NH and ME Landsat chlorophyll-a retrieval algorithms and in situ measurements 2000 (Landsat 7), 2013-2015 (Landsat 8)

Predicting algal blooms has become a priority for scientists,
municipalities, businesses, and citizens. Remote sensing offers
solutions to the spatial and temporal challenges facing existing lake
research and monitoring programs that rely primarily on
high-investment, in situ measurements. Techniques to remotely measure
chlorophyll-a (chl-a) as a proxy for algal biomass have been limited
to specific large water bodies in particular seasons and narrow chl-a
ranges. Thus, a first step toward prediction of algal blooms is
generating regionally robust algorithms using in situ and remote
sensing data. This study explores the relationship between in-lake
measured chl-a data from Maine and New Hampshire lakes and
remotely-sensed chl-a retrieval algorithm outputs. Landsat 8 images
were obtained and then processed after required atmospheric and
radiometric corrections. Six previously developed algorithms were
tested on a regional scale on eleven scenes from 2013-2015 covering
192 lakes. Additionally, data related to one Landsat 7 scene (2000)
are included in this data set. Boucher, J, K.C. Weathers, H. Norouzi, B. Steele. In Press. Assessing
the effectiveness of Landsat 8 chlorophyll-a retrieval algorithms for
regional freshwater monitoring. Ecological Applications.

预测藻华已成为科学家、市政当局、企业与普通民众的重点工作方向。遥感技术为当前主要依赖高投入原位测量（in situ measurements）的湖泊研究与监测项目所面临的时空难题提供了解决方案。以叶绿素a（chlorophyll-a，chl-a）作为藻类生物量替代指标的遥感测量技术，目前仅能在特定季节、特定大型水体以及狭窄的chl-a浓度范围内应用。因此，实现藻华预测的首要步骤，是利用原位测量与遥感数据构建适用于区域尺度的稳健算法。本研究探究了来自美国缅因州与新罕布什尔州湖泊的原位chl-a实测数据与遥感chl-a反演算法输出结果之间的关联。研究获取了陆地卫星8号（Landsat 8）影像，并按照要求完成大气校正与辐射校正后进行了处理。本研究在区域尺度上，针对2013至2015年间覆盖192个湖泊的11景遥感影像，测试了6种此前已开发的算法。此外，本数据集还包含一景2000年的陆地卫星7号（Landsat 7）影像相关数据。Boucher, J.、K.C. Weathers、H. Norouzi、B. Steele. 已录用. 评估陆地卫星8号叶绿素a反演算法在区域淡水监测中的应用效果. 《生态应用》（Ecological Applications）