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首页> 外文期刊>Ecological informatics: an international journal on ecoinformatics and computational ecology >Performance of four state-of-the-art GPP products (VPM, MOD17, BESS and PML) for grasslands in drought years
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Performance of four state-of-the-art GPP products (VPM, MOD17, BESS and PML) for grasslands in drought years

机译：在干旱岁月中为草原进行四种最先进的GPP产品（VPM，MOD17，BESS和PML）的性能

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Accurate estimation of gross primary production (GPP) is of significance for understanding the changes of carbon uptake and its responses to extreme climate events like droughts. Emerging new GPP products with higher spatial and temporal resolutions (500-1000 m, 8-day) from the Moderate Resolution Imaging Spectroradiometer (MODIS) Photosynthesis (MOD17), the Vegetation Photosynthesis Model (VPM), the Breathing Earth System Simulator (BESS), and the Penman-Monteith-Leuning (PML) models, provided unprecedented opportunities to understand the spatial and temporal variations of GPP. However, their performances under drought conditions remain obscure. Here we evaluated the performance of these four state-of-the-art GPP products in grasslands, using FLUXNET data as reference. The results showed that all the four models have reasonable accuracies under non-drought years. In drought years, the VPM performed best, followed by the MOD17, PML and BESS, with the RMSEs of 1.67, 1.69, 1.72 and 1.77 gC m(-2) day(-1), respectively. The VPM, BESS and PML overestimated annual GPP by 2%, 13% and 21%, respectively, while MOD17 underestimated annual GPP by 10% in drought years. This varied model performances under drought years could be partially attributed to the differences in quantifying the water stress effects. The water constraint factor in the VPM, which is derived from the Land Surface Water Index (LSWI) and directly indicates the overall water content of leaf, plant stand and soil background, could better capture the vegetation response to water content variation than that in MOD17, PML and BESS, all of which used an atmospheric moisture related indicator (the Vapor Pressure Deficit for MOD17 and PML, and the relative humidity for BESS). This study suggests that water stress factors, which reflect the physiological and ecological characteristics of vegetation itself (e.g., LSWI) rather than atmospheric moisture (e.g., VPD) or other meteorological surrogates, should be further considered in GPP models when applied in drought conditions
机译：准确估算总初级生产（GPP）对于了解碳吸收的变化及其对干旱等极端气候事件的反应具有重要意义。从中度分辨率成像光谱仪（MOD17），植被光合作用模拟器（MOD17），呼吸地球系统模拟器（BESS）具有较高的空间和时间分辨率（500-1000米，8天）的新型GPP产品（500-1000米，8天），植被光合作用模拟器（北部）而且Penman-Monteith-Leuning（PML）模型，提供了前所未有的机会，了解GPP的空间和时间变化。然而，他们在干旱条件下的表现仍然模糊不清。在这里，我们评估了在草地上使用Fluxnet数据作为参考的这四种最先进的GPP产品的性能。结果表明，所有四种模型都在非干旱年度下具有合理的准确性。在干旱岁时，VPM分别表现最佳，其次是MOD17，PML和BESS，分别为1.67,1.69,1.72和1.77GC M（-2）天（-1）。 VPM，BESS和PML高估年度GPP分别为2％，13％和21％，而Mod17在干旱年份低估每年的GPP 10％。在干旱岁以下的这种不同的模型性能可能部分地归因于量化水胁迫影响的差异。 VPM中的水约束因子来自陆地水指数（LSWI）并直接表明叶，植物支架和土壤背景的总含水量，可以更好地捕获植被响应水含量变化，而不是Mod17 ，PML和BESS，所有这些都使用了大气湿度相关的指示器（MOD17和PML的蒸气压力，以及BESS的相对湿度）。该研究表明，在干旱条件下，应在GPP模型中反映植被本身（例如，LSWI）的生理和生态特征而非大气水分（例如，VPD）或其他气象替代品。

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