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Science and Technology Research Project","award":["222102110038"],"award-info":[{"award-number":["222102110038"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Crop chlorophyll content measuring plays a vital role in monitoring crop growth and optimizing agricultural inputs such as water and fertilizer. However, traditional methods for measuring chlorophyll content primarily rely on labor-intensive chemical analysis. These methods not only involve destructive sampling but also are time-consuming, often resulting in obtaining monitoring results after the optimal growth period of crops. Unmanned aerial vehicle (UAV) remote sensing technology offers the potential for rapidly acquiring chlorophyll content estimations over large areas. Currently, most studies only utilize single features from UAV data and employ traditional machine learning algorithms to estimate chlorophyll content, while the potential of multisource feature fusion and stacking ensemble learning in chlorophyll content estimation research remains largely unexplored. Therefore, this study collected UAV spectral features, thermal features, structural features, as well as chlorophyll content data during maize jointing, trumpet, and big trumpet stages, creating a multisource feature dataset. Subsequently, chlorophyll content estimation models were built based on four machine learning algorithms, namely, ridge regression (RR), light gradient boosting machine (LightGBM), random forest regression (RFR), and stacking ensemble learning. The research results demonstrate that (1) the multisource feature fusion approach achieves higher estimation accuracy compared to the single-feature method, with R2 ranging from 0.699 to 0.754 and rRMSE ranging from 8.36% to 9.47%; and (2) the stacking ensemble learning outperforms traditional machine learning algorithms in chlorophyll content estimation accuracy, particularly when combined with multisource feature fusion, resulting in the best estimation results. In summary, this study proves the effective improvement in chlorophyll content estimation accuracy through multisource feature fusion and stacking ensemble learning. The combination of these methods provides reliable estimation of chlorophyll content using UAV remote sensing technology and brings new insights to precision agriculture management in this field.<\/jats:p>","DOI":"10.3390\/rs15133454","type":"journal-article","created":{"date-parts":[[2023,7,10]],"date-time":"2023-07-10T00:47:35Z","timestamp":1688950055000},"page":"3454","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":37,"title":["Exploring Multisource Feature Fusion and Stacking Ensemble Learning for Accurate Estimation of Maize Chlorophyll Content Using Unmanned Aerial Vehicle Remote Sensing"],"prefix":"10.3390","volume":"15","author":[{"given":"Weiguang","family":"Zhai","sequence":"first","affiliation":[{"name":"Institute of Farmland Irrigation, Chinese Academy of Agricultural Sciences, Xinxiang 453002, China"},{"name":"School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454003, China"},{"name":"Key Laboratory of Water-Saving Irrigation Engineering, Ministry of Agriculture & Rural Affairs, Xinxiang 453002, China"},{"name":"Key Laboratory of Water-Saving Agriculture of Henan Province, Xinxiang 453002, China"}]},{"given":"Changchun","family":"Li","sequence":"additional","affiliation":[{"name":"School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454003, China"}]},{"given":"Qian","family":"Cheng","sequence":"additional","affiliation":[{"name":"Institute of Farmland Irrigation, Chinese Academy of Agricultural Sciences, Xinxiang 453002, China"},{"name":"Key Laboratory of Water-Saving Irrigation Engineering, Ministry of Agriculture & Rural Affairs, Xinxiang 453002, China"},{"name":"Key Laboratory of Water-Saving Agriculture of Henan Province, Xinxiang 453002, China"}]},{"given":"Fan","family":"Ding","sequence":"additional","affiliation":[{"name":"Institute of Farmland Irrigation, Chinese Academy of Agricultural Sciences, Xinxiang 453002, China"},{"name":"School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454003, China"},{"name":"Key Laboratory of Water-Saving Irrigation Engineering, Ministry of Agriculture & Rural Affairs, Xinxiang 453002, China"},{"name":"Key Laboratory of Water-Saving Agriculture of Henan Province, Xinxiang 453002, China"}]},{"given":"Zhen","family":"Chen","sequence":"additional","affiliation":[{"name":"Institute of Farmland Irrigation, Chinese Academy of Agricultural Sciences, Xinxiang 453002, China"},{"name":"Key Laboratory of Water-Saving Irrigation Engineering, Ministry of Agriculture & Rural Affairs, Xinxiang 453002, China"},{"name":"Key Laboratory of Water-Saving Agriculture of Henan Province, Xinxiang 453002, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,7,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1007\/s10994-019-05848-5","article-title":"An evaluation of machine-learning for predicting phenotype: Studies in yeast, rice, and wheat","volume":"109","author":"Grinberg","year":"2020","journal-title":"Mach. Learn."