{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T08:54:03Z","timestamp":1776329643220,"version":"3.50.1"},"reference-count":84,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2019,4,8]],"date-time":"2019-04-08T00:00:00Z","timestamp":1554681600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key R&amp;D Program of China","award":["2017YFE0122400"],"award-info":[{"award-number":["2017YFE0122400"]}]},{"name":"Hainan Provincial Key R&amp;D Program of China","award":["ZDYF2018073"],"award-info":[{"award-number":["ZDYF2018073"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41801338"],"award-info":[{"award-number":["41801338"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41601467"],"award-info":[{"award-number":["41601467"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41575111"],"award-info":[{"award-number":["41575111"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004739","name":"Youth Innovation Promotion Association CAS","doi-asserted-by":"publisher","award":["2017085"],"award-info":[{"award-number":["2017085"]}],"id":[{"id":"10.13039\/501100004739","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Agri-Tech in China Newton Network+ (ATCNN)","award":["Quzhou Integrated Platform"],"award-info":[{"award-number":["Quzhou Integrated Platform"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Monitoring and discriminating co-epidemic diseases and pests at regional scales are of practical importance in guiding differential treatment. A combination of vegetation and environmental parameters could improve the accuracy for discriminating crop diseases and pests. Different diseases and pests could cause similar stresses and symptoms during the same crop growth period, so combining growth period information can be useful for discerning different changes in crop diseases and pests. Additionally, problems associated with imbalanced data often have detrimental effects on the performance of image classification. In this study, we developed an approach for discriminating crop diseases and pests based on bi-temporal Landsat-8 satellite imagery integrating both crop growth and environmental parameters. As a case study, the approach was applied to data during a period of typical co-epidemic outbreak of winter wheat powdery mildew and aphids in the Shijiazhuang area of Hebei Province, China. Firstly, bi-temporal remotely sensed features characterizing growth indices and environmental factors were calculated based on two Landsat-8 images. The synthetic minority oversampling technique (SMOTE) algorithm was used to resample the imbalanced training data set before model construction. Then, a back propagation neural network (BPNN) based on a new training data set balanced by the SMOTE approach (SMOTE-BPNN) was developed to generate the regional wheat disease and pest distribution maps. The original training data set-based BPNN and support vector machine (SVM) methods were used for comparison and testing of the initial results. Our findings suggest that the proposed approach incorporating both growth and environmental parameters of different crop periods could distinguish wheat powdery mildew and aphids at the regional scale. The bi-temporal growth indices and environmental factors-based SMOTE-BPNN, BPNN, and SVM models all had an overall accuracy high than 80%. Meanwhile, the SMOTE-BPNN method had the highest G-means among the three methods. These results revealed that the combination of bi-temporal crop growth and environmental parameters is essential for improving the accuracy of the crop disease and pest discriminating models. The combination of SMOTE and BPNN could effectively improve the discrimination accuracy of the minor disease or pest.<\/jats:p>","DOI":"10.3390\/rs11070846","type":"journal-article","created":{"date-parts":[[2019,4,8]],"date-time":"2019-04-08T11:54:52Z","timestamp":1554724492000},"page":"846","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":53,"title":["Integrating Growth and Environmental Parameters to Discriminate Powdery Mildew and Aphid of Winter Wheat Using Bi-Temporal Landsat-8 Imagery"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5577-8632","authenticated-orcid":false,"given":"Huiqin","family":"Ma","sequence":"first","affiliation":[{"name":"Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science &amp; Technology, Nanjing 210044, China"},{"name":"Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1710-8301","authenticated-orcid":false,"given":"Wenjiang","family":"Huang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuanshu","family":"Jing","sequence":"additional","affiliation":[{"name":"Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science &amp; Technology, Nanjing 210044, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chenghai","family":"Yang","sequence":"additional","affiliation":[{"name":"USDA-ARS, Southern Plains Agricultural Research