{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T08:54:05Z","timestamp":1777625645563,"version":"3.51.4"},"reference-count":44,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2018,2,14]],"date-time":"2018-02-14T00:00:00Z","timestamp":1518566400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Cash crops are agricultural crops intended to be sold for profit as opposed to subsistence crops, meant to support the producer, or to support livestock. Since cash crops are intended for future sale, they translate into large financial value when considered on a wide geographical scale, so their production directly involves financial risk. At a national level, extreme weather events including destructive rain or hail, as well as drought, can have a significant impact on the overall economic balance. It is thus important to map such crops in order to set up insurance and mitigation strategies. Using locally generated data\u2014such as municipality-level records of crop seeding\u2014for mapping purposes implies facing a series of issues like data availability, quality, homogeneity, etc. We thus opted for a different approach relying on global datasets. Global datasets ensure homogeneity and availability of data, although sometimes at the expense of precision and accuracy. A typical global approach makes use of spaceborne remote sensing, for which different land cover classification strategies are available in literature at different levels of cost and accuracy. We selected the optimal strategy in the perspective of a global processing chain. Thanks to a specifically developed strategy for fusing unsupervised classification results with environmental constraints and other geospatial inputs including ground-based data, we managed to obtain good classification results despite the constraints placed. The overall production process was composed using \u201cgood-enough\" algorithms at each step, ensuring that the precision, accuracy, and data-hunger of each algorithm was commensurate to the precision, accuracy, and amount of data available. This paper describes the tailored strategy developed on the occasion as a cooperation among different groups with diverse backgrounds, a strategy which is believed to be profitably reusable in other, similar contexts. The paper presents the problem, the constraints and the adopted solutions; it then summarizes the main findings including that efforts and costs can be saved on the side of Earth Observation data processing when additional ground-based data are available to support the mapping task.<\/jats:p>","DOI":"10.3390\/s18020591","type":"journal-article","created":{"date-parts":[[2018,2,14]],"date-time":"2018-02-14T07:01:42Z","timestamp":1518591702000},"page":"591","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["A Novel Strategy for Very-Large-Scale Cash-Crop Mapping in the Context of Weather-Related Risk Assessment, Combining Global Satellite Multispectral Datasets, Environmental Constraints, and In Situ Acquisition of Geospatial Data"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0044-2998","authenticated-orcid":false,"given":"Fabio","family":"Dell\u2019Acqua","sequence":"first","affiliation":[{"name":"Department of Electrical, Computer, Biomedical Engineering, University of Pavia, Via Adolfo Ferrata, 5, I-27100 Pavia, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gianni","family":"Iannelli","sequence":"additional","affiliation":[{"name":"Ticinum Aerospace S.r.l., I-27100 Pavia, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Marco","family":"Torres","sequence":"additional","affiliation":[{"name":"Instituto de Ingenier\u00eda, UNAM, C.P. 04510 Ciudad de M\u00e9xico, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6283-2732","authenticated-orcid":false,"given":"Mario","family":"Martina","sequence":"additional","affiliation":[{"name":"Scuola Universitaria Superiore IUSS Pavia, Piazza della Vittoria, 15, I-27100 Pavia, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2018,2,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1093\/jpe\/rtm005","article-title":"Remote sensing imagery in vegetation mapping: A review","volume":"1","author":"Xie","year":"2008","journal-title":"J. Plant Ecol."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Thenkabail, P., Lyon, J., and Huete, A. (2011). Advances in Hyperspectral Remote Sensing of Vegetation and Agricultural Croplands, CRC Press.","DOI":"10.1201\/b11222-3"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Gurenko, E.N. (2015). Climate Change and Insurance: Disaster Risk Financing in Developing Countries, Routledge.","DOI":"10.4324\/9781849775960"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"467","DOI":"10.1111\/disa.12118","article-title":"Disaster risk insurance and catastrophe models in risk-prone small Caribbean islands","volume":"39","author":"Joyette","year":"2015","journal-title":"Disasters"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"264","DOI":"10.1038\/nclimate2124","article-title":"Increasing stress on disaster-risk finance due to large floods","volume":"4","author":"Jongman","year":"2014","journal-title":"Nat. Clim. Chang."