The chlorophyll-a (Chla) products of seven processors developed for the Medium Resolution Imaging Spectrometer (MERIS) sensor were evaluated. The seven processors, based on a neural network and band height, were assessed over an optically complex water body with Chla concentrations of 8.10–187.40 mg∙m⁻³ using 10-year MERIS archival data. These processors were adopted for the Ocean and Land Color Instrument (OLCI) sensor. Results indicated that the four processors of band height (i.e. the Maximum Chlorophyll Index (MCI_L1); and Fluorescence Line Height (FLH_L1)); neural network (i.e. Eutrophic Lake (EUL); and Case 2 Regional (C2R)) possessed reasonable retrieval accuracy with root mean square error (R²) in the range of 0.42–0.65. However, these processors underestimated the retrieved Chla > 100 mg∙m⁻³, reflecting the limitation of the band height processors to eliminate the influence of non-phytoplankton matter and highlighting the need to train the neural network for highly turbid waters. MCI_L1 outperformed other processors during the calibration and validation stages (R² = 0.65, Root mean square error (RMSE) = 22.18 mg∙m⁻³, the mean absolute relative error (MARE) = 36.88%). In contrast, the results from the Boreal Lake (BOL) and Free University of Berlin (FUB) processors demonstrated their inadequacy to accurately retrieve Chla concentration > 50 mg∙m⁻³, mainly due to the limitation of the training datasets that resulted in a high MARE for BOL (56.20%) and FUB (57.00%). Mapping the spatial distribution of Chla concentrations across Lake Kasumigaura using the seven processors showed that all processors—except for the BOL and FUB—were able to accurately capture the Chla distribution for moderate and high Chla concentrations. In addition, MCI_L1 and C2R processors were evaluated over 10-years of monthly measured Chla as they demonstrated the best retrieval accuracy from both groups (i.e. band height and neural network, respectively). The retrieved Chla of MCI_L1 was more accurate at tracking seasonal and annual variation in Chla than C2R, with only slight overestimation occurring during the springtime.

Many approaches have been proposed for monitoring the eutrophication of Case 2 waters using remote sensing data. Semi-analytical algorithms and spectrum matching are two major approaches for chlorophyll-a (Chla) retrieval. Semi-analytical algorithms provide indices correlated with phytoplankton characteristics, (e.g., maximum and minimum absorption peaks). Algorithms’ indices are correlated with measured Chla through the regression process. The main drawback of the semi-analytical algorithms is that the derived relation is location and data limited. Spectrum matching and the look-up table approach rely on matching the measured reflectance with a large library of simulated references corresponding to wide ranges of water properties. The spectral matching approach taking hyperspectral measured reflectance as an input, leading to difficulties in incorporating data from multispectral satellites. Consequently, multi-algorithm indices and the look-up table (MAIN-LUT) technique is proposed to combine the merits of semi-analytical algorithms and look-up table, which can be applied to multispectral data. Eight combinations of four algorithms (i.e., 2-band, 3-band, maximum chlorophyll index, and normalized difference chlorophyll index) are investigated for the MAIN-LUT technique. In situ measurements and Medium Resolution Imaging Spectrometer (MERIS) sensor data are used to validate MAIN-LUT. In general, the MAIN-LUT provide a comparable retrieval accuracy with locally tuned algorithms. The most accurate of the locally tuned algorithms varied among datasets, revealing the limitation of these algorithms to be applied universally. In contrast, the MAIN-LUT provided relatively high retrieval accuracy for Tokyo Bay (R² = 0.692, root mean square error (RMSE) = 21.4 mg m⁻³), Lake Kasumigaura (R² = 0.866, RMSE = 11.3 mg m⁻³), and MERIS data over Lake Kasumigaura (R² = 0.57, RMSE = 36.5 mg m⁻³). The simulated reflectance library of MAIN-LUT was generated based on inherent optical properties of Tokyo Bay; however, the MAIN-LUT also provided high retrieval accuracy for Lake Kasumigaura. MAIN-LUT could capture the spatial and temporal distribution of Chla concentration for Lake Kasumigaura.

Soil extracts are useful nutrients to enhance the growth of microalgae. Therefore, the present study attempts for the use of virgin soils from Peninsular Malaysia as growth enhancer. Soils collected from Raja Musa Forest Reserve (RMFR) and Ayer Hitam Forest Reserve (AHFR) were treated using different extraction methods. The total dissolved nitrogen (TDN), total dissolved phosphorus (TDP), and dissolved organic carbon (DOC) concentrations in the autoclave methods were relatively higher than natural extraction with up to 132.0 mg N/L, 10.7 mg P/L, and 2629 mg C/L, respectively for RMFR. The results of TDN, TDP, and DOC suggested that the best extraction methods are autoclaved at 121 °C twice with increasing 87%, 84%, and 95%, respectively. Chlorella vulgaris TRG 4C dominated the growth at 121 °C twice extraction method in the RMRF and AHRF samples, with increasing 54.3% and 14%, respectively. The specific growth rate (µ) of both microalgae were relatively higher, 0.23 d⁻¹ in the Ayer Hitam Soil. This extract served well as a microalgal growth promoter, reducing the cost and the needs for synthetic medium. Mass production of microalgae as aquatic feed will be attempted eventually. The high recovery rate of nutrients has a huge potential to serve as a growth promoter for microalgae.