Center, College Station, TX 77845, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liangxiu","family":"Han","sequence":"additional","affiliation":[{"name":"School of Computing, Mathematics and Digital Technology, Manchester Metropolitan University, Manchester M1 5GD, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yingying","family":"Dong","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7836-497X","authenticated-orcid":false,"given":"Huichun","family":"Ye","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8424-6996","authenticated-orcid":false,"given":"Yue","family":"Shi","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qiong","family":"Zheng","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"College of Geoscience and Surveying Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4587-2489","authenticated-orcid":false,"given":"Linyi","family":"Liu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chao","family":"Ruan","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"School of Electronics and Information Engineering, Anhui University, Hefei 230601, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,4,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"407","DOI":"10.1007\/s001220051497","article-title":"Molecular mapping of the wheat powdery mildew resistance gene Pm24 and marker validation for molecular breeding","volume":"101","author":"Huang","year":"2000","journal-title":"Theor. Appl. Genet."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Luo, J., Wang, D., Dong, Y., Huang, W., and Wang, J. (2011, January 24\u201329). Developing an aphid damage hyperspectral index for detecting aphid (Hemiptera: Aphididae) damage levels in winter wheat. Proceedings of the Geoscience and Remote Sensing Symposium, Vancouver, BC, Canada.","DOI":"10.1109\/IGARSS.2011.6049456"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"4328","DOI":"10.1109\/JSTARS.2014.2315875","article-title":"Integrating Remotely Sensed and Meteorological Observations to Forecast Wheat Powdery Mildew at a Regional Scale","volume":"7","author":"Zhang","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"026025","DOI":"10.1117\/1.JRS.11.026025","article-title":"Evaluation of wavelet spectral features in pathological detection and discrimination of yellow rust and powdery mildew in winter wheat with hyperspectral reflectance data","volume":"11","author":"Shi","year":"2017","journal-title":"J. Appl. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"397","DOI":"10.1016\/j.cropro.2004.08.011","article-title":"A crop loss model and economic thresholds for the grain aphid, Sitobion avenae (F.), in winter wheat in southern Sweden","volume":"24","author":"Larsson","year":"2005","journal-title":"Crop Prot."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1007\/s11119-012-9283-4","article-title":"Evaluation of spectral indices and continuous wavelet analysis to;quantify aphid infestation in wheat","volume":"14","author":"Luo","year":"2013","journal-title":"Precis. Agric."},{"key":"ref_7","first-page":"676","article-title":"A new slow-release formulation of methyl salicylate optimizes the alternative control of Sitobion avenae (Fabricius) (Hemiptera: Aphididae) in wheat fields","volume":"3","author":"Wang","year":"2018","journal-title":"Pest Manag. Sci."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Shi, Y., Huang, W., Ye, H., Ruan, C., Xing, N., Geng, Y., Dong, Y., and Peng, D. (2018). Partial Least Square Discriminant Analysis Based on Normalized Two-Stage Vegetation Indices for Mapping Damage from Rice Diseases Using PlanetScope Datasets. Sensors, 18.","DOI":"10.3390\/s18061901"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"608","DOI":"10.1007\/s11119-016-9440-2","article-title":"Improved remote sensing detection of wheat powdery mildew using dual-green vegetation indices","volume":"17","author":"Feng","year":"2016","journal-title":"Precis. Agric."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1835","DOI":"10.2135\/cropsci1999.3961835x","article-title":"Leaf reflectance spectra of cereal aphid-damaged wheat","volume":"39","author":"Riedell","year":"1999","journal-title":"Crop Sci."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2516","DOI":"10.1109\/JSTARS.2013.2294961","article-title":"New Optimized Spectral Indices for Identifying and Monitoring Winter Wheat Diseases","volume":"7","author":"Huang","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"598","DOI":"10.1016\/j.ijleo.2016.11.206","article-title":"Feasibility assessment of multi-spectral satellite sensors in monitoring and discriminating wheat diseases and insects","volume":"131","author":"Yuan","year":"2017","journal-title":"Opt. Int. J. Light Electron Opt."