{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T02:34:04Z","timestamp":1760150044963,"version":"build-2065373602"},"reference-count":69,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2023,9,23]],"date-time":"2023-09-23T00:00:00Z","timestamp":1695427200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["32001129","22-CAS-TFE-03"],"award-info":[{"award-number":["32001129","22-CAS-TFE-03"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"CAS Key Laboratory of Tropical Forest Ecology","award":["32001129","22-CAS-TFE-03"],"award-info":[{"award-number":["32001129","22-CAS-TFE-03"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Although the Landsat 30 m Enhanced Vegetation Index (EVI) products are important input variables in land surface models, recurring Landsat 5\/7 EVI time series over cloud-prone, fragmented, and mosaic agricultural landscapes is still a great challenge. In this study, we put forward a simple, but effective \u201cLight and Temperature-Driven Growth model and Double Logistic function fusion algorithm\u201d (LTDG_DL). The empirical basis of the LTDG_DL algorithm was traced from the de Wit crop growth simulation model and the commonly observed nonlinear correlation between the EVI and the Leaf Area Index (LAI). It assimilates the ground daily solar radiation and air temperature to generate seasonal profiles of the empirical LAI and EVI and conducts the within-season calibration of the empirical EVI by adjusting crop growth using cloud-free Landsat EVI observations. The initial date of seedling emergence (DOYini) and the accumulated Growing Degree Days for completion of the vegetative and Flowering stage (FGDDs) were variables to which the algorithm\u2019s accuracy was most sensitive. The variable-constrained optimization of the LTDG_DL algorithm was performed by loading the seedling emergence calendar of local prevailing crops and establishing an FGDD lookup table with an exhaustive sampling without replication method. Compared to temporal interpolation functions and Landsat\u2013MODIS spatiotemporal fusion algorithms, the LTDG_DL algorithm had superior performance in the predictions of the EVI increment slope at the vegetative growth stage, the timing of the peak EVI, and the protection of key Landsat EVI observations over cloud-contaminated and complex landscape agricultural systems. Finally, the advantages and limitations of the LTDG_DL algorithm are discussed.<\/jats:p>","DOI":"10.3390\/rs15194673","type":"journal-article","created":{"date-parts":[[2023,9,24]],"date-time":"2023-09-24T10:46:21Z","timestamp":1695552381000},"page":"4673","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Improving Reliability in Reconstruction of Landsat EVI Seasonal Trajectory over Cloud-Prone, Fragmented, and Mosaic Agricultural Landscapes"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3257-7755","authenticated-orcid":false,"given":"Wei","family":"Xue","sequence":"first","affiliation":[{"name":"Key Laboratory of Oasis Ecology of Education Ministry, College of Ecology and Environment, Xinjiang University, Urumqi 830049, China"},{"name":"Xinjiang Jinghe Observation and Research Station of Temperate Desert Ecosystem, Ministry of Education, Jinghe 833300, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7974-3808","authenticated-orcid":false,"given":"Jonghan","family":"Ko","sequence":"additional","affiliation":[{"name":"Department of Applied Plant Science, Chonnam National University, Gwangju 61186, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8122-9696","authenticated-orcid":false,"given":"Ruyin","family":"Cao","sequence":"additional","affiliation":[{"name":"School of Resources and Environment, University of Electronic Science and Technology of China, 2006 Xiyuan Avenue, Chengdu 611731, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhiguo","family":"Yu","sequence":"additional","affiliation":[{"name":"School of Hydrology and Water Resources, Nanjing University of Information Science and Technology, Nanjing 210044, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,9,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"063526","DOI":"10.1117\/1.JRS.6.063526","article-title":"Mapping paddy rice agriculture in a highly fragmented area using a geographic information system object-based post classification process","volume":"6","author":"Lin","year":"2012","journal-title":"J. Appl. Remote Sens."},{"key":"ref_2","unstructured":"(2022, January 25). Available online: http:\/\/kostat.go.kr\/portal\/eng\/index.action."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"132","DOI":"10.1016\/j.agee.2014.09.005","article-title":"Carbon dioxide exchange and its regulation in the main agro-ecosystems of Haean catchment in South Korea","volume":"199","author":"Lindner","year":"2015","journal-title":"Agric. Ecosyst. Environ."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1016\/j.worlddev.2015.10.041","article-title":"The number, size, and distribution of farms, smallholder farms, and family farms worldwide","volume":"87","author":"Lowder","year":"2016","journal-title":"World Dev."