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Jiang N, Zhu XG. Modern phenomics to empower holistic crop science, agronomy, and breeding research. J Genet Genomics 2024; 51:790-800. [PMID: 38734136 DOI: 10.1016/j.jgg.2024.04.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2023] [Revised: 04/25/2024] [Accepted: 04/30/2024] [Indexed: 05/13/2024]
Abstract
Crop phenomics enables the collection of diverse plant traits for a large number of samples along different time scales, representing a greater data collection throughput compared with traditional measurements. Most modern crop phenomics use different sensors to collect reflective, emitted, and fluorescence signals, etc., from plant organs at different spatial and temporal resolutions. Such multi-modal, high-dimensional data not only accelerates basic research on crop physiology, genetics, and whole plant systems modeling, but also supports the optimization of field agronomic practices, internal environments of plant factories, and ultimately crop breeding. Major challenges and opportunities facing the current crop phenomics research community include developing community consensus or standards for data collection, management, sharing, and processing, developing capabilities to measure physiological parameters, and enabling farmers and breeders to effectively use phenomics in the field to directly support agricultural production.
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Affiliation(s)
- Ni Jiang
- Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China.
| | - Xin-Guang Zhu
- Center of Excellence for Molecular Plant Sciences, Chinese Academy of Sciences, Shanghai 200032, China.
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2
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Ji Y, Zeng S, Yang L, Wan H, Xia J. Global eight drought types: Spatio-temporal characteristics and vegetation response. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2024; 359:121069. [PMID: 38714034 DOI: 10.1016/j.jenvman.2024.121069] [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: 02/17/2024] [Revised: 04/12/2024] [Accepted: 04/30/2024] [Indexed: 05/09/2024]
Abstract
The traditional classification of drought events into seasonal and flash types oversimplified the complexity and variability of global drought phenomena, limiting a deeper understanding of drought characteristics and their impacts on vegetation. To address this issue, soil moisture percentile methods and the Soil Moisture Anomaly Percentage Index (SMAPI) were employed to create time series for flash drought (FD) and seasonal drought (SD) events globally from 1981 to 2020. A novel categorization framework was proposed to subdivide the two basic drought categories into eight distinct drought types using a set relationship identification method. The results showed fluctuating trends in the frequencies of Independent FD and Inclusion FD, which declined rapidly after 2011 at rates of 0.05 and 0.04 times/year, respectively. Independent FD frequency was highest in humid areas and decreased with increasing aridity. The spatial distributions of Inclusion FD and SD were similar, with both frequencies highest in extremely arid areas and decreasing with increasing humidity. The frequency of Independent SD, which peaked in semi-arid areas, increased significantly after 2011 at a rate of 0.01 times/year. The occurrence of FD evolving into SD or emerging at the end of SD was rare, with a global average of 0.46 events/decade and little spatial variation. Between 1981 and 2020, FD showed a U-shaped trend in drought duration, while SD showed no clear pattern. The duration of FD showed little difference across arid and humid zones, but the duration of SD decreased significantly with increasing humidity. Vegetation responses to drought varied, with arid regions showing longer response time compared to humid regions. A positive correlation between temperature and solar-induced chlorophyll fluorescence (SIF) during droughts was observed, while precipitation generally showed a negative correlation with SIF. Radiation had a minimal effect on SIF during droughts. The study offered a comprehensive categorization of drought events, enhancing our understanding of their spatiotemporal characteristics and vegetation responses on a global scale.
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Affiliation(s)
- Yongyue Ji
- Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing, 400714, China; Chongqing School, University of Chinese Academy of Sciences, Chongqing, 400714, China; Changjiang Institute of Survey, Planning, Design and Research Corporation, Key Laboratory of Water Grid Project and Regulation of Ministry of Water Resources, Wuhan, 430010, China
| | - Sidong Zeng
- Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing, 400714, China; Chongqing School, University of Chinese Academy of Sciences, Chongqing, 400714, China.
