Estimating Surface Nitrate Concentrations in the Coastal Areas of the Around Savu Sea and Southern Sumba Island Based on Remote Sensing Data

¹ Department of Marine Science, University of Trunojoyo Madura, Jl. Raya Telang PO BOX 2 Kamal-Bangkalan, East Java, Indonesia
² Laboratory of Oceanography, University of Tunojoyo Madura, Jl. Raya Telang PO BOX 2 Kamal-Bangkalan, East Java, Indonesia

^* E-mail: arygiri.dwikartika@trunojoyo.ac.id

Abstract

Nitrate is an essential nutrient in phytoplankton's photosynthesis process. In addition, phytoplankton uses nitrate for their growth and reproduction. Nitrate abundance on the coast will affect primary productivity and biogeochemical cycles. The availability of nitrate observation data, especially around the Savu Sea coast, is minimal. In this study, the estimation of nitrate in the coastal area of the southern part of Sumba Island and the eastern part of Savu Island by using the generalized additive model (GAM). Seventy-one nitrate observation data were used to build the GAM model, and remote sensing data were used as input data for nitrate estimation. Sea Surface Temperature (SST) and chlorophyll-a data were obtained from Aqua-MODIS. Sea Surface Salinity (SSS) and Sea Surface Windspeed (SSW) data were obtained from a Microwave Imaging Radiometer with Aperture Synthesis (MIRAS) Soil Moisture-Ocean Salinity (SMOS), and Advanced Scatterometer (ASCAT), respectively. This study uses the Generalized Additive Model (GAM) approach to predict the distribution of nitrate concentrations and determine the main driving factors associated with nitrate. Based on the result, temperature is the dominant factor in nitrate estimation, while chlorophyll-a has a relatively small influence. The best model to predict nitrate distribution uses four parameters, namely SST, SSS, SSW, and chlorophyll-a. The validation results of the expected nitrate value obtained from the model with the observed nitrate value obtained results with the same value range of 0 - 0.35; the difference is the value of the distribution. From the comparison results, the R2 value is 0.357.