{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T17:08:57Z","timestamp":1770743337840,"version":"3.49.0"},"reference-count":46,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2024,12,15]],"date-time":"2024-12-15T00:00:00Z","timestamp":1734220800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Joint Funds of the National Natural Science Foundation of China","award":["U22A20567"],"award-info":[{"award-number":["U22A20567"]}]},{"name":"Joint Funds of the National Natural Science Foundation of China","award":["202402AE090022"],"award-info":[{"award-number":["202402AE090022"]}]},{"name":"Joint Funds of the National Natural Science Foundation of China","award":["2024AH050625"],"award-info":[{"award-number":["2024AH050625"]}]},{"name":"Major Science and Technology Project of Yunnan Province","award":["U22A20567"],"award-info":[{"award-number":["U22A20567"]}]},{"name":"Major Science and Technology Project of Yunnan Province","award":["202402AE090022"],"award-info":[{"award-number":["202402AE090022"]}]},{"name":"Major Science and Technology Project of Yunnan Province","award":["2024AH050625"],"award-info":[{"award-number":["2024AH050625"]}]},{"name":"Natural Science Research Project of Anhui Province Educational Committee","award":["U22A20567"],"award-info":[{"award-number":["U22A20567"]}]},{"name":"Natural Science Research Project of Anhui Province Educational Committee","award":["202402AE090022"],"award-info":[{"award-number":["202402AE090022"]}]},{"name":"Natural Science Research Project of Anhui Province Educational Committee","award":["2024AH050625"],"award-info":[{"award-number":["2024AH050625"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Mixed pixels often hinder accurate cropland mapping from remote sensing images with coarse spatial resolution. Image spatial super-resolution reconstruction technology is widely applied to address this issue, typically transforming coarse-resolution remote sensing images into fine spatial resolution images, which are then used to generate fine-resolution land cover maps using classification techniques. Deep learning has been widely used for image spatial super-resolution reconstruction; however, collecting training samples is often difficult for cropland mapping. Given that the quality of spatial super-resolution reconstruction directly impacts classification accuracy, this study aims to assess the impact of different types of training samples on image spatial super-resolution reconstruction and cropland mapping results by employing a Residual Channel Attention Network (RCAN) model combined with a spatial attention mechanism. Four types of samples were used for spatial super-resolution reconstruction model training, namely fine-resolution images and their corresponding coarse-resolution images, including original Sentinel-2 and degraded Sentinel-2 images, original GF-2 and degraded GF-2 images, histogram-matched GF-2 and degraded GF-2 images, and registered original GF-2 and Sentinel-2 images. The results indicate that the samples acquired by the histogram-matched GF-2 and degraded GF-2 images can resolve spectral band mismatches when simulating training samples from fine spatial resolution imagery, while the other three methods have limitations in their inability to fully address spectral and spatial mismatches. The histogram-matched method yielded the best image quality with PSNR, SSIM, and QNR values of 42.2813, 0.9778, and 0.9872, respectively, and produced the best mapping results, achieving an overall accuracy of 0.9306. By assessing the impact of training samples on image spatial super-resolution reconstruction and classification, this study addresses data limitations and contributes to improving the accuracy of cropland mapping, which is crucial for agricultural management and decision-making.<\/jats:p>","DOI":"10.3390\/rs16244678","type":"journal-article","created":{"date-parts":[[2024,12,16]],"date-time":"2024-12-16T10:08:53Z","timestamp":1734343733000},"page":"4678","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Enhancing Cropland Mapping with Spatial Super-Resolution Reconstruction by Optimizing Training Samples for Image Super-Resolution Models"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-7644-8375","authenticated-orcid":false,"given":"Xiaofeng","family":"Jia","sequence":"first","affiliation":[{"name":"Key Laboratory for Environment and Disaster Monitoring and Evaluation of Hubei, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan 430077, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2489-4312","authenticated-orcid":false,"given":"Xinyan","family":"Li","sequence":"additional","affiliation":[{"name":"Key Laboratory of Natural Resources Monitoring