Synthetic images to map daily evapotranspiration in field scale using SEBAL model and ESTARFM algorithm

Silva, Bruno Bonemberger da; Mercante, Erivelto; Kusminski, Douglas; Cattani, Carlos Eduardo Vizzotto; Mendes, Isaque de Souza; Caon, Ivã Luis; Ganascini, Diandra; Prior, M.J.

doi:10.21475/ajcs.20.14.03.p2202

Cited by 1 publication

(1 citation statement)

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“…Finally, calculate the reflectance of the target pixel. Some experimental results show that ESTARFM can maintain fine spatial and spectral details as well as obtain high prediction accuracy in heterogeneous regions, such as agricultural areas and urban, coalfields [ 43 , 44 , 45 , 46 ]. However, ESTARFM still has several limitations and constraints in practical applications [ 36 , 40 ].…”

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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