This paper describes the selected algorithm for the ESA climate change initiative vegetation parameters project. Multi- and hyper-spectral, multi-angular, or multi-sensor top-of-canopy reflectance data call for an efficient generic retrieval system which can improve the consistent retrieval of standard canopy parameters as albedo, Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (fAPAR) and their uncertainties, and exploit the information to retrieve additional parameters (e.g. leaf pigments). We present a retrieval system for canopy and sub-canopy parameters (OptiSAIL), which is based on a model comprising SAIL (canopy reflectance), PROSPECT-D (leaf properties), TARTES (snow properties), a soil model (soil reflectance anisotropy, moisture effect), and a cloud contamination model. The inversion is gradient based and uses codes created by Automatic Differentiation. The full per pixel covariance-matrix of the retrieved parameters is computed. For this demonstration, single observation data from the Sentinel-3 SY_2_SYN (synergy) product is used. The results are compared with the MODIS 4-day LAI/fAPAR product and PhenoCam site photography. OptiSAIL produces generally consistent and credible results, at least matching the quality of the technically quite different MODIS product. The system is computationally efficient with a rate of 150 pixel s(-1) (7 ms per pixel) for a single thread on a current desktop CPU using observations on 26 bands. Not all of the model parameters are well determined in all situations. Significant correlations between the parameters are found, which can change sign and magnitude over time. OptiSAIL appears to meet the design goals and puts real-time processing with this kind of system into reach.
努普·康格鲁阿(Nuup Kangerlua)峡湾的叶绿素a(Chl a)浓度的时空分布对于气候变化有一定的指示作用。基于此,结合通过C2RCC算法对Sentinel-3 OLCI反演得到的Chl a数据、OSTIA海表面温度(SST)数据以及Nuuk气象站数据,对2016—2020年该区域的Chl a浓度分布进行时空分析。结果表明,在峡湾入口端, Chl a的分布主要受风力和海温的影响,在9月风力最强盛时达到最大;而在冰川出水口端,除风力和水温外,在冰川融水的作用下,浮游植物在春季爆发后,还伴随有秋季二次爆发。