{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T11:07:57Z","timestamp":1775560077344,"version":"3.50.1"},"reference-count":52,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2023,6,9]],"date-time":"2023-06-09T00:00:00Z","timestamp":1686268800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Omidyar Network\u2019s Property Rights Initiative, now PLACE, and NASA","award":["80NSSC18K0158"],"award-info":[{"award-number":["80NSSC18K0158"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Mapping arable field areas is crucial for assessing agricultural productivity but poses challenges in sub-Saharan agroecosystems because of diverse crop calendars, small and irregularly shaped fields, persistent cloud cover, and lack of high-quality model training data. This study proposes several methodological improvements to overcome these challenges. Specifically, it utilizes long-term MODIS data to stratify finer Sentinel-2 reflectance and Sentinel-1 backscatter image features on a per-pixel basis. It also incorporates texture features and employs a machine learning approach with over 300,000 samples. The eastern region of Ghana was stratified into seven seasonal strata exhibiting distinct vegetation seasonality, capturing diversity in crop calendars, using long-term MODIS (2001\u20132009) normalized difference vegetation index phenology. Three years (2017\u20132019) of Sentinel-1 and Sentinel-2 original bands at 20 m were composited into dry and wet seasonal features according to the strata, from which spectral, polarimetric, and texture features were extracted. The field boundaries were digitized using PlanetScope images (2018\u20132019). Random Forest classifier with 10-fold cross-validation and recursive feature elimination was used for feature selection and model building. Including topographic variables, out of 137 image features, only 11 features were found important. Sentinel-2 SWIR-based spectral features were most important, followed by Sentinel-1 polarimetric (VV) and elevation features. Half of the 11 features were variance texture features, followed by spectral features. The Random Forest classifier produced a 0.78 AUC score with overall precision, recall, and F1-score of 0.96, 0.78, and 0.85, respectively. While the precision for both classes was &gt;0.90, the recall rate for arable areas was half that of non-arable areas. Future studies could improve the technical workflow with reliable balanced sampling, narrowband hyperspectral images, and fully polarized SAR images.<\/jats:p>","DOI":"10.3390\/rs15123014","type":"journal-article","created":{"date-parts":[[2023,6,9]],"date-time":"2023-06-09T02:03:18Z","timestamp":1686276198000},"page":"3014","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Cropland Mapping in Tropical Smallholder Systems with Seasonally Stratified Sentinel-1 and Sentinel-2 Spectral and Textural Features"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9088-1427","authenticated-orcid":false,"given":"Manushi B.","family":"Trivedi","sequence":"first","affiliation":[{"name":"School of Integrative Plant Science, Cornell University, Tower Rd, Ithaca, NY 14850, USA"},{"name":"Faculty of Geo-Information Science and Earth Observation, University of Twente, Hengelosestraat 99, 7514 AE Enschede, The Netherlands"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9738-5036","authenticated-orcid":false,"given":"Michael","family":"Marshall","sequence":"additional","affiliation":[{"name":"Faculty of Geo-Information Science and Earth Observation, University of Twente, Hengelosestraat 99, 7514 AE Enschede, The Netherlands"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9358-816X","authenticated-orcid":false,"given":"Lyndon","family":"Estes","sequence":"additional","affiliation":[{"name":"Graduate School of Geography, Clark University, 950 Main Street, Worcester, MA 01610, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3944-8924","authenticated-orcid":false,"given":"C.A.J.M.","family":"de Bie","sequence":"additional","affiliation":[{"name":"Faculty of Geo-Information Science and Earth Observation, University of Twente, Hengelosestraat 99, 7514 AE Enschede, The Netherlands"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8212-7221","authenticated-orcid":false,"given":"Ling","family":"Chang","sequence":"additional","affiliation":[{"name":"Faculty of Geo-Information Science and Earth Observation, University of Twente, Hengelosestraat 99, 7514 