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Varghese R, Cherukuri AK, Doddrell NH, Doss CGP, Simkin AJ, Ramamoorthy S. Machine learning in photosynthesis: Prospects on sustainable crop development. PLANT SCIENCE : AN INTERNATIONAL JOURNAL OF EXPERIMENTAL PLANT BIOLOGY 2023; 335:111795. [PMID: 37473784 DOI: 10.1016/j.plantsci.2023.111795] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Revised: 07/10/2023] [Accepted: 07/13/2023] [Indexed: 07/22/2023]
Abstract
Improving photosynthesis is a promising avenue to increase food security. Studying photosynthetic traits with the aim to improve efficiency has been one of many strategies to increase crop yield but analyzing large data sets presents an ongoing challenge. Machine learning (ML) represents a ubiquitous tool that can provide a more elaborate data analysis. Here we review the application of ML in various domains of photosynthetic research, as well as in photosynthetic pigment studies. We highlight how correlating hyperspectral data with photosynthetic parameters to improve crop yield could be achieved through various ML algorithms. We also propose strategies to employ ML in promoting photosynthetic pigment research for furthering crop yield.
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Affiliation(s)
- Ressin Varghese
- School of Bio Sciences and Technology, VIT University, Vellore 632014, Tamil Nadu, India
| | - Aswani Kumar Cherukuri
- School of Information Technology and Engineering, VIT University, Vellore 632014, Tamil Nadu, India
| | | | - C George Priya Doss
- School of Bio Sciences and Technology, VIT University, Vellore 632014, Tamil Nadu, India
| | - Andrew J Simkin
- School of Biosciences, University of Kent, Canterbury CT2 7NJ, UK; School of Life Sciences, University of Essex, Wivenhoe Park, Colchester CO4 3SQ, UK
| | - Siva Ramamoorthy
- School of Bio Sciences and Technology, VIT University, Vellore 632014, Tamil Nadu, India.
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2
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Zhu XJ, Yu GR, Chen Z, Zhang WK, Han L, Wang QF, Chen SP, Liu SM, Wang HM, Yan JH, Tan JL, Zhang FW, Zhao FH, Li YN, Zhang YP, Shi PL, Zhu JJ, Wu JB, Zhao ZH, Hao YB, Sha LQ, Zhang YC, Jiang SC, Gu FX, Wu ZX, Zhang YJ, Zhou L, Tang YK, Jia BR, Li YQ, Song QH, Dong G, Gao YH, Jiang ZD, Sun D, Wang JL, He QH, Li XH, Wang F, Wei WX, Deng ZM, Hao XX, Li Y, Liu XL, Zhang XF, Zhu ZL. Mapping Chinese annual gross primary productivity with eddy covariance measurements and machine learning. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 857:159390. [PMID: 36243072 DOI: 10.1016/j.scitotenv.2022.159390] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Revised: 10/05/2022] [Accepted: 10/08/2022] [Indexed: 06/16/2023]
Abstract
Annual gross primary productivity (AGPP) is the basis for grain production and terrestrial carbon sequestration. Mapping regional AGPP from site measurements provides methodological support for analysing AGPP spatiotemporal variations thereby ensures regional food security and mitigates climate change. Based on 641 site-year eddy covariance measuring AGPP from China, we built an AGPP mapping scheme based on its formation and selected the optimal mapping way, which was conducted through analysing the predicting performances of divergent mapping tools, variable combinations, and mapping approaches in predicting observed AGPP variations. The reasonability of the selected optimal scheme was confirmed by assessing the consistency between its generating AGPP and previous products in spatiotemporal variations and total amount. Random forest regression tree explained 85 % of observed AGPP variations, outperforming other machine learning algorithms and classical statistical methods. Variable combinations containing climate, soil, and biological factors showed superior performance to other variable combinations. Mapping AGPP through predicting AGPP per leaf area (PAGPP) explained 86 % of AGPP variations, which was superior to other approaches. The optimal scheme was thus using a random forest regression tree, combining climate, soil, and biological variables, and predicting PAGPP. The optimal scheme generating AGPP of Chinese terrestrial ecosystems decreased from southeast to northwest, which was highly consistent with previous products. The interannual trend and interannual variation of our generating AGPP showed a decreasing trend from east to west and from southeast to northwest, respectively, which was consistent with data-oriented products. The mean total amount of generated AGPP was 7.03 ± 0.45 PgC yr-1 falling into the range of previous works. Considering the consistency between the generated AGPP and previous products, our optimal mapping way was suitable for mapping AGPP from site measurements. Our results provided a methodological support for mapping regional AGPP and other fluxes.