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"112156","DOI":"10.1016\/j.rse.2020.112156","article-title":"Reconstructing daily 30 m NDVI over complex agricultural landscapes using a crop reference curve approach","volume":"253","author":"Sun","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1016\/j.rse.2015.10.034","article-title":"Conterminous United States crop field size quantification from multi-temporal Landsat data","volume":"172","author":"Yan","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"339","DOI":"10.5194\/essd-6-339-2014","article-title":"Deriving a per-field land use and land cover map in an agricultural mosaic catchment","volume":"6","author":"Seo","year":"2014","journal-title":"Earth Syst. Sci. Data"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1038\/s41597-021-00827-9","article-title":"The 10-m crop type maps in Northeast China during 2017\u20132019","volume":"8","author":"You","year":"2021","journal-title":"Sci. Data"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1493","DOI":"10.5194\/bg-7-1493-2010","article-title":"Influence of the Asian monsoon on net ecosystem carbon exchange in two major ecosystems in Korea","volume":"7","author":"Kwon","year":"2010","journal-title":"Biogeosciences"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"17","DOI":"10.18814\/epiiugs\/2019\/0190003","article-title":"Land use and land cover changes in the Haean Basin of Korea: Impacts on soil erosion","volume":"42","author":"Lee","year":"2019","journal-title":"Episodes"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.rse.2016.07.033","article-title":"Landsat 8: The plans, the reality, and the legacy","volume":"185","author":"Loveland","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1016\/0034-4257(95)00152-2","article-title":"The status of agricultural lands in Egypt: The use of multitemporal NDVI features derived from landsat TM","volume":"56","author":"Lenney","year":"1996","journal-title":"Remote Sens. Environ."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1016\/j.rse.2017.08.027","article-title":"Detection of cropland field parcels from Landsat imagery","volume":"201","author":"Graesser","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"383","DOI":"10.1016\/j.rse.2017.01.008","article-title":"National-scale soybean mapping and area estimation in the United States using medium resolution satellite imagery and field survey","volume":"190","author":"Song","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1196","DOI":"10.1016\/j.rse.2007.08.011","article-title":"The availability of cloud-free Landsat ETM+ data over the conterminous United States and globally","volume":"112","author":"Ju","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"280","DOI":"10.1016\/j.rse.2012.12.003","article-title":"The global availability of Landsat 5 TM and Landsat 7 ETM+ land surface observations and implications for global 30m Landsat data product generation","volume":"130","author":"Kovalskyy","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Egorov, A.V., Roy, D.P., Zhang, H.K., Li, Z., Yan, L., and Huang, H. (2019). Landsat 4, 5 and 7 (1982 to 2017) analysis ready data (ARD) observation coverage over the conterminous United States and implications for terrestrial monitoring. Remote Sens., 11.","DOI":"10.3390\/rs11040447"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1109\/TGRS.2016.2580576","article-title":"Spatially and temporally weighted regression: A novel method to produce continuous cloud-free Landsat imagery","volume":"55","author":"Chen","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"111901","DOI":"10.1016\/j.rse.2020.111901","article-title":"Multispectral high resolution sensor fusion for smoothing and gap-filling in the cloud","volume":"247","author":"Maneta","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_20","first-page":"102640","article-title":"High-quality vegetation index product generation: A review of NDVI time series reconstruction techniques","volume":"105","author":"Li","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinform."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1824","DOI":"10.1109\/TGRS.2002.802519","article-title":"Seasonality extraction by function fitting to time-series of satellite sensor data","volume":"40","author":"Jonsson","year":"2002","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"332","DOI":"10.1016\/j.rse.2004.03.014","article-title":"A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky\u2013Golay filter","volume":"91","author":"Chen","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1016\/j.rse.2014.03.001","article-title":"Modeling growing season phenology in North American forests using seasonal mean vegetation indices from MODIS","volume":"147","author":"Wu","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Xue, W., Jeong, S., Ko, J., and Yeom, J.-M. (2021). Contribution of biophysical factors to regional variations of evapotranspiration and seasonal cooling effects in paddy rice in South Korea. Remote Sens., 13.","DOI":"10.3390\/rs13193992"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"176","DOI":"10.1016\/j.rse.2013.01.011","article-title":"Detecting interannual variation in deciduous broadleaf forest phenology using Landsat TM\/ETM+ data","volume":"132","author":"Melaas","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"110810","DOI":"10.1016\/j.rse.2018.06.038","article-title":"Robust Landsat-based crop time series modelling","volume":"238","author":"Roy","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"979","DOI":"10.1016\/j.rse.2017.07.036","article-title":"Reconstruction of Landsat time series in the presence of irregular and sparse observations: Development and assessment in north-eastern Alberta, Canada","volume":"204","author":"Pouliot","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"164","DOI":"10.1016\/j.rse.2016.11.023","article-title":"Global evaluation of gap-filling approaches for seasonal NDVI with considering vegetation growth trajectory, protection of key point, noise resistance and curve stability","volume":"189","author":"Liu","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"244","DOI":"10.1016\/j.rse.2018.08.022","article-title":"A simple method to improve the quality of