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Ma X, Zhu X, Xie Q, Jin J, Zhou Y, Luo Y, Liu Y, Tian J, Zhao Y. Monitoring nature's calendar from space: Emerging topics in land surface phenology and associated opportunities for science applications. GLOBAL CHANGE BIOLOGY 2022; 28:7186-7204. [PMID: 36114727 PMCID: PMC9827868 DOI: 10.1111/gcb.16436] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Revised: 09/09/2022] [Accepted: 09/11/2022] [Indexed: 06/15/2023]
Abstract
Vegetation phenology has been viewed as the nature's calendar and an integrative indicator of plant-climate interactions. The correct representation of vegetation phenology is important for models to accurately simulate the exchange of carbon, water, and energy between the vegetated land surface and the atmosphere. Remote sensing has advanced the monitoring of vegetation phenology by providing spatially and temporally continuous data that together with conventional ground observations offers a unique contribution to our knowledge about the environmental impact on ecosystems as well as the ecological adaptations and feedback to global climate change. Land surface phenology (LSP) is defined as the use of satellites to monitor seasonal dynamics in vegetated land surfaces and to estimate phenological transition dates. LSP, as an interdisciplinary subject among remote sensing, ecology, and biometeorology, has undergone rapid development over the past few decades. Recent advances in sensor technologies, as well as data fusion techniques, have enabled novel phenology retrieval algorithms that refine phenology details at even higher spatiotemporal resolutions, providing new insights into ecosystem dynamics. As such, here we summarize the recent advances in LSP and the associated opportunities for science applications. We focus on the remaining challenges, promising techniques, and emerging topics that together we believe will truly form the very frontier of the global LSP research field.
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Affiliation(s)
- Xuanlong Ma
- College of Earth and Environmental Sciences, Lanzhou UniversityLanzhouChina
| | - Xiaolin Zhu
- Department of Land Surveying and Geo‐InformaticsThe Hong Kong Polytechnic UniversityHong KongChina
| | - Qiaoyun Xie
- School of Life Sciences, Faculty of ScienceUniversity of Technology SydneySydneyNew South WalesAustralia
| | - Jiaxin Jin
- College of Hydrology and Water Resources, Hohai UniversityNanjingChina
| | - Yuke Zhou
- Key Laboratory of Ecosystem Network Observation and ModellingInstitute of Geographic Sciences and Natural Resources Research, Chinese Academy of SciencesBeijingChina
| | - Yunpeng Luo
- Swiss Federal Institute for Forest, Snow and Landscape Research WSLBirmensdorfSwitzerland
- Department of Environmental System ScienceETH ZurichZurichSwitzerland
| | - Yuxia Liu
- School of Life Sciences, Faculty of ScienceUniversity of Technology SydneySydneyNew South WalesAustralia
- Geospatial Sciences Center of Excellence (GSCE)South Dakota State UniversityBrookingsSouth DakotaUSA
| | - Jiaqi Tian
- Department of Land Surveying and Geo‐InformaticsThe Hong Kong Polytechnic UniversityHong KongChina
- Department of GeographyNational University of SingaporeSingaporeSingapore
| | - Yuhe Zhao
- College of Earth and Environmental Sciences, Lanzhou UniversityLanzhouChina
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Martini D, Sakowska K, Wohlfahrt G, Pacheco-Labrador J, van der Tol C, Porcar-Castell A, Magney TS, Carrara A, Colombo R, El-Madany TS, Gonzalez-Cascon R, Martín MP, Julitta T, Moreno G, Rascher U, Reichstein M, Rossini M, Migliavacca M. Heatwave breaks down the linearity between sun-induced fluorescence and gross primary production. THE NEW PHYTOLOGIST 2022; 233:2415-2428. [PMID: 34921419 DOI: 10.1111/nph.17920] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/16/2021] [Accepted: 11/21/2021] [Indexed: 06/14/2023]
Abstract
Sun-induced fluorescence in the far-red region (SIF) is increasingly used as a remote and proximal-sensing tool capable of tracking vegetation gross primary production (GPP). However, the use of SIF to probe changes in GPP is challenged during extreme climatic events, such as heatwaves. Here, we examined how the 2018 European heatwave (HW) affected the GPP-SIF relationship in evergreen broadleaved trees with a relatively invariant canopy structure. To do so, we combined canopy-scale SIF measurements, GPP estimated from an eddy covariance tower, and active pulse amplitude modulation fluorescence. The HW caused an inversion of the photosynthesis-fluorescence relationship at both the canopy and leaf scales. The highly nonlinear relationship was strongly shaped by nonphotochemical quenching (NPQ), that is, a dissipation mechanism to protect from the adverse effects of high light intensity. During the extreme heat stress, plants experienced a saturation of NPQ, causing a change in the allocation of energy dissipation pathways towards SIF. Our results show the complex modulation of the NPQ-SIF-GPP relationship at an extreme level of heat stress, which is not completely represented in state-of-the-art coupled radiative transfer and photosynthesis models.
