{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T09:58:00Z","timestamp":1773050280479,"version":"3.50.1"},"reference-count":50,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2024,11,14]],"date-time":"2024-11-14T00:00:00Z","timestamp":1731542400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Natural Environment Research Council (NERC)","award":["NE\/S007350\/1"],"award-info":[{"award-number":["NE\/S007350\/1"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Sugarcane is a high-impact crop used in the majority of global sugar production, with India being the second largest global producer. Understanding the timing and length of sugarcane growth stages is critical to improving the sustainability of sugarcane management. Earth observation (EO) data have been shown to be sensitive to the variation in sugarcane growth, but questions remain as to how to reliably extract sugarcane phenology over wide areas so that this information can be used for effective management. This study develops an automated approach to derive sugarcane growth stages using EO data from Landsat-8 and Sentinel-2 satellite data in the Indian state of Andhra Pradesh. The developed method is then evaluated in the State of Telangana. Normalised difference vegetation index (NDVI) EO data from Landsat-8 and Sentinel-2 were pre-processed to filter out clouds and to harmonise sensor response. Pixel-based cloud filtering was selected over filtering by scene in order to increase the temporal frequency of observations. Harmonising data from two different sensors further increased temporal resolution to 3\u20136 days (70% of sampled fields). To automate seasonal decomposition, harmonised signals were resampled at 14 days, and low-frequency components, related to seasonal growth, were extracted using a fast Fourier transform. The start and end of each season were extracted from the time series using difference of Gaussian and were compared to assessments based on visual observation for both Unit 1 (R2 = 0.72\u20130.84) and Unit 2 (R2 = 0.78\u20130.82). A trapezoidal growth model was then used to derive crop growth stages from satellite-measured phenology for better crop management information. Automated assessments of the start and the end of mid-season growth stages were compared to visual observations in Unit 1 (R2 = 0.56\u20130.72) and Unit 2 (R2 = 0.36\u20130.79). Outliers were found to result from cloud cover that was not removed by the initial screening as well as multiple crops or harvesting dates within a single field. These results demonstrate that EO time series can be used to automatically determine the growth stages of sugarcane in India over large areas, without the need for prior knowledge of planting and harvest dates, as a tool for improving sustainable production.<\/jats:p>","DOI":"10.3390\/rs16224244","type":"journal-article","created":{"date-parts":[[2024,11,14]],"date-time":"2024-11-14T08:06:32Z","timestamp":1731571592000},"page":"4244","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Automating the Derivation of Sugarcane Growth Stages from Earth Observation Time Series"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-5472-9358","authenticated-orcid":false,"given":"Neha","family":"Joshi","sequence":"first","affiliation":[{"name":"Faculty of Engineering and Applied Science, Cranfield University, Cranfield MK43 0AL, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6318-4052","authenticated-orcid":false,"given":"Daniel M.","family":"Simms","sequence":"additional","affiliation":[{"name":"Faculty of Engineering and Applied Science, Cranfield University, Cranfield MK43 0AL, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8210-3430","authenticated-orcid":false,"given":"Paul J.","family":"Burgess","sequence":"additional","affiliation":[{"name":"Faculty of Engineering and Applied Science, Cranfield University, Cranfield MK43 0AL, UK"}]}],"member":"1968","published-online":{"date-parts":[[2024,11,14]]},"reference":[{"key":"ref_1","unstructured":"FAO (2021). World Food and Agriculture\u2014Statistical Yearbook 2021, FAO."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1002\/jsfa.6369","article-title":"Understanding the impact of crop and food production on the water environment\u2014Using sugar as a model","volume":"94","author":"Hess","year":"2013","journal-title":"J. Sci. Food Agric."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1016\/j.fcr.2005.01.011","article-title":"Sugarcane physiology: Integrating from cell to crop to advance sugarcane production","volume":"92","author":"Bonnett","year":"2005","journal-title":"Field Crop. Res."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"588","DOI":"10.1007\/s12355-016-0494-2","article-title":"Sugarcane production and development of sugar industry in India","volume":"18","author":"Solomon","year":"2016","journal-title":"Sugar Tech"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"233","DOI":"10.54302\/mausam.v67i1.1187","article-title":"Managing impact of extreme weather events in sugarcane in different agro-climatic zones of Uttar Pradesh","volume":"67","author":"Mall","year":"2016","journal-title":"Mausam"},{"key":"ref_6","first-page":"13","article-title":"A study on sugarcane production in India","volume":"3","author":"Nandhini","year":"2017","journal-title":"Int. J. Adv. Res. Bot."},{"key":"ref_7","first-page":"4337","article-title":"The concept of sustainable sugarcane production: Global, African and South African perceptions","volume":"7","author":"Mnisi","year":"2012","journal-title":"Afr. J. Agric. Res."