Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China; University of Chinese Academy of Sciences, Beijing 100049, China.
Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China.
Sci Total Environ. 2021 Jun 1;771:144811. doi: 10.1016/j.scitotenv.2020.144811. Epub 2021 Jan 27.
Due to the difference of vertical distribution of algae in lakes, it is necessary to carry out remote sensing estimation of algal biomass based on the vertically heterogeneous distribution of chlorophyll in order to improve the accuracy of biomass inversion. A new algorithm is proposed and validated to measure algal biomass in Lake Chaohu based on the Moderate Resolution Imaging Spectrometer (MODIS) images. The algal biomass index (ABI) is defined as the difference in remote-sensing reflectance (R, sr) at 555 nm normalized against two baselines with one formed linearly between R(859) and R(469) and another formed linearly between R(645) and R(469). Both theory and model simulations show that ABI has a good relation with the algal biomass in the euphotic zone (R = 0.88, p < 0.01, N = 50). Field data were further used to estimate the biomass outside the euphotic layer through an empirical algorithm. The ABI algorithm was applied to MODIS Rayleigh-corrected reflectance (R) data after testing the sensitivity to sun glint and thickness of aerosols, which showed an acceptable precision (root mean square error < 21.31 mg and mean relative error < 16.08%). Spectral analyses showed that ABI algorithm was immune to concentration of colored dissolved organic matter (CDOM) but relatively sensitive to suspended particulate inorganic matter (SPIM), which can be solved by using Turbid Water Index (TWI) though in such a challenging environment. A long-term (2012-2017) estimation of algal biomass was further calculated based on the robust algorithm, which shows both seasonal and spatial variations in Lake Chaohu. Tests of ABI algorithm on Sentinel-3 OLCI demonstrates the potential for application in other remote sensors, which meets the need of observation using multi-sensor remote sensing in the future.
由于藻类在湖泊中的垂直分布存在差异，因此有必要基于叶绿素的垂直非均一分布进行藻类生物量的遥感估算，以提高生物量反演的精度。本文提出并验证了一种基于中分辨率成像光谱仪（MODIS）图像的巢湖藻类生物量测量新算法。藻类生物量指数（ABI）定义为在 555nm 处的遥感反射率（R，sr）与两个基线之差，其中一个由 R（859）和 R（469）之间的线性关系形成，另一个由 R（645）和 R（469）之间的线性关系形成。理论和模型模拟均表明，ABI 与真光层中的藻类生物量呈良好相关关系（R=0.88，p<0.01，N=50）。进一步利用野外数据通过经验算法估算了真光层之外的生物量。在测试了对太阳耀光和气溶胶厚度的敏感性之后，将 ABI 算法应用于 MODIS 瑞利校正反射率（R）数据，结果表明具有可接受的精度（均方根误差<21.31mg，平均相对误差<16.08%）。光谱分析表明，ABI 算法对有色溶解有机物（CDOM）的浓度不敏感，但对悬浮颗粒无机质（SPIM）相对敏感，尽管在这样具有挑战性的环境中，可通过使用浑浊水指数（TWI）来解决。基于稳健的算法，进一步计算了巢湖藻类生物量的长期（2012-2017 年）估算结果，显示了巢湖的季节性和空间变化。ABI 算法在 Sentinel-3 OLCI 上的测试表明了其在其他遥感传感器中的应用潜力，满足了未来多传感器遥感观测的需求。
