{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,6]],"date-time":"2026-10-06T05:51:29Z","timestamp":1791265889627,"version":"4.1.0"},"reference-count":69,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Computers and Electronics in Agriculture"],"published-print":{"date-parts":[[2026,10]]},"DOI":"10.1016\/j.compag.2026.112173","type":"journal-article","created":{"date-parts":[[2026,7,10]],"date-time":"2026-07-10T13:32:03Z","timestamp":1783690323000},"page":"112173","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["A conditional generative diffusion model enables joint retrieval of rice LAI and LCC"],"prefix":"10.1016","volume":"253","author":[{"given":"Heguang","family":"Sun","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huanming","family":"Mai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bo","family":"Han","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yi","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhongyu","family":"Jin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Changsheng","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenqian","family":"Tang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zifan","family":"Shen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ziheng","family":"Feng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoyu","family":"Song","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yubin","family":"Lan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wen","family":"Du","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5588-3443","authenticated-orcid":false,"given":"Xiaoling","family":"Deng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.compag.2026.112173_b0005","doi-asserted-by":"crossref","first-page":"2455","DOI":"10.1016\/j.biombioe.2011.02.028","article-title":"A review of remote sensing methods for biomass feedstock production","volume":"35","author":"Ahamed","year":"2011","journal-title":"Biomass Bioenergy"},{"key":"10.1016\/j.compag.2026.112173_b0010","doi-asserted-by":"crossref","first-page":"1131","DOI":"10.1080\/0143116031000116967","article-title":"Effect of sensor view angle on the assessment of agronomic traits by ground level hyper-spectral reflectance measurements in durum wheat under contrasting Mediterranean conditions","volume":"25","author":"Aparicio","year":"2004","journal-title":"Int. J. Remote Sens."},{"key":"10.1016\/j.compag.2026.112173_b0015","doi-asserted-by":"crossref","unstructured":"Awad, M., Khanna, R., 2015. Support Vector Regression, in: Efficient Learning Machines. Apress, Berkeley, CA, pp. 67\u201380. https:\/\/doi.org\/10.1007\/978-1-4302-5990-9_4.","DOI":"10.1007\/978-1-4302-5990-9_4"},{"key":"10.1016\/j.compag.2026.112173_b0020","doi-asserted-by":"crossref","first-page":"1894","DOI":"10.1109\/JSEN.2023.3337278","article-title":"GAN-based bearing fault diagnosis method for short and imbalanced vibration signal","volume":"24","author":"Bai","year":"2024","journal-title":"IEEE Sens. J."},{"key":"10.1016\/j.compag.2026.112173_b0025","doi-asserted-by":"crossref","first-page":"1869","DOI":"10.13031\/2013.27665","article-title":"Remote sensing of plant nitrogen status in corn","volume":"39","author":"Bausch","year":"1996","journal-title":"Trans. ASAE"},{"key":"10.1016\/j.compag.2026.112173_b0030","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1080\/07038992.1996.10855178","article-title":"Evaluation of vegetation indices and a modified simple ratio for boreal applications","volume":"22","author":"Chen","year":"1996","journal-title":"Can. J. Remote. Sens."},{"key":"10.1016\/j.compag.2026.112173_b0035","series-title":"New Thinking in GIScience","first-page":"207","article-title":"Next step in vegetation remote sensing: synergetic retrievals of canopy structural and leaf biochemical parameters","author":"Chen","year":"2022"},{"key":"10.1016\/j.compag.2026.112173_b0040","doi-asserted-by":"crossref","DOI":"10.1016\/j.plaphe.2025.100100","article-title":"Exploring the depth of the maize canopy LAI detected by spectroscopy based on simulations and in situ measurements","volume":"7","author":"Cheng","year":"2025","journal-title":"Plant Phenomics"},{"key":"10.1016\/j.compag.2026.112173_b0045","doi-asserted-by":"crossref","first-page":"10850","DOI":"10.1109\/TPAMI.2023.3261988","article-title":"Diffusion models in vision: A survey","volume":"45","author":"Croitoru","year":"2023","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.compag.2026.112173_b0050","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1016\/S0034-4257(00)00113-9","article-title":"Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance","volume":"74","author":"Daughtry","year":"2000","journal-title":"Remote Sens. Environ."