emission from the Earth's surface (land+ocean) to the atmosphere in the range of 2.4+/-0.2 PgC (1 Pg = 10(exp 15) g) over the period of July 2015 to June 2016. The excess CO2 flux is resulted primarily from reduction in vegetation uptake due to drought, and to a lesser degree from increased biomass burning. It is about the half of the CO2 flux anomaly (range: 4.4-6.7 PgC) estimated for the 1997/1998 El Nio. The annual total sink is estimated to be 3.9+/-0.2 PgC for the assumed fossil fuel emission of 10.1 PgC. The major uncertainty in attribution arise from error in anthropogenic emission trends, satellite data and atmospheric transport

Plant Regrowth as a Driver of Recent Enhancement of Terrestrial CO2 Uptake

The increasing strength of land CO2 uptake in the 2000s has been attributed to a stimulating effect of rising atmospheric CO2 on photosynthesis (CO2 fertilization). Using terrestrial biosphere models, we show that enhanced CO2 uptake is induced not only by CO2 fertilization but also an increasing uptake by plant regrowth (accounting for 0.33 ± 0.10 Pg C/year increase of CO2 uptake in the 2000s compared with the 1960s-1990s) with its effect most pronounced in eastern North America, southern‐eastern Europe, and southeastern temperate Eurasia. Our analysis indicates that ecosystems in North America and Europe have established the current productive state through regrowth since the 1960s, and those in temperate Eurasia are still in a stage from regrowth following active afforestation in the 1980s-1990s. As the strength of model representation of CO2 fertilization is still in debate, plant regrowth might have a greater potential to sequester carbon than indicated by this study

Summary for policymakers of the global assessment report on biodiversity and ecosystem services of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services

Fil: Díaz, Sandra. Universidad Nacional de Córdoba. Instituto Multidisciplinario de Biología Vegetal; Argentina.Fil: Díaz, Sandra. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto Multidisciplinario de Biología Vegetal; Argentina.Fil: Settele, Josef. Helmholtz-Zentrum für Umweltforschung. Department of Community Ecology; Alemania.Fil: Brondízio, Eduardo. Indiana University Bloomington. Department of Anthropology; Estados Unidos.Fil: Ngo, Hien T. Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services; Alemania.Fil: Guèze, Maximilien. Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services; Alemania.Fil: Agard, John. University of the West Indies. Department of Life Sciences; Trinidad y Tobago.Fil: Arneth, Almut. Karlsruhe Institute of Technology. Institute of Meteorology and Climate Research. Atmospheric Environmental Research; Alemania.Fil: Balvanera, Patricia. Universidad Nacional Autónoma de México. Instituto de Investigaciones en Ecosistemas y Sustentabilidad; México.Fil: Brauman, Kate A. University of Minnesota. Institute on the Environment; Estados Unidos.Fil: Butchart, Stuart H. M. BirdLife International; Reino Unido.Fil: Chan, Kai. University of British Columbia. Institute for Resources, Environment and Sustainability; Canada.Fil: Garibaldi, Lucas Alejandro. Universidad Nacional de Río Negro. Instituto de Investigaciones en Recursos Naturales, Agroecología y Desarrollo Rural; Argentina.Fil: Garibaldi, Lucas Alejandro. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones en Recursos Naturales, Agroecología y Desarrollo Rural; Argentina.Fil: Ichii, Kazuhito. National Institute for Environmental Studies. Center for Global Environmental Research; Japón.Fil: Liu, Jianguo. Michigan State University. Center for Systems Integration and Sustainability; Estados Unidos.Fil: Mazhenchery Subramanian, Suneetha. United Nations University. Institute of Advanced Studies; Japón.Fil: Midgley, Guy. Stellenbosch University. Department of Botany and Zoology; Sudáfrica.Fil: Miloslavich, Patricia. Commonwealth Scientific and Industrial Research Organisation. Oceans and Atmosphere; Australia.Fil: Molnár, Zsolt. Hungarian Academy of Sciences. Traditional Ecological Knowledge Research Group; Hungría.Fil: Obura, David. Coastal Oceans Research and Development – Indian Ocean; Kenya.Fil: Pfaff, Alexander. Duke University; Estados Unidos.Fil: Polasky, Stephen. University of Minnesota. Department of Applied Economics; Estados Unidos.Fil: Purvis, Andy. Natural History Museum. Department of Life Sciences; Reino Unido.Fil: Razzaque, Jona. University of the West of England. Faculty of Business and Law. Department of Law; Reino Unido.Fil: Reyers, Belinda. Stellenbosch University. Department of Conservation Ecology; Sudáfrica.Fil: Roy Chowdhury, Rinku. Clark University. Graduate School of Geography; Estados Unidos.Fil: Shin, Yunne J. Institute of Research for Development, Sète & Montpellier; Francia.Fil: Visseren Hamakers, Ingrid. George Mason University. Department of Environmental Science and Policy; Estados Unidos.Fil: Willis, Katherine. University of Oxford. Department of Zoology; Reino Unido.Fil: Zayas, Cynthia N. University of the Philippines. Center for International Studies; Filipinas.Summary for policymakers of the global assessment report on biodiversity and ecosystem services of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services

