Articles | Volume 18, issue 13
Reviews and syntheses 12 Jul 2021
Reviews and syntheses | 12 Jul 2021
Reviews and syntheses: Ongoing and emerging opportunities to improve environmental science using observations from the Advanced Baseline Imager on the Geostationary Operational Environmental Satellites
Anam M. Khan et al.
No articles found.
Xuanli Li, Jason B. Roberts, Jayanthi Srikishen, Jonathan L. Case, Walter A. Petersen, GyuWon Lee, and Christopher R. Hain
Geosci. Model Dev. Discuss.,
Revised manuscript under review for GMDShort summary
This research assimilated the Global Precipitation Measurement (GPM) satellite-retrieved ocean surface meteorology data into the Weather Research and Forecasting (WRF) model with the Gridpoint Statistical Interpolation (GSI) system for two snowstorms during the International Collaborative Experiments for PyeongChang 2018 Olympic and Paralympic Winter Games (ICE-POP 2018) field experiments. The result indicated positive impact of the data for short-term forecasts for both heavy snowfall events.
Paul C. Stoy, Adam A. Cook, John E. Dore, Natascha Kljun, William Kleindl, E. N. Jack Brookshire, and Tobias Gerken
Biogeosciences, 18, 961–975,Short summary
The reintroduction of American bison creates multiple environmental benefits. Ruminants like bison also emit methane – a potent greenhouse gas – to the atmosphere, which has not been measured to date in a field setting. We measured methane efflux from an American bison herd during winter using eddy covariance. Automated cameras were used to approximate their location to calculate per-animal flux. From the measurements, bison do not emit more methane than the cattle they often replace.
Mahmoud Osman, Benjamin F. Zaitchik, Hamada S. Badr, Jordan I. Christian, Tsegaye Tadesse, Jason A. Otkin, and Martha C. Anderson
Hydrol. Earth Syst. Sci., 25, 565–581,Short summary
Our study of flash droughts' definitions over the United States shows that published definitions yield markedly different inventories of flash drought geography and frequency. Results suggest there are several pathways that can lead to events that are characterized as flash droughts. Lack of consensus across definitions helps to explain apparent contradictions in the literature on trends and indicates the selection of a definition is important for accurate monitoring of different mechanisms.
Sangchul Lee, Gregory W. McCarty, Glenn E. Moglen, Haw Yen, Fangni Lei, Martha Anderson, Feng Gao, Wade Crow, In-Young Yeo, and Liang Sun
Hydrol. Earth Syst. Sci. Discuss.,
Publication in HESS not foreseen
Paul C. Stoy, Tarek S. El-Madany, Joshua B. Fisher, Pierre Gentine, Tobias Gerken, Stephen P. Good, Anne Klosterhalfen, Shuguang Liu, Diego G. Miralles, Oscar Perez-Priego, Angela J. Rigden, Todd H. Skaggs, Georg Wohlfahrt, Ray G. Anderson, A. Miriam J. Coenders-Gerrits, Martin Jung, Wouter H. Maes, Ivan Mammarella, Matthias Mauder, Mirco Migliavacca, Jacob A. Nelson, Rafael Poyatos, Markus Reichstein, Russell L. Scott, and Sebastian Wolf
Biogeosciences, 16, 3747–3775,Short summary
Key findings are the nearly optimal response of T to atmospheric water vapor pressure deficits across methods and scales. Additionally, the notion that T / ET intermittently approaches 1, which is a basis for many partitioning methods, does not hold for certain methods and ecosystems. To better constrain estimates of E and T from combined ET measurements, we propose a combination of independent measurement techniques to better constrain E and T at the ecosystem scale.
Johannes Winckler, Christian H. Reick, Sebastiaan Luyssaert, Alessandro Cescatti, Paul C. Stoy, Quentin Lejeune, Thomas Raddatz, Andreas Chlond, Marvin Heidkamp, and Julia Pongratz
Earth Syst. Dynam., 10, 473–484,Short summary
For local living conditions, it matters whether deforestation influences the surface temperature, temperature at 2 m, or the temperature higher up in the atmosphere. Here, simulations with a climate model show that at a location of deforestation, surface temperature generally changes more strongly than atmospheric temperature. Comparison across climate models shows that both for summer and winter the surface temperature response exceeds the air temperature response locally by a factor of 2.
Susanne Wiesner, Christina L. Staudhammer, Paul C. Stoy, Lindsay R. Boring, and Gregory Starr
Biogeosciences, 16, 1845–1863,Short summary
We studied entropy production in longleaf savanna sites with variations in land use legacy, plant diversity, and soil water availability which experienced drought. Sites with greater land use legacy had lower metabolic energy use efficiency, which delayed recovery from drought. Sites with more hardwood captured less solar radiation but more efficiently used absorbed energy. Future management applications could use these methods to quantify energy use efficiency across global ecosystems.
Jason A. Otkin, Yafang Zhong, David Lorenz, Martha C. Anderson, and Christopher Hain
Hydrol. Earth Syst. Sci., 22, 5373–5386,Short summary
Correlation analyses were used to explore relationships between the Evaporative Stress Index (ESI) – which depicts anomalies in evapotranspiration (ET) – and various land and atmospheric variables that impact ET. The results revealed that the ESI is more strongly correlated to anomalies in soil moisture and near-surface vapor pressure deficit than to precipitation and temperature anomalies. Large regional and seasonal dependencies in the strengths of the correlations were also observed.
Vikalp Mishra, James F. Cruise, Christopher R. Hain, John R. Mecikalski, and Martha C. Anderson
Hydrol. Earth Syst. Sci., 22, 4935–4957,Short summary
Multiple satellite observations can be used for surface and subsurface soil moisture estimations. In this study, satellite observations along with a mathematical model were used to distribute and develop multiyear soil moisture profiles over the southeastern US. Such remotely sensed profiles become particularly useful at large spatiotemporal scales, can be a significant tool in data-scarce regions of the world, can complement various land and crop models, and can act as drought indicators etc.
Tobias Gerken, Gabriel T. Bromley, Benjamin L. Ruddell, Skylar Williams, and Paul C. Stoy
Hydrol. Earth Syst. Sci., 22, 4155–4163,Short summary
An unprecedented flash drought took place across parts of the US Northern Great Plains and Canadian Prairie Provinces during the summer of 2017 that in some areas was the worst in recorded history. We show that this drought was preceded by a breakdown of land–atmosphere coupling, reducing the likelihood of convective precipitation. It may be useful to monitor land–atmosphere coupling to track and potentially forecast drought development.
Thomas R. H. Holmes, Christopher R. Hain, Wade T. Crow, Martha C. Anderson, and William P. Kustas
Hydrol. Earth Syst. Sci., 22, 1351–1369,Short summary
In an effort to apply cloud-tolerant microwave data to satellite-based monitoring of evapotranspiration (ET), this study reports on an experiment where microwave-based land surface temperature is used as the key diagnostic input to a two-source energy balance method for the estimation of ET. Comparisons of this microwave ET with the conventional thermal infrared estimates show widespread agreement in spatial and temporal patterns from seasonal to inter-annual timescales over Africa and Europe.
Min Huang, Gregory R. Carmichael, James H. Crawford, Armin Wisthaler, Xiwu Zhan, Christopher R. Hain, Pius Lee, and Alex B. Guenther
Geosci. Model Dev., 10, 3085–3104,Short summary
Various sensitivity simulations during two airborne campaigns were performed to assess the impact of different initialization methods and model resolutions on NUWRF-modeled weather states, heat fluxes, and the follow-on MEGAN isoprene emission calculations. Proper land initialization is shown to be important to the coupled weather modeling and the follow-on emission modeling, which is also critical to accurately representing other processes in air quality modeling and data assimilation.
Wade T. Crow, Eunjin Han, Dongryeol Ryu, Christopher R. Hain, and Martha C. Anderson
Hydrol. Earth Syst. Sci., 21, 1849–1862,Short summary
Terrestrial water storage is defined as the total volume of water stored within the land surface and sub-surface and is a key variable for tracking long-term variability in the global water cycle. Currently, annual variations in terrestrial water storage can only be measured at extremely coarse spatial resolutions (> 200 000 km2) using gravity-based remote sensing. Here we provide evidence that microwave-based remote sensing of soil moisture can be applied to enhance this resolution.
Jingfeng Xiao, Shuguang Liu, and Paul C. Stoy
Biogeosciences, 13, 3665–3675,Short summary
This special issue showcases recent advancements on the impacts of disturbances and extreme events on the carbon (C) cycle. Notable advancements include quantifying harvest impacts on forest structure, recovery, and carbon stocks; observed dissolved organic C and methane increases in thermokarst lakes following summer warming; disentangling the roles of herbivores and fire on forest carbon dioxide flux; and improved atmospheric inversion of regional C flux by incorporating disturbances.
Joel McCorkel, Brian Cairns, and Andrzej Wasilewski
Atmos. Meas. Tech., 9, 955–962,Short summary
The transfer and maintenance of international radiometric standards to satellite remote-sensing instruments is a labor-intensive and costly one. The goal is to provide specific examples for calibration implementation for a potential instrument mission and, with this, advance debate on the roles that the various satellite calibration techniques play in providing the best radiometric standards for Earth-observing sensors.
P. C. Stoy, M. C. Dietze, A. D. Richardson, R. Vargas, A. G. Barr, R. S. Anderson, M. A. Arain, I. T. Baker, T. A. Black, J. M. Chen, R. B. Cook, C. M. Gough, R. F. Grant, D. Y. Hollinger, R. C. Izaurralde, C. J. Kucharik, P. Lafleur, B. E. Law, S. Liu, E. Lokupitiya, Y. Luo, J. W. Munger, C. Peng, B. Poulter, D. T. Price, D. M. Ricciuto, W. J. Riley, A. K. Sahoo, K. Schaefer, C. R. Schwalm, H. Tian, H. Verbeeck, and E. Weng
Biogeosciences, 10, 6893–6909,
Related subject area
Biogeochemistry: LandAssessing the representation of the Australian carbon cycle in global vegetation modelsAssessing the response of soil carbon in Australia to changing inputs and climate using a consistent modelling frameworkFirst pan-Arctic assessment of dissolved organic carbon in lakes of the permafrost regionThe impact of wildfire on biogeochemical fluxes and water quality in boreal catchmentsExamining the sensitivity of the terrestrial carbon cycle to the expression of El NiñoSubalpine grassland productivity increased with warmer and drier conditions, but not with higher N deposition, in an altitudinal transplantation experimentReviews and syntheses: Impacts of plant-silica–herbivore interactions on terrestrial biogeochemical cyclingImplementation of nitrogen cycle in the CLASSIC land modelCombined effects of ozone and drought stress on the emission of biogenic volatile organic compounds from Quercus robur L.A bottom-up quantification of foliar mercury uptake fluxes across EuropeLagged effects regulate the inter-annual variability of the tropical carbon balanceSpatial variations in terrestrial net ecosystem productivity and its local indicatorsNitrogen cycling in CMIP6 land surface models: progress and limitationsDecomposing reflectance spectra to track gross primary production in a subalpine evergreen forestSensitivity of 21st century simulated ecosystem indicators to model parameters, prescribed climate drivers, RCP scenarios and forest management actions for two Finnish boreal forest sitesSummarizing the state of the terrestrial biosphere in few dimensionsPatterns and trends of the dominant environmental controls of net biome productivityLocalized basal area affects soil respiration temperature sensitivity in a coastal deciduous forestDissolved organic carbon mobilized from organic horizons of mature and harvested black spruce plots in a mesic boreal regionIdeas and perspectives: Proposed best practices for collaboration at cross-disciplinary observatoriesEffects of leaf length and development stage on the triple oxygen isotope signature of grass leaf water and phytoliths: insights for a proxy of continental atmospheric humidityResponse of simulated burned area to historical changes in environmental and anthropogenic factors: a comparison of seven fire modelsEstimation of coarse dead wood stocks in intact and degraded forests in the Brazilian Amazon using airborne lidarTheoretical uncertainties for global satellite-derived burned area estimatesEstimating global gross primary productivity using chlorophyll fluorescence and a data assimilation system with the BETHY-SCOPE modelHow representative are FLUXNET measurements of surface fluxes during temperature extremes?Stable carbon and nitrogen isotopic composition of leaves, litter, and soils of various ecosystems along an elevational and land-use gradient at Mount Kilimanjaro, TanzaniaComparison of CO2 and O2 fluxes demonstrate retention of respired CO2 in tree stems from a range of tree speciesTropical climate–vegetation–fire relationships: multivariate evaluation of the land surface model JSBACHA global spatially contiguous solar-induced fluorescence (CSIF) dataset using neural networksTechnical note: A simple theoretical model framework to describe plant stomatal “sluggishness” in response to elevated ozone concentrationsWater-stress-induced breakdown of carbon–water relations: indicators from diurnal FLUXNET patternsShrub type dominates the vertical distribution of leaf C : N : P stoichiometry across an extensive altitudinal gradientEstimation of gross land-use change and its uncertainty using a Bayesian data assimilation approachSmaller global and regional carbon emissions from gross land use change when considering sub-grid secondary land cohorts in a global dynamic vegetation modelNitrogen isotopic composition of plants and soil in an arid mountainous terrain: south slope versus north slopeLand-use and land-cover change carbon emissions between 1901 and 2012 constrained by biomass observationsImpacts of temperature extremes on European vegetation during the growing seasonAn assessment of geographical distribution of different plant functional types over North America simulated using the CLASS–CTEM modelling frameworkVariability in above- and belowground carbon stocks in a Siberian larch watershedPrecipitation–fire linkages in Indonesia (1997–2015)Fire-regime variability impacts forest carbon dynamics for centuries to millenniaChanging patterns of fire occurrence in proximity to forest edges, roads and rivers between NW Amazonian countriesReviews and syntheses: Flying the satellite into your model: on the role of observation operators in constraining models of the Earth system and the carbon cycleMapping the reduction in gross primary productivity in subarctic birch forests due to insect outbreaksTechnical note: Dynamic INtegrated Gap-filling and partitioning for OzFlux (DINGO)Nitrogen mineralization, not N2 fixation, alleviates progressive nitrogen limitation – Comment on “Processes regulating progressive nitrogen limitation under elevated carbon dioxide: a meta-analysis” by Liang et al. (2016)Transient dynamics of terrestrial carbon storage: mathematical foundation and its applicationsDevelopment and evaluation of an ozone deposition scheme for coupling to a terrestrial biosphere modelVariations of leaf N and P concentrations in shrubland biomes across northern China: phylogeny, climate, and soil
Lina Teckentrup, Martin G. De Kauwe, Andrew J. Pitman, Daniel S. Goll, Vanessa Haverd, Atul K. Jain, Emilie Joetzjer, Etsushi Kato, Sebastian Lienert, Danica Lombardozzi, Patrick C. McGuire, Joe R. Melton, Julia E. M. S. Nabel, Julia Pongratz, Stephen Sitch, Anthony P. Walker, and Sönke Zaehle
Biogeosciences, 18, 5639–5668,Short summary
The Australian continent is included in global assessments of the carbon cycle such as the global carbon budget, yet the performance of dynamic global vegetation models (DGVMs) over Australia has rarely been evaluated. We assessed simulations by an ensemble of dynamic global vegetation models over Australia and highlighted a number of key areas that lead to model divergence on both short (inter-annual) and long (decadal) timescales.