},{"key":"10.1016\/j.compag.2026.112173_b0055","doi-asserted-by":"crossref","DOI":"10.1016\/j.rse.2022.113232","article-title":"Improving above ground biomass estimates of Southern Africa dryland forests by combining Sentinel-1 SAR and Sentinel-2 multispectral imagery","volume":"282","author":"David","year":"2022","journal-title":"Remote Sens. Environ."},{"key":"10.1016\/j.compag.2026.112173_b0060","first-page":"240","article-title":"Advancing UAV-based wheat phenology monitoring: A dual-mode framework integrating time-series reconstruction, noise augmentation, and deep learning for robust BBCH estimation","volume":"16","author":"Feng","year":"2026","journal-title":"Artif. Intell. Agric."},{"key":"10.1016\/j.compag.2026.112173_b0065","article-title":"PIOSL: An improved version of PROSPECT model for simulating leaf optical properties by considering leaf internal structural variations","volume":"211","author":"Fenghua","year":"2023","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.compag.2026.112173_b0070","article-title":"A novel mechanism-guided retrieval framework for mangrove chlorophyll content considering leaf area index and spectral resolution based on active learning hybrid model","volume":"144","author":"Fu","year":"2025","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"10.1016\/j.compag.2026.112173_b0075","unstructured":"Garson, G.D., 2012. Testing statistical assumptions."},{"key":"10.1016\/j.compag.2026.112173_b0080","doi-asserted-by":"crossref","DOI":"10.1016\/j.rse.2025.115102","article-title":"In-season crop progress in unsurveyed regions using networks trained on synthetic data","volume":"333","author":"Worrall","year":"2026","journal-title":"Remote Sens. Environ."},{"key":"10.1016\/j.compag.2026.112173_b0085","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1078\/0176-1617-01176","article-title":"Wide dynamic range vegetation index for remote quantification of biophysical characteristics of vegetation","volume":"161","author":"Gitelson","year":"2004","journal-title":"J. Plant Physiol."},{"key":"10.1016\/j.compag.2026.112173_b0090","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":"10.1016\/j.compag.2026.112173_b0095","doi-asserted-by":"crossref","first-page":"2691","DOI":"10.1080\/014311697217558","article-title":"Remote estimation of chlorophyll content in higher plant leaves","volume":"18","author":"Gitelson","year":"1997","journal-title":"Int. J. Remote Sens."},{"key":"10.1016\/j.compag.2026.112173_b0100","unstructured":"Gitelson, A.A., Merzlyak, M.N., Zur, Y., Stark, R., Gritz, U., 2001. Non-destructive and remote sensing techniques for estimation of vegetation status."},{"key":"10.1016\/j.compag.2026.112173_b0105","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1016\/j.rse.2014.01.004","article-title":"Relationships between gross primary production, green LAI, and canopy chlorophyll content in maize: Implications for remote sensing of primary production","volume":"144","author":"Gitelson","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"10.1016\/j.compag.2026.112173_b0110","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1080\/02757259409532252","article-title":"Influences of canopy architecture on relationships between various vegetation indices and LAI and Fpar: A computer simulation","volume":"10","author":"Goel","year":"1994","journal-title":"Remote Sens. Rev."},{"key":"10.1016\/j.compag.2026.112173_b0115","doi-asserted-by":"crossref","first-page":"1355","DOI":"10.1109\/TGRS.2003.812910","article-title":"Estimation of forest leaf area index using vegetation indices derived from Hyperion hyperspectral data","volume":"41","author":"Gong","year":"2003","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"10.1016\/j.compag.2026.112173_b0120","first-page":"22","article-title":"Zero-shot text-to-image generation","author":"Guo","year":"2023","journal-title":"Geo-spatial Inf. Sci."