{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,11]],"date-time":"2026-04-11T13:13:32Z","timestamp":1775913212319,"version":"3.50.1"},"reference-count":52,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2019,6,18]],"date-time":"2019-06-18T00:00:00Z","timestamp":1560816000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002322","name":"Coordena\u00e7\u00e3o de Aperfei\u00e7oamento de Pessoal de N\u00edvel Superior","doi-asserted-by":"publisher","award":["001"],"award-info":[{"award-number":["001"]}],"id":[{"id":"10.13039\/501100002322","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003593","name":"Conselho Nacional de Desenvolvimento Cient\u00edfico e Tecnol\u00f3gico","doi-asserted-by":"publisher","award":["148636\/2016-0"],"award-info":[{"award-number":["148636\/2016-0"]}],"id":[{"id":"10.13039\/501100003593","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Monitoring agricultural crops is necessary for decision-making in the field. However, it is known that in some regions and periods, cloud cover makes this activity difficult to carry out in a systematic way throughout the phenological cycle of crops. This circumstance opens up opportunities for techniques involving radar sensors, resulting in images that are free of cloud effects. In this context, the objective of this work was to obtain a normalized different vegetation index (NDVI) cloudless product (NDVInc) by modeling Sentinel 2 NDVI using different regression techniques and the Sentinel 1 radar backscatter as input. To do this, we used four pairs of Sentinel 2 and Sentinel 1 images on coincident days, aiming to achieve the greatest range of NDVI values for agricultural crops (soybean and maize). These coincident pairs were the only ones in which the percentage of clouds was not equal to 100% for 33 central pivot areas in western Bahia, Brazil. The dataset used for NDVInc modeling was divided into two subsets: training and validation. The training and validation datasets were from the period from 24 June 2017 to 19 July 2018 (four pairs of images). The best performing model was used in a temporal analysis from 02 October 2017 to 08 August 2018, totaling 55 Sentinel 2 images and 25 Sentinel 1 images. The selection of the best regression algorithm was based on two validation methodologies: K-fold cross-validation (k = 10) and holdout. We tested four modeling approaches with eight regression algorithms. The random forest was the algorithm that presented the best statistical metrics, regardless of the validation methodology and the approach used. Therefore, this model was applied to a time series of Sentinel 1 images in order to demonstrate the robustness and applicability of the model created. We observed that the data derived from Sentinel 1 allowed us to model, with great reliability, the NDVI of agricultural crops throughout the phenological cycle, making the methodology developed in this work a relevant solution for the monitoring of various regions, regardless of cloud cover.<\/jats:p>","DOI":"10.3390\/rs11121441","type":"journal-article","created":{"date-parts":[[2019,6,19]],"date-time":"2019-06-19T02:42:46Z","timestamp":1560912166000},"page":"1441","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":102,"title":["Crop NDVI Monitoring Based on Sentinel 1"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0186-8907","authenticated-orcid":false,"given":"Roberto","family":"Filgueiras","sequence":"first","affiliation":[{"name":"Department of Agricultural Engineering, Federal University of Vi\u00e7osa (UFV), Avenue Peter Henry Rolfs, Vi\u00e7osa, 36570-900, MG, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8795-8793","authenticated-orcid":false,"given":"Everardo Chartuni","family":"Mantovani","sequence":"additional","affiliation":[{"name":"Department of Agricultural Engineering, Federal University of Vi\u00e7osa (UFV), Avenue Peter Henry Rolfs, Vi\u00e7osa, 36570-900, MG, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5390-575X","authenticated-orcid":false,"given":"Daniel","family":"Althoff","sequence":"additional","affiliation":[{"name":"Department of Agricultural Engineering, Federal University of Vi\u00e7osa (UFV), Avenue Peter Henry Rolfs, Vi\u00e7osa, 36570-900, MG, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9484-1411","authenticated-orcid":false,"given":"Elp\u00eddio In\u00e1cio","family":"Fernandes Filho","sequence":"additional","affiliation":[{"name":"Departament of Soil and Plant Nutrition, Federal University of Vi\u00e7osa (UFV), Avenue Peter Henry Rolfs, Vi\u00e7osa, 36570-900, MG, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1671-1021","authenticated-orcid":false,"given":"Fernando Fran\u00e7a da","family":"Cunha","sequence":"additional","affiliation":[{"name":"Department of Agricultural Engineering, Federal University of Vi\u00e7osa (UFV), Avenue Peter Henry Rolfs, Vi\u00e7osa, 36570-900, MG, Brazil"}]}],"member":"1968","published-online":{"date-parts":[[2019,6,18]]},"reference":[{"key":"ref_1","first-page":"1884","article-title":"Variabilidade espa\u00e7o-temporal da condi\u00e7\u00e3o da vegeta\u00e7\u00e3o na agricultura irrigada por meio de imagens SENTINEL-2a","volume":"11","author":"Ribeiro","year":"2017","journal-title":"Rev. Bras. De Agric. Irrig."