Juhwan Lee, Raphael A. Viscarra Rossel, Mingxi Zhang, Zhongkui Luo, and Ying-Ping Wang
Biogeosciences, 18, 5185–5202,Short summary
We performed Roth C simulations across Australia and assessed the response of soil carbon to changing inputs and future climate change using a consistent modelling framework. Site-specific initialisation of the C pools with measurements of the C fractions is essential for accurate simulations of soil organic C stocks and composition at a large scale. With further warming, Australian soils will become more vulnerable to C loss: natural environments > native grazing > cropping > modified grazing.
Lydia Stolpmann, Caroline Coch, Anne Morgenstern, Julia Boike, Michael Fritz, Ulrike Herzschuh, Kathleen Stoof-Leichsenring, Yury Dvornikov, Birgit Heim, Josefine Lenz, Amy Larsen, Katey Walter Anthony, Benjamin Jones, Karen Frey, and Guido Grosse
Biogeosciences, 18, 3917–3936,Short summary
Our new database summarizes DOC concentrations of 2167 water samples from 1833 lakes in permafrost regions across the Arctic to provide insights into linkages between DOC and environment. We found increasing lake DOC concentration with decreasing permafrost extent and higher DOC concentrations in boreal permafrost sites compared to tundra sites. Our study shows that DOC concentration depends on the environmental properties of a lake, especially permafrost extent, ecoregion, and vegetation.
Gustaf Granath, Christopher D. Evans, Joachim Strengbom, Jens Fölster, Achim Grelle, Johan Strömqvist, and Stephan J. Köhler
Biogeosciences, 18, 3243–3261,Short summary
We measured element losses and impacts on water quality following a wildfire in Sweden. We observed the largest carbon and nitrogen losses during the fire and a strong pulse of elements 1–3 months after the fire that showed a fast (weeks) and a slow (months) release from the catchments. Total carbon export through water did not increase post-fire. Overall, we observed a rapid recovery of the biogeochemical cycling of elements within 3 years but still an annual net release of carbon dioxide.
Lina Teckentrup, Martin G. De Kauwe, Andrew J. Pitman, and Benjamin Smith
Biogeosciences, 18, 2181–2203,Short summary
The El Niño–Southern Oscillation (ENSO) describes changes in the sea surface temperature patterns of the Pacific Ocean. This influences the global weather, impacting vegetation on land. There are two types of El Niño: central Pacific (CP) and eastern Pacific (EP). In this study, we explored the long-term impacts on the carbon balance on land linked to the two El Niño types. Using a dynamic vegetation model, we simulated what would happen if only either CP or EP El Niño events had occurred.
Matthias Volk, Matthias Suter, Anne-Lena Wahl, and Seraina Bassin
Biogeosciences, 18, 2075–2090,Short summary
Grassland ecosystem services like forage production and greenhouse gas storage in the soil depend on plant growth. In an experiment in the mountains with warming treatments, we found that despite dwindling soil water content, the grassland growth increased with up to +1.3 °C warming (annual mean) compared to present temperatures. Even at +2.4 °C the growth was still larger than at the reference site. This suggests that plant growth will increase due to global warming in the near future.
Bernice C. Hwang and Daniel B. Metcalfe
Biogeosciences, 18, 1259–1268,Short summary
Despite growing recognition of herbivores as important ecosystem engineers, many major gaps remain in our understanding of how silicon and herbivory interact to shape biogeochemical processes. We highlight the need for more research particularly in natural settings as well as on the potential effects of herbivory on terrestrial silicon cycling to understand potentially critical animal–plant–soil feedbacks.
Ali Asaadi and Vivek K. Arora
Biogeosciences, 18, 669–706,Short summary
More than a quarter of the current anthropogenic CO2 emissions are taken up by land, reducing the atmospheric CO2 growth rate. This is because of the CO2 fertilization effect which benefits 80 % of global vegetation. However, if nitrogen and phosphorus nutrients cannot keep up with increasing atmospheric CO2, the magnitude of this terrestrial ecosystem service may reduce in future. This paper implements nitrogen constraints on photosynthesis in a model to understand the mechanisms involved.
Arianna Peron, Lisa Kaser, Anne Charlott Fitzky, Martin Graus, Heidi Halbwirth, Jürgen Greiner, Georg Wohlfahrt, Boris Rewald, Hans Sandén, and Thomas Karl
Biogeosciences, 18, 535–556,Short summary
Drought events are expected to become more frequent with climate change. Along with these events atmospheric ozone is also expected to increase. Both can stress plants. Here we investigate to what extent these factors modulate the emission of volatile organic compounds (VOCs) from oak plants. We find an antagonistic effect between drought stress and ozone, impacting the emission of different BVOCs, which is indirectly controlled by stomatal opening, allowing plants to control their water budget.
Lena Wohlgemuth, Stefan Osterwalder, Carl Joseph, Ansgar Kahmen, Günter Hoch, Christine Alewell, and Martin Jiskra
Biogeosciences, 17, 6441–6456,Short summary
Mercury uptake by trees from the air represents an important but poorly quantified pathway in the global mercury cycle. We determined mercury uptake fluxes by leaves and needles at 10 European forests which were 4 times larger than mercury deposition via rainfall. The amount of mercury taken up by leaves and needles depends on their age and growing height on the tree. Scaling up our measurements to the forest area of Europe, we estimate that each year 20 t of mercury is taken up by trees.
A. Anthony Bloom, Kevin W. Bowman, Junjie Liu, Alexandra G. Konings, John R. Worden, Nicholas C. Parazoo, Victoria Meyer, John T. Reager, Helen M. Worden, Zhe Jiang, Gregory R. Quetin, T. Luke Smallman, Jean-François Exbrayat, Yi Yin, Sassan S. Saatchi, Mathew Williams, and David S. Schimel
Biogeosciences, 17, 6393–6422,Short summary
We use a model of the 2001–2015 tropical land carbon cycle, with satellite measurements of land and atmospheric carbon, to disentangle lagged and concurrent effects (due to past and concurrent meteorological events, respectively) on annual land–atmosphere carbon exchanges. The variability of lagged effects explains most 2001–2015 inter-annual carbon flux variations. We conclude that concurrent and lagged effects need to be accurately resolved to better predict the world's land carbon sink.
Erqian Cui, Chenyu Bian, Yiqi Luo, Shuli Niu, Yingping Wang, and Jianyang Xia
Biogeosciences, 17, 6237–6246,Short summary
Mean annual net ecosystem productivity (NEP) is related to the magnitude of the carbon sink of a specific ecosystem, while its inter-annual variation (IAVNEP) characterizes the stability of such a carbon sink. Thus, a better understanding of the co-varying NEP and IAVNEP is critical for locating the major and stable carbon sinks on land. Based on daily NEP observations from eddy-covariance sites, we found local indicators for the spatially varying NEP and IAVNEP, respectively.
Taraka Davies-Barnard, Johannes Meyerholt, Sönke Zaehle, Pierre Friedlingstein, Victor Brovkin, Yuanchao Fan, Rosie A. Fisher, Chris D. Jones, Hanna Lee, Daniele Peano, Benjamin Smith, David Wårlind, and Andy J. Wiltshire
Biogeosciences, 17, 5129–5148,
Rui Cheng, Troy S. Magney, Debsunder Dutta, David R. Bowling, Barry A. Logan, Sean P. Burns, Peter D. Blanken, Katja Grossmann, Sophia Lopez, Andrew D. Richardson, Jochen Stutz, and Christian Frankenberg
Biogeosciences, 17, 4523–4544,Short summary
We measured reflected sunlight from an evergreen canopy for a year to detect changes in pigments that play an important role in regulating the seasonality of photosynthesis. Results show a strong mechanistic link between spectral reflectance features and pigment content, which is validated using a biophysical model. Our results show spectrally where, why, and when spectral features change over the course of the season and show promise for estimating photosynthesis remotely.
Jarmo Mäkelä, Francesco Minunno, Tuula Aalto, Annikki Mäkelä, Tiina Markkanen, and Mikko Peltoniemi
Biogeosciences, 17, 2681–2700,Short summary
We assess the relative magnitude of uncertainty sources on ecosystem indicators of the 21st century climate change on two boreal forest sites. In addition to RCP and climate model uncertainties, we included the overlooked model parameter uncertainty and management actions in our analysis. Management was the dominant uncertainty factor for the more verdant southern site, followed by RCP, climate and parameter uncertainties. The uncertainties were estimated with canonical correlation analysis.
Guido Kraemer, Gustau Camps-Valls, Markus Reichstein, and Miguel D. Mahecha
Biogeosciences, 17, 2397–2424,Short summary
To closely monitor the state of our planet, we require systems that can monitor the observation of many different properties at the same time. We create indicators that resemble the behavior of many different simultaneous observations. We apply the method to create indicators representing the Earth's biosphere. The indicators show a productivity gradient and a water gradient. The resulting indicators can detect a large number of changes and extremes in the Earth system.
Barbara Marcolla, Mirco Migliavacca, Christian Rödenbeck, and Alessandro Cescatti
Biogeosciences, 17, 2365–2379,Short summary
This work investigates the sensitivity of terrestrial CO2 fluxes to climate drivers. We observed that CO2 flux is mostly controlled by temperature during the growing season and by radiation off season. We also observe that radiation importance is increasing over time while sensitivity to temperature is decreasing in Eurasia. Ultimately this analysis shows that ecosystem response to climate is changing, with potential repercussions for future terrestrial sink and land role in climate mitigation.
Stephanie C. Pennington, Nate G. McDowell, J. Patrick Megonigal, James C. Stegen, and Ben Bond-Lamberty
Biogeosciences, 17, 771–780,Short summary
Soil respiration (Rs) is the flow of CO2 from the soil surface to the atmosphere and is one of the largest carbon fluxes on land. This study examined the effect of local basal area (tree area) on Rs in a coastal forest in eastern Maryland, USA. Rs measurements were taken as well as distance from soil collar, diameter, and species of each tree within a 15 m radius. We found that trees within 5 m of our sampling points had a positive effect on how sensitive soil respiration was to temperature.
Keri L. Bowering, Kate A. Edwards, Karen Prestegaard, Xinbiao Zhu, and Susan E. Ziegler
Biogeosciences, 17, 581–595,Short summary
We examined the effects of season and tree harvesting on the flow of water and the organic carbon (OC) it carries from boreal forest soils. We found that more OC was lost from the harvested forest because more precipitation reached the soil surface but that during periods of flushing in autumn and snowmelt a limit on the amount of water-extractable OC is reached. These results contribute to an increased understanding of carbon loss from boreal forest soils.
Jason Philip Kaye, Susan L. Brantley, Jennifer Zan Williams, and the SSHCZO team
Biogeosciences, 16, 4661–4669,Short summary
Interdisciplinary teams can only capitalize on innovative ideas if members work well together through collegial and efficient use of field sites, instrumentation, samples, data, and model code. Thus, biogeoscience teams may benefit from developing a set of best practices for collaboration. We present one such example from a the Susquehanna Shale Hills critical zone observatory. Many of the themes from our example are universal, and they offer insights useful to other biogeoscience teams.
Anne Alexandre, Elizabeth Webb, Amaelle Landais, Clément Piel, Sébastien Devidal, Corinne Sonzogni, Martine Couapel, Jean-Charles Mazur, Monique Pierre, Frédéric Prié, Christine Vallet-Coulomb, Clément Outrequin, and Jacques Roy
Biogeosciences, 16, 4613–4625,Short summary
This calibration study shows that despite isotope heterogeneity along grass leaves, the triple oxygen isotope composition of bulk leaf phytoliths can be estimated from the Craig and Gordon model, a mixing equation and a mean leaf water–phytolith fractionation exponent (lambda) of 0.521. The results strengthen the reliability of the 17O–excess of phytoliths to be used as a proxy of atmospheric relative humidity and open tracks for its use as an imprint of leaf water 17O–excess.
Lina Teckentrup, Sandy P. Harrison, Stijn Hantson, Angelika Heil, Joe R. Melton, Matthew Forrest, Fang Li, Chao Yue, Almut Arneth, Thomas Hickler, Stephen Sitch, and Gitta Lasslop
Biogeosciences, 16, 3883–3910,Short summary
This study compares simulated burned area of seven global vegetation models provided by the Fire Model Intercomparison Project (FireMIP) since 1900. We investigate the influence of five forcing factors: atmospheric CO2, population density, land–use change, lightning and climate. We find that the anthropogenic factors lead to the largest spread between models. Trends due to climate are mostly not significant but climate strongly influences the inter-annual variability of burned area.