},{"key":"10.1016\/j.compag.2026.112173_b0125","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1080\/10095020.2023.2251540","article-title":"Synergetic inversion of leaf area index and leaf chlorophyll content using multi-spectral remote sensing data","volume":"28","author":"Guo","year":"2025","journal-title":"Geo-spatial Inf. Sci."},{"key":"10.1016\/j.compag.2026.112173_b0130","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1016\/j.rse.2003.12.013","article-title":"Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture","volume":"90","author":"Haboudane","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"10.1016\/j.compag.2026.112173_b0135","doi-asserted-by":"crossref","first-page":"416","DOI":"10.1016\/S0034-4257(02)00018-4","article-title":"Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture","volume":"81","author":"Haboudane","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"10.1016\/j.compag.2026.112173_b0140","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1016\/0034-4257(88)90106-X","article-title":"A soil-adjusted vegetation index (SAVI)","volume":"25","author":"Huete","year":"1988","journal-title":"Remote Sens. Environ."},{"key":"10.1016\/j.compag.2026.112173_b0145","doi-asserted-by":"crossref","first-page":"3833","DOI":"10.1016\/j.rse.2008.06.006","article-title":"Development of a two-band enhanced vegetation index without a blue band","volume":"112","author":"Jiang","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"10.1016\/j.compag.2026.112173_b0150","first-page":"17564","article-title":"TabDDPM: Modelling tabular data with diffusion models","author":"Kotelnikov","year":"2023","journal-title":"PMLR"},{"key":"10.1016\/j.compag.2026.112173_b0155","doi-asserted-by":"crossref","first-page":"224","DOI":"10.1016\/j.rse.2012.05.013","article-title":"Estimating crop biophysical properties from remote sensing data by inverting linked radiative transfer and ecophysiological models","volume":"124","author":"Thorp","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"10.1016\/j.compag.2026.112173_b0160","doi-asserted-by":"crossref","first-page":"3846","DOI":"10.1016\/j.rse.2008.06.005","article-title":"Calibration and validation of hyperspectral indices for the estimation of broadleaved forest leaf chlorophyll content, leaf mass per area, leaf area index and leaf canopy biomass","volume":"112","author":"le Maire","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"10.1016\/j.compag.2026.112173_b0165","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2025.110840","article-title":"Research on the optimal fertilization decision method for rice economy based on PROSAIL-WOFOST optimal coupling unit","volume":"238","author":"Li","year":"2025","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.compag.2026.112173_b0170","first-page":"39","article-title":"Collaborative inversion heavy metal stress in rice by using two-dimensional spectral feature space based on HJ-1 A HSI and radarsat-2 SAR remote sensing data","volume":"78","author":"Li","year":"2019","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"10.1016\/j.compag.2026.112173_b0175","first-page":"1","article-title":"Evaluation of reflectance and canopy scattering coefficient based vegetation indices to reduce the impacts of canopy structure and soil in estimating leaf and canopy chlorophyll contents","volume":"61","author":"Li","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"10.1016\/j.compag.2026.112173_b0180","unstructured":"Lisai Cao, Shengbo Chen, Zhijun Zhen, 2025. Zero-Shot Text-to-Image Generation. TGRS."},{"key":"10.1016\/j.compag.2026.112173_b0185","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2025.110792","article-title":"Time-space-angle scale effects and incorporation patterns in estimating rice LAI and leaf chlorophyll content by UAV multispectral remote sensing","volume":"237","author":"Luo","year":"2025","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.compag.2026.112173_b0190","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1016\/j.agrformet.2003.08.001","article-title":"Review of methods for in situ leaf area index (LAI) determination: Part II. Estimation of LAI, errors and sampling","volume":"121","author":"Weiss","year":"2004","journal-title":"Agric. For. Meteorol."