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Comparing Three Remotely Sensed Approaches for Simulating Gross Primary Productivity over Mountainous Watersheds: A Case Study in the Wanglang National Nature Reserve, China. REMOTE SENSING 2021. [DOI: 10.3390/rs13183567] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Light Use Efficiency (LUE), Vegetation Index (VI)-based, and process-based models are the main approaches for spatially continuous gross primary productivity (GPP) estimation. However, most current GPP models overlook the effects of topography on the vegetation photosynthesis process. Based on the structures of a two-leaf LUE model (TL-LUE), a VI-based model (temperature and greenness, TG), and a process-based model (Boreal Ecosystem Productivity Simulator, BEPS), three models, named mountain TL-LUE (MTL-LUE), mountain TG (MTG), and BEPS-TerrainLab, have been proposed to improve GPP estimation over mountainous areas. The GPP estimates from the three mountain models have been proven to align more closely with tower-based GPP than those from the original models at the site scale, but their abilities to characterize the spatial variation of GPP at the watershed scale are not yet known. In this work, the GPP estimates from three LUE models (i.e., MOD17, TL-LUE, and MTL-LUE), two VI-based models (i.e., TG and MTG), and two process-based models (i.e., BEPS and BEPS-TerrainLab) were compared for a mountainous watershed. At the watershed scale, the annual GPP estimates from MTL-LUE, MTG, and BTL were found to have a higher spatial variation than those from the original models (increasing the spatial coefficient of variation by 6%, 8%, and 22%), highlighting that incorporating topographic information into GPP models might improve understanding of the high spatial heterogeneity of the vegetation photosynthesis process over mountainous areas. Obvious discrepancies were also observed in the GPP estimates from MTL-LUE, MTG, and BTL, with determination coefficients ranging from 0.02–0.29 and root mean square errors ranging from 399–821 gC m−2yr−1. These GPP discrepancies mainly stem from the different (1) structures of original LUE, VI, and process models, (2) assumptions associated with the effects of topography on photosynthesis, (3) input data, and (4) values of sensitive parameters. Our study highlights the importance of considering surface topography when modeling GPP over mountainous areas, and suggests that more attention should be given to the discrepancy of GPP estimates from different models.
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Wurster PM, Maneta M, Kimball JS, Endsley KA, Beguería S. Monitoring Crop Status in the Continental United States Using the SMAP Level-4 Carbon Product. Front Big Data 2021; 3:597720. [PMID: 33693422 PMCID: PMC7931861 DOI: 10.3389/fdata.2020.597720] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2020] [Accepted: 11/19/2020] [Indexed: 12/02/2022] Open
Abstract
Accurate monitoring of crop condition is critical to detect anomalies that may threaten the economic viability of agriculture and to understand how crops respond to climatic variability. Retrievals of soil moisture and vegetation information from satellite-based remote-sensing products offer an opportunity for continuous and affordable crop condition monitoring. This study compared weekly anomalies in accumulated gross primary production (GPP) from the SMAP Level-4 Carbon (L4C) product to anomalies calculated from a state-scale weekly crop condition index (CCI) and also to crop yield anomalies calculated from county-level yield data reported at the end of the season. We focused on barley, spring wheat, corn, and soybeans cultivated in the continental United States from 2000 to 2018. We found that consistencies between SMAP L4C GPP anomalies and both crop condition and yield anomalies increased as crops developed from the emergence stage (r: 0.4–0.7) and matured (r: 0.6–0.9) and that the agreement was better in drier regions (r: 0.4–0.9) than in wetter regions (r: −0.8–0.4). The L4C provides weekly GPP estimates at a 1-km scale, permitting the evaluation and tracking of anomalies in crop status at higher spatial detail than metrics based on the state-level CCI or county-level crop yields. We demonstrate that the L4C GPP product can be used operationally to monitor crop condition with the potential to become an important tool to inform decision-making and research.
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Affiliation(s)
- Patrick M Wurster
- Regional Hydrology Lab, Geosciences Department, University of Montana, Missoula, MT, United States
| | - Marco Maneta
- Regional Hydrology Lab, Geosciences Department, University of Montana, Missoula, MT, United States.,Department of Ecosystem and Conservation Sciences, W.A. Franke College of Forestry and Conservation, University of Montana, Missoula, MT, United States
| | - John S Kimball
- Numerical Terradynamic Simulation Group, University of Montana, W.A. Franke College of Forestry and Conservation, Missoula, MT, United States
| | - K Arthur Endsley
- Numerical Terradynamic Simulation Group, University of Montana, W.A. Franke College of Forestry and Conservation, Missoula, MT, United States
| | - Santiago Beguería
- Estación Experimental de Aula Dei, Consejo Superior de Investigaciones Científicas (EEAD-CSIC), Zaragoza, Spain
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Crop Biomass Mapping Based on Ecosystem Modeling at Regional Scale Using High Resolution Sentinel-2 Data. REMOTE SENSING 2021. [DOI: 10.3390/rs13040806] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
We evaluate the potential of using a process-based ecosystem model (BEPS) for crop biomass mapping at 20 m resolution over the research site in Manitoba, western Canada driven by spatially explicit leaf area index (LAI) retrieved from Sentinel-2 spectral reflectance throughout the entire growing season. We find that overall, the BEPS-simulated crop gross primary production (GPP), net primary production (NPP), and LAI time-series can explain 82%, 83%, and 85%, respectively, of the variation in the above-ground biomass (AGB) for six selected annual crops, while an application of individual crop LAI explains only 50% of the variation in AGB. The linear relationships between the AGB and these three indicators (GPP, NPP and LAI time-series) are rather high for the six crops, while the slopes of the regression models vary for individual crop type, indicating the need for calibration of key photosynthetic parameters and carbon allocation coefficients. This study demonstrates that accumulated GPP and NPP derived from an ecosystem model, driven by Sentinel-2 LAI data and abiotic data, can be effectively used for crop AGB mapping; the temporal information from LAI is also effective in AGB mapping for some crop types.