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"3611","DOI":"10.3390\/rs6053611","article-title":"Damage mapping of powdery mildew in winter wheat with high-resolution satellite image","volume":"6","author":"Yuan","year":"2014","journal-title":"Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Ma, H., Jing, Y., Huang, W., Shi, Y., Dong, Y., Zhang, J., and Liu, L. (2018). Integrating Early Growth Information to Monitor Winter Wheat Powdery Mildew Using Multi-Temporal Landsat-8 Imagery. Sensors, 18.","DOI":"10.3390\/s18103290"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"608","DOI":"10.1166\/sl.2012.1829","article-title":"Discriminating wheat aphid damage degree using 2-dimensional feature space derived from landsat 5 TM","volume":"10","author":"Luo","year":"2012","journal-title":"Sens. Lett."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"16","DOI":"10.4236\/ars.2013.21003","article-title":"Remote monitoring of wheat streak mosaic progression using sub-pixel classification of Landsat 5 TM imagery for site specific disease management in winter wheat","volume":"2","author":"Mirik","year":"2013","journal-title":"Adv. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"322","DOI":"10.1016\/j.compag.2018.03.035","article-title":"Identification of purple spot disease on asparagus crops across spatial and spectral scales","volume":"148","author":"Navrozidis","year":"2018","journal-title":"Comput. Electron. Agric."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Zhang, J., Yuan, L., Nie, C., Wei, L., and Yang, G. (2014, January 11\u201314). Forecasting of powdery mildew disease with multi-sources of remote sensing information. Proceedings of the Third International Conference on Agro-Geoinformatics (Agro-Geoinformatics 2014), Beijing, China.","DOI":"10.1109\/Agro-Geoinformatics.2014.6910569"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1016\/j.ijleo.2017.06.071","article-title":"Habitat monitoring to evaluate crop disease and pest distributions based on multi-source satellite remote sensing imagery","volume":"145","author":"Yuan","year":"2017","journal-title":"Optik"},{"key":"ref_20","first-page":"162","article-title":"Remote sensing monitoring of wheat powdery mildew based on AdaBoost model combining mRMR algorithm","volume":"33","author":"Ma","year":"2017","journal-title":"Trans. Chin. Soc. Agric. Eng."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2236","DOI":"10.1080\/01431161.2012.743694","article-title":"Using WorldView-2 bands and indices to predict bronze bug (Thaumastocoris peregrinus) damage in plantation forests","volume":"34","author":"Oumar","year":"2013","journal-title":"Int. J. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"332","DOI":"10.1007\/s11119-015-9421-x","article-title":"Using high spatial resolution satellite imagery for mapping powdery mildew at a regional scale","volume":"17","author":"Yuan","year":"2016","journal-title":"Precis. Agric."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Zhang, C., Guo, J., and Lu, J. (2017, January 26\u201329). Research on Classification Method of High-Dimensional Class-Imbalanced Data Sets Based on SVM. Proceedings of the IEEE Second International Conference on Data Science in Cyberspace, Shenzhen, China.","DOI":"10.1109\/DSC.2017.63"},{"key":"ref_24","unstructured":"Provost, F. (2000, January 31). Machine learning from imbalanced data sets 101. Proceedings of the AAAI\u20192000 Workshop on Imbalanced Data Sets, Austin, TX, USA."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"720","DOI":"10.1016\/j.nonrwa.2005.04.006","article-title":"Evaluation of neural networks and data mining methods on a credit assessment task for class imbalance problem","volume":"7","author":"Huang","year":"2006","journal-title":"Nonlinear Anal. Real World Appl."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1613\/jair.953","article-title":"SMOTE: Synthetic minority over-sampling technique","volume":"16","author":"Chawla","year":"2002","journal-title":"J. Artif. Intell. Res."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1827016","DOI":"10.1155\/2017\/1827016","article-title":"A novel ensemble method for imbalanced data learning: Bagging of extrapolation-SMOTE SVM","volume":"2017","author":"Wang","year":"2017","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"539","DOI":"10.1109\/TSMCB.2008.2007853","article-title":"Exploratory undersampling for class-imbalance learning","volume":"39","author":"Liu","year":"2009","journal-title":"IEEE Trans. Syst. Man Cybern. Part B (Cybern.)"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/S0169-2070(97)00044-7","article-title":"Forecasting with artificial neural networks: The state of the art","volume":"14","author":"Zhang","year":"1998","journal-title":"Int. J. Forecast."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"855","DOI":"10.1016\/j.eswa.2014.08.018","article-title":"Back propagation neural network with adaptive differential evolution algorithm for time series forecasting","volume":"42","author":"Wang","year":"2015","journal-title":"Expert Syst. Appl."