| | - Linhan Yang
- Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing, 400714, China; Chongqing School, University of Chinese Academy of Sciences, Chongqing, 400714, China; Changjiang Institute of Survey, Planning, Design and Research Corporation, Key Laboratory of Water Grid Project and Regulation of Ministry of Water Resources, Wuhan, 430010, China
| | - Hui Wan
- Changjiang Institute of Survey, Planning, Design and Research Corporation, Key Laboratory of Water Grid Project and Regulation of Ministry of Water Resources, Wuhan, 430010, China
| | - Jun Xia
- State Key Laboratory of Water Resources Engineering and Management, Wuhan University, Wuhan, 430072, China
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Li M, Wang Y, Li N, Chen B, ShurenChou. Knowledge mapping on the sun-induced chlorophyll fluorescence technology research: a scientometric and visualization analysis. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2024; 31:9150-9166. [PMID: 38182958 DOI: 10.1007/s11356-023-31709-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Accepted: 12/20/2023] [Indexed: 01/07/2024]
Abstract
The sun-induced chlorophyll fluorescence (SIF) has received increasing attention over the past few years. This scientometric study analyzed the SIF research field, based on publications available in Web of Science Core Collection visualization, and cluster analysis enabled us to map the knowledge domain and intellectual landscape of this field and identify thematic trends, landmark articles, and emerging research themes. In this study, the software VOSviewer and CiteSpace were used to identify the intellectual base and research front using visualization and analysis. Then, we analyzed synthesized networks of co-authorship (author, institution, and country), co-citation (author, document, and journal), and co-occurring keywords. SIF has increased its publication output steadily since 2002. This study provided a visual knowledge map of the major research domains of SIF. Also, explanations and implications of the findings were explored, and the emerging trends were identified. This report provides a thorough overview of the current state and growth trends of SIF research, and it helps researchers and practitioners understand the major areas of attention in this area and provides insights into prospective areas for further study.
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Affiliation(s)
- Ming Li
- Gong Qing Institute of Science and Technology, Nanchang, 330044, China
- Jilin Academy of Agricultural Sciences, Changchun, 130016, China
- School of Civil and Environmental Engineering, Jilin Jianzhu University, Changchun, China
| | - Yang Wang
- Gong Qing Institute of Science and Technology, Nanchang, 330044, China
- Space Security Center, Space Engineering University, Beijing, 101416, China
- School of Civil and Environmental Engineering, Jilin Jianzhu University, Changchun, China
| | - Na Li
- School of Civil and Environmental Engineering, Jilin Jianzhu University, Changchun, China
| | - Bin Chen
- Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China
| | - ShurenChou
- Space Security Center, Space Engineering University, Beijing, 101416, China.
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4
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Sun Y, Gu L, Wen J, van der Tol C, Porcar-Castell A, Joiner J, Chang CY, Magney T, Wang L, Hu L, Rascher U, Zarco-Tejada P, Barrett CB, Lai J, Han J, Luo Z. From remotely sensed solar-induced chlorophyll fluorescence to ecosystem structure, function, and service: Part I-Harnessing theory. GLOBAL CHANGE BIOLOGY 2023; 29:2926-2952. [PMID: 36799496 DOI: 10.1111/gcb.16634] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Accepted: 11/08/2022] [Indexed: 05/03/2023]
Abstract
Solar-induced chlorophyll fluorescence (SIF) is a remotely sensed optical signal emitted during the light reactions of photosynthesis. The past two decades have witnessed an explosion in availability of SIF data at increasingly higher spatial and temporal resolutions, sparking applications in diverse research sectors (e.g., ecology, agriculture, hydrology, climate, and socioeconomics). These applications must deal with complexities caused by tremendous variations in scale and the impacts of interacting and superimposing plant physiology and three-dimensional vegetation structure on the emission and scattering of SIF. At present, these complexities have not been overcome. To advance future research, the two companion reviews aim to (1) develop an analytical framework for inferring terrestrial vegetation structures and function that are tied to SIF emission, (2) synthesize progress and identify challenges in SIF research via the lens of multi-sector applications, and (3) map out actionable solutions to tackle these challenges and offer our vision for research priorities over the next 5-10 years based on the proposed analytical framework. This paper is the first of the two companion reviews, and theory oriented. It introduces a theoretically rigorous yet practically applicable analytical framework. Guided by this framework, we offer theoretical perspectives on three overarching questions: (1) The forward (mechanism) question-How are the dynamics of SIF affected by terrestrial ecosystem structure and function? (2) The inference question: What aspects of terrestrial ecosystem structure, function, and service can be reliably inferred from remotely sensed SIF and how? (3) The innovation question: What innovations are needed to realize the full potential of SIF remote sensing for real-world applications under climate change? The analytical framework elucidates that process complexity must be appreciated in inferring ecosystem structure and function from the observed SIF; this framework can serve as a diagnosis and inference tool for versatile applications across diverse spatial and temporal scales.
