A machine learning approach to estimating the error in satellite sea surface temperature retrievals

Kumar, C. S.; Podestá, Guillermo; Kilpatrick, K. A.; Minnett, Peter J.

doi:10.1016/j.rse.2020.112227

Cited by 24 publications

(17 citation statements)

References 76 publications

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“…It is necessary to minimize the bias caused by the different measurement depths between satellite-based and in situ SSTs. Thus, we added 0.17 • C to the MODIS SST to reduce the cool skin effect and subtracted 0.03 • C from the AMSR2 SST to mitigate warm bias at night [28,[50][51][52]. Finally, the proposed approach was applied to the bias-corrected satellite SSTs at a depth of 20 cm.…”

Section: Satellite and In Situ Datamentioning

confidence: 99%

“…The presence of data (i.e., binary) was selected to distinguish the reconstructed and original SST pixels in RF [30]. Latitude affects the retrieval of satellite-based SST, temporal variability of SST, and the number of in situ matchup data [28,30,32]. Scaled DOY was used to document the seasonality of SST [25,28].…”

Section: Data Fusion For Improving Reconstructed Sstmentioning

confidence: 99%

“…Recently, machine learning has emerged as a cutting-edge technology for the reconstruction and estimation of ocean data, which can resolve the nonlinear relationship among high-dimensional and heterogeneous data [26][27][28][29][30][31][32][33]. A variety of machine learning techniques for reconstructing SST have been evaluated, including Artificial Neural Network (ANN), the patch-based NN with Kalman filter, and Random Forest (RF) [18,32,34].…”

Section: Introductionmentioning

confidence: 99%

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

Jung

Yoo

2022

Remote Sensing

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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.

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Section: Satellite and In Situ Datamentioning

confidence: 99%

Section: Data Fusion For Improving Reconstructed Sstmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Jung

Yoo

2022

Remote Sensing

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“…Generally, the generation of consistent time series datasets is challenged by temporal diversities as the Sun-sensor geometry, atmospheric conditions or limited acquisition Remote Sens. 2021, 13, 3454 2 of 17 frequency [9][10][11][12][13]. Thus, data gaps introduce uncertainty regarding the true state of investigated variables at unmonitored points in time (epistemic uncertainty).…”

Section: Introductionmentioning

confidence: 99%

Determining Temporal Uncertainty of a Global Inland Surface Water Time Series

et al. 2021

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Earth observation time series are well suited to monitoring global surface dynamics. However, data products that are aimed at assessing large-area dynamics with a high temporal resolution often face various error sources (e.g., retrieval errors, sampling errors) in their acquisition chain. Addressing uncertainties in a spatiotemporal consistent manner is challenging, as extensive high-quality validation data is typically scarce. Here we propose a new method that utilizes time series inherent information to assess the temporal interpolation uncertainty of time series datasets. For this, we utilized data from the DLR-DFD Global WaterPack (GWP), which provides daily information on global inland surface water. As the time series is primarily based on optical MODIS (Moderate Resolution Imaging Spectroradiometer) images, the requirement of data gap interpolation due to clouds constitutes the main uncertainty source of the product. With a focus on different temporal and spatial characteristics of surface water dynamics, seven auxiliary layers were derived. Each layer provides probability and reliability estimates regarding water observations at pixel-level. This enables the quantification of uncertainty corresponding to the full spatiotemporal range of the product. Furthermore, the ability of temporal layers to approximate unknown pixel states was evaluated for stratified artificial gaps, which were introduced into the original time series of four climatologic diverse test regions. Results show that uncertainty is quantified accurately (>90%), consequently enhancing the product’s quality with respect to its use for modeling and the geoscientific community.

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“…When targeting temperature differences (thermal infrared) between water and non-water surfaces, results are strongly dependent on prevalent weather and landcover conditions [49,50]. Furthermore, temporal diversities such as sun-sensor geometry and atmospheric conditions complicate the generation of consistent time-series [31,45,[51][52][53]. Especially for optical data, atmospheric distortions (e.g., clouds, dust particles) influence radiation transfer and might result in invalid observations for affected pixels and regions, thus reducing the temporal integrity of a remote-sensing time-series.…”

Section: Introductionmentioning

confidence: 99%

Systematic Water Fraction Estimation for a Global and Daily Surface Water Time-Series

et al. 2021

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Fresh water is a vital natural resource. Earth observation time-series are well suited to monitor corresponding surface dynamics. The DLR-DFD Global WaterPack (GWP) provides daily information on globally distributed inland surface water based on MODIS (Moderate Resolution Imaging Spectroradiometer) images at 250 m spatial resolution. Operating on this spatiotemporal level comes with the drawback of moderate spatial resolution; only coarse pixel-based surface water quantification is possible. To enhance the quantitative capabilities of this dataset, we systematically access subpixel information on fractional water coverage. For this, a linear mixture model is employed, using classification probability and pure pixel reference information. Classification probability is derived from relative datapoint (pixel) locations in feature space. Pure water and non-water reference pixels are located by combining spatial and temporal information inherent to the time-series. Subsequently, the model is evaluated for different input sets to determine the optimal configuration for global processing and pixel coverage types. The performance of resulting water fraction estimates is evaluated on the pixel level in 32 regions of interest across the globe, by comparison to higher resolution reference data (Sentinel-2, Landsat 8). Results show that water fraction information is able to improve the product’s performance regarding mixed water/non-water pixels by an average of 11.6% (RMSE). With a Nash-Sutcliffe efficiency of 0.61, the model shows good overall performance. The approach enables the systematic provision of water fraction estimates on a global and daily scale, using only the reflectance and temporal information contained in the input time-series.

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A machine learning approach to estimating the error in satellite sea surface temperature retrievals

Cited by 24 publications

References 76 publications

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

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

Determining Temporal Uncertainty of a Global Inland Surface Water Time Series

Systematic Water Fraction Estimation for a Global and Daily Surface Water Time-Series

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