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1080\/10629360600564874","article-title":"Comparison of ARIMA, neural networks and hybrid models in time series: Tourist arrival forecasting","volume":"77","author":"Aslanargun","year":"2007","journal-title":"J. Stat. Comput. Simul."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"015501","DOI":"10.1063\/1.4940408","article-title":"Research and application of a hybrid forecasting model based on simulated annealing algorithm: A case study of wind speed forecasting","volume":"8","author":"Jiang","year":"2016","journal-title":"J. Renew. Sustain. Energy"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"970","DOI":"10.1016\/j.snb.2012.11.071","article-title":"Comparison of random forest, support vector machine and back propagation neural network for electronic tongue data classification: Application to the recognition of orange beverage and Chinese vinegar","volume":"177","author":"Liu","year":"2013","journal-title":"Sens. Actuators B Chem."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"and Kaur, G. (2016). Satellite Image Classification using Back Propagation Neural Network. Indian J. Sci. Technol., 9.","DOI":"10.17485\/ijst\/2016\/v9i45\/97437"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"983","DOI":"10.1016\/j.neucom.2015.05.026","article-title":"New algorithm for detection and fault classification on parallel transmission line using DWT and BPNN based on Clarke\u2019s transformation","volume":"168","author":"Zin","year":"2015","journal-title":"Neurocomputing"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1016\/j.geoderma.2010.03.001","article-title":"Comparison among principal component, partial least squares and back propagation neural network analyses for accuracy of measurement of selected soil properties with visible and near infrared spectroscopy","volume":"158","author":"Mouazen","year":"2010","journal-title":"Geoderma"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1979","DOI":"10.1016\/j.atmosenv.2011.01.022","article-title":"Ozone concentration forecast method based on genetic algorithm optimized back propagation neural networks and support vector machine data classification","volume":"45","author":"Feng","year":"2011","journal-title":"Atmos. Environ."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1016\/j.apgeochem.2016.05.018","article-title":"Application of hydrochemistry and stable isotopes (\u03b434S, \u03b418O and \u03b437Cl) to trace natural and anthropogenic influences on the quality of groundwater in the piedmont region, Shijiazhuang, China","volume":"71","author":"Zhou","year":"2016","journal-title":"Appl. Geochem."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1016\/j.fcr.2012.10.008","article-title":"Effects of potassium fertilization on winter wheat under different production practices in the North China Plain","volume":"140","author":"Niu","year":"2013","journal-title":"Field Crops Res."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Liu, L., Dong, Y., Huang, W., Du, X., Luo, J., Shi, Y., and Ma, H. (2019). Enhanced Regional Monitoring of Wheat Powdery Mildew Based on an Instance-Based Transfer Learning Method. Remote Sens., 11.","DOI":"10.3390\/rs11030298"},{"key":"ref_41","first-page":"68","article-title":"Occurrence and Critical Controlling Period of Wheat Aphids in Tangshan","volume":"6","author":"Wang","year":"2016","journal-title":"Heilongjiang Agric. Sci."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"190","DOI":"10.1097\/00010694-197809000-00011","article-title":"Compendium of wheat diseases","volume":"126","author":"Wiese","year":"1978","journal-title":"Soil Sci."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"609","DOI":"10.1094\/PHYTO-98-5-0609","article-title":"Disease-weather relationships for powdery mildew and yellow rust on winter wheat","volume":"98","author":"Paveley","year":"2008","journal-title":"Phytopathology"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Zhang, J., Pu, R., Yuan, L., Wang, J., Huang, W., and Yang, G. (2014). Monitoring powdery mildew of winter wheat by using moderate resolution multi-temporal satellite imagery. PLoS ONE, 9.","DOI":"10.1371\/journal.pone.0093107"},{"key":"ref_45","first-page":"6216","article-title":"Research on spatiotemporal pattern of crop phenological characteristics and cropping system in North China based on NDVI time series data","volume":"29","author":"Li","year":"2009","journal-title":"Acta Ecol. Sin."},{"key":"ref_46","first-page":"256","article-title":"Land cover mapping of winter wheat and clover using muti-temporal Landsat NIR band in a growing season","volume":"21","author":"Li","year":"2005","journal-title":"Trans. Chin. Soc. Agric. Eng."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"423","DOI":"10.1080\/2150704X.2014.915434","article-title":"Derivation of a tasselled cap transformation based on Landsat 8 at-satellite reflectance","volume":"5","author":"Baig","year":"2014","journal-title":"Remote Sens. Lett."