Marcos A. S. Scaranello, Michael Keller, Marcos Longo, Maiza N. dos-Santos, Veronika Leitold, Douglas C. Morton, Ekena R. Pinagé, and Fernando Del Bon Espírito-Santo
Biogeosciences, 16, 3457–3474,Short summary
The coarse dead wood component of the tropical forest carbon pool is rarely measured. For the first time, we developed models for predicting coarse dead wood in Amazonian forests by using airborne laser scanning data. Our models produced site-based estimates similar to independent field estimates found in the literature. Our study provides an approach for estimating coarse dead wood pools from remotely sensed data and mapping those pools over large scales in intact and degraded forests.
James Brennan, Jose L. Gómez-Dans, Mathias Disney, and Philip Lewis
Biogeosciences, 16, 3147–3164,Short summary
We estimate the uncertainties associated with three global satellite-derived burned area estimates. The method provides unique uncertainties for the three estimates at the global scale for 2001–2013. We find uncertainties of 4 %–5.5 % in global burned area and uncertainties of 8 %–10 % in the frequently burning regions of Africa and Australia.
Alexander J. Norton, Peter J. Rayner, Ernest N. Koffi, Marko Scholze, Jeremy D. Silver, and Ying-Ping Wang
Biogeosciences, 16, 3069–3093,Short summary
This study presents an estimate of global terrestrial photosynthesis. We make use of satellite chlorophyll fluorescence measurements, a visible indicator of photosynthesis, to optimize model parameters and estimate photosynthetic carbon uptake. This new framework incorporates nonlinear, process-based understanding of the link between fluorescence and photosynthesis, an advance on past approaches. This will aid in the utility of fluorescence to quantify terrestrial carbon cycle feedbacks.
Sophie V. J. van der Horst, Andrew J. Pitman, Martin G. De Kauwe, Anna Ukkola, Gab Abramowitz, and Peter Isaac
Biogeosciences, 16, 1829–1844,Short summary
Measurements of surface fluxes are taken around the world and are extremely valuable for understanding how the land and atmopshere interact, and how the land can amplify temerature extremes. However, do these measurements sample extreme temperatures, or are they biased to the average? We examine this question and highlight data that do measure surface fluxes under extreme conditions. This provides a way forward to help model developers improve their models.
Friederike Gerschlauer, Gustavo Saiz, David Schellenberger Costa, Michael Kleyer, Michael Dannenmann, and Ralf Kiese
Biogeosciences, 16, 409–424,Short summary
Mount Kilimanjaro is an iconic environmental asset under serious threat due to increasing human pressures and climate change constraints. We studied variations in the stable isotopic composition of carbon and nitrogen in plant, litter, and soil material sampled along a strong land-use and altitudinal gradient. Our results show that, besides management, increasing temperatures in a changing climate may promote carbon and nitrogen losses, thus altering the stability of Kilimanjaro ecosystems.
Boaz Hilman, Jan Muhr, Susan E. Trumbore, Norbert Kunert, Mariah S. Carbone, Päivi Yuval, S. Joseph Wright, Gerardo Moreno, Oscar Pérez-Priego, Mirco Migliavacca, Arnaud Carrara, José M. Grünzweig, Yagil Osem, Tal Weiner, and Alon Angert
Biogeosciences, 16, 177–191,Short summary
Combined measurement of CO2 / O2 fluxes in tree stems suggested that on average 41 % of the respired CO2 was not emitted locally to the atmosphere. This finding strengthens the recognition that CO2 efflux from tree stems is not an accurate measure of respiration. The CO2 / O2 fluxes did not vary as expected if CO2 dissolution in the xylem sap was the main driver for the CO2 retention. We suggest the examination of refixation of respired CO2 as a possible mechanism for CO2 retention.
Gitta Lasslop, Thomas Moeller, Donatella D'Onofrio, Stijn Hantson, and Silvia Kloster
Biogeosciences, 15, 5969–5989,Short summary
We apply a multivariate model evaluation to the relationship between climate, vegetation and fire in the tropics using the JSBACH land surface model and two remote-sensing data sets, with the aim to identify the potential for model improvement. The overestimation of tree cover for low precipitation and a very strong relationship between tree cover and burned area indicates opportunities in the improvement of drought effects and the impact of fire on tree cover or the adaptation of trees to fire.
Yao Zhang, Joanna Joiner, Seyed Hamed Alemohammad, Sha Zhou, and Pierre Gentine
Biogeosciences, 15, 5779–5800,Short summary
Using satellite reflectance measurements and a machine learning algorithm, we generated a new solar-induced chlorophyll fluorescence (SIF) dataset that is closely linked to plant photosynthesis. This new dataset has higher spatial and temporal resolutions, and lower uncertainty compared to the existing satellite retrievals. We also demonstrated its application in monitoring drought and improving the understanding of the SIF–photosynthesis relationship.
Chris Huntingford, Rebecca J. Oliver, Lina M. Mercado, and Stephen Sitch
Biogeosciences, 15, 5415–5422,Short summary
Raised ozone levels impact plant stomatal opening and thus photosynthesis. Most models describe this as a suppression of stomata opening. Field evidence suggests more complexity, as ozone damage may make stomatal response
sluggish. In some circumstances, this causes stomata to be more open – a concern during drought conditions – by increasing transpiration. To guide interpretation and modelling of field measurements, we present an equation for sluggish effects, via a single tau parameter.
Jacob A. Nelson, Nuno Carvalhais, Mirco Migliavacca, Markus Reichstein, and Martin Jung
Biogeosciences, 15, 2433–2447,Short summary
Plants have typical daily carbon uptake and water loss cycles. However, these cycles may change under periods of duress, such as water limitation. Here we identify two types of patterns in response to water limitations: a tendency to lose more water in the morning than afternoon and a decoupling of the carbon and water cycles. The findings show differences in responses by trees and grasses and suggest that morning shifts may be more efficient at gaining carbon per unit water used.
Wenqiang Zhao, Peter B. Reich, Qiannan Yu, Ning Zhao, Chunying Yin, Chunzhang Zhao, Dandan Li, Jun Hu, Ting Li, Huajun Yin, and Qing Liu
Biogeosciences, 15, 2033–2053,Short summary
We found larger shrub leaf C, C : N and lower leaf N, N : P levels compared to other terrestrial ecosystems. Alpine shrubs exhibited the greatest leaf C at low temperatures, whereas the largest leaf N and P occurred in valley deciduous shrubs. The large heterogeneity in nutrient uptake and physiological adaptation of shrub types to environments explained the largest fraction of leaf C : N : P variations, while climate indirectly affected leaf C : N : P via its interactive effects on shrub type or soil.
Peter Levy, Marcel van Oijen, Gwen Buys, and Sam Tomlinson
Biogeosciences, 15, 1497–1513,Short summary
We present a new method for estimating land-use change using a Bayesian data assimilation approach. This allows us to constrain estimates of gross land-use change with reliable national-scale census data whilst retaining the information available from several other sources. This includes detailed spatial data; further data sources, such as new satellites, could easily be added in future. Uncertainty is propagated appropriately into the output.
Chao Yue, Philippe Ciais, and Wei Li
Biogeosciences, 15, 1185–1201,Short summary
Gross land use change such as shifting cultivation causes carbon emissions because carbon release in cleared forests is larger than absorption in regrowing ones. However, to appropriately account for this process, vegetation models have to represent sub-grid secondary forest dynamics. We found that gross land use emissions can be overestimated if sub-grid secondary forests are neglected in the model. Conversely, rotation lengths of shifting cultivation have a critical role.
Chongjuan Chen, Yufu Jia, Yuzhen Chen, Imran Mehmood, Yunting Fang, and Guoan Wang
Biogeosciences, 15, 369–377,Short summary
The south slope of Tian Shan differs from the north slope in environment. The study showed that leaf δ15N, soil δ15N and △δ15Nleaf-soil on the south slope were greater than those on the north slope. The significant influential factors of leaf and soil δ15N on the south slope were different from those on the north slope. The results suggested that the south slope has higher soil N transformation rates than the north slope and relationships between leaf and soil δ15N and environment are localized.
Wei Li, Philippe Ciais, Shushi Peng, Chao Yue, Yilong Wang, Martin Thurner, Sassan S. Saatchi, Almut Arneth, Valerio Avitabile, Nuno Carvalhais, Anna B. Harper, Etsushi Kato, Charles Koven, Yi Y. Liu, Julia E.M.S. Nabel, Yude Pan, Julia Pongratz, Benjamin Poulter, Thomas A. M. Pugh, Maurizio Santoro, Stephen Sitch, Benjamin D. Stocker, Nicolas Viovy, Andy Wiltshire, Rasoul Yousefpour, and Sönke Zaehle
Biogeosciences, 14, 5053–5067,Short summary
We used several observation-based biomass datasets to constrain the historical land-use change carbon emissions simulated by models. Compared to the range of the original modeled emissions (from 94 to 273 Pg C), the observationally constrained global cumulative emission estimate is 155 ± 50 Pg C (1σ Gaussian error) from 1901 to 2012. Our approach can also be applied to evaluate the LULCC impact of land-based climate mitigation policies.
Lukas Baumbach, Jonatan F. Siegmund, Magdalena Mittermeier, and Reik V. Donner
Biogeosciences, 14, 4891–4903,Short summary
Temperature extremes play a crucial role for vegetation growth and vitality in vast parts of the European continent. Here, we study the likelihood of simultaneous occurrences of extremes in daytime land surface temperatures and the normalized difference vegetation index (NDVI) for three main periods during the growing season. Our results reveal a particularly high vulnerability of croplands to temperature extremes, while other vegetation types are considerably less affected.
Rudra K. Shrestha, Vivek K. Arora, Joe R. Melton, and Laxmi Sushama
Biogeosciences, 14, 4733–4753,Short summary
Computer models of vegetation provide a tool to assess how future changes in climate may the affect geographical distribution of vegetation. However, such models must first be assessed for their ability to reproduce the present-day geographical distribution of vegetation. Here, we assess the ability of one such dynamic vegetation model. We find that while the model is broadly successful in reproducing the geographical distribution of trees and grasses in North America some limitations remain.
Elizabeth E. Webb, Kathryn Heard, Susan M. Natali, Andrew G. Bunn, Heather D. Alexander, Logan T. Berner, Alexander Kholodov, Michael M. Loranty, John D. Schade, Valentin Spektor, and Nikita Zimov
Biogeosciences, 14, 4279–4294,Short summary
Permafrost soils store massive amounts of C, yet estimates of soil C storage in this region are highly uncertain, primarily due to undersampling at all spatial scales; circumpolar soil C estimates lack sufficient continental spatial diversity, regional intensity, and replication at the field-site level. We aim to reduce the uncertainty of regional C estimates by providing a comprehensive assessment of vegetation, active-layer, and permafrost C stocks in a watershed in northeast Siberia, Russia.
Thierry Fanin and Guido R. van der Werf
Biogeosciences, 14, 3995–4008,Short summary
Using night fire detection and rainfall datasets during 1997–2015, we found that the number of night fires detected in 1997 was 2.2 times higher than in 2015, but with a higher fraction of peatland burned in 2015. We also confirmed the non-linearity of rainfall accumulation prior to a fire to indicate a high fire year. The influence of rainfall on the number of yearly fires varies across Indonesia. Southern Sumatra and Kalimantan need 120 days of observations, while northern Sumatra only 30.
Tara W. Hudiburg, Philip E. Higuera, and Jeffrey A. Hicke
Biogeosciences, 14, 3873–3882,Short summary
Wildfire is a dominant disturbance agent in forest ecosystems, shaping important processes including net carbon (C) balance. Our results imply that fire-regime variability is a major driver of C trajectories in stand-replacing fire regimes. Predicting carbon balance in these systems, therefore, will depend strongly on the ability of ecosystem models to represent a realistic range of fire-regime variability over the past several centuries to millennia.
Dolors Armenteras, Joan Sebastian Barreto, Karyn Tabor, Roberto Molowny-Horas, and Javier Retana
Biogeosciences, 14, 2755–2765,Short summary
Tropical forests are highly threatened by the expansion of the agricultural frontier, use of fire and subsequent deforestation. NW Amazonia is the wettest part of the basin and the role of fire is still largely unknown in this subregion. In this study, we compared fire regimes in five countries sharing this tropical biome (Venezuela, Colombia, Ecuador, Peru and Brazil). We studied fire activity in relation to proximity to roads and rivers and how fire occurs in relation to forest fragmentation.
Thomas Kaminski and Pierre-Philippe Mathieu
Biogeosciences, 14, 2343–2357,Short summary
This paper provides the formalism and examples of how observation operators can be used, in combination with data assimilation or retrieval techniques, to better ingest satellite products in a manner consistent with the dynamics of the Earth system expressed by models.
Per-Ola Olsson, Michal Heliasz, Hongxiao Jin, and Lars Eklundh
Biogeosciences, 14, 1703–1719,
Jason Beringer, Ian McHugh, Lindsay B. Hutley, Peter Isaac, and Natascha Kljun
Biogeosciences, 14, 1457–1460,Short summary
Standardised, quality-controlled and robust data from flux networks underpin the understanding of ecosystem processes and tools to manage our natural resources. The Dynamic INtegrated Gap-filling and partitioning for OzFlux (DINGO) system enables gap-filling and partitioning of fluxes and subsequently provides diagnostics and results. Quality data from robust systems like DINGO ensure the utility and uptake of flux data and facilitates synergies between flux, remote sensing and modelling.
Biogeosciences, 14, 751–754,Short summary
The response of ecosystems to elevated atmospheric CO2 is affected by nitrogen (N) availability. It has been hypothesized that N limitation becomes progressively stronger (progressive N limitation, PNL). Most long-term free air CO2 enrichment studies did not see a PNL. This paper shows that enhanced biological N2 fixation only prevents PNL in plant communities with symbiotic N2 fixation. In most ecosystems a stimulation of gross N mineralization prevents the development of a PNL.
Yiqi Luo, Zheng Shi, Xingjie Lu, Jianyang Xia, Junyi Liang, Jiang Jiang, Ying Wang, Matthew J. Smith, Lifen Jiang, Anders Ahlström, Benito Chen, Oleksandra Hararuk, Alan Hastings, Forrest Hoffman, Belinda Medlyn, Shuli Niu, Martin Rasmussen, Katherine Todd-Brown, and Ying-Ping Wang
Biogeosciences, 14, 145–161,Short summary
Climate change is strongly regulated by land carbon cycle. However, we lack the ability to predict future land carbon sequestration. Here, we develop a novel framework for understanding what determines the direction and rate of future change in land carbon storage. The framework offers a suite of new approaches to revolutionize land carbon model evaluation and improvement.