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Affiliation(s)
- David Martini
- Max Planck Institute for Biogeochemistry, 07745, Jena, Germany
| | - Karolina Sakowska
- Institute of BioEconomy, National Research Council (IBE-CNR), 38010, San Michele all'Adige (TN), Italy
| | - Georg Wohlfahrt
- Department of Ecology, University of Innsbruck, Sternwartestrasse 15, 6020, Innsbruck, Austria
| | | | - Christiaan van der Tol
- Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, 7500 AE, Enschede, the Netherlands
| | - Albert Porcar-Castell
- Optics of Photosynthesis Laboratory, Institute for Atmospheric and Earth System Research (INAR/Forest Sciences) and Viikki Plant Science Center, University of Helsinki, Finland
| | - Troy S Magney
- Department of Plant Sciences, University of California, Davis, CA, 95616, USA
| | - Arnaud Carrara
- Centro De Estudios Ambientales Del Mediterráneo, 46980, Valencia, Spain
| | - Roberto Colombo
- Earth and Environmental Sciences Department, University of Milano-Bicocca, Milan, Italy
| | | | - Rosario Gonzalez-Cascon
- Department of Environment, National Institute for Agriculture and Food Research and Technology (INIA), 28040, Madrid, Spain
| | - María Pilar Martín
- Environmental Remote Sensing and Spectroscopy Laboratory (SpecLab), Spanish National Research Council (CSIC), 28037, Madrid, Spain
| | | | | | - Uwe Rascher
- Institute of Bio- and Geosciences, IBG-2: Plant Sciences, Forschungszentrum Jülich, Jülich, Germany
| | | | - Micol Rossini
- Earth and Environmental Sciences Department, University of Milano-Bicocca, Milan, Italy
| | - Mirco Migliavacca
- Max Planck Institute for Biogeochemistry, 07745, Jena, Germany
- European Commission, Joint Research Centre, Ispra (VA), 21027, Italy
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Fu Z, Ciais P, Makowski D, Bastos A, Stoy PC, Ibrom A, Knohl A, Migliavacca M, Cuntz M, Šigut L, Peichl M, Loustau D, El-Madany TS, Buchmann N, Gharun M, Janssens I, Markwitz C, Grünwald T, Rebmann C, Mölder M, Varlagin A, Mammarella I, Kolari P, Bernhofer C, Heliasz M, Vincke C, Pitacco A, Cremonese E, Foltýnová L, Wigneron JP. Uncovering the critical soil moisture thresholds of plant water stress for European ecosystems. GLOBAL CHANGE BIOLOGY 2022; 28:2111-2123. [PMID: 34927310 DOI: 10.1111/gcb.16050] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/27/2021] [Revised: 11/18/2021] [Accepted: 12/16/2021] [Indexed: 06/14/2023]
Abstract
Understanding the critical soil moisture (SM) threshold (θcrit ) of plant water stress and land surface energy partitioning is a basis to evaluate drought impacts and improve models for predicting future ecosystem condition and climate. Quantifying the θcrit across biomes and climates is challenging because observations of surface energy fluxes and SM remain sparse. Here, we used the latest database of eddy covariance measurements to estimate θcrit across Europe by evaluating evaporative fraction (EF)-SM relationships and investigating the covariance between vapor pressure deficit (VPD) and gross primary production (GPP) during SM dry-down periods. We found that the θcrit and soil matric potential threshold in Europe are 16.5% and -0.7 MPa, respectively. Surface energy partitioning characteristics varied among different vegetation types; EF in savannas had the highest sensitivities to SM in water-limited stage, and the lowest in forests. The sign of the covariance between daily VPD and GPP consistently changed from positive to negative during dry-down across all sites when EF shifted from relatively high to low values. This sign of the covariance changed after longer period of SM decline in forests than in grasslands and savannas. Estimated θcrit from the VPD-GPP covariance method match well with the EF-SM method, showing this covariance method can be used to detect the θcrit . We further found that soil texture dominates the spatial variability of θcrit while shortwave radiation and VPD are the major drivers in determining the spatial pattern of EF sensitivities. Our results highlight for the first time that the sign change of the covariance between daily VPD and GPP can be used as an indicator of how ecosystems transition from energy to SM limitation. We also characterized the corresponding θcrit and its drivers across diverse ecosystems in Europe, an essential variable to improve the representation of water stress in land surface models.
