Improvements of the MODIS Gross Primary Productivity model based on a comprehensive uncertainty assessment over the Brazilian Amazonia

Almeida, Catherine Torres de; Delgado, Rafael Coll; Galvão, Lênio Soares; Aragão, L. E.; Ramos, M. C.

doi:10.1016/j.isprsjprs.2018.07.016

Cited by 39 publications

(24 citation statements)

References 57 publications

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“…2.5; (b) GPP sensitivity to climate variables (i.e., VPD, Ta, and PAR), LAI, and atmospheric CO 2 ; (c) contributions of climate variables (i.e., VPD, Ta, and PAR), LAI, and atmospheric CO 2 to GPP changes over 1982-2017, 1982-2000, and 2001-2017. The asterisk indicates the significant level at p value < 0.05. tation growth (de Cárcer et al, 2018;Ding et al, 2018), forest mortality (Williams et al, 2013), and crop yields (Lobell et al, 2014). It is increasingly important to integrate the atmospheric water constraint into carbon and water flux modeling.…”

Section: Model Accuracy Analysismentioning

confidence: 99%

“…Similarly, other satellite-based models exhibited a large uncertainty in the GPP simulations over tropical forest areas (Ryu et al, 2011;Yuan et al, 2014). For example, the MODIS GPP product (MOD17) underestimated GPP at high-productivity sites over the tropical evergreen forests (de Almeida et al, 2018). Regarding the quality of satellite data, a high cloud cover exists over tropical regions, introducing large uncer- Figure 10.…”

Section: Model Uncertaintymentioning

confidence: 99%

“…tainties to fraction of absorbed photosynthetically active radiation (FAPAR), LAI and other vegetation indices (e.g., normalized difference vegetation index, NDVI, and enhanced vegetation index, EVI). As suggested by de Almeida et al (2018), the lack of reliable MOD15 FAPAR data from January to April as a result of the cloudiness contamination could have substantially affected the seasonality of GPP estimates. Besides, the quality of satellite data can even affect the evaluation of the interannual variations in GPP.…”

Section: Model Uncertaintymentioning

confidence: 99%

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Improved estimate of global gross primary production for reproducing its long-term variation, 1982–2017

Zheng

Shen

Wang

et al. 2020

Earth Syst. Sci. Data

226

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Abstract. Satellite-based models have been widely used to simulate vegetation gross primary production (GPP) at the site, regional, or global scales in recent years. However, accurately reproducing the interannual variations in GPP remains a major challenge, and the long-term changes in GPP remain highly uncertain. In this study, we generated a long-term global GPP dataset at 0.05∘ latitude by 0.05∘ longitude and 8 d interval by revising a light use efficiency model (i.e., EC-LUE model). In the revised EC-LUE model, we integrated the regulations of several major environmental variables: atmospheric CO2 concentration, radiation components, and atmospheric vapor pressure deficit (VPD). These environmental variables showed substantial long-term changes, which could greatly impact the global vegetation productivity. Eddy covariance (EC) measurements at 95 towers from the FLUXNET2015 dataset, covering nine major ecosystem types around the globe, were used to calibrate and validate the model. In general, the revised EC-LUE model could effectively reproduce the spatial, seasonal, and annual variations in the tower-estimated GPP at most sites. The revised EC-LUE model could explain 71 % of the spatial variations in annual GPP over 95 sites. At more than 95 % of the sites, the correlation coefficients (R2) of seasonal changes between tower-estimated and model-simulated GPP are larger than 0.5. Particularly, the revised EC-LUE model improved the model performance in reproducing the interannual variations in GPP, and the averaged R2 between annual mean tower-estimated and model-simulated GPP is 0.44 over all 55 sites with observations longer than 5 years, which is significantly higher than those of the original EC-LUE model (R2=0.36) and other LUE models (R2 ranged from 0.06 to 0.30 with an average value of 0.16). At the global scale, GPP derived from light use efficiency models, machine learning models, and process-based biophysical models shows substantial differences in magnitude and interannual variations. The revised EC-LUE model quantified the mean global GPP from 1982 to 2017 as 106.2±2.9 Pg C yr−1 with the trend 0.15 Pg C yr−1. Sensitivity analysis indicated that GPP simulated by the revised EC-LUE model was sensitive to atmospheric CO2 concentration, VPD, and radiation. Over the period of 1982–2017, the CO2 fertilization effect on the global GPP (0.22±0.07 Pg C yr−1) could be partly offset by increased VPD (-0.17±0.06 Pg C yr−1). The long-term changes in the environmental variables could be well reflected in global GPP. Overall, the revised EC-LUE model is able to provide a reliable long-term estimate of global GPP. The GPP dataset is available at https://doi.org/10.6084/m9.figshare.8942336.v3 (Zheng et al., 2019).

