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

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
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/c3c93216d73242fa982b79092f1a02a4
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spelling	oai:doaj.org-article:c3c93216d73242fa982b79092f1a02a42021-11-25T06:19:38ZEvaluation of NPP using three models compared with MODIS-NPP data over China1932-6203https://doaj.org/article/c3c93216d73242fa982b79092f1a02a42021-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC8601518/?tool=EBIhttps://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 (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/c3c93216d73242fa982b79092f1a02a4
work_keys_str_mv	AT jinkesun evaluationofnppusingthreemodelscomparedwithmodisnppdataoverchina AT yingyue evaluationofnppusingthreemodelscomparedwithmodisnppdataoverchina AT haipengniu evaluationofnppusingthreemodelscomparedwithmodisnppdataoverchina
_version_	1718413889957265408

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

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