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Academic literature on the topic 'GPP-EVI relationships'
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Journal articles on the topic "GPP-EVI relationships"
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Hinojo-Hinojo, César, and Michael L. Goulden. "Plant Traits Help Explain the Tight Relationship between Vegetation Indices and Gross Primary Production." Remote Sensing 12, no. 9 (April 29, 2020): 1405. http://dx.doi.org/10.3390/rs12091405.
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Remotely-sensed Vegetation Indices (VIs) are often tightly correlated with terrestrial ecosystem CO2 uptake (Gross Primary Production or GPP). These correlations have been exploited to infer GPP at local to global scales and over half-hour to decadal periods, though the underlying mechanisms remain incompletely understood. We used satellite remote sensing and eddy covariance observations at 10 sites across a California climate gradient to explore the relationships between GPP, the Enhanced Vegetation Index (EVI), the Normalized Difference Vegetation Index (NDVI), and the Near InfraRed Vegetation (NIRv) index. EVI and NIRv were linearly correlated with GPP across both space and time, whereas the relationship between NDVI and GPP was less general. We explored these interactions using radiative transfer and GPP models forced with in-situ plant trait and soil reflectance observations. GPP ultimately reflects the product of Leaf Area Index (LAI) and leaf level CO2 uptake (Aleaf); a VI that is sensitive mainly to LAI will lack generality across ecosystems that differ in Aleaf. EVI and NIRv showed a strong, multiplicative sensitivity to LAI and Leaf Mass per Area (LMA). LMA was correlated with Aleaf, and EVI and NIRv consequently mimic GPP’s multiplicative sensitivity to LAI and Aleaf, as mediated by LMA. NDVI was most sensitive to LAI, and was relatively insensitive to leaf properties over realistic conditions; NDVI lacked EVI and NIRv’s sensitivity to both LAI and Aleaf. These findings carry implications for understanding the limitations of current VIs for predicting GPP, and also for devising strategies to improve predictions of GPP.
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Olofsson, P., F. Lagergren, A. Lindroth, J. Lindström, L. Klemedtsson, W. Kutsch, and L. Eklundh. "Towards operational remote sensing of forest carbon balance across Northern Europe." Biogeosciences 5, no. 3 (May 19, 2008): 817–32. http://dx.doi.org/10.5194/bg-5-817-2008.
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Abstract. Monthly averages of ecosystem respiration (ER), gross primary production (GPP) and net ecosystem exchange (NEE) over Scandinavian forest sites were estimated using regression models driven by air temperature (AT), absorbed photosynthetically active radiation (APAR) and vegetation indices. The models were constructed and evaluated using satellite data from Terra/MODIS and measured data collected at seven flux tower sites in northern Europe. Data used for model construction was excluded from the evaluation. Relationships between ground measured variables and the independent variables were investigated. It was found that the enhanced vegetation index (EVI) at 250 m resolution was highly noisy for the coniferous sites, and hence, 1 km EVI was used for the analysis. Linear relationships between EVI and the biophysical variables were found: correlation coefficients between EVI and GPP, NEE, and AT ranged from 0.90 to 0.79 for the deciduous data, and from 0.85 to 0.67 for the coniferous data. Due to saturation, there were no linear relationships between normalized difference vegetation index (NDVI) and the ground measured parameters found at any site. APAR correlated better with the parameters in question than the vegetation indices. Modeled GPP and ER were in good agreement with measured values, with more than 90% of the variation in measured GPP and ER being explained by the coniferous models. The site-specific respiration rate at 10°C (R10) was needed for describing the ER variation between sites. Even though monthly NEE was modeled with less accuracy than GPP, 61% and 75% (dec. and con., respectively) of the variation in the measured time series was explained by the model. These results are important for moving towards operational remote sensing of forest carbon balance across Northern Europe.
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Olofsson, P., F. Lagergren, A. Lindroth, J. Lindström, L. Klemedtsson, and L. Eklundh. "Towards operational remote sensing of forest carbon balance across Northern Europe." Biogeosciences Discussions 4, no. 5 (September 11, 2007): 3143–93. http://dx.doi.org/10.5194/bgd-4-3143-2007.