NDVI time-series data by integrating spatiotemporal information with the Savitzky-Golay filter","volume":"217","author":"Cao","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"5179","DOI":"10.1109\/TGRS.2020.2973762","article-title":"A new cross-fusion method to automatically determine the optimal input image pairs for NDVI spatiotemporal data fusion","volume":"58","author":"Chen","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"112009","DOI":"10.1016\/j.rse.2020.112009","article-title":"Virtual image pair-based spatio-temporal fusion","volume":"249","author":"Wang","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"174","DOI":"10.1016\/j.isprsjprs.2021.08.015","article-title":"A practical approach to reconstruct high-quality Landsat NDVI time-series data by gap filling and the Savitzky\u2013Golay filter","volume":"180","author":"Chen","year":"2021","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"2207","DOI":"10.1109\/TGRS.2006.872081","article-title":"On the blending of the Landsat and MODIS surface reflectance: Predicting daily Landsat surface reflectance","volume":"44","author":"Gao","year":"2006","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"2610","DOI":"10.1016\/j.rse.2010.05.032","article-title":"An enhanced spatial and temporal adaptive reflectance fusion model for complex heterogeneous regions","volume":"114","author":"Zhu","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1016\/j.rse.2015.11.016","article-title":"A flexible spatiotemporal method for fusing satellite images with different resolutions","volume":"172","author":"Zhu","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1016\/j.rse.2018.04.042","article-title":"STAIR: A generic and fully-automated method to fuse multiple sources of optical satellite data to generate a high-resolution, daily and cloud-\/gap-free surface reflectance product","volume":"214","author":"Luo","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1016\/j.rse.2019.03.012","article-title":"An Improved Flexible Spatiotemporal DAta Fusion (IFSDAF) method for producing high spatiotemporal resolution normalized difference vegetation index time series","volume":"227","author":"Liu","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1016\/S0034-4257(02)00096-2","article-title":"Overview of the radiometric and biophysical performance of the MODIS vegetation indices","volume":"83","author":"Huete","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1627","DOI":"10.1021\/ac60214a047","article-title":"Smoothing and differentiation of data by simplified least squares procedures","volume":"36","author":"Savitzky","year":"1964","journal-title":"Anal. Chem."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1016\/0308-521X(96)00011-X","article-title":"The \u2018School of de Wit\u2019 crop growth simulation models: A pedigree and historical overview","volume":"52","author":"Bouman","year":"1996","journal-title":"Agric. Syst."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"128204","DOI":"10.1016\/j.jhydrol.2022.128204","article-title":"Radiation estimation and crop growth trajectory reconstruction by novel algorithms improve MOD16 evapotranspiration predictability for global multi-site paddy rice ecosystems","volume":"612","author":"Xue","year":"2022","journal-title":"J. Hydrol."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Connor, D.J., Loomis, R.S., and Cassman, K.G. (2011). Crop Ecology: Productivity and Management in Agricultural Systems, Cambridge University Press.","DOI":"10.1017\/CBO9780511974199"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"927","DOI":"10.3390\/rs5020927","article-title":"Global Data sets of vegetation leaf area index (LAI)3g and fraction of photosynthetically active radiation (FPAR)3g derived from global inventory modeling and mapping studies (GIMMS) normalized difference vegetation index (NDVI3g) for the period 1981 to 2011","volume":"5","author":"Zhu","year":"2013","journal-title":"Remote Sens."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1039","DOI":"10.1080\/01431160903505310","article-title":"Integration of MODIS LAI and vegetation index products with the CSM\u2013CERES\u2013Maize model for corn yield estimation","volume":"32","author":"Fang","year":"2011","journal-title":"Int. J. Remote Sens."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"739","DOI":"10.1029\/2018RG000608","article-title":"An overview of global leaf area index (LAI): Methods, products, validation, and applications","volume":"57","author":"Fang","year":"2019","journal-title":"Rev. Geophys."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1098\/rstb.1977.0140","article-title":"Climate and the efficiency of crop production in Britain","volume":"281","author":"Monteith","year":"1977","journal-title":"Philos. Trans. R. Soc. Lond. Ser. B"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Russell, G., Jarvis, P., and Monteith, J. (1989). Plant Canopies: Their Growth, Form and Function, Cambridge University Press.","DOI":"10.1017\/CBO9780511752308"},{"key":"ref_48","unstructured":"Conte, S.D., and de Boor, C. (1965). Elementary Numerical Analysis: An Algorithmic Approach, McGraw-Hill."},{"key":"ref_49","unstructured":"Press, W.H., Teukolsky, S.A., Vetterling, W.T., and Flannery, B.P. (1992). Numerical Recipes in Fortran: The Art of Scientific Computing, Cambridge University Press. [2nd ed.]."},{"key":"ref_50","unstructured":"Yoshida, S. (1981). Rice Crop Science, The International Rice Research Institute."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1016\/j.agrformet.2019.01.023","article-title":"Predicting wheat productivity: Integrating time series of vegetation indices into crop modeling via sequential assimilation","volume":"272\u2013273","author":"Guo","year":"2019","journal-title":"Agric. For. Meteorol."