},{"key":"10.1016\/j.compag.2026.112173_b0195","doi-asserted-by":"crossref","first-page":"751","DOI":"10.1016\/j.isprsjprs.2011.08.001","article-title":"An investigation into robust spectral indices for leaf chlorophyll estimation","volume":"66","author":"Main","year":"2011","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"10.1016\/j.compag.2026.112173_b0200","series-title":"In: 2024 2nd International Conference on Networking and Communications (ICNWC). IEEE","first-page":"1","article-title":"Computer vision and generative AI for yield prediction in digital agriculture","author":"Majumder","year":"2024"},{"key":"10.1016\/j.compag.2026.112173_b0205","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":"10.1016\/j.compag.2026.112173_b0210","series-title":"Introduction to linear regression analysis","author":"Montgomery","year":"2021"},{"key":"10.1016\/j.compag.2026.112173_b0215","doi-asserted-by":"crossref","unstructured":"Moon, T., Choi, H., Kim, D., 2022. Autonomous construction of parameterizable 3D leaf models from scanned sweet pepper leaves with deep generative networks. in silico Plants.","DOI":"10.1093\/insilicoplants\/diac015"},{"key":"10.1016\/j.compag.2026.112173_b0220","series-title":"Computers and Electronics in Agriculture 214","article-title":"Analysis of stable diffusion-derived fake weeds performance for training convolutional neural networks","author":"Moreno","year":"2023"},{"key":"10.1016\/j.compag.2026.112173_b0225","unstructured":"Moriera, F., 2024. Proceedings of the 23rd European Conference on e-Learning. Academic Conferences and publishing limited."},{"key":"10.1016\/j.compag.2026.112173_b0230","doi-asserted-by":"crossref","first-page":"36322","DOI":"10.1109\/ACCESS.2019.2905015","article-title":"Recent progress on generative adversarial networks (GANs): A survey","volume":"7","author":"Pan","year":"2019","journal-title":"IEEE Access"},{"key":"10.1016\/j.compag.2026.112173_b0235","series-title":"Remote mapping of standing crop biomass for estimation of the productivity of the shortgrass prairie","author":"Pearson","year":"1972"},{"key":"10.1016\/j.compag.2026.112173_b0240","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":"10.1016\/j.compag.2026.112173_b0245","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/S0034-4257(99)00113-3","article-title":"Leaf area index estimates using remotely sensed data and BRDF models in a semiarid region","volume":"73","author":"Qi","year":"2000","journal-title":"Remote Sens. Environ."},{"key":"10.1016\/j.compag.2026.112173_b0250","doi-asserted-by":"crossref","first-page":"31","DOI":"10.17849\/insm-47-01-31-39.1","volume":"47","author":"Rigatti","year":"2017","journal-title":"Random Forest. J. Insurance Med."},{"key":"10.1016\/j.compag.2026.112173_b0255","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1016\/0034-4257(95)00186-7","article-title":"Optimization of soil-adjusted vegetation indices","volume":"55","author":"Rondeaux","year":"1996","journal-title":"Remote Sens. Environ."},{"key":"10.1016\/j.compag.2026.112173_b0260","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":"10.1016\/j.compag.2026.112173_b0265","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":"10.1016\/j.compag.2026.112173_b0270","series-title":"In: 2001 ASAE Annual Meeting","first-page":"1","article-title":"Evaluation and refinement of the nitrogen reflectance index (NRI) for site-specific fertilizer management","author":"Schleicher","year":"1998"},{"key":"10.1016\/j.compag.2026.112173_b0275","first-page":"47","article-title":"Remote estimation of nitrogen and chlorophyll contents in maize at leaf and canopy levels","volume":"25","author":"Schlemmer","year":"2013","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"10.1016\/j.compag.2026.112173_b0280","doi-asserted-by":"crossref","unstructured":"Shahi, A.P., Rai, P.K., Rabi-ul-Islam, 2023. Remote sensing data extraction and inversion techniques: A review, in: Atmospheric Remote Sensing. Elsevier, pp. 85\u2013104. https:\/\/doi.org\/10.1016\/B978-0-323-99262-6.00021-3.","DOI":"10.1016\/B978-0-323-99262-6.00021-3"},{"key":"10.1016\/j.compag.2026.112173_b0285","doi-asserted-by":"crossref","DOI":"10.1016\/j.eja.2025.127702","article-title":"Multi-variety monitoring of potato late blight severity using UAV data with improved SMOTE-CS for small sample modeling and deep feature learning","volume":"169","author":"Sun","year":"2025","journal-title":"Eur. J. Agron."