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"551","DOI":"10.1016\/j.rse.2018.11.007","article-title":"Near real-time agriculture monitoring at national scale at parcel resolution: Performance assessment of the Sen2-Agri automated system in various cropping systems around the world","volume":"221","author":"Defourny","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"16398","DOI":"10.3390\/rs71215841","article-title":"Review of Machine Learning Approaches for Biomass and Soil Moisture Retrievals from Remote Sensing Data","volume":"7","author":"Ali","year":"2015","journal-title":"Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"380","DOI":"10.1061\/(ASCE)0733-9437(2007)133:4(380)","article-title":"Satellite-based energy balance for mapping evapotranspiration with internalized calibration (METRIC)\u2014Model","volume":"133","author":"Allen","year":"2007","journal-title":"J. Irrig. Drain. Eng."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"501","DOI":"10.1016\/j.agrformet.2017.07.018","article-title":"Water productivity and crop yield: A simplified remote sensing driven operational approach","volume":"249","author":"Campos","year":"2017","journal-title":"Agric. For. Meteorol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1126","DOI":"10.18517\/ijaseit.8.4.5797","article-title":"Advances in Radar Remote Sensing of Agricultural Crops: A Review","volume":"8","author":"Sivasankar","year":"2018","journal-title":"Int. J. Adv. Sci. Eng. Inf. Technol."},{"key":"ref_7","first-page":"026010","article-title":"Use of different temporal scales to monitor phenology and its relationship with temporal evolution of normalized difference vegetation index in wheat","volume":"12","author":"Campos","year":"2018","journal-title":"J. Appl. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1080\/17538947.2011.623189","article-title":"Generation of high spatial and temporal resolution NDVI and its application in crop biomass estimation","volume":"6","author":"Meng","year":"2013","journal-title":"Int. J. Digit. Earth"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"5273","DOI":"10.1080\/01431161.2017.1338784","article-title":"A comparison of NDVI intercalibration methods","volume":"38","author":"Fan","year":"2017","journal-title":"Int. J. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1430","DOI":"10.1109\/JSTARS.2016.2519099","article-title":"Evaluation of Disaggregation Methods for Downscaling MODIS Land Surface Temperature to Landsat Spatial Resolution in Barrax Test Site","volume":"9","author":"Bisquert","year":"2016","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Boyte, S.P., Wylie, B.K., Rigge, M.B., and Dahal, D. (2017). Fusing MODIS with Landsat 8 data to downscale weekly normalized difference vegetation index estimates for central Great Basin rangelands, USA. Giscience Remote Sens., 1\u201324.","DOI":"10.1080\/15481603.2017.1382065"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1016\/j.isprsjprs.2017.02.006","article-title":"Spatiotemporal downscaling approaches for monitoring 8-day 30 m actual evapotranspiration","volume":"126","author":"Ke","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Abascal Zorrilla, N., Vantrepotte, V., Gensac, E., Huybrechts, N., and Gardel, A. (2018). The Advantages of Landsat 8-OLI-Derived Suspended Particulate Matter Maps for Monitoring the Subtidal Extension of Amazonian Coastal Mud Banks (French Guiana). Remote Sens., 10.","DOI":"10.3390\/rs10111733"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"6163","DOI":"10.3390\/rs6076163","article-title":"Combined Use of Multi-Temporal Optical and Radar Satellite Images for Grassland Monitoring","volume":"6","author":"Dusseux","year":"2014","journal-title":"Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Torbick, N., Chowdhury, D., Salas, W., and Qi, J. (2017). Monitoring Rice Agriculture across Myanmar Using Time Series Sentinel-1 Assisted by Landsat-8 and PALSAR-2. Remote Sens., 9.","DOI":"10.3390\/rs9020119"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Vreugdenhil, M., Wagner, W., Bauer-Marschallinger, B., Pfeil, I., Teubner, I., R\u00fcdiger, C., and Strauss, P. (2018). Sensitivity of Sentinel-1 Backscatter to Vegetation Dynamics: An Austrian Case Study. Remote Sens., 10.","DOI":"10.3390\/rs10091396"},{"key":"ref_17","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_18","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1016\/j.pce.2015.02.009","article-title":"Surface soil moisture retrievals from remote sensing: Current status, products & future trends","volume":"83\u201384","author":"Petropoulos","year":"2015","journal-title":"Phys. Chem. Earthparts A\/B\/C"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Navarro, A., Rolim, J., Miguel, I., Catal\u00e3o, J., Silva, J., Painho, M., and Vekerdy, Z. (2016). Crop Monitoring Based on SPOT-5 Take-5 and Sentinel-1A Data for the Estimation of Crop Water Requirements. Remote Sens., 8.","DOI":"10.3390\/rs8060525"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/j.rse.2011.05.028","article-title":"GMES Sentinel-1 mission","volume":"120","author":"Torres","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"7253","DOI":"10.1038\/s41598-018-25369-w","article-title":"Continuous, semi-automatic monitoring of ground deformation using Sentinel-1 satellites","volume":"8","author":"Raspini","year":"2018","journal-title":"Sci. Rep."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Gao, Q., Zribi, M., Escorihuela, M., and Baghdadi, N. (2017). Synergetic Use of Sentinel-1 and Sentinel-2 Data for Soil Moisture Mapping at 100 m Resolution. Sensors, 17.","DOI":"10.3390\/s17091966"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"644","DOI":"10.1111\/gcb.13841","article-title":"Spatial evaluation of Indonesia\u2019s 2015 fire-affected area and estimated carbon emissions using Sentinel-1","volume":"24","author":"Lohberger","year":"2018","journal-title":"Glob. Chang. Biol."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Bazzi, H., Baghdadi, N., El Hajj, M., Zribi, M., Minh, D.H.T., Ndikumana, E., Courault, D., and Belhouchette, H. (2019). Mapping Paddy Rice Using Sentinel-1 SAR Time Series in Camargue, France. Remote Sens., 11.","DOI":"10.3390\/rs11070887"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Frison, P.-L., Fruneau, B., Kmiha, S., Soudani, K., Dufr\u00eane, E., Toan, T.L., Koleck, T., Villard, L., Mougin, E., and Rudant, J.-P. (2018). Potential of Sentinel-1 Data for Monitoring Temperate Mixed Forest Phenology. Remote Sens., 10.","DOI":"10.3390\/rs10122049"},{"key":"ref_26","unstructured":"Miranda, E.E., Magalh\u00e3es, L.A., and Carvalho, C.C. (2014). Proposta de delimita\u00e7\u00e3o territorial do Matopiba. Nota T\u00e9cnica 1., Gite\/Embrapa."},{"key":"ref_27","unstructured":"Embrapa (2013). Sistema Brasileiro de Classifica\u00e7\u00e3o de Solos, Embrapa. 3a Edi\u00e7\u00e3o Revista e Ampliada."},{"key":"ref_28","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_29","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.rse.2011.11.026","article-title":"Sentinel-2: ESA\u2019s Optical High-Resolution Mission for GMES Operational Services","volume":"120","author":"Drusch","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"459","DOI":"10.1016\/0034-4257(88)90019-3","article-title":"An improved dark-object subtraction technique for atmospheric scattering correction of multispectral data","volume":"24","author":"Chavez","year":"1988","journal-title":"Remote Sens. Environ."},{"key":"ref_31","first-page":"309","article-title":"Monitoring vegetation systems in the Great Plains with ERTS","volume":"1","author":"Rouse","year":"1974","journal-title":"NASA"},{"key":"ref_32","unstructured":"Formaggio, A.R., and Sanches, I.D. (2017). Sensoriamento Remoto em Agricultura, Oficina de Textos."},{"key":"ref_33","unstructured":"Ponzoni, F., Shimabukuro, Y., and Kuplich, T. (2012). Sensoriamento Remoto da Vegeta\u00e7\u00e3o. 2a Edi\u00e7\u00e3o Atualizada e Ampliada, Oficina de Textos."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1036","DOI":"10.1007\/s11430-010-4002-3","article-title":"China\u2019s wetland change (1990\u20132000) determined by remote sensing","volume":"53","author":"Gong","year":"2010","journal-title":"Sci. China Earth Sci."},{"key":"ref_35","unstructured":"Veci, L. (2019, June 14). Sentinel-1 Toolbox: SAR Basics Tutorial. Available online: https:\/\/sentinel.esa.int\/web\/sentinel\/toolboxes\/sentinel-1\/tutorials."