},{"key":"ref_48","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_49","doi-asserted-by":"crossref","first-page":"1983","DOI":"10.1080\/01431160701355264","article-title":"Evaluation of MODIS derived perpendicular drought index for estimation of surface dryness over northwestern China","volume":"29","author":"Qin","year":"2008","journal-title":"Int. J. Remote Sens."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"355","DOI":"10.1023\/A:1007033503276","article-title":"Reflectance Indices Indicative of Changes in Water and Pigment Contents of Peanut and Wheat Leaves","volume":"36","author":"Inoue","year":"1999","journal-title":"Photosynthetica"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"2898","DOI":"10.3390\/rs5062898","article-title":"Relation between Seasonally Detrended Shortwave Infrared? Reflectance Data and Land Surface Moisture in Semi-Arid Sahel","volume":"5","author":"Olsen","year":"2013","journal-title":"Remote Sens."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1016\/S1360-1385(98)01213-8","article-title":"Visible and near-infrared reflectance techniques for diagnosing plant physiological status","volume":"3","author":"Filella","year":"1998","journal-title":"Trends Plant Sci."},{"key":"ref_53","unstructured":"Calero, A.D.T., Nieto, H., Guzinski, R., Mendiguren, G., Sandholt, I., and Berliner, P. (2012, January 22\u201327). Multi-scale approach of the surface temperature\/vegetation index triangle method for estimating evapotranspiration over heterogeneous landscapes. Proceedings of the EGU General Assembly, Vienna, Austria."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"893","DOI":"10.1016\/j.rse.2009.01.007","article-title":"Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI sensors","volume":"113","author":"Chander","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1080\/15481603.2015.1095471","article-title":"Spatiotemporal changes of urban impervious surface area and land surface temperature in Beijing from 1990 to 2014","volume":"53","author":"Hao","year":"2016","journal-title":"GISci. Remote Sens."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"368674","DOI":"10.1155\/2015\/368674","article-title":"Computer-aided lung nodule recognition by SVM classifier based on combination of random undersampling and SMOTE","volume":"2015","author":"Sui","year":"2015","journal-title":"Comput. Math. Methods Med."},{"key":"ref_57","unstructured":"Pears, R., Finlay, J., and Connor, A.M. (arXiv, 2014). Synthetic Minority over-sampling technique (SMOTE) for predicting software build outcomes, arXiv."},{"key":"ref_58","first-page":"1601","article-title":"MOA: Massive Online Analysis","volume":"11","author":"Bifet","year":"2010","journal-title":"J. Mach. Learn. Res."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"1097","DOI":"10.1109\/TSMCA.2009.2020804","article-title":"Evolutionary Sampling and Software Quality Modeling of High-Assurance Systems","volume":"39","author":"Drown","year":"2009","journal-title":"IEEE Trans. Syst. Man Cybern. Part A Syst. Hum."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1007\/s10462-011-9208-z","article-title":"An optimizing BP neural network algorithm based on genetic algorithm","volume":"36","author":"Ding","year":"2011","journal-title":"Artif. Intell. Rev."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Chen, K., Yang, S., and Batur, C. (2012, January 29\u201331). Effect of multi-hidden-layer structure on performance of BP neural network: Probe. Proceedings of the 2012 8th International Conference on Natural Computation, Chongqing, China.","DOI":"10.1109\/ICNC.2012.6234604"},{"key":"ref_62","first-page":"48","article-title":"Effect of Multi-hidden-layer on Performance of BP Neural Network","volume":"26","author":"Yang","year":"2013","journal-title":"J. Ningbo Univ."},{"key":"ref_63","unstructured":"Blum, A. (1992). Neural Networks in C++: An Object-Oriented Framework for Building Connectionist Systems, John Wiley & Sons, Inc."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1016\/j.jbiotec.2009.08.014","article-title":"Back propagation neural network (BPNN) prediction model and control strategies of methanogen phase reactor treating traditional Chinese medicine wastewater (TCMW)","volume":"144","author":"Shi","year":"2009","journal-title":"J. Biotechnol."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"606","DOI":"10.1016\/j.rse.2006.10.010","article-title":"Comparative assessment of the measures of thematic classification accuracy","volume":"107","author":"Liu","year":"2007","journal-title":"Remote Sens. Environ."