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Singh, P., and Tiwari, A.K. (2018). Sustainable Sugarcane Production, Apple Academic Press.","DOI":"10.1201\/9781351047760"},{"key":"ref_9","unstructured":"Gujja, B., Loganandhan, N., Vinod Goud, V., Agarwal, M., and Dalai, S. (2010). Sustainable Sugarcane Initiative\u2014Improving Sugarcane Cultivation in India\u2014Training Manual Developed by WWF India and ICRISAT, ICRISAT."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Li, Y.R. (2022). Growth and development of sugarcane (Saccharum spp. Hybrid) and its relationship with environmental factors. Agro-Industrial Perspectives on Sugarcane Production under Environmental Stress, Springer Nature.","DOI":"10.1007\/978-981-19-3955-6_1"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"519","DOI":"10.5194\/isprs-annals-V-3-2020-519-2020","article-title":"Sugarcane plantation mapping using dynamic time warping from multi-temporal Sentinel-1A radar images","volume":"V-3\u20132020","author":"Olfindo","year":"2020","journal-title":"ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"111951","DOI":"10.1016\/j.rse.2020.111951","article-title":"Mapping sugarcane plantation dynamics in Guangxi, China, by time series Sentinel-1, Sentinel-2 and Landsat images","volume":"247","author":"Wang","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1080\/17538947.2019.1610807","article-title":"A workflow for Sustainable Development Goals indicators assessment based on high-resolution satellite data","volume":"13","author":"Kussul","year":"2019","journal-title":"Int. J. Digit. Earth"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Fu, W., Ma, J., Chen, P., and Chen, F. (2019). Remote sensing satellites for digital Earth. Manual of Digital Earth, Springer.","DOI":"10.1007\/978-981-32-9915-3_3"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"832","DOI":"10.1080\/17538947.2019.1585976","article-title":"Big Earth data: Disruptive changes in Earth observation data management and analysis?","volume":"13","author":"Sudmanns","year":"2019","journal-title":"Int. J. Digit. Earth"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Stubbings, P., Peskett, J., Rowe, F., and Arribas-Bel, D. (2019). A hierarchical urban forest index using street-level imagery and deep learning. Remote Sens., 11.","DOI":"10.3390\/rs11121395"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"100046","DOI":"10.1016\/j.atech.2022.100046","article-title":"Sugarcane yield estimation through remote sensing time series and phenology metrics","volume":"2","author":"Dimov","year":"2022","journal-title":"Smart Agric. Technol."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Zheng, Y., Li, Z., Pan, B., Lin, S., Dong, J., Li, X., and Yuan, W. (2022). Development of a phenology-based method for identifying sugarcane plantation areas in china using high-resolution satellite datasets. Remote Sens., 14.","DOI":"10.3390\/rs14051274"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Yeasin, M., Haldar, D., Kumar, S., Paul, R.K., and Ghosh, S. (2022). Machine learning techniques for phenology assessment of sugarcane using conjunctive SAR and optical data. Remote Sens., 14.","DOI":"10.3390\/rs14143249"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"333","DOI":"10.1016\/j.isprsjprs.2020.05.013","article-title":"Cloud removal in Sentinel-2 imagery using a deep residual neural network and SAR-optical data fusion","volume":"166","author":"Meraner","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_21","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-Golay filter","volume":"91","author":"Chen","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1016\/j.rse.2013.10.025","article-title":"Systematic land cover bias in Collection 5 MODIS cloud mask and derived products\u2014A global overview","volume":"141","author":"Wilson","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_23","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_24","doi-asserted-by":"crossref","first-page":"176","DOI":"10.1016\/j.isprsjprs.2018.02.011","article-title":"Refined shape model fitting methods for detecting various types of phenological information on major U.S. crops\u2014ScienceDirect","volume":"138","author":"Sakamoto","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"105398","DOI":"10.1016\/j.compag.2020.105398","article-title":"Detection of phenology using an improved shape model on time-series vegetation index in wheat","volume":"173","author":"Zhou","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"113060","DOI":"10.1016\/j.rse.2022.113060","article-title":"Detecting crop phenology from vegetation index time-series data by improved shape model fitting in each phenological stage","volume":"277","author":"Liu","year":"2022","journal-title":"Remote Sens. Environ."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"473","DOI":"10.5194\/bg-21-473-2024","article-title":"Reviews and syntheses: Remotely sensed optical time series for monitoring vegetation productivity","volume":"21","author":"Kooistra","year":"2024","journal-title":"Biogeosciences"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1038\/s41592-019-0686-2","article-title":"SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python","volume":"17","author":"Virtanen","year":"2020","journal-title":"Nat. Methods"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1109\/MCSE.2007.55","article-title":"Matplotlib: A 2D graphics environment","volume":"9","author":"Hunter","year":"2007","journal-title":"Comput. Sci. Eng."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.rse.2017.06.031","article-title":"Google Earth Engine: Planetary-scale geospatial analysis for everyone","volume":"202","author":"Gorelick","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_31","first-page":"1","article-title":"Crop water requirements","volume":"24","author":"Doorenbos","year":"1977","journal-title":"FAO Irrig. Drain. Pap."