AUTHOR=Salehi Hamid , Shamsoddini Ali , Mirlatifi Seyed Majid , Mirgol Behnam , Nazari Meisam TITLE=Spatial and Temporal Resolution Improvement of Actual Evapotranspiration Maps Using Landsat and MODIS Data Fusion JOURNAL=Frontiers in Environmental Science VOLUME=Volume 9 - 2021 YEAR=2021 URL=https://www.frontiersin.org/journals/environmental-science/articles/10.3389/fenvs.2021.795287 DOI=10.3389/fenvs.2021.795287 ISSN=2296-665X ABSTRACT=

Producing daily actual evapotranspiration (ETa) maps with high spatial resolution has always been a challenge for remote sensing research. This study assessed the feasibility of producing daily ETa maps with a high spatial resolution (30 m) for the sugarcane farmlands of Amir Kabir Sugarcane Agro-industry (Khuzestan, Iran) using three different scenarios. In the first scenario, the reflectance bands of Landsat 8 were predicted from the moderate resolution imaging spectroradiometer (MODIS) imagery using the spatial and temporal adaptive reflectance fusion model (STARFM) algorithm. Also, the thermal bands of Landsat 8 were predicted by the spatiotemporal adaptive data fusion algorithm for temperature mapping (SADFAT). Then, ETa amounts were calculated employing such bands and the surface energy balance algorithm for land (SEBAL). In the second scenario, the input data needed by SEBAL were downscaled using the MODIS images and different methods. Then, using the downscaled data and SEBAL, daily ETa amounts with a spatial resolution of 30 m were calculated. In the third scenario, ETa data acquired by MODIS were downscaled to the scale of Landsat 8. In the second and third scenarios, downscaling of the data was carried out by the ratio, regression, and neural networks methods with two different approaches. In the first approach, the Landsat image on day 1 and the relationship between the two MODIS images on day 1 and the other days were used. In the second approach, the simulated image on the previous day and the relationship between the two consecutive images of MODIS were used. Comparing the simulated ETa amounts with the ETa amounts derived from Landsat 8, the first scenario had the best result with an RMSE (root mean square error) of 0.68 mm day−1. The neural networks method used in the third scenario with the second approach had the worst result with an RMSE of 2.25 mm day−1, which was however a better result than the ETa amounts derived from MODIS with an RMSE of 3.19 mm day−1. The method developed in this study offers an efficient and inexpensive way to produce daily ETa maps with a high spatial resolution. Furthermore, we suggest that STARFM and SADFAT algorithms have acceptable accuracies in the simulation of reflectance and thermal bands of Landsat 8 images for homogeneous areas.