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Abstract. Monthly averages of ecosystem respiration (ER), gross primary production (GPP) and net ecosystem exchange (NEE) over Scandinavian forest sites were estimated using regression models driven by air temperature (AT), absorbed photosynthetically active radiation (APAR) and vegetation indices. The models were constructed and evaluated using satellite data from Terra/MODIS and measured data collected at seven flux tower sites in northern Europe. Data used for model construction was excluded from the evaluation. Relationships between ground measured variables and the independent variables were investigated. It was found that the enhanced vegetation index (EVI) at 250 m resolution was highly noisy for the coniferous sites, and hence, 1 km EVI was used for the analysis. Linear relationships between EVI and the biophysical variables were found for both coniferous and deciduous data: correlation coefficients ranged from 0.91 to 0.79, and 0.85 to 0.67, respectively. Due to saturation, there were no linear relationships between normalized difference vegetation index (NDVI) and the ground measured parameters found at any site. APAR correlated better with the parameters in question than the vegetation indices. Modeled GPP and ER were in good agreement with measured values, with more than 90% of the variation in measured GPP and ER being explained by the coniferous models. The site-specific respiration rate at 10°C (R10) was needed for describing the ER variation between sites. Even though monthly NEE was modeled with less accuracy than GPP, 61% and 75% (dec. and con., respectively) of the variation in the measured time series was explained by the model. These results are important for moving towards operational remote sensing of forest carbon balance across Northern Europe.
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Jaafar, H. H., and F. A. Ahmad. "Relationships between primary production and crop yields in semi-arid and arid irrigated agro-ecosystems." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-7/W3 (April 28, 2015): 27–30. http://dx.doi.org/10.5194/isprsarchives-xl-7-w3-27-2015.
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In semi-arid areas within the MENA region, food security problems are the main problematic imposed. Remote sensing can be a promising too early diagnose food shortages and further prevent the population from famine risks. This study is aimed at examining the possibility of forecasting yield before harvest from remotely sensed MODIS-derived Enhanced Vegetation Index (EVI), Net photosynthesis (net PSN), and Gross Primary Production (GPP) in semi-arid and arid irrigated agro-ecosystems within the conflict affected country of Syria. Relationships between summer yield and remotely sensed indices were derived and analyzed. Simple regression spatially-based models were developed to predict summer crop production. The validation of these models was tested during conflict years. A significant correlation (p<0.05) was found between summer crop yield and EVI, GPP and net PSN. Results indicate the efficiency of remotely sensed-based models in predicting summer yield, mostly for cotton yields and vegetables. Cumulative summer EVI-based model can predict summer crop yield during crisis period, with deviation less than 20% where vegetables are the major yield. This approach prompts to an early assessment of food shortages and lead to a real time management and decision making, especially in periods of crisis such as wars and drought.
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Guo, Meng, Jing Li, Shubo Huang, and Lixiang Wen. "Feasibility of Using MODIS Products to Simulate Sun-Induced Chlorophyll Fluorescence (SIF) in Boreal Forests." Remote Sensing 12, no. 4 (February 19, 2020): 680. http://dx.doi.org/10.3390/rs12040680.
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Solar-induced chlorophyll fluorescence (SIF) is a novel approach to gain information about plant activity from remote sensing observations. However, there are currently no continuous SIF data produced at high spatial resolutions. Many previous studies have discussed the relationship between SIF and gross primary production (GPP) and showed a significant correlation between them, but few researchers have focused on forests, which are one the most important terrestrial ecosystems. This study takes Greater Khingan Mountains, a typical boreal forest in China, as an example to explore the feasibility of using MODerate resolution Imaging Spectroradiometer (MODIS) products and Orbiting Carbon Observatory-2 (OCO-2) SIF data to simulate continuous SIF at higher spatial resolutions. The results show that there is no significant correlation between SIF and MODIS GPP at a spatial resolution of 1 km; however, significant correlations between SIF and the enhanced vegetation index (EVI) were found during growing seasons. Furthermore, the broadleaf forest has a higher SIF than coniferous forest because of the difference in leaf and canopy bio-chemical and structural characteristic. When using MODIS EVI to model SIF, linear regression models show average performance (R2 = 0.58, Root Mean Squared Error (RMSE) = 0.14 from Julian day 145 to 257) at a 16-day time scale. However, when using MODIS EVI and temperature, multiple regressions perform better (R2 = 0.71, RMSE = 0.13 from Julian day 145 to 241). An important contribution of this paper is the analysis of the relationships between SIF and vegetation indices at different spatial resolutions and the finding that the relationships became closer with a decrease in spatial resolution. From this research, we conclude that the SIF of the boreal forest investigated can mainly be explained by EVI and air temperature.