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Ye, H., Huang, W., Huang, S., Cui, B., Dong, Y., Guo, A., Ren, Y., and Jin, Y. (2020). Recognition of banana fusarium wilt based on UAV remote sensing. Remote Sens., 12.","DOI":"10.3390\/rs12060938"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Wang, J., Zhou, Q., Shang, J., Liu, C., Zhuang, T., Ding, J., Xian, Y., Zhao, L., Wang, W., and Zhou, G. (2021). UAV-and machine learning-based retrieval of wheat SPAD values at the overwintering stage for variety screening. Remote Sens., 13.","DOI":"10.3390\/rs13245166"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Qiao, L., Gao, D., Zhang, J., Li, M., Sun, H., and Ma, J. (2020). Dynamic influence elimination and chlorophyll content diagnosis of maize using UAV spectral imagery. Remote Sens., 12.","DOI":"10.3390\/rs12162650"},{"key":"ref_5","first-page":"187","article-title":"Retrieval of crop biophysical parameters from Sentinel-2 remote sensing imagery","volume":"80","author":"Xie","year":"2019","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_6","first-page":"102618","article-title":"Winter wheat SPAD estimation from UAV hyperspectral data using cluster-regression methods","volume":"105","author":"Yang","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"106671","DOI":"10.1016\/j.compag.2021.106671","article-title":"Monitoring maize canopy chlorophyll density under lodging stress based on UAV hyperspectral imagery","volume":"193","author":"Sun","year":"2022","journal-title":"Comput. Electron. Agric."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"105331","DOI":"10.1016\/j.compag.2020.105331","article-title":"Monitoring of sugar beet growth indicators using wide-dynamic-range vegetation index (WDRVI) derived from UAV multispectral images","volume":"171","author":"Cao","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Yang, H., Hu, Y., Zheng, Z., Qiao, Y., Zhang, K., Guo, T., and Chen, J. (2022). Estimation of Potato Chlorophyll Content from UAV Multispectral Images with Stacking Ensemble Algorithm. Agronomy, 12.","DOI":"10.3390\/agronomy12102318"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"106775","DOI":"10.1016\/j.compag.2022.106775","article-title":"UAV-based chlorophyll content estimation by evaluating vegetation index responses under different crop coverages","volume":"196","author":"Qiao","year":"2022","journal-title":"Comput. Electron. Agric."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"107089","DOI":"10.1016\/j.compag.2022.107089","article-title":"Remote-sensing estimation of potato above-ground biomass based on spectral and spatial features extracted from high-definition digital camera images","volume":"198","author":"Liu","year":"2022","journal-title":"Comput. Electron. Agric."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1007\/s11119-022-09938-8","article-title":"UAV-based multi-sensor data fusion and machine learning algorithm for yield prediction in wheat","volume":"24","author":"Fei","year":"2022","journal-title":"Precis. Agric."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"9802585","DOI":"10.34133\/2022\/9802585","article-title":"Application of UAV Multisensor Data and Ensemble Approach for High-Throughput Estimation of Maize Phenotyping Traits","volume":"2022","author":"Shu","year":"2022","journal-title":"Plant Phenomics"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1768","DOI":"10.1007\/s11119-021-09811-0","article-title":"Optimization of multi-source UAV RS agro-monitoring schemes designed for field-scale crop phenotyping","volume":"22","author":"Zhu","year":"2021","journal-title":"Precis. Agric."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Lu, B., and He, Y. (2019). Evaluating empirical regression, machine learning, and radiative transfer modelling for estimating vegetation chlorophyll content using bi-seasonal hyperspectral images. Remote Sens., 11.","DOI":"10.3390\/rs11171979"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Guo, Y., Yin, G., Sun, H., Wang, H., Chen, S., Senthilnath, J., Wang, J., and Fu, Y. (2020). Scaling effects on chlorophyll content estimations with RGB camera mounted on a UAV platform using machine-learning methods. Sensors, 20.","DOI":"10.3390\/s20185130"},{"key":"ref_17","first-page":"100235","article-title":"Chlorophyll estimation using multi-spectral unmanned aerial system based on machine learning techniques","volume":"15","author":"Singhal","year":"2019","journal-title":"Remote Sens. Appl.-Soc. Environ."