{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,8]],"date-time":"2026-01-08T04:20:35Z","timestamp":1767846035317,"version":"3.49.0"},"reference-count":84,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2021,5,5]],"date-time":"2021-05-05T00:00:00Z","timestamp":1620172800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the National Funds for Distinguished Young youths","award":["No. 42025101"],"award-info":[{"award-number":["No. 42025101"]}]},{"name":"the National Key Research and Development Program of China","award":["No.2017YFA06036001"],"award-info":[{"award-number":["No.2017YFA06036001"]}]},{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["No. 31770516"],"award-info":[{"award-number":["No. 31770516"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"the 111 Project","award":["No. B18006"],"award-info":[{"award-number":["No. B18006"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>With the recent developments of unmanned aerial vehicle (UAV) remote sensing, it is possible to monitor the growth condition of trees with the high temporal and spatial resolutions of data. In this study, the daily high-throughput RGB images of pear trees were captured from a UAV platform. A new index was generated by integrating the spectral and textural information using the improved adaptive feature weighting method (IAFWM). The inter-relationships of the air climatic variables and the soil\u2019s physical properties (temperature, humidity and conductivity) were firstly assessed using principal component analysis (PCA). The climatic variables were selected to independently build a linear regression model with the new index when the cumulative variance explained reached 99.53%. The coefficient of determination (R2) of humidity (R2 = 0.120, p = 0.205) using linear regression analysis was the dominating influencing factor for the growth of the pear trees, among the air climatic variables tested. The humidity (%) in 40 cm depth of soil (R2 = 0.642, p &lt; 0.001) using a linear regression coefficient was the largest among climatic variables in the soil. The impact of climatic variables on the soil was commonly greater than those in the air, and the R2 grew larger with the increasing depth of soil. The effects of the fluctuation of the soil-climatic variables on the pear trees\u2019 growth could be detected using the sliding window method (SWM), and the maximum absolute value of coefficients with the corresponding day of year (DOY) of air temperature, soil temperature, soil humidity, and soil conductivity were confirmed as 221, 227, 228, and 226 (DOY), respectively. Thus, the impact of the fluctuation of climatic variables on the growth of pear trees can last 14, 8, 7, and 9 days, respectively. Therefore, it is highly recommended that the adoption of the integrated new index to explore the long-time impact of climate on pears growth be undertaken.<\/jats:p>","DOI":"10.3390\/rs13091795","type":"journal-article","created":{"date-parts":[[2021,5,5]],"date-time":"2021-05-05T22:51:42Z","timestamp":1620255102000},"page":"1795","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Integrating Spectral and Textural Information for Monitoring the Growth of Pear Trees Using Optical Images from the UAV Platform"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0099-0759","authenticated-orcid":false,"given":"Yahui","family":"Guo","sequence":"first","affiliation":[{"name":"College of Water Sciences, Beijing Normal University, Beijing 100875, China"}]},{"given":"Shouzhi","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Water Sciences, Beijing Normal University, Beijing 100875, China"}]},{"given":"Zhaofei","family":"Wu","sequence":"additional","affiliation":[{"name":"College of Water Sciences, Beijing Normal University, Beijing 100875, China"}]},{"given":"Shuxin","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Water Sciences, Beijing Normal University, Beijing 100875, China"}]},{"given":"Christopher","family":"Robin Bryant","sequence":"additional","affiliation":[{"name":"D\u00e9partement de G\u00e9ographie, Universit\u00e9 de Montr\u00e9al, Montr\u00e9al, QC H2V2B8, Canada"},{"name":"The School of Environmental Design and Rural Development, University of Guelph, Guelph, ON N1G 2W1, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1737-7985","authenticated-orcid":false,"given":"Jayavelu","family":"Senthilnath","sequence":"additional","affiliation":[{"name":"Institute for Infocomm Research, Agency for Science, Technology and Research (A*STAR), Singapore 138632, Singapore"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8299-324X","authenticated-orcid":false,"given":"Mario","family":"Cunha","sequence":"additional","affiliation":[{"name":"Institute for Systems and Computer Engineering, Technology and Science (INESC TEC), Campus