Hybrid Chlorophyll-a Algorithm for Assessing Trophic States of a Tropical Brazilian Reservoir Based on MSI/Sentinel-2 Data

Autor(es)

Cairo, Carolline ^[1] ; Barbosa, Claudio ^[1] ; Lobo, Felipe ^{[1, 2]} ; Novo, Evlyn ^[1] ; Carlos, Felipe ^[1] ; Maciel, Daniel ^[1] ; Flores Junior, Rogerio ^[1] ; Silva, Edson ^[1] ; Curtarelli, Victor ^[1]

Afiliação do(s) autor(es)

^[1] Nacl Inst Space Res INPE, Instrumentat Lab Aquat Syst LabISA, BR-12227010 Sao Jose Dos Campos, SP - Brazil

^[2] Fed Univ Pelotas UFPel, Ctr Technol Dev, BR-96075630 Pelotas, RS - Brazil

• Tipo de documento: Artigo Científico
• Fonte: REMOTE SENSING; v. 12, n. 1 JAN 1 2020.
• Citações Web of Science: 2

Resumo

Using remote sensing for monitoring trophic states of inland waters relies on the calibration of chlorophyll-a (chl-a) bio-optical algorithms. One of the main limiting factors of calibrating those algorithms is that they cannot accurately cope with the wide chl-a concentration ranges in optically complex waters subject to different trophic states. Thus, this study proposes an optical hybrid chl-a algorithm (OHA), which is a combined framework of algorithms for specific chl-a concentration ranges. The study area is Ibitinga Reservoir characterized by high spatiotemporal variability of chl-a concentrations (3-1000 mg/m(3)). We took the following steps to address this issue: (1) we defined optical classes of specific chl-a concentration ranges using Spectral Angle Mapper (SAM); (2) we calibrated/validated chl-a bio-optical algorithms for each trophic class using simulated Sentinel-2 MSI (Multispectral Instrument) bands; (3) and we applied a decision tree classifier in MSI/Sentinel-2 image to detect the optical classes and to switch to the suitable algorithm for the given class. The results showed that three optical classes represent different ranges of chl-a concentration: class 1 varies 2.89-22.83 mg/m(3), class 2 varies 19.51-87.63 mg/m(3), and class 3 varies 75.89-938.97 mg/m(3). The best algorithms for trophic classes 1, 2, and 3 are the 3-band (R-2 = 0.78; MAPE - Mean Absolute Percentage Error = 34.36%), slope (R-2 = 0.93; MAPE = 23.35%), and 2-band (R-2 = 0.98; MAPE = 20.12%), respectively. The decision tree classifier showed an accuracy of 95% for detecting SAM's optical trophic classes. The overall performance of OHA was satisfactory (R-2 = 0.98; MAPE = 26.33%) using in situ data but reduced in the Sentinel-2 image (R-2 = 0.42; MAPE = 28.32%) due to the temporal gap between matchups and the variability in reservoir hydrodynamics. In summary, OHA proved to be a viable method for estimating chl-a concentration in Ibitinga Reservoir and the extension of this framework allowed a more precise chl-a estimate in eutrophic inland waters. (AU)

• Processo FAPESP: 08/56252-0 - Environmental and socioeconomic impacts associated with the production and consumption of sugarcane ethanol in south central Brazil
• Beneficiário: Evlyn Márcia Leão de Moraes Novo
• Linha de fomento: Auxílio à Pesquisa - Programa BIOEN - Temático