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He L, Chen JM, Liu J, Zheng T, Wang R, Joiner J, Chou S, Chen B, Liu Y, Liu R, Rogers C. Diverse photosynthetic capacity of global ecosystems mapped by satellite chlorophyll fluorescence measurements. REMOTE SENSING OF ENVIRONMENT 2019; 232:111344. [PMID: 33149371 PMCID: PMC7608051 DOI: 10.1016/j.rse.2019.111344] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/19/2023]
Abstract
Photosynthetic capacity is often quantified by the Rubisco-limited photosynthetic capacity (i.e. maximum carboxylation rate, Vcmax). It is a key plant functional trait that is widely used in Earth System Models for simulation of the global carbon and water cycles. Measuring Vcmax is time-consuming and laborious; therefore, the spatiotemporal distribution of Vcmax is still poorly understood due to limited measurements of Vcmax. In this study, we used a data assimilation approach to map the spatial variation of Vcmax for global terrestrial ecosystems from a 11-year-long satellite-observed solar-induced chlorophyll fluorescence (SIF) record. In this SIF-derived Vcmax map, the mean Vcmax value for each plant function type (PFT) is found to be comparable to a widely used N-derived Vcmax dataset by Kattge et al. (2009). The gradient of Vcmax along PFTs is clearly revealed even without land cover information as an input. Large seasonal and spatial variations of Vcmax are found within each PFT, especially for diverse crop rotation systems. The distribution of major crop belts, characterized with high Vcmax values, is highlighted in this Vcmax map. Legume plants are characterized with high Vcmax values. This Vcmax map also clearly illustrates the emerging soybean revolution in South America where Vcmax is the highest among the world. The gradient of Vcmax in Amazon is found to follow the transition of soil types with different soil N and P contents. This study suggests that satellite-observed SIF is powerful in deriving the important plant functional trait, i.e. Vcmax, for global climate change studies.
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Affiliation(s)
- Liming He
- Department of Geography and Planning, University of Toronto, Toronto, ON M5S 3G3, Canada
- Laboratory of Environmental Model and Data Optima, Laurel, MD 20707, USA
- Corresponding author at: Department of Geography and Planning, University of Toronto, Toronto, ON M5S 3G3, Canada. (L. He)
| | - Jing M. Chen
- Department of Geography and Planning, University of Toronto, Toronto, ON M5S 3G3, Canada
- International Institute for Earth System Sciences, Nanjing University, 210023 Nanjing, China
| | - Jane Liu
- Department of Geography and Planning, University of Toronto, Toronto, ON M5S 3G3, Canada
| | - Ting Zheng
- Department of Forest and Wildlife Ecology, University of Wisconsin, Madison, WI 53706, USA
| | - Rong Wang
- Department of Geography and Planning, University of Toronto, Toronto, ON M5S 3G3, Canada
| | - Joanna Joiner
- NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA
| | - Shuren Chou
- Space Engineering University, Beijing 101419, China
| | - Bin Chen
- China State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
| | - Yang Liu
- China State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
| | - Ronggao Liu
- China State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
| | - Cheryl Rogers
- Department of Geography and Planning, University of Toronto, Toronto, ON M5S 3G3, Canada
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Cotton Yield Estimate Using Sentinel-2 Data and an Ecosystem Model over the Southern US. REMOTE SENSING 2019. [DOI: 10.3390/rs11172000] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
High-resolution data with nearly global coverage from Sentinel-2 mission open a new era for crop growth monitoring and yield estimation from remote sensing. The objective of this study is to demonstrate the potential of using Sentinel-2 biophysical data combined with an ecosystem modeling approach for estimation of cotton yield in the southern United States (US). The Boreal Ecosystems Productivity Simulator (BEPS) ecosystem model was used to simulate the cotton gross primary production (GPP) over three Sentinel-2 tiles located in Mississippi, Georgia, and Texas in 2017. Leaf area index (LAI) derived from Sentinel-2 measurements and hourly meteorological data from Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) reanalysis were used to drive the ecosystem model. The simulated GPP values at 20-m grid spacing were aggregated to the county level (17 counties in total) and compared to the cotton lint yield estimates at the county level which are available from National Agricultural Statistics Service in the United States Department of Agriculture. The results of the comparison show that the BEPS-simulated cotton GPP explains 85% of variation in cotton yield. Our study suggests that the integration of Sentinel-2 LAI time series into the ecosystem model results in reliable estimates of cotton yield.
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Zhang S, Zhang J, Bai Y, Koju UA, Igbawua T, Chang Q, Zhang D, Yao F. Evaluation and improvement of the daily boreal ecosystem productivity simulator in simulating gross primary productivity at 41 flux sites across Europe. Ecol Modell 2018. [DOI: 10.1016/j.ecolmodel.2017.11.023] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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