in Tropical and Subtropical Area of South China, Ministry of Natural Resources, Surveying and Mapping Institute of Land and Resources Department of Guangdong Province, Guangzhou 510663, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-7630-243X","authenticated-orcid":false,"given":"Zirui","family":"Wang","sequence":"additional","affiliation":[{"name":"Key Laboratory for Environment and Disaster Monitoring and Evaluation of Hubei, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan 430077, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"given":"Zhen","family":"Hao","sequence":"additional","affiliation":[{"name":"Key Laboratory for Environment and Disaster Monitoring and Evaluation of Hubei, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan 430077, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9123-2592","authenticated-orcid":false,"given":"Dong","family":"Ren","sequence":"additional","affiliation":[{"name":"The Fourth Institute of Oceanography, Ministry of Natural Resources, Beihai 536000, China"}]},{"given":"Hui","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Integrative Plant Science, Cornell University, Ithaca, New York, NY 14850, USA"}]},{"given":"Yun","family":"Du","sequence":"additional","affiliation":[{"name":"Key Laboratory for Environment and Disaster Monitoring and Evaluation of Hubei, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan 430077, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0685-4897","authenticated-orcid":false,"given":"Feng","family":"Ling","sequence":"additional","affiliation":[{"name":"Key Laboratory for Environment and Disaster Monitoring and Evaluation of Hubei, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan 430077, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,12,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"509","DOI":"10.1016\/j.rse.2017.10.005","article-title":"Sentinel-2 cropland mapping using pixel-based and object-based time-weighted dynamic time warping analysis","volume":"204","author":"Belgiu","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1038\/s43016-021-00429-z","article-title":"Global maps of cropland extent and change show accelerated cropland expansion in the twenty-first century","volume":"3","author":"Potapov","year":"2022","journal-title":"Nat. Food"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1109\/JSTARS.2019.2950466","article-title":"Mapping Plastic Greenhouses Using Spectral Metrics Derived From GaoFen-2 Satellite Data","volume":"13","author":"Shi","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Pan, Z., Xu, J., Guo, Y., Hu, Y., and Wang, G. (2020). Deep Learning Segmentation and Classification for Urban Village Using a Worldview Satellite Image Based on U-Net. Remote Sens., 12.","DOI":"10.3390\/rs12101574"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"108089","DOI":"10.1016\/j.agwat.2022.108089","article-title":"Assessing the sensitive spectral bands for soybean water status monitoring and soil moisture prediction using leaf-based hyperspectral reflectance","volume":"277","author":"Crusiol","year":"2023","journal-title":"Agric. Water Manag."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2236","DOI":"10.1016\/j.scib.2023.08.015","article-title":"Sustainable poverty reduction models for the coordinated development of the social economy and environment in China","volume":"68","author":"Ge","year":"2023","journal-title":"Sci. Bull."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"5270","DOI":"10.1038\/s41467-023-40940-4","article-title":"Effects of public-health measures for zeroing out different SARS-CoV-2 variants","volume":"14","author":"Ge","year":"2023","journal-title":"Nat. Commun"},{"key":"ref_8","first-page":"102470","article-title":"Mapping water bodies under cloud cover using remotely sensed optical images and a spatiotemporal dependence model","volume":"103","author":"Li","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"5621618","DOI":"10.1109\/TGRS.2023.3318003","article-title":"Multiobjective Memetic Spatiotemporal Subpixel Mapping for Remote Sensing Imagery","volume":"61","author":"Ma","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1038\/s41597-022-01169-w","article-title":"Validation and refinement of cropland data layer using a spatial-temporal decision tree algorithm","volume":"9","author":"Lin","year":"2022","journal-title":"Sci. Data"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"113884","DOI":"10.1016\/j.rse.2023.113884","article-title":"Very fine spatial resolution urban land cover mapping using an explicable sub-pixel mapping network based on learnable spatial correlation","volume":"299","author":"He","year":"2023","journal-title":"Remote Sens. Environ."