High-Resolution Seamless Daily Sea Surface Temperature Based on Satellite Data Fusion and Machine Learning over Kuroshio Extension

Abstract

Sea SurfaceTemperature (SST) is a critical parameter for monitoring the marine environment and understanding various ocean phenomena. While SST can be regularly retrieved from satellite data, it often suffers from missing data due to various reasons including cloud contamination. In this study, we proposed a novel two-step data fusion framework for generating high-resolution seamless daily SST from multi-satellite data sources. The proposed approach consists of (1) SST reconstruction based on Data Interpolate Convolutional AutoEncoder (DINCAE) using the SSTs derived from two satellite sensors (i.e., Moderate Resolution Imaging Spectroradiometer (MODIS) and Advanced Microwave Scanning Radiometer 2(AMSR2)), and (2) SST improvement through data fusion using random forest for consistency with in situ measurements with two schemes (i.e., scheme 1 using the reconstructed MODIS SST variables and scheme 2 using both MODIS and AMSR2 SST variables). The proposed approach was evaluated over the Kuroshio Extension in the Northwest Pacific, where a highly dynamic SST pattern can be found, from 2015 to 2019. The results showed that the reconstructed MODIS and AMSR2 SSTs through DINCAE yielded very good performance with Root Mean Square Errors (RMSEs) of 0.85 and 0.60 °C and Mean Absolute Errors (MAEs) of 0.59 and 0.45 °C, respectively. The results from the second step showed that scheme 2 and scheme 1 produced RMSEs of 0.75 and 0.98 °C and MAEs of 0.53 and 0.68 °C, respectively, compared to the in situ measurements, which proved the superiority of scheme 2 using multi-satellite data sources. Scheme 2 also showed comparable or even better performance than two operational SST products with similar spatial resolution. In particular, scheme 2 was good at simulating features with fine resolution (~50 km). The proposed approach yielded promising results over the study area, producing seamless daily SST products with high quality and high feature resolution.

Towards Digital Twins of the Oceans: The Potential of Machine Learning for Monitoring the Impacts of Offshore Wind Farms on Marine Environments

“…Including historical data for an OWF area and comparing the stages before the construction of the OWF and afterwards could provide additional information. One challenge regarding the satellite data is dealing with clouds, which could be reduced by combining different satellites and their products [ 34 , 35 ]. In general, satellite data come with many challenges but are a strong tool towards the development of digital twins due to their continuous availability and coverage.…”

Synergistic Use of Earth Observation Driven Techniques to Support the Implementation of Water Framework Directive in Europe: A Review

“…Based on our analysis, a significant but smaller number of studies explore coarser resolution (>250 m) sensors such as MODIS (Moderate Resolution Imaging Spectroradiometer) (13%) [5,52,55,[63][64][65][66][67] and MERIS (MEdium Resolution Imaging Spectrometer) (8.6%) [4,52,53,[68][69][70]. For example, Zheng and DiGiacomo (2017) [52] collected match ups for four sensors including the SeaWiFS, the MODIS onboard Aqua, the MERIS, and VIIRS to develop a new Chl-a model based on the semi-analytical approach, in a coastal zone [61] analyzed if the consistency between the Landsat-8 and Sentinel-2 products were evaluated through an extensive evaluation of spectral consistency with case studies in estuary, coast, and lake for algae monitoring.…”

“…The analysis reveals that the difference in the algae area increases with the time difference between the same-day overpass. Cabarello et al (2022)[62] examined the evolution of the key indicators of water quality during the most recent ecological crisis in 2021, which resulted in a significant loss of benthic vegetation and strange death rate events affecting various aquatic species.Based on our analysis, a significant but smaller number of studies explore coarser resolution (>250 m) sensors such as MODIS (Moderate Resolution Imaging Spectroradiometer) (13%)[5,52,55,[63][64][65][66][67] and MERIS (MEdium Resolution Imaging Spectrometer) (8.6%)[4,52,53,[68][69][70]. For example, Zheng and DiGiacomo (2017)[52] collected match ups for four sensors including the SeaWiFS, the MODIS onboard Aqua, the MERIS, and VIIRS to develop a new Chl-a model based on the semi-analytical approach, in a coastal zone…”

Application of Synthetic DINCAE–BME Spatiotemporal Interpolation Framework to Reconstruct Chlorophyll–a from Satellite Observations in the Arabian Sea

“…Furthermore, overcoming the problem affecting DINEOF, which cannot handle nonlinear relationships in temporal and spatial domains, DINCAE can utilize the powerful capacity of a neural network to handle complex interactions and nonlinear relationships. In recent years, the DINCAE method has been widely used for reconstruction of missing data in satellite remote sensing data and has proven to be a potential tool in data reconstruction for SST [13][14][15], Chl-a [6,16,17] and other variables [18].…”