{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T23:28:57Z","timestamp":1780442937005,"version":"3.54.1"},"reference-count":64,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2021,7,23]],"date-time":"2021-07-23T00:00:00Z","timestamp":1626998400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41671433"],"award-info":[{"award-number":["41671433"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Black soil is one of the most productive soils with high organic matter content. Crop residue covering is important for protecting black soil from alleviating soil erosion and increasing soil organic carbon. Mapping crop residue covered areas accurately using remote sensing images can monitor the protection of black soil in regional areas. Considering the inhomogeneity and randomness, resulting from human management difference, the high spatial resolution Chinese GF-1 B\/D image and developed MSCU-net+C deep learning method are used to mapping corn residue covered area (CRCA) in this study. The developed MSCU-net+C is joined by a multiscale convolution group (MSCG), the global loss function, and Convolutional Block Attention Module (CBAM) based on U-net and the full connected conditional random field (FCCRF). The effectiveness of the proposed MSCU-net+C is validated by the ablation experiment and comparison experiment for mapping CRCA in Lishu County, Jilin Province, China. The accuracy assessment results show that the developed MSCU-net+C improve the CRCA classification accuracy from IOUAVG = 0.8604 and KappaAVG = 0.8864 to IOUAVG = 0.9081 and KappaAVG = 0.9258 compared with U-net. Our developed and other deep semantic segmentation networks (MU-net, GU-net, MSCU-net, SegNet, and Dlv3+) improve the classification accuracy of IOUAVG\/KappaAVG with 0.0091\/0.0058, 0.0133\/0.0091, 0.044\/0.0345, 0.0104\/0.0069, and 0.0107\/0.0072 compared with U-net, respectively. The classification accuracies of IOUAVG\/KappaAVG of traditional machine learning methods, including support vector machine (SVM) and neural network (NN), are 0.576\/0.5526 and 0.6417\/0.6482, respectively. These results reveal that the developed MSCU-net+C can be used to map CRCA for monitoring black soil protection.<\/jats:p>","DOI":"10.3390\/rs13152903","type":"journal-article","created":{"date-parts":[[2021,7,25]],"date-time":"2021-07-25T22:07:00Z","timestamp":1627250820000},"page":"2903","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Corn Residue Covered Area Mapping with a Deep Learning Method Using Chinese GF-1 B\/D High Resolution Remote Sensing Images"],"prefix":"10.3390","volume":"13","author":[{"given":"Wancheng","family":"Tao","sequence":"first","affiliation":[{"name":"College of Land Science and Technology, China Agricultural University, Beijing 100083, China"},{"name":"Key Laboratory of Remote Sensing for Agri-Hazards, Ministry of Agriculture, Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zixuan","family":"Xie","sequence":"additional","affiliation":[{"name":"College of Land Science and Technology, China Agricultural University, Beijing 100083, China"},{"name":"Key Laboratory of Remote Sensing for Agri-Hazards, Ministry of Agriculture, Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ying","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Land Science and Technology, China Agricultural University, Beijing 100083, China"},{"name":"Key Laboratory of Remote Sensing for Agri-Hazards, Ministry of Agriculture, Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiayu","family":"Li","sequence":"additional","affiliation":[{"name":"College of Land Science and Technology, China Agricultural University, Beijing 100083, China"},{"name":"Key Laboratory of Remote Sensing for Agri-Hazards, Ministry of Agriculture, Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fu","family":"Xuan","sequence":"additional","affiliation":[{"name":"College of Land Science and Technology, China Agricultural University, Beijing 100083, China"},{"name":"Key Laboratory of Remote Sensing for Agri-Hazards, Ministry of Agriculture, Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0341-1983","authenticated-orcid":false,"given":"Jianxi","family":"Huang","sequence":"additional","affiliation":[{"name":"College of Land Science and Technology, China Agricultural University, Beijing 100083, China"},{"name":"Key Laboratory of Remote Sensing for Agri-Hazards, Ministry of Agriculture, Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6942-0746","authenticated-orcid":false,"given":"Xuecao","family":"Li","sequence":"additional","affiliation":[{"name":"College