{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T18:20:13Z","timestamp":1777486813019,"version":"3.51.4"},"reference-count":36,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2015,10,30]],"date-time":"2015-10-30T00:00:00Z","timestamp":1446163200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Over the recent past, there has been a growing concern on the need for  mapping cropping practices in order to improve decision-making in the agricultural sector. We developed an original method for mapping cropping practices: crop type and harvest mode, in a sugarcane landscape of western Kenya using remote sensing data. At local scale, a temporal series of 15-m resolution Landsat 8 images was obtained for Kibos sugar management zone over 20 dates (April 2013 to March 2014) to characterize cropping practices. To map the crop type and harvest mode we used ground survey and factory data over 1280 fields, digitized field boundaries, and spectral indices (the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Water Index (NDWI)) were computed for all Landsat images. The results showed NDVI classified crop type at 83.3% accuracy, while NDWI classified harvest mode at 90% accuracy. The crop map will inform better planning decisions for the sugar industry operations, while the harvest mode map will be used to plan for sensitizations forums on best management and environmental practices.<\/jats:p>","DOI":"10.3390\/rs71114428","type":"journal-article","created":{"date-parts":[[2015,11,2]],"date-time":"2015-11-02T02:53:57Z","timestamp":1446432837000},"page":"14428-14444","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":43,"title":["Mapping Cropping Practices of a Sugarcane-Based Cropping System in Kenya Using Remote Sensing"],"prefix":"10.3390","volume":"7","author":[{"given":"Betty","family":"Mulianga","sequence":"first","affiliation":[{"name":"Centre for International Research for Agricultural Development (CIRAD), Spatial Information and Analysis for Territories and Ecosystems (UMR TETIS), Maison de la T\u00e9l\u00e9d\u00e9tection, 500 rue J-F. Breton, Montpellier F-34093, France"},{"name":"Kenya Agriculture and Livestock Research Organization-Sugar Research Institute, Kisumu-Miwani Road, P.O Box 44\u201340100, Kisumu, Kenya"},{"name":"CIRAD Agro-ecology and Sustainable Intensification of Annual Crops (UPR AIDA), Avenue Agropolis, Montpellier Cedex 5, Montpellier F-34398, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9289-1052","authenticated-orcid":false,"given":"Agn\u00e8s","family":"B\u00e9gu\u00e9","sequence":"additional","affiliation":[{"name":"Centre for International Research for Agricultural Development (CIRAD), Spatial Information and Analysis for Territories and Ecosystems (UMR TETIS), Maison de la T\u00e9l\u00e9d\u00e9tection, 500 rue J-F. Breton, Montpellier F-34093, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pascal","family":"Clouvel","sequence":"additional","affiliation":[{"name":"CIRAD Agro-ecology and Sustainable Intensification of Annual Crops (UPR AIDA), Avenue Agropolis, Montpellier Cedex 5, Montpellier F-34398, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pierre","family":"Todoroff","sequence":"additional","affiliation":[{"name":"CIRAD Agro-ecology and Sustainable Intensification of Annual Crops (UPR AIDA), Station de Ligne-Paradis, 7 chemin de l\u2019IRAT, Saint-Pierre, R\u00e9union F-97410, France"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2015,10,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"430","DOI":"10.1016\/j.ecolind.2015.01.007","article-title":"Remote sensing of ecosystem services: A systematic review","volume":"52","author":"Atkinson","year":"2015","journal-title":"Ecol. Indic."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"922","DOI":"10.1126\/science.289.5486.1922","article-title":"Greenhouse gases in intensive agriculture: Contributions of individual gases to the radiative forcing of the atmosphere","volume":"289","author":"Robertson","year":"2000","journal-title":"Science"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1016\/j.rse.2004.03.001","article-title":"Optimal classification methods for mapping agricultural tillage practices","volume":"91","author":"South","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"285","DOI":"10.1016\/j.agee.2005.12.014","article-title":"Effect of sugarcane residue management (mulching versus burning) on organic matter in a clayey Oxisol from southern Brazil","volume":"115","author":"Razafimbelo","year":"2006","journal-title":"Agric. Ecosyst. Environ."