Journal of Marine Sciences ›› 2021, Vol. 39 ›› Issue (1): 9-19.DOI: 10.3969/j.issn.1001-909X.2021.01.002

Previous Articles     Next Articles

Impact of spatial variability on the validation of ocean chlorophyll-a concentration remote sensing product

JIANG Jin'gang1,2, FENG Huiyun*2, ZHANG Yaguo2, HE Xianqiang1,3   

  1. 1. Southern Marine Science and Engineering Guangdong Laboratory(Zhanjiang),Zhanjiang 524006, China;
    2. Institute of Intelligent Machines, Hefei Institutes of Physical Science, Chinese Academy of Sciences,Hefei 230031, China;
    3. Second Institute of Oceanography, Ministry of Natural Resources,Hangzhou 310012, China
  • Received:2020-11-05 Online:2021-03-15 Published:2021-03-15

Abstract: Ocean chlorophyll-a concentration remote sensing product is important data sources for the studies of ocean primary productivity and carbon sequestration capacity of ocean ecosystems. In order to ensure the data reliability, it is particularly important to verify the accuracy of remote sensing product and analyze the causes of validation errors. In the verification of remote sensing products, due to the existence of spatial variation, the measured data within the remote sensing pixel have different statistical distribution characteristics, and thus produce different statistical error results. In this work, chlorophyll-a concentration remote sensing products derived from MODIS-Aqua, MODIS-Terra, MERIS and SeaWiFS satellite sensors, were quantitatively analyzed on the relationship between spatial variability and validation accuracy. The results of statistical analysis show that spatial variation is one of the direct causes of validation error. The relationship between Mean Absolute Percentage Error (MAPE) and spatial Coefficient of Variation (CV) is in accordance with power exponential model. When CV<0.05, MAPE increases significantly with CV, and when CV>0.15, MAPE changes gently. The validation results of different chlorophyll-a concentration remote sensing products show that SeaWiFS has the highest accuracy, MERIS takes the second place, and MODIS-Terra has the worst accuracy.

Key words: spatial variability, satellite remote sensing, chlorophyll-a, validation

CLC Number: