{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T16:23:11Z","timestamp":1774542191105,"version":"3.50.1"},"reference-count":52,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2018,3,21]],"date-time":"2018-03-21T00:00:00Z","timestamp":1521590400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"NASA Land Cover Land Use Change Program (LCLUC)","award":["NNX15AK65G"],"award-info":[{"award-number":["NNX15AK65G"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>While publicly available, cost-free coarse and medium spatial resolution satellite data such as MODIS and Landsat perform well in characterizing industrial cropping systems, commercial high spatial resolution satellite data are often preferred alternative for fine scale land tenure agricultural systems such as found in Pakistan. In this article, we integrated commercial 5 m spatial resolution RapidEye and free 30 m Landsat imagery in characterizing winter wheat in Punjab province, Pakistan. Specifically, we used 5 m spatial resolution RapidEye imagery from peak of the winter wheat growing season to derive training data for the characterization of time-series Landsat data. After co-registration, each RapidEye image was classified into wheat\/no wheat labels at the 5 m resolution and then aggregated as percent cover to 30 m Landsat grid cells. We produced four maps, two using RapidEye derived continuous training data (of percent wheat cover) as input to a regression tree model, and two using RapidEye derived categorical training data as input to a classification tree model. From the RapidEye-derived 30 m continuous training data, we derived Map 1 as percent wheat per pixel, and Map 2 as binary wheat\/no wheat classification derived using a 50% threshold applied to Map 1. To create the categorical wheat\/no wheat training data, we first converted the continuous training data to a wheat\/no wheat classification, and then used these categorical RapidEye training data to produce a categorical wheat map from the Landsat data. Two methods for categorizing the training data were used. The first method used a 50% wheat\/no wheat threshold to produce Map 3, and the second method used only pure wheat (\u226575% cover) and no wheat (\u226425% cover) training pixels to produce Map 4. The approach of Map 4 is analogous to a standard method in which whole, pure, high-confidence training pixels are delineated. We validated the wheat maps with field data collected using a stratified, two-stage cluster design. Accuracy of the maps produced from the percent cover training data (Map 1 and Map 2) was not substantially better than the accuracy of the maps produced from the categorical training data as all methods yielded similar overall accuracies (\u00b1standard error): 88% (\u00b14%) for Map 1, 90% (\u00b14%) for Map 2, 90% (\u00b14%) for Map 3, and 87% (\u00b14%) for Map 4. Because the percent cover training data did not produce significantly higher accuracies, sub-pixel training data are not required for winter wheat mapping in Punjab. Given sufficient expertise in supervised classification model calibration, freely available Landsat data are sufficient for crop mapping in the fine-scale land tenure system of Punjab. For winter wheat mapping in Punjab and other like landscapes, training data for supervised classification may be collected directly from Landsat images without the need for high resolution reference imagery.<\/jats:p>","DOI":"10.3390\/rs10040489","type":"journal-article","created":{"date-parts":[[2018,3,22]],"date-time":"2018-03-22T05:14:55Z","timestamp":1521695695000},"page":"489","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":30,"title":["Evaluating Landsat and RapidEye Data for Winter Wheat Mapping and Area Estimation in Punjab, Pakistan"],"prefix":"10.3390","volume":"10","author":[{"given":"Ahmad","family":"Khan","sequence":"first","affiliation":[{"name":"Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0042-2767","authenticated-orcid":false,"given":"Matthew","family":"Hansen","sequence":"additional","affiliation":[{"name":"Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA"}]},{"given":"Peter","family":"Potapov","sequence":"additional","affiliation":[{"name":"Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA"}]},{"given":"Bernard","family":"Adusei","sequence":"additional","affiliation":[{"name":"Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA"}]},{"given":"Amy","family":"Pickens","sequence":"additional","affiliation":[{"name":"Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA"}]},{"given":"Alexander","family":"Krylov","sequence":"additional","affiliation":[{"name":"Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA"}]},{"given":"Stephen","family":"Stehman","sequence":"additional","affiliation":[{"name":"SUNY College of Environmental Science and Forestry, Syracuse, NY 13210, USA"}]}],"member":"1968","published-online":{"date-parts":[[2018,3,21]]},"reference":[{"key":"ref_1","first-page":"1517","article-title":"Mult-resolution in remote sensing for agricultural monitoring: A review","volume":"66","author":"Junior","year":"2014","journal-title":"Rev. Bras. Cartogr."