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Affiliation(s)
- Ying Sun
- School of Integrative Plant Science, Soil and Crop Sciences Section, Cornell University, Ithaca, New York, USA
| | - Lianhong Gu
- Environmental Sciences Division and Climate Change Science Institute, Oak Ridge National Laboratory, Oak Ridge, Tennessee, USA
| | - Jiaming Wen
- School of Integrative Plant Science, Soil and Crop Sciences Section, Cornell University, Ithaca, New York, USA
| | - Christiaan van der Tol
- Affiliation Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, Enschede, The Netherlands
| | - Albert Porcar-Castell
- Optics of Photosynthesis Laboratory, Institute for Atmospheric and Earth System Research (INAR)/Forest Sciences, Viikki Plant Science Center (ViPS), University of Helsinki, Helsinki, Finland
| | - Joanna Joiner
- National Aeronautics and Space Administration (NASA) Goddard Space Flight Center (GSFC), Greenbelt, Maryland, USA
| | - Christine Y Chang
- US Department of Agriculture, Agricultural Research Service, Adaptive Cropping Systems Laboratory, Beltsville, Maryland, USA
| | - Troy Magney
- Department of Plant Sciences, University of California, Davis, Davis, California, USA
| | - Lixin Wang
- Department of Earth Sciences, Indiana University-Purdue University Indianapolis (IUPUI), Indianapolis, Indiana, USA
| | - Leiqiu Hu
- Department of Atmospheric and Earth Science, University of Alabama in Huntsville, Huntsville, Alabama, USA
| | - Uwe Rascher
- Institute of Bio- and Geosciences, Forschungszentrum Jülich GmbH, Jülich, Germany
| | - Pablo Zarco-Tejada
- School of Agriculture and Food (SAF-FVAS) and Faculty of Engineering and Information Technology (IE-FEIT), University of Melbourne, Melbourne, Victoria, Australia
| | - Christopher B Barrett
- Charles H. Dyson School of Applied Economics and Management, Cornell University, Ithaca, New York, USA
| | - Jiameng Lai
- School of Integrative Plant Science, Soil and Crop Sciences Section, Cornell University, Ithaca, New York, USA
| | - Jimei Han
- School of Integrative Plant Science, Soil and Crop Sciences Section, Cornell University, Ithaca, New York, USA
| | - Zhenqi Luo
- School of Integrative Plant Science, Soil and Crop Sciences Section, Cornell University, Ithaca, New York, USA
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da Costa LM, de Mendonça GC, Araújo Santos GAD, Moraes JRDSCD, Colombo R, Panosso AR, La Scala N. High spatial resolution solar-induced chlorophyll fluorescence and its relation to rainfall precipitation across Brazilian ecosystems. ENVIRONMENTAL RESEARCH 2023; 218:114991. [PMID: 36502899 DOI: 10.1016/j.envres.2022.114991] [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: 08/25/2022] [Revised: 11/25/2022] [Accepted: 11/30/2022] [Indexed: 06/17/2023]
Abstract
The detection of Solar-Induced chlorophyll Fluorescence (SIF) by remote sensing has opened new perspectives on ecosystem studies and other related aspects such as photosynthesis. In general, fluorescence high-resolution studies were limited to proximal sensors, but new approaches were developed to improve SIF resolution by combining OCO-2 with MODIS orbital observations, improving its resolution from 0.5° to 0.05 on a global scale. Using a high-resolution dataset and rainfall data some SIF characteristics of the satellite were studied based across 06 contrasting ecosystems in Brazil: Amazonia, Caatinga, Cerrado, Atlantic Forest, Pampa, and Pantanal, from years 2015-2018. SIF spatial variability in each biome presented significant spatial variability structures with high R2 values (>0.6, Gaussian models) in all studied years. The rainfall maps were positively and similar related to SIF spatial distribution and were able to explain more than 40% of SIF's spatial variability. The Amazon biome presented the higher SIF values (>0.4 W m-2 sr-1 μm-1) and also the higher annual rainfall precipitation (around 2000 mm), while Caatinga had the lowest SIF values and precipitations (<0.1 W m-2 sr-1 μm-1, precipitation around 500 mm). The linear relationship of SIF to rainfall across biomes was mostly significant (except in Pantanal) and presented contrasting sensitivities as in Caatinga SIF was mostly affected while in the Amazon, SIF was lesser affected by precipitation events. We believe that the features presented here indicate that SIF could be highly affected by rainfall precipitation changes in some Brazilian biomes. Combining rainfall with SIF allowed us to detect the differences and similarities across Brazil's biomes improving our understanding on how these ecosystems could be affected by climate change and severe weather conditions.
