School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo, Henan, China.
School of Emergency Management, Henan Polytechnic University, Jiaozuo, Henan, China.
PLoS One. 2021 Nov 18;16(11):e0252149. doi: 10.1371/journal.pone.0252149. eCollection 2021.
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.
估算净初级生产力（NPP）对于全球气候变化研究和碳循环至关重要。然而，不同 NPP 模型结果存在许多不确定性，并且在缺乏数据的情况下，NPP 过程难以建模。在本研究中，我们使用气象数据作为输入，通过基于气候的模型、综合模型和 CASA 模型模拟植被 NPP。然后，将这三种模型的结果与 2000 年至 2015 年中国的 MODIS NPP 和观测数据进行比较。通过计算相对偏差（RB）、皮尔逊线性相关系数（CC）、均方根误差（RMSE）和纳什-苏特克利夫效率系数（NSE），对模拟 NPP 和 MODIS NPP 之间的统计评估指标进行了比较。结果表明，无论在全国还是区域尺度上，CASA 模型在 RB、RMSE、NSE 和 CC 方面的表现均优于其他两种模型。在全国范围内，CASA 模型的 CC 较高，为 0.51，RMSE 较小，为 111.96 g C·m-2·yr-1。综合模型和 CASA 模型在某些地区具有相同的优势，在中国南方（86.35 g C·m-2·yr-1）、新疆（85.53 g C·m-2·yr-1）和青藏高原（93.22 g C·m-2·yr-1）地区 RMSE 较低。基于气候的模型存在广泛的高估和较大的系统误差，表现较差（NSEmax=0.45），其他指标也不尽如人意，特别是青藏高原由于缺乏观测数据，准确性相对较低。总体而言，在缺乏数据的情况下，CASA 模型更适合估算中国各地的 NPP。本研究对不同 NPP 模拟模型进行了全面比较，为研究人员选择合适的 NPP 评价模型提供了有力帮助。
