阴叶和阳叶对冠层叶绿素荧光的贡献及其与光合作用关系

Gross primary productivity (GPP) is the indicator of vegetation canopy photosynthesis and a key component of the terrestrial carbon cycle. Accurate and rapid estimation of GPP is a requirement for improving terrestrial carbon cycle study. In recent years, sun-induced chlorophyll fluorescence (SIF) remote sensing data has been widely used to estimate GPP based on the linear SIF-GPP relationship. Previously studies indicate that the relationship between GPP and SIF changes with land cover types, canopy structure, temporal and spatial scales. As a consequence, linear SIF-GPP models tend to underestimate high GPP. One possible reason responsible for this phenomenon is that these models ignore the different contributions of shaded leaves to canopy SIF and GPP (Under the condition of high canopy density, the contribution of shaded leaves to canopy SIF is much smaller the contribution of these leaves to canopy GPP). This proposed project will use SIF retrieved from ground-based high-resolution imaging spectroscopy data, multi-angle SIF measurements over the canopy, and GPP derived from eddy-covariance observations to test this hypothesis. The research will focus on: (1) Characteristics of canopy SIF and GPP and their linkages; (2) Contributions of sunlit and shaded leaves to canopy SIF and a methods for estimating contributions of sunlit and shaded leaves; (3) Impacts of different contributions of sunlit and shaded leaves to canopy SIF and GPP on uncertainties of GPP estimation and methods for improving the model estimating GPP using SIF. The outcomes of this project will be of scientific significance and practical values for bettering GPP estimation using SIF remote sensing data.

总初级生产力（GPP）是植被光合速率的度量指标和生态系统碳循环的重要分量，其准确估算有助于生态系统碳循环研究的深入开展。近年来，依据太阳诱导叶绿素荧光（SIF）与GPP的线性关系模型，SIF遥感数据广泛用于GPP估算。研究表明，SIF与GPP之间关系随生态系统类型、冠层结构、时间和空间尺度等变化，SIF-GPP线性模型趋于低估高值GPP，其可能原因是模型未考虑阴叶对冠层SIF和GPP贡献的差异（高密度冠层时阴叶对SIF的贡献远小于对GPP的贡献）。 针对这一问题， 本项目拟利用农田高分辨率高光谱成像、冠层SIF和通量观测数据，结合模型，研究：（1）冠层SIF和GPP变化特征及其相互关系；（2）阴叶和阳叶对冠层SIF贡献及其估算模型；（3）阴叶和阳叶对冠层SIF和GPP贡献差异对冠层GPP估算的影响以及模型优化方法。成果对更好利用SIF数据监测GPP具有重要的科学意义和应用价值

日光诱导叶绿素荧光（SIF）是生态系统总初级生产力（GPP）的有效探针，但是两者关系随生态系统类型、冠层结构、时间和空间尺度等变化。本项目主要研究内容包括冠层SIF和GPP变化特征及其相互关系，阴叶和阳叶对冠层SIF贡献及其估算模型，阴叶和阳叶对冠层SIF和GPP贡献差异对冠层GPP估算的影响以及模型优化方法等。项目组建立了日光诱导叶绿素荧光（SIF）多角度观测系统，在河南商丘和江苏句容农田生态系统开展多角度SIF和光谱观测，并获取了同步的气象要素、植被参数和通量观测数据；在南京信息工程大学开展了不同土壤水分条件下，玉米冠层光谱以及叶片光合参数的观测。基于这些数据，分析了冠层光化学反射指数（PRI）和叶片非光化学猝灭系数（NPQ）对土壤干旱的响应特征；发现随着土壤含水量的上升冠层PRI增大、叶片NPQ降低；分析了观测的冠层SIF随观测角度的变化特征；发现热点方向的SIF高于暗点方向；研究了冠层SIF观测数据角度订正方法；发现将观测的SIF订正到热点方向可提高GPP的估算精度，对红光波段更加明显。鉴别了阳叶和阴叶对冠层SIF和GPP的贡献，比较了阳叶SIF与其GPP、阴叶SIF与其GPP之间关系的差异；发现阳叶对冠层SIF的贡献大于阴叶，在水稻生长的早期（叶面积指数较低）尤为明显，阳叶对冠层GPP的贡献在水稻生长的早期明显大于阴叶、在水稻生长的后期小于阴叶，阳叶GPP与SIF之间线性关系的斜率明显小于阴叶。基于收集的全球不同类型生态系统GPP、OCO-2卫星SIF和MODIS光谱数据,分析了PRI对增强SIF与GPP相关性的作用；发现引入PRI可以增强SIF与GPP之间的相关性，减小两者之间关系斜率在不同植被类型之间的差异。基于两叶光能利用率模型（TL-LUE）的模拟结果，评价了SIF遥感数据监测中国陆地生态GPP的能力；发现GOME-2 SIF与TL-LUE模拟的GPP具有高度相似的空间分布特征，但是两者之间的相关性具有季节差异。成果对于加深SIF与GPP关系认知、提高GPP估算精度具有重要参考意义。