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Affiliation(s)
- Luis Miguel da Costa
- Department of Engineering and Exact Sciences, São Paulo State University Faculty of Agrarian and Veterinary Sciences (FCAV-UNESP), Via de Acesso Prof. Paulo Donato Castellane s/n, 14884-900, Jaboticabal, São Paulo, Brazil.
| | - Gislaine Costa de Mendonça
- Department of Engineering and Exact Sciences, São Paulo State University Faculty of Agrarian and Veterinary Sciences (FCAV-UNESP), Via de Acesso Prof. Paulo Donato Castellane s/n, 14884-900, Jaboticabal, São Paulo, Brazil.
| | - Gustavo André de Araújo Santos
- Advanced Campus Porto Franco, Federal Institute of Education, Science and Technology of Maranhão - IFMA, Rua Custódio Barbosa, no 09, Centro, Porto Franco, Maranhão, 65970-000, Brazil; Center of Agricultural, Natural and Literary Sciences, State University of the Tocantins Region of Maranhão (UEMASUL), Av. Brejo do Pinto, S/N - Brejo do Pinto, Estreito, Maranhão, 65975-000, Brazil.
| | - José Reinaldo da Silva Cabral de Moraes
- Department of Engineering and Exact Sciences, São Paulo State University Faculty of Agrarian and Veterinary Sciences (FCAV-UNESP), Via de Acesso Prof. Paulo Donato Castellane s/n, 14884-900, Jaboticabal, São Paulo, Brazil.
| | - Roberto Colombo
- Remote Sensing of Environmental Dynamics Lab., DISAT, University of Milano-Bicocca, P.zza della Scienza 1, 20126, Milano, Italy.
| | - Alan Rodrigo Panosso
- Department of Engineering and Exact Sciences, São Paulo State University Faculty of Agrarian and Veterinary Sciences (FCAV-UNESP), Via de Acesso Prof. Paulo Donato Castellane s/n, 14884-900, Jaboticabal, São Paulo, Brazil.
| | - Newton La Scala
- Department of Engineering and Exact Sciences, São Paulo State University Faculty of Agrarian and Veterinary Sciences (FCAV-UNESP), Via de Acesso Prof. Paulo Donato Castellane s/n, 14884-900, Jaboticabal, São Paulo, Brazil.
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Yao T, Liu S, Hu S, Mo X. Response of vegetation ecosystems to flash drought with solar-induced chlorophyll fluorescence over the Hai River Basin, China during 2001-2019. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2022; 313:114947. [PMID: 35421694 DOI: 10.1016/j.jenvman.2022.114947] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Revised: 03/03/2022] [Accepted: 03/20/2022] [Indexed: 06/14/2023]
Abstract
With global climate change, frequent flash droughts have critically impacted vegetation productivity. Based on the new definition on flash drought onset and duration, the temporal and spatial evolution patterns of the flash drought over the Hai River Basin (HRB) was analysed. Among the events, the flash drought in 2019 lasted for 40 days, from the day of the year (DOY) 120 to DOY160, which was the strongest and mainly concentrated in the south-eastern part of the basin. Solar-induced chlorophyll fluorescence (SIF) and vegetation indices were used to explore the responses of different vegetation types to this flash drought. Compared to forest and grassland, the SIF and SIFyield (SIF normalized by the absorbed photosynthetically active radiation (APAR)) values of cropland were more sensitive to water losses and replenishment. By analysing different radiation conditions which would affect SIF and photosynthesis, low radiation was found altering the linear relationship between fluorescence and photosynthesis. The flash drought event caused gross primary productivity (GPP) losses in 40% of the basin and the maximum loss reached 0.16 kg C m-2, indicating that the impact of this flash drought on vegetation productivity was quite serious. The results obtained in this work can be used to understand the mechanisms with which the vegetation photosynthetic capacity responds to flash droughts and to evaluate the impact of flash droughts on terrestrial ecosystems.
