Water quality monitoring in Térraba Sièrpe Wetland (Costa Rica) using multi- and hyperspectral EO data

Abstract

The project MONEOWET focuses on multispectral and hyperspectral Earth Observation (EO) data to investigate water quality in relation to agricultural activities within the Térraba Sièrpe Wetland in Costa Rica. This study corresponds to an initiative focused on investigating the applicability of remote sensing data in tropical systems. The main topic of this project is the use of EO data to assess the impacts and dynamics of agricultural activities on the sensitive RAMSAR wetland ecosystem Térraba Sièrpe at the mouth of the Térraba and Sièrpe rivers. One goal of this project is to develop a first EO database and define analytical methods for water quality studies in that area and beyond. The results will provide a deeper insight into the processes of the entire wetland ecosystem and may help to detect harmful damage to the fragile environment caused by surrounding agricultural activities. The long-term goal is sustainable water and land use management that is exemplary for many other tropical wetlands in Latin America. Scientists from Germany and Costa Rica are working together to collect data with established (e.g. Sentinel 2, Landsat 8) and new Earth Observation sensors (e.g. DESIS on the ISS) to assess water quality parameters and link these parameters to agricultural land use in the surrounding area. The common goal of the project is to evaluate the applicability of Landsat 8, Sentinel-2 and DESIS multi- and hyperspectral satellite imagery for water quality studies in tropical environments. Field campaigns were carried out during wet season (November 2018 and November 2019) and dry season (March 2019 and March 2021). The sampling sites for in-situ measurements were taken in the three main meanders of the Sièrpe River and the main meander of Térraba River within the wetland. At each sampling site, the spectral signature of the river was recorded using an Ocean Optics Sensor System (OOSS). The multispectral (Sentinel 2, Landsat 8) and hyperspectral EO (DESIS) data were atmospherically corrected to Bottom-of-atmosphere (BOA) reflectance using Sen2cor (ESA) and PACO (Python-based Atmospheric Correction, DLR), respectively. The WASI-2D inversion method, a semi-analytical model, which retrieves the optically active water quality variables: chlorophyll, total suspended matter (TSM) and colored dissolved organic matter (CDOM) was used and parameterized with site - specific inherent optical properties (SIOPs) of the area and applied to time series of L2A Sentinel, Landsat 8 and DESIS images. Some of the Sentinel-2 and Landsat overpasses were coincident with available field data, however DESIS images could not be obtained during field campaigns, thus only a qualitative evaluation is presented. Although cloud cover in the tropics is a major challenge, the influence of thin clouds could be corrected and the concentrations of TSM and CDOM could be derived quantitatively. Chlorophyll could not be derived reliably in most areas, in particular not from Landsat 8, most likely because its concentration was relatively low and water absorption was dominated by CDOM. The high temporal dynamics of the river system, which is strongly influenced by tides, makes comparison of satellite data collected at different times very difficult, as is comparison with field data. Nevertheless, Sentinel 2-derived maps of water constituents and corresponding Landsat 8 and DESIS images show good agreements in the average concentrations of TSM and CDOM concentration and plausible spatial patterns, and field measurements show that they are in a plausible range. The results indicate that under favorable observational and environmental conditions, the applied atmospheric correction and the used retrieval algorithm are suitable to use DESIS, Sentinel 2 and Landsat 8 data for mapping TSM and CDOM in tropical environments, while chlorophyll is challenging. Their quantitative determination by satellite is therefore an important contribution of this project to the ecological assessment of the waters and the surrounding environment of the study area.