Solar-induced chlorophyll fluorescence (SIF) is closely linked to photosynthesis, and provides new opportunities for detecting global gross primary production (GPP). Instantaneous satellite SIF products are usually available only for clear-sky condition, which means that there is a temporal inconsistency in the direct link between these and continuous carbon flux records. In this study, we designed a method to upscale SIF from instantaneous clear-sky observations to all-sky sums, which adopted the absorbed photosynthetically active radiation (APAR) to correct for the effects of clouds on the SIF, and hereby derived a SIF product for all-sky conditions (ASSIF) from GOME-2 at 8-day and monthly intervals during 2007 and 2018. The advantage of ASSIF was evaluated using both tower-based experiments and satellite retrievals at different spatio-temporal scales, compared with the common clear-sky SIF upscaled using the cosine-based method (CSSIF). For time-series comparison, with tower-based experiments over a growing season, the all-sky upscaling method obviously corrected the overestimates of CSSIF made on cloudy days, and produced more accurate predictions with the retrieved SIF, and thus significantly reduced the variability in 8-day SIF–GPP correlations between sunny and cloudy days. It is the same case for the results of satellite products, but the improvements of 8-day SIF–GPP correlations were much weaker during longer time spans, for example several years, due to effects of time averaging and the dominance of seasonal growth dynamics. Notably, for spatial comparison, ASSIF products effectively reduced the remarkable overestimations in CSSIF for cloudy regions, with the largest reduction in the 12-year average being 31.15% at the fully humid and cool summer climate region (Dfc). This led to a decrease in the variability of the SIF–GPP slope over different climate regions, as well as a significant improvement for the correlation at yearly scale. The all-sky upscaling method presented here can eliminate the inconsistency between clear-sky satellite SIF and all-sky GPP, which is of importance to the understanding of SIF–GPP links and accurate estimation of GPP using satellite SIF.

太阳诱导的叶绿素荧光（SIF）与光合作用紧密相关，并为检测全球初级总产值（GPP）提供了新的机会。瞬时卫星SIF产品通常仅在晴空条件下可用，这意味着这些和连续碳通量记录之间的直接联系存在时间上的不一致。在这项研究中，我们设计了一种方法，可以将SIF从瞬时的晴空观测升级到全天总和，该方法采用吸收的光合有效辐射（APAR）来校正云对SIF的影响，从而得出SIF产品在2007年和2018年期间，每隔8天和每月一次，从GOME-2获得全天空条件（ASSIF）。与使用基于余弦方法（CSSIF）放大后的普通晴空SIF相比，使用基于塔的实验和不同时空尺度的卫星检索都评估了ASSIF的优势。为了进行时间序列比较，通过在生长季节进行的基于塔的实验，全天候升频方法明显纠正了多云天对CSSIF的高估，并利用检索到的SIF产生了更准确的预测，从而显着降低了SIF的变化性。晴天和阴天之间的8天SIF-GPP相关性。卫星产品的结果也是如此，但是由于时间平均和季节性增长动态的影响，在更长的时间跨度（例如几年）中，8天SIF-GPP相关性的改善要弱得多。尤其，为了进行空间比较，ASSIF产品有效地减少了多云地区CSSIF中的明显高估，在完全湿润和凉爽的夏季气候地区（Dfc），其12年平均下降幅度最大，为31.15％。这导致不同气候区域SIF-GPP斜率的变异性降低，并且在年尺度上也显着改善了相关性。本文介绍的全天候升频方法可以消除晴空卫星SIF和全天候GPP之间的不一致，这对于理解SIF-GPP链路以及使用卫星SIF精确估计GPP具有重要意义。15％在完全湿润和凉爽的夏季气候地区（Dfc）。这导致不同气候区域SIF-GPP斜率的变异性降低，并且在年尺度上也显着改善了相关性。本文介绍的全天候升频方法可以消除晴空卫星SIF和全天候GPP之间的不一致，这对于理解SIF-GPP链路以及使用卫星SIF精确估计GPP具有重要意义。15％在完全湿润和凉爽的夏季气候地区（Dfc）。这导致不同气候区域SIF-GPP斜率的变异性降低，并且在年尺度上也显着改善了相关性。本文介绍的全天候升频方法可以消除晴空卫星SIF和全天候GPP之间的不一致，这对于理解SIF-GPP链路以及使用卫星SIF精确估计GPP具有重要意义。