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.rse.2017.03.047","article-title":"A multi-resolution approach to national-scale cultivated area estimation of soybean","volume":"195","author":"King","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_3","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_4","doi-asserted-by":"crossref","first-page":"389","DOI":"10.1111\/j.1751-5823.2005.tb00155.x","article-title":"Using Remote Sensing for Agricultural Statistics","volume":"73","author":"Carfagna","year":"2005","journal-title":"Int. Stat. Rev."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"9653","DOI":"10.3390\/rs6109653","article-title":"Wheat yield forecasting for Punjab Province from Vegetation Index Time Series and Historic Crop Statistics","volume":"6","author":"Dempewolf","year":"2014","journal-title":"J. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1391","DOI":"10.1080\/01431161.2016.1151572","article-title":"Landsat-based wheat mapping in the heterogeneous cropping system of Punjab, Pakistan","volume":"37","author":"Khan","year":"2016","journal-title":"Int. J. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"358","DOI":"10.1016\/j.biosystemseng.2012.08.009","article-title":"Twenty five years of remote sensing in precision agriculture: Key advances and remaining knowledge gaps","volume":"114","author":"Mulla","year":"2013","journal-title":"Biosyst. Eng."},{"key":"ref_8","unstructured":"GoPakistan (2018, February 20). Agricultural Census 2010\u2014Pakistan Report, Available online: www.pbs.gov.pk."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Zheng, Y., Wu, B., Zhang, M., and Zeng, H. (2016). Crop Phenology Detection Using High Spatio-Temporal Resolution Data Fused from SPOT5 and MODIS Products. Sensors, 16.","DOI":"10.3390\/s16122099"},{"key":"ref_10","unstructured":"The World Bank (WB), and Food and Agriculture Organization of the United Nations (FAO) (2011). Global Strategy to Improve Agricultural and Rural Statisitcs, FAO."},{"key":"ref_11","first-page":"1","article-title":"Remote sensing based wheat acreage and spectral-trend-agrometeorological Yield Forecasting: Factor Analysis Approach","volume":"9","author":"Verma","year":"2011","journal-title":"Stat. Appl."},{"key":"ref_12","first-page":"657","article-title":"Efficient collection of training data for sub-pixel land cover classification using neural networks","volume":"13","author":"Heremans","year":"2011","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"949","DOI":"10.3390\/rs5020949","article-title":"Advances in Remote Sensing of Agriculture: Context Description, Existing Operational Monitoring Systems and Major Information Needs","volume":"5","author":"Atzberger","year":"2013","journal-title":"Remote Sens."},{"key":"ref_14","unstructured":"Akhtar, I. (2012, May 16). Pakistan Needs a New Crop Forecasting System. Available online: https:\/\/www.scidev.net\/global\/climate-change\/opinion\/pakistan-needs-a-new-crop-forecasting-system.html."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"859","DOI":"10.1007\/s12524-014-0377-5","article-title":"Corn Area Extraction by the Integration of MODIS-EVI Time Series Data and China\u2019s Enviornment Satellite (HJ-1) Data","volume":"42","author":"Yao","year":"2013","journal-title":"J. Indian Soc. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1016\/j.rse.2014.02.015","article-title":"Good practices for estimating area and assessing accuracy of land change","volume":"148","author":"Olofsson","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_17","unstructured":"SUPARCO (2012). Punjab CRS: Baseline Survey, Agriculture Information System. Building Provincial Capacity for Crop Forecasting and Estimation."},{"key":"ref_18","unstructured":"Crop Reporting Service, Punjab (2015). Rabi Crop Estimates Data Book 2014\u20132015, Crop Reporting Service, Punjab."