},{"key":"ref_12","first-page":"5503815","article-title":"Deep Hierarchical Pyramid Network With High- Frequency -Aware Differential Architecture for Super-Resolution Mapping","volume":"61","author":"He","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_13","first-page":"5406319","article-title":"Unmixing-Based Spatiotemporal Image Fusion Based on the Self-Trained Random Forest Regression and Residual Compensation","volume":"61","author":"Li","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"5631","DOI":"10.1029\/2018WR024136","article-title":"Measuring River Wetted Width From Remotely Sensed Imagery at the Subpixel Scale With a Deep Convolutional Neural Network","volume":"55","author":"Ling","year":"2019","journal-title":"Water Resour. Res."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"598","DOI":"10.1080\/2150704X.2019.1587196","article-title":"Super-resolution land cover mapping by deep learning","volume":"10","author":"Ling","year":"2019","journal-title":"Remote Sens. Lett."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1692","DOI":"10.1109\/TGRS.2014.2346535","article-title":"Fast Subpixel Mapping Algorithms for Subpixel Resolution Change Detection","volume":"53","author":"Wang","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1109\/TGRS.2019.2930764","article-title":"Information Loss-Guided Multi-Resolution Image Fusion","volume":"58","author":"Wang","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"6763","DOI":"10.1109\/TGRS.2018.2842748","article-title":"A New Spectral-Spatial Sub-Pixel Mapping Model for Remotely Sensed Hyperspectral Imagery","volume":"56","author":"Xu","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Xu, X., Tong, X., Plaza, A., Zhong, Y., Xie, H., and Zhang, L. (2016). Joint Sparse Sub-Pixel Mapping Model with Endmember Variability for Remotely Sensed Imagery. Remote Sens., 9.","DOI":"10.3390\/rs9010015"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"5614213","DOI":"10.1109\/TGRS.2021.3129789","article-title":"A Cascaded Spectral\u2013Spatial CNN Model for Super-Resolution River Mapping With MODIS Imagery","volume":"60","author":"Yin","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"8599","DOI":"10.1109\/TGRS.2020.3041724","article-title":"Object-Based Area-to-Point Regression Kriging for Pansharpening","volume":"59","author":"Zhang","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"8438","DOI":"10.1109\/TGRS.2020.2987907","article-title":"COLOR: Cycling, Offline Learning, and Online Representation Framework for Airport and Airplane Detection Using GF-2 Satellite Images","volume":"58","author":"Zhong","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"5408618","DOI":"10.1109\/TGRS.2023.3331904","article-title":"Super-Resolution Mapping With a Fraction Error Eliminating CNN Model","volume":"61","author":"Yin","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_24","first-page":"104176","article-title":"Super-resolution water body mapping with a feature collaborative CNN model by fusing Sentinel-1 and Sentinel-2 images","volume":"134","author":"Yin","year":"2024","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"2036","DOI":"10.1080\/15481603.2022.2142727","article-title":"Generating annual high resolution land cover products for 28 metropolises in China based on a deep super-resolution mapping network using Landsat imagery","volume":"59","author":"He","year":"2022","journal-title":"GIScience Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"345","DOI":"10.1016\/j.rse.2018.03.015","article-title":"Influence of reconstruction scale, spatial resolution and pixel spatial relationships on the sub-pixel mapping accuracy of a double-calculated spatial attraction model","volume":"210","author":"Wu","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"3106","DOI":"10.1109\/TMM.2019.2919431","article-title":"Deep Learning for Single Image Super-Resolution: A Brief Review","volume":"21","author":"Yang","year":"2019","journal-title":"IEEE Trans. Multimed."