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Affiliation(s)
- Xian-Jin Zhu
- College of Agronomy, Shenyang Agricultural University, Shenyang 110866, China; Liaoning Panjin Wetland Ecosystem National Observation and Research Station, Shenyang 110866, China
| | - Gui-Rui Yu
- Synthesis Research Center of Chinese Ecosystem Research Network, Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China.
| | - Zhi Chen
- Synthesis Research Center of Chinese Ecosystem Research Network, Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Wei-Kang Zhang
- Synthesis Research Center of Chinese Ecosystem Research Network, Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
| | - Lang Han
- Institute of Surface-Earth System Science, School of Earth System Science, Tianjin University, Tianjin,300072, China
| | - Qiu-Feng Wang
- Synthesis Research Center of Chinese Ecosystem Research Network, Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China.
| | - Shi-Ping Chen
- State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China
| | - Shao-Min Liu
- State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
| | - Hui-Min Wang
- Synthesis Research Center of Chinese Ecosystem Research Network, Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
| | - Jun-Hua Yan
- South China Botanical Garden, Chinese Academy of Sciences, Guangzhou 510650, China
| | - Jun-Lei Tan
- Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China
| | - Fa-Wei Zhang
- Northwest Institute of Plateau Biology, Chinese Academy of Sciences, Xining 810008, China
| | - Feng-Hua Zhao
- Synthesis Research Center of Chinese Ecosystem Research Network, Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
| | - Ying-Nian Li
- Northwest Institute of Plateau Biology, Chinese Academy of Sciences, Xining 810008, China
| | - Yi-Ping Zhang
- Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences, Mengla 666303, China
| | - Pei-Li Shi
- Synthesis Research Center of Chinese Ecosystem Research Network, Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
| | - Jiao-Jun Zhu
- Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110016, China
| | - Jia-Bing Wu
- Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110016, China
| | - Zhong-Hui Zhao
- Central South University of Forestry and Technology, Changsha 410004, China
| | - Yan-Bin Hao
- University of the Chinese Academy of Sciences, Beijing 100049, China
| | - Li-Qing Sha
- Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences, Mengla 666303, China
| | - Yu-Cui Zhang
- Center for Agricultural Resources Research, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Shijiazhuang 050021, China
| | | | - Feng-Xue Gu
- Institute of Environmental and sustainable development in agriculture, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Zhi-Xiang Wu
- Rubber research institute, Chinese Academy of tropical agricultural sciences, Haikou 570100, China
| | - Yang-Jian Zhang
- Synthesis Research Center of Chinese Ecosystem Research Network, Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Li Zhou
- Chinese Academy of Meteorological Sciences, China Meteorological Administration, Beijing 100081, China
| | - Ya-Kun Tang
- Northwest A&F University, Yangling 712100, China
| | - Bing-Rui Jia
- State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China
| | - Yu-Qiang Li
- Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China
| | - Qing-Hai Song
- Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences, Mengla 666303, China
| | - Gang Dong
- Shanxi University, Taiyuan 030006, China
| | - Yan-Hong Gao
- Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China
| | - Zheng-De Jiang
- Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110016, China
| | - Dan Sun
- South China Botanical Garden, Chinese Academy of Sciences, Guangzhou 510650, China
| | - Jian-Lin Wang
- Qingdao Agricultural University, Qingdao 266109, China
| | - Qi-Hua He
- Chengdu Institute of Biology, Chinese Academy of Sciences, Chengdu 610041, China
| | - Xin-Hu Li
- Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China
| | - Fei Wang
- Inner Mongolia Agricultural University, Hohhot 010018, China
| | - Wen-Xue Wei
- Institute of Subtropical Agriculture Chinese Academy of Sciences, Changsha 410125, China
| | - Zheng-Miao Deng
- Institute of Subtropical Agriculture Chinese Academy of Sciences, Changsha 410125, China
| | - Xiang-Xiang Hao
- Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China
| | - Yan Li
- Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China
| | - Xiao-Li Liu
- Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China
| | - Xi-Feng Zhang
- Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu 610041, China
| | - Zhi-Lin Zhu
- Synthesis Research Center of Chinese Ecosystem Research Network, Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
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3
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Zhang Z, Li X, Ju W, Zhou Y, Cheng X. Improved estimation of global gross primary productivity during 1981-2020 using the optimized P model. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 838:156172. [PMID: 35618136 DOI: 10.1016/j.scitotenv.2022.156172] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/23/2022] [Revised: 05/12/2022] [Accepted: 05/19/2022] [Indexed: 06/15/2023]
Abstract