da Faculdade de Engenharia da Universidade do Porto, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9761-5292","authenticated-orcid":false,"given":"Yongshuo H.","family":"Fu","sequence":"additional","affiliation":[{"name":"College of Water Sciences, Beijing Normal University, Beijing 100875, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,5,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1038\/nature15402","article-title":"Declining global warming effects on the phenology of spring leaf unfolding","volume":"526","author":"Fu","year":"2015","journal-title":"Nature"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1198","DOI":"10.1016\/j.foreco.2010.07.013","article-title":"Addressing climate change in the forest vegetation simulator to assess impacts on landscape forest dynamics","volume":"260","author":"Crookston","year":"2010","journal-title":"For. Ecol. Manag."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Keane, R.E., Mahalovich, M.F., Bollenbacher, B.L., Manning, M.E., Loehman, R.A., Jain, T.B., Holsinger, L.M., and Larson, A.J. (2018). Effects of climate change on forest vegetation in the Northern Rockies. Climate Change and Rocky Mountain Ecosystems, Springer.","DOI":"10.1007\/978-3-319-56928-4_5"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1016\/j.agrformet.2015.11.001","article-title":"Timing of rice maturity in China is affected more by transplanting date than by climate change","volume":"216","author":"Zhao","year":"2016","journal-title":"Agric. For. Meteorol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"926","DOI":"10.1111\/j.1469-8137.2011.03803.x","article-title":"Leaf-out phenology of temperate woody plants: From trees to ecosystems","volume":"191","author":"Polgar","year":"2011","journal-title":"New Phytol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1016\/j.tree.2007.04.003","article-title":"Shifting plant phenology in response to global change","volume":"22","author":"Cleland","year":"2007","journal-title":"Trends Ecol. Evol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1922","DOI":"10.1111\/gcb.14619","article-title":"Plant phenology and global climate change: Current progresses and challenges","volume":"25","author":"Piao","year":"2019","journal-title":"Glob. Chang. Biol."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Zhang, J., Cheng, X., Jin, Q., Su, X., Li, M., Yan, C., Jiao, X., Li, D., Lin, Y., and Cai, Y. (2017). Comparison of the transcriptomic analysis between two Chinese white pear (Pyrus bretschneideri Rehd.) genotypes of different stone cells contents. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0187114"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Zhu, W., Sun, Z., Peng, J., Huang, Y., Li, J., Zhang, J., Yang, B., and Liao, X. (2019). Estimating maize above-ground biomass using 3D point clouds of multi-source unmanned aerial vehicle data at multi-spatial scales. Remote Sens., 11.","DOI":"10.3390\/rs11222678"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Guo, Y., Senthilnath, J., Wu, W., Zhang, X., Zeng, Z., and Huang, H. (2019). Radiometric calibration for multispectral camera of different imaging conditions mounted on a UAV platform. Sustainability, 11.","DOI":"10.3390\/su11040978"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1016\/j.compag.2017.05.027","article-title":"Application of UAV imaging platform for vegetation analysis based on spectral-spatial methods","volume":"140","author":"Senthilnath","year":"2017","journal-title":"Comput. Electron. Agric."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Fu, Y., Yang, G., Song, X., Li, Z., Xu, X., Feng, H., and Zhao, C. (2021). Improved Estimation of Winter Wheat Aboveground Biomass Using Multiscale Textures Extracted from UAV-Based Digital Images and Hyperspectral Feature Analysis. Remote Sens., 13.","DOI":"10.3390\/rs13040581"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Aasen, H., Honkavaara, E., Lucieer, A., and Zarco-Tejada, P.J. (2018). Quantitative remote sensing at ultra-high resolution with UAV spectroscopy: A review of sensor technology, measurement procedures, and data correction workflows. Remote Sens., 10.","DOI":"10.3390\/rs10071091"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"6880","DOI":"10.3390\/rs5126880","article-title":"Using unmanned aerial vehicles (UAV) for high-resolution reconstruction of topography: The structure from motion approach on coastal environments","volume":"5","author":"Mancini","year":"2013","journal-title":"Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Jin, X., Zarco-Tejada, P., Schmidhalter, U., Reynolds, M.P., Hawkesford, M.J., Varshney, R.K., Yang, T., Nie, C., Li, Z., and Ming, B. (2020). High-throughput estimation of crop traits: A review of ground and aerial phenotyping platforms. IEEE Geosci. Remote Sens. Lett., 1\u201333.","DOI":"10.1109\/MGRS.2020.2998816"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Guo, Y., Wang, H., Wu, Z., Wang, S., Sun, H., Senthilnath, J., Wang, J., Robin Bryant, C., and Fu, Y. (2020). Modified Red Blue Vegetation Index for Chlorophyll Estimation and Yield Prediction of Maize from Visible Images Captured by UAV. Sensors, 20.","DOI":"10.3390\/s20185055"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s12518-013-0120-x","article-title":"UAV for 3D mapping applications: A review","volume":"6","author":"Nex","year":"2014","journal-title":"Appl. Geomat."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"105731","DOI":"10.1016\/j.compag.2020.105731","article-title":"A review on plant high-throughput phenotyping traits using UAV-based sensors","volume":"178","author":"Xie","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_19","first-page":"115","article-title":"Principal component analysis for hyperspectral image classification","volume":"62","author":"Rodarmel","year":"2002","journal-title":"Surv. Land Inf. Sci."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1109\/LGRS.2006.888109","article-title":"Hyperspectral image compression using JPEG2000 and principal component analysis","volume":"4","author":"Du","year":"2007","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Zhao, C., Luo, G., Wang, Y., Chen, C., and Wu, Z. (2021). UAV Recognition Based on Micro-Doppler Dynamic Attribute-Guided Augmentation Algorithm. Remote Sens., 13.","DOI":"10.3390\/rs13061205"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Ramos, L.P., Campos, A.B., Schwartz, C., Duarte, L.T., Alves, D.I., Pettersson, M.I., Vu, V.T., and Machado, R. (2021). A Wavelength-Resolution SAR Change Detection Method Based on Image Stack through Robust Principal Component Analysis. Remote Sens., 13.","DOI":"10.3390\/rs13050833"},{"key":"ref_23","first-page":"215","article-title":"New research methods for vegetation information extraction based on visible light remote sensing images from an unmanned aerial vehicle (UAV)","volume":"78","author":"Zhang","year":"2019","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Li, X., Li, X., Liu, W., Wei, B., and Xu, X. (2021). A UAV-based framework for crop lodging assessment. Eur. J. Agron., 123.","DOI":"10.1016\/j.eja.2020.126201"},{"key":"ref_25","first-page":"79","article-title":"Combining UAV-based plant height from crop surface models, visible, and near infrared vegetation indices for biomass monitoring in barley","volume":"39","author":"Bendig","year":"2015","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1016\/j.biosystemseng.2020.11.010","article-title":"Combining plant height, canopy coverage and vegetation index from UAV-based RGB images to estimate leaf nitrogen concentration of summer maize","volume":"202","author":"Lu","year":"2021","journal-title":"Biosyst. Eng."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1016\/j.compag.2013.10.010","article-title":"Winter wheat biomass estimation based on spectral indices, band depth analysis and partial least squares regression using hyperspectral measurements","volume":"100","author":"Fu","year":"2014","journal-title":"Comput. Electron. Agric."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"158","DOI":"10.1016\/S0034-4257(99)00067-X","article-title":"Hyperspectral vegetation indices and their relationships with agricultural crop characteristics","volume":"71","author":"Thenkabail","year":"2000","journal-title":"Remote Sens. Environ."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Wan, L., Li, Y., Cen, H., Zhu, J., Yin, W., Wu, W., Zhu, H., Sun, D., Zhou, W., and He, Y. (2018). Combining UAV-Based Vegetation Indices and Image Classification to Estimate Flower Number in Oilseed Rape. Remote Sens., 10.","DOI":"10.3390\/rs10091484"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41467-020-19520-3","article-title":"Impacts of irrigated agriculture on food\u2013energy\u2013water\u2013CO 2 nexus across metacoupled systems","volume":"11","author":"Xu","year":"2020","journal-title":"Nat. Commun."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1016\/j.envpol.2005.11.005","article-title":"Nitrogen balance and groundwater nitrate contamination: Comparison among three intensive cropping systems on the North China Plain","volume":"143","author":"Ju","year":"2006","journal-title":"Environ. Pollut."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"282","DOI":"10.1016\/j.compag.2008.03.009","article-title":"Verification of color vegetation indices for automated crop imaging applications","volume":"63","author":"Meyer","year":"2008","journal-title":"Comput. Electron. Agric."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1016\/j.rse.2017.06.007","article-title":"Estimates of plant density of wheat crops at emergence from very low altitude UAV imagery","volume":"198","author":"Jin","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1016\/j.compag.2015.01.008","article-title":"Detecting creeping thistle in sugar beet fields using vegetation indices","volume":"112","author":"Kazmi","year":"2015","journal-title":"Comput. Electron. Agric."},{"key":"ref_35","first-page":"35","article-title":"Assessment of rice leaf chlorophyll content using visible bands at different growth stages at both the leaf and canopy scale","volume":"32","author":"Saberioon","year":"2014","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1007\/s11119-005-6787-1","article-title":"Automated crop and weed monitoring in widely spaced cereals","volume":"7","author":"Hague","year":"2006","journal-title":"Precis. Agric."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"146","DOI":"10.1016\/j.foreco.2016.05.006","article-title":"Using excess greenness and green chromatic coordinate colour indices from aerial images to assess lodgepole pine vigour, mortality and disease occurrence","volume":"374","author":"Reid","year":"2016","journal-title":"For. Ecol. Manag."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1016\/j.cageo.2013.10.011","article-title":"An easy to use ArcMap based texture analysis program for extraction of flooded areas from TerraSAR-X satellite image","volume":"63","author":"Pradhan","year":"2014","journal-title":"Comput. Geosci."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1524","DOI":"10.1109\/TIP.2005.852204","article-title":"Adaptive perceptual color-texture image segmentation","volume":"14","author":"Chen","year":"2005","journal-title":"IEEE Trans. Image Process."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"688","DOI":"10.1016\/j.bspc.2013.06.011","article-title":"Shearlet-based texture feature extraction for classification of breast tumor in ultrasound image","volume":"8","author":"Zhou","year":"2013","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"3224","DOI":"10.1109\/JSTARS.2018.2851753","article-title":"Supervised and adaptive feature weighting for object-based classification on satellite images","volume":"11","author":"Chen","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"463","DOI":"10.1007\/s13042-019-00986-7","article-title":"Adaptive feature weighting for robust Lp-norm sparse representation with application to biometric image classification","volume":"11","author":"Zhu","year":"2020","journal-title":"Int. J. Mach. Learn. Cyber."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"2709","DOI":"10.1121\/1.5067373","article-title":"Automatic detection of fish sounds based on multi-stage classification including logistic regression via adaptive feature weighting","volume":"144","author":"Harakawa","year":"2018","journal-title":"J. Acoust. Soc. Am."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Zhao, H., Xu, L., Shi, S., Jiang, H., and Chen, D. (2018). A High Throughput Integrated Hyperspectral Imaging and 3D Measurement System. Sensors, 18.","DOI":"10.3390\/s18041068"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"397","DOI":"10.1016\/j.agrformet.2017.10.015","article-title":"Fine-scale perspectives on landscape phenology from unmanned aerial vehicle (UAV) photography","volume":"248","author":"Klosterman","year":"2018","journal-title":"Agric. For. Meteo."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"134","DOI":"10.1007\/s11119-017-9504-y","article-title":"Phenological analysis of unmanned aerial vehicle based time series of barley imagery with high temporal resolution","volume":"19","author":"Burkart","year":"2018","journal-title":"Precis. Agric."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Lisein, J., Michez, A., Claessens, H., and Lejeune, P. (2015). Discrimination of deciduous tree species from time series of unmanned aerial system imagery. PLoS ONE, 10.","DOI":"10.1371\/journal.pone.0141006"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"455","DOI":"10.1016\/j.rse.2006.09.014","article-title":"Monitoring yield and fruit quality parameters in open-canopy tree crops under water stress. Implications for ASTER","volume":"107","author":"Sobrino","year":"2007","journal-title":"Remote Sens. Environ."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Johansen, K., Raharjo, T., and McCabe, M.F. (2018). Using multi-spectral UAV imagery to extract tree crop structural properties and assess pruning effects. Remote Sens., 10.","DOI":"10.20944\/preprints201804.0198.v1"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Ramos, A.P.M., Osco, L.P., Furuya, D.E.G., Gon\u00e7alves, W.N., Santana, D.C., Teodoro, L.P.R., da Silva Junior, C.A., Capristo-Silva, G.F., Li, J., and Baio, F.H.R. (2020). A random forest ranking approach to predict yield in maize with uav-based vegetation spectral indices. Comput. Electron. Agric., 178.","DOI":"10.1016\/j.compag.2020.105791"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Maimaitijiang, M., Sagan, V., Sidike, P., Hartling, S., Esposito, F., and Fritschi, F.B. (2020). Soybean yield prediction from UAV using multimodal data fusion and deep learning. Remote Sens. Environ., 237.","DOI":"10.1016\/j.rse.2019.111599"},{"key":"ref_52","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_53","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13007-019-0507-8","article-title":"Remote estimation of rice LAI based on Fourier spectrum texture from UAV image","volume":"15","author":"Duan","year":"2019","journal-title":"Plant Methods"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Maimaitijiang, M., Sagan, V., Sidike, P., Daloye, A.M., Erkbol, H., and Fritschi, F.B. (2020). Crop Monitoring Using Satellite\/UAV Data Fusion and Machine Learning. Remote Sens., 12.","DOI":"10.3390\/rs12091357"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1016\/0034-4257(91)90009-U","article-title":"Potentials and limits of vegetation indices for LAI and APAR assessment","volume":"35","author":"Baret","year":"1991","journal-title":"Remote Sens. Environ."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"457","DOI":"10.1080\/17429145.2017.1392623","article-title":"Salicylic acid induced changes on antioxidant capacity, pigments and grain yield of soybean genotypes in water deficit condition","volume":"12","author":"Razmi","year":"2017","journal-title":"J. Plant Interact."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1016\/j.envexpbot.2017.06.001","article-title":"Distinct growth light and gibberellin regimes alter leaf anatomy and reveal their influence on leaf optical properties","volume":"140","author":"Falcioni","year":"2017","journal-title":"Environ. Exp. Bot."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Gr\u00fcner, E., Wachendorf, M., and Astor, T. (2020). The potential of UAV-borne spectral and textural information for predicting aboveground biomass and N fixation in legume-grass mixtures. PLoS ONE, 15.","DOI":"10.1371\/journal.pone.0234703"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Guo, A., Huang, W., Ye, H., Dong, Y., Ma, H., Ren, Y., and Ruan, C. (2020). Identification of wheat yellow rust using spectral and texture features of hyperspectral images. Remote Sens., 12.","DOI":"10.3390\/rs12091419"},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Liu, M., Yu, T., Gu, X., Sun, Z., Yang, J., Zhang, Z., Mi, X., Cao, W., and Li, J. (2020). The Impact of Spatial Resolution on the Classification of Vegetation Types in Highly Fragmented Planting Areas Based on Unmanned Aerial Vehicle Hyperspectral Images. Remote Sens., 12.","DOI":"10.3390\/rs12010146"},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Li, S., Yuan, F., Ata-UI-Karim, S.T., Zheng, H., Cheng, T., Liu, X., Tian, Y., Zhu, Y., Cao, W., and Cao, Q. (2019). Combining color indices and textures of UAV-based digital imagery for rice LAI estimation. Remote Sens., 11.","DOI":"10.3390\/rs11151763"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Zhang, J., Qiu, X., Wu, Y., Zhu, Y., Cao, Q., Liu, X., and Cao, W. (2021). Combining texture, color, and vegetation indices from fixed-wing UAS imagery to estimate wheat growth parameters using multivariate regression methods. Comput. Electron. Agric., 185.","DOI":"10.1016\/j.compag.2021.106138"},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Chi, Y., Sun, J., Sun, Y., Liu, S., and Fu, Z. (2020). Multi-temporal characterization of land surface temperature and its relationships with normalized difference vegetation index and soil moisture content in the Yellow River Delta, China. Glob. Ecol. Conserv., 23.","DOI":"10.1016\/j.gecco.2020.e01092"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"3437","DOI":"10.1109\/TGRS.2019.2956194","article-title":"Solar-induced chlorophyll fluorescence measured from an Unmanned Aircraft System: Sensor etaloning and platform motion correction","volume":"58","author":"Bendig","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_65","first-page":"33","article-title":"Winter Crop Growth Monitoring using Multi-Temporal NDVI Profiles in Kapadvanj Taluka, Gujarat State","volume":"8","author":"MEHTA","year":"2021","journal-title":"Int. J. Environ. Geoinf."