{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T01:16:45Z","timestamp":1760231805565,"version":"build-2065373602"},"reference-count":45,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2022,10,3]],"date-time":"2022-10-03T00:00:00Z","timestamp":1664755200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of Guangdong","award":["2020A1515011409","2022KJ147","202201010273","2021B0101190003","2021A1414030004","U1801263","U2001201","2020B1212060069"],"award-info":[{"award-number":["2020A1515011409","2022KJ147","202201010273","2021B0101190003","2021A1414030004","U1801263","U2001201","2020B1212060069"]}]},{"name":"Provincial Agricultural Science and Technology Innovation and Extension Project of Guangdong Province","award":["2020A1515011409","2022KJ147","202201010273","2021B0101190003","2021A1414030004","U1801263","U2001201","2020B1212060069"],"award-info":[{"award-number":["2020A1515011409","2022KJ147","202201010273","2021B0101190003","2021A1414030004","U1801263","U2001201","2020B1212060069"]}]},{"name":"Guangzhou Fundamental and Applied Research","award":["2020A1515011409","2022KJ147","202201010273","2021B0101190003","2021A1414030004","U1801263","U2001201","2020B1212060069"],"award-info":[{"award-number":["2020A1515011409","2022KJ147","202201010273","2021B0101190003","2021A1414030004","U1801263","U2001201","2020B1212060069"]}]},{"name":"Key-Area Research and Development Program of Guangdong Province","award":["2020A1515011409","2022KJ147","202201010273","2021B0101190003","2021A1414030004","U1801263","U2001201","2020B1212060069"],"award-info":[{"award-number":["2020A1515011409","2022KJ147","202201010273","2021B0101190003","2021A1414030004","U1801263","U2001201","2020B1212060069"]}]},{"name":"Special Project of Science and Technology Innovation Strategy of Guangdong Province","award":["2020A1515011409","2022KJ147","202201010273","2021B0101190003","2021A1414030004","U1801263","U2001201","2020B1212060069"],"award-info":[{"award-number":["2020A1515011409","2022KJ147","202201010273","2021B0101190003","2021A1414030004","U1801263","U2001201","2020B1212060069"]}]},{"name":"Key Program of NSFC-Guangdong Joint Funds","award":["2020A1515011409","2022KJ147","202201010273","2021B0101190003","2021A1414030004","U1801263","U2001201","2020B1212060069"],"award-info":[{"award-number":["2020A1515011409","2022KJ147","202201010273","2021B0101190003","2021A1414030004","U1801263","U2001201","2020B1212060069"]}]},{"name":"Guangdong Provincial Key Laboratory of Cyber-Physical System","award":["2020A1515011409","2022KJ147","202201010273","2021B0101190003","2021A1414030004","U1801263","U2001201","2020B1212060069"],"award-info":[{"award-number":["2020A1515011409","2022KJ147","202201010273","2021B0101190003","2021A1414030004","U1801263","U2001201","2020B1212060069"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The use of multi-mode remote sensing data for biomass prediction is of potential value to aid planting management and yield maximization. In this study, an advanced biomass estimation approach for sugarcane fields is proposed based on multi-source remote sensing data. Since feature interpretability in agricultural data mining is significant, a feature extraction method of deep archetypal analysis (DAA) that has good model interpretability is introduced and aided by principal component analysis (PCA) for feature mining from the multi-mode multispectral and light detection and ranging (LiDAR) remote sensing data pertaining to sugarcane. In addition, an integrated regression model integrating random forest regression, support vector regression, K-nearest neighbor regression and deep network regression is developed after feature extraction by DAA to precisely predict biomass of sugarcane. In this study, the biomass prediction performance achieved using the proposed integrated learning approach is found to be predominantly better than that achieved by using conventional linear methods in all the time periods of plant growth. Of more significance, according to model interpretability of DAA, only a small set of informative features maintaining their physical meanings (four informative spectral indices and four key LiDAR metrics) can be extracted which eliminates the redundancy of multi-mode data and plays a vital role in accurate biomass prediction. Therefore, the findings in this study provide hands-on experience to planters with indications of the key or informative spectral or LiDAR metrics relevant to the biomass to adjust the corresponding planting management design.