Seamless observations of chlorophyll-a from OLCI and VIIRS measurements in inland lakes

Abstract

The Visible Infrared Imaging Radiometer Suite (VIIRS) and Ocean and Land Colour Instrument (OLCI) are two main instruments for the ocean color community to observe the global lake environment in the following decades. Despite their applications to retrieve various water optical parameters, the spatial and temporal resolutions of individual sensors cannot meet the requirements for lake monitoring effectively. To date, the possibility of complementary observations through the OLCI-VIIRS data to lake aquatic environments remains unclear. Here, we evaluated the agreement between OLCI and VIIRS-derived remote sensing reflectance (R_rs(λ)) and chlorophyll-a (Chl-a) in Chinese lakes spanning a variety of lake characteristics. We find that OLCI R_rs(λ) data generated by the NOAA Multi-Sensor Level-1 to Level-2 (MSL12) system perform satisfactory accuracy in 20 Chinese lakes with less than 30% uncertainty from 490 nm to 865 nm and show good agreements with VIIRS R_rs(λ) in more than 200 large lakes in China (> 0.90 correlation). The deep neural network algorithm outperformed several state-of-the-art algorithms in Chl-a estimates from OLCI images (23% bias). The spatial and temporal patterns of OLCI and VIIRS-derived Chl-a presented an excellent consistency with ∼20% difference, suggesting the feasibility of seamless OLCI-VIIRS observations in Chl-a for lakes. With the OLCI data and well-validated algorithm, we revealed the high-resolution maps of Chl-a in 681 lakes of larger than 10 km² in China, which significantly filled the results in small-medium lakes where VIIRS did not observe before. This study demonstrates the reasonable agreement of OLCI-VIIRS observations in lakes and proposes an initiative to generate seamless data records in inland lakes through OLCI-VIIRS data.