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Affiliation(s)
- Zheng Fu
- Laboratoire des Sciences du Climat et de l'Environnement, LSCE/IPSL, CEA-CNRS-UVSQ, Université Paris-Saclay, Gif-sur-Yvette, France
| | - Philippe Ciais
- Laboratoire des Sciences du Climat et de l'Environnement, LSCE/IPSL, CEA-CNRS-UVSQ, Université Paris-Saclay, Gif-sur-Yvette, France
| | - David Makowski
- Unit Applied Mathematics and Computer Science (UMR 518), INRAE AgroParisTech Université Paris-Saclay, Paris, France
| | - Ana Bastos
- Department Biogeochemical Integration, Max Planck Institute for Biogeochemistry, Jena, Germany
| | - Paul C Stoy
- Department of Biological Systems Engineering, University of Wisconsin - Madison, Madison, Wisconsin, USA
| | - Andreas Ibrom
- Department of Environmental Engineering, Technical University of Denmark, Lyngby, Denmark
| | - Alexander Knohl
- Bioclimatology, Faculty of Forest Sciences and Forest Ecology, University of Goettingen, Göttingen, Germany
| | - Mirco Migliavacca
- Department Biogeochemical Integration, Max Planck Institute for Biogeochemistry, Jena, Germany
| | - Matthias Cuntz
- AgroParisTech, INRAE, UMR Silva, Université de Lorraine, Nancy, France
| | - Ladislav Šigut
- Global Change Research Institute of the Czech Academy of Sciences, Brno, Czech Republic
| | - Matthias Peichl
- Department of Forest Ecology and Management, Swedish University of Agricultural Sciences, Umeå, Sweden
| | - Denis Loustau
- ISPA, Bordeaux Sciences Agro, INRAE, Villenave d'Ornon, France
| | - Tarek S El-Madany
- Department Biogeochemical Integration, Max Planck Institute for Biogeochemistry, Jena, Germany
| | - Nina Buchmann
- Department of Environmental Systems Science, ETH Zurich, Zurich, Switzerland
| | - Mana Gharun
- Department of Environmental Systems Science, ETH Zurich, Zurich, Switzerland
| | - Ivan Janssens
- Center of Excellence Global Change Ecology, Department of Biology, University of Antwerp, Wilrijk, Belgium
| | - Christian Markwitz
- Bioclimatology, Faculty of Forest Sciences and Forest Ecology, University of Goettingen, Göttingen, Germany
| | - Thomas Grünwald
- Faculty of Environmental Sciences, Institute of Hydrology and Meteorology, Technische Universit ̈at Dresden, Dresden, Germany
| | - Corinna Rebmann
- Department Computational Hydrosystems, Helmholtz Centre for Environmental Research - UFZ, Leipzig, Germany
| | - Meelis Mölder
- Department of Physical Geography and Ecosystem Science, Lund University, Lund, Sweden
| | - Andrej Varlagin
- A.N. Severtsov Institute of Ecology and Evolution, Russian Academy of Sciences, Moscow, Russia
| | - Ivan Mammarella
- Institute for Atmospheric and Earth System Research/Physics, Faculty of Science, University of Helsinki, Helsinki, Finland
| | - Pasi Kolari
- Institute for Atmospheric and Earth System Research/Physics, Faculty of Science, University of Helsinki, Helsinki, Finland
| | - Christian Bernhofer
- Faculty of Environmental Sciences, Institute of Hydrology and Meteorology, Technische Universit ̈at Dresden, Dresden, Germany
| | - Michal Heliasz
- Centre for Environmental and Climate Research, Lund University, Lund, Sweden
| | - Caroline Vincke
- Earth and Life Institute, Université Catholique de Louvain, Louvain-la-Neuve, Belgium
| | | | - Edoardo Cremonese
- Climate Change Unit, Environmental Protection Agency of Aosta Valley, Saint Christophe, Italy
| | - Lenka Foltýnová
- Global Change Research Institute of the Czech Academy of Sciences, Brno, Czech Republic
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Abstract
