Abstract Accurate estimation of grassland Net Primary Productivity (NPP) is one of the most important steps in monitoring and further analysing the status of the grassland ecosystem. However, the quality of regional or even global grassland NPP estimations are commonly limited by the two most important input datasets: Photosynthetic Active Radiation (PAR) and climatic data, which make the NPP estimate unrepresentative or even inaccurate. This paper uses a Light Use Efficiency with Vegetation Photosynthesis Model (LUE-VPM) to estimate the high-quality grassland NPP in Zeku. By using the PAR data derived from high resolution DEM products and the improved land surface temperature data, the NPP produced by LUE-VPM shows that it is statistically the same as in situ converted NPP with a p-value of t-test equalling 0.90 (the RMSE between the two is 97.77 gC/m2), while MODIS NPP in Zeku is statically different from the all the in-situ sampling results with a p-value of t-text smaller than 0.03. The results of this paper provided a better estimate than MODIS NPP, and LUE-VPM can be used for the high-quality regional grassland NPP estimation when the quality of PAR data and climatic data improved.

中文翻译：

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光利用效率与植被光合作用模型对青藏地区草地净初级生产力的改进估算

摘要 准确估算草地净初级生产力（NPP）是监测和进一步分析草地生态系统状况的最重要步骤之一。然而，区域甚至全球草地 NPP 估计的质量通常受到两个最重要的输入数据集的限制：光合有效辐射 (PAR) 和气候数据，这使得 NPP 估计不具有代表性甚至不准确。本文使用光利用效率与植被光合作用模型 (LUE-VPM) 来估计泽库的优质草地 NPP。通过使用高分辨率DEM产品的PAR数据和改进的地表温度数据，LUE-VPM产生的NPP表明它与原位转换的NPP在统计上相同，t检验的p值等于0.90（两者之间的 RMSE 是 97。77 gC/m2)，而 Zeku 的 MODIS NPP 与所有原位采样结果静态不同，t-text 的 p 值小于 0.03。本文的结果提供了比 MODIS NPP 更好的估计，当 PAR 数据和气候数据质量提高时，LUE-VPM 可用于高质量区域草地 NPP 估计。