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Chen, Feiyan, Aiwen Lin, Hongji Zhu, and Jiqiang Niu. "Quantifying Climate Change and Ecological Responses within the Yangtze River Basin, China." Sustainability 10, no. 9 (August 25, 2018): 3026. http://dx.doi.org/10.3390/su10093026.
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The interactions between climate change and vegetation have a significant impact on the dynamics of the global carbon cycle. Based on the observed meteorological data from 1961 to 2013 and the temperature and precipitation data simulated by various climate models (simulations phase 5 of the Climate Model Intercomparison Project dataset), this paper analyzes the temperature and precipitation changes of the Yangtze River Basin (YRB) and finds that they are a similar trend, that is, the temperature presents a significant upward trend (R2 = 0.49, p < 0.01), and the variation trend of precipitation is not significant (R2 = 0.01). Specifically, based on observed meteorological data, the annual mean temperature increased significantly and the area of increasing temperature accounted for 99.94% of the total region (p < 0.05); however, there was no significant change in annual precipitation. Ecological indicators (normalized difference vegetation index (NDVI); enhanced vegetation index (EVI); leaf area index (LAI); gross primary production (GPP); and net primary production (NPP)) of the YRB showed an increasing trend, and annual NDVI, annual EVI, LAI, annual total GPP and annual total NPP increased at respective rates of 0.002 yr−1, 0.001 yr−1, 0.07 m2m−2decade−1, 9 TgCyr−1yr−1, and 6 TgCyr−1yr−1, respectively. Correlation analysis between temperature/precipitation and NDVI/EVI/LAI/GPP/NPP was used to determine the relationships between climatic parameters and ecological indicators. Specifically, the temperature is significantly positively correlated with annual NDVI (R2 = 0.37, p < 0.05), with annual mean LAI (R2 = 0.35, p < 0.05) and with annual GPP (R2 = 0.37, p < 0.05). In addition, there is a moderate positive correlation between mean EVI and mean growing season air temperature (R2 = 0.24); annual mean air temperature is a moderate positive correlation with annual NPP (R2 = 0.28). Our findings confirm that temperature is more closely related to ecological factors than precipitation over the YRB in these decades.
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Tagesson, Torbern, Jonas Ardö, Bernard Cappelaere, Laurent Kergoat, Abdulhakim Abdi, Stéphanie Horion, and Rasmus Fensholt. "Modelling spatial and temporal dynamics of gross primary production in the Sahel from earth-observation-based photosynthetic capacity and quantum efficiency." Biogeosciences 14, no. 5 (March 17, 2017): 1333–48. http://dx.doi.org/10.5194/bg-14-1333-2017.
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Abstract. It has been shown that vegetation growth in semi-arid regions is important to the global terrestrial CO2 sink, which indicates the strong need for improved understanding and spatially explicit estimates of CO2 uptake (gross primary production; GPP) in semi-arid ecosystems. This study has three aims: (1) to evaluate the MOD17A2H GPP (collection 6) product against GPP based on eddy covariance (EC) for six sites across the Sahel; (2) to characterize relationships between spatial and temporal variability in EC-based photosynthetic capacity (Fopt) and quantum efficiency (α) and vegetation indices based on earth observation (EO) (normalized difference vegetation index (NDVI), renormalized difference vegetation index (RDVI), enhanced vegetation index (EVI) and shortwave infrared water stress index (SIWSI)); and (3) to study the applicability of EO upscaled Fopt and α for GPP modelling purposes. MOD17A2H GPP (collection 6) drastically underestimated GPP, most likely because maximum light use efficiency is set too low for semi-arid ecosystems in the MODIS algorithm. Intra-annual dynamics in Fopt were closely related to SIWSI being sensitive to equivalent water thickness, whereas α was closely related to RDVI being affected by chlorophyll abundance. Spatial and inter-annual dynamics in Fopt and α were closely coupled to NDVI and RDVI, respectively. Modelled GPP based on Fopt and α upscaled using EO-based indices reproduced in situ GPP well for all except a cropped site that was strongly impacted by anthropogenic land use. Upscaled GPP for the Sahel 2001–2014 was 736 ± 39 g C m−2 yr−1. This study indicates the strong applicability of EO as a tool for spatially explicit estimates of GPP, Fopt and α; incorporating EO-based Fopt and α in dynamic global vegetation models could improve estimates of vegetation production and simulations of ecosystem processes and hydro-biochemical cycles.