},{"key":"ref_5","first-page":"77","article-title":"Greenhouse gas balance due to the conversion of sugarcane areas from burned to green harvest in Brazil","volume":"141","year":"2001","journal-title":"Agric. Ecosyst. Environ."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"190","DOI":"10.1016\/j.still.2010.10.002","article-title":"Soil CO2 emission and its relation to soil properties in sugarcane areas under slash-and-burn and green harvest","volume":"111","author":"Panosso","year":"2011","journal-title":"Soil Tillage Res."},{"key":"ref_7","unstructured":"Jamoza, J.E., Amolo, R.A., and Muturi, S.M. (2013). A Baseline Survey on the Status of Sugarcane Production Technologies in Western Kenya, International Society of Sugar Cane Technologists."},{"key":"ref_8","unstructured":"KSB (2012). Year Book of Statistics, Kenya Sugar Board."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2184","DOI":"10.3390\/rs5052184","article-title":"Forecasting regional sugarcane yield based on time integral and spatial aggregation of MODIS NDVI","volume":"5","author":"Mulianga","year":"2013","journal-title":"Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1016\/j.isprsjprs.2009.06.004","article-title":"Object based image analysis for remote sensing","volume":"65","author":"Blaschke","year":"2010","journal-title":"ISPRS J. Photogram. Remote Sens."},{"key":"ref_11","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_12","unstructured":"Longley, P.A., Goodchild, M.F., Maguire, D.J., and Rhind, D.W. (2005). Geographical Information Systems: Principles, Techniques, Management, and Applications, John Wiley & Sons, Inc."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"5391","DOI":"10.1080\/01431160903349057","article-title":"Spatio-temporal variability of sugarcane fields and recommendations for yield forecast using NDVI","volume":"31","author":"Begue","year":"2010","journal-title":"Int. J. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1057","DOI":"10.3390\/rs2041057","article-title":"Studies on the rapid expansion of sugarcane for ethanol production in S\u00e3o Paulo State (Brazil) using Landsat data","volume":"2","author":"Rudorff","year":"2010","journal-title":"Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2052","DOI":"10.1016\/j.rse.2009.04.009","article-title":"Integrating SPOT-5 time series, crop growth modeling and expert knowledge for monitoring agricultural practices: The case of sugarcane harvest on Reunion Island","volume":"113","author":"Guillaume","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"553","DOI":"10.1016\/j.rse.2012.04.011","article-title":"Object based image analysis and data mining applied to a remotely sensed Landsat time-series to map sugarcane over large areas","volume":"123","author":"Vieira","year":"2012","journal-title":"Remote Sens.Environ."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2890","DOI":"10.3390\/rs4102890","article-title":"An Automated Cropland Classification Algorithm (ACCA) for Tajikistan by combining Landsat, MODIS, and secondary data","volume":"4","author":"Thenkabail","year":"2012","journal-title":"Remote Sens."},{"key":"ref_18","unstructured":"Mulianga, B., B\u00e9gu\u00e9, A., Simoes, M., Todoroff, P., and Clouvel, P. (2012, January 23\u201327). MODIS data for forecasting sugarcane yield in Kenya through a zonal approach. Proceedings of the Sentinel-2 Preparatory Symposium, Frascati, Italy."},{"key":"ref_19","unstructured":"Amolo, R., Abayo, G., Muturi, S., and Rono, J. (2009). The Impact of Planting and Harvesting Time on Sugarcane Productivity in Kenyan Sugar Industry, KESREF."},{"key":"ref_20","unstructured":"Mulianga, B. Assessing spatial heterogeneity and temporal dynamics of sugarcane landscape in western Kenya by remote sensing: Implications for environmental services. Availavble online: http:\/\/agritrop.cirad.fr\/574730\/."