{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,18]],"date-time":"2026-04-18T17:13:51Z","timestamp":1776532431075,"version":"3.51.2"},"reference-count":54,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2019,5,9]],"date-time":"2019-05-09T00:00:00Z","timestamp":1557360000000},"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>Space-based remote sensing imagery can provide a valuable and cost-effective set of observations for mapping crop-productivity differences. The effectiveness of such signals is dependent on several conditions that are related to crop and sensor characteristics. In this paper, we present the dynamic behavior of signals from five Synthetic Aperture Radar (SAR) sensors and optical sensors with growing sugarcane, focusing on saturation effects and the influence of precipitation events. In addition, we analyzed the level of agreement within and between these spaceborne datasets over space and time. As a result, we produced a list of conditions during which the acquisition of satellite imagery is most effective for sugarcane productivity monitoring. For this, we analyzed remote sensing data from two C-band SAR (Sentinel-1 and Radarsat-2), one L-band SAR (ALOS-2), and two optical sensors (Landsat-8 and WorldView-2), in conjunction with detailed ground-reference data acquired over several sugarcane fields in the state of S\u00e3o Paulo, Brazil. We conclude that satellite imagery from L-band SAR and optical sensors is preferred for monitoring sugarcane biomass growth in time and space. Additionally, C-band SAR imagery offers the potential for mapping spatial variations during specific time windows and may be further exploited for its precipitation sensitivity.<\/jats:p>","DOI":"10.3390\/rs11091109","type":"journal-article","created":{"date-parts":[[2019,5,9]],"date-time":"2019-05-09T08:19:59Z","timestamp":1557389999000},"page":"1109","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":32,"title":["Sugarcane Productivity Mapping through C-Band and L-Band SAR and Optical Satellite Imagery"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3439-0548","authenticated-orcid":false,"given":"Ramses","family":"Molijn","sequence":"first","affiliation":[{"name":"Geoscience and Remote Sensing, Delft University of Technology, 2628 CN Delft, The Netherlands"}]},{"given":"Lorenzo","family":"Iannini","sequence":"additional","affiliation":[{"name":"Geoscience and Remote Sensing, Delft University of Technology, 2628 CN Delft, The Netherlands"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3337-4394","authenticated-orcid":false,"given":"Jansle","family":"Vieira Rocha","sequence":"additional","affiliation":[{"name":"Faculdade de Engenharia Agr\u00edcola (FEAGRI), Unicamp, Campinas 13083-875, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6067-7561","authenticated-orcid":false,"given":"Ramon","family":"Hanssen","sequence":"additional","affiliation":[{"name":"Geoscience and Remote Sensing, Delft University of Technology, 2628 CN Delft, The Netherlands"}]}],"member":"1968","published-online":{"date-parts":[[2019,5,9]]},"reference":[{"key":"ref_1","unstructured":"Food and Agriculture Organization of the United Nations (2017). 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