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Fernández-Martínez, Marcos, Rong Yu, John Gamon, Gabriel Hmimina, Iolanda Filella, Manuela Balzarolo, Benjamin Stocker, and Josep Peñuelas. "Monitoring Spatial and Temporal Variabilities of Gross Primary Production Using MAIAC MODIS Data." Remote Sensing 11, no. 7 (April 11, 2019): 874. http://dx.doi.org/10.3390/rs11070874.
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Remotely sensed vegetation indices (RSVIs) can be used to efficiently estimate terrestrial primary productivity across space and time. Terrestrial productivity, however, has many facets (e.g., spatial and temporal variability, including seasonality, interannual variability, and trends), and different vegetation indices may not be equally good at predicting them. Their accuracy in monitoring productivity has been mostly tested in single-ecosystem studies, but their performance in different ecosystems distributed over large areas still needs to be fully explored. To fill this gap, we identified the facets of terrestrial gross primary production (GPP) that could be monitored using RSVIs. We compared the temporal and spatial patterns of four vegetation indices (NDVI, EVI, NIRV, and CCI), derived from the MODIS MAIAC data set and of GPP derived from data from 58 eddy-flux towers in eight ecosystems with different plant functional types (evergreen needle-leaved forest, evergreen broad-leaved forest, deciduous broad-leaved forest, mixed forest, open shrubland, grassland, cropland, and wetland) distributed throughout Europe, covering Mediterranean, temperate, and boreal regions. The RSVIs monitored temporal variability well in most of the ecosystem types, with grasslands and evergreen broad-leaved forests most strongly and weakly correlated with weekly and monthly RSVI data, respectively. The performance of the RSVIs monitoring temporal variability decreased sharply, however, when the seasonal component of the time series was removed, suggesting that the seasonal cycles of both the GPP and RSVI time series were the dominant drivers of their relationships. Removing winter values from the analyses did not affect the results. NDVI and CCI identified the spatial variability of average annual GPP, and all RSVIs identified GPP seasonality well. The RSVI estimates, however, could not estimate the interannual variability of GPP across sites or monitor the trends of GPP. Overall, our results indicate that RSVIs are suitable to track different facets of GPP variability at the local scale, therefore they are reliable sources of GPP monitoring at larger geographical scales.
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Liu, Yang, Huizhi Liu, Fengquan Li, Qun Du, Lujun Xu, and Yaohui Li. "Interannual Variations of Water and Carbon Dioxide Fluxes over a Semiarid Alpine Steppe on the Tibetan Plateau." Advances in Meteorology 2022 (October 6, 2022): 1–13. http://dx.doi.org/10.1155/2022/7368882.
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Water and carbon exchanges between grassland and the atmosphere are important processes for water balance and carbon balance. Based on eddy covariance observations over a semiarid alpine steppe ecosystem in Bange on the Tibetan Plateau during the growing season from 2014 to 2017, the variations in evapotranspiration (ET), net ecosystem exchange (NEE), and their components and the associated driving factors were analyzed. Linear and nonlinear models were applied to investigate the relationships between fluxes and their controlling factors over different timescales. The results show that the average ET for the growing season ranged from 1.1 to 2.4 mm/d with an average of 2.0 mm/d for the four consecutive years. Drought conditions reduced the surface conductance and hence the Priestley–Taylor coefficient. Mean T/ET was low (0.34) due to low vegetation cover. Plant growth increased the T/ET ratio during the growing season, whereas soil water content (SWC) explained most of the variation of ET and E on daily and monthly scales. The Enhanced Vegetation Index (EVI) was the most important controlling factor for temperature. Transpiration increased with SWC in dry conditions. For the growing season in 2014, 2016, and 2017, Bange was a carbon sink, while it was a carbon source in 2015. The largest CO2 flux was higher and the temperature sensitivity coefficient (Q10) was lower for 2015 than for the other three years. SWC affected these photosynthesis and respiration parameters. The ratio of respiration (Re) to gross primary production (GPP) was the highest during the 2015 growing season. Both on daily and monthly scales, Re was positively and linearly correlated with GPP. The most important controlling factor for the CO2 flux was EVI on daily and monthly scales.
