{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T13:35:57Z","timestamp":1773840957816,"version":"3.50.1"},"reference-count":67,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2022,11,7]],"date-time":"2022-11-07T00:00:00Z","timestamp":1667779200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["42074004"],"award-info":[{"award-number":["42074004"]}]},{"name":"National Natural Science Foundation of China","award":["0733-20180876\/1"],"award-info":[{"award-number":["0733-20180876\/1"]}]},{"name":"Ministry of Natural Resources of the People\u2019s Republic of China","award":["42074004"],"award-info":[{"award-number":["42074004"]}]},{"name":"Ministry of Natural Resources of the People\u2019s Republic of China","award":["0733-20180876\/1"],"award-info":[{"award-number":["0733-20180876\/1"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>To address the problem caused by mixed pixels in MODIS images for high-resolution crop mapping, this paper presents a novel spatial\u2013temporal deep learning-based approach for sub-pixel mapping (SPM) of different crop types within mixed pixels from MODIS images. High-resolution cropland data layer (CDL) data were used as ground references. The contributions of this paper are summarized as follows. First, we designed a novel spatial\u2013temporal depth-wise residual network (ST-DRes) model that can simultaneously address both spatial and temporal data in MODIS images in efficient and effective manners for improving SPM accuracy. Second, we systematically compared different ST-DRes architecture variations with fine-tuned parameters for identifying and utilizing the best neural network architecture and hyperparameters. We also compared the proposed method with several classical SPM methods and state-of-the-art (SOTA) deep learning approaches. Third, we evaluated feature importance by comparing model performances with inputs of different satellite-derived metrics and different combinations of reflectance bands in MODIS. Last, we conducted spatial and temporal transfer experiments to evaluate model generalization abilities across different regions and years. Our experiments show that the ST-DRes outperforms the other classical SPM methods and SOTA backbone-based methods, particularly in fragmented categories, with the mean intersection over union (mIoU) of 0.8639 and overall accuracy (OA) of 0.8894 in Sherman County. Experiments in the datasets of transfer areas and transfer years also demonstrate better spatial\u2013temporal generalization capabilities of the proposed method.<\/jats:p>","DOI":"10.3390\/rs14215605","type":"journal-article","created":{"date-parts":[[2022,11,7]],"date-time":"2022-11-07T03:02:22Z","timestamp":1667790142000},"page":"5605","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["A Spatial\u2013Temporal Depth-Wise Residual Network for Crop Sub-Pixel Mapping from MODIS Images"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5194-275X","authenticated-orcid":false,"given":"Yuxian","family":"Wang","sequence":"first","affiliation":[{"name":"School of Land Science and Technology, China University of Geosciences, Beijing 100083, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5740-9526","authenticated-orcid":false,"given":"Yuan","family":"Fang","sequence":"additional","affiliation":[{"name":"Department of Systems Design Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada"}]},{"given":"Wenlong","family":"Zhong","sequence":"additional","affiliation":[{"name":"School of Land Science and Technology, China University of Geosciences, Beijing 100083, China"}]},{"given":"Rongming","family":"Zhuo","sequence":"additional","affiliation":[{"name":"Aerospace Era Feihong Technology Co., Ltd., Beijing 100094, China"}]},{"given":"Junhuan","family":"Peng","sequence":"additional","affiliation":[{"name":"School of Land Science and Technology, China University of Geosciences, Beijing 100083, China"}]},{"given":"Linlin","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Land Science and Technology, China University of Geosciences, Beijing 100083, China"},{"name":"Department of Systems Design Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"111912","DOI":"10.1016\/j.rse.2020.111912","article-title":"A generalized approach based on convolutional neural networks for large area cropland mapping at very high resolution","volume":"247","author":"Zhang","year":"2020","journal-title":"Remote Sens. 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