of Land Science and Technology, China Agricultural University, Beijing 100083, China"},{"name":"Key Laboratory of Remote Sensing for Agri-Hazards, Ministry of Agriculture, Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8726-5858","authenticated-orcid":false,"given":"Wei","family":"Su","sequence":"additional","affiliation":[{"name":"College of Land Science and Technology, China Agricultural University, Beijing 100083, China"},{"name":"Key Laboratory of Remote Sensing for Agri-Hazards, Ministry of Agriculture, Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2080-3463","authenticated-orcid":false,"given":"Dongqin","family":"Yin","sequence":"additional","affiliation":[{"name":"College of Land Science and Technology, China Agricultural University, Beijing 100083, China"},{"name":"Key Laboratory of Remote Sensing for Agri-Hazards, Ministry of Agriculture, Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,7,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"104824","DOI":"10.1016\/j.catena.2020.104824","article-title":"\u201cBlack soils\u201d in the Russian Soil Classification system, the US Soil Taxonomy and the WRB: Quantitative correlation and implications for pedodiversity assessment","volume":"196","author":"Sorokin","year":"2021","journal-title":"CATENA"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"87","DOI":"10.17221\/155\/2009-PSE","article-title":"Soil degradation: A problem threatening the sustainable development of agriculture in Northeast China","volume":"56","author":"Liu","year":"2020","journal-title":"Plant Soil Environ."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1016\/j.agee.2017.07.031","article-title":"Effects of over 30-year of different fertilization regimes on fungal community compositions in the black soils of northeast China","volume":"248","author":"Hu","year":"2017","journal-title":"Agric. Ecosyst. Environ."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"8107","DOI":"10.3390\/rs70608107","article-title":"Spatial variability mapping of crop residue using Hyperion (EO-1) hyperspectral data","volume":"7","author":"Bannari","year":"2015","journal-title":"Remote Sens."},{"key":"ref_5","first-page":"341","article-title":"Measuring crop residue cover","volume":"36","author":"Laflen","year":"1981","journal-title":"J. Soil Water Conserv."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1016\/j.landusepol.2008.02.001","article-title":"Adoption of conservation agriculture in Europe: Lessons of the KASSA project","volume":"27","author":"Lahmar","year":"2010","journal-title":"Land Use Policy"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"753","DOI":"10.2134\/agronj1991.00021962008300040020x","article-title":"Reflectances from four wheat residue cover densities as influenced by three soil backgrounds","volume":"83","author":"Aase","year":"1991","journal-title":"Agron. J."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"125938","DOI":"10.1016\/j.eja.2019.125938","article-title":"Super-resolution enhancement of Sentinel-2 image for retrieving LAI and chlorophyll content of summer corn","volume":"111","author":"Zhang","year":"2019","journal-title":"Eur. J. Agron."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Su, W., Zhang, M.Z., Bian, D.H., Liu, Z., Huang, J.X., Wang, W., Wu, J.Y., and Guo, H. (2019). Phenotyping of corn plants using unmanned aerial vehicle (UAV) images. Remote Sens., 11.","DOI":"10.3390\/rs11172021"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Quemada, M., and Daughtry, C.S.T. (2016). Spectral indices to improve crop residue cover estimation under varying moisture conditions. Remote Sens., 8.","DOI":"10.3390\/rs8080660"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1479","DOI":"10.13031\/trans.12172","article-title":"A Method for Reflectance Index Wavelength Selection from Moisture-Controlled Soil and Crop Residue Samples","volume":"60","author":"Hamidisepehr","year":"2017","journal-title":"Trans. ASABE"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"354","DOI":"10.1109\/JSTARS.2012.2194696","article-title":"Hyperspectral unmixing overview: Geometrical, statistical, and sparse regression-based approaches","volume":"5","author":"Omar","year":"2012","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1080\/07038992.1993.10874543","article-title":"Mapping corn residue cover on agricultural fields in oxford county, ontario, using thematic mapper","volume":"19","author":"Mcnairn","year":"1993","journal-title":"Can. J. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"14559","DOI":"10.3390\/rs71114559","article-title":"Estimation of maize residue cover using Landsat-8 OLI image spectral information and textural features","volume":"7","author":"Jin","year":"2015","journal-title":"Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Yue, J., Tian, Q., Dong, X., Xu, K., and Zhou, C. (2019). Using hyperspectral crop residue angle index to estimate maize and winter-wheat residue cover: A laboratory study. Remote Sens., 11.","DOI":"10.3390\/rs11070807"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1652","DOI":"10.2134\/agronj2012.0133","article-title":"Modeling the Effect of Moisture on the Reflectance of Crop Residues","volume":"104","author":"Wang","year":"2012","journal-title":"Agron. J."},{"key":"ref_17","first-page":"306","article-title":"A dynamic soil endmember spectrum selection approach for soil and crop residue linear spectral unmixing analysis","volume":"78","author":"Yue","year":"2019","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1016\/j.rse.2016.02.028","article-title":"A meta-analysis of remote sensing research on supervised pixel-based land-cover image classification processes: General guidelines for practitioners and future research","volume":"177","author":"Khatami","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"516","DOI":"10.1016\/j.rse.2012.01.003","article-title":"Image texture as a remotely sensed measure of vegetation structure","volume":"121","author":"Wood","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1109\/LGRS.2013.2246538","article-title":"Object detection in high-resolution remote sensing images using rotation invariant parts based model","volume":"11","author":"Zhang","year":"2013","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"45","DOI":"10.5589\/m02-004","article-title":"An analysis of co-occurrence texture statistics as a function of grey level quantization","volume":"28","author":"Clausi","year":"2002","journal-title":"Can. J. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1109\/36.905239","article-title":"A new approach for the morphological segmentation of high-resolution satellite imagery","volume":"39","author":"Pesaresi","year":"2001","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"7405","DOI":"10.1109\/TGRS.2016.2601622","article-title":"Learning rotation-invariant convolutional neural networks for object detection in VHR optical remote sensing images","volume":"54","author":"Cheng","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"2290","DOI":"10.1109\/TPAMI.2009.96","article-title":"Turbopixels: Fast superpixels using geometric flows","volume":"31","author":"Levinshtein","year":"2009","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.cviu.2017.03.007","article-title":"Superpixels: An evaluation of the state-of-the-art","volume":"166","author":"Stutz","year":"2018","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1016\/j.jvcir.2015.09.015","article-title":"Automated coronal hole segmentation from Solar EUV Images using the watershed transform","volume":"33","author":"Ciecholewski","year":"2015","journal-title":"J. Vis. Commun. Image Represent."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"925","DOI":"10.1109\/TPAMI.2009.71","article-title":"Watershed cuts: Thinnings, shortest path forests, and topological watersheds","volume":"32","author":"Cousty","year":"2009","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1016\/j.neucom.2013.09.058","article-title":"Geometric active curve for selective entropy optimization","volume":"139","author":"Gao","year":"2014","journal-title":"Neurocomputing"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"224","DOI":"10.1016\/j.sigpro.2016.12.021","article-title":"Active contours driven by region-scalable fitting and optimized Laplacian of Gaussian energy for image segmentation","volume":"134","author":"Ding","year":"2017","journal-title":"Signal Process."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Wang, X., Huang, J., Feng, Q., and Yin, D. (2020). Winter Wheat Yield Prediction at County Level and Uncertainty Analysis in Main Wheat-producing Regions of China with Deep Learning Approaches. Remote Sens., 12.","DOI":"10.3390\/rs12111744"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"107609","DOI":"10.1016\/j.agrformet.2019.06.008","article-title":"Assimilation of remote sensing into crop growth models: Current status and perspectives","volume":"276\u2013277","author":"Huang","year":"2019","journal-title":"Agric. For. Meteorol."