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Section: Model Accuracy Analysismentioning

confidence: 99%

Section: Model Uncertaintymentioning

confidence: 99%

Section: Model Uncertaintymentioning

confidence: 99%

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Improved estimate of global gross primary production for reproducing its long-term variation, 1982–2017

Zheng

Shen

Wang

et al. 2020

Earth Syst. Sci. Data

226

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“…O principal processo envolvendo a contenção de carbono pelos ecossistemas é chamado de Produtividade Primária Bruta -PPB (Gross Primary Productivity), que ocorre a partir da conversão de energia luminosa em fitomassa. A PPB refere-se, portanto, ao processo denominado de fotossíntese, que constitui um dos principais processos que controlam a troca de dióxido de carbono (CO2) entre a biosfera e a atmosfera, sendo ferramenta bastante relevante para compensar as emissões de CO2 (ALMEIDA et al, 2018).…”

Section: Introductionunclassified

“…Tal fato contribui para o crescimento da aplicação do sensoriamento remoto utilizando imagens de satélite, com ampla cobertura espacial, baixo custo e maior operacionalidade. Muitos modelos da PPB baseados em sensoriamento remoto se fundamentam na relação entre eficiência de uso da luz (ε), fração da radiação fotossinteticamente ativa absorvida (APAR) e radiação fotossinteticamente ativa (RFA) (ALMEIDA et al, 2018).…”

Section: Introductionunclassified

Estimativa Da Produtividade Primária Bruta Da Vegetação Nativa E Cultivo Irrigado Através De Sensoriamento Remoto

Bezerra¹,

Tomaz²,

Silva

et al. 2020

RBAI

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A cultura da bananeira (Musa spp.) é de extrema importância para a região semiárida do Brasil. No entanto, fatores edafoclimáticos, como a seca, contribuem para a redução da produtividade. Algumas técnicas de monitoramento através do sensoriamento remoto vêm auxiliando produtores no manejo da propriedade. Objetivou-se com este trabalho, estimar a Produtividade Primaria Bruta (PPB) para vegetação nativa e cultivo irrigado da bananeira, na região do Cariri Cearense, através de sensoriamento remoto. O experimento foi realizado em área localizada na região sul do estado do Ceará que contêm unidades produtoras de banana irrigada, denominadas de sítios 1, 2 e 3, e uma área de vegetação nativa da caatinga. As avaliações foram realizadas em cinco datas distintas. As imagens foram geradas pelos sensores OLI/TIRS do satélite Landsat 8, órbita 217 e ponto 65. A determinação da PPB foi realizada a partir da compilação dos modelos da radiação fotossinteticamente ativa. Verificou-se que o déficit hídrico reduziu a PPB na área de vegetação nativa, entretanto, observou-se pouca ou nenhuma redução na área irrigada. A deficiência hídrica, portanto, afetou diretamente a PPB, comprometendo a atividade fotossintética e consequentemente a produtividade da cultura.Palavras-Chave: musa spp., landsat, uso e ocupação do solo.

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Tracking of Vegetation Carbon Dynamics from 2001 to 2016 by MODIS GPP in HKH Region

Chao

Che

Shang

et al. 2019

Carbon Management for Promoting Local Livelihood in the Hindu Kush Himalayan (HKH) Region

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Improvements of the MODIS Gross Primary Productivity model based on a comprehensive uncertainty assessment over the Brazilian Amazonia

Cited by 39 publications

References 57 publications

Improved estimate of global gross primary production for reproducing its long-term variation, 1982–2017

Improved estimate of global gross primary production for reproducing its long-term variation, 1982–2017

Estimativa Da Produtividade Primária Bruta Da Vegetação Nativa E Cultivo Irrigado Através De Sensoriamento Remoto

Tracking of Vegetation Carbon Dynamics from 2001 to 2016 by MODIS GPP in HKH Region

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