The global assessment report on biodiversity and ecosystem services: Summary for policy makers

This report represents a critical assessment, the first in almost 15 years (since the release of the Millennium Ecosystem Assessment in 2005) and the first ever carried out by an intergovernmental body, of the status and trends of the natural world, the social implications of these trends, their direct and indirect causes, and, importantly, the actions that can still be taken to ensure a better future for all. These complex links have been assessed using a simple, yet very inclusive framework that should resonate with a wide range of stakeholders, since it recognizes diverse world views, values and knowledge systems.Fil: Díaz, Sandra Myrna. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto Multidisciplinario de Biología Vegetal. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas Físicas y Naturales. Instituto Multidisciplinario de Biología Vegetal; ArgentinaFil: Settele, Josef. Helmholtz Centre for Environmental Research; AlemaniaFil: Brondízio, Eduardo. Indiana University; Estados UnidosFil: Ngo, Hien. Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services; AlemaniaFil: Guèze, Maximilien. Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services; AlemaniaFil: Agard, John. University of The West Indies; Trinidad y TobagoFil: Arneth, Almut. Karlsruher Institut fur Technologie; AlemaniaFil: Balvanera, Patricia. Universidad Nacional Autónoma de México; MéxicoFil: Brauman, Kate. University of Minnesota; Estados UnidosFil: Butchart, Stuart. University of Cambridge; Reino UnidoFil: Chan, Kai M. A.. University of British Columbia; CanadáFil: Garibaldi, Lucas Alejandro. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Centro Cientifico Tecnologico Conicet - Patagonia Norte. Instituto de Investigaciones En Recursos Naturales, Agroecologia y Desarrollo Rural. - Universidad Nacional de Rio Negro. Instituto de Investigaciones En Recursos Naturales, Agroecologia y Desarrollo Rural.; ArgentinaFil: Ichii, Kazuhito. Chiba University; JapónFil: Liu, Jianguo. Michigan State University; Estados UnidosFil: Subramanian, Suneetha. United Nations University; JapónFil: Midgley, Guy. Stellenbosch University; SudáfricaFil: Miloslavich, Patricia. Universidad Simon Bolivar.; VenezuelaFil: Molnár, Zsolt. Hungarian Academy of Sciences; HungríaFil: Obura, David. Coastal Oceans Research and Development Indian Ocean; KeniaFil: Pfaff, Alexander. University of Duke; Estados UnidosFil: Polasky, Stephen. University of Minnesota; Estados UnidosFil: Purvis, Andy. Natural History Museum; Reino UnidoFil: Razzaque, Jona. University of the West of England; Reino UnidoFil: Reyers, Belinda. Stellenbosch University; SudáfricaFil: Roy Chowdhury, Rinku. Clark University; Estados UnidosFil: Shin, Yunne-Jai. Centre National de la Recherche Scientifique; FranciaFil: Visseren-Hamakers, Ingrid. Radboud Universiteit Nijmegen; Países BajosFil: Willis, Katherine. University of Oxford; Reino UnidoFil: Zayas, Cynthia. University of the Philippines; Filipina