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"202","DOI":"10.1109\/MGRS.2022.3171836","article-title":"Deep Learning for Downscaling Remote Sensing Images: Fusion and super-resolution","volume":"10","author":"Sdraka","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"4105511","DOI":"10.1109\/TGRS.2024.3415051","article-title":"Spatial Downscaling of Downward Surface Shortwave Radiation Based on Image Super-Resolution","volume":"62","author":"Cheng","year":"2024","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_30","first-page":"3000224","article-title":"Single-Image Super-Resolution for Remote Sensing Images Using a Deep Generative Adversarial Network With Local and Global Attention Mechanisms","volume":"60","author":"Li","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_31","first-page":"102543","article-title":"Image super-resolution with dense-sampling residual channel-spatial attention networks for multi-temporal remote sensing image classification","volume":"104","author":"Zhu","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"5799","DOI":"10.1109\/TGRS.2019.2902431","article-title":"Edge-Enhanced GAN for Remote Sensing Image Superresolution","volume":"57","author":"Jiang","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Masi, G., Cozzolino, D., Verdoliva, L., and Scarpa, G. (2016). Pansharpening by Convolutional Neural Networks. Remote Sens., 8.","DOI":"10.3390\/rs8070594"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"4607","DOI":"10.1109\/JSTARS.2020.3016135","article-title":"HISTIF: A New Spatiotemporal Image Fusion Method for High-Resolution Monitoring of Crops at the Subfield Level","volume":"13","author":"Jiang","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"5509814","DOI":"10.1109\/TGRS.2023.3269892","article-title":"Local Information-Enhanced Graph-Transformer for Hyperspectral Image Change Detection With Limited Training Samples","volume":"61","author":"Dong","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_36","first-page":"5404715","article-title":"MSWAGAN: Multispectral Remote Sensing Image Super-Resolution Based on Multiscale Window Attention Transformer","volume":"62","author":"Wang","year":"2024","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_37","first-page":"4301418","article-title":"A Framework for Fine-Resolution and Spatially Continuous Arctic Sea Ice Drift Retrieval Using Multisensor Data","volume":"62","author":"Wang","year":"2024","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Li, K., Li, K., Wang, L., Zhong, B., and Fu, Y. (2018, January 8\u201314). Image Super-Resolution Using Very Deep Residual Channel Attention Networks. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_18"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"7957","DOI":"10.1109\/JSTARS.2024.3382136","article-title":"A Novel Remote Sensing Spatiotemporal Data Fusion Framework Based on the Combination of Deep-Learning Downscaling and Traditional Fusion Algorithm","volume":"17","author":"Cui","year":"2024","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"5605209","DOI":"10.1109\/TGRS.2023.3256373","article-title":"Building a Bridge of Bounding Box Regression Between Oriented and Horizontal Object Detection in Remote Sensing Images","volume":"61","author":"Qian","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"4101717","DOI":"10.1109\/TGRS.2023.3261545","article-title":"A Dehazing Method for Remote Sensing Image Under Nonuniform Hazy Weather Based on Deep Learning Network","volume":"61","author":"Jiang","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Zhu, Y., Fan, L., Li, Q., and Chang, J. (2023). Multi-Scale Discrete Cosine Transform Network for Building Change Detection in Very-High-Resolution Remote Sensing Images. Remote Sens., 15.","DOI":"10.3390\/rs15215243"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Wang, B., Jia, K., Liang, S., Xie, X., Wei, X., Zhao, X., Yao, Y., and Zhang, X. (2018). Assessment of Sentinel-2 MSI Spectral Band Reflectances for Estimating Fractional Vegetation Cover. Remote Sens., 10.","DOI":"10.3390\/rs10121927"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Scheffler, D., Hollstein, A., Diedrich, H., Segl, K., and Hostert, P. (2017). AROSICS: An Automated and Robust Open-Source Image Co-Registration Software for Multi-Sensor Satellite Data. Remote Sens., 9.","DOI":"10.3390\/rs9070676"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"45334","DOI":"10.1109\/ACCESS.2023.3256086","article-title":"IRE: Improved Image Super-Resolution Based on Real-ESRGAN","volume":"11","author":"Zhu","year":"2023","journal-title":"IEEE Access"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Zhou, R., and Susstrunk, S. (2019\u20132, January 27). Kernel Modeling Super-Resolution on Real Low-Resolution Images. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Republic of Korea.","DOI":"10.1109\/ICCV.2019.00252"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/24\/4678\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:55:41Z","timestamp":1760115341000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/24\/4678"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,15]]},"references-count":46,"journal-issue":{"issue":"24","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["rs16244678"],"URL":"https:\/\/doi.org\/10.3390\/rs16244678","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,15]]}}}