This paper begins with a review of the history of remote sensing algorithms for the determination of particulate inorganic carbon (PIC; aka calcium carbonate), primarily associated with haptophyte phytoplankton known as coccolithophores. These algae have strong optical particle backscattering (b_bp) which can dominate ocean color properties._. In non-bloom conditions, coccolithophore b_bp typically accounts for ∼10-20% of the total b_bp, whereas in turbid coccolithophore blooms, coccolithophore b_bp can account for >90% of total b_bp. Since total b_bp features heavily in a number of algorithms for the determination of phytoplankton standing stock, disproportionate coccolithophore b_bp can cause significant errors in a wide variety of other ocean-color algorithms. Here we discriminate between qualitative coccolithophore algorithms (coccolith flags), quantitative algorithms to determine the concentration of coccolithophore PIC and algorithms that focus on coccolithophore biomass. Algorithms from satellite sensors, such as the AVHRR and MISR, not typically used for phytoplankton remote sensing, are discussed as well as an improved method to model the backscattering cross-section of PIC. We also cover remote sensing algorithms for determination of calcification rates, modeling vertical profiles of PIC for the remote sensing of integrated euphotic PIC, and the effect of coccolithophore species variation on PIC retrievals. The second part of this review paper covers what we have learned about the cycling of PIC from remotely-sensed satellite measurements since the first satellite observations in 1982. The analysis begins from the global perspective, then focuses on five sub-regions which have become notorious for their regular, high-reflectance coccolithophore blooms (Southern Ocean, Atlantic Ocean, Arctic Ocean, Black Sea and Bering Sea). We end with a discussion of future directions for the PIC algorithms using machine-learning approaches and hyperspectral applications during the upcoming PACE era.

本文首先回顾了用于测定颗粒无机碳（PIC；又名碳酸钙）的遥感算法的历史，主要与称为球石藻的触生浮游植物有关。这些藻类具有很强的光学粒子反向散射 (b _bp )，可以控制海洋颜色特性。_. 在非水华条件下，coccolithophore b _bp通常占总 b _bp的 ~10-20% ，而在浑浊的 coccolithophore 水华中，coccolithophore b _bp可占总 b _bp的 >90% 。由于总 b _bp在许多用于确定浮游植物存量、不成比例的球石藻 b _{bp的算法中具有重要特征}可能会导致各种其他海洋颜色算法出现重大错误。在这里，我们区分定性球石藻算法（球石标志）、确定球石藻 PIC 浓度的定量算法和侧重于球石藻生物量的算法。来自卫星传感器的算法，如 AVHRR 和 MISR，通常不用于浮游植物遥感，被讨论以及一种改进的方法来模拟 PIC 的反向散射截面。我们还涵盖了用于确定钙化率的遥感算法、用于综合透光 PIC 遥感的 PIC 垂直剖面建模，以及球石藻物种变异对 PIC 检索的影响。本综述的第二部分涵盖了自 1982 年首次卫星观测以来我们从遥感卫星测量中了解到的 PIC 循环。分析从全球视角开始，然后重点关注五个已经臭名昭著的次区域因其定期、高反射率的球石藻大量繁殖（南大洋、大西洋、北冰洋、黑海和白令海）。最后，我们讨论了在即将到来的 PACE 时代使用机器学习方法和高光谱应用的 PIC 算法的未来方向。北冰洋、黑海和白令海）。最后，我们讨论了在即将到来的 PACE 时代使用机器学习方法和高光谱应用的 PIC 算法的未来方向。北冰洋、黑海和白令海）。最后，我们讨论了在即将到来的 PACE 时代使用机器学习方法和高光谱应用的 PIC 算法的未来方向。