Accurate estimation of terrestrial gross primary productivity (GPP) is essential for quantifying the net carbon exchange between the atmosphere and biosphere. Light use efficiency (LUE) models are widely used to estimate GPP at different spatial scales. However, difficulties in proper determination of maximum LUE (LUEmax) and downregulation of LUEmax into actual LUE result in uncertainties in GPP estimated by LUE models. The recently developed P model, as a LUE-like model, captures the deep mechanism of photosynthesis and simplifies parameterization. Site level studies have proved the outperformance of P model over LUE models. However, the global application of the P model is still lacking. Thus, the effectiveness of 5 water stress factors integrated into the P model was compared. The optimal P model was used to generate a new long-term (1981-2020) global monthly GPP dataset at a spatial resolution of 0.1° × 0.1°, called PGPP. Validation at globally distributed 109 FLUXNET sites indicated that PGPP is better than three widely-used GPP products. R2 between PGPP and observed GPP equals to 0.75, the corresponding root mean squared error (RMSE) and mean absolute error (MAE) equal to 1.77 g C m-2 d-1 and 1.28 g C m-2 d-1. During the period from 1981 to 2020, PGPP significantly increased in 69.02% of global vegetated regions (p < 0.05). Overall, PGPP provides a new GPP product choice for global ecology studies and the comparison of various water stress factors provides a new idea for the improvement of GPP model in the future.
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Affiliation(s)
- Zhenyu Zhang
- International Institute of Earth System Science, Nanjing University, Nanjing 210023, China; School of Geography and Ocean Science, Nanjing University, Nanjing 210023, China; State Key Laboratory of Subtropical Silviculture, Zhejiang A&F University, Hangzhou 311300, Zhejiang, China; Jiangsu Center for Collaborative Innovation in Geographic Information Resource Development and Application, Nanjing, Jiangsu 210023, China
| | - Xiaoyu Li
- State Key Laboratory of Subtropical Silviculture, Zhejiang A&F University, Hangzhou 311300, Zhejiang, China
| | - Weimin Ju
- International Institute of Earth System Science, Nanjing University, Nanjing 210023, China; Jiangsu Center for Collaborative Innovation in Geographic Information Resource Development and Application, Nanjing, Jiangsu 210023, China.
| | - Yanlian Zhou
- School of Geography and Ocean Science, Nanjing University, Nanjing 210023, China; Jiangsu Center for Collaborative Innovation in Geographic Information Resource Development and Application, Nanjing, Jiangsu 210023, China
| | - Xianfu Cheng
- Key Laboratory of Earth Surface Processes and Regional Response in the Yangtze-Huaihe River Basin, Anhui Province, Wuhu 241003, China
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4
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Kohonen KM, Dewar R, Tramontana G, Mauranen A, Kolari P, Kooijmans LMJ, Papale D, Vesala T, Mammarella I. Intercomparison of methods to estimate gross primary production based on CO 2 and COS flux measurements. BIOGEOSCIENCES (ONLINE) 2022; 19:4067-4088. [PMID: 36171741 PMCID: PMC7613647 DOI: 10.5194/bg-19-4067-2022] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Separating the components of ecosystem-scale carbon exchange is crucial in order to develop better models and future predictions of the terrestrial carbon cycle. However, there are several uncertainties and unknowns related to current photosynthesis estimates. In this study, we evaluate four different methods for estimating photosynthesis at a boreal forest at the ecosystem scale, of which two are based on carbon dioxide (CO2) flux measurements and two on carbonyl sulfide (COS) flux measurements. The CO2-based methods use traditional flux partitioning and artificial neural networks to separate the net CO2 flux into respiration and photosynthesis. The COS-based methods make use of a unique 5-year COS flux data set and involve two different approaches to determine the leaf-scale relative uptake ratio of COS and CO2 (LRU), of which one (LRUCAP) was developed in this study. LRUCAP was based on a previously tested stomatal optimization theory (CAP), while LRUPAR was based on an empirical relation to measured radiation. For the measurement period 2013-2017, the artificial neural network method gave a GPP estimate very close to that of traditional flux partitioning at all timescales. On average, the COS-based methods gave higher GPP estimates than the CO2-based estimates on daily (23% and 7% higher, using LRUPAR and LRUCAP, respectively) and monthly scales (20% and 3% higher), as well as a higher cumulative sum over 3 months in all years (on average 25% and 3% higher). LRUCAP was higher than LRU estimated from chamber measurements at high radiation, leading to underestimation of midday GPP relative to other GPP methods. In general, however, use of LRUCAP gave closer agreement with CO2-based estimates of GPP than use of LRUPAR. When extended to other sites, LRUCAP may be more robust than LRUPAR because it is based on a physiological model whose parameters can be estimated from simple measurements or obtained from the literature. In contrast, the empirical radiation relation in LRUPAR may be more site-specific. However, this requires further testing at other measurement sites.