AE Enschede, The Netherlands"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7249-3778","authenticated-orcid":false,"given":"Andrew","family":"Nelson","sequence":"additional","affiliation":[{"name":"Faculty of Geo-Information Science and Earth Observation, University of Twente, Hengelosestraat 99, 7514 AE Enschede, The Netherlands"}]}],"member":"1968","published-online":{"date-parts":[[2023,6,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"United Nations (2019). The Sustainable Development Goals Report 2019, United Nations. Available online: https:\/\/unstats.un.org\/sdgs\/report\/2019\/The-Sustainable-Development-Goals-Report-2019.pdf.","DOI":"10.18356\/5d04ad97-en"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1016\/j.gfs.2014.10.004","article-title":"Improved global cropland data as an essential ingredient for food security","volume":"4","author":"See","year":"2015","journal-title":"Glob. Food Secur."},{"key":"ref_3","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_4","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_5","doi-asserted-by":"crossref","first-page":"D14","DOI":"10.1029\/2007JD009175","article-title":"Crop area estimation using high and medium resolution satellite imagery in areas with complex topography","volume":"113","author":"Husak","year":"2008","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Estes, L.D., Ye, S., Song, L., Luo, B., Eastman, J.R., Meng, Z., Zhang, Q., McRitchie, D., Debats, S.R., and Muhando, J. (2022). High Resolution, Annual Maps of Field Boundaries for Smallholder-Dominated Croplands at National Scales. Front. Artif. Intell., 4.","DOI":"10.3389\/frai.2021.744863"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"210","DOI":"10.1016\/j.rse.2016.03.010","article-title":"A generalized computer vision approach to mapping crop fields in heterogeneous agricultural landscapes","volume":"179","author":"Debats","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1007\/s12571-015-0538-6","article-title":"In search of a global model of cultivation: Using remote sensing to examine the characteristics and constraints of agricultural production in the developing world","volume":"8","author":"Husak","year":"2016","journal-title":"Food Sec."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1016\/j.isprsjprs.2017.01.019","article-title":"Automated cropland mapping of continental Africa using Google Earth Engine cloud computing","volume":"126","author":"Xiong","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1016\/j.isprsjprs.2013.04.007","article-title":"Evaluating the capabilities of Sentinel-2 for quantitative estimation of biophysical variables in vegetation","volume":"82","author":"Frampton","year":"2013","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"105078","DOI":"10.1016\/j.compag.2019.105078","article-title":"Automating field boundary delineation with multi-temporal Sentinel-2 imagery","volume":"167","author":"Watkins","year":"2019","journal-title":"Comput. Electron. Agric."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2150","DOI":"10.1109\/TGRS.2019.2953652","article-title":"A Method for the Analysis of Small Crop Fields in Sentinel-2 Dense Time Series","volume":"58","author":"Bovolo","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"2249","DOI":"10.1109\/JSTARS.2016.2639043","article-title":"Radar Remote Sensing of Agricultural Canopies: A Review","volume":"10","author":"McNairn","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1016\/j.rse.2019.04.016","article-title":"Smallholder maize area and yield mapping at national scales with Google Earth Engine","volume":"228","author":"Jin","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_15","first-page":"388","article-title":"Investigating the impact of classification features and classifiers on crop mapping performance in heterogeneous agricultural landscapes","volume":"102","author":"Zhang","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1016\/j.rse.2018.04.025","article-title":"Smallholder crop area mapped with wall-to-wall WorldView sub-meter panchromatic image texture: A test case for Tigray, Ethiopia","volume":"212","author":"Neigh","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1016\/j.rse.2017.06.040","article-title":"Extracting smallholder cropped area in Tigray, Ethiopia with wall-to-wall sub-meter WorldView and moderate resolution Landsat 8 