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13\/2022"],"award-info":[{"award-number":["PI 13\/2022"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Precision agriculture integrates multiple sensors and data types to support farmers with informed decision-making tools throughout crop cycles. This study evaluated Aboveground Biomass (AGB) estimates of Rye using attributes derived from PlanetScope (PS) optical, Sentinel-1 Synthetic Aperture Radar (SAR), and hybrid (optical plus SAR) datasets. Optical attributes encompassed surface reflectance from PS\u2019s blue, green, red, and near-infrared (NIR) bands, alongside the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI). Sentinel-1 SAR attributes included the C-band Synthetic Aperture Radar Ground Range Detected, VV and HH polarizations, and both Ratio and Polarization (Pol) indices. Ground reference AGB data for Rye (Secale cereal L.) were collected from 50 samples and four dates at a farm located in southern Brazil, aligning with image acquisition dates. Multiple linear regression models were trained and validated. AGB was estimated based on individual (optical PS or Sentinel-1 SAR) and combined datasets (optical plus SAR). This process was repeated 100 times, and variable importance was extracted. Results revealed improved Rye AGB estimates with integrated optical and SAR data. Optical vegetation indices displayed higher correlation coefficients (r) for AGB estimation (r = +0.67 for both EVI and NDVI) compared to SAR attributes like VV, Ratio, and polarization (r ranging from \u22120.52 to \u22120.58). However, the hybrid regression model enhanced AGB estimation (R2 = 0.62, p &lt; 0.01), reducing RMSE to 579 kg\u00b7ha\u22121. Using only optical or SAR data yielded R2 values of 0.51 and 0.42, respectively (p &lt; 0.01). In the hybrid model, the most important predictors were VV, NIR, blue, and EVI. Spatial distribution analysis of predicted Rye AGB unveiled agricultural zones associated with varying biomass throughout the cover crop development. Our findings underscored the complementarity of optical with SAR data to enhance AGB estimates of cover crops, offering valuable insights for agricultural zoning to support soil and cash crop management.<\/jats:p>","DOI":"10.3390\/rs16152686","type":"journal-article","created":{"date-parts":[[2024,7,23]],"date-time":"2024-07-23T07:57:23Z","timestamp":1721721443000},"page":"2686","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Monitoring Cover Crop Biomass in Southern Brazil Using Combined PlanetScope and Sentinel-1 SAR Data"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0405-9603","authenticated-orcid":false,"given":"F\u00e1bio Marcelo","family":"Breunig","sequence":"first","affiliation":[{"name":"Departamento de Geografia, Setor de Ci\u00eancias da Terra, Universidade Federal do Paran\u00e1 (DGEOG\/SCT\/UFPR), Polit\u00e9cnico, Curitiba 81530-900, PR, Brazil"},{"name":"Campus de Frederico Westphalen, Universidade Federal de Santa Maria (UFSM-FW), Frederico Westphalen 98400-000, RS, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7151-8697","authenticated-orcid":false,"given":"Ricardo","family":"Dalagnol","sequence":"additional","affiliation":[{"name":"Center for Tropical Research, Institute of the Environment and Sustainability, University of California, Los Angeles, CA 90095, USA"},{"name":"NASA-Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8313-0497","authenticated-orcid":false,"given":"L\u00eanio Soares","family":"Galv\u00e3o","sequence":"additional","affiliation":[{"name":"Earth Observation and Geoinformatics Division (DIOTG), National Institute for Space Research (INPE), S\u00e3o Jos\u00e9 dos Campos 12245-970, SP, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0247-8449","authenticated-orcid":false,"given":"Polyanna da Concei\u00e7\u00e3o","family":"Bispo","sequence":"additional","affiliation":[{"name":"Department of Geography, School of Environment, Education and Development, University of Manchester, Manchester M13 9PL, UK"}]},{"given":"Qing","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Geography, School of Environment, Education and Development, University of Manchester, Manchester M13 9PL, UK"}]},{"given":"Elias Fernando","family":"Berra","sequence":"additional","affiliation":[{"name":"Departamento de Geografia, Setor de Ci\u00eancias da Terra, Universidade Federal do Paran\u00e1 (DGEOG\/SCT\/UFPR), Polit\u00e9cnico, Curitiba 81530-900, PR, Brazil"}]},{"given":"William","family":"Gaida","sequence":"additional","affiliation":[{"name":"Campus de Frederico Westphalen, Universidade Federal de Santa Maria (UFSM-FW), Frederico Westphalen 98400-000, RS, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0564-7818","authenticated-orcid":false,"given":"Veraldo","family":"Liesenberg","sequence":"additional","affiliation":[{"name":"College of Agronomy and Veterinary\u2014CAV, Santa Catarina State University (UDESC), Lages 88520-000, SC, Brazil"}]},{"given":"Tony Vinicius Moreira","family":"Sampaio","sequence":"additional","affiliation":[{"name":"Departamento de Geografia, Setor de Ci\u00eancias da Terra, Universidade Federal do Paran\u00e1 (DGEOG\/SCT\/UFPR), Polit\u00e9cnico, Curitiba 81530-900, PR, Brazil"}]}],"member":"1968","published-online":{"date-parts":[[2024,7,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Cherubin, M.R., Damian, J.M., Tavares, T.R., Trevisan, R.G., Cola\u00e7o, A.F., Eitelwein, M.T., Martello, M., Inamasu, R.Y., Pias, O.H.D.C., and Molin, J.P. 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