Abstract. The use of Sentinel-3 Ocean and Land Color Instrument (OLCI) images in estimating chlorophyll-a (total and class-differentiated)a concentration is promising owing to Sentinel-3’s 21 bands. This was investigated for the case of Laguna de Bay (or Laguna Lake), Philippines. Field surveys were conducted on 13–17 November 2018 using FluoroProbe, a submersible fluorimeter capable of quantifying concentrations of spectral classes of microalgae. These were regressed with reflectance data obtained from 10-day composite Sentinel-3 reflectance images as well as ten empirical algorithms (indices) for OLCI. Compared to band reflectance, the 10 indices yielded stronger correlations, especially with R665/R709, R674/R709, and (1/R665-1/R709)xR754 with the following respective correlation values: −0.623, −0.646, and 0.628. Multiple regression results indicates that 48% of the variability of total chl-a concentration is explained by five explanatory (reflectance) variables (R412, R443, R560, R681, and R754) with RMSE of 2.814 μg/l. In contrast, the two indices R674/R754 and (1/R665-1/R709)xR754 accounted for about 46% of the variability of total chl-a concentration with RMSE of 2.475 μg/l. For diatoms and bluegreen microalgae, R560/R665 and (1/R665-1/R709)xR754 constitute the models with R2 of 0.21 and 0.435, and RMSE of 2.516 and 2.163 ug/l, respectively. Green microalgal concentration is jointly described by three indices: R560/R665, R674/R754, and R709-R754, with R2 = 0.182 and RMSE = 1.219 μg/l. From cryptophytes, the model comprising of R560/R665, (1/R665-1/R709)xR754, and R709-R754 produced an R2 = 0.289 and RMSE = 0.767 μg/l. It can be said that the empirical algorithms can be used for Sentinel-3 OLCI data providing acceptable estimations of total and spectral class-differentiated chl-a concentration.

Abstract. Laguna Lake, the Philippines’ largest freshwater lake, has always been historically, economically, and ecologically significant to the people living near it. However, as it lies at the center of urban development in Metro Manila, it suffers from water quality degradation. Water quality sampling by current field methods is not enough to assess the spatial and temporal variations of water quality in the lake. Regular water quality monitoring is advised, and remote sensing addresses the need for a synchronized and frequent observation and provides an efficient way to obtain bio-optical water quality parameters. Optimization of bio-optical models is done as local parameters change regionally and seasonally, thus requiring calibration. Field spectral measurements and in-situ water quality data taken during simultaneous satellite overpass were used to calibrate the bio-optical modelling tool WASI-2D to get estimates of chlorophyll-a concentration from the corresponding Landsat-8 images. The initial output values for chlorophyll-a concentration, which ranges from 10–40 μg/L, has an RMSE of up to 10 μg/L when compared with in situ data. Further refinements in the initial and constant parameters of the model resulted in an improved chlorophyll-a concentration retrieval from the Landsat-8 images. The outputs provided a chlorophyll-a concentration range from 5–12 μg/L, well within the usual range of measured values in the lake, with an RMSE of 2.28 μg/L compared to in situ data.

Abstract. Laguna Lake is significant to its surrounding cities and municipalities as it serves multiple purposes: flood basin, aquaculture, water source for irrigation and domestic use, among others. Monitoring the lake’s water quality is an integral part ensuring that the lake would continue to serve its purposes. Bio-optical modelling is a type of empirical model that relates the inherent optical properties of water to different biological properties like chlorophyll-a. The BOMBER (Bio-Optical Model Based tool for Estimating water quality and bottom properties from Remote sensing images) tool makes use of the different IOPs apparent optical properties (AOPs) of satellite images to be able to produce water quality maps. To localize the parameters used by the BOMBER tool, the use of WASI (The Water Color Simulator) tool was introduced. Inverting in situ spectral measurements of the lake, WASI tool was able to produce parameters localized for the lake. This research used 2018 Landsat 8 Images to produce images and used a water profiler to validate results. Results show the bio-optical model provided a R-squared value of 0.6912 and an RMSE of 2.43 μg/l which shows good correlation between the in-situ and the bio-optical model results.

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ABSTRACT:Remote sensing has been an effective technology in mapping natural resources by reducing the costs and field data gathering time and bringing in timely information. With the launch of several earth observation satellites, an increase in the availability of satellite imageries provides an immense selection of data for the users. The Philippines has recently embarked in a program which will enable the gathering of LiDAR data in the whole country. The capacity of the Philippines to take advantage of these advancements and opportunities is lacking. There is a need to transfer the knowledge of remote sensing technology to other institutions to better utilize the available data. Being an archipelagic country with approximately 36,000 kilometers of coastline, and most of its people depending on its coastal resources, remote sensing is an optimal choice in mapping such resources. A project involving fifteen (15) state universities and colleges and higher education institutions all over the country headed by the University of the Philippines Training Center for Applied Geodesy and Photogrammetry and funded by the Department of Science and Technology was formed to carry out the task of capacity building in mapping the country's coastal resources using LiDAR and other remotely sensed datasets. This paper discusses the accomplishments and the future activities of the project.