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"6985","DOI":"10.1109\/JSTARS.2022.3200735","article-title":"Combining Sentinel-1 and-3 Imagery for Retrievals of Regional Multitemporal Biophysical Parameters Under a Deep Learning Framework","volume":"15","author":"Han","year":"2022","journal-title":"IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens."},{"key":"ref_19","first-page":"1","article-title":"Deep spatial-spectral global reasoning network for hyperspectral image denoising","volume":"60","author":"Cao","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"369","DOI":"10.1109\/JSTARS.2022.3225150","article-title":"Improved Swin Transformer-Based Semantic Segmentation of Postearthquake Dense Buildings in Urban Areas Using Remote Sensing Images","volume":"16","author":"Cui","year":"2022","journal-title":"IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TGRS.2020.3040277","article-title":"Convolutional neural networks for multimodal remote sensing data classification","volume":"60","author":"Wu","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"5514415","DOI":"10.1109\/TGRS.2023.3284671","article-title":"Extended Vision Transformer (ExViT) for Land Use and Land Cover Classification: A Multimodal Deep Learning Framework","volume":"61","author":"Yao","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Shao, J., Tang, L., Liu, M., Shao, G., Sun, L., and Qiu, Q. (2020). BDD-Net: A general protocol for mapping buildings damaged by a wide range of disasters based on satellite imagery. Remote Sens., 12.","DOI":"10.3390\/rs12101670"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1016\/j.isprsjprs.2021.05.011","article-title":"Multimodal remote sensing benchmark datasets for land cover classification with a shared and specific feature learning model","volume":"178","author":"Hong","year":"2021","journal-title":"ISPRS-J. Photogramm. Remote Sens."},{"key":"ref_25","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_26","doi-asserted-by":"crossref","first-page":"4026","DOI":"10.3390\/rs70404026","article-title":"Evaluating multispectral images and vegetation indices for precision farming applications from UAV images","volume":"7","author":"Candiago","year":"2015","journal-title":"Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"562","DOI":"10.3390\/rs2020562","article-title":"Value of using different vegetative indices to quantify agricultural crop characteristics at different growth stages under varying management practices","volume":"2","author":"Hatfield","year":"2010","journal-title":"Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1532","DOI":"10.3389\/fpls.2017.01532","article-title":"Multi-spectral imaging from an unmanned aerial vehicle enables the assessment of seasonal leaf area dynamics of sorghum breeding lines","volume":"8","author":"Potgieter","year":"2017","journal-title":"Front. Plant Sci."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"663","DOI":"10.2307\/1936256","article-title":"Derivation of leaf-area index from quality of light on the forest floor","volume":"50","author":"Jordan","year":"1969","journal-title":"Ecology"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1016\/j.fcr.2013.12.018","article-title":"Improving estimation of summer maize nitrogen status with red edge-based spectral vegetation indices","volume":"157","author":"Li","year":"2014","journal-title":"Field Crop. Res."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"375","DOI":"10.1016\/0034-4257(94)00114-3","article-title":"Estimating PAR absorbed by vegetation from bidirectional reflectance measurements","volume":"51","author":"Roujean","year":"1995","journal-title":"Remote Sens. Environ."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1016\/S0034-4257(96)00072-7","article-title":"Use of a green channel in remote sensing of global vegetation from EOS-MODIS","volume":"58","author":"Gitelson","year":"1996","journal-title":"Remote Sens. Environ."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1016\/S0034-4257(01)00332-7","article-title":"Deriving green crop area index and canopy chlorophyll density of winter wheat from spectral reflectance data","volume":"81","author":"Broge","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1078\/0176-1617-00887","article-title":"Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves","volume":"160","author":"Gitelson","year":"2003","journal-title":"J. Plant Physiol."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/0034-4257(94)90134-1","article-title":"A modified soil adjusted vegetation index","volume":"48","author":"Qi","year":"1994","journal-title":"Remote Sens. Environ."},{"key":"ref_36","first-page":"309","article-title":"Monitoring vegetation systems in the Great Plains with ERTS","volume":"351","author":"Rouse","year":"1974","journal-title":"NASA Spec. Publ."