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Optical sensors onboard satellite platforms face a tradeoff between temporal and spatial resolutions. Spatiotemporal fusion models can produce high spatiotemporal data, while existing models are not designed to produce moderate-spatial-resolution data, like Moderate-Resolution Imaging Spectroradiometer (MODIS), which has moderate spatial detail and frequent temporal coverage. This limitation arises from the challenge of combining coarse- and fine-spatial-resolution data, due to their large spatial resolution gap. This study presents a novel model, named multi-scale convolutional neural network for spatiotemporal fusion (MSCSTF), to generate MODIS-like data by addressing the large spatial-scale gap in blending the Advanced Very-High-Resolution Radiometer (AVHRR) and Landsat images. To mitigate the considerable biases between AVHRR and Landsat with MODIS images, an image correction module is included into the model using deep supervision. The outcomes show that the modeled MODIS-like images are consistent with the observed ones in five tested areas, as evidenced by the root mean square errors (RMSE) of 0.030, 0.022, 0.075, 0.036, and 0.045, respectively. The model makes reasonable predictions on reconstructing retrospective MODIS-like data when evaluating against Landsat data. The proposed MSCSTF model outperforms six other comparative models in accuracy, with regional average RMSE values being lower by 0.005, 0.007, 0.073, 0.062, 0.070, and 0.060, respectively, compared to the counterparts in the other models. The developed method does not rely on MODIS images as input, and it has the potential to reconstruct MODIS-like data prior to 2000 for retrospective studies and applications.<\/jats:p>","DOI":"10.3390\/rs16061086","type":"journal-article","created":{"date-parts":[[2024,3,20]],"date-time":"2024-03-20T09:14:33Z","timestamp":1710926073000},"page":"1086","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Developing a Multi-Scale Convolutional Neural Network for Spatiotemporal Fusion to Generate MODIS-like Data Using AVHRR and Landsat Images"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3447-141X","authenticated-orcid":false,"given":"Zhicheng","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0444-8139","authenticated-orcid":false,"given":"Zurui","family":"Ao","sequence":"additional","affiliation":[{"name":"Faculty of Engineering, Beidou Research Institute, South China Normal University, Guangzhou 510631, China"}]},{"given":"Wei","family":"Wu","sequence":"additional","affiliation":[{"name":"Mining College, Guizhou University, Guiyang 550025, China"}]},{"given":"Yidan","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Civil and Environmental Engineering, the Hong Kong University of Science and Technology, Hong Kong 999077, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1146-4874","authenticated-orcid":false,"given":"Qinchuan","family":"Xin","sequence":"additional","affiliation":[{"name":"School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,3,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1126\/science.1087910","article-title":"Global Warming Trend of Mean Tropospheric Temperature Observed by Satellites","volume":"302","author":"Vinnikov","year":"2003","journal-title":"Science"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"3517","DOI":"10.1029\/2000GL011719","article-title":"Global warming: Evidence from satellite observations","volume":"27","author":"Prabhakara","year":"2000","journal-title":"Geophys. 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