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"12070","DOI":"10.3390\/rs61212070","article-title":"Global Land Cover Mapping: A Review and Uncertainty Analysis","volume":"6","author":"Congalton","year":"2014","journal-title":"Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1303","DOI":"10.1080\/014311600210191","article-title":"Development of a global land cover characteristics database and IGBP DISCover from 1 km AVHRR data","volume":"21","author":"Loveland","year":"2000","journal-title":"Int. J. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1331","DOI":"10.1080\/014311600210209","article-title":"Global land cover classification at 1 km spatial resolution using a classification tree approach","volume":"21","author":"Hansen","year":"2000","journal-title":"Int. J. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1959","DOI":"10.1080\/01431160412331291297","article-title":"GLC2000: A new approach to global land cover mapping from Earth observation data","volume":"26","author":"Belward","year":"2005","journal-title":"Int. J. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Manakos, I., and Braun, M. (2014). CORINE Land Cover and Land Cover Change Products. Land Use and Land Cover Mapping in Europe: Practices & Trends, Springer.","DOI":"10.1007\/978-94-007-7969-3"},{"key":"ref_11","unstructured":"(2017, August 30). Corine Land Cover. Available online: http:\/\/land.copernicus.eu\/pan-european\/corine-land-cover\/view."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"373","DOI":"10.1080\/17538947.2012.713190","article-title":"Global characterization and monitoring of forest cover using Landsat data: opportunities and challenges","volume":"5","author":"Townshend","year":"2012","journal-title":"Int. J. Digit. Earth"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"12356","DOI":"10.3390\/rs70912356","article-title":"Assessment of an Operational System for Crop Type Map Production Using High Temporal and Spatial Resolution Satellite Optical Imagery","volume":"7","author":"Inglada","year":"2015","journal-title":"Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1496","DOI":"10.1109\/LGRS.2015.2409982","article-title":"First results from the phenology-based synthesis classifier using Landsat 8 imagery","volume":"12","author":"Simonetti","year":"2015","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1016\/0034-4257(84)90061-0","article-title":"Classification of corn and soybeans using multitemporal thematic mapper data","volume":"16","author":"Badhwar","year":"1984","journal-title":"Remote Sens. Environ."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"4871","DOI":"10.1080\/0143116031000070490","article-title":"Classification of wheat crop with multi-temporal images: Performance of maximum likelihood and artificial neural networks","volume":"24","author":"Murthy","year":"2003","journal-title":"Int. J. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1016\/j.rse.2012.11.009","article-title":"Classifying multiyear agricultural land use data from Mato Grosso using time-series MODIS vegetation index data","volume":"130","author":"Brown","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"412","DOI":"10.1016\/j.rse.2004.08.002","article-title":"Cropland distributions from temporal unmixing of MODIS data","volume":"93","author":"Lobell","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_19","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 250m NDVI data: An assessment for the U.S. Central Great Plains","volume":"112","author":"Wardlow","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1190","DOI":"10.1016\/j.rse.2010.01.006","article-title":"The spatial distribution of crop types from MODIS data: Temporal unmixing using Independent Component Analysis","volume":"114","author":"Ozdogan","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_21","first-page":"486","article-title":"Sub-pixel classification of SPOT-VEGETATION time series for the assessment of regional crop areas in Belgium","volume":"10","author":"Verbeiren","year":"2008","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_22","unstructured":"(2018, January 15). USGS\/NASA Land Processes Distributed Active Archive Center, Available online: https:\/\/earthdata.nasa.gov\/about\/daacs\/daac-lpdaac."},{"key":"ref_23","unstructured":"(2018, January 15). Copernicus Open Access Hub. Available online: https:\/\/scihub.copernicus.eu\/."