Martina Franz, David Simpson, Almut Arneth, and Sönke Zaehle
Biogeosciences, 14, 45–71,Short summary
Ozone is a toxic air pollutant that can damage plant leaves and impact their carbon uptake from the atmosphere. We extend a terrestrial biosphere model to account for ozone damage of plants and investigate the impact on the terrestrial carbon cycle. Our approach accounts for ozone transport from the free troposphere to leaf level. We find that this substantially affects simulated ozone uptake into the plants. Simulations indicate that ozone damages plants less than expected from previous studies
Xian Yang, Xiulian Chi, Chengjun Ji, Hongyan Liu, Wenhong Ma, Anwar Mohhammat, Zhaoyong Shi, Xiangping Wang, Shunli Yu, Ming Yue, and Zhiyao Tang
Biogeosciences, 13, 4429–4438,Short summary
Leaf chemical concentrations are key traits in ecosystem functioning. Previous studies were biased for trees and grasses. Here, we explored the patterns of leaf N and P concentrations in relation to climate, soil, and evolutionary history in northern China. We found that climate influenced the community chemical traits through the shift in species composition, whereas soil directly influenced the community chemical traits.
Aerts, R., Wagendorp, T., November, E., Behailu, M., Deckers, J., and Muys, B.: Ecosystem thermal buffer capacity as an indicator of the restoration status of protected areas in the northern ethiopian highlands, Restor. Ecol., 12, 586–596, https://doi.org/10.1111/j.1061-2971.2004.00324.x, 2004.
Ahl, D. E., Gower, S. T., Mackay, D. S., Burrows, S. N., Norman, J. M., and Diak, G. R.: Heterogeneity of light use efficiency in a northern Wisconsin forest: implications for modeling net primary production with remote sensing, Remote Sens. Environ., 93, 168–178, https://doi.org/10.1016/j.rse.2004.07.003, 2004.
Ahn, J.-H., Park, Y.-J., Ryu, J.-H., Lee, B., and Oh, I. S.: Development of atmospheric correction algorithm for Geostationary Ocean Color Imager (GOCI), Ocean Sci. J., 47, 247–259, https://doi.org/10.1007/s12601-012-0026-2, 2012.
Anderson, M., Norman, J. M., Diak, G. R., Kustas, W. P., and Mecikalski, J. R.: A Two-Source Time-Integrated Model for Estimating Surface Fluxes Using Thermal Infrared Remote Sensing, Remote Sens. Environ., 60, 195–216, https://doi.org/10.1016/S0034-4257(96)00215-5, 1997.
Anderson, M., Norman, J., Kustas, W., Houborg, R., Starks, P., and Agam, N.: A thermal-based remote sensing technique for routine mapping of land-surface carbon, water and energy fluxes from field to regional scales, Remote Sens. Environ., 112, 4227–4241, https://doi.org/10.1016/j.rse.2008.07.009, 2008.
Anderson, M., Diak, G., Gao, F., Knipper, K., Hain, C., Eichelmann, E., Hemes, K., Baldocchi, D., Kustas, W., and Yang, Y.: Impact of Insolation Data Source on Remote Sensing Retrievals of Evapotranspiration over the California Delta, Remote Sens., 11, 216, https://doi.org/10.3390/rs11030216, 2019.
Anderson, M. C., Norman, J. M., Meyers, T. P., and Diak, G. R.: An analytical model for estimating canopy transpiration and carbon assimilation fluxes based on canopy light-use efficiency, Agr. Forest Meteorol., 101, 265–289, https://doi.org/10.1016/S0168-1923(99)00170-7, 2000.
Anderson, M. C., Norman, J. M., Mecikalski, J. R., Otkin, J. A., and Kustas, W. P.: A climatological study of evapotranspiration and moisture stress across the continental United States based on thermal remote sensing: 1. Model formulation, J. Geophys. Res., 112, D10117, https://doi.org/10.1029/2006JD007506, 2007a.
Anderson, M. C., Norman, J. M., Mecikalski, J. R., Otkin, J. A., and Kustas, W. P.: A climatological study of evapotranspiration and moisture stress across the continental United States based on thermal remote sensing: 2. Surface moisture climatology, J. Geophys. Res., 112, D11112, https://doi.org/10.1029/2006JD007507, 2007b.
Anderson, M. C., Kustas, W. P., Norman, J. M., Hain, C. R., Mecikalski, J. R., Schultz, L., González-Dugo, M. P., Cammalleri, C., d'Urso, G., Pimstein, A., and Gao, F.: Mapping daily evapotranspiration at field to continental scales using geostationary and polar orbiting satellite imagery, Hydrol. Earth Syst. Sci., 15, 223–239, https://doi.org/10.5194/hess-15-223-2011, 2011.
Anderson, M. C., Hain, C., Otkin, J., Zhan, X., Mo, K., Svoboda, M., Wardlow, B., and Pimstein, A.: An Intercomparison of Drought Indicators Based on Thermal Remote Sensing and NLDAS-2 Simulations with U.S. Drought Monitor Classifications, J. Hydrometeor., 14, 1035–1056, https://doi.org/10.1175/JHM-D-12-0140.1, 2013a.
Anderson, M. C., Cammalleri, C., Hain, C. R., Otkin, J., Zhan, X., and Kustas, W.: Using a Diagnostic Soil-Plant-Atmosphere Model for Monitoring Drought at Field to Continental Scales, Procedia Environ. Sci., 19, 47–56, https://doi.org/10.1016/j.proenv.2013.06.006, 2013b.
Anderson, M. C., Zolin, C., Sentelhas, P., Hain, C., Semmens, K., Yilmaz, M. T., Gao, F., Otkin, J., and Tetrault, R.: Assessing correlations of satellite-derived evapotranspiration, precipitation and leaf area index anomalies with yields of major Brazilian crop, Remote Sens. Environ., 174, 82–99, 2016.
Anderson, M. C., Yang, Y., Xue, J., Knipper, K. R., Yang, Y., Gao, F., Hain, C. R., Kustas, W. P., Cawse-Nicholson, K., Hulley, G., Fisher, J. B., Alfieri, J. G., Meyers, T. P., Prueger, J., Baldocchi, D. D., and Rey-Sanchez, C.: Interoperability of ECOSTRESS and Landsat for mapping evapotranspiration time series at sub-field scales, Remote Sens. Environ., 252, 112189, https://doi.org/10.1016/j.rse.2020.112189, 2020.
Badgley, G., Field, C. B., and Berry, J. A.: Canopy near-infrared reflectance and terrestrial photosynthesis, Sci. Adv., 3, e1602244, https://doi.org/10.1126/sciadv.1602244, 2017.
Badgley, G., Anderegg, L. D. L., Berry, J. A., and Field, C. B.: Terrestrial gross primary production: Using NIRV to scale from site to globe, Glob. Change Biol., 25, 3731–3740, https://doi.org/10.1111/gcb.14729, 2019.
Baguskas, S. A., Oliphant, A. J., Clemesha, R. E. S., and Loik, M. E.: Water and light-use efficiency are enhanced under summer coastal fog in a California agricultural system, J. Geophys. Res.-Biogeo., 126, e2020JG006193, https://doi.org/10.1029/2020JG006193, 2021.
Baldocchi, D. D., Ryu, Y., Dechant, B., Eichelmann, E., Hemes, K., Ma, S., Sanchez, C. R., Shortt, R., Szutu, D., Valach, A., Verfaillie, J., Badgley, G., Zeng, Y., and Berry, J. A.: Outgoing near-infrared radiation from vegetation scales with canopy photosynthesis across a spectrum of function, structure, physiological capacity, and weather, J. Geophys. Res.-Biogeo., 125, e2019JG005534, https://doi.org/10.1029/2019JG005534, 2020.
Bauerle, W. L., Oren, R., Way, D. A., Qian, S. S., Stoy, P. C., Thornton, P. E., Bowden, J. D., Hoffman, F. M., and Reynolds, R. F.: Photoperiodic regulation of the seasonal pattern of photosynthetic capacity and the implications for carbon cycling, P. Natl. Acad. Sci. USA, 109, 8612–8617, https://doi.org/10.1073/pnas.1119131109, 2012.
Bieliński, T.: A parallax shift effect correction based on cloud height for geostationary satellites and radar observations, Remote Sens., 12, 365, https://doi.org/10.3390/rs12030365, 2020.
Bradley, N. L., Leopold, A. C., Ross, J., and Huffaker, W.: Phenological changes reflect climate change in Wisconsin, P. Natl. Acad. Sci. USA, 96, 9701–9704, https://doi.org/10.1073/pnas.96.17.9701, 1999.
Brown, T. B., Hultine, K. R., Steltzer, H., Denny, E. G., Denslow, M. W., Granados, J., Henderson, S., Moore, D., Nagai, S., SanClements, M., Sánchez-Azofeifa, A., Sonnentag, O., Tazik, D., and Richardson, A. D.: Using phenocams to monitor our changing Earth: toward a global phenocam network, Front. Ecol. Environ., 14, 84–93, https://doi.org/10.1002/fee.1222, 2016.
Brunsell, N. A., Schymanski, S. J., and Kleidon, A.: Quantifying the thermodynamic entropy budget of the land surface: is this useful?, Earth Syst. Dynam., 2, 87–103, https://doi.org/10.5194/esd-2-87-2011, 2011.
Cammalleri, C., Anderson, M. C., Gao, F., Hain, C. R., and Kustas, W. P.: A data fusion approach for mapping daily evapotranspiration at field scale, Water Resour. Res., 49, 4672–4686, https://doi.org/10.1002/wrcr.20349, 2013.
Carrer, D., Ceamanos, X., Moparthy, S., Vincent, C., C. Freitas, S., and Trigo, I. F.: Satellite Retrieval of Downwelling Shortwave Surface Flux and Diffuse Fraction under All Sky Conditions in the Framework of the LSA SAF Program (Part 1: Methodology), Remote Sens., 11, 2532, https://doi.org/10.3390/rs11212532, 2019.
Chambers, J. Q., Fisher, J. I., Zeng, H., Chapman, E. L., Baker, D. B., and Hurtt, G. C.: Hurricane Katrina's carbon footprint on U.S. Gulf Coast forests, Science, 318, 1107, https://doi.org/10.1126/science.1148913, 2007.
Chen, D., Huang, J., and Jackson, T. J.: Vegetation water content estimation for corn and soybeans using spectral indices derived from MODIS near- and short-wave infrared bands, Remote Sens. Environ., 98, 225–236, https://doi.org/10.1016/j.rse.2005.07.008, 2005.
Choi, J.-K., Min, J.-E., Noh, J. H., Han, T.-H., Yoon, S., Park, Y. J., Moon, J.-E., Ahn, J.-H., Ahn, S. M., and Park, J.-H.: Harmful algal bloom (HAB) in the East Sea identified by the Geostationary Ocean Color Imager (GOCI), Harmful Algae, 39, 295–302, https://doi.org/10.1016/j.hal.2014.08.010, 2014.
Chudnovsky, A., Ben-Dor, E., and Saaroni, H.: Diurnal thermal behavior of selected urban objects using remote sensing measurements, Energ. Build., 36, 1063–1074, https://doi.org/10.1016/j.enbuild.2004.01.052, 2004.
Concha, J., Mannino, A., Franz, B., and Kim, W.: Uncertainties in the Geostationary Ocean Color Imager (GOCI) Remote Sensing Reflectance for Assessing Diurnal Variability of Biogeochemical Processes, Remote Sens., 11, 295, https://doi.org/10.3390/rs11030295, 2019.
Coyle, D. B., Stysley, P. R., Poulios, D., Clarke, G. B., and Kay, R. B.: Laser transmitter development for NASA's Global Ecosystem Dynamics Investigation (GEDI) lidar, in: Lidar Remote Sensing for Environmental Monitoring XV, Vol. 9612, edited by: Singh, U. N., SPIE, 961208, https://doi.org/10.1117/12.2191569, 2015.
Dannenberg, M., Wang, X., Yan, D., and Smith, W.: Phenological characteristics of global ecosystems based on optical, fluorescence, and microwave remote sensing, Remote Sens., 12, 671, https://doi.org/10.3390/rs12040671, 2020.
De Araujo Barbosa, C. C., Atkinson, P. M., and Dearing, J. A.: Remote sensing of ecosystem services: A systematic review, Ecol. Ind., 52, 430–443, https://doi.org/10.1016/j.ecolind.2015.01.007, 2015.
Diak, G. R.: Investigations of improvements to an operational GOES-satellite-data-based insolation system using pyranometer data from the U.S. Climate Reference Network (USCRN), Remote Sens. Environ., 195, 79–95, https://doi.org/10.1016/j.rse.2017.04.002, 2017.
Diak, G. R. and Gautier, C.: Improvements to a Simple Physical Model for Estimating Insolation from GOES Data, J. Climate Appl. Meteor., 22, 505–508, https://doi.org/10.1175/1520-0450(1983)022<0505:ITASPM>2.0.CO;2, 1983.
Diak, G. R. and Stewart, T. R.: Assessment of surface turbulent fluxes using geostationary satellite surface skin temperatures and a mixed layer planetary boundary layer scheme, J. Geophys. Res., 94, 6357, https://doi.org/10.1029/JD094iD05p06357, 1989.
Diak, G. R. and Whipple, M. S.: Note on estimating surface sensible heat fluxes using surface temperatures measured from a geostationary satellite during FIFE 1989, J. Geophys. Res., 100, 25453, https://doi.org/10.1029/95JD00729, 1995.
Dutkiewicz, S., Hickman, A. E., Jahn, O., Henson, S., Beaulieu, C., and Monier, E.: Ocean colour signature of climate change, Nat. Commun., 10, 578, https://doi.org/10.1038/s41467-019-08457-x, 2019.