},{"key":"10.1016\/j.compag.2026.112173_b0290","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":"10.1016\/j.compag.2026.112173_b0295","article-title":"Introduction to the mathematics of inversion in remote sensing and indirect measurements","author":"Twomey","year":"2019","journal-title":"Courier Dover"},{"key":"10.1016\/j.compag.2026.112173_b0300","article-title":"Exploring the depth of the maize canopy LAI detected by spectroscopy based on simulations and in situ measurements","volume":"7","author":"Wang","year":"2025","journal-title":"Plant Phenomics"},{"key":"10.1016\/j.compag.2026.112173_b0305","first-page":"e2","volume":"1","author":"Wattenberg","year":"2016","journal-title":"How to Use t-SNE Effectively. Distill"},{"key":"10.1016\/j.compag.2026.112173_b0310","doi-asserted-by":"crossref","DOI":"10.1016\/j.rse.2025.114962","article-title":"RSPECT: A PROSPECT-based model incorporating the real structure of rice leaves","volume":"330","author":"Xiang","year":"2025","journal-title":"Remote Sens. Environ."},{"key":"10.1016\/j.compag.2026.112173_b0315","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.rse.2019.01.039","article-title":"Retrieving leaf chlorophyll content using a matrix-based vegetation index combination approach","volume":"224","author":"Xu","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"10.1016\/j.compag.2026.112173_b0320","first-page":"1","article-title":"Diffusion models: A comprehensive survey of methods and applications","volume":"56","author":"Yang","year":"2024","journal-title":"ACM Comput. Surv."},{"key":"10.1016\/j.compag.2026.112173_b0325","doi-asserted-by":"crossref","first-page":"8654","DOI":"10.3390\/app12178654","article-title":"A review of ensemble learning algorithms used in remote sensing applications","volume":"12","author":"Zhang","year":"2022","journal-title":"Appl. Sci."},{"key":"10.1016\/j.compag.2026.112173_b0330","doi-asserted-by":"crossref","DOI":"10.1016\/j.rse.2023.113759","article-title":"Evaluating the potential of airborne hyperspectral LiDAR for assessing forest insects and diseases with 3D radiative transfer modeling","volume":"297","author":"Zhao","year":"2023","journal-title":"Remote Sens. Environ."},{"key":"10.1016\/j.compag.2026.112173_b0335","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.isprsjprs.2025.11.031","article-title":"National mapping of wetland vegetation leaf area index in China using hybrid model with Sentinel-2 and Landsat-8 data","volume":"232","author":"Zhen","year":"2026","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"10.1016\/j.compag.2026.112173_b0340","doi-asserted-by":"crossref","DOI":"10.1016\/j.fcr.2023.109077","article-title":"Estimation of canopy water content for wheat through combining radiative transfer model and machine learning","volume":"302","author":"Zhu","year":"2023","journal-title":"Field Crop Res"},{"key":"10.1016\/j.compag.2026.112173_b0345","doi-asserted-by":"crossref","first-page":"2525","DOI":"10.3390\/rs15102525","article-title":"An approach for joint estimation of grassland leaf area index and leaf chlorophyll content from UAV hyperspectral data","volume":"15","author":"Zhu","year":"2023","journal-title":"Remote Sens. (Basel)"}],"container-title":["Computers and Electronics in Agriculture"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0168169926007696?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0168169926007696?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,10,6]],"date-time":"2026-10-06T04:55:28Z","timestamp":1791262528000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0168169926007696"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,10]]},"references-count":69,"alternative-id":["S0168169926007696"],"URL":"https:\/\/doi.org\/10.1016\/j.compag.2026.112173","relation":{},"ISSN":["0168-1699"],"issn-type":[{"value":"0168-1699","type":"print"}],"subject":[],"published":{"date-parts":[[2026,10]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"A conditional generative diffusion model enables joint retrieval of rice LAI and LCC","name":"articletitle","label":"Article Title"},{"value":"Computers and Electronics in Agriculture","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.compag.2026.112173","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"112173"}}