Evaluation of NPP using three models compared with MODIS-NPP data over China.

Evaluation of NPP using three models compared with MODIS-NPP data over China.

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Estimating net primary productivity (NPP) is significant in global climate change research and carbon cycle. However, there are many uncertainties in different NPP modeling results and the process of NPP is challenging to model on the absence of data. In this study, we used meteorological data as in...

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Autores principales: Jinke Sun, Ying Yue, Haipeng Niu
Formato: article
Lenguaje:EN
Publicado: Public Library of Science (PLoS) 2021
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Acceso en línea:https://doaj.org/article/ac5dad6c3cf049a48d40915854ce6bd8
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spelling oai:doaj.org-article:ac5dad6c3cf049a48d40915854ce6bd82021-12-02T20:12:51ZEvaluation of NPP using three models compared with MODIS-NPP data over China.1932-620310.1371/journal.pone.0252149https://doaj.org/article/ac5dad6c3cf049a48d40915854ce6bd82021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0252149https://doaj.org/toc/1932-6203Estimating net primary productivity (NPP) is significant in global climate change research and carbon cycle. However, there are many uncertainties in different NPP modeling results and the process of NPP is challenging to model on the absence of data. In this study, we used meteorological data as input to simulate vegetation NPP through climate-based model, synthetic model and CASA model. Then, the results from three models were compared with MODIS NPP and observed data over China from 2000 to 2015. The statistics evaluation metrics (Relative Bias (RB), Pearson linear Correlation Coefficient (CC), Root Mean Square Error (RMSE), and Nash-Sutcliffe efficiency coefficient (NSE)) between simulated NPP and MODIS NPP were calculated. The results implied that the CASA-model performed better than the other two models in terms of RB, RMSE, NSE and CC whether on the national or the regional scale. It has a higher CC with 0.51 and a smaller RMSE with 111.96 g C·m-2·yr-1 in the whole country. The synthetic model and CASA-model has the same advantages at some regions, and there are lower RMSE in Southern China (86.35 g C·m-2·yr-1), Xinjiang (85.53 g C·m-2·yr-1) and Qinghai-Tibet Plateau (93.22 g C·m-2·yr-1). The climate-based model has widespread overestimation and large systematic errors, along with worse performances (NSEmax = 0.45) and other metric indexes unsatisfactory, especially Qinghai-Tibet Plateau with relatively lower accuracy because of the unavailable observation data. Overall, the CASA-model is much more ideal for estimating NPP all over China in the absence of data. This study provides a comprehensive intercomparison of different NPP-simulated models and can provide powerful help for researchers to select the appropriate NPP evaluation model.Jinke SunYing YueHaipeng NiuPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 11, p e0252149 (2021)
institution DOAJ
collection DOAJ
language EN
topic Medicine
R
Science
Q
spellingShingle Medicine
R
Science
Q
Jinke Sun
Ying Yue
Haipeng Niu
Evaluation of NPP using three models compared with MODIS-NPP data over China.
description Estimating net primary productivity (NPP) is significant in global climate change research and carbon cycle. However, there are many uncertainties in different NPP modeling results and the process of NPP is challenging to model on the absence of data. In this study, we used meteorological data as input to simulate vegetation NPP through climate-based model, synthetic model and CASA model. Then, the results from three models were compared with MODIS NPP and observed data over China from 2000 to 2015. The statistics evaluation metrics (Relative Bias (RB), Pearson linear Correlation Coefficient (CC), Root Mean Square Error (RMSE), and Nash-Sutcliffe efficiency coefficient (NSE)) between simulated NPP and MODIS NPP were calculated. The results implied that the CASA-model performed better than the other two models in terms of RB, RMSE, NSE and CC whether on the national or the regional scale. It has a higher CC with 0.51 and a smaller RMSE with 111.96 g C·m-2·yr-1 in the whole country. The synthetic model and CASA-model has the same advantages at some regions, and there are lower RMSE in Southern China (86.35 g C·m-2·yr-1), Xinjiang (85.53 g C·m-2·yr-1) and Qinghai-Tibet Plateau (93.22 g C·m-2·yr-1). The climate-based model has widespread overestimation and large systematic errors, along with worse performances (NSEmax = 0.45) and other metric indexes unsatisfactory, especially Qinghai-Tibet Plateau with relatively lower accuracy because of the unavailable observation data. Overall, the CASA-model is much more ideal for estimating NPP all over China in the absence of data. This study provides a comprehensive intercomparison of different NPP-simulated models and can provide powerful help for researchers to select the appropriate NPP evaluation model.
format article
author Jinke Sun
Ying Yue
Haipeng Niu
author_facet Jinke Sun
Ying Yue
Haipeng Niu
author_sort Jinke Sun
title Evaluation of NPP using three models compared with MODIS-NPP data over China.
title_short Evaluation of NPP using three models compared with MODIS-NPP data over China.
title_full Evaluation of NPP using three models compared with MODIS-NPP data over China.
title_fullStr Evaluation of NPP using three models compared with MODIS-NPP data over China.
title_full_unstemmed Evaluation of NPP using three models compared with MODIS-NPP data over China.
title_sort evaluation of npp using three models compared with modis-npp data over china.
publisher Public Library of Science (PLoS)
publishDate 2021
url https://doaj.org/article/ac5dad6c3cf049a48d40915854ce6bd8
work_keys_str_mv AT jinkesun evaluationofnppusingthreemodelscomparedwithmodisnppdataoverchina
AT yingyue evaluationofnppusingthreemodelscomparedwithmodisnppdataoverchina
AT haipengniu evaluationofnppusingthreemodelscomparedwithmodisnppdataoverchina
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