Lakes/reservoirs are rapidly deteriorating from cultural eutrophication due to anthropogenic factors. In this study, we aimed to (1) explore nutrient levels in the Sabalan dam reservoir (SDR) of northwest Iran, (2) determine the reservoir water fertility using the total phosphorus (TP) based and total nitrogen (TN) based Carlson trophic state indices, and (3) specify primary limiting factors for the reservoir eutrophication. Our field observations showed a state of hyper-nutrient enrichment in the SDR. The highest variation of TN in the reservoir water column happened when the reservoir was severely stratified (in August) while the highest variation of TP took place when the thermocline was attenuated with the deepening of the epilimnion (in October). Both TP and TN based trophic indicators classified the SDR as a hypereutrophic lake. TN:TP molar ratio averaged at the epilimnion indicated a P–deficiency in the reservoir during warm months whilst it suggested a co–deficiency of P and N in cold months. Given the hyper-nutrient enrichment state in the reservoir, other drivers such as water residence time (WRT) can also act as the main contributor of eutrophication in the SDR. We found that WRT in the SDR varied from hundreds to thousands of days, which was much longer than that of other reservoirs/lakes with the same and even much greater storage capacity. Therefore, both hyper-nutrient enrichment and WRT mainly controlled eutrophication in the reservoir. Given time consuming and expensive management practices for reducing nutrients in the watershed, changes in the SDR operation are suggested to somewhat recover its hypereutrophic state in the short-term. However, strategic long-term recovery plans are required to reduce the transition of nutrients from the watershed to the SDR.
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Dynamic Threshold of Carbon Phenology in Two Cold Temperate Grasslands in China. REMOTE SENSING 2021. [DOI: 10.3390/rs13040574] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
Plant phenology, especially the timing of the start and the end of the vegetation growing season (SOS and EOS), plays a major role in grassland ecosystem carbon cycles. As the second-largest grassland country in the world, China’s grasslands are mainly distributed in the northern cold temperate climate zone. The accuracies and relations of plant phenology estimations from multialgorithms and data resources are poorly understood. Here, we investigated vegetation phenology in two typical cold temperate grasslands, Haibei (HB) and Inner Mongolia (NM) grasslands, in China from 2001 to 2017. Compared to ground vegetation phenology observations, we analyzed the performance of the moderate resolution imaging spectroradiometer MODIS phenology products (MCD12Q2) and two remote sensing-based vegetation phenology algorithms from the normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) time series (five satellite-based phenology algorithms). The optimal algorithm was used to compare with eddy covariance (EC)-based carbon phenology, and to calculate the thresholds of carbon phenology periods (SOSt and EOSt) in each site. Results showed that satellite-based phenology estimations (all five algorithms in this study) were strongly coupled with the temporal variation of the observed phenological period but significantly overestimated the SOS, predicting it to be over 21 days later than the field data. The carbon phenology thresholds of HB grassland (HB_SOSt and HB_EOSt) had a significant upward trend, with the multiyear average values being 0.14 and 0.29, respectively. In contrast, the thresholds of NM grasslands (NM_SOSt and NM_EOSt) also showed a certain upward trend, but it was not significant (p > 0.05), with the multiyear average values being 0.17 and 0.2, respectively. Our study suggested the thresholds of carbon phenology periods (SOSt and EOSt, %) could be simply and effectively estimated based on their significant relationship with the EC-based maximum of gross primary productivity observations (GPPmax) at a specific site and time. Therefore, this study suggested the thresholds of carbon phenology were not fixed even in a specific ecosystem, which also provided simple bridges between satellite-based vegetation phenology and EC-based carbon phenology in similar grasslands.
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