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Affiliation(s)
- Kukka-Maaria Kohonen
- Institute for Atmospheric and Earth System Research/Physics, Faculty of Science, University of Helsinki, Helsinki, Finland
| | - Roderick Dewar
- Institute for Atmospheric and Earth System Research/Physics, Faculty of Science, University of Helsinki, Helsinki, Finland
- Division of Plant Sciences, Research School of Biology, The Australian National University, Canberra, ACT 2601, Australia
| | - Gianluca Tramontana
- Image Processing Laboratory (IPL), Parc Científic Universitat de València, Universitat de València, Paterna, Spain
- Terrasystem s.r.l, Viterbo, Italy
| | - Aleksanteri Mauranen
- 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
| | - Linda M. J. Kooijmans
- Meteorology and Air Quality, Wageningen University and Research, Wageningen, the Netherlands
| | - Dario Papale
- DIBAF, Department for Innovation in Biological, Agro-food and Forest Systems, University of Tuscia, Viterbo, Italy
- IAFES, Euro-Mediterranean Center for Climate Change (CMCC), Viterbo, Italy
| | - Timo Vesala
- Institute for Atmospheric and Earth System Research/Physics, Faculty of Science, University of Helsinki, Helsinki, Finland
- Institute for Atmospheric and Earth System Research/Forest Sciences, University of Helsinki, Helsinki, Finland
| | - Ivan Mammarella
- Institute for Atmospheric and Earth System Research/Physics, Faculty of Science, University of Helsinki, Helsinki, Finland
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5
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Cabon A, Kannenberg SA, Arain A, Babst F, Baldocchi D, Belmecheri S, Delpierre N, Guerrieri R, Maxwell JT, McKenzie S, Meinzer FC, Moore DJP, Pappas C, Rocha AV, Szejner P, Ueyama M, Ulrich D, Vincke C, Voelker SL, Wei J, Woodruff D, Anderegg WRL. Cross-biome synthesis of source versus sink limits to tree growth. Science 2022; 376:758-761. [PMID: 35549405 DOI: 10.1126/science.abm4875] [Citation(s) in RCA: 35] [Impact Index Per Article: 17.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
Abstract
Uncertainties surrounding tree carbon allocation to growth are a major limitation to projections of forest carbon sequestration and response to climate change. The prevalence and extent to which carbon assimilation (source) or cambial activity (sink) mediate wood production are fundamentally important and remain elusive. We quantified source-sink relations across biomes by combining eddy-covariance gross primary production with extensive on-site and regional tree ring observations. We found widespread temporal decoupling between carbon assimilation and tree growth, underpinned by contrasting climatic sensitivities of these two processes. Substantial differences in assimilation-growth decoupling between angiosperms and gymnosperms were determined, as well as stronger decoupling with canopy closure, aridity, and decreasing temperatures. Our results reveal pervasive sink control over tree growth that is likely to be increasingly prominent under global climate change.