<\/jats:p>","DOI":"10.3390\/rs14194944","type":"journal-article","created":{"date-parts":[[2022,10,10]],"date-time":"2022-10-10T03:07:28Z","timestamp":1665371248000},"page":"4944","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Sugarcane Biomass Prediction with Multi-Mode Remote Sensing Data Using Deep Archetypal Analysis and Integrated Learning"],"prefix":"10.3390","volume":"14","author":[{"given":"Zhuowei","family":"Wang","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Guangdong University and Technology, Guangzhou 510006, China"}]},{"given":"Yusheng","family":"Lu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Guangdong University and Technology, Guangzhou 510006, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3360-1756","authenticated-orcid":false,"given":"Genping","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Guangdong University and Technology, Guangzhou 510006, China"},{"name":"Key Laboratory of Environment Change and Resources Use in Beibu Gulf, Ministry of Education, Nanning Normal University, Nanning 530001, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8219-9153","authenticated-orcid":false,"given":"Chuanliang","family":"Sun","sequence":"additional","affiliation":[{"name":"Institute of Agricultural Information, Jiangsu Academy of Agricultural Science, Nanjing 210014, China"}]},{"given":"Fuhua","family":"Zhang","sequence":"additional","affiliation":[{"name":"Beijing Aerospace TITAN Technology Co., Ltd., Beijing 100070, China"}]},{"given":"Su","family":"He","sequence":"additional","affiliation":[{"name":"College of Mechanical and Electrical Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,10,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"246","DOI":"10.1016\/j.isprsjprs.2017.05.003","article-title":"Predicting grain yield in rice using multi-temporal vegetation indices from UAV-based multispectral and digital imagery","volume":"130","author":"Zhou","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/j.rse.2016.10.005","article-title":"Development of methods to improve soybean yield estimation and predict plant maturity with an unmanned aerial vehicle based platform","volume":"187","author":"Yu","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"673","DOI":"10.3390\/rs2030673","article-title":"Application of vegetation indices for agricultural crop yield prediction using neural network techniques","volume":"2","author":"Panda","year":"2010","journal-title":"Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"178","DOI":"10.1016\/j.fcr.2014.05.001","article-title":"Predicting grain yield and protein content in wheat by fusing multi-sensor and multi-temporal remote-sensing images","volume":"164","author":"Wang","year":"2014","journal-title":"Field Crop. Res."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Wan, L., Cen, H., Zhu, J., Zhang, J., Zhu, Y., Sun, D., Du, X., Zhai, L., Weng, H., and Li, Y. (2020). Grain yield prediction of rice using multi-temporal UAV-based RGB and multispectral images and model transfer\u2014A case study of small farmlands in the South of China. Agric. For. Meteorol., 291.","DOI":"10.1016\/j.agrformet.2020.108096"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/0034-4257(79)90013-0","article-title":"Red and photographic infrared linear combinations for monitoring vegetation","volume":"8","author":"Tucker","year":"1979","journal-title":"Remote Sens. Environ."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1016\/j.rse.2016.06.016","article-title":"Spectral considerations for modeling yield of canola","volume":"184","author":"Sulik","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"da Silva, E.E., Baio, F.H.R., Teodoro, L.P.R., da Silva Junior, C.A., Borges, R.S., and Teodoro, P.E. (2020). UAV-multispectral and vegetation indices in soybean grain yield prediction based on in situ observation. Remote Sens. Appl. Soc. Environ., 18.","DOI":"10.1016\/j.rsase.2020.100318"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"10193","DOI":"10.3390\/rs61010193","article-title":"Assessing the performance of MODIS NDVI and EVI for seasonal crop yield forecasting at the ecodistrict scale","volume":"6","author":"Kouadio","year":"2014","journal-title":"Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1016\/j.isprsjprs.2017.10.011","article-title":"Unmanned Aerial System (UAS)-based phenotyping of soybean using multi-sensor data fusion and extreme learning machine","volume":"134","author":"Maimaitijiang","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Christiansen, M.P., Laursen, M.S., J\u00f8rgensen, R.N., Skovsen, S., and Gislum, R. (2017). Designing and testing a UAV mapping system for agricultural field surveying. Sensors, 