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"5283","DOI":"10.1109\/TGRS.2015.2420659","article-title":"Remote Sensing Image Matching Based on Adaptive Binning SIFT Descriptor","volume":"53","author":"Sedaghat","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"317","DOI":"10.1109\/LGRS.2009.2012878","article-title":"Mode-based method for matching of pre-and postevent remotely sensed images","volume":"6","author":"Aldrighi","year":"2009","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_26","unstructured":"Awad, A.I., and Hassaballah, M. (2016). Satellite Image Matching and Registration: A Comparative Study Using Invariant Local Features. Image Feature Detectors and Descriptors: Foundations and Applications, Springer International Publishing."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"De Vecchi, D., Harb, M., Iannelli, G.C., Gamba, P., Dell\u2019Acqua, F., and Feitosa, R.Q. (April, January 30). A feature-based approach to register CBERS CCD and HRC imagery for built-up area extraction purposes. Proceedings of the 2015 Joint Urban Remote Sensing Event (JURSE), Lausanne, Switzerland.","DOI":"10.1109\/JURSE.2015.7120350"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1532","DOI":"10.1109\/JSTARS.2016.2514274","article-title":"Automatic Delineation of Clouds and Their Shadows in Landsat and CBERS (HRCC) Data","volume":"9","author":"Harb","year":"2016","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Harb, M., Vecchi, D.D., Gamba, P., Dell\u2019Acqua, F., and Feitosa, R. (2015, January 26\u201331). Automatic clouds\/shadows extraction method from CBERS-2 CCD and LANDSAT data. Proceedings of the 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Milan, Italy.","DOI":"10.1109\/IGARSS.2015.7326851"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Harb, M., De Vecchi, D., and Dell\u2019Acqua, F. (April, January 30). Automatic hybrid-based built-up area extraction from Landsat 5, 7, and 8 data sets. Proceedings of the 2015 Joint Urban Remote Sensing Event (JURSE), Lausanne, Switzerland.","DOI":"10.1109\/JURSE.2015.7120475"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"291","DOI":"10.1080\/17538940902951401","article-title":"A new global raster water mask at 250 m resolution","volume":"2","author":"Carroll","year":"2009","journal-title":"Int. J. Digit. Earth"},{"key":"ref_32","unstructured":"FAO (1996). Agro-Ecological Zoning: Guidelines, Food & Agriculture Organization. Number 73."},{"key":"ref_33","unstructured":"(2017, August 30). Potencial Productivo de Especies Agr\u00edcolas de Importancia Socioecon\u00f3mica en M\u00e9xico. (In Spanish)."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1965","DOI":"10.1002\/joc.1276","article-title":"Very high resolution interpolated climate surfaces for global land areas","volume":"25","author":"Hijmans","year":"2005","journal-title":"Int. J. Climatol."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"4302","DOI":"10.1002\/joc.5086","article-title":"Worldclim 2: New 1-km spatial resolution climate surfaces for global land areas","volume":"37","author":"Fick","year":"2017","journal-title":"Int. J. Climatol."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Farr, T.G., Rosen, P.A., Caro, E., Crippen, R., Duren, R., Hensley, S., Kobrick, M., Paller, M., Rodriguez, E., and Roth, L. (2007). The shuttle radar topography mission. Rev. Geophys., 45.","DOI":"10.1029\/2005RG000183"},{"key":"ref_37","unstructured":"LPDAAC (2018, January 15). Land Processes Distributed Active Archive Center, Available online: https:\/\/lpdaac.usgs.gov\/."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"168","DOI":"10.1016\/j.rse.2009.08.016","article-title":"MODIS Collection 5 global land cover: Algorithm refinements and characterization of new datasets","volume":"114","author":"Friedl","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_39","first-page":"1013","article-title":"The IGBP-DIS Global 1-km LandCover Data Set DISCover: A Project Overview","volume":"65","author":"Belward","year":"1999","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_40","first-page":"1051","article-title":"Thematic validation of high-resolution global land-cover data sets","volume":"65","author":"Scepan","year":"1999","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"287","DOI":"10.1016\/S0034-4257(02)00078-0","article-title":"Global land cover mapping from MODIS: algorithms and early results","volume":"83","author":"Friedl","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1380","DOI":"10.1109\/36.649788","article-title":"Estimation of global leaf area index and absorbed par using radiative transfer models","volume":"35","author":"Myneni","year":"1997","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_43","first-page":"77","article-title":"A vegetation classification logic-based on remote-sensing for use in global biogeochemical models","volume":"23","author":"Running","year":"1994","journal-title":"Ambio"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"5-1","DOI":"10.1029\/2000GB001360","article-title":"Landscapes as patches of plant functional types: An integrating concept for climate and ecosystem models","volume":"16","author":"Bonan","year":"2002","journal-title":"Glob. Biogeochem. 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