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Affiliation(s)
- Antoine Cabon
- School of Biological Sciences, University of Utah, Salt Lake City, UT, USA
| | | | - Altaf Arain
- McMaster Centre for Climate Change, McMaster University, Hamilton, Ontario L8S 4K1, Canada.,School of Earth, Environment and Society, McMaster University, Hamilton, Ontario L8S 4K1, Canada
| | - Flurin Babst
- School of Natural Resources and the Environment, University of Arizona, Tucson, AZ, USA.,Laboratory of Tree-Ring Research, University of Arizona, Tucson, AZ, USA
| | - Dennis Baldocchi
- Department of Environmental Science, Policy and Management, University of California, Berkeley, CA, USA
| | - Soumaya Belmecheri
- Laboratory of Tree-Ring Research, University of Arizona, Tucson, AZ, USA
| | - Nicolas Delpierre
- Université Paris-Saclay, CNRS, AgroParisTech, Ecologie Systématique et Evolution, 91405 Orsay, France.,Institut Universitaire de France, 75231 Paris Cedex 05, France
| | | | - Justin T Maxwell
- Department of Geography, Indiana University, Bloomington, IN, USA
| | - Shawn McKenzie
- McMaster Centre for Climate Change, McMaster University, Hamilton, Ontario L8S 4K1, Canada.,School of Earth, Environment and Society, McMaster University, Hamilton, Ontario L8S 4K1, Canada
| | | | - David J P Moore
- School of Natural Resources and the Environment, University of Arizona, Tucson, AZ, USA
| | - Christoforos Pappas
- Centre d'étude de la forêt, Université du Québec à Montréal, C.P. 8888, Succursale Centre-ville, Montréal, Quebec H3C 3P8, Canada.,Département Science et Technologie, Téluq, Université du Québec, Bureau 1105, Montréal, Quebec H2S 3L5, Canada
| | - Adrian V Rocha
- Department of Biological Sciences, University of Notre Dame, Notre Dame, IN, USA
| | - Paul Szejner
- Geology Institute, National Autonomous University of Mexico, Coyoacán, CDMX, Mexico
| | - Masahito Ueyama
- Graduate School of Life and Environmental Sciences, Osaka Prefecture University, 1-1 Gakuen-cho, Naka-ku, Sakai 599-8531, Japan
| | - Danielle Ulrich
- Department of Ecology, Montana State University, Bozeman, MT, USA
| | - Caroline Vincke
- Earth and Life Institute, Université Catholique de Louvain, Louvain-la-Neuve, Belgium
| | - Steven L Voelker
- College of Forest Resources and Environmental Science, Michigan Technological University, Houghton, MI, USA
| | - Jingshu Wei
- Department of Ecology, W. Szafer Institute of Botany, Polish Academy of Sciences, 31-512 Kraków, Poland
| | - David Woodruff
- USDA Forest Service, Pacific Northwest Research Station, Corvallis, OR, USA
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6
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Eichelmann E, Mantoani MC, Chamberlain SD, Hemes KS, Oikawa PY, Szutu D, Valach A, Verfaillie J, Baldocchi DD. A novel approach to partitioning evapotranspiration into evaporation and transpiration in flooded ecosystems. GLOBAL CHANGE BIOLOGY 2022; 28:990-1007. [PMID: 34735731 DOI: 10.1111/gcb.15974] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/28/2021] [Revised: 10/22/2021] [Accepted: 10/25/2021] [Indexed: 06/13/2023]
Abstract
Reliable partitioning of micrometeorologically measured evapotranspiration (ET) into evaporation (E) and transpiration (T) would greatly enhance our understanding of the water cycle and its response to climate change related shifts in local-to-regional climate conditions and rising global levels of vapor pressure deficit (VPD). While some methods on ET partitioning have been developed, their underlying assumptions make them difficult to apply more generally, especially in sites with large contributions of E. Here, we report a novel ET partitioning method using artificial neural networks (ANNs) in combination with a range of environmental input variables to predict daytime E from nighttime ET measurements. The study uses eddy covariance data from four restored wetlands in the Sacramento-San Joaquin Delta, California, USA, as well as leaf-level T data for validation. The four wetlands vary in their vegetation make-up and structure, representing a range of ET conditions. The ANNs were built with increasing complexity by adding the input variable that resulted in the next highest average value of model testing R2 across all sites. The order of variable inclusion (and importance) was: VPD > gap-filled sensible heat flux (H_gf) > air temperature (Tair ) > friction velocity (u∗ ) > other variables. The model using VPD, H_gf, Tair , and u∗ showed the best performance during validation with independent data and had a mean testing R2 value of 0.853 (averaged across all sites, range from 0.728 to 0.910). In comparison to other methods, our ANN method generated T/ET partitioning results which were more consistent with CO2 exchange data especially for more heterogeneous sites with large E contributions. Our method improves the understanding of T/ET partitioning. While it may be particularly suited to flooded ecosystems, it can also improve T/ET partitioning in other systems, increasing our knowledge of the global water cycle and ecosystem functioning.