},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Bekkar, M., Djemaa, H.K., and Alitouche, T.A. (2013). Evaluation measures for models assessment over imbalanced datasets. J. Inf. Eng. Appl., 3.","DOI":"10.5121\/ijdkp.2013.3402"},{"key":"ref_67","first-page":"76","article-title":"Improved AdaBoost algorithm based on multi-class unbalance classification","volume":"33","author":"Wu","year":"2018","journal-title":"J. Beijing Inf. Sci. Technol. Univ."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"861","DOI":"10.1016\/j.patrec.2005.10.010","article-title":"An introduction to ROC analysis","volume":"27","author":"Fawcett","year":"2006","journal-title":"Pattern Recognit. Lett."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"412","DOI":"10.1002\/sam.10061","article-title":"Roughly balanced bagging for imbalanced data","volume":"2","author":"Hido","year":"2009","journal-title":"Stat. Anal. Data Min. ASA Data Sci. J."},{"key":"ref_70","first-page":"397","article-title":"Accuracy assessment: A user\u2019s perspective","volume":"52","author":"Story","year":"1986","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_71","first-page":"223","article-title":"A coefficient of agreement as a measure of thematic classification accuracy","volume":"52","author":"Rosenfield","year":"1986","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.compag.2010.02.007","article-title":"A review of advanced techniques for detecting plant diseases","volume":"72","author":"Sankaran","year":"2010","journal-title":"Comput. Electron. Agric."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1007\/s00271-007-0072-1","article-title":"Identification and discrimination of water stress in wheat leaves (Triticum aestivum L.) by means of reflectance measurements","volume":"26","author":"Graeff","year":"2007","journal-title":"Irrig. Sci."},{"key":"ref_74","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_75","doi-asserted-by":"crossref","first-page":"305","DOI":"10.1016\/j.asr.2004.09.008","article-title":"Changes in spectral reflectance of wheat leaves in response to specific macronutrient deficiency","volume":"35","author":"Beyl","year":"2005","journal-title":"Adv. Space Res. Off. J. Comm. Space Res."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"370","DOI":"10.1016\/S0034-4257(03)00079-8","article-title":"Thermal remote sensing of urban climates","volume":"86","author":"Voogt","year":"2003","journal-title":"Remote Sens. Environ."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"168","DOI":"10.1016\/j.rse.2005.10.029","article-title":"Comparative analysis of urban reflectance and surface temperature","volume":"104","author":"Small","year":"2006","journal-title":"Remote Sens. Environ."},{"key":"ref_78","first-page":"256","article-title":"Bi-temporal characterization of land surface temperature in relation to impervious surface area, NDVI and NDBI, using a sub-pixel image analysis","volume":"11","author":"Zhang","year":"2009","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_79","doi-asserted-by":"crossref","unstructured":"Liu, Q., Guo, Y., Liu, G., and Zhao, J. (2014, January 19\u201321). Classification of Landsat 8 OLI image using support vector machine with Tasseled Cap Transformation. Proceedings of the International Conference on Natural Computation, Xiamen, China.","DOI":"10.1109\/ICNC.2014.6975915"},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"738","DOI":"10.1631\/jzus.2007.B0738","article-title":"Characterizing and estimating rice brown spot disease severity using stepwise regression, principal component regression and partial least-square regression","volume":"8","author":"Liu","year":"2007","journal-title":"J. Zhejiang Univ. Sci. B"},{"key":"ref_81","unstructured":"Kang, H. (2012). Dynamic and control of wheat powdery mildew in Xingtang County, Hebei Province. [Master\u2019s Thesis, Chinese Academy of Agricultural Sciences Dissertation]."},{"key":"ref_82","first-page":"1263","article-title":"Learning from imbalanced data","volume":"21","author":"He","year":"2008","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_83","unstructured":"Douzas, G., and Bacao, F. (arXiv, 2017). Geometric SMOTE: Effective oversampling for imbalanced learning through a geometric extension of SMOTE, arXiv."},{"key":"ref_84","doi-asserted-by":"crossref","unstructured":"Han, H., Wang, W.-Y., and Mao, B.-H. (2005, January 23\u201326). Borderline-SMOTE: A new over-sampling method in imbalanced data sets learning. Proceedings of the International Conference on Intelligent Computing, Hefei, China.","DOI":"10.1007\/11538059_91"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/7\/846\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:43:44Z","timestamp":1760186624000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/7\/846"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,4,8]]},"references-count":84,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2019,4]]}},"alternative-id":["rs11070846"],"URL":"https:\/\/doi.org\/10.3390\/rs11070846","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,4,8]]}}}