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Gitelson, A.A., Vi\u00f1a, A., Ciganda, V., Rundquist, D.C., and Arkebauer, T.J. (2005). Remote estimation of canopy chlorophyll content in crops. Geophys. Res. Lett., 32.","DOI":"10.1029\/2005GL022688"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1388","DOI":"10.1016\/j.rse.2010.01.021","article-title":"The use of MERIS Terrestrial Chlorophyll Index to study spatio-temporal variation in vegetation phenology over India","volume":"114","author":"Dash","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1016\/j.agwat.2017.05.001","article-title":"Thermal imaging and passive reflectance sensing to estimate the water status and grain yield of wheat under different irrigation regimes","volume":"189","author":"Elsayed","year":"2017","journal-title":"Agric. Water Manag."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1016\/j.compag.2014.02.009","article-title":"Multi-temporal mapping of the vegetation fraction in early-season wheat fields using images from UAV","volume":"103","year":"2014","journal-title":"Comput. Electron. Agric."},{"key":"ref_41","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_42","first-page":"3149","article-title":"Lightgbm: A highly efficient gradient boosting decision tree","volume":"30","author":"Ke","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"106906","DOI":"10.1016\/j.agwat.2021.106906","article-title":"Mapping maize crop coefficient Kc using random forest algorithm based on leaf area index and UAV-based multispectral vegetation indices","volume":"252","author":"Shao","year":"2021","journal-title":"Agric. Water Manag."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1080\/00401706.1970.10488634","article-title":"Ridge regression: Biased estimation for nonorthogonal problems","volume":"12","author":"Hoerl","year":"1970","journal-title":"Technometrics"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"107530","DOI":"10.1016\/j.agwat.2022.107530","article-title":"Estimation of soil moisture content under high maize canopy coverage from UAV multimodal data and machine learning","volume":"264","author":"Cheng","year":"2022","journal-title":"Agric. Water Manag."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1016\/j.cj.2019.06.005","article-title":"Deep neural network algorithm for estimating maize biomass based on simulated Sentinel 2A vegetation indices and leaf area index","volume":"8","author":"Jin","year":"2020","journal-title":"Crop J."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"111599","DOI":"10.1016\/j.rse.2019.111599","article-title":"Soybean yield prediction from UAV using multimodal data fusion and deep learning","volume":"237","author":"Maimaitijiang","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1551","DOI":"10.1093\/plphys\/kiab322","article-title":"Estimating leaf area index using unmanned aerial vehicle data: Shallow vs. deep machine learning algorithms","volume":"187","author":"Liu","year":"2021","journal-title":"Plant Physiol."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Ding, F., Li, C., Zhai, W., Fei, S., Cheng, Q., and Chen, Z. (2022). Estimation of Nitrogen Content in Winter Wheat Based on Multi-Source Data Fusion and Machine Learning. Agriculture, 12.","DOI":"10.3390\/agriculture12111752"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"961","DOI":"10.1007\/s11119-021-09870-3","article-title":"Phenotyping a diversity panel of quinoa using UAV-retrieved leaf area index, SPAD-based chlorophyll and a random forest approach","volume":"23","author":"Jiang","year":"2022","journal-title":"Precis. Agric."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"106603","DOI":"10.1016\/j.compag.2021.106603","article-title":"Improving estimation of LAI dynamic by fusion of morphological and vegetation indices based on UAV imagery","volume":"192","author":"Qiao","year":"2022","journal-title":"Comput. Electron. Agric."},{"key":"ref_52","first-page":"102397","article-title":"Combining spectral and textural information in UAV hyperspectral images to estimate rice grain yield","volume":"102","author":"Wang","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"109057","DOI":"10.1016\/j.agrformet.2022.109057","article-title":"Combining multi-indicators with machine-learning algorithms for maize yield early prediction at the county-level in China","volume":"323","author":"Cheng","year":"2022","journal-title":"Agric. For. Meteorol."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Shah, S.H., Angel, Y., Houborg, R., Ali, S., and McCabe, M.F. (2019). A random forest machine learning approach for the retrieval of leaf chlorophyll content in wheat. Remote Sens., 11.","DOI":"10.3390\/rs11080920"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Yang, Y., Zhang, Q., Duan, R., Liu, J., Qin, Y., and Wang, X. (2022). Toward Multi-Stage Phenotyping of Soybean with Multimodal UAV Sensor Data: A Comparison of Machine Learning Approaches for Leaf Area Index Estimation. 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