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"330","DOI":"10.1590\/1807-1929\/agriambi.v23n5p330-335","article-title":"Sugarcane spatial-temporal monitoring and crop coefficient estimation through NDVI","volume":"23","author":"Alface","year":"2019","journal-title":"Rev. Bras. Eng. Agr\u00edCola Ambient."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Xu, F., Wang, Z., Lu, G., Zeng, R., and Que, Y. (2021). Sugarcane ratooning ability: Research status, shortcomings, and prospects. Biology, 10.","DOI":"10.3390\/biology10101052"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"454","DOI":"10.1080\/00103624.2014.997385","article-title":"Soil-root interface changes in sugarcane plant and ratoon crops under subtropical conditions: Implications for dry-matter accumulation","volume":"46","author":"Singh","year":"2014","journal-title":"Commun. Soil Sci. Plant Anal."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"482","DOI":"10.1016\/j.rse.2018.04.031","article-title":"Characterization of Sentinel-2A and Landsat-8 top of atmosphere, surface, and nadir BRDF adjusted reflectance and NDVI differences","volume":"215","author":"Zhang","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1016\/j.isprsjprs.2024.07.031","article-title":"From satellite-based phenological metrics to crop planting dates: Deriving field-level planting dates for corn and soybean in the U.S. Midwest","volume":"216","author":"Zhou","year":"2024","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1585","DOI":"10.1080\/01431169208904212","article-title":"The Best Index Slope Extraction (BISE): A method for reducing noise in NDVI time-series","volume":"13","author":"Viovy","year":"1992","journal-title":"Int. J. Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Xu, X., Conrad, C., and Doktor, D. (2017). Optimising phenological metrics extraction for different crop types in Germany using the moderate resolution imaging spectrometer (MODIS). Remote Sens., 9.","DOI":"10.3390\/rs9030254"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"367","DOI":"10.1016\/j.rse.2016.10.025","article-title":"On the performance of remote sensing time series reconstruction methods\u2014A spatial comparison","volume":"187","author":"Zhou","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_40","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. Geoinf."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Satopaa, V., Albrecht, J., Irwin, D., and Raghavan, B. (2011, January 20\u201324). Finding a \u201cKneedle\u201d in a Haystack: Detecting Knee Points in System Behavior. Proceedings of the 2011 31st International Conference on Distributed Computing Systems Workshops, Minneapolis, MI, USA.","DOI":"10.1109\/ICDCSW.2011.20"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Vavlas, N.C., Waine, T.W., Meersmans, J., Burgess, P.J., Fontanelli, G., and Richter, G.M. (2020). Deriving Wheat Crop Productivity Indicators Using Sentinel-1 Time Series. Remote Sens., 12.","DOI":"10.3390\/rs12152385"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1016\/j.isprsjprs.2023.07.023","article-title":"Vegetation descriptors from Sentinel-1 SAR data for crop growth monitoring","volume":"203","author":"Bao","year":"2023","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"113555","DOI":"10.1016\/j.rse.2023.113555","article-title":"Understanding Sentinel-1 backscatter response to sugarcane yield variability and waterlogging","volume":"290","author":"Mahmud","year":"2023","journal-title":"Remote Sens. Environ."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Zhu, W., He, B., Xie, Z., Zhao, C., Zhuang, H., and Li, P. (2022). Reconstruction of vegetation index time series based on self-weighting function fitting from curve features. Remote Sens., 14.","DOI":"10.3390\/rs14092247"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Misra, V., Mall, A., Solomon, S., and Ansari, M.I. (2022). Post-harvest biology and recent advances of storage technologies in sugarcane. Biotechnol. Rep., 33.","DOI":"10.1016\/j.btre.2022.e00705"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"819","DOI":"10.1007\/s12355-020-00945-5","article-title":"Sugarcane yield and yield components as affected by harvest time","volume":"23","author":"Marin","year":"2021","journal-title":"Sugar Tech"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"6620","DOI":"10.3390\/rs6076620","article-title":"Toward a satellite-based system of sugarcane yield estimation and forecasting in smallholder farming conditions: A case study on Reunion Island","volume":"6","author":"Morel","year":"2014","journal-title":"Remote Sens."},{"key":"ref_49","unstructured":"Birdi, P.K., and Kale, K. (2017, January 23\u201327). Identification of growth stage of sugarcane crop using decision tree for Landsat-8 data. Proceedings of the 38th Asian Conference on Remote Sensing, New Delhi, India."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Cruz-Sanabria, H., Sanchez, M.G., Rivera-Caicedo, J.P., and Avila-George, H. (2020, January 21\u201323). Identification of phenological stages of sugarcane cultivation using Sentinel-2 images. Proceedings of the 2020 9th International Conference On Software Process Improvement (CIMPS), Mazatlan, Sinaloa, Mexico.","DOI":"10.1109\/CIMPS52057.2020.9390095"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/22\/4244\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:32:25Z","timestamp":1760113945000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/22\/4244"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,14]]},"references-count":50,"journal-issue":{"issue":"22","published-online":{"date-parts":[[2024,11]]}},"alternative-id":["rs16224244"],"URL":"https:\/\/doi.org\/10.3390\/rs16224244","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11,14]]}}}