DOC, NOAA, NESDIS and NASA: Product Definition and User's Guide (PUG): Volume 3: Level 1b Products, available at: https://www.goes-r.gov/users/docs/PUG-L1b-vol3.pdf (last access: 7 August 2020), 2019.
Emmel, C., D'Odorico, P., Revill, A., Hörtnagl, L., Ammann, C., Buchmann, N., and Eugster, W.: Canopy photosynthesis of six major arable crops is enhanced under diffuse light due to canopy architecture, Glob. Change Biol., 26, 5164–5177, https://doi.org/10.1111/gcb.15226, 2020.
Erbs, D. G., Klein, S. A., and Duffie, J. A.: Estimation of the diffuse radiation fraction for hourly, daily and monthly-average global radiation, Sol. Energy, 28, 293–302, https://doi.org/10.1016/0038-092X(82)90302-4, 1982.
Fang, L., Zhan, X., Schull, M., Kalluri, S., Laszlo, I., Yu, P., Carter, C., Hain, C., and Anderson, M.: Evapotranspiration Data Product from NESDIS GET-D System Upgraded for GOES-16 ABI Observations, Remote Sens., 11, 2639, https://doi.org/10.3390/rs11222639, 2019.
Fensholt, R. and Sandholt, I.: Derivation of a shortwave infrared water stress index from MODIS near- and shortwave infrared data in a semiarid environment, Remote Sens. Environ., 87, 111–121, https://doi.org/10.1016/j.rse.2003.07.002, 2003.
Fensholt, R., Sandholt, I., Stisen, S., and Tucker, C.: Analysing NDVI for the African continent using the geostationary meteosat second generation SEVIRI sensor, Remote Sens. Environ., 101, 212–229, https://doi.org/10.1016/j.rse.2005.11.013, 2006.
Filippa, G., Cremonese, E., Migliavacca, M., Galvagno, M., Sonnentag, O., Humphreys, E., Hufkens, K., Ryu, Y., Verfaillie, J., Morra di Cella, U., and Richardson, A. D.: NDVI derived from near-infrared-enabled digital cameras: Applicability across different plant functional types, Agr. Forest Meteorol., 249, 275–285, https://doi.org/10.1016/j.agrformet.2017.11.003, 2018.
Fu, Z., Stoy, P. C., Luo, Y., Chen, J., Sun, J., Montagnani, L., Wohlfahrt, G., Rahman, A. F., Rambal, S., Bernhofer, C., Wang, J., Shirkey, G., and Niu, S.: Climate controls over the net carbon uptake period and amplitude of net ecosystem production in temperate and boreal ecosystems, Agr. Forest Meteorol., 243, 9–18, https://doi.org/10.1016/j.agrformet.2017.05.009, 2017.
Fu, Z., Stoy, P. C., Poulter, B., Gerken, T., Zhang, Z., Wakbulcho, G., and Niu, S.: Maximum carbon uptake rate dominates the interannual variability of global net ecosystem exchange, Glob. Change Biol., 25, 3381–3394, https://doi.org/10.1111/gcb.14731, 2019.
Gamon, J. A., Field, C. B., Goulden, M. L., Griffin, K. L., Hartley, A. E., Joel, G., Penuelas, J., and Valentini, R.: Relationships between NDVI, canopy structure, and photosynthesis in three californian vegetation types, Ecol. Appl., 5, 28–41, https://doi.org/10.2307/1942049, 1995.
Gamon, J. A., Huemmrich, K. F., Wong, C. Y. S., Ensminger, I., Garrity, S., Hollinger, D. Y., Noormets, A., and Peñuelas, J.: A remotely sensed pigment index reveals photosynthetic phenology in evergreen conifers, P. Natl. Acad. Sci. USA, 113, 13087–13092, https://doi.org/10.1073/pnas.1606162113, 2016.
Ganguly, S., Friedl, M. A., Tan, B., Zhang, X., and Verma, M.: Land surface phenology from MODIS: Characterization of the Collection 5 global land cover dynamics product, Remote Sens. Environ., 114, 1805–1816, https://doi.org/10.1016/j.rse.2010.04.005, 2010.
Gao, B.: NDWI – A normalized difference water index for remote sensing of vegetation liquid water from space, Remote Sens. Environ., 58, 257–266, https://doi.org/10.1016/S0034-4257(96)00067-3, 1996.
Gao, F., Hilker, T., Zhu, X., Anderson, M., Masek, J., Wang, P., and Yang, Y.: Fusing landsat and MODIS data for vegetation monitoring, IEEE Geosci. Remote Sens. Mag., 3, 47–60, https://doi.org/10.1109/MGRS.2015.2434351, 2015.
García-Haro, F. J., Campos-Taberner, M., Moreno, Á., Tagesson, H. T., Camacho, F., Martínez, B., Sánchez, S., Piles, M., Camps-Valls, G., Yebra, M., and Gilabert, M. A.: A global canopy water content product from AVHRR/Metop, ISPRS J. Photogramm., 162, 77–93, https://doi.org/10.1016/j.isprsjprs.2020.02.007, 2020.
Gautier, C., Diak, G., and Masse, S.: A Simple Physical Model to Estimate Incident Solar Radiation at the Surface from GOES Satellite Data, J. Appl. Meteor., 19, 1005–1012, https://doi.org/10.1175/1520-0450(1980)019<1005:ASPMTE>2.0.CO;2, 1980.
Ghilain, N., Arboleda, A., and Gellens-Meulenberghs, F.: Evapotranspiration modelling at large scale using near-real time MSG SEVIRI derived data, Hydrol. Earth Syst. Sci., 15, 771–786, https://doi.org/10.5194/hess-15-771-2011, 2011.
Goetz, S. J., Fiske, G. J., and Bunn, A. G.: Using satellite time-series data sets to analyze fire disturbance and forest recovery across Canada, Remote Sens. Environ., 101, 352–365, https://doi.org/10.1016/j.rse.2006.01.011, 2006.
Govaerts, Y. M., Wagner, S., Lattanzio, A., and Watts, P.: Joint retrieval of surface reflectance and aerosol optical depth from MSG/SEVIRI observations with an optimal estimation approach: 1. Theory, J. Geophys. Res., 115, D02203, https://doi.org/10.1029/2009JD011779, 2010.
Grant, I. F., Prata, A. J., and Cechet, R. P.: The impact of the diurnal variation of albedo on the remote sensing of the daily mean albedo of grassland, J. Appl. Meteor., 39, 231–244, https://doi.org/10.1175/1520-0450(2000)039<0231:TIOTDV>2.0.CO;2, 2000.
Gu, L., Baldocchi, D. D., Wofsy, S. C., Munger, J. W., Michalsky, J. J., Urbanski, S. P., and Boden, T. A.: Response of a deciduous forest to the Mount Pinatubo eruption: enhanced photosynthesis, Science, 299, 2035–2038, https://doi.org/10.1126/science.1078366, 2003.
Hardisky, M., Klemas, V., and Smart, R. M.: The influence of soil salinity, growth form, and leaf moisture on the spectral radiance of Spartina Alterniflora canopies, Photogramm. Engin. Remote Sens., 49, 77–83, 1983.
Hashimoto, H., Wang, W., Dungan, J. L., Li, S., Michaelis, A. R., Takenaka, H., Higuchi, A., Myneni, R. B., and Nemani, R. R.: New generation geostationary satellite observations support seasonality in greenness of the Amazon evergreen forests, Nat. Commun., 12, 684, https://doi.org/10.1038/s41467-021-20994-y, 2021.
Heidinger, A. and Straka, W. C.: Algorithm Theoretical Basis Document ABI Cloud Mask, NOAA NESDIS Center for Satellite Applications and Research, Version 3.0, available at: https://github.com/anmikhan/envirogoes.git (last access: 1 January 2021), 2012.
Hemes, K. S., Verfaillie, J., and Baldocchi, D. D.: Wildfire-smoke aerosols lead to increased light use efficiency among agricultural and restored wetland land uses in california's central valley, J. Geophys. Res.-Biogeo., 125, e2019JG005380, https://doi.org/10.1029/2019JG005380, 2020.
He, T., Liang, S., Wang, D., Wu, H., Yu, Y., and Wang, J.: Estimation of surface albedo and directional reflectance from Moderate Resolution Imaging Spectroradiometer (MODIS) observations, Remote Sens. Environ., 119, 286–300, https://doi.org/10.1016/j.rse.2012.01.004, 2012.
He, T., Zhang, Y., Liang, S., Yu, Y., and Wang, D.: Developing Land Surface Directional Reflectance and Albedo Products from Geostationary GOES-R and Himawari Data: Theoretical Basis, Operational Implementation, and Validation, Remote Sens., 11, 2655, https://doi.org/10.3390/rs11222655, 2019.
Higuchi, A.: Toward more integrated utilizations of geostationary satellite data for disaster management and risk mitigation, Remote Sens., 13, 1553, https://doi.org/10.3390/rs13081553, 2021.
Hilker, T., Wulder, M. A., Coops, N. C., Linke, J., McDermid, G., Masek, J. G., Gao, F., and White, J. C.: A new data fusion model for high spatial- and temporal-resolution mapping of forest disturbance based on Landsat and MODIS, Remote Sens. Environ., 113, 1613–1627, https://doi.org/10.1016/j.rse.2009.03.007, 2009.
Holdaway, R. J., Sparrow, A. D., and Coomes, D. A.: Trends in entropy production during ecosystem development in the Amazon Basin, Philos. T. R. Soc. Lond. B, 365, 1437–1447, https://doi.org/10.1098/rstb.2009.0298, 2010.
Huete, A., Didan, K., Miura, T., Rodriguez, E. P., Gao, X., and Ferreira, L. G.: Overview of the radiometric and biophysical performance of the MODIS vegetation indices, Remote Sens. Environ., 83, 195–213, https://doi.org/10.1016/S0034-4257(02)00096-2, 2002.
Hulley, G., Hook, S., Fisher, J., and Lee, C.: ECOSTRESS, A NASA Earth-Ventures Instrument for studying links between the water cycle and plant health over the diurnal cycle, in 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), IEEE, 5494–5496, https://doi.org/10.1109/IGARSS.2017.8128248, 2017.
Jackson, T. J., Chen, D., Cosh, M., Li, F., Anderson, M., Walthall, C., Doriaswamy, P., and Hunt, E. R.: Vegetation water content mapping using Landsat data derived normalized difference water index for corn and soybeans, Remote Sens. Environ., 92, 475–482, https://doi.org/10.1016/j.rse.2003.10.021, 2004.
Janjai, S. and Wattan, R.: Development of a model for the estimation of photosynthetically active radiation from geostationary satellite data in a tropical environment, Remote Sens. Environ., 115, 1680–1693, https://doi.org/10.1016/j.rse.2011.02.026, 2011.
Jolliff, J. K., Lewis, M. D., Ladner, S., and Crout, R. L.: Observing the Ocean Submesoscale with Enhanced-Color GOES-ABI Visible Band Data, Sensors, 19, 3900, https://doi.org/10.3390/s19183900, 2019.
Jordan, C. F.: Derivation of Leaf-Area Index from Quality of Light on the Forest Floor, Ecology, 50, 663–666, https://doi.org/10.2307/1936256, 1969.
Kannenberg, S. A., Bowling, D. R., and Anderegg, W. R. L.: Hot moments in ecosystem fluxes: High GPP anomalies exert outsized influence on the carbon cycle and are differentially driven by moisture availability across biomes, Environ. Res. Lett., 15, 054004, https://doi.org/10.1088/1748-9326/ab7b97, 2020.
Kay, J. J. and Schneider, E. D.: Thermodynamics and measures of ecological integrity, in: Ecological Indicators, edited by: McKenzie, D. H., Hyatt, D. E., and McDonald, V. J., Springer US, Boston, MA, 159–182, https://doi.org/10.1007/978-1-4615-4659-7_12, 1992.
Kerr, J. T. and Ostrovsky, M.: From space to species: ecological applications for remote sensing, Trends. Ecol. Evol., 18, 299–305, https://doi.org/10.1016/S0169-5347(03)00071-5, 2003.
Kim, H.-W., Yeom, J.-M., Shin, D., Choi, S., Han, K.-S., and Roujean, J.-L.: An assessment of thin cloud detection by applying bidirectional reflectance distribution function model-based background surface reflectance using Geostationary Ocean Color Imager (GOCI): A case study for South Korea, J. Geophys. Res.-Atmos., 122, 8153–8172, https://doi.org/10.1002/2017JD026707, 2017.
Knauer, K., Gessner, U., Fensholt, R., and Kuenzer, C.: An ESTARFM Fusion Framework for the Generation of Large-Scale Time Series in Cloud-Prone and Heterogeneous Landscapes, Remote Sens., 8, 425, https://doi.org/10.3390/rs8050425, 2016.
LeBauer, D., Wang, D., Feng, X., and Dietz, M.: PECAn: workflow management for data assimilation and forecasting, Nat. Prec., https://doi.org/10.1038/npre.2011.5533.1, 2011.
Leitão, P. J., Schwieder, M., Pötzschner, F., Pinto, J. R. R., Teixeira, A. M. C., Pedroni, F., Sanchez, M., Rogass, C., van der Linden, S., Bustamante, M. M. C., and Hostert, P.: From sample to pixel: multi-scale remote sensing data for upscaling aboveground carbon data in heterogeneous landscapes, Ecosphere, 9, e02298, https://doi.org/10.1002/ecs2.2298, 2018.
Lin, H., Cao, M., Stoy, P. C., and Zhang, Y.: Assessing self-organization of plant communities – A thermodynamic approach, Ecol. Modell., 220, 784–790, https://doi.org/10.1016/j.ecolmodel.2009.01.003, 2009.
Liu, Y., Hill, M. J., Zhang, X., Wang, Z., Richardson, A. D., Hufkens, K., Filippa, G., Baldocchi, D. D., Ma, S., Verfaillie, J., and Schaaf, C. B.: Using data from Landsat, MODIS, VIIRS and PhenoCams to monitor the phenology of California oak/grass savanna and open grassland across spatial scales, Agr. Forest Meteorol., 237/238, 311–325, https://doi.org/10.1016/j.agrformet.2017.02.026, 2017.
