MODIS-derived green Noctiluca blooms in the upper Gulf of Thailand: Algorithm development and seasonal variation mapping

In recent decades, red tides of non-toxic harmful algal blooms have frequently occurred in monsoon-influenced tropical areas, particularly the green form of Noctiluca scintillans (hereafter green Noctiluca). However, our understanding of the mechanism of red tide formation is hindered by spatial and temporal constraints of field data. In this study, we used moderate resolution imaging spectroradiometer (MODIS) ocean color data along with a locally developed algal-bloom classification algorithm to investigate the seasonal variability of dominant red tides across the upper Gulf of Thailand (uGoT). During our July 2018 observation, a super green Noctiluca bloom with extraordinarily high chl-a (>1,469 mg m-3) displayed a distinct spectral reflectance characteristic among red tides in blue-to-green and red-to-near infrared wavelengths. According to the distinctive in situ hyperspectral characteristics of uGoT algal blooms, we developed a classification algorithm for MODIS normalized at 488, 531, and 667 nm, which successfully discriminated green Noctiluca in three levels of blooms, namely, super (100%), strong (>80%), and weak (>40%), from other algal blooms (i.e., dinoflagellates, diatoms, cyanobacteria, and mixed red tide species) as well as non-bloom oceanic and coastal waters using MODIS data, as confirmed by uGoT red tide reports. Monthly MODIS-based discrimination composites from 2003 to 2021 revealed seasonal variability in the surface distribution and bloom frequency of green Noctiluca and other red tides according to the Asian monsoon seasons: the southwest monsoon (May–September) and the northeast monsoon (October–January of the following year). Green Noctiluca blooms occurred farther from the shore and estuaries than other red tides (dinoflagellates and cyanobacteria), and were much more frequent than other red tides between the Tha Chin and Chao Phraya River mouths during the non-monsoon period (February to April). The frequency and distribution of green Noctiluca blooms, as well as other algal blooms, varied with the monsoon season. By comparing MODIS-derived algal blooms to monsoon-induced factors (i.e., sea surface winds, precipitation, and river discharge), we present an unprecedented overview of the spatial and temporal dynamics of red tides throughout the uGoT under Asian monsoon conditions. This research contributes to our understanding of the impact of climate change on phytoplankton dynamics

A Simple Procedure to Preprocess and Ingest Level-2 Ocean Color Data into Google Earth Engine

Data from ocean color (OC) remote sensing are considered a cost-effective tool for the study of biogeochemical processes globally. Satellite-derived chlorophyll, for instance, is considered an essential climate variable since it is helpful in detecting climate change impacts. Google Earth Engine (GEE) is a planetary scale tool for remote sensing data analysis. Along with OC data, such tools allow an unprecedented spatial and temporal scale analysis of water quality monitoring in a way that has never been done before. Although OC data have been routinely collected at medium (~1 km) and more recently at higher (~250 m) spatial resolution, only coarse resolution (≥4 km) data are available in GEE, making them unattractive for applications in the coastal regions. Data reprojection is needed prior to making OC data readily available in the GEE. In this paper, we introduce a simple but practical procedure to reproject and ingest OC data into GEE at their native resolution. The procedure is applicable to OC swath (Level-2) data and is easily adaptable to higher-level products. The results showed consistent distributions between swath and reprojected data, building confidence in the introduced framework. The study aims to start a discussion on making OC data at native resolution readily available in GEE

A Simple Procedure to Preprocess and Ingest Level-2 Ocean Color Data into Google Earth Engine

Data from ocean color (OC) remote sensing are considered a cost-effective tool for the study of biogeochemical processes globally. Satellite-derived chlorophyll, for instance, is considered an essential climate variable since it is helpful in detecting climate change impacts. Google Earth Engine (GEE) is a planetary scale tool for remote sensing data analysis. Along with OC data, such tools allow an unprecedented spatial and temporal scale analysis of water quality monitoring in a way that has never been done before. Although OC data have been routinely collected at medium (~1 km) and more recently at higher (~250 m) spatial resolution, only coarse resolution (≥4 km) data are available in GEE, making them unattractive for applications in the coastal regions. Data reprojection is needed prior to making OC data readily available in the GEE. In this paper, we introduce a simple but practical procedure to reproject and ingest OC data into GEE at their native resolution. The procedure is applicable to OC swath (Level-2) data and is easily adaptable to higher-level products. The results showed consistent distributions between swath and reprojected data, building confidence in the introduced framework. The study aims to start a discussion on making OC data at native resolution readily available in GEE

Improved MODIS-Aqua Chlorophyll-a Retrievals in the Turbid Semi-Enclosed Ariake Bay, Japan

The accurate retrieval of chlorophyll-a concentration (Chl-a) from ocean color satellite data is extremely challenging in turbid, optically complex coastal waters. Ariake Bay in Japan is a turbid semi-enclosed bay of great socio-economic significance, but it suffers from serious water quality problems, particularly due to red tide events. Chl-a derived from the MODerate resolution Imaging Spectroradiometer (MODIS) sensor on satellite Aqua in Ariake Bay was investigated, and it was determined that the causes of the errors were from inaccurate atmospheric correction and inappropriate in-water algorithms. To improve the accuracy of MODIS remote sensing reflectance (Rrs) in the blue and green bands, a simple method was adopted using in situ Rrs data. This method assumes that the error in MODIS Rrs(547) is small, and MODIS Rrs(412) can be estimated from MODIS Rrs(547) using a linear relation between in situ Rrs(412) and Rrs(547). We also showed that the standard MODIS Chl-a algorithm, OC3M, underestimated Chl-a, which was mostly due to water column turbidity. A new empirical switching algorithm was generated based on the relationship between in situ Chl-a and the blue-to-green band ratio, max(Rrs(443), Rrs(448)/Rrs(547), which was the same as the OC3M algorithm. The criterion of Rrs(667) of 0.005 sr−1 was used to evaluate the extent of turbidity for the switching algorithm. The results showed that the switching algorithm performed better than OC3M, and the root mean square error (RMSE) of estimated Chl-a decreased from 0.414 to 0.326. The RMSE for MODIS Chl-a using the recalculated Rrs and the switching algorithm was 0.287, which was a significant improvement from the RMSE of 0.610, which was obtained using standard MODIS Chl-a. Finally, the accuracy of our method was tested with an independent dataset collected by the local Fisheries Research Institute, and the results revealed that the switching algorithm with the recalculated Rrs reduced the RMSE of MODIS Chl-a from 0.412 of the standard to 0.335