imagery","volume":"202","author":"McCarty","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_18","first-page":"22","article-title":"Detecting long-duration cloud contamination in hyper-temporal NDVI imagery","volume":"24","author":"Ali","year":"2013","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Xiong, J., Thenkabail, P.S., Tilton, J.C., Gumma, M.K., Teluguntla, P., Oliphant, A., Cogalton, R.G., Yadav, K., and Gorelick, N. (2017). Nominal 30-m Cropland Extent Map of Continental Africa by Integrating Pixel-Based and Object-Based Algorithms Using Sentinel-2 and Landsat-8 Data on Google Earth Engine. Remote Sens., 9.","DOI":"10.3390\/rs9101065"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"6673","DOI":"10.1080\/01431161.2010.512939","article-title":"Analysis of multi-temporal SPOT NDVI images for small-scale land-use mapping","volume":"32","author":"Khan","year":"2011","journal-title":"Int. J. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1016\/j.isprsjprs.2020.01.024","article-title":"blended census and multiscale remote sensing approach to probabilistic cropland mapping in complex landscapes","volume":"161","author":"Mohammed","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Elmes, A., Alemohammad, H., Avery, R., Caylor, K., Eastman, J.R., Fishgold, L., Friedl, M.A., Jain, M., Kohli, D., and Laso Bayas, J.C. (2020). Accounting for Training Data Error in Machine Learning Applied to Earth Observations. Remote Sens., 12.","DOI":"10.3390\/rs12061034"},{"key":"ref_23","unstructured":"PlanetTeam (2023, March 26). Planet Application Program Interface. Space for Life on Earth. Available online: https:\/\/api.planet.com."},{"key":"ref_24","unstructured":"Ministry of Food and Agriculture (MoFA)\u2014Statistics, Research and Information Directorate (SRID) (2023, January 02). Agriculture in Ghana Facts and Figures 2016, Available online: https:\/\/mofa.gov.gh\/site\/images\/pdf\/Agric%20in%20Ghana%20F&F%202016_Final.pdf."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Baidu, M., Amekudzi, L.K., Aryee, J.N.A., and Annor, T. (2017). Assessment of Long-Term Spatio-Temporal Rainfall Variability over Ghana using Wavelet Analysis. Climate, 5.","DOI":"10.3390\/cli5020030"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"e2049","DOI":"10.1002\/met.2049","article-title":"Climatic zoning of Ghana using selected meteorological variables for the period 1976\u20132018","volume":"29","author":"Bessah","year":"2022","journal-title":"Meteorol. Appl."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random Forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_28","unstructured":"Wilm, U.M. (2022, May 24). Sen2Cor Configuration and User Manual. S2-PDGS-MPC-L2A-SUM-V2.5.5. Available online: http:\/\/step.esa.int\/thirdparties\/sen2cor\/2.5.5\/docs\/S2-PDGS-MPC-L2A-SUM-V2.5.5_V2.pdf."},{"key":"ref_29","unstructured":"GEE (2022, May 24). Sentinel-1 Algorithms|Google Earth Engine. Google Developers. Available online: https:\/\/developers.google.com\/earth-engine\/guides\/sentinel1."},{"key":"ref_30","unstructured":"Ball, G.H., and Hall, D.J. (1965). ISODATA, a Novel Method of Data Analysis and Pattern Classification, Stanford Research Institute. Available online: https:\/\/apps.dtic.mil\/sti\/citations\/AD0699616."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"3171","DOI":"10.1080\/01431160701442146","article-title":"An unsupervised method of classifying remotely sensed images using Kohonen self-organizing maps and agglomerative hierarchical clustering methods","volume":"29","author":"Netto","year":"2008","journal-title":"Int. J. Remote Sens."},{"key":"ref_32","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_33","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1016\/0034-4257(89)90035-7","article-title":"Munsell Soft Color and Soil Reflectance in the Visible Spectral Bands of Landsat MSS and TM Data","volume":"27","author":"Escadafal","year":"1989","journal-title":"Remote Sens. Environ."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1078\/0176-1617-00887","article-title":"Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves","volume":"160","author":"Gitelson","year":"2003","journal-title":"J. Plant Physiol."