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Kaplan, G., and Avdan, U. (2018). Monthly Analysis of Wetlands Dynamics Using Remote Sensing Data. ISPRS Int. J. Geo-Inf., 7.","DOI":"10.3390\/ijgi7100411"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"2355","DOI":"10.1016\/j.asr.2017.09.006","article-title":"Comparison of Landsat-8, ASTER and Sentinel 1 satellite remote sensing data in automatic lineaments extraction: A case study of Sidi Flah-Bouskour inlier, Moroccan Anti Atlas","volume":"60","author":"Adiri","year":"2017","journal-title":"Adv. Space Res."},{"key":"ref_38","unstructured":"Aldroubi, A., Laine, A.F., and Unser, M.A. (1997). Speckle Filtering of SAR Images: A Comparative Study between Complex-Wavelet-Based and Standard Filters, SPIE."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"2990","DOI":"10.1080\/01431161.2016.1192304","article-title":"Sentinel-1-based flood mapping: A fully automated processing chain","volume":"37","author":"Twele","year":"2016","journal-title":"Int. J. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1016\/j.eja.2019.03.001","article-title":"New approach to determining the surface temperature without thermal band of satellites","volume":"106","author":"Filgueiras","year":"2019","journal-title":"Eur. J. Agron."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Kuhn, M., and Johnson, K. (2013). Applied Predictive Modeling, Springer.","DOI":"10.1007\/978-1-4614-6849-3"},{"key":"ref_42","unstructured":"Hijmans, R.J. (2019, June 14). Raster: Geographic Data Analysis and Modeling, R package version 2.8-19. Available online: https:\/\/CRAN.R-project.org\/package=raster."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"158","DOI":"10.1109\/MCOM.2018.1700560","article-title":"Machine Learning for Cognitive Network Management","volume":"56","author":"Ayoubi","year":"2018","journal-title":"IEEE Commun. Mag."},{"key":"ref_44","unstructured":"Kennedy, J.B., and Neville, A.M. (1986). Basic Statistical Methods for Engineers and Scientists, Harper & Row. [3rd ed.]."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"282","DOI":"10.1016\/0022-1694(70)90255-6","article-title":"River flow forecasting through conceptual models: Part I\u2014A discussion of principles","volume":"10","author":"Nash","year":"1970","journal-title":"J. Hydrol."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1214\/09-SS054","article-title":"A survey of cross-validation procedures for model selection","volume":"4","author":"Arlot","year":"2010","journal-title":"Statist. Surv."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Bousbih, S., Zribi, M., Lili-Chabaane, Z., Baghdadi, N., El Hajj, M., Gao, Q., and Mougenot, B. (2017). Potential of Sentinel-1 Radar Data for the Assessment of Soil and Cereal Cover Parameters. Sensors, 17.","DOI":"10.3390\/s17112617"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1016\/j.compag.2017.05.036","article-title":"Pan evaporation modeling using four different heuristic approaches","volume":"140","author":"Wang","year":"2017","journal-title":"Comput. Electron. Agric."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"314","DOI":"10.1590\/1413-70542018423006818","article-title":"Heuristic methods applied in reference evapotranspiration modeling","volume":"42","author":"Althoff","year":"2018","journal-title":"Ci\u00eancia E Agrotecnologia"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Alexakis, D.D., Mexis, F.-D., Vozinaki, A.-E.K., Daliakopoulos, I.N., and Tsanis, I.K. (2017). Soil Moisture Content Estimation Based on Sentinel-1 and Auxiliary Earth Observation Products. A Hydrological Approach. Sensors, 17.","DOI":"10.3390\/s17061455"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Rodr\u00edguez-Fern\u00e1ndez, N., Al Bitar, A., Colliander, A., and Zhao, T. (2019). Soil Moisture Remote Sensing across Scales. Remote Sens., 11.","DOI":"10.3390\/rs11020190"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Bazzi, H., Baghdadi, N., El Hajj, M., and Zribi, M. (2019). Potential of Sentinel-1 Surface Soil Moisture Product for Detecting Heavy Rainfall in the South of France. Sensors, 19.","DOI":"10.3390\/s19040802"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/12\/1441\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:59:12Z","timestamp":1760187552000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/12\/1441"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,6,18]]},"references-count":52,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2019,6]]}},"alternative-id":["rs11121441"],"URL":"https:\/\/doi.org\/10.3390\/rs11121441","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,6,18]]}}}