17.","DOI":"10.3390\/s17122703"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Sofonia, J., Shendryk, Y., Phinn, S., Roelfsema, C., Kendoul, F., and Skocaj, D. (2019). Monitoring sugarcane growth response to varying nitrogen application rates: A comparison of UAV SLAM LiDAR and photogrammetry. Int. J. Appl. Earth Obs. Geoinf., 82.","DOI":"10.1016\/j.jag.2019.05.011"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Zhao, G., Sanchez-Azofeifa, A., Laakso, K., Sun, C., and Fei, L. (2021). Hyperspectral and Full-Waveform LiDAR Improve Mapping of Tropical Dry Forest\u2019s Successional Stages. Remote Sens., 13.","DOI":"10.3390\/rs13193830"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"de Almeida, C.T., Galvao, L.S., Ometto, J.P.H.B., Jacon, A.D., de Souza Pereira, F.R., Sato, L.Y., Lopes, A.P., de Alencastro Gra\u00e7a, P.M.L., de Jesus Silva, C.V., and Ferreira-Ferreira, J. (2019). Combining LiDAR and hyperspectral data for aboveground biomass modeling in the Brazilian Amazon using different regression algorithms. Remote Sens. Environ., 232.","DOI":"10.1016\/j.rse.2019.111323"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Cao, J., Zhang, Z., Tao, F., Zhang, L., Luo, Y., Han, J., and Li, Z. (2020). Identifying the contributions of multi-source data for winter wheat yield prediction in China. Remote Sens., 12.","DOI":"10.3390\/rs12050750"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1016\/j.isprsjprs.2020.02.013","article-title":"Above-ground biomass estimation and yield prediction in potato by using UAV-based RGB and hyperspectral imaging","volume":"162","author":"Li","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Zhang, N., Chen, M., Yang, F., Yang, C., Yang, P., Gao, Y., Shang, Y., and Peng, D. (2022). Forest Height Mapping Using Feature Selection and Machine Learning by Integrating Multi-Source Satellite Data in Baoding City, North China. Remote Sens., 14.","DOI":"10.3390\/rs14184434"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Shendryk, Y., Sofonia, J., Garrard, R., Rist, Y., Skocaj, D., and Thorburn, P. (2020). Fine-scale prediction of biomass and leaf nitrogen content in sugarcane using UAV LiDAR and multispectral imaging. Int. J. Appl. Earth Obs. Geoinf., 92.","DOI":"10.1016\/j.jag.2020.102177"},{"key":"ref_19","first-page":"1","article-title":"A Biologically Interpretable Two-Stage Deep Neural Network (BIT-DNN) for Vegetation Recognition From Hyperspectral Imagery","volume":"60","author":"Shi","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1109\/MGRS.2021.3064051","article-title":"Interpretable hyperspectral artificial intelligence: When nonconvex modeling meets hyperspectral remote sensing","volume":"9","author":"Hong","year":"2021","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1016\/j.neucom.2011.06.033","article-title":"Archetypal analysis for machine learning and data mining","volume":"80","author":"Hansen","year":"2012","journal-title":"Neurocomputing"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"338","DOI":"10.1080\/00401706.1994.10485840","article-title":"Archetypal analysis","volume":"36","author":"Cutler","year":"1994","journal-title":"Technometrics"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1007\/s10994-015-5498-8","article-title":"Probabilistic archetypal analysis","volume":"102","author":"Seth","year":"2016","journal-title":"Mach. Learn."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"805","DOI":"10.1007\/s11263-020-01390-3","article-title":"Learning extremal representations with deep archetypal analysis","volume":"129","author":"Keller","year":"2021","journal-title":"Int. J. Comput. Vis."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Xu, J.X., Ma, J., Tang, Y.N., Wu, W.X., Shao, J.H., Wu, W.B., Wei, S.Y., Liu, Y.F., Wang, Y.C., and Guo, H.Q. (2020). Estimation of sugarcane yield using a machine learning approach based on uav-lidar data. Remote Sens., 12.","DOI":"10.3390\/rs12172823"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Stateras, D., and Kalivas, D. (2020). Assessment of olive tree canopy characteristics and yield forecast model using high resolution UAV imagery. Agriculture, 10.","DOI":"10.3390\/agriculture10090385"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Wang, Y., Zhang, Z., Feng, L., Du, Q., and Runge, T. (2020). Combining multi-source data and machine learning approaches to predict winter wheat yield in the conterminous United States. Remote Sens., 12.","DOI":"10.3390\/rs12081232"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_29","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_30","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1007\/BF00153759","article-title":"Instance-based learning algorithms","volume":"6","author":"Aha","year":"1991","journal-title":"Mach. Learn."