Transformation of the Normalized Difference Chlorophyll Index to Retrieve Chlorophyll-a Concentrations in Manila Bay

Abstract

Abstract. Manila Bay is one of the most significant harbors in the region, enabling commerce and trade between the Philippines and other countries. Its abundant natural resources have provided for generations of its inhabitants and have driven socio-economic development for centuries. Like other water bodies adjacent to highly urbanized cities, the increased organic and nutrient loading from untreated domestic, industrial, and agricultural wastes resulted to further degradation of its water quality. While frequent water quality monitoring is ideal, data from traditional field sampling methods might not be sufficient to assess the spatial and temporal variations of water quality in Manila Bay. Remote sensing fills the need for a frequent and full overview of the bay’s water quality. Sentinel-3 images were initially processed through the Case 2 Regional CoastColour (C2RCC) model to retrieve the remote sensing reflectance. The Normalized Difference Chlorophyll Index (NDCI) was computed for the images and were modeled against the C2RCC-derived chlorophyll-a estimates. From this, we were able to get a general equation (TNDCI – transformed NDCI) to retrieve chlorophyll-a concentrations from two reflectance bands (Oa8 and Oa11). TNDCI gave an R2 of 0.9 and RMSE = 5.12 µg/L as compared to C2RCC values, and an R2 = 0.85 and RMSE = 2.44 µg/L with field data.

High-Throughput Analysis of Leaf Chlorophyll Content in Aquaponically Grown Lettuce Using Hyperspectral Reflectance and RGB Images

“…Also, reasonable prediction accuracy (R 2 p = 0.82) was obtained for NDI under AutoML, as shown in Figure 9b. In this context, our results agree with Manuel and Blanco, who used the NDI as an estimator for chlorophyll, achieving good predictive quality with an R 2 p of 0.9 [70]. GLI index came in third place in terms of the quality of color vegetation indicators as estimators of chlorophyll, obtaining R 2 p of 0.81 developed under AutoML modeling, as shown in Figure 9c and Table 5.…”

Riverine inputs into Manila Bay, Philippines potentially drive changes in chlorophyll-a and total suspended matter concentrations measured from MODIS

“…MODIS is carried by satellite platforms Terra and Aqua to generate images every 1 to 2 days to observe global dynamics. Chlorophyll-a (chl-a) and total suspended matter (TSM) concentrations have been utilized to examine water quality conditions, and recent studies have shown that chl-a and TSM concentrations in Manila Bay can be determined acurately using satellites such as Himawari-8 and Sentinel-3 [6,7,8,9]. Chl-a is related to biological activity because of its correlation with nutrient concentration and algal production; and TSM indicates the presence of suspended sediments which in turn affects the amount of light that is scattered or transmitted through the water column [6].…”