We detail our attempts at empirical modeling of MODIS derived Chlorophyll a (Chl a) distribution on Lake Victoria in East Africa and consequently its trophic status. This was motivated by the need for Lake Victoria specific algorithms, as the current satellite based standard algorithms overestimate derived Chl a. In situ Chl a data was hence collected in three field campaigns in November 2014, March 2015 and July 2015. In situ reflectances were collected during the July campaign only. We first developed models from in situ reflectances and in situ Chl a, which when applied to MODIS bands performed dismally (R² = 0.03). We then proceeded to derive empirical models by directly comparing MODIS bands with in situ Chl a based on data collected in November 2014 and July 2015. The March 2015 dataset couldn’t be used due to cloud cover hence no matchups could be obtained. The best model derived (R² = 0.88) was based on the ratio 488 nm/645 nm, and was then used to determine the trophic status of Lake Victoria using Carlson’s Chl a Trophic State Index (TSI). The results show that large areas of the lake are mesotrophic with eutrophic displays closer to the shores. The modeled TSI was then validated against in situ TSI derived from the March dataset and posted an 80% matchup. One of the main challenges, however is the prevalence of cloud cover, which hinders synoptic mapping of the lake. That notwithstanding, the study demonstrates the potential of earth observation in providing accurate TSI information for improved management of Lake Victoria.

我们详细介绍了我们尝试对 MODIS 派生的叶绿素a ( Chl a ) 在东非维多利亚湖上的分布及其营养状态进行经验建模的尝试。这是由于需要维多利亚湖特定算法，因为当前基于卫星的标准算法高估了派生的Chl a。因此，在 2014 年 11 月、2015 年 3 月和 2015 年 7 月的三个现场活动中收集了原位Chl a数据。仅在 7 月活动期间收集了原位反射率。我们首先从原位反射率和原位Chl a开发了模型，当应用于 MODIS 波段时，其表现不佳（R ² = 0.03）。然后，我们根据 2014 年 11 月和 2015 年 7 月收集的数据，通过直接比较 MODIS 波段与原位Chl a来推导经验模型。由于云量覆盖，无法使用 2015 年 3 月的数据集，因此无法获得匹配。导出的最佳模型 (R ²  = 0.88) 是基于 488 nm/645 nm 的比率，然后用于使用 Carlson's Chl a确定维多利亚湖的营养状况营养状态指数 (TSI)。结果表明，湖泊的大部分区域都处于中营养状态，靠近海岸的地方呈现富营养状态。然后根据来自 March 数据集的原位 TSI 验证建模的 TSI，并发布了 80% 的匹配。然而，主要挑战之一是云层的普遍存在，这阻碍了湖泊的天气图绘制。尽管如此，该研究证明了地球观测在提供准确的 TSI 信息以改善维多利亚湖管理方面的潜力。