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Huang, Xiaojuan, Jingfeng Xiao, and Mingguo Ma. "Evaluating the Performance of Satellite-Derived Vegetation Indices for Estimating Gross Primary Productivity Using FLUXNET Observations across the Globe." Remote Sensing 11, no. 15 (August 4, 2019): 1823. http://dx.doi.org/10.3390/rs11151823.
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Satellite-derived vegetation indices (VIs) have been widely used to approximate or estimate gross primary productivity (GPP). However, it remains unclear how the VI-GPP relationship varies with indices, biomes, timescales, and the bidirectional reflectance distribution function (BRDF) effect. We examined the relationship between VIs and GPP for 121 FLUXNET sites across the globe and assessed how the VI-GPP relationship varied among a variety of biomes at both monthly and annual timescales. We used three widely-used VIs: normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and 2-band EVI (EVI2) as well as a new VI - NIRV and used surface reflectance both with and without BRDF correction from the moderate resolution imaging spectroradiometer (MODIS) to calculate these indices. The resulting traditional (NDVI, EVI, EVI2, and NIRV) and BRDF-corrected (NDVIBRDF, EVIBRDF, EVI2BRDF, and NIRV, BRDF) VIs were used to examine the VI-GPP relationship. At the monthly scale, all VIs were moderate or strong predictors of GPP, and the BRDF correction improved their performance. EVI2BRDF and NIRV, BRDF had similar performance in capturing the variations in tower GPP as did the MODIS GPP product. The VIs explained lower variance in tower GPP at the annual scale than at the monthly scale. The BRDF-correction of surface reflectance did not improve the VI-GPP relationship at the annual scale. The VIs had similar capability in capturing the interannual variability in tower GPP as MODIS GPP. VIs were influenced by temperature and water stresses and were more sensitive to temperature stress than to water stress. VIs in combination with environmental factors could improve the prediction of GPP than VIs alone. Our findings can help us better understand how the VI-GPP relationship varies among indices, biomes, and timescales and how the BRDF effect influences the VI-GPP relationship.
Dissertations / Theses on the topic "GPP-EVI relationships"
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Shi, Hao. "A joint analysis of gross primary production and evapotranspiration of Australia using eddy covariance, remote sensing and land surface modelling approaches." Thesis, 2017. http://hdl.handle.net/10453/90289.
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University of Technology Sydney. Faculty of Science.The aim of this thesis is to analyze the patterns of gross primary production (GPP) and evapotranspiration (ET) across Australian biomes in combination of eddy covariance, remote sensing and land surface model (LSM) methods, taking advantage of their respective applicability on different space and time scales. To do this, I (1) used the wavelet method to decompose eddy covariance observed half-hourly GPP and ET into different frequencies from hourly to annual to investigate the coupling of GPP and ET and their interactions with climate and vegetation variability over hourly to annual time-scales, (2) established GPP-EVI relationships across multiple biomes using observed GPP and MODIS EVI and applied them to the global scale, (3) developed an pure remote sensing ET model (TG-SM) in combination of MODIS EVI, LST and microwave soil moisture data, (4) identified and optimized key above- and below-ground processes of GPP and ET in the CABLE model across 10 Australian flux sites, and (5) benchmarked the CABLE model across the whole Australia through integrative use of remote sensing products of GPP and ET predicted by both my own remote sensing models and other available products. Each chapter provides new insights into the popular approach for estimating GPP or ET, while together they form a strong example in joint analysis of GPP and ET across various spatio-temporal scales.