Li, F., Zhang, X., Roy, D. P., and Kondragunta, S.: Estimation of biomass-burning emissions by fusing the fire radiative power retrievals from polar-orbiting and geostationary satellites across the conterminous United States, Atmos. Environ., 211, 274–287, https://doi.org/10.1016/j.atmosenv.2019.05.017, 2019a.
Li, S., Wang, W., Hashimoto, H., Xiong, J., Vandal, T., Yao, J., Qian, L., Ichii, K., Lyapustin, A., Wang, Y., and Nemani, R.: First Provisional Land Surface Reflectance Product from Geostationary Satellite Himawari-8 AHI, Remote Sens., 11, 2990, https://doi.org/10.3390/rs11242990, 2019b.
Li, X., Xiao, J., Fisher, J. B., and Baldocchi, D. D.: ECOSTRESS estimates gross primary production with fine spatial resolution for different times of day from the International Space Station, Remote Sens. Environ., 258, 112360, https://doi.org/10.1016/j.rse.2021.112360, 2021.
Li, Z.-L., Tang, B.-H., Wu, H., Ren, H., Yan, G., Wan, Z., Trigo, I. F., and Sobrino, J. A.: Satellite-derived land surface temperature: Current status and perspectives, Remote Sens. Environ., 131, 14–37, https://doi.org/10.1016/j.rse.2012.12.008, 2013.
Mahadevan, P., Wofsy, S. C., Matross, D. M., Xiao, X., Dunn, A. L., Lin, J. C., Gerbig, C., Munger, J. W., Chow, V. Y., and Gottlieb, E. W.: A satellite-based biosphere parameterization for net ecosystem CO2 exchange: Vegetation Photosynthesis and Respiration Model (VPRM), Global Biogeochem. Cy., 22, GB2005, https://doi.org/10.1029/2006GB002735, 2008.
Ma, X., Huete, A., Tran, N. N., Bi, J., Gao, S., and Zeng, Y.: Sun-Angle Effects on Remote-Sensing Phenology Observed and Modelled Using Himawari-8, Remote Sens., 12, 1339, https://doi.org/10.3390/rs12081339, 2020.
McCallum, I., Wagner, W., Schmullius, C., Shvidenko, A., Obersteiner, M., Fritz, S., and Nilsson, S.: Satellite-based terrestrial production efficiency modeling, Carb. Balance Manag., 4, 8, https://doi.org/10.1186/1750-0680-4-8, 2009.
McCorkel, J., Efremova, B., Hair, J., Andrade, M., and Holben, B.: GOES-16 ABI solar reflective channel validation for earth science application, Remote Sens. Environ., 237, 111438, https://doi.org/10.1016/j.rse.2019.111438, 2020.
Medlyn, B. E.: Physiological basis of the light use efficiency model, Tree Physiol., 18, 167–176, https://doi.org/10.1093/treephys/18.3.167, 1998.
Meng, R., Wu, J., Zhao, F., Cook, B. D., Hanavan, R. P., and Serbin, S. P.: Measuring short-term post-fire forest recovery across a burn severity gradient in a mixed pine-oak forest using multi-sensor remote sensing techniques, Remote Sens. Environ., 210, 282–296, https://doi.org/10.1016/j.rse.2018.03.019, 2018.
Menzel, W. P.: History of geostationary weather satellites, in: The GOES-R Series, Elsevier, 5–11, https://doi.org/10.1016/B978-0-12-814327-8.00002-0, 2020.
Mercado, L. M., Bellouin, N., Sitch, S., Boucher, O., Huntingford, C., Wild, M., and Cox, P. M.: Impact of changes in diffuse radiation on the global land carbon sink, Nature, 458, 1014–1017, https://doi.org/10.1038/nature07949, 2009.
Miura, T., Nagai, S., Takeuchi, M., Ichii, K., and Yoshioka, H.: Improved Characterisation of Vegetation and Land Surface Seasonal Dynamics in Central Japan with Himawari-8 Hypertemporal Data, Sci. Rep., 9, 15692, https://doi.org/10.1038/s41598-019-52076-x, 2019.
Mladenova, I. E., Bolten, J. D., Crow, W. T., Anderson, M. C., Hain, C. R., Johnson, D. M., and Mueller, R.: Intercomparison of soil moisture, evaporative stress, and vegetation indices for estimating corn and soybean yields over the U.S., IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens., 10, 1328–1343, https://doi.org/10.1109/JSTARS.2016.2639338, 2017.
Monteith, J. L.: Solar radiation and productivity in tropical ecosystems, J. Appl. Ecol., 9, 747–766, https://doi.org/10.2307/2401901, 1972.
Neukermans, G., Ruddick, K., Bernard, E., Ramon, D., Nechad, B., and Deschamps, P.-Y.: Mapping total suspended matter from geostationary satellites: a feasibility study with SEVIRI in the Southern North Sea, Opt. Express, 17, 14029–14052, https://doi.org/10.1364/oe.17.014029, 2009.
Nieke, J., Borde, F., Mavrocordatos, C., Berruti, B., Delclaud, Y., Riti, J. B., and Garnier, T.: The Ocean and Land Colour Imager (OLCI) for the Sentinel 3 GMES Mission: status and first test results, in: Earth Observing Missions and Sensors: Development, Implementation, and Characterization II, Vol. 8528, edited by: Shimoda, H., Xiong, X., Cao, C., Gu, X., Kim, C., and Kiran Kumar, A. S., SPIE, 85280C, https://doi.org/10.1117/12.977247, 2012.
NOAA: Summary of the GOES-17 Cooling System Issue, GOES Image Viewer, available at: https://www.star.nesdis.noaa.gov/GOES/loopheatpipeanomaly.php, last access: 29 October 2020.
NOAA and NASA: GOES-17 ABI Performance, GOES-R, available at: https://www.goes-r.gov/users/GOES-17-ABI-Performance.html, last access: 4 November 2020.
Noh, J. H., Kim, W., Son, S. H., Ahn, J.-H., and Park, Y.-J.: Remote quantification of Cochlodinium polykrikoides blooms occurring in the East Sea using geostationary ocean color imager (GOCI), Harmful Algae, 73, 129–137, https://doi.org/10.1016/j.hal.2018.02.006, 2018.
Norman, J. M., Kustas, W. P., and Humes, K. S.: Source approach for estimating soil and vegetation energy fluxes in observations of directional radiometric surface temperature, Agr. Forest Meteorol., 77, 263–293, https://doi.org/10.1016/0168-1923(95)02265-Y, 1995.
Norris, C., Hobson, P., and Ibisch, P. L.: Microclimate and vegetation function as indicators of forest thermodynamic efficiency, J. Appl. Ecol. 49, 562–570, https://doi.org/10.1111/j.1365-2664.2011.02084.x, 2012.
Odum, E. P.: The Strategy of Ecosystem Development, Science, 164, 262–270, https://doi.org/10.1126/science.164.3877.262, 1969.
Oliphant, A. J. and Stoy, P. C.: An evaluation of semiempirical models for partitioning photosynthetically active radiation into diffuse and direct beam components, J. Geophys. Res.-Biogeo., 123, 889–901, https://doi.org/10.1002/2017JG004370, 2018.
Otkin, J. A., Anderson, M. C., Mecikalski, J. R., and Diak, G. R.: Validation of GOES-Based Insolation Estimates Using Data from the U.S. Climate Reference Network, J. Hydrometeor., 6, 460–475, https://doi.org/10.1175/JHM440.1, 2005.
Otkin, J. A., Anderson, M. C., Hain, C., Mladenova, I. E., Basara, J. B., and Svoboda, M.: Examining rapid onset drought development using the thermal infrared–based evaporative stress index, J. Hydrometeor., 14, 1057–1074, https://doi.org/10.1175/JHM-D-12-0144.1, 2013.
Otkin, J. A., Anderson, M. C., Hain, C., and Svoboda, M.: Examining the Relationship between Drought Development and Rapid Changes in the Evaporative Stress Index, J. Hydrometeor., 15, 938–956, https://doi.org/10.1175/JHM-D-13-0110.1, 2014.
Otkin, J. A., Shafer, M., Svoboda, M., Wardlow, B., Anderson, M. C., Hain, C., and Basara, J.: Facilitating the Use of Drought Early Warning Information through Interactions with Agricultural Stakeholders, Bull. Am. Meteorol. Soc., 96, 1073–1078, https://doi.org/10.1175/BAMS-D-14-00219.1, 2015.
Otkin, J. A., Anderson, M. C., Hain, C., Svoboda, M., Johnson, D., Mueller, R., Tadesse, T., Wardlow, B., and Brown, J.: Assessing the evolution of soil moisture and vegetation conditions during the 2012 United States flash drought, Agr. Forest Meteorol., 218/219, 230–242, https://doi.org/10.1016/j.agrformet.2015.12.065, 2016.
Otkin, J. A., Haigh, T., Mucia, A., Anderson, M. C., and Hain, C.: Comparison of Agricultural Stakeholder Survey Results and Drought Monitoring Datasets during the 2016 U.S. Northern Plains Flash Drought, Wea. Climate Soc., 10, 867–883, https://doi.org/10.1175/WCAS-D-18-0051.1, 2018a.
Otkin, J. A., Svoboda, M., Hunt, E. D., Ford, T. W., Anderson, M. C., Hain, C., and Basara, J. B.: Flash droughts: a review and assessment of the challenges imposed by rapid onset droughts in the united states, Bull. Am. Meteorol. Soc., 99, 911–915, https://doi.org/10.1175/BAMS-D-17-0149.1, 2018b.
Otkin, J. A., Zhong, Y., Hunt, E. D., Basara, J., Svoboda, M., Anderson, M. C., and Hain, C.: Assessing The Evolution Of Soil Moisture And Vegetation Conditions During A Flash Drought – Flash Recovery Sequence Over The South-Central United States, J. Hydrometeor., 20, 549–562, https://doi.org/10.1175/JHM-D-18-0171.1, 2019.
Ouaknine, J., Gode, S., Napierala, B., Viard, T., Foerster, U., Fray, S., Peacoke, P., Hartl, M., Hallibert, P., and Durand, Y.: MTG Flexible Combined Imager optical design and performances, in: Earth Observing Systems XVIII, vol. 8866, edited by: Butler, J. J., (Jack) Xiong, X., and Gu, X., SPIE, 88661A, https://doi.org/10.1117/12.2023078, 2013.
Park, K.-A., Woo, H.-J., and Ryu, J.-H.: Spatial scales of mesoscale eddies from GOCI Chlorophyll-a concentration images in the East/Japan Sea, Ocean Sci. J., 47, 347–358, https://doi.org/10.1007/s12601-012-0033-3, 2012.
Peres, L. F. and DaCamara, C. C.: Emissivity maps to retrieve land-surface temperature from MSG/SEVIRI, IEEE Trans. Geosci. Remote Sens., 43, 1834–1844, https://doi.org/10.1109/TGRS.2005.851172, 2005.
Peschoud, C., Minghelli, A., Mathieu, S., Lei, M., Pairaud, I., and Pinazo, C.: Fusion of Sun-Synchronous and Geostationary Images for Coastal and Ocean Color Survey Application to OLCI (Sentinel-3) and FCI (MTG), IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens., 10, 45–56, https://doi.org/10.1109/JSTARS.2016.2558819, 2017.
Piao, S., Liu, Q., Chen, A., Janssens, I. A., Fu, Y., Dai, J., Liu, L., Lian, X., Shen, M., and Zhu, X.: Plant phenology and global climate change: Current progresses and challenges, Glob. Change Biol., 25, 1922–1940, https://doi.org/10.1111/gcb.14619, 2019.
Pinker, R. T. and Ewing, J. A.: Modeling surface solar radiation: model formulation and validation, J. Clim. Appl. Meteor., 24, 389–401, https://doi.org/10.1175/1520-0450(1985)024<0389:MSSRMF>2.0.CO;2, 1985.
Pinker, R. T., Laszlo, I., Tarpley, J. D., and Mitchell, K.: Geostationary satellite parameters for surface energy balance, Adv. Space Res., 30, 2427–2432, https://doi.org/10.1016/S0273-1177(02)80296-4, 2002.
Pinker, R. T., Ma, Y., Chen, W., Hulley, G., Borbas, E., Islam, T., Hain, C., Cawse-Nicholson, K., Hook, S., and Basara, J.: Towards a Unified and Coherent Land Surface Temperature Earth System Data Record from Geostationary Satellites, Remote Sens., 11, 1399, https://doi.org/10.3390/rs11121399, 2019.
Prins, E. M. and Menzel, W. P.: Trends in South American biomass burning detected with the GOES visible infrared spin scan radiometer atmospheric sounder from 1983 to 1991, J. Geophys. Res., 99, 16719, https://doi.org/10.1029/94JD01208, 1994.
Qi, W., Lee, S.-K., Hancock, S., Luthcke, S., Tang, H., Armston, J., and Dubayah, R.: Improved forest height estimation by fusion of simulated GEDI Lidar data and TanDEM-X InSAR data, Remote Sens. Environ., 221, 621–634, https://doi.org/10.1016/j.rse.2018.11.035, 2019.
Ramo, R., Roteta, E., Bistinas, I., van Wees, D., Bastarrika, A., Chuvieco, E., and van der Werf, G. R.: African burned area and fire carbon emissions are strongly impacted by small fires undetected by coarse resolution satellite data, P. Natl. Acad. Sci. USA, 118, e2011160118, https://doi.org/10.1073/pnas.2011160118, 2021.
Randazzo, N. A., Michalak, A. M., and Desai, A. R.: Synoptic meteorology explains temperate forest carbon uptake, J. Geophys. Res.-Biogeo., 125, e2019JG005476, https://doi.org/10.1029/2019JG005476, 2020.
Richardson, A. D., Keenan, T. F., Migliavacca, M., Ryu, Y., Sonnentag, O., and Toomey, M.: Climate change, phenology, and phenological control of vegetation feedbacks to the climate system, Agr. Forest Meteorol., 169, 156–173, https://doi.org/10.1016/j.agrformet.2012.09.012, 2013.