},{"key":"ref_35","first-page":"170","article-title":"SENTINEL-2A red-edge spectral indices suitability for discriminating burn severity","volume":"50","author":"Quintano","year":"2016","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"709","DOI":"10.1109\/TGRS.1989.1398243","article-title":"Multitemporal and Dual-Polaization Observations of Agicultural Vegetation Covers by X-Band SAR Images","volume":"27","author":"Laur","year":"1989","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"3165","DOI":"10.1109\/TGRS.2007.903698","article-title":"Inferring Vegetation Water Content From C- and L-Band SAR Images","volume":"45","author":"Notarnicola","year":"2007","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Nasirzadehdizaji, R., Sanli, F.B., Abdikan, S., Cakir, Z., Sekertekin, A., and Ustuner, M. (2019). Sensitivity Analysis of Multi-Temporal Sentinel-1 SAR Parameters to Crop Height and Canopy Coverage. Appl. Sci., 9.","DOI":"10.3390\/app9040655"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"791","DOI":"10.1109\/TGRS.2005.860969","article-title":"C-band polarimetric indexes for maize monitoring based on a validated radiative transfer model","volume":"44","author":"Blaes","year":"2006","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1016\/j.rse.2017.07.015","article-title":"Understanding the temporal behavior of crops using Sentinel-1 and Sentinel-2-like data for agricultural applications","volume":"199","author":"Veloso","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Kakooei, M., Nascetti, A., and Ban, Y. (2018). IGARSS 2018, Proceedings of the 2018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain, 22\u201327 July 2018, IEEE.","DOI":"10.1109\/IGARSS.2018.8519098"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"610","DOI":"10.1109\/TSMC.1973.4309314","article-title":"Textural Features for Image Classification","volume":"SMC-3","author":"Haralick","year":"1973","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Zakeri, H., Yamazaki, F., and Liu, W. (2017). Texture Analysis and Land Cover Classification of Tehran Using Polarimetric Synthetic Aperture Radar Imagery. Appl. Sci., 7.","DOI":"10.3390\/app7050452"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/0034-4257(79)90013-0","article-title":"Red and photographic infrared linear combinations for monitoring vegetation","volume":"8","author":"Tucker","year":"1979","journal-title":"Remote Sens. Environ."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"257","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_46","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.isprsjprs.2016.01.011","article-title":"Random forest in remote sensing: A review of applications and future directions","volume":"114","author":"Belgiu","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.isprsjprs.2015.01.001","article-title":"Global land cover mapping using Earth observation satellite data: Recent progresses and challenges","volume":"103","author":"Ban","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"389","DOI":"10.1023\/A:1012487302797","article-title":"Gene Selection for Cancer Classification using Support Vector Machines","volume":"46","author":"Guyon","year":"2003","journal-title":"Mach. Learn."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1189","DOI":"10.1214\/aos\/1013203451","article-title":"Greedy function approximation: A gradient boosting machine","volume":"29","author":"Friedman","year":"2001","journal-title":"Ann. Stat."},{"key":"ref_50","unstructured":"Molnar, C. (2023, April 04). 8.1 Partial Dependence Plot (PDP)|Interpretable Machine Learning. Available online: https:\/\/christophm.github.io\/interpretable-ml-book\/pdp.html."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"111954","DOI":"10.1016\/j.rse.2020.111954","article-title":"Dual polarimetric radar vegetation index for crop growth monitoring using sentinel-1 SAR data","volume":"247","author":"Mandal","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"111375","DOI":"10.1016\/j.rse.2019.111375","article-title":"Needle in a haystack: Mapping rare and infrequent crops using satellite imagery and data balancing methods","volume":"233","author":"Waldner","year":"2019","journal-title":"Remote Sens. Environ."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/12\/3014\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:51:19Z","timestamp":1760125879000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/12\/3014"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,9]]},"references-count":52,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2023,6]]}},"alternative-id":["rs15123014"],"URL":"https:\/\/doi.org\/10.3390\/rs15123014","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,6,9]]}}}