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1349","DOI":"10.1109\/TGRS.2015.2478379","article-title":"Unsupervised deep feature extraction for remote sensing image classification","volume":"54","author":"Romero","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Liu, S., Ding, W., Liu, C., Liu, Y., Wang, Y., and Li, H. (2018). ERN: Edge Loss Reinforced Semantic Segmentation Network for Remote Sensing Images. Remote Sens., 10.","DOI":"10.3390\/rs10091339"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.isprsjprs.2018.04.014","article-title":"Algorithms for semantic segmentation of multispectral remote sensing imagery using deep learning","volume":"145","author":"Kemker","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., and Xiao, B. (2020). Deep high-resolution representation learning for visual recognition. IEEE Trans. Pattern Anal. Mach. Intell.","DOI":"10.1109\/TPAMI.2020.2983686"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"632","DOI":"10.4283\/JMAG.2020.25.4.632","article-title":"Deeplab v3+ Based Automatic Diagnosis Model for Dental X-ray: Preliminary Study","volume":"25","author":"Jung","year":"2020","journal-title":"J. Magn."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015, January 5\u20139). U-net: Convolutional networks for biomedical image segmentation. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Munich, Germany.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1016\/j.isprsjprs.2020.01.013","article-title":"Resunet-a: A deep learning framework for semantic segmentation of remotely sensed data","volume":"162","author":"Diakogiannis","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Zhou, Z.W., Siddiquee, M.M.R., Tajbakhsh, N., and Liang, J.M. (2018). Unet++: A nested u-net architecture for medical image segmentation. Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support, Springer.","DOI":"10.1007\/978-3-030-00889-5_1"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J.Y., and Kweon, I.S. (2018, January 8\u201314). Cbam: Convolutional block attention module. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Chen, L., Tian, X., Chai, G., Zhang, X., and Chen, E. (2021). A New CBAM-P-Net Model for Few-Shot Forest Species Classification Using Airborne Hyperspectral Images. Remote Sens., 13.","DOI":"10.3390\/rs13071269"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"4594","DOI":"10.1109\/TIP.2019.2910052","article-title":"Multi-level semantic feature augmentation for one-shot learning","volume":"28","author":"Chen","year":"2019","journal-title":"IEEE Trans. Image Process."},{"key":"ref_43","first-page":"224","article-title":"Evaluation of optical remote sensing models for crop residue cover assessment","volume":"59","author":"Thoma","year":"2004","journal-title":"J. Soil Water Conserv."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1016\/j.still.2005.11.013","article-title":"Remote sensing of crop residue cover and soil tillage intensity","volume":"91","author":"Daughtry","year":"2006","journal-title":"Soil Tillage Res."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"688","DOI":"10.1109\/TGRS.2010.2059706","article-title":"Modeling and classifying hyperspectral imagery by CRFs with sparse higher order potentials","volume":"49","author":"Zhong","year":"2010","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Zhang, P., Ke, Y., Zhang, Z., Wang, M., Li, P., and Zhang, S. (2018). Urban land use and land cover classification using novel deep learning models based on high spatial resolution satellite imagery. Sensors, 18.","DOI":"10.3390\/s18113717"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Garg, L., Shukla, P., Singh, S.K., Bajpai, V., and Yadav, U. (2019, January 25\u201327). Land Use Land Cover Classification from Satellite Imagery using mUnet: A Modified Unet Architecture. Proceedings of the VISIGRAPP (4: VISAPP), Prague, Czech Republic.","DOI":"10.5220\/0007370603590365"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1254","DOI":"10.1109\/34.730558","article-title":"A model of saliency-based visual attention for rapid scene analysis","volume":"20","author":"Itti","year":"1998","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_49","first-page":"1243","article-title":"Learning to combine foveal glimpses with a third-order boltzmann machine","volume":"23","author":"Larochelle","year":"2010","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"5455","DOI":"10.1007\/s10462-020-09825-6","article-title":"A survey of the recent architectures of deep convolutional neural networks","volume":"53","author":"Khan","year":"2020","journal-title":"Artif. Intell. Rev."