},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Vargas, J.Q., Bendig, J., Mac Arthur, A., Burkart, A., Julitta, T., Maseyk, K., Thomas, R., Siegmann, B., Rossini, M., and Celesti, M. (2020). Unmanned aerial systems (UAS)-based methods for solar induced chlorophyll fluorescence (SIF) retrieval with non-imaging spectrometers: State of the art. Remote Sens., 12.","DOI":"10.3390\/rs12101624"},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Chang, C.Y., Zhou, R., Kira, O., Marri, S., Skovira, J., Gu, L., and Sun, Y. (2020). An Unmanned Aerial System (UAS) for concurrent measurements of solar-induced chlorophyll fluorescence and hyperspectral reflectance toward improving crop monitoring. Agric. For. Meteor., 294.","DOI":"10.1016\/j.agrformet.2020.108145"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1007\/s00704-015-1572-1","article-title":"NDVI-based vegetation responses to climate change in an arid area of China","volume":"126","author":"Xu","year":"2016","journal-title":"Theor. Appl. Clim."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"503","DOI":"10.1016\/j.tree.2005.05.011","article-title":"Using the satellite-derived NDVI to assess ecological responses to environmental change","volume":"20","author":"Pettorelli","year":"2005","journal-title":"Trends Ecol. Evol."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.isprsjprs.2018.12.015","article-title":"Automated detection and measurement of individual sorghum panicles using density-based clustering of terrestrial lidar data","volume":"149","author":"Malambo","year":"2019","journal-title":"ISPRS J. Photogram. Remote Sens."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"2330","DOI":"10.1016\/j.rse.2011.04.033","article-title":"Estimating zero-plane displacement height and aerodynamic roughness length using synthesis of LiDAR and SPOT-5 data","volume":"115","author":"Tian","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Lin, Y.-C., Cheng, Y.-T., Zhou, T., Ravi, R., Hasheminasab, S.M., Flatt, J.E., Troy, C., and Habib, A. (2019). Evaluation of UAV LiDAR for Mapping Coastal Environments. Remote Sens., 11.","DOI":"10.3390\/rs11242893"},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1016\/j.rse.2014.11.001","article-title":"Urban land cover classification using airborne LiDAR data: A review","volume":"158","author":"Yan","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1016\/j.rse.2018.09.027","article-title":"LiDAR derived topography and forest stand characteristics largely explain the spatial variability observed in MODIS land surface phenology","volume":"218","author":"Misra","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Guo, Y., Fu, Y., Hao, F., Zhang, X., Wu, W., Jin, X., Robin Bryant, C., and Senthilnath, J. (2021). Integrated phenology and climate in rice yields prediction using machine learning methods. Ecol. Indic., 120.","DOI":"10.1016\/j.ecolind.2020.106935"},{"key":"ref_76","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_77","doi-asserted-by":"crossref","unstructured":"Han, L., Yang, G., Dai, H., Xu, B., Yang, H., Feng, H., Li, Z., and Yang, X. (2019). Modeling maize above-ground biomass based on machine learning approaches using UAV remote-sensing data. Plant Methods, 15.","DOI":"10.1186\/s13007-019-0394-z"},{"key":"ref_78","doi-asserted-by":"crossref","unstructured":"Cao, J., Zhang, Z., Tao, F., Zhang, L., Luo, Y., Zhang, J., Han, J., and Xie, J. (2021). Integrating Multi-Source Data for Rice Yield Prediction across China using Machine Learning and Deep Learning Approaches. Agric. For. Meteor., 297.","DOI":"10.1016\/j.agrformet.2020.108275"},{"key":"ref_79","first-page":"1","article-title":"Two-step machine learning enables optimized nanoparticle synthesis","volume":"7","author":"Ren","year":"2021","journal-title":"NPJ Comput. Mater."},{"key":"ref_80","doi-asserted-by":"crossref","unstructured":"Dempewolf, J., Nagol, J., Hein, S., Thiel, C., and Zimmermann, R. (2017). Measurement of within-season tree height growth in a mixed forest stand using UAV imagery. Forests, 8.","DOI":"10.3390\/f8070231"},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"4175","DOI":"10.1109\/JSTARS.2019.2918572","article-title":"Estimation of forest structural parameters using UAV-LiDAR data and a process-based model in ginkgo planted forests","volume":"12","author":"Cao","year":"2019","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"322","DOI":"10.1080\/02827581.2020.1806350","article-title":"Use of UAV photogrammetric data in forest genetic trials: Measuring tree height, growth, and phenology in Norway spruce (Picea abies L. Karst.)","volume":"35","author":"Solvin","year":"2020","journal-title":"Scand. J. For. Res."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1016\/j.rse.2012.11.024","article-title":"Tradeoffs between lidar pulse density and forest measurement accuracy","volume":"130","author":"Jakubowski","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"3417","DOI":"10.1109\/COMST.2019.2906228","article-title":"Survey on UAV cellular communications: Practical aspects, standardization advancements, regulation, and security challenges","volume":"21","author":"Fotouhi","year":"2019","journal-title":"IEEE Commun. Surv. 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