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Qin, Q., Ren, H., Sun, Y., Li, M., Zhang, T., and Ren, S. (2018). Optimal hyperspectral characteristics determination for winter wheat yield prediction. Remote Sens., 10.","DOI":"10.3390\/rs10122015"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Xu, C., Ding, Y., Zheng, X., Wang, Y., Zhang, R., Zhang, H., Dai, Z., and Xie, Q. (2022). A Comprehensive Comparison of Machine Learning and Feature Selection Methods for Maize Biomass Estimation Using Sentinel-1 SAR, Sentinel-2 Vegetation Indices, and Biophysical Variables. Remote Sens., 14.","DOI":"10.3390\/rs14164083"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Han, J., Zhang, Z., Cao, J., Luo, Y., Zhang, L., Li, Z., and Zhang, J. (2020). Prediction of winter wheat yield based on multi-source data and machine learning in China. Remote Sens., 12.","DOI":"10.3390\/rs12020236"},{"key":"ref_34","unstructured":"Haykin, S. (1999). Neural Networks, a Comprehensive Foundation, Prentice-Hall Inc."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Kross, A., Znoj, E., Callegari, D., Kaur, G., Sunohara, M., Lapen, D.R., and McNairn, H. (2020). Using artificial neural networks and remotely sensed data to evaluate the relative importance of variables for prediction of within-field corn and soybean yields. Remote Sens., 12.","DOI":"10.3390\/rs12142230"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1016\/j.fcr.2019.02.022","article-title":"Deep convolutional neural networks for rice grain yield estimation at the ripening stage using UAV-based remotely sensed images","volume":"235","author":"Yang","year":"2019","journal-title":"Field Crop. Res."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Feng, L., Zhang, Z., Ma, Y., Du, Q., Williams, P., Drewry, J., and Luck, B. (2020). Alfalfa yield prediction using UAV-based hyperspectral imagery and ensemble learning. Remote Sens., 12.","DOI":"10.3390\/rs12122028"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"2557","DOI":"10.1109\/TGRS.2019.2952319","article-title":"An adaptive multiview active learning approach for spectral\u2013spatial classification of hyperspectral images","volume":"58","author":"Zhang","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Fei, S., Hassan, M.A., He, Z., Chen, Z., Shu, M., Wang, J., Li, C., and Xiao, Y. (2021). Assessment of ensemble learning to predict wheat grain yield based on UAV-multispectral reflectance. Remote Sens., 13.","DOI":"10.3390\/rs13122338"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1016\/S0893-6080(05)80023-1","article-title":"Stacked generalization","volume":"5","author":"Wolpert","year":"1992","journal-title":"Neural Netw."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"717","DOI":"10.1016\/j.rse.2017.09.029","article-title":"Mapping forest change using stacked generalization: An ensemble approach","volume":"204","author":"Healey","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Feng, L., Li, Y., Wang, Y., and Du, Q. (2020). Estimating hourly and continuous ground-level PM2. 5 concentrations using an ensemble learning algorithm: The ST-stacking model. Atmos. Environ., 223.","DOI":"10.1016\/j.atmosenv.2019.117242"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1016\/j.agrformet.2019.03.010","article-title":"Integrating satellite and climate data to predict wheat yield in Australia using machine learning approaches","volume":"274","author":"Cai","year":"2019","journal-title":"Agric. For. Meteorol."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1016\/j.agrformet.2014.06.007","article-title":"A comparative analysis of multitemporal MODIS EVI and NDVI data for large-scale rice yield estimation","volume":"197","author":"Son","year":"2014","journal-title":"Agric. For. Meteorol."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1016\/0034-4257(83)90032-9","article-title":"Remote sensing estimators of potential and actual crop yield","volume":"13","author":"Hatfield","year":"1983","journal-title":"Remote Sens. Environ."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/19\/4944\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:46:00Z","timestamp":1760143560000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/19\/4944"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,3]]},"references-count":45,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2022,10]]}},"alternative-id":["rs14194944"],"URL":"https:\/\/doi.org\/10.3390\/rs14194944","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2022,10,3]]}}}