},{"key":"ref_21","unstructured":"KSB (2010). Year Book of Statistics, Kenya Sugar Board."},{"key":"ref_22","unstructured":"Monroy, L., Mulinge, W., and Witwer, M. (2012). Analysis of Incentives and Disincentives for Sugar in Kenya, FAO."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2682","DOI":"10.3390\/rs3122682","article-title":"Remote sensing images in support of environmental protocol: Monitoring the sugarcane harvest in Sao Paulo State, Brazil","volume":"3","author":"Aguiar","year":"2011","journal-title":"Remote Sens."},{"key":"ref_24","first-page":"109","article-title":"Improving harvest and planting monitoring for smallholders with geospatial technology: The Reunion Island experience","volume":"1298","author":"Lebourgeois","year":"2010","journal-title":"Int. Sugar J."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"36","DOI":"10.4236\/ijg.2011.21004","article-title":"Rainfall variability and its impact on normalized difference vegetation index in arid and semi-arid lands of Kenya","volume":"2","author":"Shisanya","year":"2012","journal-title":"Int. J. Geosci."},{"key":"ref_26","unstructured":"Cochran, W.G. (1977). Sampling Techniques, John Wiley and Sons. [3rd ed.]."},{"key":"ref_27","unstructured":"Rouse, J.W. (1974). Monitoring the Vernal Advancement of Retrogradation of Natural Vegetation, NASA\/GSFC. Type III, Final Report."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1016\/S0034-4257(96)00067-3","article-title":"NDWI, a normalized difference water index for remote sensing of vegetation liquid water from space","volume":"58","author":"Gao","year":"1996","journal-title":"Remote Sens. Environ."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Gu, Y., Hunt, E., Wardlow, B., Basara, J.B., Brown, J.F., and Verdin, J.P. (2008). Evaluation of MODIS NDVI and NDWI for vegetation drought monitoring using Oklahoma Mesonet soil moisture data. Geophys. Res. Lett., 35.","DOI":"10.1029\/2008GL035772"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1096","DOI":"10.1016\/j.rse.2007.07.019","article-title":"Large area crop mapping using time-series MODIS 250 m NDVI data: An assessment for the US Central Great Plains","volume":"112","author":"Wardlow","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_31","unstructured":"Congalton, R.G., and Green, K. (1991). Assessing the Accuracy of Remotely Sensed Data: Principles and Practices, CRC Press."},{"key":"ref_32","first-page":"83","article-title":"Crop area mapping in West Africa using landscape stratification of MODIS time series and comparison with existing global land products","volume":"14","author":"Vintrou","year":"2012","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"641","DOI":"10.2134\/agronj2003.0257","article-title":"Temporal and spatial relationships between within-field yield variability in cotton and high-spatial hyperspectral remote sensing imagery","volume":"97","author":"Ustin","year":"2005","journal-title":"Agronomy J."},{"key":"ref_34","first-page":"341","article-title":"Satellite change detection of forest harvest patterns on an industrial forest landscape","volume":"49","author":"Sader","year":"2003","journal-title":"For. Sci."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1016\/j.rse.2005.07.008","article-title":"Vegetation water content estimation for corn and soybeans using spectral indices derived from MODIS near- and short-wave infrared bands","volume":"98","author":"Chen","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_36","first-page":"509","article-title":"Landsat wildland mapping accuracy","volume":"46","author":"Todd","year":"1980","journal-title":"Photogram. Eng. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/7\/11\/14428\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T20:51:12Z","timestamp":1760215872000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/7\/11\/14428"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2015,10,30]]},"references-count":36,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2015,11]]}},"alternative-id":["rs71114428"],"URL":"https:\/\/doi.org\/10.3390\/rs71114428","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2015,10,30]]}}}