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Affiliation(s)
- Elke Eichelmann
- School of Biology and Environmental Science, University College Dublin, Science Centre West, Dublin 4, Ireland
| | - Mauricio C Mantoani
- School of Biology and Environmental Science, University College Dublin, Science Centre West, Dublin 4, Ireland
| | - Samuel D Chamberlain
- Department of Environmental Science, Policy & Management, UC Berkeley, Berkeley, California, USA
| | - Kyle S Hemes
- Department of Environmental Science, Policy & Management, UC Berkeley, Berkeley, California, USA
| | - Patricia Y Oikawa
- Department of Earth and Environmental Sciences, California State University, East Bay, Hayward, California, USA
| | - Daphne Szutu
- Department of Environmental Science, Policy & Management, UC Berkeley, Berkeley, California, USA
| | - Alex Valach
- Department of Environmental Science, Policy & Management, UC Berkeley, Berkeley, California, USA
| | - Joseph Verfaillie
- Department of Environmental Science, Policy & Management, UC Berkeley, Berkeley, California, USA
| | - Dennis D Baldocchi
- Department of Environmental Science, Policy & Management, UC Berkeley, Berkeley, California, USA
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7
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Estimating Gross Primary Productivity (GPP) over Rice–Wheat-Rotation Croplands by Using the Random Forest Model and Eddy Covariance Measurements: Upscaling and Comparison with the MODIS Product. REMOTE SENSING 2021. [DOI: 10.3390/rs13214229] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Despite advances in remote sensing–based gross primary productivity (GPP) modeling, the calibration of the Moderate Resolution Imaging Spectroradiometer (MODIS) GPP product (GPPMOD) is less well understood over rice–wheat-rotation cropland. To improve the performance of GPPMOD, a random forest (RF) machine learning model was constructed and employed over the rice–wheat double-cropping fields of eastern China. The RF-derived GPP (GPPRF) agreed well with the eddy covariance (EC)-derived GPP (GPPEC), with a coefficient of determination of 0.99 and a root-mean-square error of 0.42 g C m−2 d−1. Therefore, it was deemed reliable to upscale GPPEC to regional scales through the RF model. The upscaled cumulative seasonal GPPRF was higher for rice (924 g C m−2) than that for wheat (532 g C m−2). By comparing GPPMOD and GPPEC, we found that GPPMOD performed well during the crop rotation periods but underestimated GPP during the rice/wheat active growth seasons. Furthermore, GPPMOD was calibrated by GPPRF, and the error range of GPPMOD (GPPRF minus GPPMOD) was found to be 2.5–3.25 g C m−2 d−1 for rice and 0.75–1.25 g C m−2 d−1 for wheat. Our findings suggest that RF-based GPP products have the potential to be applied in accurately evaluating MODIS-based agroecosystem carbon cycles at regional or even global scales.
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8
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Coupling Photosynthetic Measurements with Biometric Data to Estimate Gross Primary Productivity (GPP) in Mediterranean Pine Forests of Different Post-Fire Age. FORESTS 2021. [DOI: 10.3390/f12091256] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Quantification of forest Gross Primary Productivity (GPP) is important for understanding ecosystem function and designing appropriate carbon mitigation strategies. Coupling forest biometric data with canopy photosynthesis models can provide a means to simulate GPP across different stand ages. In this study we developed a simple framework to integrate biometric and leaf gas-exchange measurements, and to estimate GPP across four Mediterranean pine forests of different post-fire age. We used three different methods to estimate the Leaf Area Index (LAI) of the stands, and monthly gas exchange data to calibrate the photosynthetic light response of the leaves. Upscaling of carbon sequestration at the canopy level was made by implementing a Big Leaf and a Sun/Shade model, using both average and variant (monthly) photosynthetic capacity values. The Big Leaf model simulations systematically underestimated GPP compared to the Sun/Shade model simulations. Our simulations suggest an increasing GPP with age up to a stand maturity stage. The shape of the GPP trend with stand age was not affected by the method used to parameterise the model. At the scale of our study, variability in stand and canopy structure among the study sites seems to be the key determinant of GPP.
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9
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Towards neural Earth system modelling by integrating artificial intelligence in Earth system science. NAT MACH INTELL 2021. [DOI: 10.1038/s42256-021-00374-3] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
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