Robinson, N. P., Allred, B. W., Smith, W. K., Jones, M. O., Moreno, A., Erickson, T. A., Naugle, D. E., and Running, S. W.: Terrestrial primary production for the conterminous United States derived from Landsat 30 m and MODIS 250 m, Remote Sens. Ecol. Conserv., 4, 264–280, https://doi.org/10.1002/rse2.74, 2018.
Rouse, J. W., Haas, R. H. J., Schell, J. A., and Deering, D. W.: Monitoring vegetation systems in the great plains with ERTS, Vol. 1, edited by: Freden, S. C., Mercanti, E. P., and Becker, M. A., NASA Scientific and Technical Information Office, 309–317, available at: https://ntrs.nasa.gov/archive/nasa/casi.ntrs.nasa.gov/19740022614.pdf (last access: 4 May 2021), 1974.
Ruddick, K., Neukermans, G., Vanhellemont, Q., and Jolivet, D.: Challenges and opportunities for geostationary ocean colour remote sensing of regional seas: A review of recent results, Remote Sens. Environ., 146, 63–76, https://doi.org/10.1016/j.rse.2013.07.039, 2014.
Running, S. W., Peterson, D. L., Spanner, M. A., and Teuber, K. B.: Remote sensing of coniferous forest leaf area, Ecology, 67, 273–276, https://doi.org/10.2307/1938532, 1986.
Running, S. W., Nemani, R. R., Heinsch, F. A., Zhao, M., Reeves, M., and Hashimoto, H.: A Continuous Satellite-Derived Measure of Global Terrestrial Primary Production, Bioscience, 54, 547–560, https://doi.org/10.1641/0006-3568(2004)054[0547:ACSMOG]2.0.CO;2, 2004.
Ryu, J.-H. and Ishizaka, J.: GOCI data processing and ocean applications, Ocean Sci. J., 47, 221–221, https://doi.org/10.1007/s12601-012-0023-5, 2012.
Ryu, J.-H., Han, H.-J., Cho, S., Park, Y.-J., and Ahn, Y.-H.: Overview of geostationary ocean color imager (GOCI) and GOCI data processing system (GDPS), Ocean Sci. J., 47, 223–233, https://doi.org/10.1007/s12601-012-0024-4, 2012.
Schaaf, C. and Wang, Z.: MCD43A4 MODIS/Terra+Aqua BRDF/Albedo Nadir BRDF Adjusted Ref Daily L3 Global – 500m V006, distributed by NASA EOSDIS Land Processes DAAC, https://doi.org/10.5067/MODIS/MCD43A4.006 (last access: 1 April 2021), 2015.
Schmetz, J., Pili, P., Tjemkes, S., Just, D., Kerkmann, J., Rota, S., and Ratier, A.: An introduction to meteosat second generation (MSG), Bull. Am. Meteorol. Soc., 83, 977–992, https://doi.org/10.1175/1520-0477(2002)083<0977:AITMSG>2.3.CO;2, 2002.
Schmidt, C. C., Hoffman, J., Prins, E., and Lindstrom, S.: GOES-R Advanced Baseline Imager (ABI) Algorithm TheoreticalBasis Document For Fire/Hot Spot Characterization, Noaa Nesdis Center For Satellite Applications And Research, 2012.
Schmit, T. J. and Gunshor, M. M.: ABI Imagery from the GOES-R Series, in: The GOES-R Series, Elsevier, 23–34, https://doi.org/10.1016/B978-0-12-814327-8.00004-4, 2020.
Schmit, T., Gunshor, M., Fu, G., Rink, T., Bah, K., Zhang, W., and Wolf, W.: GOES-R Advanced Baseline Imager (ABI) Algorithm Theoretical Basis Document for Cloud and Moisture Imagery Product (CMIP), NOAA NESDIS Center for Satellite Applications and Research, Version 3.0, available at: https://registry.opendata.aws/noaa-goes/ (last access: 1 January 2021), 2012.
Schmit, T. J., Goodman, S. J., Gunshor, M. M., Sieglaff, J., Heidinger, A. K., Bachmeier, A. S., Lindstrom, S. S., Terborg, A., Feltz, J., Bah, K., Rudlosky, S., Lindsey, D. T., Rabin, R. M., and Schmidt, C. C.: Rapid refresh information of significant events: preparing users for the next generation of geostationary operational satellites, Bull. Am. Meteorol. Soc., 96, 561–576, https://doi.org/10.1175/BAMS-D-13-00210.1, 2015.
Schmit, T. J., Griffith, P., Gunshor, M. M., Daniels, J. M., Goodman, S. J., and Lebair, W. J.: A Closer Look at the ABI on the GOES-R Series, Bull. Am. Meteorol. Soc., 98, 681–698, https://doi.org/10.1175/BAMS-D-15-00230.1, 2017.
Schneider, E. D. and Kay, J. J.: Complexity and thermodynamics, Futures, 26, 626–647, https://doi.org/10.1016/0016-3287(94)90034-5, 1994.
Schumacher, D. L., Keune, J., and Miralles, D. G.: Atmospheric heat and moisture transport to energy- and water-limited ecosystems, Ann. N. Y. Acad. Sci., 1472, 123–138, https://doi.org/10.1111/nyas.14357, 2020.
Semmens, K. A., Anderson, M. C., Kustas, W. P., Gao, F., Alfieri, J. G., McKee, L., Prueger, J. H., Hain, C. R., Cammalleri, C., Yang, Y., Xia, T., Sanchez, L., Mar Alsina, M., and Vélez, M.: Monitoring daily evapotranspiration over two California vineyards using Landsat 8 in a multi-sensor data fusion approach, Remote Sens. Environ., 185, 155–170, https://doi.org/10.1016/j.rse.2015.10.025, 2016.
Seong, N.-H., Jung, D., Kim, J., and Han, K.-S.: Evaluation of NDVI Estimation Considering Atmospheric and BRDF Correction through Himawari-8/AHI, Asia-Pacific J. Atmos. Sci., 56, 265–274, https://doi.org/10.1007/s13143-019-00167-0, 2020.
Seyednasrollah, B., Young, A. M., Hufkens, K., Milliman, T., Friedl, M. A., Frolking, S., and Richardson, A. D.: Tracking vegetation phenology across diverse biomes using Version 2.0 of the PhenoCam Dataset, Sci. Data, 6, 222, https://doi.org/10.1038/s41597-019-0229-9, 2019.
Sims, D. A., Rahman, A. F., Cordova, V. D., El-Masri, B. Z., Baldocchi, D. D., Flanagan, L. B., Goldstein, A. H., Hollinger, D. Y., Misson, L., Monson, R. K., Oechel, W. C., Schmid, H. P., Wofsy, S. C., and Xu, L.: On the use of MODIS EVI to assess gross primary productivity of North American ecosystems, J. Geophys. Res., 111, G04015, https://doi.org/10.1029/2006JG000162, 2006.
Smith, W. K., Dannenberg, M. P., Yan, D., Herrmann, S., Barnes, M. L., Barron-Gafford, G. A., Biederman, J. A., Ferrenberg, S., Fox, A. M., Hudson, A., Knowles, J. F., MacBean, N., Moore, D. J. P., Nagler, P. L., Reed, S. C., Rutherford, W. A., Scott, R. L., Wang, X., and Yang, J.: Remote sensing of dryland ecosystem structure and function: Progress, challenges, and opportunities, Remote Sens. Environ., 233, 111401, https://doi.org/10.1016/j.rse.2019.111401, 2019.
Stocker, B. D., Zscheischler, J., Keenan, T. F., Prentice, I. C., Seneviratne, S. I., and Peñuelas, J.: Drought impacts on terrestrial primary production underestimated by satellite monitoring, Nat. Geosci., 12, 264–270, https://doi.org/10.1038/s41561-019-0318-6, 2019.
Stoy, P., Lin, H., Novick, K., Siqueira, M., and Juang, J.-Y.: The role of vegetation on the ecosystem radiative entropy budget and trends along ecological succession, Entropy, 16, 3710–3731, https://doi.org/10.3390/e16073710, 2014.
Stoy, P. C., Katul, G. G., Siqueira, M. B. S., Juang, J.-Y., McCarthy, H. R., Kim, H.-S., Oishi, A. C., and Oren, R.: Variability in net ecosystem exchange from hourly to inter-annual time scales at adjacent pine and hardwood forests: a wavelet analysis, Tree Physiol., 25, 887–902, https://doi.org/10.1093/treephys/25.7.887, 2005.
Stoy, P. C., Trowbridge, A. M., and Bauerle, W. L.: Controls on seasonal patterns of maximum ecosystem carbon uptake and canopy-scale photosynthetic light response: contributions from both temperature and photoperiod, Photosyn. Res., 119, 49–64, https://doi.org/10.1007/s11120-013-9799-0, 2014.
Sullivan, P., Gallagher, F. W., Boukabara, S. A., Lindsey, D. T., and Grigsby, E.: What Follows GOES-R?, available at: https://ams.confex.com/ams/2020Annual/meetingapp.cgi/Paper/370685, last access: 24 August 2020.
Sun, D. and Pinker, R. T.: Estimation of land surface temperature from a Geostationary Operational Environmental Satellite (GOES-8), J. Geophys. Res., 108, 4326, https://doi.org/10.1029/2002JD002422, 2003.
Suomi, V. E. and Parent, R. J.: a color view of planet Earth, Bull. Am. Meteorol. Soc., 49, 74–75, https://doi.org/10.1175/1520-0477-49.2.74, 1968.
Tan, B., Dellomo, J., Wolfe, R. E., and Reth, A. D.: GOES-16 ABI navigation assessment, in: Earth Observing Systems XXIII, edited by: Butler, J. J., (Jack) Xiong, X., and Gu, X., SPIE, p. 15, https://doi.org/10.1117/12.2321170, 2018.
Tan, B., Dellomo, J. J., Wolfe, R. E., and Reth, A. D.: GOES-16 and GOES-17 ABI INR assessment, in: Earth Observing Systems XXIV, edited by: Butler, J. J., (Jack) Xiong, X., and Gu, X., SPIE, p. 49, https://doi.org/10.1117/12.2529336, 2019.
Tan, B., Dellomo, J. J., Folley, C. N., Grycewicz, T. J., Houchin, S., Isaacson, P. J., Johnson, P. D., Porter, B. C., Reth, A. D., Thiyanaratnam, P., and Wolfe, R. E.: GOES-R series image navigation and registration performance assessment tool set, J. Appl. Remote Sens., 14, 032405, https://doi.org/10.1117/1.JRS.14.032405, 2020.
Tian, Y., Romanov, P., Yu, Y., Xu, H., and Tarpley, D.: Analysis of vegetation index NDVI anisotropy to improve the accuracy of the GOES-R green vegetation fraction product, in 2010 IEEE International Geoscience and Remote Sensing Symposium, IEEE, 2091–2094, https://doi.org/10.1109/IGARSS.2010.5651925, 2010.
Tran, N. N., Huete, A., Nguyen, H., Grant, I., Miura, T., Ma, X., Lyapustin, A., Wang, Y., and Ebert, E.: Seasonal Comparisons of Himawari-8 AHI and MODIS Vegetation Indices over Latitudinal Australian Grassland Sites, Remote Sens., 12, 2494, https://doi.org/10.3390/rs12152494, 2020.
Trigo, I. F., Dacamara, C. C., Viterbo, P., Roujean, J.-L., Olesen, F., Barroso, C., Camacho-de-Coca, F., Carrer, D., Freitas, S. C., García-Haro, J., Geiger, B., Gellens-Meulenberghs, F., Ghilain, N., Meliá, J., Pessanha, L., Siljamo, N., and Arboleda, A.: The satellite application facility for land surface analysis, Int. J. Remote Sens., 32, 2725–2744, https://doi.org/10.1080/01431161003743199, 2011.
Tucker, C. J.: Red and photographic infrared linear combinations for monitoring vegetation, Remote Sens. Environ., 8, 127–150, https://doi.org/10.1016/0034-4257(79)90013-0, 1979.
Tucker, C. J.: Remote sensing of leaf water content in the near infrared, Remote Sens. Environ., 10, 23–32, https://doi.org/10.1016/0034-4257(80)90096-6, 1980.
Tucker, C. J., Townshend, J. R., and Goff, T. E.: African land-cover classification using satellite data, Science, 227, 369–375, https://doi.org/10.1126/science.227.4685.369, 1985.
Ulivieri, C. and Cannizzaro, G.: Land surface temperature retrievals from satellite measurements, Acta Astronaut., 12, 977–985, https://doi.org/10.1016/0094-5765(85)90026-8, 1985.
Verger, A., Baret, F., and Weiss, M.: Near Real-Time Vegetation Monitoring at Global Scale, IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens., 7, 3473–3481, https://doi.org/10.1109/JSTARS.2014.2328632, 2014.
Vermote, E. F., Tanre, D., Deuze, J. L., Herman, M., and Morcette, J. J.: Second Simulation of the Satellite Signal in the Solar Spectrum, 6S: an overview, IEEE Trans. Geosci. Remote Sens., 35, 675–686, https://doi.org/10.1109/36.581987, 1997.
Wagner, S. C., Govaerts, Y. M., and Lattanzio, A.: Joint retrieval of surface reflectance and aerosol optical depth from MSG/SEVIRI observations with an optimal estimation approach: 2. Implementation and evaluation, J. Geophys. Res., 115, D02204, https://doi.org/10.1029/2009JD011780, 2010.
Wang, M., Ahn, J.-H., Jiang, L., Shi, W., Son, S., Park, Y.-J., and Ryu, J.-H.: Ocean color products from the Korean Geostationary Ocean Color Imager (GOCI), Opt. Express, 21, 3835–3849, https://doi.org/10.1364/OE.21.003835, 2013.
Wang, W., Qu, J. J., Hao, X., Liu, Y., and Stanturf, J. A.: Post-hurricane forest damage assessment using satellite remote sensing, Agr. Forest Meteorol., 150, 122–132, https://doi.org/10.1016/j.agrformet.2009.09.009, 2010.