},{"key":"ref_19","unstructured":"Qasim, M. (2012). Determinants of Farm Income and Agricultural Risk Management Strategies: The Case of Rain-Fed Farm Households in Pakistan\u2019s Punjab, Kassel University Press GmbH. International Rural Development."},{"key":"ref_20","unstructured":"Ministry of Finance, Government of Pakistan (2014, July 03). Pakistan Economic Survey 2013\u20132014, Available online: http:\/\/www.finance.gov.pk\/survey_1314.html."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1516","DOI":"10.4236\/jwarp.2015.717125","article-title":"The Challenges of Water Pollution, Threat to Public Health, Flaws of Water Laws and Policies in Pakistan","volume":"7","author":"Jabeen","year":"2015","journal-title":"J. Water Resour. Protect."},{"key":"ref_22","unstructured":"GoPakistan (2015, April 01). Provisional Province Wise Population by Sex and Rurual\/Urban, Available online: www.pbs.gov.pk."},{"key":"ref_23","unstructured":"Branca, G., McCarthy, N., Lipper, L., and Jolejole, M.C. (2011). Climate-Smart Agriculture: A Systhesis of Empirical Evidence of Food Security and Mitigation Benefits from Improved Cropland Management, FAO."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Dempewolf, J., Adusei, B., Becker-Reschef, I., Barker, B., Potapov, P., Hansen, M., and Justice, C. (2013, January 21\u201326). Wheat Production Forecasting for Pakistan from Satellite Data. Proceedings of the 2013 IEEE International on Geoscience and Remote Sensing Symposium (IGARSS), Melbourne, Australia.","DOI":"10.1109\/IGARSS.2013.6723517"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1016\/S0034-4257(99)00054-1","article-title":"Operational Atmospheric Correction of Landsat TM Data","volume":"70","author":"Ouaidrari","year":"1999","journal-title":"Remote Sens. Environ."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/0034-4257(79)90013-0","article-title":"Photographic Infrared Linear Combinations for Monitoring Vegetation","volume":"8","author":"Tucker","year":"1979","journal-title":"Remote Sens. Environ."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"258","DOI":"10.1016\/S0034-4257(96)00067-3","article-title":"NDWI\u2014A 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_28","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1016\/j.rse.2011.08.027","article-title":"Quantifying forest cover loss in Democratic Republic of the Congo, 2000\u20132010, with Landsat ETM plus data","volume":"122","author":"Potapov","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"893","DOI":"10.1016\/j.rse.2009.01.007","article-title":"Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI sensors","volume":"113","author":"Chander","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"2495","DOI":"10.1016\/j.rse.2007.11.012","article-title":"A method for integrating MODIS and Landsat data for systematic monitoring of forest cover and change in the Congo Basin","volume":"112","author":"Hansen","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"695","DOI":"10.1007\/s10021-004-0243-3","article-title":"Detecting long-term global forest change using continuous fields of tree cover maps from 8-km Advanced Very High Resolution Radiometer (AVHRR) data for the years 1982\u20131999","volume":"7","author":"Hansen","year":"2004","journal-title":"Ecosystems"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1654","DOI":"10.2134\/agronj2007.0170","article-title":"Corn and soybean mapping in the United States using MODIS time-series data sets","volume":"99","author":"Chang","year":"2007","journal-title":"Am. Soc. Agron."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"20867","DOI":"10.1029\/95JD01536","article-title":"Mapping the land surface for global atmospher-biosphere models: Toward continous distributions of vegetation\u2019s functional properties","volume":"100","author":"DeFries","year":"1995","journal-title":"J. Geophys. Res."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"703","DOI":"10.2307\/3235884","article-title":"Measuring phenological variability from satellite imagery","volume":"5","author":"Reed","year":"1994","journal-title":"J. Veg. Sci."