A PSNR Review of ESTARFM Cloud Removal Method with Sentinel 2 and Landsat 8 Combination

Tarigan, Dietrich G. P.; Isa, Sani Muhamad

doi:10.14569/ijacsa.2021.0120923

Cited by 2 publications

(2 citation statements)

References 17 publications

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“…The main step of the STNLFFM is to first construct the transformation relationship between high and low resolution data, filter similar image elements, and then calculate the weight function using the correlation relationship between images for the target image elements (Cheng et al., 2017; Shen et al., 2019). Since the cloud volume of Sentinel‐2A images on 5 April and 15 May is large compared with the cloud volume of images at the rest of the time periods, it is affected by cloud coverage during the fusion process, resulting in a decrease in fusion accuracy (Knauer et al., 2016; X. Li et al., 2019; Tarigan & Isa, 2021), making the predicted results on 24 June with poor correlation with the corresponding time Sentinel‐2A. Meanwhile, the fusion accuracy is correlated with the temporal phase difference within the prediction time period (Lei et al., 2021), and the prediction accuracy degrades if there is a sudden change in the feature within a certain time period.…”

Section: Discussionmentioning

confidence: 99%

Yield estimation of summer maize based on multi‐source remote‐sensing data

Wang

Liu

et al. 2022

Agronomy Journal

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Accurately estimating regional‐scale crop yields is substantial in determining current agricultural production performance and effective agricultural land management. The Yuncheng Basin is an important grain‐producing area in the Shanxi Province. This paper used Sentinel 2A with a spatial resolution of 10 m and MODIS with a temporal resolution of 1 d in 2020. The spatial and temporal nonlocal filter‐based fusion model (STNLFFM) was used to obtain fused data with a spatial resolution of 10 m and a temporal resolution of 1 d, combined with the Carnegie–Ames–Stanford Approach (CASA) and light‐use efficiency model to achieve summer maize (Zea mays L.) yield estimation. The results showed that the fused normalized difference vegetation index (NDVI) could inherit the spatial Sentinel‐2A NDVI details and express the spatial differences between smaller features more effectively. The STNLFFM NDVI curve was consistent with the actual summer maize growth condition, which accurately reflects the NDVI trend and local abrupt change information during the summer maize growth period. Moreover, the fused NDVI was influenced by topographic differences and artificial irrigation factors, whereas the summer maize yield in mountainous and plateau areas of the Yuncheng Basin was <5,000 kg ha−1 and those in the alluvial plain of the Sushui River reached 8,000 kg ha−1. The accuracy of the yield estimation model constructed based on STNLFFM NDVI (mean absolute percentage error [MAPE] = 5.47%, −13.74% ≤ relative error [RE] ≤0.12%) was significantly higher than that of the model based on MODIS NDVI (MAPE = 15.65%, −19.67% ≤ RE ≤ 20.88%), indicating that the use of spatio‐temporal fusion technology can effectively improve the summer maize yield estimation accuracy.

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Section: Discussionmentioning

confidence: 99%

Yield estimation of summer maize based on multi‐source remote‐sensing data

Wang

Liu

et al. 2022

Agronomy Journal

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“…There are some limitations of ESTARFM when applied to snow-covered mountain areas [ 51 , 52 ]. First, snow reflectance is higher than non-snow pixels in the visible band, which leads to a high threshold of selecting similar pixels, resulting in large errors.…”

Section: Introductionmentioning

confidence: 99%

An Improved Spatiotemporal Data Fusion Method for Snow-Covered Mountain Areas Using Snow Index and Elevation Information

Gao

Liu

et al. 2022

Sensors

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Remote sensing images with high spatial and temporal resolution in snow-covered areas are important for forecasting avalanches and studying the local weather. However, it is difficult to obtain images with high spatial and temporal resolution by a single sensor due to the limitations of technology and atmospheric conditions. The enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) can fill in the time-series gap of remote sensing images, and it is widely used in spatiotemporal fusion. However, this method cannot accurately predict the change when there is a change in surface types. For example, a snow-covered surface will be revealed as the snow melts, or the surface will be covered with snow as snow falls. These sudden changes in surface type may not be predicted by this method. Thus, this study develops an improved spatiotemporal method ESTARFM (iESTARFM) for the snow-covered mountain areas in Nepal by introducing NDSI and DEM information to simulate the snow-covered change to improve the accuracy of selecting similar pixels. Firstly, the change in snow cover is simulated according to NDSI and DEM. Then, similar pixels are selected according to the change in snow cover. Finally, NDSI is added to calculate the weights to predict the pixels at the target time. Experimental results show that iESTARFM can reduce the bright abnormal patches in the land area compared to ESTARFM. For spectral accuracy, iESTARFM performs better than ESTARFM with the root mean square error (RMSE) being reduced by 0.017, the correlation coefficient (r) being increased by 0.013, and the Structural Similarity Index Measure (SSIM) being increased by 0.013. For spatial accuracy, iESTARFM can generate clearer textures, with Robert’s edge (Edge) being reduced by 0.026. These results indicate that iESTARFM can obtain higher prediction results and maintain more spatial details, which can be used to generate dense time series images for snow-covered mountain areas.

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A PSNR Review of ESTARFM Cloud Removal Method with Sentinel 2 and Landsat 8 Combination

Cited by 2 publications

References 17 publications

Yield estimation of summer maize based on multi‐source remote‐sensing data

Yield estimation of summer maize based on multi‐source remote‐sensing data

An Improved Spatiotemporal Data Fusion Method for Snow-Covered Mountain Areas Using Snow Index and Elevation Information

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