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"2814","DOI":"10.1080\/01431161.2012.750020","article-title":"Relationships between vegetation indices and root zone soil moisture under maize and soybean canopies in the US Corn Belt: A comparative study using a close-range sensing approach","volume":"34","author":"Swain","year":"2013","journal-title":"Int. J. Remote Sens."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"2441","DOI":"10.1080\/01431161.2018.1425567","article-title":"Application of an unmanned aerial system for monitoring paddy productivity using the GRAMI-rice model","volume":"39","author":"Jeong","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"4","DOI":"10.18637\/jss.v043.i04","article-title":"Analyzing remote sensing data in R: The landsat package","volume":"43","author":"Goslee","year":"2011","journal-title":"J. Stat. Softw."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"390","DOI":"10.1016\/j.rse.2015.08.030","article-title":"Evaluation of the Landsat-5 TM and Landsat-7 ETM+ surface reflectance products","volume":"169","author":"Claverie","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/j.rse.2015.12.024","article-title":"Characterization of Landsat-7 to Landsat-8 reflective wavelength and normalized difference vegetation index continuity","volume":"185","author":"Roy","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"1798","DOI":"10.3390\/rs70201798","article-title":"Comparison of spatiotemporal fusion models: A review","volume":"7","author":"Chen","year":"2015","journal-title":"Remote Sens."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1016\/j.isprsjprs.2014.04.023","article-title":"Improved maize cultivated area estimation over a large scale combining MODIS\u2013EVI time series data and crop phenological information","volume":"94","author":"Zhang","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"2146","DOI":"10.1016\/j.rse.2010.04.019","article-title":"A Two-Step Filtering approach for detecting maize and soybean phenology with time-series MODIS data","volume":"114","author":"Sakamoto","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"347","DOI":"10.1016\/j.rse.2017.03.029","article-title":"PhenoRice: A method for automatic extraction of spatio-temporal information on rice crops using satellite data time series","volume":"194","author":"Boschetti","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Huang, X., Liu, J., Zhu, W., Atzberger, C., and Liu, Q. (2019). The optimal threshold and vegetation index time series for retrieving crop phenology based on a modified dynamic threshold method. Remote Sens., 11.","DOI":"10.3390\/rs11232725"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"2753","DOI":"10.5194\/essd-13-2753-2021","article-title":"GLC_FCS30: Global land-cover product with fine classification system at 30 m using time-series Landsat imagery","volume":"13","author":"Zhang","year":"2021","journal-title":"Earth Syst. Sci. Data"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1016\/j.eja.2015.11.021","article-title":"Assessing uncertainty and complexity in regional-scale crop model simulations","volume":"88","author":"Koehler","year":"2017","journal-title":"Eur. J. Agron."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"751868","DOI":"10.3389\/fpls.2021.751868","article-title":"Assessing drought and heat stress-induced changes in the cotton leaf metabolome and their relationship with hyperspectral reflectance","volume":"12","author":"Melandri","year":"2021","journal-title":"Front. Plant Sci."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"503","DOI":"10.1016\/j.agrformet.2017.08.038","article-title":"A spatially hierarchical integration of close-range remote sensing, leaf structure and physiology assists in diagnosing spatiotemporal dimensions of field-scale ecosystem photosynthetic productivity","volume":"247","author":"Xue","year":"2017","journal-title":"Agric. For. Meteorol."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1016\/j.ecoinf.2018.05.006","article-title":"CropPhenology: An R package for extracting crop phenology from time series remotely sensed vegetation index imagery","volume":"46","author":"Araya","year":"2018","journal-title":"Ecol. Inform."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1016\/j.fcr.2012.11.023","article-title":"Use of agro-climatic zones to upscale simulated crop yield potential","volume":"143","author":"Wolf","year":"2013","journal-title":"Field Crop. Res."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"108865","DOI":"10.1016\/j.agrformet.2022.108865","article-title":"Climate warming outweighed agricultural managements in affecting wheat phenology across China during 1981\u20132018","volume":"316","author":"Tao","year":"2022","journal-title":"Agric. For. Meteorol."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"394","DOI":"10.1038\/nature13893","article-title":"Agricultural Green Revolution as a driver of increasing atmospheric CO2 seasonal amplitude","volume":"515","author":"Zeng","year":"2014","journal-title":"Nature"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/19\/4673\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:56:51Z","timestamp":1760129811000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/19\/4673"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9,23]]},"references-count":69,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2023,10]]}},"alternative-id":["rs15194673"],"URL":"https:\/\/doi.org\/10.3390\/rs15194673","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2023,9,23]]}}}