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Affiliation(s)
- Tingting Yao
- Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Science, Beijing, 100101, China; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, 100190, China
| | - Suxia Liu
- Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Science, Beijing, 100101, China; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, 100190, China; Sino-Danish College, University of Chinese Academy of Sciences, Beijing, 100049, China.
| | - Shi Hu
- Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Science, Beijing, 100101, China
| | - Xingguo Mo
- Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Science, Beijing, 100101, China; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, 100190, China; Sino-Danish College, University of Chinese Academy of Sciences, Beijing, 100049, China
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Evaluation of Plant Stress Monitoring Capabilities Using a Portable Spectrometer and Blue-Red Grow Light. SENSORS 2022; 22:s22093411. [PMID: 35591102 PMCID: PMC9099694 DOI: 10.3390/s22093411] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Revised: 04/11/2022] [Accepted: 04/21/2022] [Indexed: 11/25/2022]
Abstract
Remote sensing offers a non-destructive method to detect plant physiological response to the environment by measuring chlorophyll fluorescence (CF). Most methods to estimate CF require relatively complex retrieval, spectral fitting, or modelling methods. An investigation was undertaken to evaluate measurements of CF using a relatively straightforward technique to detect and monitor plant stress with a spectroradiometer and blue-red light emitting diode (LED). CF spectral response of tomato plants treated with a photosystem inhibitor were assessed and compared to traditional reflectance-based indices: normalized difference vegetation index (NDVI) and photochemical reflectance index (PRI). The blue-red LEDs provided input irradiance and a “window” in the CF emission range of plants (~650 to 850 nm) sufficient to capture distinctive “two-peak” spectra and to distinguish plant health from day to day of the experiment, while within day differences were noisy. CF-based metrics calculated from CF spectra clearly captured signs of vegetation stress earlier than reflectance-based indices and by visual inspection. This CF monitoring technique is a flexible and scalable option for collecting plant function data, especially for indicating early signs of stress. The technique can be applied to a single plant or larger canopies using LED in dark conditions by an individual, or a manned or unmanned vehicle for agricultural or military purposes.
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Li Z, Ding L, Xu D. Exploring the potential role of environmental and multi-source satellite data in crop yield prediction across Northeast China. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 815:152880. [PMID: 34998760 DOI: 10.1016/j.scitotenv.2021.152880] [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: 10/25/2021] [Revised: 12/20/2021] [Accepted: 12/30/2021] [Indexed: 06/14/2023]
Abstract
Developing an accurate crop yield predicting system at a large scale is of paramount importance for agricultural resource management and global food security. Earth observation provides a unique source of information to monitor crops from a diversity of spectral ranges. However, the integrated use of these data and their values in crop yield prediction is still understudied. Here we proposed the combination of environmental data (climate, soil, geography, and topography) with multiple satellite data (optical-based vegetation indices, solar-induced fluorescence (SIF), land surface temperature (LST), and microwave vegetation optical depth (VOD)) into the framework to estimate crop yield for maize, rice, and soybean in northeast China, and their unique value and relative influence on yield prediction was assessed. Two linear regression methods, three machine learning (ML) methods, and one ML ensemble model were adopted to build yield prediction models. Results showed that the individual ML methods outperformed the linear regression methods, the ML ensemble model further improved the single ML models. Moreover, models with more inputs achieved better performance, the combination of satellite data with environmental data, which explained 72%, 69%, and 57% of maize, rice, and soybean yield variability, respectively, demonstrated higher yield prediction performance than individual inputs. While satellite data contributed to crop yield prediction mainly at the early-peak of the growing season, climate data offered extra information mainly at the peak-late season. We also found that the combined use of EVI, LST and SIF has improved the model accuracy compared to the benchmark EVI model. However, the optical-based vegetation indices shared similar information and did not provide much extra information beyond EVI. The within-season yield forecasting showed that crop yields can be satisfactorily forecasted at two to three months prior to harvest. Geography, topography, VOD, EVI, soil hydraulic and nutrient parameters are more important for crop yield prediction.