Wang, W., Li, S., Hashimoto, H., Takenaka, H., Higuchi, A., Kalluri, S., and Nemani, R.: An Introduction to the Geostationary-NASA Earth Exchange (GeoNEX) Products: 1. Top-of-Atmosphere Reflectance and Brightness Temperature, Remote Sens., 12, 1267, https://doi.org/10.3390/rs12081267, 2020.
Weiss, A. and Norman, J. M.: Partitioning solar radiation into direct and diffuse, visible and near-infrared components, Agr. Forest Meteorol., 34, 205–213, https://doi.org/10.1016/0168-1923(85)90020-6, 1985.
Wheeler, K. I. and Dietze, M. C.: A Statistical Model for Estimating Midday NDVI from the Geostationary Operational Environmental Satellite (GOES) 16 and 17, Remote Sens., 11, 2507, https://doi.org/10.3390/rs11212507, 2019.
Wheeler, K. I. and Dietze, M. C.: Improving the monitoring of deciduous broadleaf phenology using the Geostationary Operational Environmental Satellite (GOES) 16 and 17, Biogeosciences, 18, 1971–1985, https://doi.org/10.5194/bg-18-1971-2021, 2021.
White, M. A., de BEURS, K. M., Didan, K., Inouye, D. W., Richardson, A. D., Jensen, O. P., O'Keefe, J., Zhang, G., Nemani, R. R., van LEEUWEN, W. J. D., Brown, J. F., de WIT, A., Schaepman, M., Lin, X., Dettinger, M., Bailey, A. S., Kimball, J., Schwartz, M. D., Baldocchi, D. D., Lee, J. T., and Lauenroth, W. K.: Intercomparison, interpretation, and assessment of spring phenology in North America estimated from remote sensing for 1982–2006, Glob. Change Biol., 15, 2335–2359, https://doi.org/10.1111/j.1365-2486.2009.01910.x, 2009.
Whittaker, T.: Explore the effects of parallax, available at: http://cimss.ssec.wisc.edu/goes/webapps/parallax/goes16_conus.html (last access: 5 May 2021), 2014.
Wiesner, S., Staudhammer, C. L., Stoy, P. C., Boring, L. R., and Starr, G.: Quantifying energy use efficiency via entropy production: a case study from longleaf pine ecosystems, Biogeosciences, 16, 1845–1863, https://doi.org/10.5194/bg-16-1845-2019, 2019.
Williams, M., Richardson, A. D., Reichstein, M., Stoy, P. C., Peylin, P., Verbeeck, H., Carvalhais, N., Jung, M., Hollinger, D. Y., Kattge, J., Leuning, R., Luo, Y., Tomelleri, E., Trudinger, C. M., and Wang, Y. P.: Improving land surface models with FLUXNET data, Biogeosciences, 6, 1341–1359, https://doi.org/10.5194/bg-6-1341-2009, 2009.
Wong, C. Y. S., D'Odorico, P., Bhathena, Y., Arain, M. A., and Ensminger, I.: Carotenoid based vegetation indices for accurate monitoring of the phenology of photosynthesis at the leaf-scale in deciduous and evergreen trees, Remote Sens. Environ., 233, 111407, https://doi.org/10.1016/j.rse.2019.111407, 2019.
Wonsook, S. H., R. Diak, G., and F. Krajewski, W.: Estimating Near Real-Time Hourly Evapotranspiration Using Numerical Weather Prediction Model Output and GOES Remote Sensing Data in Iowa, Remote Sens., 12, 2337, https://doi.org/10.3390/rs12142337, 2020.
Wooster, M.: Fire radiative energy for quantitative study of biomass burning: derivation from the BIRD experimental satellite and comparison to MODIS fire products, Remote Sens. Environ., 86, 83–107, https://doi.org/10.1016/S0034-4257(03)00070-1, 2003.
Wu, G., Guan, K., Jiang, C., Peng, B., Kimm, H., Chen, M., Yang, X., Wang, S., Suyker, A. E., Bernacchi, C. J., Moore, C. E., Zeng, Y., Berry, J. A., and Cendrero-Mateo, M. P.: Radiance-based NIRv as a proxy for GPP of corn and soybean, Environ. Res. Lett., 15, 034009, https://doi.org/10.1088/1748-9326/ab65cc, 2020.
Wu, P., Shen, H., Zhang, L., and Göttsche, F.-M.: Integrated fusion of multi-scale polar-orbiting and geostationary satellite observations for the mapping of high spatial and temporal resolution land surface temperature, Remote Sens. Environ., 156, 169–181, https://doi.org/10.1016/j.rse.2014.09.013, 2015.
Xiao, J., Chevallier, F., Gomez, C., Guanter, L., Hicke, J. A., Huete, A. R., Ichii, K., Ni, W., Pang, Y., Rahman, A. F., Sun, G., Yuan, W., Zhang, L., and Zhang, X.: Remote sensing of the terrestrial carbon cycle: A review of advances over 50 years, Remote Sens. Environ., 233, 111383, https://doi.org/10.1016/j.rse.2019.111383, 2019.
Xu, W., Wooster, M. J., Roberts, G., and Freeborn, P.: New GOES imager algorithms for cloud and active fire detection and fire radiative power assessment across North, South and Central America, Remote Sens. Environ., 114, 1876–1895, https://doi.org/10.1016/j.rse.2010.03.012, 2010.
Xu, X., Riley, W. J., Koven, C. D., Jia, G., and Zhang, X.: Earlier leaf-out warms air in the north, Nat. Clim. Change, 10, 370–375, https://doi.org/10.1038/s41558-020-0713-4, 2020.
Yang, Y., Anderson, M. C., Gao, F., Wardlow, B., Hain, C. R., Otkin, J. A., Alfieri, J., Yang, Y., Sun, L., and Dulaney, W.: Field-scale mapping of evaporative stress indicators of crop yield: An application over Mead, NE, USA, Remote Sens. Environ., 210, 387–402, https://doi.org/10.1016/j.rse.2018.02.020, 2018.
Yang, Y., Anderson, M., Gao, F., Hain, C., Noormets, A., Sun, G., Wynne, R., Thomas, V., and Sun, L.: Investigating impacts of drought and disturbance on evapotranspiration over a forested landscape in North Carolina, USA using high spatiotemporal resolution remotely sensed data, Remote Sens. Environ., 238, 111018, https://doi.org/10.1016/j.rse.2018.12.017, 2020.
Yan, D., Zhang, X., Yu, Y., and Guo, W.: A Comparison of Tropical Rainforest Phenology Retrieved From Geostationary (SEVIRI) and Polar-Orbiting (MODIS) Sensors Across the Congo Basin, IEEE Trans. Geosci. Remote Sens., 54, 4867–4881, https://doi.org/10.1109/TGRS.2016.2552462, 2016.
Yan, D., Zhang, X., Nagai, S., Yu, Y., Akitsu, T., Nasahara, K. N., Ide, R., and Maeda, T.: Evaluating land surface phenology from the Advanced Himawari Imager using observations from MODIS and the Phenological Eyes Network, Int. J. Appl. Earth Obs., 79, 71–83, https://doi.org/10.1016/j.jag.2019.02.011, 2019.
Yeom, J.-M. and Kim, H.-O.: Comparison of NDVIs from GOCI and MODIS Data towards Improved Assessment of Crop Temporal Dynamics in the Case of Paddy Rice, Remote Sens., 7(9), 11326–11343, https://doi.org/10.3390/rs70911326, 2015.
Yeom, J.-M., Ko, J., and Kim, H.-O.: Application of GOCI-derived vegetation index profiles to estimation of paddy rice yield using the GRAMI rice model, Comput. Electron. Agr., 118, 1–8, https://doi.org/10.1016/j.compag.2015.08.017, 2015.
Yeom, J.-M., Roujean, J.-L., Han, K.-S., Lee, K.-S., and Kim, H.-W.: Thin cloud detection over land using background surface reflectance based on the BRDF model applied to Geostationary Ocean Color Imager (GOCI) satellite data sets, Remote Sens. Environ., 239, 111610, https://doi.org/10.1016/j.rse.2019.111610, 2020.
Yu, F., Wu, X., Yoo, H., Wang, Z., Qian, H., and Shao, X.: Radiometric calibration performance of GOES-17 Advanced Baseline Imager (ABI), in: Earth Observing Systems XXIV, edited by: Butler, J. J., (Jack) Xiong, X., and Gu, X., SPIE, p. 48, https://doi.org/10.1117/12.2531407, 2019.
Yu, Y., Tarpley, D., Privette, J. L., Goldberg, M. D., Rama Varma Raja, M. K., Vinnikov, K. Y., and Hui Xu: Developing Algorithm for Operational GOES-R Land Surface Temperature Product, IEEE Trans. Geosci. Remote Sens., 47, 936–951, https://doi.org/10.1109/TGRS.2008.2006180, 2009.
Yu, Y., Tarpley, D., and Xu, H.: GOES-R Advanced Baseline Imager (ABI) Algorithm Theoretical Basis Document For Land Surface Temperature (Version 2.5), Noaa Nesdis Center For Satellite Applications And Research, 2012.
Yuan, W., Liu, S., Zhou, G., Zhou, G., Tieszen, L. L., Baldocchi, D., Bernhofer, C., Gholz, H., Goldstein, A. H., Goulden, M. L., Hollinger, D. Y., Hu, Y., Law, B. E., Stoy, P. C., Vesala, T., and Wofsy, S. C.: Deriving a light use efficiency model from eddy covariance flux data for predicting daily gross primary production across biomes, Agr. Forest Meteorol., 143, 189–207, https://doi.org/10.1016/j.agrformet.2006.12.001, 2007.
Yuan, W., Cai, W., Xia, J., Chen, J., Liu, S., Dong, W., Merbold, L., Law, B., Arain, A., Beringer, J., Bernhofer, C., Black, A., Blanken, P. D., Cescatti, A., Chen, Y., Francois, L., Gianelle, D., Janssens, I. A., Jung, M., Kato, T., and Wohlfahrt, G.: Global comparison of light use efficiency models for simulating terrestrial vegetation gross primary production based on the LaThuile database, Agr. Forest Meteorol., 192/193, 108–120, https://doi.org/10.1016/j.agrformet.2014.03.007, 2014.
Zakšek, K., Hort, M., Zaletelj, J., and Langmann, B.: Monitoring volcanic ash cloud top height through simultaneous retrieval of optical data from polar orbiting and geostationary satellites, Atmos. Chem. Phys., 13, 2589–2606, https://doi.org/10.5194/acp-13-2589-2013, 2013.
Zeng, L., Wardlow, B. D., Xiang, D., Hu, S., and Li, D.: A review of vegetation phenological metrics extraction using time-series, multispectral satellite data, Remote Sens. Environ., 237, 111511, https://doi.org/10.1016/j.rse.2019.111511, 2020.
Zhang, X., Friedl, M. A., Schaaf, C. B., Strahler, A. H., Hodges, J. C. F., Gao, F., Reed, B. C., and Huete, A.: Monitoring vegetation phenology using MODIS, Remote Sens. Environ., 84, 471–475, https://doi.org/10.1016/S0034-4257(02)00135-9, 2003.
Zhang, X., Liang, S., Zhou, G., Wu, H., and Zhao, X.: Generating Global LAnd Surface Satellite incident shortwave radiation and photosynthetically active radiation products from multiple satellite data, Remote Sens. Environ., 152, 318–332, https://doi.org/10.1016/j.rse.2014.07.003, 2014.
Zhang, Y., Song, C., Sun, G., Band, L. E., McNulty, S., Noormets, A., Zhang, Q., and Zhang, Z.: Development of a coupled carbon and water model for estimating global gross primary productivity and evapotranspiration based on eddy flux and remote sensing data, Agr. Forest Meteorol., 223, 116–131, https://doi.org/10.1016/j.agrformet.2016.04.003, 2016.
Zhao, J., Chen, X., Zhang, J., Zhao, H., and Song, Y.: Higher temporal evapotranspiration estimation with improved SEBS model from geostationary meteorological satellite data, Sci. Rep., 9, 14981, https://doi.org/10.1038/s41598-019-50724-w, 2019.
Zhao, M., Heinsch, F. A., Nemani, R. R., and Running, S. W.: Improvements of the MODIS terrestrial gross and net primary production global data set, Remote Sens. Environ., 95, 164–176, https://doi.org/10.1016/j.rse.2004.12.011, 2005.
Zhao, W. and Duan, S.-B.: Reconstruction of daytime land surface temperatures under cloud-covered conditions using integrated MODIS/Terra land products and MSG geostationary satellite data, Remote Sens. Environ., 247, 111931, https://doi.org/10.1016/j.rse.2020.111931, 2020.
Zheng, T., Liang, S., and Wang, K.: Estimation of Incident Photosynthetically Active Radiation from GOES Visible Imagery, J. Appl. Meteor. Climatol., 47, 853–868, https://doi.org/10.1175/2007JAMC1475.1, 2008.
Zhou, Y., Zhang, L., Xiao, J., Chen, S., Kato, T., and Zhou, G.: A Comparison of Satellite-Derived Vegetation Indices for Approximating Gross Primary Productivity of Grasslands, Rangeland Ecol. Manag., 67, 9–18, https://doi.org/10.2111/REM-D-13-00059.1, 2014.
Zscheischler, J., Fatichi, S., Wolf, S., Blanken, P. D., Bohrer, G., Clark, K., Desai, A. R., Hollinger, D., Keenan, T., Novick, K. A., and Seneviratne, S. I.: Short-term favorable weather conditions are an important control of interannual variability in carbon and water fluxes, J. Geophys. Res.-Biogeo., 121, 2186–2198, https://doi.org/10.1002/2016JG003503, 2016.
Remote sensing has played an important role in the study of land surface processes. Geostationary satellites, such as the GOES-R series, can observe the Earth every 5–15 min, providing us with more observations than widely used polar-orbiting satellites. Here, we outline current efforts utilizing geostationary observations in environmental science and look towards the future of GOES observations in the carbon cycle, ecosystem disturbance, and other areas of application in environmental science.
Remote sensing has played an important role in the study of land surface processes....