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1844","DOI":"10.3390\/rs2071844","article-title":"Estimating Global Cropland extent with Multi-year MODIS Data","volume":"2","author":"Pittman","year":"2010","journal-title":"Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Stoll, E., Konstanski, H., Anderson, C., Douglas, K., and Oxfort, M. (2012, January 3\u201310). The RapidEye Constellation and Its Data Products. Proceedings of the 2012 IEEE on Aerospace Conference, Big Sky, MT, USA.","DOI":"10.1109\/AERO.2012.6187173"},{"key":"ref_37","first-page":"239","article-title":"Detecting forest damage after a low severity fire using remote sensing at multiple scale","volume":"35","author":"Arnette","year":"2015","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1335","DOI":"10.3390\/rs5031335","article-title":"Mapping the Spatial Distribution of Winter Crops at Sub-Pixel Level Using AVHRR NDVI Time Series and Neural Nets","volume":"5","author":"Atzberger","year":"2013","journal-title":"Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1117\/1.JRS.8.083512","article-title":"Classification of small agricultural fields using combined Landsat-8 and RapidEye imagery: Case study of northern Serbia","volume":"8","author":"Crnojevic","year":"2014","journal-title":"J. Appl. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"5583","DOI":"10.1080\/01431161.2012.666812","article-title":"Testing the red edge channel for improving land-use classifications based on high-resolution multi-spectral satellite data","volume":"33","author":"Schuster","year":"2012","journal-title":"Int. J. Remote Sens."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1007\/BF00058655","article-title":"Bagging predictors","volume":"24","author":"Breiman","year":"1996","journal-title":"Mach. Learn."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1075","DOI":"10.1080\/01431169608949069","article-title":"Classification trees: An alternative to traditional land cover classifiers","volume":"17","author":"Hansen","year":"1996","journal-title":"Int. J. Remote Sens."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1007\/s11273-012-9277-z","article-title":"Identifying nascent wetland forest conversion in the Democratic Republic of the Congo","volume":"21","author":"Bwangoy","year":"2013","journal-title":"Wetl. Ecol. Manag."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Ripley, B.D. (1996). Pattern Recognition and Neural Networks, Cambridge University Press.","DOI":"10.1017\/CBO9780511812651"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"279","DOI":"10.1080\/01431161.2010.519002","article-title":"Continous fields of land cover for the conterminous United States using Landsat data: First results from the Web-Enabled Landsat Data (WELD) project","volume":"2","author":"Hansen","year":"2011","journal-title":"Remote Sens. Lett."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1016\/j.rse.2009.08.004","article-title":"Wetland mapping in the Congo Basin using optical and radar remotely sensed data and derived topographical indices","volume":"114","author":"Bwangoy","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_47","unstructured":"Breiman, L., Friedman, J., Olshen, R., and Stone, C. (1984). Classification and Regression Trees, Wadsworth and Brooks\/Cole."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"320","DOI":"10.1016\/S0034-4257(02)00080-9","article-title":"Development of a MODIS percent tree cover validation data set for Western Province, Zambia","volume":"83","author":"Hansen","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1312","DOI":"10.1016\/j.rse.2010.01.010","article-title":"A generalized regression-based model for forecasting winter wheat yields in Kansas and Ukraine using MODIS data","volume":"114","author":"Vermote","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"124012","DOI":"10.1088\/1748-9326\/9\/12\/124012","article-title":"National satellite-based humid tropical forest change assessment in Peru in support of REDD+ implementation","volume":"9","author":"Potapov","year":"2014","journal-title":"Environ. Res. Lett."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Sarndal, C.-E., Swensson, B., and Wretman, J. (1992). Model Assisted Survey Sampling, Springer.","DOI":"10.1007\/978-1-4612-4378-6"},{"key":"ref_52","unstructured":"SUPARCO (2015). Rabi Crop 2014\u201315."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/4\/489\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T14:57:54Z","timestamp":1760194674000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/4\/489"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,3,21]]},"references-count":52,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2018,4]]}},"alternative-id":["rs10040489"],"URL":"https:\/\/doi.org\/10.3390\/rs10040489","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,3,21]]}}}