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Affiliation(s)
- Zhenwang Li
- State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China.
| | - Lei Ding
- College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China
| | - Dawei Xu
- National Field Scientific Observation and Research Station of Hulunbuir Grassland Ecosystem in Inner Mongolia, Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China
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Remotely Monitoring Vegetation Productivity in Two Contrasting Subtropical Forest Ecosystems Using Solar-Induced Chlorophyll Fluorescence. REMOTE SENSING 2022. [DOI: 10.3390/rs14061328] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Subtropical forests can sequester a larger amount of atmospheric carbon dioxide (CO2) relative to other terrestrial ecosystems through photosynthetic activity and act as an important role in mitigating global climate warming. Compared with the model-based gross primary production (GPP) products, satellite-derived solar-induced fluorescence (SIF) opens a new window for quantification. Here, we used the remotely sensed SIF retrievals, two satellite-driven GPP products including MODIS (GPPMOD) and BESS (GPPBESS), and tower-based GPP measurements at two contrasting subtropical forests to provide a systematic analysis. Our results revealed that GPP and the associated environmental factors exhibited distinct seasonal patterns. However, the peak GPP values had large differences, with stronger GPP in the evergreen needleleaf forest site (8.76 ± 0.71 g C m−2 d−1) than that in the evergreen broadleaf forest site (5.71 ± 0.31 g C m−2 d−1). The satellite-derived SIF retrievals showed great potential in quantifying the variability in GPP, especially for the evergreen needleleaf forest with r reaching up to 0.909 (p < 0.01). GPPMOD and GPPBESS showed distinctly different performances for the two subtropical forests, whereas the GPP estimates by exclusive use of satellite-based SIF data promised well to the tower-based GPP observations. Multi-year evaluation again confirmed the good performance of the SIF-based GPP estimates. These findings will provide an alternative framework for quantifying the magnitude of forest GPP and advance our understanding of the carbon sequestration capacity of subtropical forest ecosystems.
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Li Y, Liu Y, Bohrer G, Cai Y, Wilson A, Hu T, Wang Z, Zhao K. Impacts of forest loss on local climate across the conterminous United States: Evidence from satellite time-series observations. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 802:149651. [PMID: 34525747 DOI: 10.1016/j.scitotenv.2021.149651] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/16/2021] [Revised: 08/09/2021] [Accepted: 08/09/2021] [Indexed: 06/13/2023]
Abstract
Forest disturbances alter land biophysics. Their impacts on local climate and land surface temperature (LST) cannot be directly measured by comparing pre- and post-disturbance observations of the same site over time (e.g., due to confounding such as background climate fluctuations); a common remedy is to compare spatially-adjacent undisturbed sites instead. This space-for-time substitution ignores the inherent biases in vegetation between two paired sites, interannual variations, and temporal dynamics of forest recovery. Besides, there is a lack of observation-based analyses at fine spatial resolutions capable of capturing spatial heterogeneity of small-scale forest disturbances. To address these limitations, here we report new satellite analyses on local climate impacts of forest loss at 30 m resolution. Our analyses combined multiple long-term satellite products (e.g., albedo and evapotranspiration [ET]) at 700 sites across major climate zones in the conterminous United States, using time-series trend and changepoint detection methods. Our method helped isolate the biophysical changes attributed to disturbances from those attributed to climate backgrounds and natural growth. On average, forest loss increased surface albedo, decreased ET, and reduced leaf area index (LAI). Net annual warming-an increase in LST-was observed after forest loss in the arid/semiarid, northern, tropical, and temperate regions, dominated by the warming from decreased ET and attenuated by the cooling from increased albedo. The magnitude of post-disturbance warming was related to precipitation; climate zones with greater precipitation showed stronger and longer warming. Reduction in leaf or LAI was larger in evergreen than deciduous forests, but the recovery in LAI did not always synchronize with those of albedo and ET. Overall, this study presents new evidence of biophysical effects of forest loss on LST at finer spatial resolutions; our time-series method can be further leveraged to derive local policy-relevant ecosystem climate regulation metrics or support model-based climate-biosphere studies.