},{"key":"ref_51","first-page":"448","article-title":"Batch normalization: Accelerating deep network training by reducing internal covariate shift","volume":"37","author":"Ioffe","year":"2015","journal-title":"Int. Conf. Mach. Learn."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Buslaev, A., Iglovikov, V.I., Khvedchenya, E., Parinov, A., Druzhinin, M., and Kalinin, A.A. (2020). Albumentations: Fast and flexible image augmentations. Information, 11.","DOI":"10.3390\/info11020125"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/0034-4257(91)90048-B","article-title":"A review of assessing the accuracy of classifications of remotely sensed data","volume":"37","author":"Congalton","year":"1991","journal-title":"Remote Sens. Environ."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Du, Z.R., Yang, J.Y., Ou, C., and Zhang, T.T. (2019). Smallholder crop area mapped with a semantic segmentation deep learning method. Remote Sens., 11.","DOI":"10.3390\/rs11070888"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Zheng, S., Jayasumana, S., Romera-Paredes, B., Vineet, V., Su, Z.Z., Du, D.L., and Torr, P.H. (2015, January 11\u201318). Conditional random fields as recurrent neural networks. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.179"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1007\/BF00994018","article-title":"Support-vector networks","volume":"20","author":"Cortes","year":"1995","journal-title":"Mach. Learn."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1016\/S1364-8152(99)00007-9","article-title":"Neural networks for the prediction and forecasting of water resources variables: A review of modelling issues and applications","volume":"15","author":"Maier","year":"2000","journal-title":"Environ. Model. Softw."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"1146","DOI":"10.1111\/gcbb.12622","article-title":"A spatiotemporal assessment of field residues of rice, maize, and wheat at provincial and county levels in China","volume":"11","author":"Lin","year":"2019","journal-title":"GCB Bioenergy"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"95","DOI":"10.2134\/agronj2017.06.0307","article-title":"Response of maize root growth to residue management strategies","volume":"110","author":"Gao","year":"2018","journal-title":"Agron. J."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"4500","DOI":"10.1038\/s41598-018-22822-8","article-title":"Effect of tillage and crop residue on soil temperature following planting for a Black soil in Northeast China","volume":"8","author":"Shen","year":"2018","journal-title":"Sci. Rep."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"322","DOI":"10.1017\/S174217051900005X","article-title":"Effects of maize residue and mineral nitrogen applications on maize yield in conservation-agriculture-based cropping systems of Southern Africa","volume":"35","author":"Mupangwa","year":"2020","journal-title":"Renew. Agric. Food Syst."},{"key":"ref_62","unstructured":"Gao, T.Y., Han, X., Liu, Z.Y., and Sun, M.S. (February, January 27). Hybrid attention-based prototypical networks for noisy few-shot relation classification. Proceedings of the AAAI Conference on Artificial Intelligence, Honolulu, HI, USA."},{"key":"ref_63","first-page":"65","article-title":"Land cover classification of polarimetric SAR with fully convolution network and conditional random field","volume":"49","author":"Zhao","year":"2020","journal-title":"Acta Geod. Cartogr. Sin."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"126","DOI":"10.1016\/j.rse.2003.10.023","article-title":"Assessing crop residue cover using shortwave infrared reflectance","volume":"90","author":"Daughtry","year":"2004","journal-title":"Remote Sens. Environ."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/15\/2903\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:34:15Z","timestamp":1760164455000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/15\/2903"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,7,23]]},"references-count":64,"journal-issue":{"issue":"15","published-online":{"date-parts":[[2021,8]]}},"alternative-id":["rs13152903"],"URL":"https:\/\/doi.org\/10.3390\/rs13152903","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,7,23]]}}}