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Affiliation(s)
- Yang Li
- Environmental Science Graduate Program, The Ohio State University, Columbus, OH 43210, USA; School of Environment and Natural Resources, The Ohio State University, Columbus, OH 43210, USA.
| | - Yanlan Liu
- School of Environment and Natural Resources, The Ohio State University, Columbus, OH 43210, USA; School of Earth Sciences, The Ohio State University, Columbus, OH 43210, USA
| | - Gil Bohrer
- Environmental Science Graduate Program, The Ohio State University, Columbus, OH 43210, USA; Department of Civil, Environmental and Geodetic Engineering, The Ohio State University, Columbus, OH 43210, USA
| | - Yongyang Cai
- Department of Agricultural, Environmental, and Development Economics, The Ohio State University, Columbus, OH 43210, USA
| | - Aaron Wilson
- Byrd Polar and Climate Research Center, The Ohio State University, Columbus, OH 43210, USA; Department of Extension, The Ohio State University, Columbus, OH 43210, USA
| | - Tongxi Hu
- Environmental Science Graduate Program, The Ohio State University, Columbus, OH 43210, USA; School of Environment and Natural Resources, The Ohio State University, Columbus, OH 43210, USA
| | - Zhihao Wang
- Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA
| | - Kaiguang Zhao
- Environmental Science Graduate Program, The Ohio State University, Columbus, OH 43210, USA; School of Environment and Natural Resources, The Ohio State University, Columbus, OH 43210, USA.
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11
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Chen A, Mao J, Ricciuto D, Lu D, Xiao J, Li X, Thornton PE, Knapp AK. Seasonal changes in GPP/SIF ratios and their climatic determinants across the Northern Hemisphere. GLOBAL CHANGE BIOLOGY 2021; 27:5186-5197. [PMID: 34185345 DOI: 10.1111/gcb.15775] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/07/2020] [Accepted: 05/24/2021] [Indexed: 06/13/2023]
Abstract
Satellite-derived sun-induced chlorophyll fluorescence (SIF) has been increasingly used for estimating gross primary production (GPP). However, the relationship between SIF and GPP has not been well defined, impeding the translation of satellite observed SIF to GPP. Previous studies have generally assumed a linear relationship between SIF and GPP at daily and longer time scales, but support for this assumption is lacking. Here, we used the GPP/SIF ratio to investigate seasonal variations in the relationship between SIF and GPP over the Northern Hemisphere (NH). Based on multiple SIF products and MODIS and FLUXCOM GPP data, we found strong seasonal hump-shaped patterns for the GPP/SIF ratio over northern latitudes, with higher values in the summer than in the spring or autumn. This hump-shaped GPP/SIF seasonal variation was confirmed by examining different SIF products and was evident for most vegetation types except evergreen broadleaf forests. The seasonal amplitude of the GPP/SIF ratio decreased from the boreal/arctic region to drylands and the tropics. For most of the NH, the lowest GPP/SIF values occurred in October or September, while the maximum GPP/SIF values were evident in June and July. The most pronounced seasonal amplitude of GPP/SIF occurred in intermediate temperature and precipitation ranges. GPP/SIF was positively related to temperature in the early and late parts of the growing season, but not during the peak growing months. These shifting relationships between temperature and GPP/SIF across different months appeared to play a key role in the seasonal dynamics of GPP/SIF. Several mechanisms may explain the patterns we observed, and future research encompassing a broad range of climate and vegetation settings is needed to improve our understanding of the spatial and temporal relationships between SIF and GPP. Nonetheless, the strong seasonal variation in GPP/SIF we identified highlights the importance of incorporating this behavior into SIF-based GPP estimations.
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Affiliation(s)
- Anping Chen
- Department of Biology and Graduate Degree Program in Ecology, Colorado State University, Fort Collins, CO, USA
| | - Jiafu Mao
- Environmental Sciences Division and Climate Change Science Institute, Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - Daniel Ricciuto
- Environmental Sciences Division and Climate Change Science Institute, Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - Dan Lu
- Environmental Sciences Division and Climate Change Science Institute, Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - Jingfeng Xiao
- Earth Systems Research Center, Institute for the Study of Earth, Oceans, and Space, University of New Hampshire, Durham, NH, USA
| | - Xing Li
- Earth Systems Research Center, Institute for the Study of Earth, Oceans, and Space, University of New Hampshire, Durham, NH, USA
| | - Peter E Thornton
- Environmental Sciences Division and Climate Change Science Institute, Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - Alan K Knapp
- Department of Biology and Graduate Degree Program in Ecology, Colorado State University, Fort Collins, CO, USA
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The spatial heterogeneity of the relationship between gross primary production and sun-induced chlorophyll fluorescence regulated by climate conditions during 2007–2018. Glob Ecol Conserv 2021. [DOI: 10.1016/j.gecco.2021.e01721] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
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